Development of demand forecasting tool for natural resources recouping from municipal solid waste.

PubMed

Zaman, Atiq Uz; Lehmann, Steffen

2013-10-01

Sustainable waste management requires an integrated planning and design strategy for reliable forecasting of waste generation, collection, recycling, treatment and disposal for the successful development of future residential precincts. The success of the future development and management of waste relies to a high extent on the accuracy of the prediction and on a comprehensive understanding of the overall waste management systems. This study defies the traditional concepts of waste, in which waste was considered as the last phase of production and services, by putting forward the new concept of waste as an intermediate phase of production and services. The study aims to develop a demand forecasting tool called 'zero waste index' (ZWI) for measuring the natural resources recouped from municipal solid waste. The ZWI (ZWI demand forecasting tool) quantifies the amount of virgin materials recovered from solid waste and subsequently reduces extraction of natural resources. In addition, the tool estimates the potential amount of energy, water and emissions avoided or saved by the improved waste management system. The ZWI is tested in a case study of waste management systems in two developed cities: Adelaide (Australia) and Stockholm (Sweden). The ZWI of waste management systems in Adelaide and Stockholm is 0.33 and 0.17 respectively. The study also enumerates per capita energy savings of 2.9 GJ and 2.83 GJ, greenhouse gas emissions reductions of 0.39 tonnes (CO2e) and 0.33 tonnes (CO2e), as well as water savings of 2.8 kL and 0.92 kL in Adelaide and Stockholm respectively.

Household waste compositional analysis variation from insular communities in the framework of waste prevention strategy plans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zorpas, Antonis A., E-mail: antonis.zorpas@ouc.ac.cy; Lasaridi, Katia, E-mail: klasaridi@hua.gr; Voukkali, Irene

Highlights: • Waste framework directive has set clear waste prevention procedures. • Household Compositional analysis. • Waste management plans. • Zero waste approach. • Waste generation. - Abstract: Waste management planning requires reliable data regarding waste generation, affecting factors on waste generation and forecasts of waste quantities based on facts. In order to decrease the environmental impacts of waste management the choice of prevention plan as well as the treatment method must be based on the features of the waste that are produced in a specific area. Factors such as culture, economic development, climate, and energy sources have an impactmore » on waste composition; composition influences the need of collecting waste more or less frequently of waste collection and disposition. The research question was to discover the main barriers concerning the compositional analysis in Insular Communities under warm climate conditions and the findings from this study enabled the main contents of a waste management plan to be established. These included advice to residents on waste minimisation, liaison with stakeholders and the expansion of kerbside recycling schemes.« less

Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Younes, Mohammed Y

2016-09-01

Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%. Copyright © 2015 Elsevier Ltd. All rights reserved.

Implementation of SAP Waste Management System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frost, M.L.; LaBorde, C.M.; Nichols, C.D.

2008-07-01

The Y-12 National Security Complex (Y-12) assumed responsibility for newly generated waste on October 1, 2005. To ensure effective management and accountability of newly generated waste, Y-12 has opted to utilize SAP, Y-12's Enterprise Resource Planning (ERP) tool, to track low-level radioactive waste (LLW), mixed waste (MW), hazardous waste, and non-regulated waste from generation through acceptance and disposal. SAP Waste will include the functionality of the current waste tracking system and integrate with the applicable modules of SAP already in use. The functionality of two legacy systems, the Generator Entry System (GES) and the Waste Information Tracking System (WITS), andmore » peripheral spreadsheets, databases, and e-mail/fax communications will be replaced by SAP Waste. Fundamentally, SAP Waste will promote waste acceptance for certification and disposal, not storage. SAP Waste will provide a one-time data entry location where waste generators can enter waste container information, track the status of their waste, and maintain documentation. A benefit of the new system is that it will provide a single data repository where Y-12's Waste Management organization can establish waste profiles, verify and validate data, maintain inventory control utilizing hand-held data transfer devices, schedule and ship waste, manage project accounting, and report on waste handling activities. This single data repository will facilitate the production of detailed waste generation reports for use in forecasting and budgeting, provide the data for required regulatory reports, and generate metrics to evaluate the performance of the Waste Management organization and its subcontractors. SAP Waste will replace the outdated and expensive legacy system, establish tools the site needs to manage newly generated waste, and optimize the use of the site's ERP tool for integration with related business processes while promoting disposition of waste. (authors)« less

Analysis of municipal waste generation rate in Poland compared to selected European countries

NASA Astrophysics Data System (ADS)

Klojzy-Karczmarczyk, Beata; Makoudi, Said

2017-10-01

The generated municipal waste rates provided in the planning documents are a tool for forecasting the mass of waste generated in individual waste management regions. An important issue is the decisive separation of two concepts: waste generated and waste collected. The study includes analysis of the generation rate for Poland with division into urban and rural areas. The estimated and projected rate of municipal waste generation for Poland provided in subsequent editions of National Waste Management Plans (KPGO) changed since 2000 within wide range from about 300 to more than 500 kg per capita in an individual year (kg/pc/year). Currently, the National Waste Management Plan for the years 2017-2022 estimates municipal waste generation rate at approx. 270 kg/per capita/year with a projected increase to 330 kg/per capita/year in 2030. Most European countries adopt higher municipal waste generation rate, often exceeding 600 kg/per capita/year. The objective of the paper is therefore to analyze the causes of this difference in the declared values. The morphological composition of municipal waste stream in Poland and in selected European countries (e.g. France, Belgium, Switzerland) was analyzed. At present it is not possible to balance the value of the generation rate with the rate of waste collection in Poland. The conducted analyzes allow for determining a number of reasons for variation of the rate value in particular countries, mostly morphological composition of municipal waste, inclusion of household-like waste from infrastructure facilities or not and amount of waste collected in rural areas. The differences in the generation rates and provided possible reasons indicate the need to harmonize the methodology for estimating rates of municipal waste generation in various countries, including Poland.

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

PubMed Central

Song, Jingwei; He, Jiaying; Zhu, Menghua; Tan, Debao; Zhang, Yu; Ye, Song; Shen, Dingtao; Zou, Pengfei

2014-01-01

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%. PMID:25301508

Household waste compositional analysis variation from insular communities in the framework of waste prevention strategy plans.

PubMed

Zorpas, Antonis A; Lasaridi, Katia; Voukkali, Irene; Loizia, Pantelitsa; Chroni, Christina

2015-04-01

Waste management planning requires reliable data regarding waste generation, affecting factors on waste generation and forecasts of waste quantities based on facts. In order to decrease the environmental impacts of waste management the choice of prevention plan as well as the treatment method must be based on the features of the waste that are produced in a specific area. Factors such as culture, economic development, climate, and energy sources have an impact on waste composition; composition influences the need of collecting waste more or less frequently of waste collection and disposition. The research question was to discover the main barriers concerning the compositional analysis in Insular Communities under warm climate conditions and the findings from this study enabled the main contents of a waste management plan to be established. These included advice to residents on waste minimisation, liaison with stakeholders and the expansion of kerbside recycling schemes. Copyright © 2015 Elsevier Ltd. All rights reserved.

WASTE TREATMENT PLANT (WTP) LIQUID EFFLUENT TREATABILITY EVALUATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

LUECK, K.J.

2004-10-18

A forecast of the radioactive, dangerous liquid effluents expected to be produced by the Waste Treatment Plant (WTP) was provided by Bechtel National, Inc. (BNI 2004). The forecast represents the liquid effluents generated from the processing of Tank Farm waste through the end-of-mission for the WTP. The WTP forecast is provided in the Appendices. The WTP liquid effluents will be stored, treated, and disposed of in the Liquid Effluent Retention Facility (LERF) and the Effluent Treatment Facility (ETF). Both facilities are located in the 200 East Area and are operated by Fluor Hanford, Inc. (FH) for the US. Department ofmore » Energy (DOE). The treatability of the WTP liquid effluents in the LERF/ETF was evaluated. The evaluation was conducted by comparing the forecast to the LERF/ETF treatability envelope (Aromi 1997), which provides information on the items which determine if a liquid effluent is acceptable for receipt and treatment at the LERF/ETF. The format of the evaluation corresponds directly to the outline of the treatability envelope document. Except where noted, the maximum annual average concentrations over the range of the 27 year forecast was evaluated against the treatability envelope. This is an acceptable approach because the volume capacity in the LERF Basin will equalize the minimum and maximum peaks. Background information on the LERF/ETF design basis is provided in the treatability envelope document.« less

Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lebersorger, S.; Beigl, P., E-mail: peter.beigl@boku.ac.at

Waste management planning requires reliable data concerning waste generation, influencing factors on waste generation and forecasts of waste quantities based on facts. This paper aims at identifying and quantifying differences between different municipalities' municipal solid waste (MSW) collection quantities based on data from waste management and on socio-economic indicators. A large set of 116 indicators from 542 municipalities in the Province of Styria was investigated. The resulting regression model included municipal tax revenue per capita, household size and the percentage of buildings with solid fuel heating systems. The model explains 74.3% of the MSW variation and the model assumptions aremore » met. Other factors such as tourism, home composting or age distribution of the population did not significantly improve the model. According to the model, 21% of MSW collected in Styria was commercial waste and 18% of the generated MSW was burned in domestic heating systems. While the percentage of commercial waste is consistent with literature data, practically no literature data are available for the quantity of MSW burned, which seems to be overestimated by the model. The resulting regression model was used as basis for a waste prognosis model (Beigl and Lebersorger, in preparation).« less

Municipal solid waste generation in municipalities: quantifying impacts of household structure, commercial waste and domestic fuel.

PubMed

Lebersorger, S; Beigl, P

2011-01-01

Waste management planning requires reliable data concerning waste generation, influencing factors on waste generation and forecasts of waste quantities based on facts. This paper aims at identifying and quantifying differences between different municipalities' municipal solid waste (MSW) collection quantities based on data from waste management and on socio-economic indicators. A large set of 116 indicators from 542 municipalities in the Province of Styria was investigated. The resulting regression model included municipal tax revenue per capita, household size and the percentage of buildings with solid fuel heating systems. The model explains 74.3% of the MSW variation and the model assumptions are met. Other factors such as tourism, home composting or age distribution of the population did not significantly improve the model. According to the model, 21% of MSW collected in Styria was commercial waste and 18% of the generated MSW was burned in domestic heating systems. While the percentage of commercial waste is consistent with literature data, practically no literature data are available for the quantity of MSW burned, which seems to be overestimated by the model. The resulting regression model was used as basis for a waste prognosis model (Beigl and Lebersorger, in preparation). Copyright © 2011 Elsevier Ltd. All rights reserved.

A system dynamic modeling approach for evaluating municipal solid waste generation, landfill capacity and related cost management issues.

PubMed

Kollikkathara, Naushad; Feng, Huan; Yu, Danlin

2010-11-01

As planning for sustainable municipal solid waste management has to address several inter-connected issues such as landfill capacity, environmental impacts and financial expenditure, it becomes increasingly necessary to understand the dynamic nature of their interactions. A system dynamics approach designed here attempts to address some of these issues by fitting a model framework for Newark urban region in the US, and running a forecast simulation. The dynamic system developed in this study incorporates the complexity of the waste generation and management process to some extent which is achieved through a combination of simpler sub-processes that are linked together to form a whole. The impact of decision options on the generation of waste in the city, on the remaining landfill capacity of the state, and on the economic cost or benefit actualized by different waste processing options are explored through this approach, providing valuable insights into the urban waste-management process. Copyright © 2010 Elsevier Ltd. All rights reserved.

A system dynamic modeling approach for evaluating municipal solid waste generation, landfill capacity and related cost management issues

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kollikkathara, Naushad, E-mail: naushadkp@gmail.co; Feng Huan; Yu Danlin

2010-11-15

As planning for sustainable municipal solid waste management has to address several inter-connected issues such as landfill capacity, environmental impacts and financial expenditure, it becomes increasingly necessary to understand the dynamic nature of their interactions. A system dynamics approach designed here attempts to address some of these issues by fitting a model framework for Newark urban region in the US, and running a forecast simulation. The dynamic system developed in this study incorporates the complexity of the waste generation and management process to some extent which is achieved through a combination of simpler sub-processes that are linked together to formmore » a whole. The impact of decision options on the generation of waste in the city, on the remaining landfill capacity of the state, and on the economic cost or benefit actualized by different waste processing options are explored through this approach, providing valuable insights into the urban waste-management process.« less

Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Intharathirat, Rotchana, E-mail: rotchana.in@gmail.com; Abdul Salam, P., E-mail: salam@ait.ac.th; Kumar, S., E-mail: kumar@ait.ac.th

Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developingmore » countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.« less

Development of a hybrid model to predict construction and demolition waste: China as a case study.

PubMed

Song, Yiliao; Wang, Yong; Liu, Feng; Zhang, Yixin

2017-01-01

Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation. Therefore, this paper presents a forecasting procedure for C&DW in China that can forecast the quantity of each component in such waste. The proposed approach is based on a GM-SVR model that improves the forecasting effectiveness of the gray model (GM), which is achieved by adjusting the residual series by a support vector regression (SVR) method and a transition matrix that aims to estimate the discharge of each component in the C&DW. Through the proposed method, future C&DW volume are listed and analyzed containing their potential components and distribution in different provinces in China. Besides, model testing process provides mathematical evidence to validate the proposed model is an effective way to give future information of C&DW for policy makers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Forecasting of municipal solid waste quantity in a developing country using multivariate grey models.

PubMed

Intharathirat, Rotchana; Abdul Salam, P; Kumar, S; Untong, Akarapong

2015-05-01

In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developing countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435-44,994 tonnes per day in 2013 to 55,177-56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period. Copyright © 2015 Elsevier Ltd. All rights reserved.

Implementation of a multi-variable regression analysis in the assessment of the generation rate and composition of hospital solid waste for the design of a sustainable management system in developing countries.

PubMed

Al-Khatib, Issam A; Abu Fkhidah, Ismail; Khatib, Jumana I; Kontogianni, Stamatia

2016-03-01

Forecasting of hospital solid waste generation is a critical challenge for future planning. The composition and generation rate of hospital solid waste in hospital units was the field where the proposed methodology of the present article was applied in order to validate the results and secure the outcomes of the management plan in national hospitals. A set of three multiple-variable regression models has been derived for estimating the daily total hospital waste, general hospital waste, and total hazardous waste as a function of number of inpatients, number of total patients, and number of beds. The application of several key indicators and validation procedures indicates the high significance and reliability of the developed models in predicting the hospital solid waste of any hospital. Methodology data were drawn from existent scientific literature. Also, useful raw data were retrieved from international organisations and the investigated hospitals' personnel. The primal generation outcomes are compared with other local hospitals and also with hospitals from other countries. The main outcome, which is the developed model results, are presented and analysed thoroughly. The goal is this model to act as leverage in the discussions among governmental authorities on the implementation of a national plan for safe hospital waste management in Palestine. © The Author(s) 2016.

Assessment of municipal solid waste generation and recyclable materials potential in Kuala Lumpur, Malaysia.

PubMed

Saeed, Mohamed Osman; Hassan, Mohd Nasir; Mujeebu, M Abdul

2009-07-01

This paper presents a forecasting study of municipal solid waste generation (MSWG) rate and potential of its recyclable components in Kuala Lumpur (KL), the capital city of Malaysia. The generation rates and composition of solid wastes of various classes such as street cleansing, landscape and garden, industrial and constructional, institutional, residential and commercial are analyzed. The past and present trends are studied and extrapolated for the coming years using Microsoft office 2003 Excel spreadsheet assuming a linear behavior. The study shows that increased solid waste generation of KL is alarming. For instance, the amount of daily residential SWG is found to be about 1.62 kg/capita; with the national average at 0.8-0.9 kg/capita and is expected to be increasing linearly, reaching to 2.23 kg/capita by 2024. This figure seems reasonable for an urban developing area like KL city. It is also found that, food (organic) waste is the major recyclable component followed by mix paper and mix plastics. Along with estimated population growth and their business activities, it has been observed that the city is still lacking in terms of efficient waste treatment technology, sufficient fund, public awareness, maintaining the established norms of industrial waste treatment etc. Hence it is recommended that the concerned authority (DBKL) shall view this issue seriously.

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oribe-Garcia, Iraia, E-mail: iraia.oribe@deusto.es; Kamara-Esteban, Oihane; Martin, Cristina

Highlights: • We have modelled household waste generation in Biscay municipalities. • We have identified relevant characteristics regarding household waste generation. • Factor models are used in order to identify the best subset of explicative variables. • Biscay’s municipalities are grouped by means of hierarchical clustering. - Abstract: The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The presentmore » works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.« less

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay.

PubMed

Oribe-Garcia, Iraia; Kamara-Esteban, Oihane; Martin, Cristina; Macarulla-Arenaza, Ana M; Alonso-Vicario, Ainhoa

2015-05-01

The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The present works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Disposal and improvement of contaminated by waste extraction of copper mining in chile

NASA Astrophysics Data System (ADS)

Naranjo Lamilla, Pedro; Blanco Fernández, David; Díaz González, Marcos; Robles Castillo, Marcelo; Decinti Weiss, Alejandra; Tapia Alvarez, Carolina; Pardo Fabregat, Francisco; Vidal, Manuel Miguel Jordan; Bech, Jaume; Roca, Nuria

2016-04-01

This project originated from the need of a mining company, which mines and processes copper ore. High purity copper is produced with an annual production of 1,113,928 tons of concentrate to a law of 32%. This mining company has generated several illegal landfills and has been forced by the government to make a management center Industrial Solid Waste (ISW). The forecast volume of waste generated is 20,000 tons / year. Chemical analysis established that the studied soil has a high copper content, caused by nature or from the spread of contaminants from mining activities. Moreover, in some sectors, soil contamination by mercury, hydrocarbons and oils and fats were detected, likely associated with the accumulation of waste. The waters are also impacted by mining industrial tasks, specifically copper ores, molybdenum, manganese, sulfates and have an acidic pH. The ISW management center dispels the pollution of soil and water and concentrating all activities in a technically suitable place. In this center the necessary guidelines for the treatment and disposal of soil contamination caused by uncontrolled landfills are given, also generating a leachate collection system and a network of fluid monitoring physicochemical water quality and soil environment. Keywords: Industrial solid waste, soil contamination, Mining waste

Final Inventory Work-Off Plan for ORNL transuranic wastes (1986 version)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dickerson, L.S.

1988-05-01

The Final Inventory Work-Off Plan (IWOP) for ORNL Transuranic Wastes addresses ORNL's strategy for retrieval, certification, and shipment of its stored and newly generated contact-handled (CH) and remote-handled (RH) transuranic (TRU) wastes to the Waste Isolation Pilot Plant (WIPP), the proposed geologic repository near Carlsbad, New Mexico. This document considers certification compliance with the WIPP waste acceptance criteria (WAC) and is consistent with the US Department of Energy's Long-Range Master Plan for Defense Transuranic Waste Management. This document characterizes Oak Ridge National Laboratory's (ORNL's) TRU waste by type and estimates the number of shipments required to dispose of it; describesmore » the methods, facilities, and systems required for its certification and shipment; presents work-off strategies and schedules for retrieval, certification, and transportation; discusses the resource needs and additions that will be required for the effort and forecasts costs for the long-term TRU waste management program; and lists public documentation required to support certification facilities and strategies. 22 refs., 6 figs., 10 tabs.« less

Comparison of waste composition in a continuing-care retirement community.

PubMed

Kim, T; Shanklin, C W; Su, A Y; Hackes, B L; Ferris, D

1997-04-01

To determine the composition of wastes generated in a continuing-care retirement community (CCRC) and to analyze the effects of source-reduction activities and meal delivery system change on the amount of waste generated in the facility. A waste stream analysis was conducted at the same CCRC during spring 1994 (period 1: baseline), spring 1995 (period 2: source reduction intervention), and fall 1995 (period 3: service delivery intervention). Weight, volume, and collapsed volume were determined for food and packaging wastes. Tray service and wait staff service are provided to 70 residents in a health care unit, and family-style service is an optional service available to 130 residents in the independent-living units. A mean of 229 meals are served per day. Intervention included the implementation of source-reduction activities and a change in a service-delivery system in periods 2 and 3, respectively. Descriptive statistics were used to determine the composition of waste. Analysis of variance and a multiple comparison method (least significant difference) were used to compare mean weight and volume of waste generated in period 1 with data collected during periods 2 and 3. Mean waste generated per meal by weight and volume ranged from 0.93 to 1.00 lb and 1.44 to 1.65 gal, respectively. Significantly less production waste by weight (0.18 lb/meal) and volume (0.12 gal/meal) was generated in period 2 than in period 1 (0.32 lb/meal and 0.16 gal/meal, respectively). Significantly less service waste by weight (0.31 lb/meal) and volume (0.05 gal/meal) was discarded in period 3 than in period 1 (0.37 lb/meal and 0.15 gal/meal, respectively). Significantly less total waste and plastic by weight was disposed of after the interventions. The study conclusions indicated that implementing source-reduction practices and changing the meal-delivery system affected the composition of waste generated. Knowledge of waste stream composition can help other foodservice professionals and consulting dietitians identify waste-reduction activities and recycling opportunities. The quantity and type of waste generated should be considered when operational decisions are made relative to market form of food, menu choices, service-delivery systems, and production forecast and controls.

Forecasting waste compositions: A case study on plastic waste of electronic display housings.

PubMed

Peeters, Jef R; Vanegas, Paul; Kellens, Karel; Wang, Feng; Huisman, Jaco; Dewulf, Wim; Duflou, Joost R

2015-12-01

Because of the rapid succession of technological developments, the architecture and material composition of many products used in daily life have drastically changed over the last decades. As a result, well-adjusted recycling technologies need to be developed and installed to cope with these evolutions. This is essential to guarantee continued access to materials and to reduce the ecological impact of our material consumption. However, limited information is currently available on the material composition of arising waste streams and even less on how these waste streams will evolve. Therefore, this paper presents a methodology to forecast trends in the material composition of waste streams. To demonstrate the applicability and value of the proposed methodology, it is applied to forecast the evolution of plastic housing waste from flat panel display (FPD) TVs, FPD monitors, cathode ray tube (CRT) TVs and CRT monitors. The results of the presented forecasts indicate that a wide variety of plastic types and additives, such as flame retardants, are found in housings of similar products. The presented case study demonstrates that the proposed methodology allows the identification of trends in the evolution of the material composition of waste streams. In addition, it is demonstrated that the recycling sector will need to adapt its processes to deal with the increasing complexity of plastics of end-of-life electronic displays while respecting relevant directives. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessing the variables affecting on the rate of solid waste generation and recycling: An empirical analysis in Prespa Park.

PubMed

Grazhdani, Dorina

2016-02-01

Economic development, urbanization, and improved living standards increase the quantity and complexity of generated solid waste. Comprehensive study of the variables influencing household solid waste production and recycling rate is crucial and fundamental for exploring the generation mechanism and forecasting future dynamics of household solid waste. The present study is employed in the case study of Prespa Park. A model, based on the interrelationships of economic, demographic, housing structure and waste management policy variables influencing the rate of solid waste generation and recycling is developed and employed. The empirical analysis is based on the information derived from a field questionnaire survey conducted in Prespa Park villages for the year 2014. Another feature of this study is to test whether a household's waste generation can be decoupled from its population growth. Descriptive statistics, bivariate correlation analysis and F-tests are used to know the relationship between variables. One-way and two-way fixed effects models data analysis techniques are used to identify variables that determine the effectiveness of waste generation and recycling at household level in the study area. The results reveal that households with heterogeneous characteristics, such as education level, mean building age and income, present different challenges of waste reduction goals. Numerically, an increase of 1% in education level of population corresponds to a waste reduction of 3kg on the annual per capita basis. A village with older buildings, in the case of one year older of the median building age, corresponds to a waste generation increase of 12kg. Other economic and policy incentives such as the mean household income, pay-as-you-throw, percentage of population with access to curbside recycling, the number of drop-off recycling facilities available per 1000 persons and cumulative expenditures on recycling education per capita are also found to be effective measures in waste reduction. The mean expenditure for recycling education spent on a person for years 2010 and 2014 is 12 and 14 cents, respectively and it vary from 0 to €1. For years 2010 and 2014, the mean percentage of population with access to curbside recycling services is 38.6% and 40.3%, and the mean number of drop-off recycling centers per 1000 persons in the population is 0.29 and 0.32, respectively. Empirical evidence suggests that population growth did not necessarily result in increases in waste generation. The results provided are useful when planning, changing or implementing sustainable municipal solid waste management. Copyright © 2015 Elsevier Ltd. All rights reserved.

Forecasting of construction and demolition waste in Brazil.

PubMed

Paz, Diogo Hf; Lafayette, Kalinny Pv

2016-08-01

The objective of this article is to develop a computerised tool (software) that facilitates the analysis of strategies for waste management on construction sites through the use of indicators of construction and demolition waste generation. The development involved the following steps: knowledge acquisition, structuring the system, coding and system evaluation. The step of knowledge acquisition aims to provide subsidies for the representation of them through models. In the step of structuring the system, it was presented the structuring and formalisation of knowledge for the development of the system, and has two stages: the construction of the conceptual model and the subsequent instantiation of the model. The coding system aims to implement (code) the conceptual model developed in a model played by computer (digital). The results showed that the system is very useful and applicable in construction sites, helping to improve the quality of waste management, and creating a database that will support new research. © The Author(s) 2016.

A financial feasibility model of gasification and anaerobic digestion waste-to-energy (WTE) plants in Saudi Arabia.

PubMed

Hadidi, Laith A; Omer, Mohamed Mahmoud

2017-01-01

Municipal Solid Waste (MSW) generation in Saudi Arabia is increasingly growing at a fast rate, as it hurtles towards ever increasing urban development coupled with rapid developments and expanding population. Saudi Arabia's energy demands are also rising at a faster rate. Therefore, the importance of an integrated waste management system in Saudi Arabia is increasingly rising and introducing Waste to Energy (WTE) facilities is becoming an absolute necessity. This paper analyzes the current situation of MSW management in Saudi Arabia and proposes a financial model to assess the viability of WTE investments in Saudi Arabia in order to address its waste management challenges and meet its forecasted energy demands. The research develops a financial model to investigate the financial viability of WTE plants utilizing gasification and Anaerobic Digestion (AD) conversion technologies. The financial model provides a cost estimate of establishing both gasification and anaerobic digestion WTE plants in Saudi Arabia through a set of financial indicators, i.e. net present value (NPV), internal rate of return (IRR), modified internal rate of return (MIRR), profitability index (PI), payback period, discounted payback period, Levelized Cost of Electricity (LCOE) and Levelized Cost of Waste (LCOW). Finally, the analysis of the financial model reveals the main affecting factors of the gasification plants investment decision, namely: facility generation capacity, generated electricity revenue, and the capacity factor. Similarly, the paper also identifies facility waste capacity and the capacity factor as the main affecting factors on the AD plants' investment decision. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mitigation of Hydrogen Gas Generation from the Reaction of Uranium Metal with Water in K Basin Sludge and Sludge Waste Forms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sinkov, Sergey I.; Delegard, Calvin H.; Schmidt, Andrew J.

2011-06-08

Prior laboratory testing identified sodium nitrate and nitrite to be the most promising agents to minimize hydrogen generation from uranium metal aqueous corrosion in Hanford Site K Basin sludge. Of the two, nitrate was determined to be better because of higher chemical capacity, lower toxicity, more reliable efficacy, and fewer side reactions than nitrite. The present lab tests were run to determine if nitrate’s beneficial effects to lower H2 generation in simulated and genuine sludge continued for simulated sludge mixed with agents to immobilize water to help meet the Waste Isolation Pilot Plant (WIPP) waste acceptance drainable liquid criterion. Testsmore » were run at ~60°C, 80°C, and 95°C using near spherical high-purity uranium metal beads and simulated sludge to emulate uranium-rich KW containerized sludge currently residing in engineered containers KW-210 and KW-220. Immobilization agents tested were Portland cement (PC), a commercial blend of PC with sepiolite clay (Aquaset II H), granulated sepiolite clay (Aquaset II G), and sepiolite clay powder (Aquaset II). In all cases except tests with Aquaset II G, the simulated sludge was mixed intimately with the immobilization agent before testing commenced. For the granulated Aquaset II G clay was added to the top of the settled sludge/solution mixture according to manufacturer application directions. The gas volumes and compositions, uranium metal corrosion mass losses, and nitrite, ammonia, and hydroxide concentrations in the interstitial solutions were measured. Uranium metal corrosion rates were compared with rates forecast from the known uranium metal anoxic water corrosion rate law. The ratios of the forecast to the observed rates were calculated to find the corrosion rate attenuation factors. Hydrogen quantities also were measured and compared with quantities expected based on non-attenuated H2 generation at the full forecast anoxic corrosion rate to arrive at H2 attenuation factors. The uranium metal corrosion rates in water alone and in simulated sludge were near or slightly below the metal-in-water rate while nitrate-free sludge/Aquaset II decreased rates by about a factor of 3. Addition of 1 M nitrate to simulated sludge decreased the corrosion rate by a factor of ~5 while 1 M nitrate in sludge/Aquaset II mixtures decreased the corrosion rate by ~2.5 compared with the nitrate-free analogues. Mixtures of simulated sludge with Aquaset II treated with 1 M nitrate had uranium corrosion rates about a factor of 8 to 10 lower than the water-only rate law. Nitrate was found to provide substantial hydrogen mitigation for immobilized simulant sludge waste forms containing Aquaset II or Aquaset II G clay. Hydrogen attenuation factors of 1000 or greater were determined at 60°C for sludge-clay mixtures at 1 M nitrate. Hydrogen mitigation for tests with PC and Aquaset II H (which contains PC) were inconclusive because of suspected failure to overcome induction times and fully enter into anoxic corrosion. Lessening of hydrogen attenuation at ~80°C and ~95°C for simulated sludge and Aquaset II was observed with attenuation factors around 100 to 200 at 1 M nitrate. Valuable additional information has been obtained on the ability of nitrate to attenuate hydrogen gas generation from solution, simulant K Basin sludge, and simulant sludge with immobilization agents. Details on characteristics of the associated reactions were also obtained. The present testing confirms prior work which indicates that nitrate is an effective agent to attenuate hydrogen from uranium metal corrosion in water and simulated K Basin sludge to show that it is also effective in potential candidate solidified K Basin waste forms for WIPP disposal. The hydrogen mitigation afforded by nitrate appears to be sufficient to meet the hydrogen generation limits for shipping various sludge waste streams based on uranium metal concentrations and assumed waste form loadings.« less

Photolytic AND Catalytic Destruction of Organic Waste Water Pollutants

NASA Astrophysics Data System (ADS)

Torosyan, V. F.; Torosyan, E. S.; Kryuchkova, S. O.; Gromov, V. E.

2017-01-01

The system: water supply source - potable and industrial water - wastewater - sewage treatment - water supply source is necessary for water supply and efficient utilization of water resources. Up-to-date technologies of waste water biological treatment require for special microorganisms, which are technologically complex and expensive but unable to solve all the problems. Application of photolytic and catalytically-oxidizing destruction is quite promising. However, the most reagents are strong oxidizers in catalytic oxidation of organic substances and can initiate toxic substance generation. Methodic and scientific approaches to assess bread making industry influence on the environment have been developed in this paper in order to support forecasting and taking technological decisions concerning reduction of this influence. Destructive methods have been tested: ultra violet irradiation and catalytic oxidation for extraction of organic compounds from waste water by natural reagents.

Future trends in computer waste generation in India.

PubMed

Dwivedy, Maheshwar; Mittal, R K

2010-11-01

The objective of this paper is to estimate the future projection of computer waste in India and to subsequently analyze their flow at the end of their useful phase. For this purpose, the study utilizes the logistic model-based approach proposed by Yang and Williams to forecast future trends in computer waste. The model estimates future projection of computer penetration rate utilizing their first lifespan distribution and historical sales data. A bounding analysis on the future carrying capacity was simulated using the three parameter logistic curve. The observed obsolete generation quantities from the extrapolated penetration rates are then used to model the disposal phase. The results of the bounding analysis indicate that in the year 2020, around 41-152 million units of computers will become obsolete. The obsolete computer generation quantities are then used to estimate the End-of-Life outflows by utilizing a time-series multiple lifespan model. Even a conservative estimate of the future recycling capacity of PCs will reach upwards of 30 million units during 2025. Apparently, more than 150 million units could be potentially recycled in the upper bound case. However, considering significant future investment in the e-waste recycling sector from all stakeholders in India, we propose a logistic growth in the recycling rate and estimate the requirement of recycling capacity between 60 and 400 million units for the lower and upper bound case during 2025. Finally, we compare the future obsolete PC generation amount of the US and India. Copyright © 2010 Elsevier Ltd. All rights reserved.

Scenario analysis of the benefit of municipal organic-waste composting over landfill, Cambodia.

PubMed

Seng, Bunrith; Hirayama, Kimiaki; Katayama-Hirayama, Keiko; Ochiai, Satoru; Kaneko, Hidehiro

2013-01-15

This paper presents insight into the benefits of organic waste recycling through composting over landfill, in terms of landfill life extension, compost product, and mitigation of greenhouse gases (GHGs). Future waste generation from 2003 to 2020 was forecast, and five scenarios of organic waste recycling in the municipality of Phnom Penh (MPP), Cambodia, were carried out. Organic waste-specifically food and garden waste-was used for composting, and the remaining waste was landfilled. The recycling scenarios were set based on organic waste generated from difference sources: households, restaurants, shops, markets, schools, hotels, offices, and street sweeping. Through the five scenarios, the minimum volume reductions of waste disposal were about 56, 123, and 219 m(3) d(-1) in 2003, 2012, and 2020, respectively, whereas the maximum volume reductions in these years were about 325, 643, and 1025 m(3) d(-1). These volume reductions reflect a landfill life extension of a minimum of half a year and a maximum of about four years. Compost product could be produced at a minimum of 14, 30, and 54 tons d(-1) in 2003, 2012, and 2020, respectively, and at a maximum in those years of about 80, 158, and 252 tons d(-1). At the same time benefit is gained in compost product, GHG emissions could be reduced by a minimum of 12.8% and a maximum of 65.0% from 2003 to 2020. This means about 3.23 (minimum) and 5.79 million tons CO(2)eq (maximum) contributed to GHG mitigation. In this regard, it is strongly recommended that MPP should try to initiate an organic-waste recycling strategy in a best fit scenario. Copyright © 2012 Elsevier Ltd. All rights reserved.

Improving the Success of Strategic Management Using Big Data.

PubMed

Desai, Sapan S; Wilkerson, James; Roberts, Todd

2016-01-01

Strategic management involves determining organizational goals, implementing a strategic plan, and properly allocating resources. Poor access to pertinent and timely data misidentifies clinical goals, prevents effective resource allocation, and generates waste from inaccurate forecasting. Loss of operational efficiency diminishes the value stream, adversely impacts the quality of patient care, and hampers effective strategic management. We have pioneered an approach using big data to create competitive advantage by identifying trends in clinical practice, accurately anticipating future needs, and strategically allocating resources for maximum impact.

Forecasting quantities of disused household CRT appliances--a regional case study approach and its application to Baden-Württemberg.

PubMed

Walk, Wolfgang

2009-02-01

Due to special requirements regarding logistics and recycling, disused cathode ray tube (CRT) appliances are handled in some countries as a separate waste fraction. This article presents a forecast of future household waste CRT quantities based on the past and present equipment of households with television sets and computer monitors. Additional aspects taken into consideration are the product life time distribution and the ongoing change in display technology. Although CRT technology is fading out, the findings of this forecast show that quantities of waste CRT appliances will not decrease before 2012 in Baden-Württemberg, Germany. The results of this regional case study are not quantitatively transferable without further analysis. The method provided allows analysts to consider how the time shift between production and discard could impact recycling options, and the method could be valuable for future similar analyses elsewhere.

Requirements Development Issues for Advanced Life Support Systems: Solid Waste Management

NASA Technical Reports Server (NTRS)

Levri, Julie A.; Fisher, John W.; Alazraki, Michael P.; Hogan, John A.

2002-01-01

Long duration missions pose substantial new challenges for solid waste management in Advanced Life Support (ALS) systems. These possibly include storing large volumes of waste material in a safe manner, rendering wastes stable or sterilized for extended periods of time, and/or processing wastes for recovery of vital resources. This is further complicated because future missions remain ill-defined with respect to waste stream quantity, composition and generation schedule. Without definitive knowledge of this information, development of requirements is hampered. Additionally, even if waste streams were well characterized, other operational and processing needs require clarification (e.g. resource recovery requirements, planetary protection constraints). Therefore, the development of solid waste management (SWM) subsystem requirements for long duration space missions is an inherently uncertain, complex and iterative process. The intent of this paper is to address some of the difficulties in writing requirements for missions that are not completely defined. This paper discusses an approach and motivation for ALS SWM requirements development, the characteristics of effective requirements, and the presence of those characteristics in requirements that are developed for uncertain missions. Associated drivers for life support system technological capability are also presented. A general means of requirements forecasting is discussed, including successive modification of requirements and the need to consider requirements integration among subsystems.

EnergySolution's Clive Disposal Facility Operational Research Model - 13475

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nissley, Paul; Berry, Joanne

2013-07-01

EnergySolutions owns and operates a licensed, commercial low-level radioactive waste disposal facility located in Clive, Utah. The Clive site receives low-level radioactive waste from various locations within the United States via bulk truck, containerised truck, enclosed truck, bulk rail-cars, rail boxcars, and rail inter-modals. Waste packages are unloaded, characterized, processed, and disposed of at the Clive site. Examples of low-level radioactive waste arriving at Clive include, but are not limited to, contaminated soil/debris, spent nuclear power plant components, and medical waste. Generators of low-level radioactive waste typically include nuclear power plants, hospitals, national laboratories, and various United States government operatedmore » waste sites. Over the past few years, poor economic conditions have significantly reduced the number of shipments to Clive. With less revenue coming in from processing shipments, Clive needed to keep its expenses down if it was going to maintain past levels of profitability. The Operational Research group of EnergySolutions were asked to develop a simulation model to help identify any improvement opportunities that would increase overall operating efficiency and reduce costs at the Clive Facility. The Clive operations research model simulates the receipt, movement, and processing requirements of shipments arriving at the facility. The model includes shipment schedules, processing times of various waste types, labor requirements, shift schedules, and site equipment availability. The Clive operations research model has been developed using the WITNESS{sup TM} process simulation software, which is developed by the Lanner Group. The major goals of this project were to: - identify processing bottlenecks that could reduce the turnaround time from shipment arrival to disposal; - evaluate the use (or idle time) of labor and equipment; - project future operational requirements under different forecasted scenarios. By identifying processing bottlenecks and unused equipment and/or labor, improvements to operating efficiency could be determined and appropriate cost saving measures implemented. Model runs forecasting various scenarios helped illustrate potential impacts of certain conditions (e.g. 20% decrease in shipments arrived), variables (e.g. 20% decrease in labor), or other possible situations. (authors)« less

Proceedings of the American Power Conference. Volume 58-I

DOE Office of Scientific and Technical Information (OSTI.GOV)

McBride, A.E.

1996-10-01

This is volume 58-I of the proceedings of the American Power Conference, 1996, Technology for Competition and Globalization. The topics of the papers include power plant DC issues; cost of environmental compliance; advanced coal systems -- environmental performance; technology for competition in dispersed generation; superconductivity technologies for electric utility applications; power generation trends and challenges in China; aging in nuclear power plants; innovative and competitive repowering options; structural examinations, modifications and repairs; electric load forecasting; distribution planning; EMF effects; fuzzy logic and neural networks for power plant applications; electrokinetic decontamination of soils; integrated gasification combined cycle; advances in fusion; coolingmore » towers; relays; plant controls; flue gas desulfurization; waste product utilization; and improved technologies.« less

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Recent Trends in Variable Generation Forecasting and Its Value to the Power System

DOE PAGES

Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; ...

2014-12-23

We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.« less

Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid (Spanish Version)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Tian; Chernyakhovskiy, Ilya; Brancucci Martinez-Anido, Carlo

This document is the Spanish version of 'Greening the Grid- Forecasting Wind and Solar Generation Improving System Operations'. It discusses improving system operations with forecasting with and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

The Use of Ambient Humidity Conditions to Improve Influenza Forecast

NASA Astrophysics Data System (ADS)

Shaman, J. L.; Kandula, S.; Yang, W.; Karspeck, A. R.

2017-12-01

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing. These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast and provide further evidence that humidity modulates rates of influenza transmission.

The use of ambient humidity conditions to improve influenza forecast.

PubMed

Shaman, Jeffrey; Kandula, Sasikiran; Yang, Wan; Karspeck, Alicia

2017-11-01

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.

The use of ambient humidity conditions to improve influenza forecast

PubMed Central

Kandula, Sasikiran; Karspeck, Alicia

2017-01-01

Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1–4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast. PMID:29145389

Waste incineration industry and development policies in China.

PubMed

Li, Yun; Zhao, Xingang; Li, Yanbin; Li, Xiaoyu

2015-12-01

The growing pollution from municipal solid waste due to economic growth and urbanization has brought great challenge to China. The main method of waste disposal has gradually changed from landfill to incineration, because of the enormous land occupation by landfills. The paper presents the results of a study of the development status of the upstream and downstream of the waste incineration industry chain in China, reviews the government policies for the waste incineration power industry, and provides a forecast of the development trend of the waste incineration industry. Copyright © 2015 Elsevier Ltd. All rights reserved.

Generational forecasting in academic medicine: a unique method of planning for success in the next two decades.

PubMed

Howell, Lydia Pleotis; Joad, Jesse P; Callahan, Edward; Servis, Gregg; Bonham, Ann C

2009-08-01

Multigenerational teams are essential to the missions of academic health centers (AHCs). Generational forecasting using Strauss and Howe's predictive model, "the generational diagonal," can be useful for anticipating and addressing issues so that each generation is effective. Forecasts are based on the observation that cyclical historical events are experienced by all generations, but the response of each generation differs according to its phase of life and previous defining experiences. This article relates Strauss and Howe's generational forecasts to AHCs. Predicted issues such as work-life balance, indebtedness, and succession planning have existed previously, but they now have different causes or consequences because of the unique experiences and life stages of current generations. Efforts to address these issues at the authors' AHC include a work-life balance workgroup, expanded leave, and intramural grants.

Ensemble forecasting for renewable energy applications - status and current challenges for their generation and verification

NASA Astrophysics Data System (ADS)

Pinson, Pierre

2016-04-01

The operational management of renewable energy generation in power systems and electricity markets requires forecasts in various forms, e.g., deterministic or probabilistic, continuous or categorical, depending upon the decision process at hand. Besides, such forecasts may also be necessary at various spatial and temporal scales, from high temporal resolutions (in the order of minutes) and very localized for an offshore wind farm, to coarser temporal resolutions (hours) and covering a whole country for day-ahead power scheduling problems. As of today, weather predictions are a common input to forecasting methodologies for renewable energy generation. Since for most decision processes, optimal decisions can only be made if accounting for forecast uncertainties, ensemble predictions and density forecasts are increasingly seen as the product of choice. After discussing some of the basic approaches to obtaining ensemble forecasts of renewable power generation, it will be argued that space-time trajectories of renewable power production may or may not be necessitate post-processing ensemble forecasts for relevant weather variables. Example approaches and test case applications will be covered, e.g., looking at the Horns Rev offshore wind farm in Denmark, or gridded forecasts for the whole continental Europe. Eventually, we will illustrate some of the limitations of current frameworks to forecast verification, which actually make it difficult to fully assess the quality of post-processing approaches to obtain renewable energy predictions.

Short-Term Energy Outlook Model Documentation: Electricity Generation and Fuel Consumption Models

EIA Publications

2014-01-01

The electricity generation and fuel consumption models of the Short-Term Energy Outlook (STEO) model provide forecasts of electricity generation from various types of energy sources and forecasts of the quantities of fossil fuels consumed for power generation. The structure of the electricity industry and the behavior of power generators varies between different areas of the United States. In order to capture these differences, the STEO electricity supply and fuel consumption models are designed to provide forecasts for the four primary Census regions.

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Wu, Hongyu; Florita, Anthony R.

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

DOE PAGES

Wang, Qin; Wu, Hongyu; Florita, Anthony R.; ...

2016-11-11

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was comparedmore » through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.« less

When mechanism matters: Bayesian forecasting using models of ecological diffusion

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

A New Multivariate Approach in Generating Ensemble Meteorological Forcings for Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Khajehei, Sepideh; Moradkhani, Hamid

2015-04-01

Producing reliable and accurate hydrologic ensemble forecasts are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model structure, and model parameters. Producing reliable and skillful precipitation ensemble forecasts is one approach to reduce the total uncertainty in hydrological applications. Currently, National Weather Prediction (NWP) models are developing ensemble forecasts for various temporal ranges. It is proven that raw products from NWP models are biased in mean and spread. Given the above state, there is a need for methods that are able to generate reliable ensemble forecasts for hydrological applications. One of the common techniques is to apply statistical procedures in order to generate ensemble forecast from NWP-generated single-value forecasts. The procedure is based on the bivariate probability distribution between the observation and single-value precipitation forecast. However, one of the assumptions of the current method is fitting Gaussian distribution to the marginal distributions of observed and modeled climate variable. Here, we have described and evaluated a Bayesian approach based on Copula functions to develop an ensemble precipitation forecast from the conditional distribution of single-value precipitation forecasts. Copula functions are known as the multivariate joint distribution of univariate marginal distributions, which are presented as an alternative procedure in capturing the uncertainties related to meteorological forcing. Copulas are capable of modeling the joint distribution of two variables with any level of correlation and dependency. This study is conducted over a sub-basin in the Columbia River Basin in USA using the monthly precipitation forecasts from Climate Forecast System (CFS) with 0.5x0.5 Deg. spatial resolution to reproduce the observations. The verification is conducted on a different period and the superiority of the procedure is compared with Ensemble Pre-Processor approach currently used by National Weather Service River Forecast Centers in USA.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced.more » The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) ismore » analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Comprehensive Flood Plain Studies Using Spatial Data Management Techniques.

DTIC Science & Technology

1978-06-01

Hydrologic Engineer- ing Center computer programs that forecast urban storm water quality and dynamic in- stream water quality response to waste...determination. Water Quality The water quality analysis planned for the pilot study includes urban storm water quality forecasting and in-streamn...analysis is performed under the direction of Tony Thomas. Chief, Research Branch, by Jess Abbott for storm water quality analysis, R. G. Willey for

An Interactive Real-time Decision Support System for Leachate Irrigation on Evapotranspiration Landfill Covers

NASA Astrophysics Data System (ADS)

Wang, Y.

2015-12-01

Landfill disposal is still the most common and economical practice for municipal solid waste in most countries. However, heavily polluted leachate generated by excess rainwater percolating through the landfill waste is the major drawback of this practice. Evapotranspiration (ET) cover systems are increasingly being used as alternative cover systems to minimize percolation by evapotranspiration. Leachate recirculation is one of the least expensive options for leachate treatment. The combination of ET cover systems and leachate recirculation can be an economical and environment-friendly practice for landfill leachate management. An interactive real-time decision support system is being developed to better manage leachate irrigation using historical and forecasting weather data, and real time soil moisture data. The main frame of this system includes soil water modules, and plant-soil modules. An inverse simulation module is also included to calibrate certain parameters based on observed data when necessary. It would be an objectives-oriented irrigation management tool to minimize landfill operation costs and negative environmental impacts.

Monitoring of waste disposal in deep geological formations

NASA Astrophysics Data System (ADS)

German, V.; Mansurov, V.

2003-04-01

In the paper application of kinetic approach for description of rock failure process and waste disposal microseismic monitoring is advanced. On base of two-stage model of failure process the capability of rock fracture is proved. The requests to monitoring system such as real time mode of data registration and processing and its precision range are formulated. The method of failure nuclei delineation in a rock masses is presented. This method is implemented in a software program for strong seismic events forecasting. It is based on direct use of the fracture concentration criterion. The method is applied to the database of microseismic events of the North Ural Bauxite Mine. The results of this application, such as: efficiency, stability, possibility of forecasting rockburst are discussed.

Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts - A Hydrologic Model Output Statistics (HMOS) approach

NASA Astrophysics Data System (ADS)

Regonda, Satish Kumar; Seo, Dong-Jun; Lawrence, Bill; Brown, James D.; Demargne, Julie

2013-08-01

We present a statistical procedure for generating short-term ensemble streamflow forecasts from single-valued, or deterministic, streamflow forecasts produced operationally by the U.S. National Weather Service (NWS) River Forecast Centers (RFCs). The resulting ensemble streamflow forecast provides an estimate of the predictive uncertainty associated with the single-valued forecast to support risk-based decision making by the forecasters and by the users of the forecast products, such as emergency managers. Forced by single-valued quantitative precipitation and temperature forecasts (QPF, QTF), the single-valued streamflow forecasts are produced at a 6-h time step nominally out to 5 days into the future. The single-valued streamflow forecasts reflect various run-time modifications, or "manual data assimilation", applied by the human forecasters in an attempt to reduce error from various sources in the end-to-end forecast process. The proposed procedure generates ensemble traces of streamflow from a parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecast, QPF, and the most recent streamflow observation. For parameter estimation and evaluation, we used a multiyear archive of the single-valued river stage forecast produced operationally by the NWS Arkansas-Red River Basin River Forecast Center (ABRFC) in Tulsa, Oklahoma. As a by-product of parameter estimation, the procedure provides a categorical assessment of the effective lead time of the operational hydrologic forecasts for different QPF and forecast flow conditions. To evaluate the procedure, we carried out hindcasting experiments in dependent and cross-validation modes. The results indicate that the short-term streamflow ensemble hindcasts generated from the procedure are generally reliable within the effective lead time of the single-valued forecasts and well capture the skill of the single-valued forecasts. For smaller basins, however, the effective lead time is significantly reduced by short basin memory and reduced skill in the single-valued QPF.

An easy-to-use tool for the evaluation of leachate production at landfill sites.

PubMed

Grugnaletti, Matteo; Pantini, Sara; Verginelli, Iason; Lombardi, Francesco

2016-09-01

A simulation program for the evaluation of leachate generation at landfill sites is herein presented. The developed tool is based on a water balance model that accounts for all the key processes influencing leachate generation through analytical and empirical equations. After a short description of the tool, different simulations on four Italian landfill sites are shown. The obtained results revealed that when literature values were assumed for the unknown input parameters, the model provided a rough estimation of the leachate production measured in the field. In this case, indeed, the deviations between observed and predicted data appeared, in some cases, significant. Conversely, by performing a preliminary calibration for some of the unknown input parameters (e.g. initial moisture content of wastes, compression index), in nearly all cases the model performances significantly improved. These results although showed the potential capability of a water balance model to estimate the leachate production at landfill sites also highlighted the intrinsic limitation of a deterministic approach to accurately forecast the leachate production over time. Indeed, parameters such as the initial water content of incoming waste and the compression index, that have a great influence on the leachate production, may exhibit temporal variation due to seasonal changing of weather conditions (e.g. rainfall, air humidity) as well as to seasonal variability in the amount and type of specific waste fractions produced (e.g. yard waste, food, plastics) that make their prediction quite complicated. In this sense, we believe that a tool such as the one proposed in this work that requires a limited number of unknown parameters, can be easier handled to quantify the uncertainties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolinger, Mark; Wiser, Ryan; Golove, William

2003-08-13

Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projectedmore » costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e.g., futures, swaps, and fixed-price physical supply contracts) to contemporaneous forecasts of spot natural gas prices, with the purpose of identifying any systematic differences between the two. Although our data set is quite limited, we find that over the past three years, forward gas prices for durations of 2-10 years have been considerably higher than most natural gas spot price forecasts, including the reference case forecasts developed by the Energy Information Administration (EIA). This difference is striking, and implies that resource planning and modeling exercises based on these forecasts over the past three years have yielded results that are biased in favor of gas-fired generation (again, presuming that long-term stability is desirable). As discussed later, these findings have important ramifications for resource planners, energy modelers, and policy-makers.« less

Where next on e-waste in Australia?

PubMed

Golev, Artem; Schmeda-Lopez, Diego R; Smart, Simon K; Corder, Glen D; McFarland, Eric W

2016-12-01

For almost two decades waste electrical and electronic equipment, WEEE or e-waste, has been considered a growing problem that has global consequences. The value of recovered materials, primarily in precious and base metals, has prompted some parts of the world to informally and inappropriately process e-waste causing serious environmental and human health issues. Efforts in tackling this issue have been limited and in many ways unsuccessful. The global rates for formal e-waste treatment are estimated to be below the 20% mark, with the majority of end-of-life (EoL) electronic devices still ending up in the landfills or processed through rudimentary means. Industrial confidentiality regarding device composition combined with insufficient reporting requirements has made the task of simply characterizing the problem difficult at a global scale. To address some of these key issues, this paper presents a critical overview of existing statistics and estimations for e-waste in an Australia context, including potential value and environmental risks associated with metals recovery. From our findings, in 2014, on average per person, Australians purchased 35kg of electrical and electronic equipment (EEE) while disposed of 25kg of WEEE, and possessed approximately 320kg of EEE. The total amount of WEEE was estimated at 587kt worth about US$ 370million if all major metals are fully recovered. These results are presented over the period 2010-2014, detailed for major EEE product categories and metals, and followed by 2015-2024 forecast. Our future projection, with the base scenario fixing EEE sales at 35kg per capita, predicts stabilization of e-waste generation in Australia at 28-29kg per capita, with the total amount continuing to grow along with the population growth. Copyright © 2016 Elsevier Ltd. All rights reserved.

NOAA's weather forecasts go hyper-local with next-generation weather

Science.gov Websites

model NOAA HOME WEATHER OCEANS FISHERIES CHARTING SATELLITES CLIMATE RESEARCH COASTS CAREERS with next-generation weather model New model will help forecasters predict a storm's path, timing and intensity better than ever September 30, 2014 This is a comparison of two weather forecast models looking

EnrollForecast for Excel: K-12 Enrollment Forecasting Program. Software & User's Guide. [Computer Diskette].

ERIC Educational Resources Information Center

Smith, Curtis A.

"EnrollForecast for Excel" will generate a 5-year forecast of K-12 student enrollment. It will also work for any combination of grades between kindergarten and twelth. The forecasts can be printed as either a table or a graph. The user must provide birth history (only if forecasting kindergarten) and enrollment history information. The user also…

Uncertainty estimation of long-range ensemble forecasts of snowmelt flood characteristics

NASA Astrophysics Data System (ADS)

Kuchment, L.

2012-04-01

Long-range forecasts of snowmelt flood characteristics with the lead time of 2-3 months have important significance for regulation of flood runoff and mitigation of flood damages at almost all large Russian rivers At the same time, the application of current forecasting techniques based on regression relationships between the runoff volume and the indexes of river basin conditions can lead to serious errors in forecasting resulted in large economic losses caused by wrong flood regulation. The forecast errors can be caused by complicated processes of soil freezing and soil moisture redistribution, too high rate of snow melt, large liquid precipitation before snow melt. or by large difference of meteorological conditions during the lead-time periods from climatologic ones. Analysis of economic losses had shown that the largest damages could, to a significant extent, be avoided if the decision makers had an opportunity to take into account predictive uncertainty and could use more cautious strategies in runoff regulation. Development of methodology of long-range ensemble forecasting of spring/summer floods which is based on distributed physically-based runoff generation models has created, in principle, a new basis for improving hydrological predictions as well as for estimating their uncertainty. This approach is illustrated by forecasting of the spring-summer floods at the Vyatka River and the Seim River basins. The application of the physically - based models of snowmelt runoff generation give a essential improving of statistical estimates of the deterministic forecasts of the flood volume in comparison with the forecasts obtained from the regression relationships. These models had been used also for the probabilistic forecasts assigning meteorological inputs during lead time periods from the available historical daily series, and from the series simulated by using a weather generator and the Monte Carlo procedure. The weather generator consists of the stochastic models of daily temperature and precipitation. The performance of the probabilistic forecasts were estimated by the ranked probability skill scores. The application of Monte Carlo simulations using weather generator has given better results then using the historical meteorological series.

The meta-Gaussian Bayesian Processor of forecasts and associated preliminary experiments

NASA Astrophysics Data System (ADS)

Chen, Fajing; Jiao, Meiyan; Chen, Jing

2013-04-01

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.

Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

NASA Astrophysics Data System (ADS)

Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

2018-03-01

Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

Verification of FLYSAFE Clear Air Turbulence (CAT) objects against aircraft turbulence measurements

NASA Astrophysics Data System (ADS)

Lunnon, R.; Gill, P.; Reid, L.; Mirza, A.

2009-09-01

Prediction of gridded CAT fields The main causes of CAT are (a) Vertical wind shear - low Richardson Number (b) Mountain waves (c) Convection. All three causes contribute roughly equally to CAT occurrences, globally Prediction of shear induced CAT The predictions of shear induced CAT has a longer history than either mountain-wave induced CAT or convectively induced CAT. Both Global Aviation Forecasting Centres are currently using the Ellrod TI1 algorithm (Ellrod and Knapp, 1992). This predictor is the scalar product of deformation [akm1]and vertical wind shear. More sophisticated algorithms can amplify errors in non-linear, differentiated quantities so it is very likely that Ellrod will out-perform other algorithms when verified globally. Prediction of mountain wave CAT The Global Aviation Forecasting Centre in the UK has been generating automated forecasts of mountain wave CAT since the late 1990s, based on the diagnosis of gravity wave drag. Generation of CAT objects In the FLYSAFE project it was decided at an early stage that short range forecasts of meteorological hazards, i.e. icing, Clear Air Turbulence, Cumulonimbus Clouds, should be represented as weather objects, that is, descriptions of individual hazardous volumes of airspace. For CAT, the forecast information on which the weather objects were based was gridded, that comprised a representation of a hazard level for all points in a pre-defined 3-D grid, for a range of forecast times. A "grid-to-objects" capability was generated. This is discussed further in Mirza and Drouin (this conference). Verification of CAT forecasts Verification was performed using digital accelerometer data from aircraft in the British Airways Boeing 747 fleet. A preliminary processing of the aircraft data were performed to generate a truth field on a scale similar to that used to provide gridded forecasts to airlines. This truth field was binary, i.e. each flight segment was characterised as being either "turbulent" or "benign". A gridded forecast field is a continuously changing variable. In contrast, a simple weather object must be characterised by a specific threshold. For a gridded forecast and a binary truth measure it is possible to generate Relative Operating Characteristic (ROC) curves. For weather objects, a single point in the hit-rate/false-alarm-rate space can be generated. If this point is plotted on a ROC curve graph then the skill of the forecast using weather objects can be compared with the skill of the gridded forecast.

Villacidro solar demo plant: Integration of small-scale CSP and biogas power plants in an industrial microgrid

NASA Astrophysics Data System (ADS)

Camerada, M.; Cau, G.; Cocco, D.; Damiano, A.; Demontis, V.; Melis, T.; Musio, M.

2016-05-01

The integration of small scale concentrating solar power (CSP) in an industrial district, in order to develop a microgrid fully supplied by renewable energy sources, is presented in this paper. The plant aims to assess in real operating conditions, the performance, the effectiveness and the reliability of small-scale concentrating solar power technologies in the field of distributed generation. In particular, the potentiality of small scale CSP with thermal storage to supply dispatchable electricity to an industrial microgrid will be investigated. The microgrid will be realized in the municipal waste treatment plant of the Industrial Consortium of Villacidro, in southern Sardinia (Italy), which already includes a biogas power plant. In order to achieve the microgrid instantaneous energy balance, the analysis of the time evolution of the waste treatment plant demand and of the generation in the existing power systems has been carried out. This has allowed the design of a suitable CSP plant with thermal storage and an electrochemical storage system for supporting the proposed microgrid. At the aim of obtaining the expected energy autonomy, a specific Energy Management Strategy, which takes into account the different dynamic performances and characteristics of the demand and the generation, has been designed. In this paper, the configuration of the proposed small scale concentrating solar power (CSP) and of its thermal energy storage, based on thermocline principle, is initially described. Finally, a simulation study of the entire power system, imposing scheduled profiles based on weather forecasts, is presented.

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-01-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the loadmore » and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.« less

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Waste to Watts and Water: Enabling Self-Contained Facilities Using Microbial Fuel Cells

DTIC Science & Technology

2009-03-01

will require in future facilities is the ability to operate apart from the infrastructure net- work and line of communications (LOC) in a clean and ef...in future technologies, observes that “forecasters are im- prisoned by their times.”33 Humans tend to look at today’s crisis and project it into the...2030. In 2007 the United States Department of Energy (DOE) forecast international power demand to double by 2030.34 Today’s energy crisis is well

Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.

2010-09-01

The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and windmore » forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.« less

The Ability of Analysts' Recommendations to Predict Optimistic and Pessimistic Forecasts

PubMed Central

Biglari, Vahid; Alfan, Ervina Binti; Ahmad, Rubi Binti; Hajian, Najmeh

2013-01-01

Previous researches show that buy (growth) companies conduct income increasing earnings management in order to meet forecasts and generate positive forecast Errors (FEs). This behavior however, is not inherent in sell (non-growth) companies. Using the aforementioned background, this research hypothesizes that since sell companies are pressured to avoid income increasing earnings management, they are capable, and in fact more inclined, to pursue income decreasing Forecast Management (FM) with the purpose of generating positive FEs. Using a sample of 6553 firm-years of companies that are listed in the NYSE between the years 2005–2010, the study determines that sell companies conduct income decreasing FM to generate positive FEs. However, the frequency of positive FEs of sell companies does not exceed that of buy companies. Using the efficiency perspective, the study suggests that even though buy and sell companies have immense motivation in avoiding negative FEs, they exploit different but efficient strategies, respectively, in order to meet forecasts. Furthermore, the findings illuminated the complexities behind informative and opportunistic forecasts that falls under the efficiency versus opportunistic theories in literature. PMID:24146741

The ability of analysts' recommendations to predict optimistic and pessimistic forecasts.

PubMed

Biglari, Vahid; Alfan, Ervina Binti; Ahmad, Rubi Binti; Hajian, Najmeh

2013-01-01

Previous researches show that buy (growth) companies conduct income increasing earnings management in order to meet forecasts and generate positive forecast Errors (FEs). This behavior however, is not inherent in sell (non-growth) companies. Using the aforementioned background, this research hypothesizes that since sell companies are pressured to avoid income increasing earnings management, they are capable, and in fact more inclined, to pursue income decreasing Forecast Management (FM) with the purpose of generating positive FEs. Using a sample of 6553 firm-years of companies that are listed in the NYSE between the years 2005-2010, the study determines that sell companies conduct income decreasing FM to generate positive FEs. However, the frequency of positive FEs of sell companies does not exceed that of buy companies. Using the efficiency perspective, the study suggests that even though buy and sell companies have immense motivation in avoiding negative FEs, they exploit different but efficient strategies, respectively, in order to meet forecasts. Furthermore, the findings illuminated the complexities behind informative and opportunistic forecasts that falls under the efficiency versus opportunistic theories in literature.

Dispersion Modeling Using Ensemble Forecasts Compared to ETEX Measurements.

NASA Astrophysics Data System (ADS)

Straume, Anne Grete; N'dri Koffi, Ernest; Nodop, Katrin

1998-11-01

Numerous numerical models are developed to predict long-range transport of hazardous air pollution in connection with accidental releases. When evaluating and improving such a model, it is important to detect uncertainties connected to the meteorological input data. A Lagrangian dispersion model, the Severe Nuclear Accident Program, is used here to investigate the effect of errors in the meteorological input data due to analysis error. An ensemble forecast, produced at the European Centre for Medium-Range Weather Forecasts, is then used as model input. The ensemble forecast members are generated by perturbing the initial meteorological fields of the weather forecast. The perturbations are calculated from singular vectors meant to represent possible forecast developments generated by instabilities in the atmospheric flow during the early part of the forecast. The instabilities are generated by errors in the analyzed fields. Puff predictions from the dispersion model, using ensemble forecast input, are compared, and a large spread in the predicted puff evolutions is found. This shows that the quality of the meteorological input data is important for the success of the dispersion model. In order to evaluate the dispersion model, the calculations are compared with measurements from the European Tracer Experiment. The model manages to predict the measured puff evolution concerning shape and time of arrival to a fairly high extent, up to 60 h after the start of the release. The modeled puff is still too narrow in the advection direction.

Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model

PubMed Central

Zhang, Jinlun

2015-01-01

Abstract Arctic sea ice drift forecasts of 6 h–9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h–8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high‐resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast. PMID:27818852

Advancing solar energy forecasting through the underlying physics

NASA Astrophysics Data System (ADS)

Yang, H.; Ghonima, M. S.; Zhong, X.; Ozge, B.; Kurtz, B.; Wu, E.; Mejia, F. A.; Zamora, M.; Wang, G.; Clemesha, R.; Norris, J. R.; Heus, T.; Kleissl, J. P.

2017-12-01

As solar power comprises an increasingly large portion of the energy generation mix, the ability to accurately forecast solar photovoltaic generation becomes increasingly important. Due to the variability of solar power caused by cloud cover, knowledge of both the magnitude and timing of expected solar power production ahead of time facilitates the integration of solar power onto the electric grid by reducing electricity generation from traditional ancillary generators such as gas and oil power plants, as well as decreasing the ramping of all generators, reducing start and shutdown costs, and minimizing solar power curtailment, thereby providing annual economic value. The time scales involved in both the energy markets and solar variability range from intra-hour to several days ahead. This wide range of time horizons led to the development of a multitude of techniques, with each offering unique advantages in specific applications. For example, sky imagery provides site-specific forecasts on the minute-scale. Statistical techniques including machine learning algorithms are commonly used in the intra-day forecast horizon for regional applications, while numerical weather prediction models can provide mesoscale forecasts on both the intra-day and days-ahead time scale. This talk will provide an overview of the challenges unique to each technique and highlight the advances in their ongoing development which come alongside advances in the fundamental physics underneath.

Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

1997-04-01

Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.

Flexible reserve markets for wind integration

NASA Astrophysics Data System (ADS)

Fernandez, Alisha R.

The increased interconnection of variable generation has motivated the use of improved forecasting to more accurately predict future production with the purpose to lower total system costs for balancing when the expected output exceeds or falls short of the actual output. Forecasts are imperfect, and the forecast errors associated with utility-scale generation from variable generators need new balancing capabilities that cannot be handled by existing ancillary services. Our work focuses on strategies for integrating large amounts of wind generation under the flex reserve market, a market that would called upon for short-term energy services during an under or oversupply of wind generation to maintain electric grid reliability. The flex reserve market would be utilized for time intervals that fall in-between the current ancillary services markets that would be longer than second-to-second energy services for maintaining system frequency and shorter than reserve capacity services that are called upon for several minutes up to an hour during an unexpected contingency on the grid. In our work, the wind operator would access the flex reserve market as an energy service to correct for unanticipated forecast errors, akin to paying the generators participating in the market to increase generation during a shortfall or paying the other generators to decrease generation during an excess of wind generation. Such a market does not currently exist in the Mid-Atlantic United States. The Pennsylvania-New Jersey-Maryland Interconnection (PJM) is the Mid-Atlantic electric grid case study that was used to examine if a flex reserve market can be utilized for integrating large capacities of wind generation in a lowcost manner for those providing, purchasing and dispatching these short-term balancing services. The following work consists of three studies. The first examines the ability of a hydroelectric facility to provide short-term forecast error balancing services via a flex reserve market, identifying the operational constraints that inhibit a multi-purpose dam facility to meet the desired flexible energy demand. The second study transitions from the hydroelectric facility as the decision maker providing flex reserve services to the wind plant as the decision maker purchasing these services. In this second study, methods for allocating the costs of flex reserve services under different wind policy scenarios are explored that aggregate farms into different groupings to identify the least-cost strategy for balancing the costs of hourly day-ahead forecast errors. The least-cost strategy may be different for an individual wind plant and for the system operator, noting that the least-cost strategy is highly sensitive to cost allocation and aggregation schemes. The latter may also cause cross-subsidies in the cost for balancing wind forecast errors among the different wind farms. The third study builds from the second, with the objective to quantify the amount of flex reserves needed for balancing future forecast errors using a probabilistic approach (quantile regression) to estimating future forecast errors. The results further examine the usefulness of separate flexible markets PJM could use for balancing oversupply and undersupply events, similar to the regulation up and down markets used in Europe. These three studies provide the following results and insights to large-scale wind integration using actual PJM wind farm data that describe the markets and generators within PJM. • Chapter 2 provides an in-depth analysis of the valuable, yet highly-constrained, energy services multi-purpose hydroelectric facilities can provide, though the opportunity cost for providing these services can result in large deviations from the reservoir policies with minimal revenue gain in comparison to dedicating the whole of dam capacity to providing day-ahead, baseload generation. • Chapter 3 quantifies the system-wide efficiency gains and the distributive effects of PJM's decision to act as a single balancing authority, which means that it procures ancillary services across its entire footprint simultaneously. This can be contrasted to Midwest Independent System Operator (MISO), which has several balancing authorities operating under its footprint. • Chapter 4 uses probabilistic methods to estimate the uncertainty in the forecast errors and the quantity of energy needed to balance these forecast errors at a certain percentile. Current practice is to use a point forecast that describes the conditional expectation of the dependent variable at each time step. The approach here uses quantile regression to describe the relationship between independent variable and the conditional quantiles (equivalently the percentiles) of the dependent variable. An estimate of the conditional density is performed, which contains information about the covariate relationship of the sign of the forecast errors (negative for too much wind generation and positive for too little wind generation) and the wind power forecast. This additional knowledge may be implemented in the decision process to more accurately schedule day-ahead wind generation bids and provide an example for using separate markets for balancing an oversupply and undersupply of generation. Such methods are currently used for coordinating large footprints of wind generation in Europe.

Towards an improved ensemble precipitation forecast: A probabilistic post-processing approach

NASA Astrophysics Data System (ADS)

Khajehei, Sepideh; Moradkhani, Hamid

2017-03-01

Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carson, K.S.

The presence of overpopulation or unsustainable population growth may place pressure on the food and water supplies of countries in sensitive areas of the world. Severe air or water pollution may place additional pressure on these resources. These pressures may generate both internal and international conflict in these areas as nations struggle to provide for their citizens. Such conflicts may result in United States intervention, either unilaterally, or through the United Nations. Therefore, it is in the interests of the United States to identify potential areas of conflict in order to properly train and allocate forces. The purpose of thismore » research is to forecast the probability of conflict in a nation as a function of it s environmental conditions. Probit, logit and ordered probit models are employed to forecast the probability of a given level of conflict. Data from 95 countries are used to estimate the models. Probability forecasts are generated for these 95 nations. Out-of sample forecasts are generated for an additional 22 nations. These probabilities are then used to rank nations from highest probability of conflict to lowest. The results indicate that the dependence of a nation`s economy on agriculture, the rate of deforestation, and the population density are important variables in forecasting the probability and level of conflict. These results indicate that environmental variables do play a role in generating or exacerbating conflict. It is unclear that the United States military has any direct role in mitigating the environmental conditions that may generate conflict. A more important role for the military is to aid in data gathering to generate better forecasts so that the troops are adequntely prepared when conflicts arises.« less

Personalized glucose forecasting for type 2 diabetes using data assimilation

PubMed Central

Albers, David J.; Gluckman, Bruce; Ginsberg, Henry; Hripcsak, George; Mamykina, Lena

2017-01-01

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual’s blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges. PMID:28448498

Mathematic simulation of mining company’s power demand forecast (by example of “Neryungri” coal strip mine)

NASA Astrophysics Data System (ADS)

Antonenkov, D. V.; Solovev, D. B.

2017-10-01

The article covers the aspects of forecasting and consideration of the wholesale market environment in generating the power demand forecast. Major mining companies that operate in conditions of the present day power market have to provide a reliable energy demand request for a certain time period ahead, thus ensuring sufficient reduction of financial losses associated with deviations of the actual power demand from the expected figures. Normally, under the power supply agreement, the consumer is bound to provide a per-month and per-hour request annually. It means that the consumer has to generate one-month-ahead short-term and medium-term hourly forecasts. The authors discovered that empiric distributions of “Yakutugol”, Holding Joint Stock Company, power demand belong to the sustainable rank parameter H-distribution type used for generating forecasts based on extrapolation of such distribution parameters. For this reason they justify the need to apply the mathematic rank analysis in short-term forecasting of the contracted power demand of “Neryungri” coil strip mine being a component of the technocenosis-type system of the mining company “Yakutugol”, Holding JSC.

Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coimbra, Carlos F. M.

2016-02-25

In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior inmore » real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.« less

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE PAGES

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.; ...

2017-07-11

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Staid, Andrea; Watson, Jean -Paul; Wets, Roger J. -B.

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power timemore » series. We estimate non-parametric forecast error densities, specifically using epi-spline basis functions, allowing us to capture the skewed and non-parametric nature of error densities observed in real-world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured.We compare the performance of our approach to the current state-of-the-art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Here, our methodology is embodied in the joint Sandia – University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.« less

Forecasting the Amount of Waste-Sewage Water Discharged into the Yangtze River Basin Based on the Optimal Fractional Order Grey Model

PubMed Central

Li, Shuliang; Meng, Wei; Xie, Yufeng

2017-01-01

With the rapid development of the Yangtze River economic belt, the amount of waste-sewage water discharged into the Yangtze River basin increases sharply year by year, which has impeded the sustainable development of the Yangtze River basin. The water security along the Yangtze River basin is very important for China, It is something about water security of roughly one-third of China’s population and the sustainable development of the 19 provinces, municipalities and autonomous regions among the Yangtze River basin. Therefore, a scientific prediction of the amount of waste-sewage water discharged into Yangtze River basin has a positive significance on sustainable development of industry belt along with Yangtze River basin. This paper builds the fractional DWSGM (1,1) (DWSGM (1,1) model is short for Discharge amount of Waste Sewage Grey Model for one order equation and one variable) model based on the fractional accumulating generation operator and fractional reducing operator, and calculates the optimal order of “r” by using particle swarm optimization (PSO) algorithm for solving the minimum average relative simulation error. Meanwhile, the simulation performance of DWSGM (1,1) model with the optimal fractional order is tested by comparing the simulation results of grey prediction models with different orders. Finally, the optimal fractional order DWSGM (1,1) grey model is applied to predict the amount of waste-sewage water discharged into the Yangtze River basin, and corresponding countermeasures and suggestions are put forward through analyzing and comparing the prediction results. This paper has positive significance on enriching the fractional order modeling method of the grey system. PMID:29295517

Forecasting the Amount of Waste-Sewage Water Discharged into the Yangtze River Basin Based on the Optimal Fractional Order Grey Model.

PubMed

Li, Shuliang; Meng, Wei; Xie, Yufeng

2017-12-23

With the rapid development of the Yangtze River economic belt, the amount of waste-sewage water discharged into the Yangtze River basin increases sharply year by year, which has impeded the sustainable development of the Yangtze River basin. The water security along the Yangtze River basin is very important for China, It is something aboutwater security of roughly one-third of China's population and the sustainable development of the 19 provinces, municipalities and autonomous regions among the Yangtze River basin. Therefore, a scientific prediction of the amount of waste-sewage water discharged into Yangtze River basin has a positive significance on sustainable development of industry belt along with Yangtze River basin. This paper builds the fractional DWSGM(1,1)(DWSGM(1,1) model is short for Discharge amount of Waste Sewage Grey Model for one order equation and one variable) model based on the fractional accumulating generation operator and fractional reducing operator, and calculates the optimal order of "r" by using particle swarm optimization(PSO)algorithm for solving the minimum average relative simulation error. Meanwhile, the simulation performance of DWSGM(1,1)model with the optimal fractional order is tested by comparing the simulation results of grey prediction models with different orders. Finally, the optimal fractional order DWSGM(1,1)grey model is applied to predict the amount of waste-sewage water discharged into the Yangtze River basin, and corresponding countermeasures and suggestions are put forward through analyzing and comparing the prediction results. This paper has positive significance on enriching the fractional order modeling method of the grey system.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

Estimation of construction and demolition waste using waste generation rates in Chennai, India.

PubMed

Ram, V G; Kalidindi, Satyanarayana N

2017-06-01

A large amount of construction and demolition waste is being generated owing to rapid urbanisation in Indian cities. A reliable estimate of construction and demolition waste generation is essential to create awareness about this stream of solid waste among the government bodies in India. However, the required data to estimate construction and demolition waste generation in India are unavailable or not explicitly documented. This study proposed an approach to estimate construction and demolition waste generation using waste generation rates and demonstrated it by estimating construction and demolition waste generation in Chennai city. The demolition waste generation rates of primary materials were determined through regression analysis using waste generation data from 45 case studies. Materials, such as wood, electrical wires, doors, windows and reinforcement steel, were found to be salvaged and sold on the secondary market. Concrete and masonry debris were dumped in either landfills or unauthorised places. The total quantity of construction and demolition debris generated in Chennai city in 2013 was estimated to be 1.14 million tonnes. The proportion of masonry debris was found to be 76% of the total quantity of demolition debris. Construction and demolition debris forms about 36% of the total solid waste generated in Chennai city. A gross underestimation of construction and demolition waste generation in some earlier studies in India has also been shown. The methodology proposed could be utilised by government bodies, policymakers and researchers to generate reliable estimates of construction and demolition waste in other developing countries facing similar challenges of limited data availability.

Modeling and forecasting U.S. sex differentials in mortality.

PubMed

Carter, L R; Lee, R D

1992-11-01

"This paper examines differentials in observed and forecasted sex-specific life expectancies and longevity in the United States from 1900 to 2065. Mortality models are developed and used to generate long-run forecasts, with confidence intervals that extend recent work by Lee and Carter (1992). These results are compared for forecast accuracy with univariate naive forecasts of life expectancies and those prepared by the Actuary of the Social Security Administration." excerpt

The causes of the municipal solid waste and the greenhouse gas emissions from the waste sector in the United States.

PubMed

Lee, Seungtaek; Kim, Jonghoon; Chong, Wai K O

2016-10-01

The United States generated approximately 730kg of waste per capita in 2013, which is the highest amount of waste among OECD countries. The waste has adverse effects to human health and the environment. One of the most serious adverse effects is greenhouse gas emissions, especially methane (CH4), which causes global warming. However, the United States' amount of waste generation is not decreasing, and the recycling rate is only 26%, which is lower than other OECD countries. In order to decrease waste generation and greenhouse gas emissions, identifying the causality of the waste generation and greenhouse gas emissions from waste sector should be made a priority. The research objective is to verify whether the Environmental Kuznets Curve relationship is supported for waste generation and GDP across the U.S. Moreover, it also confirmed that total waste generation and recycling of waste influences carbon dioxide emissions from the waste sector. Based on the results, critical insight and suggestions were offered to policymakers, which is the potential way to lower the solid waste and greenhouse gas emissions from the waste sector. This research used annually based U.S. data from 1990 to 2012, and these data were collected from various data sources. To verify the causal relationship, the Granger causality test was applied. The results showed that there is no causality between GDP and waste generation, but total waste and recycling generate significantly increasing and decreasing greenhouse gas emissions from the waste sector, respectively. This implies that waste generation will not decrease even if GDP increases. And, if waste generation decreases or the recycling rate increases, greenhouse gas emission will decrease. Based on these results, increasing the recycling rate is first suggested. The second suggestion is to break the causal relationship between MSW and greenhouse gas emission from the waste sector. The third is that the U.S. government should benchmark a successful case of waste management. Based on the research, it is expected that waste generation and carbon dioxide emission from the waste sector can be decreased more efficiently. Copyright © 2016 Elsevier Ltd. All rights reserved.

Teaching ocean wave forecasting using computer-generated visualization and animation—Part 1: sea forecasting

NASA Astrophysics Data System (ADS)

Whitford, Dennis J.

2002-05-01

Ocean waves are the most recognized phenomena in oceanography. Unfortunately, undergraduate study of ocean wave dynamics and forecasting involves mathematics and physics and therefore can pose difficulties with some students because of the subject's interrelated dependence on time and space. Verbal descriptions and two-dimensional illustrations are often insufficient for student comprehension. Computer-generated visualization and animation offer a visually intuitive and pedagogically sound medium to present geoscience, yet there are very few oceanographic examples. A two-part article series is offered to explain ocean wave forecasting using computer-generated visualization and animation. This paper, Part 1, addresses forecasting of sea wave conditions and serves as the basis for the more difficult topic of swell wave forecasting addressed in Part 2. Computer-aided visualization and animation, accompanied by oral explanation, are a welcome pedagogical supplement to more traditional methods of instruction. In this article, several MATLAB ® software programs have been written to visualize and animate development and comparison of wave spectra, wave interference, and forecasting of sea conditions. These programs also set the stage for the more advanced and difficult animation topics in Part 2. The programs are user-friendly, interactive, easy to modify, and developed as instructional tools. By using these software programs, teachers can enhance their instruction of these topics with colorful visualizations and animation without requiring an extensive background in computer programming.

Verifying Operational and Developmental Air Force Weather Cloud Analysis and Forecast Products Using Lidar Data from Department of Energy Atmospheric Radiation Measurement (ARM) Sites

NASA Astrophysics Data System (ADS)

Hildebrand, E. P.

2017-12-01

Air Force Weather has developed various cloud analysis and forecast products designed to support global Department of Defense (DoD) missions. A World-Wide Merged Cloud Analysis (WWMCA) and short term Advected Cloud (ADVCLD) forecast is generated hourly using data from 16 geostationary and polar-orbiting satellites. Additionally, WWMCA and Numerical Weather Prediction (NWP) data are used in a statistical long-term (out to five days) cloud forecast model known as the Diagnostic Cloud Forecast (DCF). The WWMCA and ADVCLD are generated on the same polar stereographic 24 km grid for each hemisphere, whereas the DCF is generated on the same grid as its parent NWP model. When verifying the cloud forecast models, the goal is to understand not only the ability to detect cloud, but also the ability to assign it to the correct vertical layer. ADVCLD and DCF forecasts traditionally have been verified using WWMCA data as truth, but this might over-inflate the performance of those models because WWMCA also is a primary input dataset for those models. Because of this, in recent years, a WWMCA Reanalysis product has been developed, but this too is not a fully independent dataset. This year, work has been done to incorporate data from external, independent sources to verify not only the cloud forecast products, but the WWMCA data itself. One such dataset that has been useful for examining the 3-D performance of the cloud analysis and forecast models is Atmospheric Radiation Measurement (ARM) data from various sites around the globe. This presentation will focus on the use of the Department of Energy (DoE) ARM data to verify Air Force Weather cloud analysis and forecast products. Results will be presented to show relative strengths and weaknesses of the analyses and forecasts.

Monthly to seasonal low flow prediction: statistical versus dynamical models

NASA Astrophysics Data System (ADS)

Ionita-Scholz, Monica; Klein, Bastian; Meissner, Dennis; Rademacher, Silke

2016-04-01

While the societal and economical impacts of floods are well documented and assessable, the impacts of lows flows are less studied and sometimes overlooked. For example, over the western part of Europe, due to intense inland waterway transportation, the economical loses due to low flows are often similar compared to the ones due to floods. In general, the low flow aspect has the tendency to be underestimated by the scientific community. One of the best examples in this respect is the facts that at European level most of the countries have an (early) flood alert system, but in many cases no real information regarding the development, evolution and impacts of droughts. Low flows, occurring during dry periods, may result in several types of problems to society and economy: e.g. lack of water for drinking, irrigation, industrial use and power production, deterioration of water quality, inland waterway transport, agriculture, tourism, issuing and renewing waste disposal permits, and for assessing the impact of prolonged drought on aquatic ecosystems. As such, the ever-increasing demand on water resources calls for better a management, understanding and prediction of the water deficit situation and for more reliable and extended studies regarding the evolution of the low flow situations. In order to find an optimized monthly to seasonal forecast procedure for the German waterways, the Federal Institute of Hydrology (BfG) is exploring multiple approaches at the moment. On the one hand, based on the operational short- to medium-range forecasting chain, existing hydrological models are forced with two different hydro-meteorological inputs: (i) resampled historical meteorology generated by the Ensemble Streamflow Prediction approach and (ii) ensemble (re-) forecasts of ECMWF's global coupled ocean-atmosphere general circulation model, which have to be downscaled and bias corrected before feeding the hydrological models. As a second approach BfG evaluates in cooperation with the Alfred Wegener Institute a purely statistical scheme to generate streamflow forecasts for several months ahead. Instead of directly using teleconnection indices (e.g. NAO, AO) the idea is to identify regions with stable teleconnections between different global climate information (e.g. sea surface temperature, geopotential height etc.) and streamflow at different gauges relevant for inland waterway transport. So-called stability (correlation) maps are generated showing regions where streamflow and climate variable from previous months are significantly correlated in a 21 (31) years moving window. Finally, the optimal forecast model is established based on a multiple regression analysis of the stable predictors. We will present current results of the aforementioned approaches with focus on the River Rhine (being one of the world's most frequented waterways and the backbone of the European inland waterway network) and the Elbe River. Overall, our analysis reveals the existence of a valuable predictability of the low flows at monthly and seasonal time scales, a result that may be useful to water resources management. Given that all predictors used in the models are available at the end of each month, the forecast scheme can be used operationally to predict extreme events and to provide early warnings for upcoming low flows.

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias

With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by firstmore » layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.« less

Nationwide validation of ensemble streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Lee, H. S.; Liu, Y.; Ward, J.; Brown, J.; Maestre, A.; Herr, H.; Fresch, M. A.; Wells, E.; Reed, S. M.; Jones, E.

2017-12-01

The National Weather Service's (NWS) Office of Water Prediction (OWP) recently launched a nationwide effort to verify streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for a majority of forecast locations across the 13 River Forecast Centers (RFCs). Known as the HEFS Baseline Validation (BV), the project involves a joint effort between the OWP and the RFCs. It aims to provide a geographically consistent, statistically robust validation, and a benchmark to guide the operational implementation of the HEFS, inform practical applications, such as impact-based decision support services, and to provide an objective framework for evaluating strategic investments in the HEFS. For the BV, HEFS hindcasts are issued once per day on a 12Z cycle for the period of 1985-2015 with a forecast horizon of 30 days. For the first two weeks, the hindcasts are forced with precipitation and temperature ensemble forecasts from the Global Ensemble Forecast System of the National Centers for Environmental Prediction, and by resampled climatology for the remaining period. The HEFS-generated ensemble streamflow hindcasts are verified using the Ensemble Verification System. Skill is assessed relative to streamflow hindcasts generated from NWS' current operational system, namely climatology-based Ensemble Streamflow Prediction. In this presentation, we summarize the results and findings to date.

Ensemble forecast of human West Nile virus cases and mosquito infection rates

NASA Astrophysics Data System (ADS)

Defelice, Nicholas B.; Little, Eliza; Campbell, Scott R.; Shaman, Jeffrey

2017-02-01

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

Ensemble forecast of human West Nile virus cases and mosquito infection rates.

PubMed

DeFelice, Nicholas B; Little, Eliza; Campbell, Scott R; Shaman, Jeffrey

2017-02-24

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

Between the Rock and a Hard Place: The CCMC as a Transit Station Between Modelers and Forecasters

NASA Technical Reports Server (NTRS)

Hesse, Michael

2009-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involved model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the latter element. Specifically, we will discuss the process of transition research models, or information generated by research models, to Space Weather Forecasting organizations. We will analyze successes as well as obstacles to further progress, and we will suggest avenues for increased transitioning success.

Teaching ocean wave forecasting using computer-generated visualization and animation—Part 2: swell forecasting

NASA Astrophysics Data System (ADS)

Whitford, Dennis J.

2002-05-01

This paper, the second of a two-part series, introduces undergraduate students to ocean wave forecasting using interactive computer-generated visualization and animation. Verbal descriptions and two-dimensional illustrations are often insufficient for student comprehension. Fortunately, the introduction of computers in the geosciences provides a tool for addressing this problem. Computer-generated visualization and animation, accompanied by oral explanation, have been shown to be a pedagogical improvement to more traditional methods of instruction. Cartographic science and other disciplines using geographical information systems have been especially aggressive in pioneering the use of visualization and animation, whereas oceanography has not. This paper will focus on the teaching of ocean swell wave forecasting, often considered a difficult oceanographic topic due to the mathematics and physics required, as well as its interdependence on time and space. Several MATLAB ® software programs are described and offered to visualize and animate group speed, frequency dispersion, angular dispersion, propagation, and wave height forecasting of deep water ocean swell waves. Teachers may use these interactive visualizations and animations without requiring an extensive background in computer programming.

Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China

NASA Astrophysics Data System (ADS)

Ma, Feng; Ye, Aizhong; Duan, Qingyun

2017-03-01

An experimental seasonal drought forecasting system is developed based on 29-year (1982-2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model Ensemble (NMME) project. This system made use of a bias correction and spatial downscaling method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash-Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978-1995) and validation (1996-2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.

Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, WanYin; Zhang, Jie; Florita, Anthony

2015-12-08

Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance,more » cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.« less

New Aspects of Probabilistic Forecast Verification Using Information Theory

NASA Astrophysics Data System (ADS)

Tödter, Julian; Ahrens, Bodo

2013-04-01

This work deals with information-theoretical methods in probabilistic forecast verification, particularly concerning ensemble forecasts. Recent findings concerning the "Ignorance Score" are shortly reviewed, then a consistent generalization to continuous forecasts is motivated. For ensemble-generated forecasts, the presented measures can be calculated exactly. The Brier Score (BS) and its generalizations to the multi-categorical Ranked Probability Score (RPS) and to the Continuous Ranked Probability Score (CRPS) are prominent verification measures for probabilistic forecasts. Particularly, their decompositions into measures quantifying the reliability, resolution and uncertainty of the forecasts are attractive. Information theory sets up a natural framework for forecast verification. Recently, it has been shown that the BS is a second-order approximation of the information-based Ignorance Score (IGN), which also contains easily interpretable components and can also be generalized to a ranked version (RIGN). Here, the IGN, its generalizations and decompositions are systematically discussed in analogy to the variants of the BS. Additionally, a Continuous Ranked IGN (CRIGN) is introduced in analogy to the CRPS. The useful properties of the conceptually appealing CRIGN are illustrated, together with an algorithm to evaluate its components reliability, resolution, and uncertainty for ensemble-generated forecasts. This algorithm can also be used to calculate the decomposition of the more traditional CRPS exactly. The applicability of the "new" measures is demonstrated in a small evaluation study of ensemble-based precipitation forecasts.

The IDEA model: A single equation approach to the Ebola forecasting challenge.

PubMed

Tuite, Ashleigh R; Fisman, David N

2018-03-01

Mathematical modeling is increasingly accepted as a tool that can inform disease control policy in the face of emerging infectious diseases, such as the 2014-2015 West African Ebola epidemic, but little is known about the relative performance of alternate forecasting approaches. The RAPIDD Ebola Forecasting Challenge (REFC) tested the ability of eight mathematical models to generate useful forecasts in the face of simulated Ebola outbreaks. We used a simple, phenomenological single-equation model (the "IDEA" model), which relies only on case counts, in the REFC. Model fits were performed using a maximum likelihood approach. We found that the model performed reasonably well relative to other more complex approaches, with performance metrics ranked on average 4th or 5th among participating models. IDEA appeared better suited to long- than short-term forecasts, and could be fit using nothing but reported case counts. Several limitations were identified, including difficulty in identifying epidemic peak (even retrospectively), unrealistically precise confidence intervals, and difficulty interpolating daily case counts when using a model scaled to epidemic generation time. More realistic confidence intervals were generated when case counts were assumed to follow a negative binomial, rather than Poisson, distribution. Nonetheless, IDEA represents a simple phenomenological model, easily implemented in widely available software packages that could be used by frontline public health personnel to generate forecasts with accuracy that approximates that which is achieved using more complex methodologies. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Energy: An annotated selected bibliography

NASA Technical Reports Server (NTRS)

Blow, S. J. (Compiler); Peacock, R. W. (Compiler); Sholy, J. J. (Compiler)

1979-01-01

This updated bibliography contains approximately 7,000 selected references on energy and energy related topics from bibliographic and other data sources from June 1977. Under each subject heading the entries are arranged by the data, with the latest works first. Subject headings include: resources supply/demand, and forecasting; policy, legislation, and regulation; environment; consumption, conservation, and economics; analysis, systems, and modeling, and information sources and documentation. Fossil fuels, hydrogen and other fuels, liquid/solid wastes and biomass, waste heat utilization, and nuclear power sources are also included.

Assessing the skill of seasonal precipitation and streamflow forecasts in sixteen French catchments

NASA Astrophysics Data System (ADS)

Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

2015-04-01

Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful. Streamflow forecasting is one of the many applications than can benefit from these efforts. Seasonal flow forecasts generated using seasonal ensemble precipitation forecasts as input to a hydrological model can help to take anticipatory measures for water supply reservoir operation or drought risk management. The objective of the study is to assess the skill of seasonal precipitation and streamflow forecasts in France. First, we evaluated the skill of ECMWF SYS4 seasonal precipitation forecasts for streamflow forecasting in sixteen French catchments. Daily flow forecasts were produced using raw seasonal precipitation forecasts as input to the GR6J hydrological model. Ensemble forecasts are issued every month with 15 or 51 members according to the month of the year and evaluated for up to 90 days ahead. In a second step, we applied eight variants of bias correction approaches to the precipitation forecasts prior to generating the flow forecasts. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble forecasts was assessed in accuracy (MAE), reliability (PIT Diagram) and overall performance (CRPS). The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are more skilful in terms of accuracy and overall performance than a reference prediction based on historic observed precipitation and watershed initial conditions at the time of forecast. Reliability is the only attribute that is not significantly improved. The skill of the forecasts is, in general, improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments: the simple linear scaling of monthly values and the empirical distribution mapping of daily values. L. Crochemore is funded by the Interreg IVB DROP Project (Benefit of governance in DROught adaPtation).

Superensemble forecasts of dengue outbreaks

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2016-01-01

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems. PMID:27733698

Online probabilistic learning with an ensemble of forecasts

NASA Astrophysics Data System (ADS)

Thorey, Jean; Mallet, Vivien; Chaussin, Christophe

2016-04-01

Our objective is to produce a calibrated weighted ensemble to forecast a univariate time series. In addition to a meteorological ensemble of forecasts, we rely on observations or analyses of the target variable. The celebrated Continuous Ranked Probability Score (CRPS) is used to evaluate the probabilistic forecasts. However applying the CRPS on weighted empirical distribution functions (deriving from the weighted ensemble) may introduce a bias because of which minimizing the CRPS does not produce the optimal weights. Thus we propose an unbiased version of the CRPS which relies on clusters of members and is strictly proper. We adapt online learning methods for the minimization of the CRPS. These methods generate the weights associated to the members in the forecasted empirical distribution function. The weights are updated before each forecast step using only past observations and forecasts. Our learning algorithms provide the theoretical guarantee that, in the long run, the CRPS of the weighted forecasts is at least as good as the CRPS of any weighted ensemble with weights constant in time. In particular, the performance of our forecast is better than that of any subset ensemble with uniform weights. A noteworthy advantage of our algorithm is that it does not require any assumption on the distributions of the observations and forecasts, both for the application and for the theoretical guarantee to hold. As application example on meteorological forecasts for photovoltaic production integration, we show that our algorithm generates a calibrated probabilistic forecast, with significant performance improvements on probabilistic diagnostic tools (the CRPS, the reliability diagram and the rank histogram).

Developing models for the prediction of hospital healthcare waste generation rate.

PubMed

Tesfahun, Esubalew; Kumie, Abera; Beyene, Abebe

2016-01-01

An increase in the number of health institutions, along with frequent use of disposable medical products, has contributed to the increase of healthcare waste generation rate. For proper handling of healthcare waste, it is crucial to predict the amount of waste generation beforehand. Predictive models can help to optimise healthcare waste management systems, set guidelines and evaluate the prevailing strategies for healthcare waste handling and disposal. However, there is no mathematical model developed for Ethiopian hospitals to predict healthcare waste generation rate. Therefore, the objective of this research was to develop models for the prediction of a healthcare waste generation rate. A longitudinal study design was used to generate long-term data on solid healthcare waste composition, generation rate and develop predictive models. The results revealed that the healthcare waste generation rate has a strong linear correlation with the number of inpatients (R(2) = 0.965), and a weak one with the number of outpatients (R(2) = 0.424). Statistical analysis was carried out to develop models for the prediction of the quantity of waste generated at each hospital (public, teaching and private). In these models, the number of inpatients and outpatients were revealed to be significant factors on the quantity of waste generated. The influence of the number of inpatients and outpatients treated varies at different hospitals. Therefore, different models were developed based on the types of hospitals. © The Author(s) 2015.

Forecasting Electric Power Generation of Photovoltaic Power System for Energy Network

NASA Astrophysics Data System (ADS)

Kudo, Mitsuru; Takeuchi, Akira; Nozaki, Yousuke; Endo, Hisahito; Sumita, Jiro

Recently, there has been an increase in concern about the global environment. Interest is growing in developing an energy network by which new energy systems such as photovoltaic and fuel cells generate power locally and electric power and heat are controlled with a communications network. We developed the power generation forecast method for photovoltaic power systems in an energy network. The method makes use of weather information and regression analysis. We carried out forecasting power output of the photovoltaic power system installed in Expo 2005, Aichi Japan. As a result of comparing measurements with a prediction values, the average prediction error per day was about 26% of the measured power.

Gas demand forecasting by a new artificial intelligent algorithm

NASA Astrophysics Data System (ADS)

Khatibi. B, Vahid; Khatibi, Elham

2012-01-01

Energy demand forecasting is a key issue for consumers and generators in all energy markets in the world. This paper presents a new forecasting algorithm for daily gas demand prediction. This algorithm combines a wavelet transform and forecasting models such as multi-layer perceptron (MLP), linear regression or GARCH. The proposed method is applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the proposed method.

Regional PV power estimation and forecast to mitigate the impact of high photovoltaic penetration on electric grid.

NASA Astrophysics Data System (ADS)

Pierro, Marco; De Felice, Matteo; Maggioni, Enrico; Moser, David; Perotto, Alessandro; Spada, Francesco; Cornaro, Cristina

2017-04-01

The growing photovoltaic generation results in a stochastic variability of the electric demand that could compromise the stability of the grid and increase the amount of energy reserve and the energy imbalance cost. On regional scale, solar power estimation and forecast is becoming essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators of generation. Indeed the estimation of regional PV power can be used for PV power supervision and real time control of residual load. Mid-term PV power forecast can be employed for transmission scheduling to reduce energy imbalance and related cost of penalties, residual load tracking, trading optimization, secondary energy reserve assessment. In this context, a new upscaling method was developed and used for estimation and mid-term forecast of the photovoltaic distributed generation in a small area in the north of Italy under the control of a local DSO. The method was based on spatial clustering of the PV fleet and neural networks models that input satellite or numerical weather prediction data (centered on cluster centroids) to estimate or predict the regional solar generation. It requires a low computational effort and very few input information should be provided by users. The power estimation model achieved a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% while the one and two days forecast achieve to a RMSE of 7% and 7.5%. A model to estimate the forecast error and the prediction intervals was also developed. The photovoltaic production in the considered region provided the 6.9% of the electric consumption in 2015. Since the PV penetration is very similar to the one observed at national level (7.9%), this is a good case study to analyse the impact of PV generation on the electric grid and the effects of PV power forecast on transmission scheduling and on secondary reserve estimation. It appears that, already with 7% of PV penetration, the distributed PV generation could have a great impact both on the DSO energy need and on the transmission scheduling capability. Indeed, for some hours of the days in summer time, the photovoltaic generation can provide from 50% to 75% of the energy that the local DSO should buy from Italian TSO to cover the electrical demand. Moreover, mid-term forecast can reduce the annual energy imbalance between the scheduled transmission and the actual one from 10% of the TSO energy supply (without considering the PV forecast) to 2%. Furthermore, it was shown that prediction intervals could be used not only to estimate the probability of a specific PV generation bid on the energy market, but also to reduce the energy reserve predicted for the next day. Two different methods for energy reserve estimation were developed and tested. The first is based on a clear sky model while the second makes use of the PV prediction intervals with the 95% of confidence level. The latter reduces the amount of the day-ahead energy reserve of 36% with respect the clear sky method.

Waste Management Improvement Initiatives at Atomic Energy of Canada Limited - 13091

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chan, Nicholas; Adams, Lynne; Wong, Pierre

2013-07-01

Atomic Energy of Canada Limited's (AECL) Chalk River Laboratories (CRL) has been in operation for over 60 years. Radioactive, mixed, hazardous and non-hazardous wastes have been and continue to be generated at CRL as a result of research and development, radioisotope production, reactor operation and facility decommissioning activities. AECL has implemented several improvement initiatives at CRL to simplify the interface between waste generators and waste receivers: - Introduction of trained Waste Officers representing their facilities or activities at CRL; - Establishment of a Waste Management Customer Support Service as a Single-Point of Contact to provide guidance to waste generators formore » all waste management processes; and - Implementation of a streamlined approach for waste identification with emphasis on early identification of waste types and potential disposition paths. As a result of implementing these improvement initiatives, improvements in waste management and waste transfer efficiencies have been realized at CRL. These included: 1) waste generators contacting the Customer Support Service for information or guidance instead of various waste receivers; 2) more clear and consistent guidance provided to waste generators for waste management through the Customer Support Service; 3) more consistent and correct waste information provided to waste receivers through Waste Officers, resulting in reduced time and resources required for waste management (i.e., overall cost); 4) improved waste minimization and segregation approaches, as identified by in-house Waste Officers; and 5) enhanced communication between waste generators and waste management groups. (authors)« less

Do we need demographic data to forecast plant population dynamics?

USGS Publications Warehouse

Tredennick, Andrew T.; Hooten, Mevin B.; Adler, Peter B.

2017-01-01

Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts.Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction.In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types.In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist for many plant species. Modelers should exploit these data to predict the impacts of environmental change.

Construction and demolition waste generation rates for high-rise buildings in Malaysia.

PubMed

Mah, Chooi Mei; Fujiwara, Takeshi; Ho, Chin Siong

2016-12-01

Construction and demolition waste continues to sharply increase in step with the economic growth of less developed countries. Though the construction industry is large, it is composed of small firms with individual waste management practices, often leading to the deleterious environmental outcomes. Quantifying construction and demolition waste generation allows policy makers and stakeholders to understand the true internal and external costs of construction, providing a necessary foundation for waste management planning that may overcome deleterious environmental outcomes and may be both economically and environmentally optimal. This study offers a theoretical method for estimating the construction and demolition project waste generation rate by utilising available data, including waste disposal truck size and number, and waste volume and composition. This method is proposed as a less burdensome and more broadly applicable alternative, in contrast to waste estimation by on-site hand sorting and weighing. The developed method is applied to 11 projects across Malaysia as the case study. This study quantifies waste generation rate and illustrates the construction method in influencing the waste generation rate, estimating that the conventional construction method has a waste generation rate of 9.88 t 100 m -2 , the mixed-construction method has a waste generation rate of 3.29 t 100 m -2 , and demolition projects have a waste generation rate of 104.28 t 100 m -2 . © The Author(s) 2016.

Waste Generated from LMR-AMTEC Reactor Concept

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasan, Ahmed; Mohamed, Yasser, T.; Mohammaden, Tarek, F.

2003-02-25

The candidate Liquid Metal Reactor-Alkali Metal Thermal -to- Electric Converter (LMR-AMTEC) is considered to be the first reactor that would use pure liquid potassium as a secondary coolant, in which potassium vapor aids in the conversion of thermal energy to electric energy. As with all energy production, the thermal generation of electricity produces wastes. These wastes must be managed in ways which safeguard human health and minimize their impact on the environment. Nuclear power is the only energy industry, which takes full responsibility for all its wastes. Based on the candidate design of the LMR-AMTEC components and the coolant types,more » different wastes will be generated from LMR. These wastes must be classified and characterized according to the U.S. Code of Federal Regulation, CFR. This paper defines the waste generation and waste characterization from LMR-AMTEC and reviews the applicable U.S. regulations that govern waste transportation, treatment, storage and final disposition. The wastes generated from LMR-AMTEC are characterized as: (1) mixed waste which is generated from liquid sodium contaminated by fission products and activated corrosion products; (2) hazardous waste which is generated from liquid potassium contaminated by corrosion products; (3) spent nuclear fuel; and (4) low-level radioactive waste which is generated from the packing materials (e.g. activated carbon in cold trap and purification units). The regulations and management of these wastes are summarized in this paper.« less

Integrating Solar Power onto the Electric Grid - Bridging the Gap between Atmospheric Science, Engineering and Economics

NASA Astrophysics Data System (ADS)

Ghonima, M. S.; Yang, H.; Zhong, X.; Ozge, B.; Sahu, D. K.; Kim, C. K.; Babacan, O.; Hanna, R.; Kurtz, B.; Mejia, F. A.; Nguyen, A.; Urquhart, B.; Chow, C. W.; Mathiesen, P.; Bosch, J.; Wang, G.

2015-12-01

One of the main obstacles to high penetrations of solar power is the variable nature of solar power generation. To mitigate variability, grid operators have to schedule additional reliability resources, at considerable expense, to ensure that load requirements are met by generation. Thus despite the cost of solar PV decreasing, the cost of integrating solar power will increase as penetration of solar resources onto the electric grid increases. There are three principal tools currently available to mitigate variability impacts: (i) flexible generation, (ii) storage, either virtual (demand response) or physical devices and (iii) solar forecasting. Storage devices are a powerful tool capable of ensuring smooth power output from renewable resources. However, the high cost of storage is prohibitive and markets are still being designed to leverage their full potential and mitigate their limitation (e.g. empty storage). Solar forecasting provides valuable information on the daily net load profile and upcoming ramps (increasing or decreasing solar power output) thereby providing the grid advance warning to schedule ancillary generation more accurately, or curtail solar power output. In order to develop solar forecasting as a tool that can be utilized by the grid operators we identified two focus areas: (i) develop solar forecast technology and improve solar forecast accuracy and (ii) develop forecasts that can be incorporated within existing grid planning and operation infrastructure. The first issue required atmospheric science and engineering research, while the second required detailed knowledge of energy markets, and power engineering. Motivated by this background we will emphasize area (i) in this talk and provide an overview of recent advancements in solar forecasting especially in two areas: (a) Numerical modeling tools for coastal stratocumulus to improve scheduling in the day-ahead California energy market. (b) Development of a sky imager to provide short term forecasts (0-20 min ahead) to improve optimization and control of equipment on distribution feeders with high penetration of solar. Leveraging such tools that have seen extensive use in the atmospheric sciences supports the development of accurate physics-based solar forecast models. Directions for future research are also provided.

Forecasting peaks of seasonal influenza epidemics.

PubMed

Nsoesie, Elaine; Mararthe, Madhav; Brownstein, John

2013-06-21

We present a framework for near real-time forecast of influenza epidemics using a simulation optimization approach. The method combines an individual-based model and a simple root finding optimization method for parameter estimation and forecasting. In this study, retrospective forecasts were generated for seasonal influenza epidemics using web-based estimates of influenza activity from Google Flu Trends for 2004-2005, 2007-2008 and 2012-2013 flu seasons. In some cases, the peak could be forecasted 5-6 weeks ahead. This study adds to existing resources for influenza forecasting and the proposed method can be used in conjunction with other approaches in an ensemble framework.

International Cooperative for Aerosol Prediction Workshop on Aerosol Forecast Verification

NASA Technical Reports Server (NTRS)

Benedetti, Angela; Reid, Jeffrey S.; Colarco, Peter R.

2011-01-01

The purpose of this workshop was to reinforce the working partnership between centers who are actively involved in global aerosol forecasting, and to discuss issues related to forecast verification. Participants included representatives from operational centers with global aerosol forecasting requirements, a panel of experts on Numerical Weather Prediction and Air Quality forecast verification, data providers, and several observers from the research community. The presentations centered on a review of current NWP and AQ practices with subsequent discussion focused on the challenges in defining appropriate verification measures for the next generation of aerosol forecast systems.

Workshop Summary: International Cooperative for Aerosol Prediction Workshop On Aerosol Forecast Verification

NASA Technical Reports Server (NTRS)

Benedetti, Angela; Reid, Jeffrey S.; Colarco, Peter R.

2011-01-01

The purpose of this workshop was to reinforce the working partnership between centers who are actively involved in global aerosol forecasting, and to discuss issues related to forecast verification. Participants included representatives from operational centers with global aerosol forecasting requirements, a panel of experts on Numerical Weather Prediction and Air Quality forecast verification, data providers, and several observers from the research community. The presentations centered on a review of current NWP and AQ practices with subsequent discussion focused on the challenges in defining appropriate verification measures for the next generation of aerosol forecast systems.

A system for forecasting and monitoring cash flow : phase I : forecasting payments on construction contracts.

DOT National Transportation Integrated Search

1983-01-01

The research on which this paper is based was performed as part of a study to develop a system for generating a one-to-two year forecast of monthly cash flows for the Virginia Department of Highways and Transportation. It revealed that presently used...

NCEP Data Products

Science.gov Websites

Image of NCEP Logo WHERE AMERICA'S CLIMATE AND WEATHER SERVICES BEGIN Inventory of Data Products on Generated Products Image of horizontal rule Global Forecast System (GFS) GFS Ensemble Forecast System (GEFS of horizontal rule External Products Image of horizontal rule Canadian Ensemble Forecast System

Influence of Forecast Accuracy of Photovoltaic Power Output on Facility Planning and Operation of Microgrid under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Kato, Takeyoshi; Sone, Akihito; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on the demonstrative studies on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization and daily operation evaluated with the cost. The main results are as follows. The required capacity of NaS battery must be increased by 10-40% against the ideal situation without the forecast error of PVS power output. The influence of forecast error on the received grid electricity would not be so significant on annual basis because the positive and negative forecast error varies with days. The annual total cost of facility and operation increases by 2-7% due to the forecast error applied in this study. The impact of forecast error on the facility optimization and operation optimization is almost the same each other at a few percentages, implying that the forecast accuracy should be improved in terms of both the number of times with large forecast error and the average error.

Verification of Weather Running Estimate-Nowcast (WRE-N) Forecasts Using a Spatial-Categorical Method

DTIC Science & Technology

2017-07-01

forecasts and observations on a common grid, which enables the application a number of different spatial verification methods that reveal various...forecasts of continuous meteorological variables using categorical and object-based methods . White Sands Missile Range (NM): Army Research Laboratory (US... Research version of the Weather Research and Forecasting Model adapted for generating short-range nowcasts and gridded observations produced by the

Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

EIA Publications

2010-01-01

The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables

NASA Astrophysics Data System (ADS)

Huang, Laura X.; Isaac, George A.; Sheng, Grant

2014-01-01

This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0-6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.

Tsunami Forecast Progress Five Years After Indonesian Disaster

NASA Astrophysics Data System (ADS)

Titov, Vasily V.; Bernard, Eddie N.; Weinstein, Stuart A.; Kanoglu, Utku; Synolakis, Costas E.

2010-05-01

Almost five years after the 26 December 2004 Indian Ocean tragedy, tsunami warnings are finally benefiting from decades of research toward effective model-based forecasts. Since the 2004 tsunami, two seminal advances have been (i) deep-ocean tsunami measurements with tsunameters and (ii) their use in accurately forecasting tsunamis after the tsunami has been generated. Using direct measurements of deep-ocean tsunami heights, assimilated into numerical models for specific locations, greatly improves the real-time forecast accuracy over earthquake-derived magnitude estimates of tsunami impact. Since 2003, this method has been used to forecast tsunamis at specific harbors for different events in the Pacific and Indian Oceans. Recent tsunamis illustrated how this technology is being adopted in global tsunami warning operations. The U.S. forecasting system was used by both research and operations to evaluate the tsunami hazard. Tests demonstrated the effectiveness of operational tsunami forecasting using real-time deep-ocean data assimilated into forecast models. Several examples also showed potential of distributed forecast tools. With IOC and USAID funding, NOAA researchers at PMEL developed the Community Model Interface for Tsunami (ComMIT) tool and distributed it through extensive capacity-building sessions in the Indian Ocean. Over hundred scientists have been trained in tsunami inundation mapping, leading to the first generation of inundation models for many Indian Ocean shorelines. These same inundation models can also be used for real-time tsunami forecasts as was demonstrated during several events. Contact Information Vasily V. Titov, Seattle, Washington, USA, 98115

Impact of socioeconomic status on municipal solid waste generation rate.

PubMed

Khan, D; Kumar, A; Samadder, S R

2016-03-01

The solid waste generation rate was expected to vary in different socioeconomic groups due to many environmental and social factors. This paper reports the assessment of solid waste generation based on different socioeconomic parameters like education, occupation, income of the family, number of family members etc. A questionnaire survey was conducted in the study area to identify the different socioeconomic groups that may affect the solid waste generation rate and composition. The average waste generated in the municipality is 0.41 kg/capita/day in which the maximum waste was found to be generated by lower middle socioeconomic group (LMSEG) with average waste generation of 0.46 kg/capita/day. Waste characterization indicated that there was no much difference in the composition of wastes among different socioeconomic groups except ash residue and plastic. Ash residue is found to increase as we move lower down the socioeconomic groups with maximum (31%) in lower socioeconomic group (LSEG). The study area is a coal based city hence application of coal and wood as fuel for cooking in the lower socioeconomic group is the reason for high amount of ash content. Plastic waste is maximum (15%) in higher socioeconomic group (HSEG) and minimum (1%) in LSEG. Food waste is a major component of generated waste in almost every socioeconomic group with maximum (38%) in case of HSEG and minimum (28%) in LSEG. This study provides new insights on the role of various socioeconomic parameters on generation of household wastes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Long chain fatty acids (LCFA) evolution for inhibition forecasting during anaerobic treatment of lipid-rich wastes: Case of milk-fed veal slaughterhouse waste.

PubMed

Rodríguez-Méndez, R; Le Bihan, Y; Béline, F; Lessard, P

2017-09-01

A detailed study of a solid slaughterhouse waste (SHW) anaerobic treatment is presented. The waste used in this study is rich in lipids and proteins residue. Long chain fatty acids (LCFA), coming from the hydrolysis of lipids were inhibitory to anaerobic processes at different degrees. Acetogenesis and methanogenesis processes were mainly affected by inhibition whereas disintegration and hydrolysis processes did not seem to be affected by high LCFA concentrations. Nevertheless, because of the high energy content, this kind of waste is very suitable for anaerobic digestion but strict control of operating conditions is required to prevent inhibition. For that, two inhibition indicators were identified in this study. Those two indicators, LCFA dynamics and LCFA/VS biomass ratio proved to be useful to predict and to estimate the process inhibition degree. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hanford Facility Annual Dangerous Waste Report Calendar Year 2002

DOE Office of Scientific and Technical Information (OSTI.GOV)

FREEMAN, D.A.

2003-02-01

Hanford CY 2002 dangerous waste generation and management forms. The Hanford Facility Annual Dangerous Waste Report (ADWR) is prepared to meet the requirements of Washington Administrative Code Sections 173-303-220, Generator Reporting, and 173-303-390, Facility Reporting. In addition, the ADWR is required to meet Hanford Facility RCRA Permit Condition I.E.22, Annual Reporting. The ADWR provides summary information on dangerous waste generation and management activities for the Calendar Year for the Hanford Facility EPA ID number assigned to the Department of Energy for RCRA regulated waste, as well as Washington State only designated waste and radioactive mixed waste. The Solid Waste Informationmore » and Tracking System (SWITS) database is utilized to collect and compile the large array of data needed for preparation of this report. Information includes details of waste generated on the Hanford Facility, waste generated offsite and sent to Hanford for management, and other waste management activities conducted at Hanford, including treatment, storage, and disposal. Report details consist of waste descriptions and weights, waste codes and designations, and waste handling codes. In addition, for waste shipped to Hanford for treatment and/or disposal, information on manifest numbers, the waste transporter, the waste receiving facility, and the original waste generators are included. In addition to paper copies, electronic copies of the report are also transmitted to the regulatory agency.« less

Influence of Forecast Accuracy of Photovoltaic Power Output on Capacity Optimization of Microgrid Composition under 30 min Power Balancing Control

NASA Astrophysics Data System (ADS)

Sone, Akihito; Kato, Takeyoshi; Shimakage, Toyonari; Suzuoki, Yasuo

A microgrid (MG) is one of the measures for enhancing the high penetration of renewable energy (RE)-based distributed generators (DGs). If a number of MGs are controlled to maintain the predetermined electricity demand including RE-based DGs as negative demand, they would contribute to supply-demand balancing of whole electric power system. For constructing a MG economically, the capacity optimization of controllable DGs against RE-based DGs is essential. By using a numerical simulation model developed based on a demonstrative study on a MG using PAFC and NaS battery as controllable DGs and photovoltaic power generation system (PVS) as a RE-based DG, this study discusses the influence of forecast accuracy of PVS output on the capacity optimization. Three forecast cases with different accuracy are compared. The main results are as follows. Even with no forecast error during every 30 min. as the ideal forecast method, the required capacity of NaS battery reaches about 40% of PVS capacity for mitigating the instantaneous forecast error within 30 min. The required capacity to compensate for the forecast error is doubled with the actual forecast method. The influence of forecast error can be reduced by adjusting the scheduled power output of controllable DGs according to the weather forecast. Besides, the required capacity can be reduced significantly if the error of balancing control in a MG is acceptable for a few percentages of periods, because the total periods of large forecast error is not so often.

Segregation of biomedical waste in an South Indian tertiary care hospital.

PubMed

Sengodan, Vetrivel Chezian

2014-07-01

Hospital wastes pose significant public health hazard if not properly managed. Hence, it is necessary to develop and adopt optimal waste management systems in the hospitals. Biomedical waste generated in Coimbatore Medical College Hospital was color coded (blue, yellow, and red) and the data was analyzed retrospectively on a daily basis for 3 years (January 2010-December 2012). Effective segregation protocols significantly reduced biomedical waste generated from 2011 to 2012. While biomedical waste of red category was significantly higher (>50%), the category yellow was the least. Per unit (per bed per day) total biomedical waste generated was 68.5, 68.8, and 61.3 grams in 2010, 2011, and 2012, respectively. Segregation of biomedical waste at the source of generation is the first and essential step in biomedical waste management. Continuous training, fixing the responsibility on the nursing persons, and constant supervision are the key criteria's in implementing biomedical waste segregation process, which can significantly reduce per unit biomedical waste generated. We highly recommend all hospitals to adopt our protocol and effectively implement them to reduce generation of biomedical waste.

Examining of solid waste generation and community awareness between city center and suburban area in Medan City, Indonesia

NASA Astrophysics Data System (ADS)

Khair, H.; Putri, C. N.; Dalimunthe, R. A.; Matsumoto, T.

2018-02-01

Municipal solid waste (MSW) management is still an issue in many cities in Indonesia including Medan. Understanding the waste generation, its characteristic and communities involvement could provide effective solid waste management. This research compares waste generation from people who live in the city center and suburban area. The research also examines the willingness and participation of community about environmental aspect, especially solid waste management. The method of waste generation used Indonesian Nasional Standard 19-3964-1994. The city center generates 0.295 kg/person/day of solid waste and 0.180 kg/person/day for suburbs. The result showed that there are the common amount of waste compositions between the city center and suburban area. The majority waste composition was an organic fraction. Questionnaires were distributed to examine the community awareness. The descriptive statistic used to analyze the data. The result showed that people living in the city center are slightly higher in community awareness than in the suburb. This paper highlights that area of living could give some effect to solid waste generation, waste composition and rate of awareness.

Forecasting hotspots using predictive visual analytics approach

DOEpatents

Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

2014-12-30

A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

Factors affecting waste generation: a study in a waste management program in Dhaka City, Bangladesh.

PubMed

Afroz, Rafia; Hanaki, Keisuke; Tudin, Rabaah

2011-08-01

Information on waste generation, socioeconomic characteristics, and willingness of the households to separate waste was obtained from interviews with 402 respondents in Dhaka city. Ordinary least square regression was used to determine the dominant factors that might influence the waste generation of the households. The results showed that the waste generation of the households in Dhaka city was significantly affected by household size, income, concern about the environment, and willingness to separate the waste. These factors are necessary to effectively improve waste management, growth and performance, as well as to reduce the environmental degradation of the household waste.

Hydrological Forecasting Practices in Brazil

NASA Astrophysics Data System (ADS)

Fan, Fernando; Paiva, Rodrigo; Collischonn, Walter; Ramos, Maria-Helena

2016-04-01

This work brings a review on current hydrological and flood forecasting practices in Brazil, including the main forecasts applications, the different kinds of techniques that are currently being employed and the institutions involved on forecasts generation. A brief overview of Brazil is provided, including aspects related to its geography, climate, hydrology and flood hazards. A general discussion about the Brazilian practices on hydrological short and medium range forecasting is presented. Detailed examples of some hydrological forecasting systems that are operational or in a research/pre-operational phase using the large scale hydrological model MGB-IPH are also presented. Finally, some suggestions are given about how the forecasting practices in Brazil can be understood nowadays, and what are the perspectives for the future.

Waste heat generation: A comprehensive review.

PubMed

Yeşiller, Nazli; Hanson, James L; Yee, Emma H

2015-08-01

A comprehensive review of heat generation in various types of wastes and of the thermal regime of waste containment facilities is provided in this paper. Municipal solid waste (MSW), MSW incineration ash, and mining wastes were included in the analysis. Spatial and temporal variations of waste temperatures, thermal gradients, thermal properties of wastes, average temperature differentials, and heat generation values are provided. Heat generation was influenced by climatic conditions, mean annual earth temperatures, waste temperatures at the time of placement, cover conditions, and inherent heat generation potential of the specific wastes. Time to onset of heat generation varied between months and years, whereas timelines for overall duration of heat generation varied between years and decades. For MSW, measured waste temperatures were as high as 60-90°C and as low as -6°C. MSW incinerator ash temperatures varied between 5 and 87°C. Mining waste temperatures were in the range of -25 to 65°C. In the wastes analyzed, upward heat flow toward the surface was more prominent than downward heat flow toward the subsurface. Thermal gradients generally were higher for MSW and incinerator ash and lower for mining waste. Based on thermal properties, MSW had insulative qualities (low thermal conductivity), while mining wastes typically were relatively conductive (high thermal conductivity) with ash having intermediate qualities. Heat generation values ranged from -8.6 to 83.1MJ/m(3) and from 0.6 to 72.6MJ/m(3) for MSW and mining waste, respectively and was 72.6MJ/m(3) for ash waste. Conductive thermal losses were determined to range from 13 to 1111MJ/m(3)yr. The data and analysis provided in this review paper can be used in the investigation of heat generation and thermal regime of a wide range of wastes and waste containment facilities located in different climatic regions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using forecast modelling to evaluate treatment effects in single-group interrupted time series analysis.

PubMed

Linden, Ariel

2018-05-11

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied serially over time and the intervention is expected to "interrupt" the level and/or trend of that outcome. ITSA is commonly evaluated using methods which may produce biased results if model assumptions are violated. In this paper, treatment effects are alternatively assessed by using forecasting methods to closely fit the preintervention observations and then forecast the post-intervention trend. A treatment effect may be inferred if the actual post-intervention observations diverge from the forecasts by some specified amount. The forecasting approach is demonstrated using the effect of California's Proposition 99 for reducing cigarette sales. Three forecast models are fit to the preintervention series-linear regression (REG), Holt-Winters (HW) non-seasonal smoothing, and autoregressive moving average (ARIMA)-and forecasts are generated into the post-intervention period. The actual observations are then compared with the forecasts to assess intervention effects. The preintervention data were fit best by HW, followed closely by ARIMA. REG fit the data poorly. The actual post-intervention observations were above the forecasts in HW and ARIMA, suggesting no intervention effect, but below the forecasts in the REG (suggesting a treatment effect), thereby raising doubts about any definitive conclusion of a treatment effect. In a single-group ITSA, treatment effects are likely to be biased if the model is misspecified. Therefore, evaluators should consider using forecast models to accurately fit the preintervention data and generate plausible counterfactual forecasts, thereby improving causal inference of treatment effects in single-group ITSA studies. © 2018 John Wiley & Sons, Ltd.

Using a cross correlation technique to refine the accuracy of the Failure Forecast Method: Application to Soufrière Hills volcano, Montserrat

NASA Astrophysics Data System (ADS)

Salvage, R. O.; Neuberg, J. W.

2016-09-01

Prior to many volcanic eruptions, an acceleration in seismicity has been observed, suggesting the potential for this as a forecasting tool. The Failure Forecast Method (FFM) relates an accelerating precursor to the timing of failure by an empirical power law, with failure being defined in this context as the onset of an eruption. Previous applications of the FFM have used a wide variety of accelerating time series, often generating questionable forecasts with large misfits between data and the forecast, as well as the generation of a number of different forecasts from the same data series. Here, we show an alternative approach applying the FFM in combination with a cross correlation technique which identifies seismicity from a single active source mechanism and location at depth. Isolating a single system at depth avoids additional uncertainties introduced by averaging data over a number of different accelerating phenomena, and consequently reduces the misfit between the data and the forecast. Similar seismic waveforms were identified in the precursory accelerating seismicity to dome collapses at Soufrière Hills volcano, Montserrat in June 1997, July 2003 and February 2010. These events were specifically chosen since they represent a spectrum of collapse scenarios at this volcano. The cross correlation technique generates a five-fold increase in the number of seismic events which could be identified from continuous seismic data rather than using triggered data, thus providing a more holistic understanding of the ongoing seismicity at the time. The use of similar seismicity as a forecasting tool for collapses in 1997 and 2003 greatly improved the forecasted timing of the dome collapse, as well as improving the confidence in the forecast, thereby outperforming the classical application of the FFM. We suggest that focusing on a single active seismic system at depth allows a more accurate forecast of some of the major dome collapses from the ongoing eruption at Soufrière Hills volcano, and provides a simple addition to the well-used methodology of the FFM.

78 FR 41116 - Agency Information Collection Activities: Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-09

... Agreement State regulations. All generators, collectors, and processors of low-level waste intended for... which facilitates tracking the identity of the waste generator. That tracking becomes more complicated... waste shipped from a waste processor may contain waste from several different generators. The...

Forecasting the progress towards the target of Millennium Development Goal 1C in children under 5 years of age in Bangladesh.

PubMed

Hasan, Md Tanvir; Soares Magalhaes, Ricardo J; Williams, Gail M; Mamun, Abdullah A

2015-07-01

To estimate the average annual rates of reduction of stunting, underweight and wasting for the period 1996 to 2011, and to evaluate whether Bangladesh will be expected to achieve the target of Millennium Development Goal 1C of reducing the prevalence of underweight by half by 2015. We used five nationwide, cross-sectional, Demographic and Health Survey data sets to estimate prevalence of undernutrition defined by stunting, underweight and wasting among children under 5 years of age using the WHO child growth standards. We then computed the average annual rates of reduction of prevalence of undernutrition using the formula derived by UNICEF. Finally, we projected the prevalence of undernutrition for the year 2015 using the estimated average annual rates of reduction. Nationwide covering Bangladesh. Children under 5 years of age (n 28,941). The prevalence of stunting decreased by 18.8% (from 60.0% to 41.2%), underweight by 16.0% (from 52.2% to 36.2%) and wasting by 5.1% (from 20.6% to 15.5%) during 1996 to 2011. The overall average annual rates of reduction were 2.84%, 2.69 % and 2.47%, respectively, for stunting, underweight and wasting. We forecast that in 2015, the prevalence of stunting, underweight and wasting will be 36.7%, 32.5% and 14.0%, respectively, at the national level. The prevalence of undernutrition is likely to remain high in rural areas, in the Sylhet division and in the poorest wealth quintile. Bangladesh is likely to achieve the Millennium Development Goal 1C target of reducing the prevalence of underweight by half by 2015. However, it is falling behind in reducing stunting and further investment is needed to reduce individual, household and environmental determinants of stunting in Bangladesh.

Acceptable knowledge document for INEEL stored transuranic waste -- Rocky Flats Plant waste. Revision 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1998-01-23

This document and supporting documentation provide a consistent, defensible, and auditable record of acceptable knowledge for waste generated at the Rocky Flats Plant which is currently in the accessible storage inventory at the Idaho National Engineering and Environmental Laboratory. The inventory consists of transuranic (TRU) waste generated from 1972 through 1989. Regulations authorize waste generators and treatment, storage, and disposal facilities to use acceptable knowledge in appropriate circumstances to make hazardous waste determinations. Acceptable knowledge includes information relating to plant history, process operations, and waste management, in addition to waste-specific data generated prior to the effective date of the RCRAmore » regulations. This document is organized to provide the reader a comprehensive presentation of the TRU waste inventory ranging from descriptions of the historical plant operations that generated and managed the waste to specific information about the composition of each waste group. Section 2 lists the requirements that dictate and direct TRU waste characterization and authorize the use of the acceptable knowledge approach. In addition to defining the TRU waste inventory, Section 3 summarizes the historical operations, waste management, characterization, and certification activities associated with the inventory. Sections 5.0 through 26.0 describe the waste groups in the inventory including waste generation, waste packaging, and waste characterization. This document includes an expanded discussion for each waste group of potential radionuclide contaminants, in addition to other physical properties and interferences that could potentially impact radioassay systems.« less

Is it growing exponentially fast? -- Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics.

PubMed

Chowell, Gerardo; Viboud, Cécile

2016-10-01

The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forecasts. Improved models for epidemic forecasting could be achieved by identifying signature features of epidemic growth, which could inform the design of models of disease spread and reveal important characteristics of the transmission process. In particular, it is often taken for granted that the early growth phase of different growth processes in nature follow early exponential growth dynamics. In the context of infectious disease spread, this assumption is often convenient to describe a transmission process with mass action kinetics using differential equations and generate analytic expressions and estimates of the reproduction number. In this article, we carry out a simulation study to illustrate the impact of incorrectly assuming an exponential-growth model to characterize the early phase (e.g., 3-5 disease generation intervals) of an infectious disease outbreak that follows near-exponential growth dynamics. Specifically, we assess the impact on: 1) goodness of fit, 2) bias on the growth parameter, and 3) the impact on short-term epidemic forecasts. Designing transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit, improved estimates of key transmission parameters, and more realistic epidemic forecasts.

Bioremediation of waste under ocean acidification: Reviewing the role of Mytilus edulis.

PubMed

Broszeit, Stefanie; Hattam, Caroline; Beaumont, Nicola

2016-02-15

Waste bioremediation is a key regulating ecosystem service, removing wastes from ecosystems through storage, burial and recycling. The bivalve Mytilus edulis is an important contributor to this service, and is used in managing eutrophic waters. Studies show that they are affected by changes in pH due to ocean acidification, reducing their growth. This is forecasted to lead to reductions in M. edulis biomass of up to 50% by 2100. Growth reduction will negatively affect the filtering capacity of each individual, potentially leading to a decrease in bioremediation of waste. This paper critically reviews the current state of knowledge of bioremediation of waste carried out by M. edulis, and the current knowledge of the resultant effect of ocean acidification on this key service. We show that the effects of ocean acidification on waste bioremediation could be a major issue and pave the way for empirical studies of the topic. Copyright © 2016 Elsevier Ltd. All rights reserved.

Production patterns of packaging waste categories generated at typical Mediterranean residential building worksites

DOE Office of Scientific and Technical Information (OSTI.GOV)

González Pericot, N., E-mail: natalia.gpericot@upm.es; Villoria Sáez, P., E-mail: paola.villoria@upm.es; Del Río Merino, M., E-mail: mercedes.delrio@upm.es

2014-11-15

Highlights: • On-site segregation level: 1.80%; training and motivation strategies were not effective. • 70% Cardboard waste: from switches and sockets during the building services stage. • 40% Plastic waste: generated during structures and partition works due to palletizing. • >50% Wood packaging waste, basically pallets, generated during the envelope works. - Abstract: The construction sector is responsible for around 28% of the total waste volume generated in Europe, which exceeds the amount of household waste. This has led to an increase of different research studies focusing on construction waste quantification. However, within the research studies made, packaging waste hasmore » been analyzed to a limited extent. This article focuses on the packaging waste stream generated in the construction sector. To this purpose current on-site waste packaging management has been assessed by monitoring ten Mediterranean residential building works. The findings of the experimental data collection revealed that the incentive measures implemented by the construction company to improve on-site waste sorting failed to achieve the intended purpose, showing low segregation ratios. Subsequently, through an analytical study the generation patterns for packaging waste are established, leading to the identification of the prevailing kinds of packaging and the products responsible for their generation. Results indicate that plastic waste generation maintains a constant trend throughout the whole construction process, while cardboard becomes predominant towards the end of the construction works with switches and sockets from the electricity stage. Understanding the production patterns of packaging waste will be beneficial for adapting waste management strategies to the identified patterns for the specific nature of packaging waste within the context of construction worksites.« less

Hazardous medical waste generation in Greece: case studies from medical facilities in Attica and from a small insular hospital.

PubMed

Komilis, Dimitrios; Katsafaros, Nikolaos; Vassilopoulos, Panagiotis

2011-08-01

The accurate calculation of the unit generation rates and composition of medical waste generated from medical facilities is necessary in order to design medical waste treatment systems. In this work, the unit medical waste generation rates of 95 public and private medical facilities in the Attica region were calculated based on daily weight records from a central medical waste incineration facility. The calculated medical waste generation rates (in kg bed(-1) day( -1)) varied widely with average values at 0.27 ± 113% and 0.24 ± 121%, for public and private medical facilities, respectively. The hazardous medical waste generation was measured, at the source, in the 40 bed hospital of the island of Ikaria for a period of 42 days during a 6 month period. The average hazardous medical waste generation rate was 1.204 kg occupied bed(-1) day(-1) or 0.33 kg (official) bed( -1) day(-1). From the above amounts, 54% resulted from the patients' room (solid and liquid wastes combined), 24% from the emergency department (solid waste), 17% from the clinical pathology lab and 6% from the X-ray lab. In average, 17% of the total hazardous medical waste was solely infectious. Conclusively, no correlation among the number of beds and the unit medical waste generation rate could be established. Each hospital should be studied separately, since medical waste generation and composition depends on the number and type of departments/laboratories at each hospital, number of external patients and number of occupied beds.

Validation of Seasonal Forecast of Indian Summer Monsoon Rainfall

NASA Astrophysics Data System (ADS)

Das, Sukanta Kumar; Deb, Sanjib Kumar; Kishtawal, C. M.; Pal, Pradip Kumar

2015-06-01

The experimental seasonal forecast of Indian summer monsoon (ISM) rainfall during June through September using Community Atmosphere Model (CAM) version 3 has been carried out at the Space Applications Centre Ahmedabad since 2009. The forecasts, based on a number of ensemble members (ten minimum) of CAM, are generated in several phases and updated on regular basis. On completion of 5 years of experimental seasonal forecasts in operational mode, it is required that the overall validation or correctness of the forecast system is quantified and that the scope is assessed for further improvements of the forecast over time, if any. The ensemble model climatology generated by a set of 20 identical CAM simulations is considered as the model control simulation. The performance of the forecast has been evaluated by assuming the control simulation as the model reference. The forecast improvement factor shows positive improvements, with higher values for the recent forecasted years as compared to the control experiment over the Indian landmass. The Taylor diagram representation of the Pearson correlation coefficient (PCC), standard deviation and centered root mean square difference has been used to demonstrate the best PCC, in the order of 0.74-0.79, recorded for the seasonal forecast made during 2013. Further, the bias score of different phases of experiment revealed the fact that the ISM rainfall forecast is affected by overestimation in predicting the low rain-rate (less than 7 mm/day), but by underestimation in the medium and high rain-rate (higher than 11 mm/day). Overall, the analysis shows significant improvement of the ISM forecast over the last 5 years, viz. 2009-2013, due to several important modifications that have been implemented in the forecast system. The validation exercise has also pointed out a number of shortcomings in the forecast system; these will be addressed in the upcoming years of experiments to improve the quality of the ISM prediction.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States.

PubMed

Yamana, Teresa K; Kandula, Sasikiran; Shaman, Jeffrey

2017-11-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time.

Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States

PubMed Central

Kandula, Sasikiran; Shaman, Jeffrey

2017-01-01

Recent research has produced a number of methods for forecasting seasonal influenza outbreaks. However, differences among the predicted outcomes of competing forecast methods can limit their use in decision-making. Here, we present a method for reconciling these differences using Bayesian model averaging. We generated retrospective forecasts of peak timing, peak incidence, and total incidence for seasonal influenza outbreaks in 48 states and 95 cities using 21 distinct forecast methods, and combined these individual forecasts to create weighted-average superensemble forecasts. We compared the relative performance of these individual and superensemble forecast methods by geographic location, timing of forecast, and influenza season. We find that, overall, the superensemble forecasts are more accurate than any individual forecast method and less prone to producing a poor forecast. Furthermore, we find that these advantages increase when the superensemble weights are stratified according to the characteristics of the forecast or geographic location. These findings indicate that different competing influenza prediction systems can be combined into a single more accurate forecast product for operational delivery in real time. PMID:29107987

Conceptual framework for the study of food waste generation and prevention in the hospitality sector.

PubMed

Papargyropoulou, Effie; Wright, Nigel; Lozano, Rodrigo; Steinberger, Julia; Padfield, Rory; Ujang, Zaini

2016-03-01

Food waste has significant detrimental economic, environmental and social impacts. The magnitude and complexity of the global food waste problem has brought it to the forefront of the environmental agenda; however, there has been little research on the patterns and drivers of food waste generation, especially outside the household. This is partially due to weaknesses in the methodological approaches used to understand such a complex problem. This paper proposes a novel conceptual framework to identify and explain the patterns and drivers of food waste generation in the hospitality sector, with the aim of identifying food waste prevention measures. This conceptual framework integrates data collection and analysis methods from ethnography and grounded theory, complemented with concepts and tools from industrial ecology for the analysis of quantitative data. A case study of food waste generation at a hotel restaurant in Malaysia is used as an example to illustrate how this conceptual framework can be applied. The conceptual framework links the biophysical and economic flows of food provisioning and waste generation, with the social and cultural practices associated with food preparation and consumption. The case study demonstrates that food waste is intrinsically linked to the way we provision and consume food, the material and socio-cultural context of food consumption and food waste generation. Food provisioning, food consumption and food waste generation should be studied together in order to fully understand how, where and most importantly why food waste is generated. This understanding will then enable to draw detailed, case specific food waste prevention plans addressing the material and socio-economic aspects of food waste generation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Real-time forecasts of dengue epidemics

NASA Astrophysics Data System (ADS)

Yamana, T. K.; Shaman, J. L.

2015-12-01

Dengue is a mosquito-borne viral disease prevalent in the tropics and subtropics, with an estimated 2.5 billion people at risk of transmission. In many areas with endemic dengue, disease transmission is seasonal but prone to high inter-annual variability with occasional severe epidemics. Predicting and preparing for periods of higher than average transmission is a significant public health challenge. Here we present a model of dengue transmission and a framework for optimizing model simulations with real-time observational data of dengue cases and environmental variables in order to generate ensemble-based forecasts of the timing and severity of disease outbreaks. The model-inference system is validated using synthetic data and dengue outbreak records. Retrospective forecasts are generated for a number of locations and the accuracy of these forecasts is quantified.

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

Techniques for water demand analysis and forecasting: Puerto Rico, a case study

USGS Publications Warehouse

Attanasi, E.D.; Close, E.R.; Lopez, M.A.

1975-01-01

The rapid economic growth of the Commonwealth-of Puerto Rico since 1947 has brought public pressure on Government agencies for rapid development of public water supply and waste treatment facilities. Since 1945 the Puerto Rico Aqueduct and Sewer Authority has had the responsibility for planning, developing and operating water supply and waste treatment facilities on a municipal basis. The purpose of this study was to develop operational techniques whereby a planning agency, such as the Puerto Rico Aqueduct and Sewer Authority, could project the temporal and spatial distribution of .future water demands. This report is part of a 2-year cooperative study between the U.S. Geological Survey and the Environmental Quality Board of the Commonwealth of Puerto Rico, for the development of systems analysis techniques for use in water resources planning. While the Commonwealth was assisted in the development of techniques to facilitate ongoing planning, the U.S. Geological Survey attempted to gain insights in order to better interface its data collection efforts with the planning process. The report reviews the institutional structure associated with water resources planning for the Commonwealth. A brief description of alternative water demand forecasting procedures is presented and specific techniques and analyses of Puerto Rico demand data are discussed. Water demand models for a specific area of Puerto Rico are then developed. These models provide a framework for making several sets of water demand forecasts based on alternative economic and demographic assumptions. In the second part of this report, the historical impact of water resources investment on regional economic development is analyzed and related to water demand .forecasting. Conclusions and future data needs are in the last section.

Segregation of biomedical waste in an South Indian tertiary care hospital

PubMed Central

Sengodan, Vetrivel Chezian

2014-01-01

Introduction: Hospital wastes pose significant public health hazard if not properly managed. Hence, it is necessary to develop and adopt optimal waste management systems in the hospitals. Material and method: Biomedical waste generated in Coimbatore Medical College Hospital was color coded (blue, yellow, and red) and the data was analyzed retrospectively on a daily basis for 3 years (January 2010-December 2012). Results: Effective segregation protocols significantly reduced biomedical waste generated from 2011 to 2012. While biomedical waste of red category was significantly higher (>50%), the category yellow was the least. Per unit (per bed per day) total biomedical waste generated was 68.5, 68.8, and 61.3 grams in 2010, 2011, and 2012, respectively. Discussion: Segregation of biomedical waste at the source of generation is the first and essential step in biomedical waste management. Continuous training, fixing the responsibility on the nursing persons, and constant supervision are the key criteria's in implementing biomedical waste segregation process, which can significantly reduce per unit biomedical waste generated. Conclusion: We highly recommend all hospitals to adopt our protocol and effectively implement them to reduce generation of biomedical waste. PMID:25097419

Factors determining waste generation in Spanish towns and cities.

PubMed

Prades, Miriam; Gallardo, Antonio; Ibàñez, Maria Victoria

2015-01-01

This paper analyzes the generation and composition of municipal solid waste in Spanish towns and cities with more than 5000 inhabitants, which altogether account for 87% of the Spanish population. To do so, the total composition and generation of municipal solid waste fractions were obtained from 135 towns and cities. Homogeneity tests revealed heterogeneity in the proportions of municipal solid waste fractions from one city to another. Statistical analyses identified significant differences in the generation of glass in cities of different sizes and in the generation of all fractions depending on the hydrographic area. Finally, linear regression models and residuals analysis were applied to analyze the effect of different demographic, geographic, and socioeconomic variables on the generation of waste fractions. The conclusions show that more densely populated towns, a hydrographic area, and cities with over 50,000 inhabitants have higher waste generation rates, while certain socioeconomic variables (people/car) decrease that generation. Other socioeconomic variables (foreigners and unemployment) show a positive and null influence on that waste generation, respectively.

The global economic and regulatory determinants of household food waste generation: A cross-country analysis.

PubMed

Chalak, Ali; Abou-Daher, Chaza; Chaaban, Jad; Abiad, Mohamad G

2016-02-01

Food is generally wasted all along the supply chain, with an estimated loss of 35percent generated at the consumer level. Consequently, household food waste constitutes a sizable proportion of the total waste generated throughout the food supply chain. Yet such wastes vary drastically between developed and developing countries. Using data collected from 44 countries with various income levels, this paper investigates the impact of legislation and economic incentives on household food waste generation. The obtained results indicate that well-defined regulations, policies and strategies are more effective than fiscal measures in mitigating household food waste generation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Data analytics approach to create waste generation profiles for waste management and collection.

PubMed

Niska, Harri; Serkkola, Ari

2018-04-30

Extensive monitoring data on waste generation is increasingly collected in order to implement cost-efficient and sustainable waste management operations. In addition, geospatial data from different registries of the society are opening for free usage. Novel data analytics approaches can be built on the top of the data to produce more detailed, and in-time waste generation information for the basis of waste management and collection. In this paper, a data-based approach based on the self-organizing map (SOM) and the k-means algorithm is developed for creating a set of waste generation type profiles. The approach is demonstrated using the extensive container-level waste weighting data collected in the metropolitan area of Helsinki, Finland. The results obtained highlight the potential of advanced data analytic approaches in producing more detailed waste generation information e.g. for the basis of tailored feedback services for waste producers and the planning and optimization of waste collection and recycling. Copyright © 2018 Elsevier Ltd. All rights reserved.

A retrospective evaluation of traffic forecasting techniques.

DOT National Transportation Integrated Search

2016-08-01

Traffic forecasting techniquessuch as extrapolation of previous years traffic volumes, regional travel demand models, or : local trip generation rateshelp planners determine needed transportation improvements. Thus, knowing the accuracy of t...

Municipal solid waste characterization and quantification as a measure towards effective waste management in Ghana.

PubMed

Miezah, Kodwo; Obiri-Danso, Kwasi; Kádár, Zsófia; Fei-Baffoe, Bernard; Mensah, Moses Y

2015-12-01

Reliable national data on waste generation and composition that will inform effective planning on waste management in Ghana is absent. To help obtain this data on a regional basis, selected households in each region were recruited to obtain data on rate of waste generation, physical composition of waste, sorting and separation efficiency and per capita of waste. Results show that rate of waste generation in Ghana was 0.47 kg/person/day, which translates into about 12,710 tons of waste per day per the current population of 27,043,093. Nationally, biodegradable waste (organics and papers) was 0.318 kg/person/day and non-biodegradable or recyclables (metals, glass, textiles, leather and rubbers) was 0.096 kg/person/day. Inert and miscellaneous waste was 0.055 kg/person/day. The average household waste generation rate among the metropolitan cities, except Tamale, was high, 0.72 kg/person/day. Metropolises generated higher waste (average 0.63 kg/person/day) than the municipalities (0.40 kg/person/day) and the least in the districts (0.28 kg/person/day) which are less developed. The waste generation rate also varied across geographical locations, the coastal and forest zones generated higher waste than the northern savanna zone. Waste composition was 61% organics, 14% plastics, 6% inert, 5% miscellaneous, 5% paper, 3% metals, 3% glass, 1% leather and rubber, and 1% textiles. However, organics and plastics, the two major fractions of the household waste varied considerably across the geographical areas. In the coastal zone, the organic waste fraction was highest but decreased through the forest zone towards the northern savanna. However, through the same zones towards the north, plastic waste rather increased in percentage fraction. Households did separate their waste effectively averaging 80%. However, in terms of separating into the bin marked biodegradables, 84% effectiveness was obtained whiles 76% effectiveness for sorting into the bin labeled other waste was achieved. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Solid waste generation and characterization in the University of Lagos for a sustainable waste management.

PubMed

Adeniran, A E; Nubi, A T; Adelopo, A O

2017-09-01

Waste characterization is the first step to any successful waste management policy. In this paper, the characterization and the trend of solid waste generated in University of Lagos, Nigeria was carried out using ASTM D5231-92 and Resource Conservation Reservation Authority RCRA Waste Sampling Draft Technical Guidance methods. The recyclable potential of the waste is very high constituting about 75% of the total waste generated. The estimated average daily solid waste generation in Unilag Akoka campus was estimated to be 32.2tons. The solid waste characterization was found to be: polythene bags 24% (7.73tons/day), paper 15% (4.83tons/day), organic matters 15%, (4.83tons/day), plastic 9% (2.90tons/day), inert materials 8% (2.58tons/day), sanitary 7% (2.25tons/day), textile 7% (2.25tons/day), others 6% (1.93tons/day), leather 4% (1.29tons/day) metals 3% (0.97tons/day), glass 2% (0.64tons/day) and e-waste 0% (0.0tons/day). The volume and distribution of polythene bags generated on campus had a positive significant statistical correlation with the distribution of commercial and academic structures on campus. Waste management options to optimize reuse, recycling and reduce waste generation were discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Forecast Inaccuracies in Power Plant Projects From Project Managers' Perspectives

NASA Astrophysics Data System (ADS)

Sanabria, Orlando

Guided by organizational theory, this phenomenological study explored the factors affecting forecast preparation and inaccuracies during the construction of fossil fuel-fired power plants in the United States. Forecast inaccuracies can create financial stress and uncertain profits during the project construction phase. A combination of purposeful and snowball sampling supported the selection of participants. Twenty project managers with over 15 years of experience in power generation and project experience across the United States were interviewed within a 2-month period. From the inductive codification and descriptive analysis, 5 themes emerged: (a) project monitoring, (b) cost control, (c) management review frequency, (d) factors to achieve a precise forecast, and (e) factors causing forecast inaccuracies. The findings of the study showed the factors necessary to achieve a precise forecast includes a detailed project schedule, accurate labor cost estimates, monthly project reviews and risk assessment, and proper utilization of accounting systems to monitor costs. The primary factors reported as causing forecast inaccuracies were cost overruns by subcontractors, scope gaps, labor cost and availability of labor, and equipment and material cost. Results of this study could improve planning accuracy and the effective use of resources during construction of power plants. The study results could contribute to social change by providing a framework to project managers to lessen forecast inaccuracies, and promote construction of power plants that will generate employment opportunities and economic development.

Modeling of urban solid waste management system: The case of Dhaka city

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sufian, M.A.; Bala, B.K.

2007-07-01

This paper presents a system dynamics computer model to predict solid waste generation, collection capacity and electricity generation from solid waste and to assess the needs for waste management of the urban city of Dhaka, Bangladesh. Simulated results show that solid waste generation, collection capacity and electricity generation potential from solid waste increase with time. Population, uncleared waste, untreated waste, composite index and public concern are projected to increase with time for Dhaka city. Simulated results also show that increasing the budget for collection capacity alone does not improve environmental quality; rather an increased budget is required for both collectionmore » and treatment of solid wastes of Dhaka city. Finally, this model can be used as a computer laboratory for urban solid waste management (USWM) policy analysis.« less

77 FR 36447 - Hazardous Waste Management System; Identification and Listing of Hazardous Waste

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-19

... the point of waste generation. C. How did ExxonMobil sample and analyze the data in this petition? To support its petition, ExxonMobil submitted: (1) Historical information on waste generation and management... North Landfarm underflow water twice during the first six months of waste generation. ExxonMobil would...

EPA issues interim final waste minimization guidance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bergeson, L.L.

1993-08-01

The U.S. Environmental Protection Agency (EPA) has released a new and detailed interim final guidance to assist hazardous waste generators in certifying they have a waste minimization program in place under the Resource Conservation and Recovery Act (RCRA). EPA's guidance identifies the basic elements of a waste minimization program in place that, if present, will allow people to certify they have implemented a program to reduce the volume and toxicity of hazardous waste to the extent economically practical. The guidance is directly applicable to generators of 1000 or more kilograms per month of hazardous waste, or large-quantity generators, and tomore » owners and operators of hazardous waste treatment, storage or disposal facilities who manage their own hazardous waste on site. Small-quantity generators that generate more than 100 kilograms, but less than 1,000 kilograms, per month of hazardous waste are not subject to the same program in place certification requirement. Rather, they must certify on their manifests that they have made a good faith effort to minimize their waste generation.« less

A system for forecasting and monitoring cash flow : phase II, forecasting federal and state revenues, maintenance contracts, other expenditures, and cash balances.

DOT National Transportation Integrated Search

1985-01-01

The research on which this report is based was performed as part of a study to develop an improved system for generating a two-year forecast of monthly cash flows for the Virginia Department of Highways and Transportation. It revealed that current te...

Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions

NASA Astrophysics Data System (ADS)

Aksoy, Hafzullah; Dahamsheh, Ahmad

2018-07-01

For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions.

HANFORD FACILITY ANNUAL DANGEROUS WASTE REPORT CY2005

DOE Office of Scientific and Technical Information (OSTI.GOV)

SKOLRUD, J.O.

2006-02-15

The Hanford Facility Annual Dangerous Waste Report (ADWR) is prepared to meet the requirements of Washington Administrative Code Sections 173-303-220, Generator Reporting, and 173-303-390, Facility Reporting. In addition, the ADWR is required to meet Hanford Facility RCR4 Permit Condition I.E.22, Annual Reporting. The ADWR provides summary information on dangerous waste generation and management activities for the Calendar Year for the Hanford Facility EPA ID number assigned to the Department of Energy for RCRA regulated waste, as well as Washington State only designated waste and radioactive mixed waste. An electronic database is utilized to collect and compile the large array ofmore » data needed for preparation of this report. Information includes details of waste generated on the Hanford Facility, waste generated offsite and sent to Hanford for management, and other waste management activities conducted at Hanford, including treatment, storage, and disposal. Report details consist of waste descriptions and weights, waste codes and designations, and waste handling codes, In addition, for waste shipped to Hanford for treatment and/or disposal, information on manifest numbers, the waste transporter, the waste receiving facility, and the original waste generators are included. In addition to paper copies, the report is also transmitted electronically to a web site maintained by the Washington State Department of Ecology.« less

Quasi-most unstable modes: a window to 'À la carte' ensemble diversity?

NASA Astrophysics Data System (ADS)

Homar Santaner, Victor; Stensrud, David J.

2010-05-01

The atmospheric scientific community is nowadays facing the ambitious challenge of providing useful forecasts of atmospheric events that produce high societal impact. The low level of social resilience to false alarms creates tremendous pressure on forecasting offices to issue accurate, timely and reliable warnings.Currently, no operational numerical forecasting system is able to respond to the societal demand for high-resolution (in time and space) predictions in the 12-72h time span. The main reasons for such deficiencies are the lack of adequate observations and the high non-linearity of the numerical models that are currently used. The whole weather forecasting problem is intrinsically probabilistic and current methods aim at coping with the various sources of uncertainties and the error propagation throughout the forecasting system. This probabilistic perspective is often created by generating ensembles of deterministic predictions that are aimed at sampling the most important sources of uncertainty in the forecasting system. The ensemble generation/sampling strategy is a crucial aspect of their performance and various methods have been proposed. Although global forecasting offices have been using ensembles of perturbed initial conditions for medium-range operational forecasts since 1994, no consensus exists regarding the optimum sampling strategy for high resolution short-range ensemble forecasts. Bred vectors, however, have been hypothesized to better capture the growing modes in the highly nonlinear mesoscale dynamics of severe episodes than singular vectors or observation perturbations. Yet even this technique is not able to produce enough diversity in the ensembles to accurately and routinely predict extreme phenomena such as severe weather. Thus, we propose a new method to generate ensembles of initial conditions perturbations that is based on the breeding technique. Given a standard bred mode, a set of customized perturbations is derived with specified amplitudes and horizontal scales. This allows the ensemble to excite growing modes across a wider range of scales. Results show that this approach produces significantly more spread in the ensemble prediction than standard bred modes alone. Several examples that illustrate the benefits from this approach for severe weather forecasts will be provided.

Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model ensembles downscaled by analog ensemble using self-organizing maps

NASA Astrophysics Data System (ADS)

Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji

2016-04-01

Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the downscaling technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.

Integrated Forecast-Decision Systems For River Basin Planning and Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.

2005-12-01

A central application of climatology, meteorology, and hydrology is the generation of reliable forecasts for water resources management. In principle, effective use of forecasts could improve water resources management by providing extra protection against floods, mitigating the adverse effects of droughts, generating more hydropower, facilitating recreational activities, and minimizing the impacts of extreme events on the environment and the ecosystems. In practice, however, realization of these benefits depends on three requisite elements. First is the skill and reliability of forecasts. Second is the existence of decision support methods/systems with the ability to properly utilize forecast information. And third is the capacity of the institutional infrastructure to incorporate the information provided by the decision support systems into the decision making processes. This presentation discusses several decision support systems (DSS) using ensemble forecasting that have been developed by the Georgia Water Resources Institute for river basin management. These DSS are currently operational in Africa, Europe, and the US and address integrated water resources and energy planning and management in river basins with multiple water uses, multiple relevant temporal and spatial scales, and multiple decision makers. The article discusses the methods used and advocates that the design, development, and implementation of effective forecast-decision support systems must bring together disciplines, people, and institutions necessary to address today's complex water resources challenges.

Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Florita, A.; Hodge, B. M.; Milligan, M.

2012-08-01

The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites andmore » for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.« less

Forecasting paratransit services demand : review and recommendations.

DOT National Transportation Integrated Search

2013-06-01

Travel demand forecasting tools for Floridas paratransit services are outdated, utilizing old national trip : generation rate generalities and simple linear regression models. In its guidance for the development of : mandated Transportation Disadv...

Characterization of urban solid waste in Chihuahua, Mexico.

PubMed

Gomez, Guadalupe; Meneses, Montserrat; Ballinas, Lourdes; Castells, Francesc

2008-12-01

The characterization of urban solid waste generation is fundamental for adequate decision making in the management strategy of urban solid waste in a city. The objective of this study is to characterize the waste generated in the households of Chihuahua city, and to compare the results obtained in areas of the city with three different socioeconomic levels. In order to identify the different socioeconomic trends in waste generation and characterization, 560 samples of solid waste were collected during 1 week from 80 households in Chihuahua and were hand sorted and classified into 15 weighted fractions. The average waste generation in Chihuahua calculated in this study was 0.676 kg per capita per day in April 2006. The main fractions were: organic (48%), paper (16%) and plastic (12%). Results show an increased waste generation associated with the socioeconomic level. The characterization in amount and composition of urban waste is the first step needed for the successful implementation of an integral waste management system.

Stock flow diagram analysis on solid waste management in Malaysia

NASA Astrophysics Data System (ADS)

Zulkipli, Faridah; Nopiah, Zulkifli Mohd; Basri, Noor Ezlin Ahmad; Kie, Cheng Jack

2016-10-01

The effectiveness on solid waste management is a major importance to societies. Numerous generation of solid waste from our daily activities has risked for our communities. These due to rapid population grow and advance in economic development. Moreover, the complexity of solid waste management is inherently involved large scale, diverse and element of uncertainties that must assist stakeholders with deviating objectives. In this paper, we proposed a system dynamics simulation by developing a stock flow diagram to illustrate the solid waste generation process and waste recycle process. The analysis highlights the impact on increasing the number of population toward the amount of solid waste generated and the amount of recycled waste. The results show an increment in the number of population as well as the amount of recycled waste will decrease the amount of waste generated. It is positively represent the achievement of government aim to minimize the amount of waste to be disposed by year 2020.

Safe disposal of radionuclides in low-level radioactive-waste repository sites; Low-level radioactive-waste disposal workshop, U.S. Geological Survey, July 11-16, 1987, Big Bear Lake, Calif., Proceedings

USGS Publications Warehouse

Bedinger, Marion S.; Stevens, Peter R.

1990-01-01

In the United States, low-level radioactive waste is disposed by shallow-land burial. Low-level radioactive waste generated by non-Federal facilities has been buried at six commercially operated sites; low-level radioactive waste generated by Federal facilities has been buried at eight major and several minor Federally operated sites (fig. 1). Generally, low-level radioactive waste is somewhat imprecisely defined as waste that does not fit the definition of high-level radioactive waste and does not exceed 100 nCi/g in the concentration of transuranic elements. Most low-level radioactive waste generated by non-Federal facilities is generated at nuclear powerplants; the remainder is generated primarily at research laboratories, hospitals, industrial facilities, and universities. On the basis of half lives and concentrations of radionuclides in low-level radioactive waste, the hazard associated with burial of such waste generally lasts for about 500 years. Studies made at several of the commercially and Federally operated low-level radioactive-waste repository sites indicate that some of these sites have not provided containment of waste nor the expected protection of the environment.

HANFORD FACILITY ANNUAL DANGEROUS WASTE REPORT CY2003 [SEC 1 & 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

FREEMAN, D.A.

2004-02-17

The Hanford Facility Annual Dangerous Waste Report (ADWR) is prepared to meet the requirements of Washington Administrative Code Sections 173-303-220, Generator Reporting, and 173-303-390, Facility Reporting. In addition, the ADWR is required to meet Hanford Facility RCRA Permit Condition I.E.22, Annual Reporting. The ADWR provides summary information on dangerous waste generation and management activities for the Calendar Year for the Hanford Facility EPA ID number assigned to the Department of Energy for RCRA regulated waste, as well as Washington State only designated waste and radioactive mixed waste. The Solid Waste Information and Tracking System (SWITS) database is utilized to collectmore » and compile the large array of data needed for preparation of this report. Information includes details of waste generated on the Hanford Facility, waste generated offsite and sent to Hanford for management, and other waste management activities conducted at Hanford, including treatment, storage, and disposal. Report details consist of waste descriptions and weights, waste codes and designations, and waste handling codes. In addition, for waste shipped to Hanford for treatment and or disposal, information on manifest numbers, the waste transporter, the waste receiving facility, and the original waste generators are included. In addition to paper copies, the report is also transmitted electronically to a web site maintained by the Washington State Department of Ecology.« less

Freeway travel-time estimation and forecasting.

DOT National Transportation Integrated Search

2012-09-01

This project presents a microsimulation-based framework for generating short-term forecasts of travel time on freeway corridors. The microsimulation model that is developed (GTsim), replicates freeway capacity drop and relaxation phenomena critical f...

Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, C. W.; Hood, Raleigh R.; Long, Wen

The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat modelsmore » of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.« less

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2015-09-01

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Medium range forecasting of Hurricane Harvey flash flooding using ECMWF and social vulnerability data

NASA Astrophysics Data System (ADS)

Pillosu, F. M.; Jurlina, T.; Baugh, C.; Tsonevsky, I.; Hewson, T.; Prates, F.; Pappenberger, F.; Prudhomme, C.

2017-12-01

During hurricane Harvey the greater east Texas area was affected by extensive flash flooding. Their localised nature meant they were too small for conventional large scale flood forecasting systems to capture. We are testing the use of two real time forecast products from the European Centre for Medium-range Weather Forecasts (ECMWF) in combination with local vulnerability information to provide flash flood forecasting tools at the medium range (up to 7 days ahead). Meteorological forecasts are the total precipitation extreme forecast index (EFI), a measure of how the ensemble forecast probability distribution differs from the model-climate distribution for the chosen location, time of year and forecast lead time; and the shift of tails (SOT) which complements the EFI by quantifying how extreme an event could potentially be. Both products give the likelihood of flash flood generating precipitation. For hurricane Harvey, 3-day EFI and SOT products for the period 26th - 29th August 2017 were used, generated from the twice daily, 18 km, 51 ensemble member ECMWF Integrated Forecast System. After regridding to 1 km resolution the forecasts were combined with vulnerable area data to produce a flash flood hazard risk area. The vulnerability data were floodplains (EU Joint Research Centre), road networks (Texas Department of Transport) and urban areas (Census Bureau geographic database), together reflecting the susceptibility to flash floods from the landscape. The flash flood hazard risk area forecasts were verified using a traditional approach against observed National Weather Service flash flood reports, a total of 153 reported flash floods have been detected in that period. Forecasts performed best for SOT = 5 (hit ratio = 65%, false alarm ratio = 44%) and EFI = 0.7 (hit ratio = 74%, false alarm ratio = 45%) at 72 h lead time. By including the vulnerable areas data, our verification results improved by 5-15%, demonstrating the value of vulnerability information within natural hazard forecasts. This research shows that flash flooding from hurricane Harvey was predictable up to 4 days ahead and that filtering the forecasts to vulnerable areas provides a more focused guidance to civil protection agencies planning their emergency response.

Non-seismic tsunamis: filling the forecast gap

NASA Astrophysics Data System (ADS)

Moore, C. W.; Titov, V. V.; Spillane, M. C.

2015-12-01

Earthquakes are the generation mechanism in over 85% of tsunamis. However, non-seismic tsunamis, including those generated by meteorological events, landslides, volcanoes, and asteroid impacts, can inundate significant area and have a large far-field effect. The current National Oceanographic and Atmospheric Administration (NOAA) tsunami forecast system falls short in detecting these phenomena. This study attempts to classify the range of effects possible from these non-seismic threats, and to investigate detection methods appropriate for use in a forecast system. Typical observation platforms are assessed, including DART bottom pressure recorders and tide gauges. Other detection paths include atmospheric pressure anomaly algorithms for detecting meteotsunamis and the early identification of asteroids large enough to produce a regional hazard. Real-time assessment of observations for forecast use can provide guidance to mitigate the effects of a non-seismic tsunami.

Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri; Hug, Gabriela; Li, Xin

Energy storage systems (ESS) have the potential to be very beneficial for applications such as reducing the ramping of generators, peak shaving, and balancing not only the variability introduced by renewable energy sources, but also the uncertainty introduced by errors in their forecasts. Optimal usage of storage may result in reduced generation costs and an increased use of renewable energy. However, optimally sizing these devices is a challenging problem. This paper aims to provide the tools to optimally size an ESS under the assumption that it will be operated under a model predictive control scheme and that the forecast ofmore » the renewable energy resources include prediction errors. A two-stage stochastic model predictive control is formulated and solved, where the optimal usage of the storage is simultaneously determined along with the optimal generation outputs and size of the storage. Wind forecast errors are taken into account in the optimization problem via probabilistic constraints for which an analytical form is derived. This allows for the stochastic optimization problem to be solved directly, without using sampling-based approaches, and sizing the storage to account not only for a wide range of potential scenarios, but also for a wide range of potential forecast errors. In the proposed formulation, we account for the fact that errors in the forecast affect how the device is operated later in the horizon and that a receding horizon scheme is used in operation to optimally use the available storage.« less

NASA Products to Enhance Energy Utility Load Forecasting

NASA Technical Reports Server (NTRS)

Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.

2012-01-01

Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.

Municipal solid waste flow and waste generation characteristics in an urban--rural fringe area in Thailand.

PubMed

Hiramatsu, Ai; Hara, Yuji; Sekiyama, Makiko; Honda, Ryo; Chiemchaisri, Chart

2009-12-01

In the urban-rural fringe of the Bangkok Metropolitan Region, rapid urbanization is creating a land-use mixture of agricultural fields and residential areas. To develop appropriate policies to enhance recycling of municipal solid waste (MSW), current MSW management was investigated in the oboto (local administrative district) of Bang Maenang in Nonthaburi Province, adjoining Bangkok. The authors conducted a structural interview survey with waste-related organizations and local residents, analysed household waste generation, and performed global positioning system (GPS) tracking of municipal garbage trucks. It was found that MSW was collected and treated by local government, private-sector entities, and the local community separately. Lack of integrated management of these entities complicated waste flow in the study area, and some residences were not served by MSW collection. Organic waste, such as kitchen garbage and yard waste, accounted for a large proportion of waste generation but was underutilized. Through GPS/GIS analysis, the waste collection rate of the generated waste amount was estimated to be 45.5- 51.1% of total generation.

Selective inspection planning with ageing forecast for sewer types.

PubMed

Baur, R; Herz, R

2002-01-01

Investments in sewer rehabilitation must be based on inspection and evaluation of sewer conditions with respect to the severity of sewer damage and to environmental risks. This paper deals with the problems of forecasting the condition of sewers in a network from a small sample of inspected sewers. Transition functions from one into the next poorer condition class, which were empirically derived from this sample, are used to forecast the condition of sewers. By the same procedure, transition functions were subsequently calibrated for sub-samples of different types of sewers. With these transition functions, the most probable date of entering a critical condition class can be forecast from sewer characteristics, such as material, period of construction, location, use for waste and/or storm water, profile, diameter and gradient. Results are shown for the estimates about the actual condition of the Dresden sewer network and its deterioration in case of doing nothing about it. A procedure is proposed for scheduling the inspection dates for sewers which have not yet been inspected and for those which have been inspected before.

Production patterns of packaging waste categories generated at typical Mediterranean residential building worksites.

PubMed

González Pericot, N; Villoria Sáez, P; Del Río Merino, M; Liébana Carrasco, O

2014-11-01

The construction sector is responsible for around 28% of the total waste volume generated in Europe, which exceeds the amount of household waste. This has led to an increase of different research studies focusing on construction waste quantification. However, within the research studies made, packaging waste has been analyzed to a limited extent. This article focuses on the packaging waste stream generated in the construction sector. To this purpose current on-site waste packaging management has been assessed by monitoring ten Mediterranean residential building works. The findings of the experimental data collection revealed that the incentive measures implemented by the construction company to improve on-site waste sorting failed to achieve the intended purpose, showing low segregation ratios. Subsequently, through an analytical study the generation patterns for packaging waste are established, leading to the identification of the prevailing kinds of packaging and the products responsible for their generation. Results indicate that plastic waste generation maintains a constant trend throughout the whole construction process, while cardboard becomes predominant towards the end of the construction works with switches and sockets from the electricity stage. Understanding the production patterns of packaging waste will be beneficial for adapting waste management strategies to the identified patterns for the specific nature of packaging waste within the context of construction worksites. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, J.; Hodge, B. M.; Florita, A.

2013-10-01

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The resultsmore » show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.« less

Mathematical modeling to predict residential solid waste generation.

PubMed

Benítez, Sara Ojeda; Lozano-Olvera, Gabriela; Morelos, Raúl Adalberto; Vega, Carolina Armijo de

2008-01-01

One of the challenges faced by waste management authorities is determining the amount of waste generated by households in order to establish waste management systems, as well as trying to charge rates compatible with the principle applied worldwide, and design a fair payment system for households according to the amount of residential solid waste (RSW) they generate. The goal of this research work was to establish mathematical models that correlate the generation of RSW per capita to the following variables: education, income per household, and number of residents. This work was based on data from a study on generation, quantification and composition of residential waste in a Mexican city in three stages. In order to define prediction models, five variables were identified and included in the model. For each waste sampling stage a different mathematical model was developed, in order to find the model that showed the best linear relation to predict residential solid waste generation. Later on, models to explore the combination of included variables and select those which showed a higher R(2) were established. The tests applied were normality, multicolinearity and heteroskedasticity. Another model, formulated with four variables, was generated and the Durban-Watson test was applied to it. Finally, a general mathematical model is proposed to predict residential waste generation, which accounts for 51% of the total.

Transportation Sector Model of the National Energy Modeling System. Volume 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1998-01-01

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. The NEMS Transportation Model comprises a series of semi-independent models which address different aspects of the transportation sector. The primary purpose of this model is to provide mid-term forecasts of transportation energy demand by fuel type including, but not limited to, motor gasoline, distillate, jet fuel, and alternative fuels (such as CNG) not commonly associated with transportation. Themore » current NEMS forecast horizon extends to the year 2010 and uses 1990 as the base year. Forecasts are generated through the separate consideration of energy consumption within the various modes of transport, including: private and fleet light-duty vehicles; aircraft; marine, rail, and truck freight; and various modes with minor overall impacts, such as mass transit and recreational boating. This approach is useful in assessing the impacts of policy initiatives, legislative mandates which affect individual modes of travel, and technological developments. The model also provides forecasts of selected intermediate values which are generated in order to determine energy consumption. These elements include estimates of passenger travel demand by automobile, air, or mass transit; estimates of the efficiency with which that demand is met; projections of vehicle stocks and the penetration of new technologies; and estimates of the demand for freight transport which are linked to forecasts of industrial output. Following the estimation of energy demand, TRAN produces forecasts of vehicular emissions of the following pollutants by source: oxides of sulfur, oxides of nitrogen, total carbon, carbon dioxide, carbon monoxide, and volatile organic compounds.« less

A comparison between EDA-EnVar and ETKF-EnVar data assimilation techniques using radar observations at convective scales through a case study of Hurricane Ike (2008)

NASA Astrophysics Data System (ADS)

Shen, Feifei; Xu, Dongmei; Xue, Ming; Min, Jinzhong

2017-07-01

This study examines the impacts of assimilating radar radial velocity (Vr) data for the simulation of hurricane Ike (2008) with two different ensemble generation techniques in the framework of the hybrid ensemble-variational (EnVar) data assimilation system of Weather Research and Forecasting model. For the generation of ensemble perturbations we apply two techniques, the ensemble transform Kalman filter (ETKF) and the ensemble of data assimilation (EDA). For the ETKF-EnVar, the forecast ensemble perturbations are updated by the ETKF, while for the EDA-EnVar, the hybrid is employed to update each ensemble member with perturbed observations. The ensemble mean is analyzed by the hybrid method with flow-dependent ensemble covariance for both EnVar. The sensitivity of analyses and forecasts to the two applied ensemble generation techniques is investigated in our current study. It is found that the EnVar system is rather stable with different ensemble update techniques in terms of its skill on improving the analyses and forecasts. The EDA-EnVar-based ensemble perturbations are likely to include slightly less organized spatial structures than those in ETKF-EnVar, and the perturbations of the latter are constructed more dynamically. Detailed diagnostics reveal that both of the EnVar schemes not only produce positive temperature increments around the hurricane center but also systematically adjust the hurricane location with the hurricane-specific error covariance. On average, the analysis and forecast from the ETKF-EnVar have slightly smaller errors than that from the EDA-EnVar in terms of track, intensity, and precipitation forecast. Moreover, ETKF-EnVar yields better forecasts when verified against conventional observations.

Review, mapping and analysis of the agricultural plastic waste generation and consolidation in Europe.

PubMed

Briassoulis, Demetres; Babou, Epifania; Hiskakis, Miltiadis; Scarascia, Giacomo; Picuno, Pietro; Guarde, Dorleta; Dejean, Cyril

2013-12-01

A review of agricultural plastic waste generation and consolidation in Europe is presented. A detailed geographical mapping of the agricultural plastic use and waste generation in Europe was conducted focusing on areas of high concentration of agricultural plastics. Quantitative data and analysis of the agricultural plastic waste generation by category, geographical distribution and compositional range, and physical characteristics of the agricultural plastic waste per use and the temporal distribution of the waste generation are presented. Data were collected and cross-checked from a variety of sources, including European, national and regional services and organizations, local agronomists, retailers and farmers, importers and converters. Missing data were estimated indirectly based on the recorded cultivated areas and the characteristics of the agricultural plastics commonly used in the particular regions. The temporal distribution, the composition and physical characteristics of the agricultural plastic waste streams were mapped by category and by application. This study represents the first systematic effort to map and analyse agricultural plastic waste generation and consolidation in Europe.

Optimizing Microgrid Architecture on Department of Defense Installations

DTIC Science & Technology

2014-09-01

PPA power purchase agreement PV photovoltaic QDR Quadrennial Defense Review SNL Sandia National Laboratory SPIDERS Smart Power Infrastructure...a MILP that dispatches fuel-based generators with consideration to an ensemble of forecasted inputs from renewable power sources, subject to physical...wind power project costs by region: 2012 projects, from [30]. 6. Weather Forecasts Weather forecasts are often presented as a single prediction

Prediction of kharif rice yield at Kharagpur using disaggregated extended range rainfall forecasts

NASA Astrophysics Data System (ADS)

Dhekale, B. S.; Nageswararao, M. M.; Nair, Archana; Mohanty, U. C.; Swain, D. K.; Singh, K. K.; Arunbabu, T.

2017-08-01

The Extended Range Forecasts System (ERFS) has been generating monthly and seasonal forecasts on real-time basis throughout the year over India since 2009. India is one of the major rice producer and consumer in South Asia; more than 50% of the Indian population depends on rice as staple food. Rice is mainly grown in kharif season, which contributed 84% of the total annual rice production of the country. Rice cultivation in India is rainfed, which depends largely on rains, so reliability of the rainfall forecast plays a crucial role for planning the kharif rice crop. In the present study, an attempt has been made to test the reliability of seasonal and sub-seasonal ERFS summer monsoon rainfall forecasts for kharif rice yield predictions at Kharagpur, West Bengal by using CERES-Rice (DSSATv4.5) model. These ERFS forecasts are produced as monthly and seasonal mean values and are converted into daily sequences with stochastic weather generators for use with crop growth models. The daily sequences are generated from ERFS seasonal (June-September) and sub-seasonal (July-September, August-September, and September) summer monsoon (June to September) rainfall forecasts which are considered as input in CERES-rice crop simulation model for the crop yield prediction for hindcast (1985-2008) and real-time mode (2009-2015). The yield simulated using India Meteorological Department (IMD) observed daily rainfall data is considered as baseline yield for evaluating the performance of predicted yields using the ERFS forecasts. The findings revealed that the stochastic disaggregation can be used to disaggregate the monthly/seasonal ERFS forecasts into daily sequences. The year to year variability in rice yield at Kharagpur is efficiently predicted by using the ERFS forecast products in hindcast as well as real time, and significant enhancement in the prediction skill is noticed with advancement in the season due to incorporation of observed weather data which reduces uncertainty of yield prediction. The findings also recommend that the normal and above normal yields are predicted well in advance using the ERFS forecasts. The outcomes of this study are useful to farmers for taking appropriate decisions well in advance for climate risk management in rice production during different stages of the crop growing season at Kharagpur.

Heterogeneity: The key to failure forecasting

PubMed Central

Vasseur, Jérémie; Wadsworth, Fabian B.; Lavallée, Yan; Bell, Andrew F.; Main, Ian G.; Dingwell, Donald B.

2015-01-01

Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power. PMID:26307196

Heterogeneity: The key to failure forecasting.

PubMed

Vasseur, Jérémie; Wadsworth, Fabian B; Lavallée, Yan; Bell, Andrew F; Main, Ian G; Dingwell, Donald B

2015-08-26

Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power.

Heterogeneity: The key to failure forecasting

NASA Astrophysics Data System (ADS)

Vasseur, Jérémie; Wadsworth, Fabian B.; Lavallée, Yan; Bell, Andrew F.; Main, Ian G.; Dingwell, Donald B.

2015-08-01

Elastic waves are generated when brittle materials are subjected to increasing strain. Their number and energy increase non-linearly, ending in a system-sized catastrophic failure event. Accelerating rates of geophysical signals (e.g., seismicity and deformation) preceding large-scale dynamic failure can serve as proxies for damage accumulation in the Failure Forecast Method (FFM). Here we test the hypothesis that the style and mechanisms of deformation, and the accuracy of the FFM, are both tightly controlled by the degree of microstructural heterogeneity of the material under stress. We generate a suite of synthetic samples with variable heterogeneity, controlled by the gas volume fraction. We experimentally demonstrate that the accuracy of failure prediction increases drastically with the degree of material heterogeneity. These results have significant implications in a broad range of material-based disciplines for which failure forecasting is of central importance. In particular, the FFM has been used with only variable success to forecast failure scenarios both in the field (volcanic eruptions and landslides) and in the laboratory (rock and magma failure). Our results show that this variability may be explained, and the reliability and accuracy of forecast quantified significantly improved, by accounting for material heterogeneity as a first-order control on forecasting power.

Spatial Pattern Classification for More Accurate Forecasting of Variable Energy Resources

NASA Astrophysics Data System (ADS)

Novakovskaia, E.; Hayes, C.; Collier, C.

2014-12-01

The accuracy of solar and wind forecasts is becoming increasingly essential as grid operators continue to integrate additional renewable generation onto the electric grid. Forecast errors affect rate payers, grid operators, wind and solar plant maintenance crews and energy traders through increases in prices, project down time or lost revenue. While extensive and beneficial efforts were undertaken in recent years to improve physical weather models for a broad spectrum of applications these improvements have generally not been sufficient to meet the accuracy demands of system planners. For renewables, these models are often used in conjunction with additional statistical models utilizing both meteorological observations and the power generation data. Forecast accuracy can be dependent on specific weather regimes for a given location. To account for these dependencies it is important that parameterizations used in statistical models change as the regime changes. An automated tool, based on an artificial neural network model, has been developed to identify different weather regimes as they impact power output forecast accuracy at wind or solar farms. In this study, improvements in forecast accuracy were analyzed for varying time horizons for wind farms and utility-scale PV plants located in different geographical regions.

The UK waste input-output table: Linking waste generation to the UK economy.

PubMed

Salemdeeb, Ramy; Al-Tabbaa, Abir; Reynolds, Christian

2016-10-01

In order to achieve a circular economy, there must be a greater understanding of the links between economic activity and waste generation. This study introduces the first version of the UK waste input-output table that could be used to quantify both direct and indirect waste arisings across the supply chain. The proposed waste input-output table features 21 industrial sectors and 34 waste types and is for the 2010 time-period. Using the waste input-output table, the study results quantitatively confirm that sectors with a long supply chain (i.e. manufacturing and services sectors) have higher indirect waste generation rates compared with industrial primary sectors (e.g. mining and quarrying) and sectors with a shorter supply chain (e.g. construction). Results also reveal that the construction, mining and quarrying sectors have the highest waste generation rates, 742 and 694 tonne per £1m of final demand, respectively. Owing to the aggregated format of the first version of the waste input-output, the model does not address the relationship between waste generation and recycling activities. Therefore, an updated version of the waste input-output table is expected be developed considering this issue. Consequently, the expanded model would lead to a better understanding of waste and resource flows in the supply chain. © The Author(s) 2016.

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions

NASA Astrophysics Data System (ADS)

Pérez, B.; Brouwer, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hackett, B.; Verlaan, M.; Fanjul, E. A.

2012-03-01

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.

40 CFR 761.64 - Disposal of wastes generated as a result of research and development activities authorized under...

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 32 2012-07-01 2012-07-01 false Disposal of wastes generated as a..., AND USE PROHIBITIONS Storage and Disposal § 761.64 Disposal of wastes generated as a result of... section provides disposal requirements for wastes generated during and as a result of research and...

40 CFR 761.64 - Disposal of wastes generated as a result of research and development activities authorized under...

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 31 2011-07-01 2011-07-01 false Disposal of wastes generated as a..., AND USE PROHIBITIONS Storage and Disposal § 761.64 Disposal of wastes generated as a result of... section provides disposal requirements for wastes generated during and as a result of research and...

40 CFR 761.64 - Disposal of wastes generated as a result of research and development activities authorized under...

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 32 2013-07-01 2013-07-01 false Disposal of wastes generated as a..., AND USE PROHIBITIONS Storage and Disposal § 761.64 Disposal of wastes generated as a result of... section provides disposal requirements for wastes generated during and as a result of research and...

40 CFR 761.64 - Disposal of wastes generated as a result of research and development activities authorized under...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 31 2014-07-01 2014-07-01 false Disposal of wastes generated as a..., AND USE PROHIBITIONS Storage and Disposal § 761.64 Disposal of wastes generated as a result of... section provides disposal requirements for wastes generated during and as a result of research and...

First Assessment of Itaipu Dam Ensemble Inflow Forecasting System

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Machado Vieira Lisboa, Auder; Gomes Villa Trinidad, Giovanni; Rógenes Monteiro Pontes, Paulo; Collischonn, Walter; Tucci, Carlos; Costa Buarque, Diogo

2017-04-01

Inflow forecasting for Hydropower Plants (HPP) Dams is one of the prominent uses for hydrological forecasts. A very important HPP in terms of energy generation for South America is the Itaipu Dam, located in the Paraná River, between Brazil and Paraguay countries, with a drainage area of 820.000km2. In this work, we present the development of an ensemble forecasting system for Itaipu, operational since November 2015. The system is based in the MGB-IPH hydrological model, includes hydrodynamics simulations of the main river, and is run every day morning forced by seven different rainfall forecasts: (i) CPTEC-ETA 15km; (ii) CPTEC-BRAMS 5km; (iii) SIMEPAR WRF Ferrier; (iv) SIMEPAR WRF Lin; (v) SIMEPAR WRF Morrison; (vi) SIMEPAR WRF WDM6; (vii) SIMEPAR MEDIAN. The last one (vii) corresponds to the median value of SIMEPAR WRF model versions (iii to vi) rainfall forecasts. Besides the developed system, the "traditional" method for inflow forecasting generation for the Itaipu Dam is also run every day. This traditional method consists in the approximation of the future inflow based on the discharge tendency of upstream telemetric gauges. Nowadays, after all the forecasts are run, the hydrology team of Itaipu develop a consensus forecast, based on all obtained results, which is the one used for the Itaipu HPP Dam operation. After one year of operation a first evaluation of the Ensemble Forecasting System was conducted. Results show that the system performs satisfactory for rising flows up to five days lead time. However, some false alarms were also issued by most ensemble members in some cases. And not in all cases the system performed better than the traditional method, especially during hydrograph recessions. In terms of meteorological forecasts, some members usage are being discontinued. In terms of the hydrodynamics representation, it seems that a better information of rivers cross section could improve hydrographs recession curves forecasts. Those opportunities for improvements are currently being addressed in the system next update.

Using Analog Ensemble to generate spatially downscaled probabilistic wind power forecasts

NASA Astrophysics Data System (ADS)

Delle Monache, L.; Shahriari, M.; Cervone, G.

2017-12-01

We use the Analog Ensemble (AnEn) method to generate probabilistic 80-m wind power forecasts. We use data from the NCEP GFS ( 28 km resolution) and NCEP NAM (12 km resolution). We use forecasts data from NAM and GFS, and analysis data from NAM which enables us to: 1) use a lower-resolution model to create higher-resolution forecasts, and 2) use a higher-resolution model to create higher-resolution forecasts. The former essentially increases computing speed and the latter increases forecast accuracy. An aggregated model of the former can be compared against the latter to measure the accuracy of the AnEn spatial downscaling. The AnEn works by taking a deterministic future forecast and comparing it with past forecasts. The model searches for the best matching estimates within the past forecasts and selects the predictand value corresponding to these past forecasts as the ensemble prediction for the future forecast. Our study is based on predicting wind speed and air density at more than 13,000 grid points in the continental US. We run the AnEn model twice: 1) estimating 80-m wind speed by using predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind, 2) estimating air density by using predictors such as temperature, pressure, and relative humidity. We use the air density values to correct the standard wind power curves for different values of air density. The standard deviation of the ensemble members (i.e. ensemble spread) will be used as the degree of difficulty to predict wind power at different locations. The value of the correlation coefficient between the ensemble spread and the forecast error determines the appropriateness of this measure. This measure is prominent for wind farm developers as building wind farms in regions with higher predictability will reduce the real-time risks of operating in the electricity markets.

The Past, Present and Future of the Meteorological Phenomena Identification Near the Ground (mPING) Project

NASA Astrophysics Data System (ADS)

Elmore, K. L.

2016-12-01

The Metorological Phenomemna Identification NeartheGround (mPING) project is an example of a crowd-sourced, citizen science effort to gather data of sufficeint quality and quantity needed by new post processing methods that use machine learning. Transportation and infrastructure are particularly sensitive to precipitation type in winter weather. We extract attributes from operational numerical forecast models and use them in a random forest to generate forecast winter precipitation types. We find that random forests applied to forecast soundings are effective at generating skillful forecasts of surface ptype with consideralbly more skill than the current algorithms, especuially for ice pellets and freezing rain. We also find that three very different forecast models yuield similar overall results, showing that random forests are able to extract essentially equivalent information from different forecast models. We also show that the random forest for each model, and each profile type is unique to the particular forecast model and that the random forests developed using a particular model suffer significant degradation when given attributes derived from a different model. This implies that no single algorithm can perform well across all forecast models. Clearly, random forests extract information unavailable to "physically based" methods because the physical information in the models does not appear as we expect. One intersting result is that results from the classic "warm nose" sounding profile are, by far, the most sensitive to the particular forecast model, but this profile is also the one for which random forests are most skillful. Finally, a method for calibrarting probabilties for each different ptype using multinomial logistic regression is shown.

75 FR 20942 - Hazardous Waste Management System; Identification and Listing of Hazardous Waste; Removal of...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-22

... of the waste generation and management information for saccharin and its salts, which demonstrate... partnership with the States, biennially collects information regarding the generation, management, and final... Based on the Available Toxicological Information and Waste Generation and Management Information for...

Using snow data assimilation to improve ensemble streamflow forecasting for the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Micheletty, P. D.; Perrot, D.; Day, G. N.; Lhotak, J.; Quebbeman, J.; Park, G. H.; Carney, S.

2017-12-01

Water supply forecasting in the western United States is inextricably linked to snowmelt processes, as approximately 70-85% of total annual runoff comes from water stored in seasonal mountain snowpacks. Snowmelt-generated streamflow is vital to a variety of downstream uses; the Upper Colorado River Basin (UCRB) alone provides water supply for 25 million people, irrigation water for 3.5 million acres, and drives hydropower generation at Lake Powell. April-July water supply forecasts produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC) are critical to basin water management. The primary objective of this project as part of the NASA Water Resources Applied Science Program, is to improve water supply forecasting for the UCRB by assimilating satellite and ground snowpack observations into a distributed hydrologic model at various times during the snow accumulation and melt seasons. To do this, we have built a framework that uses an Ensemble Kalman Filter (EnKF) to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) with spatially interpolated SNOTEL snow water equivalent (SWE) observations and products from the MODIS Snow Covered-Area and Grain size retrieval algorithm (when available). We have generated April-July water supply reforecasts for a 20-year period (1991-2010) for several headwater catchments in the UCRB using HL-RDHM and snow data assimilation in the Ensemble Streamflow Prediction (ESP) framework. The existing CBRFC ESP reforecasts will provide a baseline for comparison to determine whether the data assimilation process adds skill to the water supply forecasts. Preliminary results from one headwater basin show improved skill in water supply forecasting when HL-RDHM is run with the data assimilation step compared to HL-RDHM run without the data assimilation step, particularly in years when MODSCAG data were available (2000-2010). The final forecasting framework developed during this project will be delivered to CBRFC and run operationally for a set of pilot basins.

Scientific assessment of accuracy, skill and reliability of ocean probabilistic forecast products.

NASA Astrophysics Data System (ADS)

Wei, M.; Rowley, C. D.; Barron, C. N.; Hogan, P. J.

2016-02-01

As ocean operational centers are increasingly adopting and generating probabilistic forecast products for their customers with valuable forecast uncertainties, how to assess and measure these complicated probabilistic forecast products objectively is challenging. The first challenge is how to deal with the huge amount of the data from the ensemble forecasts. The second one is how to describe the scientific quality of probabilistic products. In fact, probabilistic forecast accuracy, skills, reliability, resolutions are different attributes of a forecast system. We briefly introduce some of the fundamental metrics such as the Reliability Diagram, Reliability, Resolution, Brier Score (BS), Brier Skill Score (BSS), Ranked Probability Score (RPS), Ranked Probability Skill Score (RPSS), Continuous Ranked Probability Score (CRPS), and Continuous Ranked Probability Skill Score (CRPSS). The values and significance of these metrics are demonstrated for the forecasts from the US Navy's regional ensemble system with different ensemble members. The advantages and differences of these metrics are studied and clarified.

Methodology for quantification of waste generated in Spanish railway construction works

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guzman Baez, Ana de; Villoria Saez, Paola; Rio Merino, Mercedes del

Highlights: Black-Right-Pointing-Pointer Two equations for C and D waste estimation in railway construction works are developed. Black-Right-Pointing-Pointer Mixed C and D waste is the most generated category during railway construction works. Black-Right-Pointing-Pointer Tunnel construction is essential to quantify the waste generated during the works. Black-Right-Pointing-Pointer There is a relationship between C and D waste generated and railway functional units. Black-Right-Pointing-Pointer The methodology proposed can be used to obtain new constants for other areas. - Abstract: In the last years, the European Union (EU) has been focused on the reduction of construction and demolition (C and D) waste. Specifically, in 2006,more » Spain generated roughly 47 million tons of C and D waste, of which only 13.6% was recycled. This situation has lead to the drawing up of many regulations on C and D waste during the past years forcing EU countries to include new measures for waste prevention and recycling. Among these measures, the mandatory obligation to quantify the C and D waste expected to be originated during a construction project is mandated. However, limited data is available on civil engineering projects. Therefore, the aim of this research study is to improve C and D waste management in railway projects, by developing a model for C and D waste quantification. For this purpose, we develop two equations which estimate in advance the amount, both in weight and volume, of the C and D waste likely to be generated in railway construction projects, including the category of C and D waste generated for the entire project.« less

Using NCAR Yellowstone for PhotoVoltaic Power Forecasts with Artificial Neural Networks and an Analog Ensemble

NASA Astrophysics Data System (ADS)

Cervone, G.; Clemente-Harding, L.; Alessandrini, S.; Delle Monache, L.

2016-12-01

A methodology based on Artificial Neural Networks (ANN) and an Analog Ensemble (AnEn) is presented to generate 72-hour deterministic and probabilistic forecasts of power generated by photovoltaic (PV) power plants using input from a numerical weather prediction model and computed astronomical variables. ANN and AnEn are used individually and in combination to generate forecasts for three solar power plant located in Italy. The computational scalability of the proposed solution is tested using synthetic data simulating 4,450 PV power stations. The NCAR Yellowstone supercomputer is employed to test the parallel implementation of the proposed solution, ranging from 1 node (32 cores) to 4,450 nodes (141,140 cores). Results show that a combined AnEn + ANN solution yields best results, and that the proposed solution is well suited for massive scale computation.

Flash flood forecasting using simplified hydrological models, radar rainfall forecasts and data assimilation

NASA Astrophysics Data System (ADS)

Smith, P. J.; Beven, K.; Panziera, L.

2012-04-01

The issuing of timely flood alerts may be dependant upon the ability to predict future values of water level or discharge at locations where observations are available. Catchments at risk of flash flooding often have a rapid natural response time, typically less then the forecast lead time desired for issuing alerts. This work focuses on the provision of short-range (up to 6 hours lead time) predictions of discharge in small catchments based on utilising radar forecasts to drive a hydrological model. An example analysis based upon the Verzasca catchment (Ticino, Switzerland) is presented. Parsimonious time series models with a mechanistic interpretation (so called Data-Based Mechanistic model) have been shown to provide reliable accurate forecasts in many hydrological situations. In this study such a model is developed to predict the discharge at an observed location from observed precipitation data. The model is shown to capture the snow melt response at this site. Observed discharge data is assimilated to improve the forecasts, of up to two hours lead time, that can be generated from observed precipitation. To generate forecasts with greater lead time ensemble precipitation forecasts are utilised. In this study the Nowcasting ORographic precipitation in the Alps (NORA) product outlined in more detail elsewhere (Panziera et al. Q. J. R. Meteorol. Soc. 2011; DOI:10.1002/qj.878) is utilised. NORA precipitation forecasts are derived from historical analogues based on the radar field and upper atmospheric conditions. As such, they avoid the need to explicitly model the evolution of the rainfall field through for example Lagrangian diffusion. The uncertainty in the forecasts is represented by characterisation of the joint distribution of the observed discharge, the discharge forecast using the (in operational conditions unknown) future observed precipitation and that forecast utilising the NORA ensembles. Constructing the joint distribution in this way allows the full historic record of data at the site to inform the predictive distribution. It is shown that, in part due to the limited availability of forecasts, the uncertainty in the relationship between the NORA based forecasts and other variates dominated the resulting predictive uncertainty.

Neural networks and traditional time series methods: a synergistic combination in state economic forecasts.

PubMed

Hansen, J V; Nelson, R D

1997-01-01

Ever since the initial planning for the 1997 Utah legislative session, neural-network forecasting techniques have provided valuable insights for analysts forecasting tax revenues. These revenue estimates are critically important since agency budgets, support for education, and improvements to infrastructure all depend on their accuracy. Underforecasting generates windfalls that concern taxpayers, whereas overforecasting produces budget shortfalls that cause inadequately funded commitments. The pattern finding ability of neural networks gives insightful and alternative views of the seasonal and cyclical components commonly found in economic time series data. Two applications of neural networks to revenue forecasting clearly demonstrate how these models complement traditional time series techniques. In the first, preoccupation with a potential downturn in the economy distracts analysis based on traditional time series methods so that it overlooks an emerging new phenomenon in the data. In this case, neural networks identify the new pattern that then allows modification of the time series models and finally gives more accurate forecasts. In the second application, data structure found by traditional statistical tools allows analysts to provide neural networks with important information that the networks then use to create more accurate models. In summary, for the Utah revenue outlook, the insights that result from a portfolio of forecasts that includes neural networks exceeds the understanding generated from strictly statistical forecasting techniques. In this case, the synergy clearly results in the whole of the portfolio of forecasts being more accurate than the sum of the individual parts.

78 FR 67402 - Agency Information Collection Activities: Submission for the Office of Management and Budget (OMB...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-12

... Regulations (10 CFR) or equivalent Agreement State regulations. All generators, collectors, and processors of... which facilitates tracking the identity of the waste generator. That tracking becomes more complicated... waste shipped from a waste processor may contain waste from several different generators. The...

40 CFR 436.21 - Specialized definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... natural deposits. (e) The term “process generated waste water” shall mean any waste water used in the... of the mine operator. However, if a mine is also used for treatment of process generated waste water, discharges of commingled water from the facilities shall be deemed discharges of process generated waste...

Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

NASA Astrophysics Data System (ADS)

Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

2010-12-01

Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on individual wind turbines. The information is utilized by several technologies including: a) the Weather Research and Forecasting (WRF) model, which generates finely detailed simulations of future atmospheric conditions, b) the Real-Time Four-Dimensional Data Assimilation System (RTFDDA), which performs continuous data assimilation providing the WRF model with continuous updates of the initial atmospheric state, 3) the Dynamic Integrated Forecast System (DICast®), which statistically optimizes the forecasts using all predictors, and 4) a suite of wind-to-power algorithms that convert wind speed to power for a wide range of wind farms with varying real-time data availability capabilities. In addition to these core wind energy prediction capabilities, NCAR implemented a high-resolution (10 km grid increment) 30-member ensemble RTFDDA prediction system that provides information on the expected range of wind power over a 72-hour forecast period covering Xcel Energy’s service areas. This talk will include descriptions of these capabilities and report on several topics including initial results of next-day forecasts and nowcasts of wind energy ramp events, influence of local observations on forecast skill, and overall lessons learned to date.

Translating landfill methane generation parameters among first-order decay models.

PubMed

Krause, Max J; Chickering, Giles W; Townsend, Timothy G

2016-11-01

Landfill gas (LFG) generation is predicted by a first-order decay (FOD) equation that incorporates two parameters: a methane generation potential (L 0 ) and a methane generation rate (k). Because non-hazardous waste landfills may accept many types of waste streams, multiphase models have been developed in an attempt to more accurately predict methane generation from heterogeneous waste streams. The ability of a single-phase FOD model to predict methane generation using weighted-average methane generation parameters and tonnages translated from multiphase models was assessed in two exercises. In the first exercise, waste composition from four Danish landfills represented by low-biodegradable waste streams was modeled in the Afvalzorg Multiphase Model and methane generation was compared to the single-phase Intergovernmental Panel on Climate Change (IPCC) Waste Model and LandGEM. In the second exercise, waste composition represented by IPCC waste components was modeled in the multiphase IPCC and compared to single-phase LandGEM and Australia's Solid Waste Calculator (SWC). In both cases, weight-averaging of methane generation parameters from waste composition data in single-phase models was effective in predicting cumulative methane generation from -7% to +6% of the multiphase models. The results underscore the understanding that multiphase models will not necessarily improve LFG generation prediction because the uncertainty of the method rests largely within the input parameters. A unique method of calculating the methane generation rate constant by mass of anaerobically degradable carbon was presented (k c ) and compared to existing methods, providing a better fit in 3 of 8 scenarios. Generally, single phase models with weighted-average inputs can accurately predict methane generation from multiple waste streams with varied characteristics; weighted averages should therefore be used instead of regional default values when comparing models. Translating multiphase first-order decay model input parameters by weighted average shows that single-phase models can predict cumulative methane generation within the level of uncertainty of many of the input parameters as defined by the Intergovernmental Panel on Climate Change (IPCC), which indicates that decreasing the uncertainty of the input parameters will make the model more accurate rather than adding multiple phases or input parameters.

Hazardous Waste Generators

EPA Pesticide Factsheets

Many industries generate hazardous waste. EPA regulates hazardous waste under the Resource Conservation and Recovery Act to ensure these wastes are managed in ways that are protective of human health and the environment.

[Demography perspectives and forecasts of the demand for electricity].

PubMed

Roy, L; Guimond, E

1995-01-01

"Demographic perspectives form an integral part in the development of electric load forecasts. These forecasts in turn are used to justify the addition and repair of generating facilities that will supply power in the coming decades. The goal of this article is to present how demographic perspectives are incorporated into the electric load forecasting in Quebec. The first part presents the methods, hypotheses and results of population and household projections used by Hydro-Quebec in updating its latest development plan. The second section demonstrates applications of such demographic projections for forecasting the electric load, with a focus on the residential sector." (SUMMARY IN ENG AND SPA) excerpt

Integrated management of hazardous waste generated from community sources in Thailand

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yodnane, P.; Spaeder, D.J.

A system for the collection, transport, disposal and recycling of hazardous waste was developed as part of an overall master plan for the management of hazardous waste generated from community sources in Thailand. Results of a waste generation survey conducted as part of the study indicated that over 300 million kilograms per year of hazardous waste is generated from non-industrial, community sources such as automotive repair shops, gas stations, hospitals, farms, and households in Thailand. Hazardous waste from community sources consists primarily of used oils, lead-acid and dry cell batteries, cleaning chemicals, pesticides, medical wastes, solvents and fuels. Most ofmore » this waste was found to be mismanaged by codisposing with municipal waste in burning, unlined dumps, dumping directly to land or water courses, dumping into sewers, or recycling improperly, all of which pose serious threats to human health and the environment. The survey data on waste generation quantities and data from a reconnaissance survey of the conditions and operations of 86 existing waste disposal facilities was incorporated into a nationwide Geographic Information System (GIS) database. Based on this data, problems associated with hazardous waste were identified and needs for waste management systems were tabulated. A system was developed for ranking geographic regions according to hazardous waste management problems and needs, in order to prioritize implementation of waste management programs. The data were also used in developing solutions for hazardous waste management, which addressed methods for storing, collecting, transporting, disposing, and recycling the waste. It was recommended that centralized waste management facilities be utilized which included hazardous waste and medical waste incinerators, waste stabilization units, and secure landfills.« less

Assessment of the health care waste generation rates and its management system in hospitals of Addis Ababa, Ethiopia, 2011

PubMed Central

2013-01-01

Background Healthcare waste management options are varying in Ethiopia. One of the first critical steps in the process of developing a reliable waste management plan requires a widespread understanding of the amount and the management system. This study aimed to assess the health care waste generation rate and its management system in some selected hospitals located in Addis Ababa, Ethiopia. Methods Six hospitals in Addis Ababa, (three private and three public), were selected using simple random sampling method for this work. Data was recorded by using an appropriately designed questionnaire, which was completed for the period of two months. The calculations were based on the weights of the health care wastes that were regularly generated in the selected hospitals over a one week period during the year 2011. Average generation indexes were determined in relation to certain important factors, like the type of hospitals (public vs private). Results The median waste generation rate was found to be varied from 0.361- 0.669 kg/patient/day, comprised of 58.69% non-hazardous and 41.31% hazardous wastes. The amount of waste generated was increased as the number of patients flow increased (rs=1). Public hospitals generated high proportion of total health care wastes (59.22%) in comparison with private hospitals (40.48%). The median waste generation rate was significantly vary between hospitals with Kruskal-Wallis test (X2=30.65, p=0.0001). The amount of waste was positively correlated with the number of patients (p < 0.05). The waste separation and treatment practices were very poor. Other alternatives for waste treatment rather than incineration such as a locally made autoclave should be evaluated and implemented. Conclusion These findings revealed that the management of health care waste at hospitals in Addis Ababa city was poor. PMID:23311573

Hazardous waste generation and management in China: a review.

PubMed

Duan, Huabo; Huang, Qifei; Wang, Qi; Zhou, Bingyan; Li, Jinhui

2008-10-30

Associated with the rapid economic growth and tremendous industrial prosperity, continues to be the accelerated increase of hazardous waste generation in China. The reported generation of industrial hazardous waste (IHW) was 11.62 million tons in 2005, which accounted for 1.1% of industrial solid waste (ISW) volume. An average of 43.4% of IHW was recycled, 33.0% was stored, 23.0% was securely disposed, and 0.6% was discharged without pollution controlling. By the end of 2004, there were 177 formal treatment and disposal centers for IHW management. The reported quantity of IHW disposed in these centers was only 416,000 tons, 65% of which was landfilled, 35% was incinerated. The quantity of waste alkali and acid ranked the first among IHW categories, which accounted for 30.9%. And 39.0% of IHW was generated from the raw chemical materials and chemical products industry sectors. South west China had the maximum generation of IHW, accounted for 40.0%. In addition, it was extrapolated that 740,000 tons of medical wastes were generated per year, of which only 10% was soundly managed. The generation of discarded household hazardous waste (HHW) is another important source of hazardous waste. A great proportion of HHW was managed as municipal solid waste (MSW). Hazardous waste pollution controlling has come into being a huge challenge faced to Chinese environmental management.

Municipal solid waste generation and disposal in Robe town, Ethiopia.

PubMed

Erasu, Duguma; Faye, Tesfaye; Kiros, Amaha; Balew, Abel

2018-04-20

The amount of solid waste generated in developing countries is rising from time to time due to economic growth, change in consumer behavior and lifestyles of people. But it is hard to manage and handle the increase of solid waste with existing waste management infrastructure. Thus, the management system of solid waste is very poor and become a serious problem. The main purpose of this study is to quantify the volume of solid waste generated and investigate factors affecting generation and disposal of wastes in the study area. The result of this study indicated that total waste generated from households was about 97.092kg/day.Furthermore, the study reveals that the solid waste generation rate of the town is 0.261kg/person/day.About 57.5% of solid waste is properly disposed of to landfill site whereas the remaining 42.5% is illegally dumped at the roadsides and open fields. Implication Statement Nowadays, in developing countries there is high concentration of people in urban areas and cause for the generation of enormous concentration of municipal waste in urban areas. Therefore this finding will be important for various policy makers and town planners. It may also serve as a benchmark for the municipal authorities of the town for whom the problem is still invisible and negligible and can push environmental protection authorities to reexamine the implementation of their policies and strategies with regard to the broader issues of human and environmental health condition of town dwellers.

Waste Generation Overview Refresher, Course 21464

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, Lewis Edward

This course, Waste Generation Overview Refresher (COURSE 21464), provides an overview of federal and state waste management regulations, as well as Los Alamos National Laboratory (LANL) policies and procedures for waste management operations. The course covers the activities involved in the cradle-to- grave waste management process and focuses on waste characterization, waste compatibility determinations and classification, and the storage requirements for temporary waste accumulation areas at LANL.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Solid Waste from the Operation and Decommissioning of Power Plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Marilyn Ann; D'Arcy, Daniel; Lapsa, Melissa Voss

This baseline report examines the solid waste generated by the U.S. electric power industry, including both waste streams resulting from electricity generation and wastes resulting from the decommissioning of power plants. Coal and nuclear plants produce large volumes of waste during electricity generation, and this report describes the policies and procedures for handling these materials. Natural gas and oil-fired power plants face similar waste challenges. Renewables considered in this baseline report include hydropower, wind and solar.

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

NASA Astrophysics Data System (ADS)

Wani, Omar; Beckers, Joost V. L.; Weerts, Albrecht H.; Solomatine, Dimitri P.

2017-08-01

A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.

Hazardous Waste Generator Regulations: A User-Friendly Reference Document

EPA Pesticide Factsheets

User-friendly reference to assist EPA and state staff, industrial facilities generating and managing hazardous wastes as well as the general public, in locating and understanding RCRA hazardous waste generator regulations.

Solid Waste Composition and Quantification at Taman Melewar, Parit Raja, Batu Pahat

NASA Astrophysics Data System (ADS)

Kadir, A. A.; Abidin, S. S. S. Z.

2016-07-01

The poor management of solid waste is noticeable through the increasing of the solid waste each year and the difficulties in disposing the waste in the current available landfill. This study was undertaken to analyze the quantity and composition of waste generation in Taman melewar. Taman Melewar is a student residential area and this study is focusing on student's daily waste composition. The objective of this study was to identify the amount of solid waste generation, analyze and classify the composition of solid waste in Taman Melewar. The waste collection was conducted for 50 houses on a daily basis for two weeks. The average household waste generation rate was 0.082 kg/person/day. Organic waste was the major constituent of waste production. The average of organic waste represents about 72.4% followed by paper (9%), plastics film (5.5%), plastics rigid (4.7%), napkins (3.8%), tetrapek (1.3%), glass (1.1%), household hazardous waste (0.85%), textiles (0.52%), metal (0.51%) and rubber (0.34%). The moisture content was ranging from 27.67% to 28.68%. An evaluation was made based on student's behavior towards waste production and recycling. In conclusion, the results revealed that organic waste is the highest waste generated and recycling habits is also poor in Taman Melewar.

40 CFR 761.208 - Use of the manifest.

Code of Federal Regulations, 2010 CFR

2010-07-01

... PROHIBITIONS PCB Waste Disposal Records and Reports § 761.208 Use of the manifest. (a)(1) The generator of PCB... accompany the shipment of PCB waste. (2) For bulk shipments of PCB waste within the United States... PCB waste within the United States which originate at the site of generation, the generator shall send...

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

Real-time short-term forecast of water inflow into Bureyskaya reservoir

NASA Astrophysics Data System (ADS)

Motovilov, Yury

2017-04-01

During several recent years, a methodology for operational optimization in hydrosystems including forecasts of the hydrological situation has been developed on example of Burea reservoir. The forecasts accuracy improvement of the water inflow into the reservoir during planning of water and energy regime was one of the main goals for implemented research. Burea river is the second left largest Amur tributary after Zeya river with its 70.7 thousand square kilometers watershed and 723 km-long river course. A variety of natural conditions - from plains in the southern part to northern mountainous areas determine a significant spatio-temporal variability in runoff generation patterns and river regime. Bureyskaya hydropower plant (HPP) with watershed area 65.2 thousand square kilometers is a key station in the Russian Far Eastern energy system providing its reliable operation. With a spacious reservoir, Bureyskaya HPP makes a significant contribution to the protection of the Amur region from catastrophic floods. A physically-based distributed model of runoff generation based on the ECOMAG (ECOlogical Model for Applied Geophysics) hydrological modeling platform has been developed for the Burea River basin. The model describes processes of interception of rainfall/snowfall by the canopy, snow accumulation and melt, soil freezing and thawing, water infiltration into unfrozen and frozen soil, evapotranspiration, thermal and water regime of soil, overland, subsurface, ground and river flow. The governing model's equations are derived from integration of the basic hydro- and thermodynamics equations of water and heat vertical transfer in snowpack, frozen/unfrozen soil, horizontal water flow under and over catchment slopes, etc. The model setup for Bureya river basin included watershed and river network schematization with GIS module by DEM analysis, meteorological time-series preparation, model calibration and validation against historical observations. The results showed good model performance as compared to observed inflow data into the Bureya reservoir and high diagnostic potential of data-modeling system of the runoff formation. With the use of this system the following flowchart for short-range forecasting inflow into Bureyskoe reservoir and forecast correction technique using continuously updated hydrometeorological data has been developed: 1 - Daily renewal of weather observations and forecasts database via the Internet; 2 - Daily runoff calculation from the beginning of the current year to current date is conducted; 3 - Short-range (up to 7 days) forecast is generated based on weather forecast. The idea underlying the model assimilation of newly obtained hydro meteorological information to adjust short-range hydrological forecasts lies in the assumption of the forecast errors inertia. Then the difference between calculated and observed streamflow at the forecast release date is "scattered" with specific weights to calculated streamflow for the forecast lead time. During 2016 this forecasts method of the inflow into the Bureyskaya reservoir up to 7 days is tested in online mode. Satisfactory evaluated short-range inflow forecast success rate is obtained. Tests of developed method have shown strong sensitivity to the results of short-term precipitation forecasts.

40 CFR 271.10 - Requirements for generators of hazardous wastes.

Code of Federal Regulations, 2012 CFR

2012-07-01

... hazardous wastes. 271.10 Section 271.10 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) REQUIREMENTS FOR AUTHORIZATION OF STATE HAZARDOUS WASTE PROGRAMS Requirements for Final Authorization § 271.10 Requirements for generators of hazardous wastes. (a) The State...

40 CFR 271.10 - Requirements for generators of hazardous wastes.

Code of Federal Regulations, 2013 CFR

2013-07-01

... hazardous wastes. 271.10 Section 271.10 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) REQUIREMENTS FOR AUTHORIZATION OF STATE HAZARDOUS WASTE PROGRAMS Requirements for Final Authorization § 271.10 Requirements for generators of hazardous wastes. (a) The State...

40 CFR 271.10 - Requirements for generators of hazardous wastes.

Code of Federal Regulations, 2011 CFR

2011-07-01

... hazardous wastes. 271.10 Section 271.10 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) REQUIREMENTS FOR AUTHORIZATION OF STATE HAZARDOUS WASTE PROGRAMS Requirements for Final Authorization § 271.10 Requirements for generators of hazardous wastes. (a) The State...

Specifying the Concept of Future Generations for Addressing Issues Related to High-Level Radioactive Waste.

PubMed

Kermisch, Celine

2016-12-01

The nuclear community frequently refers to the concept of "future generations" when discussing the management of high-level radioactive waste. However, this notion is generally not defined. In this context, we have to assume a wide definition of the concept of future generations, conceived as people who will live after the contemporary people are dead. This definition embraces thus each generation following ours, without any restriction in time. The aim of this paper is to show that, in the debate about nuclear waste, this broad notion should be further specified and to clarify the related implications for nuclear waste management policies. Therefore, we provide an ethical analysis of different management strategies for high-level waste in the light of two principles, protection of future generations-based on safety and security-and respect for their choice. This analysis shows that high-level waste management options have different ethical impacts across future generations, depending on whether the memory of the waste and its location is lost, or not. We suggest taking this distinction into account by introducing the notions of "close future generations" and "remote future generations", which has important implications on nuclear waste management policies insofar as it stresses that a retrievable disposal has fewer benefits than usually assumed.

Forecasting Medicaid Expenditures for Antipsychotic Medications.

PubMed

Slade, Eric P; Simoni-Wastila, Linda

2015-07-01

The ongoing transition from use of mostly branded to mostly generic second-generation antipsychotic medications could bring about a substantial reduction in Medicaid expenditures for antipsychotic medications, a change with critical implications for formulary restrictions on second-generation antipsychotics in Medicaid. This study provided a forecast of the impact of generics on Medicaid expenditures for antipsychotic medications. Quarterly (N=816) state-level aggregate data on outpatient antipsychotic prescriptions in Medicaid between 2008 and 2011 were drawn from the Medicaid state drug utilization database. Annual numbers of prescriptions, expenditures, and cost per prescription were constructed for each antipsychotic medication. Forecasts of antipsychotic expenditures in calendar years 2016 and 2019 were developed on the basis of the estimated percentage reduction in Medicaid expenditures for risperidone, the only second-generation antipsychotic available generically throughout the study period. Two models of savings from generic risperidone use were estimated, one based on constant risperidone prices and the other based on variable risperidone prices. The sensitivity of the expenditure forecast to expected changes in Medicaid enrollment was also examined. In the main model, annual Medicaid expenditures for antipsychotics were forecasted to decrease by $1,794 million (48.8%) by 2016 and by $2,814 million (76.5%) by 2019. Adjustment for variable prices of branded medications and changes in Medicaid enrollment only moderately affected the magnitude of these reductions. Within five years, antipsychotic expenditures in Medicaid may decline to less than half their current levels. Such a spending reduction warrants a reassessment of the continued necessity of formulary restrictions for second-generation antipsychotics in Medicaid.

Chemical Waste Management for the Conditionally Exempt Small Quantity Generator

NASA Astrophysics Data System (ADS)

Zimmer, Steven W.

1999-06-01

Management of hazardous chemical wastes generated as a part of the curriculum poses a significant task for the individual responsible for maintaining compliance with all rules and regulations from the Environmental Protection Agency and the Department of Transportation while maintaining the principles of OSHA's Lab Standard and the Hazard Communication Standard. For schools that generate relatively small quantities of waste, an individual can effectively manage the waste program without becoming overly burdened by the EPA regulations required for those generating large quantities of waste, if given the necessary support from the institution.

Forecasting Occurrences of Activities.

PubMed

Minor, Bryan; Cook, Diane J

2017-07-01

While activity recognition has been shown to be valuable for pervasive computing applications, less work has focused on techniques for forecasting the future occurrence of activities. We present an activity forecasting method to predict the time that will elapse until a target activity occurs. This method generates an activity forecast using a regression tree classifier and offers an advantage over sequence prediction methods in that it can predict expected time until an activity occurs. We evaluate this algorithm on real-world smart home datasets and provide evidence that our proposed approach is most effective at predicting activity timings.

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Martinez-Anido, Carlo Brancucci; Wu, Hongyu

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reducing the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multi-timescale unit commitment (UC) and economicmore » dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, FESTIV, was used to calculate the system's area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross-validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind fore-casting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple timescales, including the day-ahead UC, 4-hour-ahead UC, and 5-min real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.« less

Dynamical Downscaling of Seasonal Climate Prediction over Nordeste Brazil with ECHAM3 and NCEP's Regional Spectral Models at IRI.

NASA Astrophysics Data System (ADS)

Nobre, Paulo; Moura, Antonio D.; Sun, Liqiang

2001-12-01

This study presents an evaluation of a seasonal climate forecast done with the International Research Institute for Climate Prediction (IRI) dynamical forecast system (regional model nested into a general circulation model) over northern South America for January-April 1999, encompassing the rainy season over Brazil's Nordeste. The one-way nesting is one in two tiers: first the NCEP's Regional Spectral Model (RSM) runs with an 80-km grid mesh forced by the ECHAM3 atmospheric general circulation model (AGCM) outputs; then the RSM runs with a finer grid mesh (20 km) forced by the forecasts generated by the RSM-80. An ensemble of three realizations is done. Lower boundary conditions over the oceans for both ECHAM and RSM model runs are sea surface temperature forecasts over the tropical oceans. Soil moisture is initialized by ECHAM's inputs. The rainfall forecasts generated by the regional model are compared with those of the AGCM and observations. It is shown that the regional model at 80-km resolution improves upon the AGCM rainfall forecast, reducing both seasonal bias and root-mean-square error. On the other hand, the RSM-20 forecasts presented larger errors, with spatial patterns that resemble those of local topography. The better forecast of the position and width of the intertropical convergence zone (ITCZ) over the tropical Atlantic by the RSM-80 model is one of the principal reasons for better-forecast scores of the RSM-80 relative to the AGCM. The regional model improved the spatial as well as the temporal details of rainfall distribution, and also presenting the minimum spread among the ensemble members. The statistics of synoptic-scale weather variability on seasonal timescales were best forecast with the regional 80-km model over the Nordeste. The possibility of forecasting the frequency distribution of dry and wet spells within the rainy season is encouraging.

Ensemble Flow Forecasts for Risk Based Reservoir Operations of Lake Mendocino in Mendocino County, California: A Framework for Objectively Leveraging Weather and Climate Forecasts in a Decision Support Environment

NASA Astrophysics Data System (ADS)

Delaney, C.; Hartman, R. K.; Mendoza, J.; Whitin, B.

2017-12-01

Forecast informed reservoir operations (FIRO) is a methodology that incorporates short to mid-range precipitation and flow forecasts to inform the flood operations of reservoirs. The Ensemble Forecast Operations (EFO) alternative is a probabilistic approach of FIRO that incorporates ensemble streamflow predictions (ESPs) made by NOAA's California-Nevada River Forecast Center (CNRFC). With the EFO approach, release decisions are made to manage forecasted risk of reaching critical operational thresholds. A water management model was developed for Lake Mendocino, a 111,000 acre-foot reservoir located near Ukiah, California, to evaluate the viability of the EFO alternative to improve water supply reliability but not increase downstream flood risk. Lake Mendocino is a dual use reservoir, which is owned and operated for flood control by the United States Army Corps of Engineers and is operated for water supply by the Sonoma County Water Agency. Due to recent changes in the operations of an upstream hydroelectric facility, this reservoir has suffered from water supply reliability issues since 2007. The EFO alternative was simulated using a 26-year (1985-2010) ESP hindcast generated by the CNRFC. The ESP hindcast was developed using Global Ensemble Forecast System version 10 precipitation reforecasts processed with the Hydrologic Ensemble Forecast System to generate daily reforecasts of 61 flow ensemble members for a 15-day forecast horizon. Model simulation results demonstrate that the EFO alternative may improve water supply reliability for Lake Mendocino yet not increase flood risk for downstream areas. The developed operations framework can directly leverage improved skill in the second week of the forecast and is extendable into the S2S time domain given the demonstration of improved skill through a reliable reforecast of adequate historical duration and consistent with operationally available numerical weather predictions.

Municipal solid waste generation in Kathmandu, Nepal.

PubMed

Dangi, Mohan B; Pretz, Christopher R; Urynowicz, Michael A; Gerow, Kenneth G; Reddy, J M

2011-01-01

Waste stream characteristics must be understood to tackle waste management problems in Kathmandu Metropolitan City (KMC), Nepal. Three-stage stratified cluster sampling was used to evaluate solid waste data collected from 336 households in KMC. This information was combined with data collected regarding waste from restaurants, hotels, schools and streets. The study found that 497.3 g capita(-1) day(-1) of solid waste was generated from households and 48.5, 113.3 and 26.1 kg facility(-1) day(-1) of waste was generated from restaurants, hotels and schools, respectively. Street litter measured 69.3 metric tons day(-1). The average municipal solid waste generation rate was 523.8 metric tons day(-1) or 0.66 kg capita(-1) day(-1) as compared to the 320 metric tons day(-1) reported by the city. The coefficient of correlation between the number of people and the amount of waste produced was 0.94. Key household waste constituents included 71% organic wastes, 12% plastics, 7.5% paper and paper products, 5% dirt and construction debris and 1% hazardous wastes. Although the waste composition varied depending on the source, the composition analysis of waste from restaurants, hotels, schools and streets showed a high percentage of organic wastes. These numbers suggest a greater potential for recovery of organic wastes via composting and there is an opportunity for recycling. Because there is no previous inquiry of this scale in reporting comprehensive municipal solid waste generation in Nepal, this study can be treated as a baseline for other Nepalese municipalities. Copyright © 2010 Elsevier Ltd. All rights reserved.

Waste Generation Overview, Course 23263

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, Lewis Edward

This course, Waste Generation Overview Live (COURSE 23263), provides an overview of federal and state waste management regulations, as well as Los Alamos National Laboratory (LANL) policies and procedures for waste management operations. The course covers the activities involved in the cradle-to-grave waste management process and focuses on waste characterization, waste compatibility determinations and classification, and the storage requirements for temporary waste accumulation areas at LANL. When you have completed this course, you will be able to recognize federal, state, and LANL environmental requirements and their impact on waste operations; recognize the importance of the cradle-to-grave waste management process; identifymore » the roles and responsibilities of key LANL waste management personnel (e.g., Waste Generator, Waste Management Coordinator, Waste Stream Profile approver, and Waste Certification Official); characterize a waste stream to determine whether it meets the definition of a hazardous waste, as well as characterize the use and minimum requirements for use of acceptable knowledge (AK) for waste characterization and waste compatibility documentation requirements; and identify the requirements for setting up and managing temporary waste accumulation areas.« less

Hazardous-waste analysis plan for LLNL operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roberts, R.S.

The Lawrence Livermore National Laboratory is involved in many facets of research ranging from nuclear weapons research to advanced Biomedical studies. Approximately 80% of all programs at LLNL generate hazardous waste in one form or another. Aside from producing waste from industrial type operations (oils, solvents, bottom sludges, etc.) many unique and toxic wastes are generated such as phosgene, dioxin (TCDD), radioactive wastes and high explosives. One key to any successful waste management program must address the following: proper identification of the waste, safe handling procedures and proper storage containers and areas. This section of the Waste Management Plan willmore » address methodologies used for the Analysis of Hazardous Waste. In addition to the wastes defined in 40 CFR 261, LLNL and Site 300 also generate radioactive waste not specifically covered by RCRA. However, for completeness, the Waste Analysis Plan will address all hazardous waste.« less

Quality and quantity of construction and demolition waste in Tehran.

PubMed

Asgari, Alireza; Ghorbanian, Tahereh; Yousefi, Nader; Dadashzadeh, Dariush; Khalili, Fatemeh; Bagheri, Amin; Raei, Mehdi; Mahvi, Amir Hossein

2017-01-01

In recent years the generation rate of construction and demolition waste (C&D) has significantly augmented. The aim of this study was to assessed the quality and quantity of construction and demolition waste in Tehran (capital of Iran). Questionnaire methods were used for estimating the amount of generated C&D wastes national statistical data and typical waste generation data. In order to defining the composition of C&D waste, trucks were randomly selected and their wastes were separated and weighted. According to obtained results, about 82,646,051 m 3 of C&D waste (average 16,529,210 m 3 per year) were generated during 2011 to 2016 which only about 26% of them has been recycled. Mixing sand and cement, concrete, broken bricks and soil have the highest amount of the composition of C&D waste in Tehran that was 30, 19, 18 and 11%, respectively. Based on the results, about 2,784,158 t of the waste will generate in 2025 and this is approximately 122% higher than wastes generate in 2016. Based on MAPSA's data, 360 teams of personnel cruise and control the illegal disposals, but due to the expansion of Tehran this number of teams is inadequate and can't be effective in controlling the situation. In general, the overall condition of C&D waste management in Tehran seems undesirable and needs to be updated based on the experience of successful countries in this field.

Technical assistance for hazardous-waste reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, F.M.; McComas, C.A.

1987-12-01

Minnesota's Waste Management Board has established, developed, and funded the Minnesota Technical Assistance Program (MnTAP). The MnTAP programs offers technical assistance to generators of hazardous waste by offering telephone and onsite consultation, a waste reduction resource bank, information dissemination, a student intern program, and research awards for waste reduction projects. The program has completed three years of successful operation. The increasing interest in and use of MnTAP's services by hazardous-waste generators has justified the belief that state technical assistance programs have an important role to play in helping generators to reduce their waste production.

Challenges and potential solutions for European coastal ocean modelling

NASA Astrophysics Data System (ADS)

She, Jun; Stanev, Emil

2017-04-01

Coastal operational oceanography is a science and technological platform to integrate and transform the outcomes in marine monitoring, new knowledge generation and innovative technologies into operational information products and services in the coastal ocean. It has been identified as one of the four research priorities by EuroGOOS (She et al. 2016). Coastal modelling plays a central role in such an integration and transformation. A next generation coastal ocean forecasting system should have following features: i) being able to fully exploit benefits from future observations, ii) generate meaningful products in finer scales e.g., sub-mesoscale and in estuary-coast-sea continuum, iii) efficient parallel computing and model grid structure, iv) provide high quality forecasts as forcing to NWP and coastal climate models, v) resolving correctly inter-basin and inter-sub-basin water exchange, vi) resolving synoptic variability and predictability in marine ecosystems, e.g., for algae bloom, vi) being able to address critical and relevant issues in coastal applications, e.g., marine spatial planning, maritime safety, marine pollution protection, disaster prevention, offshore wind energy, climate change adaptation and mitigation, ICZM (integrated coastal zone management), the WFD (Water Framework Directive), and the MSFD (Marine Strategy Framework Directive), especially on habitat, eutrophication, and hydrographic condition descriptors. This presentation will address above challenges, identify limits of current models and propose correspondent research needed. The proposed roadmap will address an integrated monitoring-modelling approach and developing Unified European Coastal Ocean Models. In the coming years, a few new developments in European Sea observations can expected, e.g., more near real time delivering on profile observations made by research vessels, more shallow water Argo floats and bio-Argo floats deployed, much more high resolution sea level data from SWOT and on-going altimetry missions, contributing to resolving (sub-)mesoscale eddies, more currents measurements from ADCPs and HF radars, geostationary data for suspended sediment and diurnal observations from satellite SST products. These developments will make it possible to generate new knowledge and build up new capacities for modelling and forecasting systems, e.g., improved currents forecast, improved water skin temperature and surface winds forecast, improved modelling and forecast of (sub) mesoscale activities and drift forecast, new forecast capabilities on SPM (Suspended Particle Matter) and algae bloom. There will be much more in-situ and satellite data available for assimilation. The assimilation of sea level, chl-a, ferrybox and profile observations will greatly improves the ocean-ice-ecosystem forecast quality.

Application of Satellite information (JASON-2) in improvement of Flood Forecasting and Early Warning Service in Bangladesh

NASA Astrophysics Data System (ADS)

Hossain, M. A.; Anderson, E. R.; Bhuiyan, M. A.; Hossain, F.; Shah-Newaz, S. M.

2014-12-01

Bangladesh is the lowest riparian of the huge system of the Ganges, Brahmaputra and Meghna (GBM) basins, second to that of Amazan, with 1.75 million sq-km catchment area, only 7% is inside Bangladesh. High inflow from GBM associated with the intense rainfall is the source of flood in Bangladesh. Flood Forecasting and Early Warning (FFEW) is the mandate and responsibility of Bangladesh Water Development Board (BWDB) and Flood Forecasting and Warning Center (FFWC) under BWDB has been carrying out this responsibility since 1972 and operational on 7-days a week during monsoon (May to October). FFEW system started with few hours lead time has been upgraded up to to 5-days with reasonable accuracy. At FFWC numerical Hydrodynamic model is used for generating water level (WL) forecast upto 5-days at 54 points on 29 rivers based on real-time observed WL of 83 and rainfall of 56 stations with boundary estimationa on daily basis. Main challenge of this system is the boundary estimation is the limited upstream data of the transboundary rivers, obstacle for increasing lead-time for FFEW. The satellite based upper catchment data may overcome this limitation. Recent NASA-French joint Satellite mission JASON-2 records Water Elevation (WE) and it may be used within 24 hours. Using JASON-2 recorded WE data of 4 and 3 virtual stations on the Ganges and Brahmaputra rivers , respectively (upper catchment), a new methodology has been developed for increasing lead time of forecast. Correlation between the JASON-2 recorded WE on the virtual stations at the upper catchment and WL of 2 dominating boundary stations at model boundary on the Ganges and Brahmaputra has been derived for generating WL forecast at those 2 boundary stations, which used as input in model. FFWC has started experimental 8-days lead-time WL forecast at 09 stations (5 in Brahmaputra and 4 in Ganges) using generated boundary data and regularly updating the results in the website. The trend of the forecasted WL using JASON-2 data is similar to those upto 5-days forecast generated in the existing system. This is a new approach in FFEW in Bangladesh where boundary estimation becomes possible using JASON-2 observed WE data of the Transboundary rivers. There is scope of further development of this system along with increase of lead time. Reference: www.ffwc.gov.bd

A Machine LearningFramework to Forecast Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in computational expense. The low computational cost (and by association low computer-power requirement) means that the machine learning algorithms could be installed on a wave-energy converter as a form of "edge computing" where a device could forecast its own 48-hour energy production.

Moving from recycling to waste prevention: A review of barriers and enables.

PubMed

Bartl, Andreas

2014-09-01

Current European waste policy does not mainly aim to treat waste streams but rather place in the foreground of interest the complete supply chain of a product. Waste prevention and re-use do have the highest priority and they take effect before the end-of-life phase of a product or a material is reached. Recycling only takes the third place whereas recovery and disposal represent the least favourable options. Recycling can help to decrease the consumption of primary resources but it does not tackle the causes but only the symptoms. In principle, recycling processes require energy and will generate side streams (i.e. waste). Furthermore, there are insuperable barriers and the practice is far from 100% recycling. The philosophy of waste prevention and re-use is completely different since they really tackle the causes. It is self-evident that a decrease of waste will also decrease the consumption of resources, energy and money to process the waste. However, even if European legislation is proceeding in the right direction, a clear decrease in waste generation did not occur up to now. Unfortunately, waste generation represents a positive factor of economic growth. Basically, waste generation is a huge business and numerous stakeholders are not interested to reduce waste. More sophisticated incentives are required to decouple economic growth from waste generation. © The Author(s) 2014.

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions

NASA Astrophysics Data System (ADS)

Pérez, B.; Brower, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hacket, B.; Verlaan, M.; Alvarez Fanjul, E.

2011-04-01

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of existing storm surge or circulation models today operational in Europe, as well as near-real time tide gauge data in the region, with the following main goals: - providing an easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool - generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average Technique (BMA) The system was developed and implemented within ECOOP (C.No. 036355) European Project for the NOOS and the IBIROOS regions, based on MATROOS visualization tool developed by Deltares. Both systems are today operational at Deltares and Puertos del Estado respectively. The Bayesian Modelling Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the probability that a model will give the correct forecast PDF and are determined and updated operationally based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. Results of validation of the different models and BMA implementation for the main harbours will be presented for the IBIROOS and Western Mediterranean regions, where this kind of activity is performed for the first time. The work has proved to be useful to detect problems in some of the circulation models not previously well calibrated with sea level data, to identify the differences on baroclinic and barotropic models for sea level applications and to confirm the general improvement of the BMA forecasts.

Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power

NASA Astrophysics Data System (ADS)

Bracale, Antonio; Carpinelli, Guido; Di Fazio, Annarita; Khormali, Shahab

2014-01-01

Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems' losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.

Waste electrical and electronic equipment (WEEE) estimation: A case study of Ahvaz City, Iran.

PubMed

Alavi, Nadali; Shirmardi, Mohammad; Babaei, Aliakbar; Takdastan, Afshin; Bagheri, Nastaran

2015-03-01

The development of new technologies and the increasing consumption of electronic and electrical equipment have led to increased generation of e-waste in the municipal waste streams. This waste due to the presence of hazardous substances in its composition needs specific attention and management. The present study was carried out in Ahvaz metropolis using a survey method in 2011. For estimating the amount of waste electrical and electronic equipment (WEEE) generated, the "use and consumption" method was used. In order to determine the amounts of the electrical and electronic equipment that were used and their lifetime, and for investigating the current status of e-waste management in Ahvaz, an appropriate questionnaire was devised. In 2011, the total number of discarded electronic items was 2,157,742 units. According to the average weight of the equipment, the total generation of e-waste was 9952.25 metric tons per year and was 9.95 kg per capita per year. The highest e-waste generated was related to air conditioners, with 3125.36 metric tons per year, followed by the wastes from refrigerators and freezers, washing machines, and televisions. The wastes from desktop computers and laptops were 418 and 63 metric tons/year, respectively, and the corresponding values per capita were 0.42 and 0.063 kg, respectively. These results also showed that 10 tons fixed phones, 25 tons mobile phones, and by considering an average lifetime of 3 years for each lamp about 320 tons lamps were generated as e-waste in Ahvaz in the year 2011. Based on this study, currently there is not an integrated system for proper management of WEEE in Ahvaz, and this waste stream is collected and disposed of with other municipal waste. Some measures, including a specific collection system, recycling of valuable substances, and proper treatment and disposal, should be done about such waste. Ahvaz is one of the most important economic centers of Iran, and to the best of our knowledge, no study has been carried out to estimate the generation of waste electrical and electronic equipment (WEEE) in this city. Therefore, the authors estimated the generation of the WEEE by the "use and consumption" method. The results of this study can be useful not only for decision-making organizations of Ahvaz to manage and recycle this type of waste but also can be used as a method to estimate the generation of e-waste in different locations of the world, especially in places where the generation of such waste could be a risk to human health and the environment.

Monthly forecasting of agricultural pests in Switzerland

NASA Astrophysics Data System (ADS)

Hirschi, M.; Dubrovsky, M.; Spirig, C.; Samietz, J.; Calanca, P.; Weigel, A. P.; Fischer, A. M.; Rotach, M. W.

2012-04-01

Given the repercussions of pests and diseases on agricultural production, detailed forecasting tools have been developed to simulate the degree of infestation depending on actual weather conditions. The life cycle of pests is most successfully predicted if the micro-climate of the immediate environment (habitat) of the causative organisms can be simulated. Sub-seasonal pest forecasts therefore require weather information for the relevant habitats and the appropriate time scale. The pest forecasting system SOPRA (www.sopra.info) currently in operation in Switzerland relies on such detailed weather information, using hourly weather observations up to the day the forecast is issued, but only a climatology for the forecasting period. Here, we aim at improving the skill of SOPRA forecasts by transforming the weekly information provided by ECMWF monthly forecasts (MOFCs) into hourly weather series as required for the prediction of upcoming life phases of the codling moth, the major insect pest in apple orchards worldwide. Due to the probabilistic nature of operational monthly forecasts and the limited spatial and temporal resolution, their information needs to be post-processed for use in a pest model. In this study, we developed a statistical downscaling approach for MOFCs that includes the following steps: (i) application of a stochastic weather generator to generate a large pool of daily weather series consistent with the climate at a specific location, (ii) a subsequent re-sampling of weather series from this pool to optimally represent the evolution of the weekly MOFC anomalies, and (iii) a final extension to hourly weather series suitable for the pest forecasting model. Results show a clear improvement in the forecast skill of occurrences of upcoming codling moth life phases when incorporating MOFCs as compared to the operational pest forecasting system. This is true both in terms of root mean squared errors and of the continuous rank probability scores of the probabilistic forecasts vs. the mean absolute errors of the deterministic system. Also, the application of the climate conserving recalibration (CCR, Weigel et al. 2009) technique allows for successful correction of the under-confidence in the forecasted occurrences of codling moth life phases. Reference: Weigel, A. P.; Liniger, M. A. & Appenzeller, C. (2009). Seasonal Ensemble Forecasts: Are Recalibrated Single Models Better than Multimodels? Mon. Wea. Rev., 137, 1460-1479.

40 CFR 262.216 - Non-laboratory hazardous waste generated at an eligible academic entity.

Code of Federal Regulations, 2012 CFR

2012-07-01

... generated at an eligible academic entity. 262.216 Section 262.216 Protection of Environment ENVIRONMENTAL... Laboratories Owned by Eligible Academic Entities § 262.216 Non-laboratory hazardous waste generated at an eligible academic entity. An eligible academic entity that generates hazardous waste outside of a...

40 CFR 262.216 - Non-laboratory hazardous waste generated at an eligible academic entity.

Code of Federal Regulations, 2010 CFR

2010-07-01

... generated at an eligible academic entity. 262.216 Section 262.216 Protection of Environment ENVIRONMENTAL... Laboratories Owned by Eligible Academic Entities § 262.216 Non-laboratory hazardous waste generated at an eligible academic entity. An eligible academic entity that generates hazardous waste outside of a...

40 CFR 262.216 - Non-laboratory hazardous waste generated at an eligible academic entity.

Code of Federal Regulations, 2013 CFR

2013-07-01

... generated at an eligible academic entity. 262.216 Section 262.216 Protection of Environment ENVIRONMENTAL... Laboratories Owned by Eligible Academic Entities § 262.216 Non-laboratory hazardous waste generated at an eligible academic entity. An eligible academic entity that generates hazardous waste outside of a...

40 CFR 262.216 - Non-laboratory hazardous waste generated at an eligible academic entity.

Code of Federal Regulations, 2014 CFR

2014-07-01

... generated at an eligible academic entity. 262.216 Section 262.216 Protection of Environment ENVIRONMENTAL... Laboratories Owned by Eligible Academic Entities § 262.216 Non-laboratory hazardous waste generated at an eligible academic entity. An eligible academic entity that generates hazardous waste outside of a...

40 CFR 262.216 - Non-laboratory hazardous waste generated at an eligible academic entity.

Code of Federal Regulations, 2011 CFR

2011-07-01

... generated at an eligible academic entity. 262.216 Section 262.216 Protection of Environment ENVIRONMENTAL... Laboratories Owned by Eligible Academic Entities § 262.216 Non-laboratory hazardous waste generated at an eligible academic entity. An eligible academic entity that generates hazardous waste outside of a...

Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.

In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based onmore » a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.« less

Minimization and management of wastes from biomedical research.

PubMed Central

Rau, E H; Alaimo, R J; Ashbrook, P C; Austin, S M; Borenstein, N; Evans, M R; French, H M; Gilpin, R W; Hughes, J; Hummel, S J; Jacobsohn, A P; Lee, C Y; Merkle, S; Radzinski, T; Sloane, R; Wagner, K D; Weaner, L E

2000-01-01

Several committees were established by the National Association of Physicians for the Environment to investigate and report on various topics at the National Leadership Conference on Biomedical Research and the Environment held at the 1--2 November 1999 at the National Institutes of Health in Bethesda, Maryland. This is the report of the Committee on Minimization and Management of Wastes from Biomedical Research. Biomedical research facilities contribute a small fraction of the total amount of wastes generated in the United States, and the rate of generation appears to be decreasing. Significant reductions in generation of hazardous, radioactive, and mixed wastes have recently been reported, even at facilities with rapidly expanding research programs. Changes in the focus of research, improvements in laboratory techniques, and greater emphasis on waste minimization (volume and toxicity reduction) explain the declining trend in generation. The potential for uncontrolled releases of wastes from biomedical research facilities and adverse impacts on the general environment from these wastes appears to be low. Wastes are subject to numerous regulatory requirements and are contained and managed in a manner protective of the environment. Most biohazardous agents, chemicals, and radionuclides that find significant use in research are not likely to be persistent, bioaccumulative, or toxic if they are released. Today, the primary motivations for the ongoing efforts by facilities to improve minimization and management of wastes are regulatory compliance and avoidance of the high disposal costs and liabilities associated with generation of regulated wastes. The committee concluded that there was no evidence suggesting that the anticipated increases in biomedical research will significantly increase generation of hazardous wastes or have adverse impacts on the general environment. This conclusion assumes the positive, countervailing trends of enhanced pollution prevention efforts by facilities and reductions in waste generation resulting from improvements in research methods will continue. PMID:11121362

Bio-Medical Waste Managment in a Tertiary Care Hospital: An Overview.

PubMed

Pandey, Anita; Ahuja, Sanjiv; Madan, Molly; Asthana, Ajay Kumar

2016-11-01

Bio-Medical Waste (BMW) management is of utmost importance as its improper management poses serious threat to health care workers, waste handlers, patients, care givers, community and finally the environment. Simultaneously, the health care providers should know the quantity of waste generated in their facility and try to reduce the waste generation in day-to-day work because lesser amount of BMW means a lesser burden on waste disposal work and cost saving. To have an overview of management of BMW in a tertiary care teaching hospital so that effective interventions and implementations can be carried out for better outcome. The observational study was carried out over a period of five months from January 2016 to May 2016 in Chhatrapati Shivaji Subharti Hospital, Meerut by the Infection Control Team (ICT). Assessment of knowledge was carried out by asking set of questions individually and practice regarding awareness of BMW Management among the Health Care Personnel (HCP) was carried out by direct observation in the workplace. Further, the total BMW generated from the present setup in kilogram per bed per day was calculated by dividing the mean waste generated per day by the number of occupied beds. Segregation of BMW was being done at the site of generation in almost all the areas of the hospital in color coded polythene bags as per the hospital protocol. The different types of waste being collected were infectious solid waste in red bag, soiled infectious waste in yellow bag and sharp waste in puncture proof container and blue bag. Though awareness (knowledge) about segregation of BMW was seen in 90% of the HCP, 30%-35% did not practice. Out of the total waste generated (57912 kg.), 8686.8 kg. (15%) was infectious waste. Average infectious waste generated was 0.341 Kg per bed per day. The transport, treatment and disposal of each collected waste were outsourced and carried out by 'Synergy' waste management Pvt. Ltd. The practice of BMW Management was lacking in 30-35% HCP which may lead to mixing of the 15% infectious waste with the remaining non-infectious. Therefore, training courses and awareness programs about BMW management will be carried out every month targeting smaller groups.

Analyzing Effect of System Inertia on Grid Frequency Forecasting Usnig Two Stage Neuro-Fuzzy System

NASA Astrophysics Data System (ADS)

Chourey, Divyansh R.; Gupta, Himanshu; Kumar, Amit; Kumar, Jitesh; Kumar, Anand; Mishra, Anup

2018-04-01

Frequency forecasting is an important aspect of power system operation. The system frequency varies with load-generation imbalance. Frequency variation depends upon various parameters including system inertia. System inertia determines the rate of fall of frequency after the disturbance in the grid. Though, inertia of the system is not considered while forecasting the frequency of power system during planning and operation. This leads to significant errors in forecasting. In this paper, the effect of inertia on frequency forecasting is analysed for a particular grid system. In this paper, a parameter equivalent to system inertia is introduced. This parameter is used to forecast the frequency of a typical power grid for any instant of time. The system gives appreciable result with reduced error.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mendes, J.; Bessa, R.J.; Keko, H.

Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highlymore » dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewis, M.S.

The Barnwell Waste Management Facility (BWMF) is scheduled to restrict access to waste generators outside of the Atlantic Compact (SC, CT, NJ) on July 1, 2008. South Carolina, authorized under the Low-Level Waste Policy Act of 1980 and Amendments Act of 1985, and in agreement with the other Atlantic Compact states, will only accept Class A, B, and C low-level radioactive waste (LLRW) generated within compact. For many years, the BWMF has been the only LLRW disposal facility to accept Class B and C waste from LLRW generators throughout the country, except those that have access to the Northwest Compactmore » Site. Many Class B/C waste generators consider this to be a national crisis situation requiring interim or possible permanent storage, changes in operation, significant cost impacts, and/or elimination of services, especially in the health care and non-power generation industries. With proper in-house waste management practices and utilization of commercial processor services, a national crisis can be avoided, although some generators with specific waste forms or radionuclides will remain without options. In summary: It is unknown what the future will bring for commercial LLRW disposal. Could the anticipated post Barnwell Class B/C crisis be avoided by any of the following? - Barnwell Site remains open for the nation's commercial Class B/C waste; - Richland Site opens back up to the nation for commercial Class B/C waste; - Texas Site opens up to the nation for commercial Class B/C waste; - Federal Government intervenes by keeping a commercial Class B/C site open for the nation's commercial Class B/C waste; - Federal Government makes a DOE site available for commercial Class B/C waste; - Federal Government revisits the LLRW Policy Act of 1980 and Amendments Act of 1985. Without a future LLRW site capable of accepting Class B/C currently on the horizon, commercial LLRW generators are faced with waste volume elimination, reduction, or storage. With proper in-house waste management practices, utilization of commercial processor services and regulatory relief, a national crisis can be avoided. Waste volumes for storage can be reduced to as little as 10% of the current Class B/C volume. Although a national LLRW crisis can be avoided, some generators with specific waste forms or radionuclides will have a significant financial and/or operational impact due to a lack of commercial LLRW management options. (authors)« less

Household hazardous wastes as a potential source of pollution: a generation study.

PubMed

Ojeda-Benítez, Sara; Aguilar-Virgen, Quetzalli; Taboada-González, Paul; Cruz-Sotelo, Samantha E

2013-12-01

Certain domestic wastes exhibit characteristics that render them dangerous, such as explosiveness, flammability, spontaneous combustion, reactivity, toxicity and corrosiveness. The lack of information about their generation and composition hinders the creation of special programs for their collection and treatment, making these wastes a potential threat to human health and the environment. We attempted to quantify the levels of hazardous household waste (HHW) generated in Mexicali, Mexico. The analysis considered three socioeconomic strata and eight categories. The sampling was undertaken on a house-by-house basis, and hypothesis testing was based on differences between two proportions for each of the eight categories. In this study, HHW comprised 3.49% of the total generated waste, which exceeded that reported in previous studies in Mexico. The greatest quantity of HHW was generated by the middle stratum; in the upper stratum, most packages were discarded with their contents remaining. Cleaning products represent 45.86% of the HHW generated. Statistical differences were not observed for only two categories among the three social strata. The scarcity of studies on HHW generation limits direct comparisons. Any decrease in waste generation within the middle social stratum will have a large effect on the total amount of waste generated, and decrease their impact on environmental and human health.

Generation and collection of restaurant waste: Characterization and evaluation at a case study in Italy.

PubMed

Tatàno, Fabio; Caramiello, Cristina; Paolini, Tonino; Tripolone, Luca

2017-03-01

Because restaurants (as a division of the hospitality sector) contribute to the generation of commercial and institutional waste, thus representing both a challenge and an opportunity, the objective of the present study was to deepen the knowledge of restaurant waste in terms of the qualitative and quantitative characteristics of waste generation and the performance achievable by the implementation of a separate collection scheme. In this study, the generated waste was characterized and the implemented separate collection was evaluated at a relevant case study restaurant in a coastal tourist area of Central Italy (Marche Region, Adriatic Sea side). The qualitative (compositional) characterization of the generated total restaurant waste showed considerable incidences of, in decreasing order, food (28.2%), glass (22.6%), paper/cardboard (19.1%), and plastic (17.1%). The quantitative (parametric) characterization of the generated restaurant waste determined the unit generation values of total waste and individual fractions based on the traditional employee and area parameters and the peculiar meal parameter. In particular, the obtained representative values per meal were: 0.72kgmeal -1 for total waste, and ranging, for individual fractions, from 0.20 (for food) to 0.008kgmeal -1 (for textile). Based on the critical evaluation of some of the resulting unit waste generation values, possible influences of restaurant practices, conditions, or characteristics were pointed out. In particular, food waste generation per meal can likely be limited by: promoting and using local, fresh, and quality food; standardizing and limiting daily menu items; basing food recipes on consolidated cooking knowledge and experience; and limiting plate sizes. The evaluation of the monthly variation of the monitored separate collection, ranging from an higher level of 52.7% to a lower level of 41.4%, indicated the following: a reduction in the separate collection level can be expected at times of high working pressure or the closing of a seasonal business (typical for restaurants in tourist areas); and the monthly variation of the separate collection level is inversely correlated with that of the unit generation of total waste per meal. The interception rates of the different restaurant waste fractions collected separately presented a ranking order (i.e., 96.0% for glass, 67.7% for paper/cardboard, 34.4% for food, 20.6% for metal, and 17.9% for plastic) similar to the order of efficiencies achievable at both small and large urban levels. Finally, the original concept of the customer equivalent person (P ce ) was introduced and behaviorally evaluated at the case study restaurant, providing the values of 0.42 and 0.39kgP ce -1 day -1 for the food waste generation and the landfilling of biodegradable waste by the customer equivalent person, respectively. These values were compared, respectively, with the food waste generation per person at the household level and the landfilling of biodegradable waste per inhabitant at the territorial level. Copyright © 2017 Elsevier Ltd. All rights reserved.

Use of temperature to improve West Nile virus forecasts

PubMed Central

Schneider, Zachary D.; Caillouet, Kevin A.; Campbell, Scott R.; Damian, Dan; Irwin, Patrick; Jones, Herff M. P.; Townsend, John

2018-01-01

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs. PMID:29522514

Short-term integrated forecasting system : 1993 model documentation report

DOT National Transportation Integrated Search

1993-12-01

The purpose of this report is to define the Short-Term Integrated Forecasting System (STIFS) and describe its basic properties. The Energy Information Administration (EIA) of the U.S. Energy Department (DOE) developed the STIFS model to generate shor...

Assessment of plastic waste generation and its potential recycling of household solid waste in Can Tho City, Vietnam.

PubMed

Thanh, Nguyen Phuc; Matsui, Yasuhiro; Fujiwara, Takeshi

2011-04-01

Plastic solid waste has become a serious problem when considering the disposal alternatives following the sequential hierarchy of sound solid waste management. This study was undertaken to assess the quantity and composition of household solid waste, especially plastic waste to identify opportunities for waste recycling. A 1-month survey of 130 households was carried out in Can Tho City, the capital city of the Mekong Delta region in southern Vietnam. Household solid waste was collected from each household and classified into ten physical categories; especially plastic waste was sorted into 22 subcategories. The average household solid waste generation rate was 281.27 g/cap/day. The compostable and recyclable shares respectively accounted for high percentage as 80.74% and 11%. Regarding plastic waste, the average plastic waste generation rate was 17.24 g/cap/day; plastic packaging and plastic containers dominated with the high percentage, 95.64% of plastic waste. Plastic shopping bags were especially identified as the major component, accounting for 45.72% of total plastic waste. Relevant factors such as household income and household size were found to have an existing correlation to plastic waste generation in detailed composition. The household habits and behaviors of plastic waste discharge and the aspects of environmental impacts and resource consumption for plastic waste disposal alternatives were also evaluated.

The Weather Forecast Using Data Mining Research Based on Cloud Computing.

NASA Astrophysics Data System (ADS)

Wang, ZhanJie; Mazharul Mujib, A. B. M.

2017-10-01

Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

Municipal solid waste generation in growing urban areas in Africa: current practices and relation to socioeconomic factors in Jimma, Ethiopia.

PubMed

Getahun, T; Mengistie, E; Haddis, A; Wasie, F; Alemayehu, E; Dadi, D; Van Gerven, T; Van der Bruggen, B

2012-10-01

As one of cities in the developing countries, a rapid population growth and industrial activities pose many environmental challenges for Jimma city, Ethiopia. One aspect of urban growth posing a threat on sustainable development is poor solid waste management, which results in environmental pollution. The purpose of this study is to evaluate the quantity, composition, sources of waste generated, their current disposal practices, and to recommend appropriate management technologies. The total waste generated daily in Jimma city was ca. 88,000 kg, and the average per capita generation rate was 0.55 ± 0.17 kg/capita/day. Eighty-seven percent of the waste was produced by households and 13% by institutions, and a negligible fraction (0.1%) was generated by street sweepings. During the rainy season, 40% more waste was generated than in the dry season because of the increased availability of agricultural food product. Further analysis showed that biodegradable organic waste constitutes 54% by weight with an average moisture content of 60% that falls within the required limits for composting. The nonbiodegradable components constitute 46% of which 30% of it was nonrecyclable material. Only 25% of the community uses municipal containers for disposal at the selected landfill site. Fifty-one percent of the households disposed their waste in individually chosen spots, whereas 22% burned their waste. Finally 2% of households use private waste collectors. The socioeconomic analysis showed that higher family income and educational status is associated more with private or municipal waste collection and less with the application of backyard or open dumping. These insights into generated waste and management practice in Jimma city allow making suggestions for improved collection, treatment, and disposal methods. A primary conclusion is that the biodegradable waste is a major fraction having suitable properties for recycling. As such an economic benefit can be obtained from this waste while avoiding the need for disposal.

Counteracting structural errors in ensemble forecast of influenza outbreaks.

PubMed

Pei, Sen; Shaman, Jeffrey

2017-10-13

For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.Inaccuracy of influenza forecasts based on dynamical models is partly due to nonlinear error growth. Here the authors address the error structure of a compartmental influenza model, and develop a new improved forecast approach combining dynamical error correction and statistical filtering techniques.

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keser, Saniye; Duzgun, Sebnem; Department of Geodetic and Geographic Information Technologies, Middle East Technical University, 06800 Ankara

Highlights: Black-Right-Pointing-Pointer Spatial autocorrelation exists in municipal solid waste generation rates for different provinces in Turkey. Black-Right-Pointing-Pointer Traditional non-spatial regression models may not provide sufficient information for better solid waste management. Black-Right-Pointing-Pointer Unemployment rate is a global variable that significantly impacts the waste generation rates in Turkey. Black-Right-Pointing-Pointer Significances of global parameters may diminish at local scale for some provinces. Black-Right-Pointing-Pointer GWR model can be used to create clusters of cities for solid waste management. - Abstract: In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation datamore » is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.« less

Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

NASA Astrophysics Data System (ADS)

van der Zwan, Rene

2013-04-01

The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water management, including temporary lower storage basin levels and a reduction in extra investments for infrastructural measures.

Municipal Solid Waste Composition Study of Selected Area in Gambang, Pahang

NASA Astrophysics Data System (ADS)

Mokhtar, Nadiah; Ishak, Wan Faizal Wan; Suraya Romali, Noor; Fatimah Che Osmi, Siti; Armi Abu Samah, Mohd

2013-06-01

The amount of municipal solid waste (MSW) generated continue to increase in response to rapid growth in population, change in life style and accelerated urbanization and industrialization process. The study on MSW is important in order to determine the composition further seeks an immediate remedy to minimize the waste generated at the early stage. As most of the MSW goes to the landfill or dumping sites, particularly in Malaysia, closure of filled-up landfill may become an alarm clock for an immediate action of proper solid waste management. This research aims to determine the waste composition generated from selected residential area at Gambang, Kuantan, Pahang which represent Old residential area (ORA), Intermediate residential area (IRA) and New residential area (NRA). The study was conducted by segregating and weighing solid waste in the residential area into 6 main components ie., food waste, paper, plastic, glass, metal and others. In a period of four weeks, samples from the residential unit were taken and analyzed. The MSW generation rates were recorded vary from 0.217 to 0.388 kg person-1day-1. Food waste has become the major solid waste component generated daily which mounted up to 50%. From this research, the result revealed that the recyclable composition of waste generated by residents have a potential to be reuse, recycle and reduce at the point sources.

Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region.

PubMed

Purcell, M; Magette, W L

2009-04-01

Both planning and design of integrated municipal solid waste management systems require accurate prediction of waste generation. This research predicted the quantity and distribution of biodegradable municipal waste (BMW) generation within a diverse 'landscape' of residential areas, as well as from a variety of commercial establishments (restaurants, hotels, hospitals, etc.) in the Dublin (Ireland) region. Socio-economic variables, housing types, and the sizes and main activities of commercial establishments were hypothesized as the key determinants contributing to the spatial variability of BMW generation. A geographical information system (GIS) 'model' of BMW generation was created using ArcMap, a component of ArcGIS 9. Statistical data including socio-economic status and household size were mapped on an electoral district basis. Historical research and data from scientific literature were used to assign BMW generation rates to residential and commercial establishments. These predictions were combined to give overall BMW estimates for the region, which can aid waste planning and policy decisions. This technique will also aid the design of future waste management strategies, leading to policy and practice alterations as a function of demographic changes and development. The household prediction technique gave a more accurate overall estimate of household waste generation than did the social class technique. Both techniques produced estimates that differed from the reported local authority data; however, given that local authority reported figures for the region are below the national average, with some of the waste generated from apartment complexes being reported as commercial waste, predictions arising from this research are believed to be closer to actual waste generation than a comparison to reported data would suggest. By changing the input data, this estimation tool can be adapted for use in other locations. Although focusing on waste in the Dublin region, this method of waste prediction can have significant potential benefits if a universal method can be found to apply it effectively.

Next-Day Earthquake Forecasts for California

NASA Astrophysics Data System (ADS)

Werner, M. J.; Jackson, D. D.; Kagan, Y. Y.

2008-12-01

We implemented a daily forecast of m > 4 earthquakes for California in the format suitable for testing in community-based earthquake predictability experiments: Regional Earthquake Likelihood Models (RELM) and the Collaboratory for the Study of Earthquake Predictability (CSEP). The forecast is based on near-real time earthquake reports from the ANSS catalog above magnitude 2 and will be available online. The model used to generate the forecasts is based on the Epidemic-Type Earthquake Sequence (ETES) model, a stochastic model of clustered and triggered seismicity. Our particular implementation is based on the earlier work of Helmstetter et al. (2006, 2007), but we extended the forecast to all of Cali-fornia, use more data to calibrate the model and its parameters, and made some modifications. Our forecasts will compete against the Short-Term Earthquake Probabilities (STEP) forecasts of Gersten-berger et al. (2005) and other models in the next-day testing class of the CSEP experiment in California. We illustrate our forecasts with examples and discuss preliminary results.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

PubMed

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

PubMed Central

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

Forecasting Dust Storms Using the CARMA-Dust Model and MM5 Weather Data

NASA Astrophysics Data System (ADS)

Barnum, B. H.; Winstead, N. S.; Wesely, J.; Hakola, A.; Colarco, P.; Toon, O. B.; Ginoux, P.; Brooks, G.; Hasselbarth, L. M.; Toth, B.; Sterner, R.

2002-12-01

An operational model for the forecast of dust storms in Northern Africa, the Middle East and Southwest Asia has been developed for the United States Air Force Weather Agency (AFWA). The dust forecast model uses the 5th generation Penn State Mesoscale Meteorology Model (MM5), and a modified version of the Colorado Aerosol and Radiation Model for Atmospheres (CARMA). AFWA conducted a 60 day evaluation of the dust model to look at the model's ability to forecast dust storms for short, medium and long range (72 hour) forecast periods. The study used satellite and ground observations of dust storms to verify the model's effectiveness. Each of the main mesoscale forecast theaters was broken down into smaller sub-regions for detailed analysis. The study found the forecast model was able to forecast dust storms in Saharan Africa and the Sahel region with an average Probability of Detection (POD)exceeding 68%, with a 16% False Alarm Rate (FAR). The Southwest Asian theater had average POD's of 61% with FAR's averaging 10%.

Assessing the management of healthcare waste in Hawassa city, Ethiopia.

PubMed

Israel Deneke Haylamicheal; Mohamed Aqiel Dalvie; Biruck Desalegn Yirsaw; Hanibale Atsbeha Zegeye

2011-08-01

Inadequate management of healthcare waste is a serious concern in many developing countries due to the risks posed to human health and the environment. This study aimed to evaluate healthcare waste management in Hawassa city, Ethiopia. The study was conducted in nine healthcare facilities (HCFs) including hospitals (four), health centres (two) and higher clinics (three) in two phases, first to assess the waste management aspect and second to determine daily waste generation rate. The result showed that the median quantity of waste generated at the facilities was 3.46 kg bed(-1) day(-1) (range: 1.48-8.19 kg bed(-1) day(-1)). The quantity of waste per day generated at a HCF increased as occupancy increased (p < 0.001). The percentage hazardous waste generated at government HCFs was more than at private HCFs (p < 0.05). The proportion of hazardous waste (20-63.1%) generated at the different HCFs was much higher than the WHO recommendation (10-25%). There was no waste segregation in most HCFs and only one used a complete color coding system. Solid waste and wastewater were stored, transported, treated and disposed inappropriately at all HCFs. Needle-stick injuries were prevalent in 25-100% of waste handlers employed at these HCFs. Additionally, low levels of training and awareness of waste legislation was prevalent amongst staff. The study showed that management of healthcare waste at HCFs to be poor. Waste management practices need to be improved through improved legislation and enforcement, and training of staff in the healthcare facilities in Hawassa.

Characterizing Urban Household Waste Generation and Metabolism Considering Community Stratification in a Rapid Urbanizing Area of China.

PubMed

Xiao, Lishan; Lin, Tao; Chen, Shaohua; Zhang, Guoqin; Ye, Zhilong; Yu, Zhaowu

2015-01-01

The relationship between social stratification and municipal solid waste generation remains uncertain under current rapid urbanization. Based on a multi-object spatial sampling technique, we selected 191 households in a rapidly urbanizing area of Xiamen, China. The selected communities were classified into three types: work-unit, transitional, and commercial communities in the context of housing policy reform in China. Field survey data were used to characterize household waste generation patterns considering community stratification. Our results revealed a disparity in waste generation profiles among different households. The three community types differed with respect to family income, living area, religious affiliation, and homeowner occupation. Income, family structure, and lifestyle caused significant differences in waste generation among work-unit, transitional, and commercial communities, respectively. Urban waste generation patterns are expected to evolve due to accelerating urbanization and associated community transition. A multi-scale integrated analysis of societal and ecosystem metabolism approach was applied to waste metabolism linking it to particular socioeconomic conditions that influence material flows and their evolution. Waste metabolism, both pace and density, was highest for family structure driven patterns, followed by lifestyle and income driven. The results will guide community-specific management policies in rapidly urbanizing areas.

Waste Characterization Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vigil-Holterman, Luciana R.; Naranjo, Felicia Danielle

2016-02-02

This report discusses ways to classify waste as outlined by LANL. Waste Generators must make a waste determination and characterize regulated waste by appropriate analytical testing or use of acceptable knowledge (AK). Use of AK for characterization requires several source documents. Waste characterization documentation must be accurate, sufficient, and current (i.e., updated); relevant and traceable to the waste stream’s generation, characterization, and management; and not merely a list of information sources.

Waste Information Record Keeping System (WIRKS) in Romania

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dogaru, D.M.; Raducea, D.; Dogaru, G.

2006-07-01

In Romania there is no common national WIRKS used by all waste management organizations. Each waste management organization uses an own WIRKS. The regulatory authority approves the WIRKS of each radioactive waste facility and checks the recordings during the process of authorization. This paper summarizes the regulatory requirements regarding to WIRKS, the types of the waste generators, facilities and their waste classification of radioactive waste. Also the paper summarizes the WIRKS applied to the most important waste generators. (authors)

Pyrolysis and co-composting of municipal organic waste in Bangladesh: A quantitative estimate of recyclable nutrients, greenhouse gas emissions, and economic benefits.

PubMed

Mia, Shamim; Uddin, Md Ektear; Kader, Md Abdul; Ahsan, Amimul; Mannan, M A; Hossain, Mohammad Monjur; Solaiman, Zakaria M

2018-05-01

Waste causes environmental pollution and greenhouse gas (GHG) emissions when it is not managed sustainably. In Bangladesh, municipal organic waste (MOW) is partially collected and landfilled. Thus, it causes deterioration of the environment urging a recycle-oriented waste management system. In this study, we propose a waste management system through pyrolysis of selective MOW for biochar production and composting of the remainder with biochar as an additive. We estimated the carbon (C), nitrogen (N), phosphorus (P) and potassium (K) recycling potentials in the new techniques of waste management. Waste generation of a city was calculated using population density and per capita waste generation rate (PWGR). Two indicators of economic development, i.e., gross domestic product (GDP) and per capita gross national income (GNI) were used to adopt PWGR with a projected contribution of 5-20% to waste generation. The projected PWGR was then validated with a survey. The waste generation from urban areas of Bangladesh in 2016 was estimated between 15,507 and 15,888 t day -1 with a large share (∼75%) of organic waste. Adoption of the proposed system could produce 3936 t day -1 biochar blended compost with an annual return of US $210 million in 2016 while it could reduce GHG emission substantially (-503 CO 2 e t -1 municipal waste). Moreover, the proposed system would able to recover ∼46%, 54%, 54% and 61% of total C, N, P and K content in the initial waste, respectively. We also provide a projection of waste generation and nutrient recycling potentials for the year 2035. The proposed method could be a self-sustaining policy option for waste management as it would generate ∼US$51 from each tonne of waste. Moreover, a significant amount of nutrients can be recycled to agriculture while contributing to the reduction in environmental pollution and GHG emission. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quantitative assessment of medical waste generation in the capital city of Bangladesh

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patwary, Masum A.; O'Hare, William Thomas; Street, Graham

2009-08-15

There is a concern that mismanagement of medical waste in developing countries may be a significant risk factor for disease transmission. Quantitative estimation of medical waste generation is needed to estimate the potential risk and as a basis for any waste management plan. Dhaka City, the capital of Bangladesh, is an example of a major city in a developing country where there has been no rigorous estimation of medical waste generation based upon a thorough scientific study. These estimates were obtained by stringent weighing of waste in a carefully chosen, representative, sample of HCEs, including non-residential diagnostic centres. This studymore » used a statistically designed sampling of waste generation in a broad range of Health Care Establishments (HCEs) to indicate that the amount of waste produced in Dhaka can be estimated to be 37 {+-} 5 ton per day. The proportion of this waste that would be classified as hazardous waste by World Health Organisation (WHO) guidelines was found to be approximately 21%. The amount of waste, and the proportion of hazardous waste, was found to vary significantly with the size and type of HCE.« less

Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

NASA Astrophysics Data System (ADS)

Christensen, Hannah; Moroz, Irene; Palmer, Tim

2015-04-01

Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.

Assessing the Value of Post-processed State-of-the-art Long-term Weather Forecast Ensembles within An Integrated Agronomic Modelling Framework

NASA Astrophysics Data System (ADS)

LI, Y.; Castelletti, A.; Giuliani, M.

2014-12-01

Over recent years, long-term climate forecast from global circulation models (GCMs) has been demonstrated to show increasing skills over the climatology, thanks to the advances in the modelling of coupled ocean-atmosphere dynamics. Improved information from long-term forecast is supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping time) and for more effectively coping with the adverse impacts of climate variability. Yet, evaluating how valuable this information can be is not straightforward and farmers' response must be taken into consideration. Indeed, while long-range forecast are traditionally evaluated in terms of accuracy by comparison of hindcast and observed values, in the context of agricultural systems, potentially useful forecast information should alter the stakeholders' expectation, modify their decisions and ultimately have an impact on their annual benefit. Therefore, it is more desirable to assess the value of those long-term forecasts via decision-making models so as to extract direct indication of probable decision outcomes from farmers, i.e. from an end-to-end perspective. In this work, we evaluate the operational value of thirteen state-of-the-art long-range forecast ensembles against climatology forecast and subjective prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of farmers' behavior. Collected ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in order to address the mismatch of the spatio-temporal scale between forecast data from GCMs and distributed crop simulation model. The agronomic model is first simulated using the forecast information (ex-ante), followed by a second run with actual climate (ex-post). Multi-year simulations are performed to account for climate variability and the value of the different climate forecast is evaluated against the perfect foresight scenario based on the expected crop productivity as well as the land-use decisions. Our results show that not all the products generate beneficial effects to farmers and that the forecast errors might be amplified by the farmers decisions.

Extended Range Prediction of Indian Summer Monsoon: Current status

NASA Astrophysics Data System (ADS)

Sahai, A. K.; Abhilash, S.; Borah, N.; Joseph, S.; Chattopadhyay, R.; S, S.; Rajeevan, M.; Mandal, R.; Dey, A.

2014-12-01

The main focus of this study is to develop forecast consensus in the extended range prediction (ERP) of monsoon Intraseasonal oscillations using a suit of different variants of Climate Forecast system (CFS) model. In this CFS based Grand MME prediction system (CGMME), the ensemble members are generated by perturbing the initial condition and using different configurations of CFSv2. This is to address the role of different physical mechanisms known to have control on the error growth in the ERP in the 15-20 day time scale. The final formulation of CGMME is based on 21 ensembles of the standalone Global Forecast System (GFS) forced with bias corrected forecasted SST from CFS, 11 low resolution CFST126 and 11 high resolution CFST382. Thus, we develop the multi-model consensus forecast for the ERP of Indian summer monsoon (ISM) using a suite of different variants of CFS model. This coordinated international effort lead towards the development of specific tailor made regional forecast products over Indian region. Skill of deterministic and probabilistic categorical rainfall forecast as well the verification of large-scale low frequency monsoon intraseasonal oscillations has been carried out using hindcast from 2001-2012 during the monsoon season in which all models are initialized at every five days starting from 16May to 28 September. The skill of deterministic forecast from CGMME is better than the best participating single model ensemble configuration (SME). The CGMME approach is believed to quantify the uncertainty in both initial conditions and model formulation. Main improvement is attained in probabilistic forecast which is because of an increase in the ensemble spread, thereby reducing the error due to over-confident ensembles in a single model configuration. For probabilistic forecast, three tercile ranges are determined by ranking method based on the percentage of ensemble members from all the participating models falls in those three categories. CGMME further added value to both deterministic and probability forecast compared to raw SME's and this better skill is probably flows from large spread and improved spread-error relationship. CGMME system is currently capable of generating ER prediction in real time and successfully delivering its experimental operational ER forecast of ISM for the last few years.

A Local Forecast of Land Surface Wetness Conditions, Drought, and St. Louis Encephalitis Virus Transmission Derived from Seasonal Climate Predictions

NASA Astrophysics Data System (ADS)

Shaman, J.; Stieglitz, M.; Zebiak, S.; Cane, M.; Day, J. F.

2002-12-01

We present an ensemble local hydrologic forecast derived from the seasonal forecasts of the International Research Institute (IRI) for Climate Prediction. Three- month seasonal forecasts were used to resample historical meteorological conditions and generate ensemble forcing datasets for a TOPMODEL-based hydrology model. Eleven retrospective forecasts were run at a Florida and New York site. Forecast skill was assessed for mean area modeled water table depth (WTD), i.e. near surface soil wetness conditions, and compared with WTD simulated with observed data. Hydrology model forecast skill was evident at the Florida site but not at the New York site. At the Florida site, persistence of hydrologic conditions and local skill of the IRI seasonal forecast contributed to the local hydrologic forecast skill. This forecast will permit probabilistic prediction of future hydrologic conditions. At the Florida site, we have also quantified the link between modeled WTD (i.e. drought) and the amplification and transmission of St. Louis Encephalitis virus (SLEV). We derive an empirical relationship between modeled land surface wetness and levels of SLEV transmission associated with human clinical cases. We then combine the seasonal forecasts of local, modeled WTD with this empirical relationship and produce retrospective probabilistic seasonal forecasts of epidemic SLEV transmission in Florida. Epidemic SLEV transmission forecast skill is demonstrated. These findings will permit real-time forecast of drought and resultant SLEV transmission in Florida.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edjabou, Maklawe Essonanawe, E-mail: vine@env.dtu.dk; Jensen, Morten Bang; Götze, Ramona

Highlights: • Tiered approach to waste sorting ensures flexibility and facilitates comparison of solid waste composition data. • Food and miscellaneous wastes are the main fractions contributing to the residual household waste. • Separation of food packaging from food leftovers during sorting is not critical for determination of the solid waste composition. - Abstract: Sound waste management and optimisation of resource recovery require reliable data on solid waste generation and composition. In the absence of standardised and commonly accepted waste characterisation methodologies, various approaches have been reported in literature. This limits both comparability and applicability of the results. In thismore » study, a waste sampling and sorting methodology for efficient and statistically robust characterisation of solid waste was introduced. The methodology was applied to residual waste collected from 1442 households distributed among 10 individual sub-areas in three Danish municipalities (both single and multi-family house areas). In total 17 tonnes of waste were sorted into 10–50 waste fractions, organised according to a three-level (tiered approach) facilitating comparison of the waste data between individual sub-areas with different fractionation (waste from one municipality was sorted at “Level III”, e.g. detailed, while the two others were sorted only at “Level I”). The results showed that residual household waste mainly contained food waste (42 ± 5%, mass per wet basis) and miscellaneous combustibles (18 ± 3%, mass per wet basis). The residual household waste generation rate in the study areas was 3–4 kg per person per week. Statistical analyses revealed that the waste composition was independent of variations in the waste generation rate. Both, waste composition and waste generation rates were statistically similar for each of the three municipalities. While the waste generation rates were similar for each of the two housing types (single-family and multi-family house areas), the individual percentage composition of food waste, paper, and glass was significantly different between the housing types. This indicates that housing type is a critical stratification parameter. Separating food leftovers from food packaging during manual sorting of the sampled waste did not have significant influence on the proportions of food waste and packaging materials, indicating that this step may not be required.« less

Healthcare waste generation and management practice in government health centers of Addis Ababa, Ethiopia.

PubMed

Tadesse, Menelik Legesse; Kumie, Abera

2014-11-25

Healthcare wastes are hazardous organic and inorganic wastes. The waste disposal management in Addis Ababa city is seen unscientific manner. The waste management practice in the health facilities are poor and need improvement. This study will help different organizations, stakeholders and policy makers to correct and improve the existing situation of healthcare waste legislation and enforcement and training of staff in the healthcare facilities in Addis Ababa. The study aimed to assess the existing generation and management practice of healthcare waste in selected government health centers of Addis Ababa. The cross-sectional study was conducted to quantify waste generation rate and evaluate its management system. The study area was Addis Ababa. The sample size was determined by simple random sampling technique, the sampling procedure involved 10 sub-cities of Addis Ababa. Data were collected using both waste collecting and measuring equipment and check list. The Data was entered by EPI INFO version 6.04d and analyzed by and SPSS for WINDOW version15. The mean (±SD) healthcare waste generation rate was 9.61 ± 3.28 kg/day of which (38%) 3.64 ± 1.45 kg/day was general or non-hazardous waste and (62%) 5.97 ± 2.31 kg/day was hazardous. The mean healthcare waste generation rate between health centers was a significant different with Kurskal-Wallis test (χ2 = 21.83, p-value = 0.009). All health centers used safety boxes for collection of sharp wastes and all health centers used plastic buckets without lid for collection and transportation of healthcare waste. Pre treatment of infectious wastes was not practiced by any of the health centers. All health centers used incinerators and had placenta pit for disposal of pathological waste however only seven out of ten pits had proper covering material. Segregation of wastes at point of generation with appropriate collection materials and pre- treatment of infectious waste before disposal should be practiced. Training should be given to healthcare workers and waste handlers. Incinerators must be constructed in a manner that facilitates complete combustion and the lining of placenta pit should be constructed in water tight material.

Use of Temperature to Improve West Nile Virus Forecasts

NASA Astrophysics Data System (ADS)

Shaman, J. L.; DeFelice, N.; Schneider, Z.; Little, E.; Barker, C.; Caillouet, K.; Campbell, S.; Damian, D.; Irwin, P.; Jones, H.; Townsend, J.

2017-12-01

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether the inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that were on average 5%, 10%, 12%, and 6% more accurate, respectively, than the baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperatures influence rates of WNV transmission. The findings help build a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

Aging in America in the Twenty-first Century: Demographic Forecasts from the MacArthur Foundation Research Network on an Aging Society

PubMed Central

Olshansky, S Jay; Goldman, Dana P; Zheng, Yuhui; Rowe, John W

2009-01-01

Context: The aging of the baby boom generation, the extension of life, and progressive increases in disability-free life expectancy have generated a dramatic demographic transition in the United States. Official government forecasts may, however, have inadvertently underestimated life expectancy, which would have major policy implications, since small differences in forecasts of life expectancy produce very large differences in the number of people surviving to an older age. This article presents a new set of population and life expectancy forecasts for the United States, focusing on transitions that will take place by midcentury. Methods: Forecasts were made with a cohort-components methodology, based on the premise that the risk of death will be influenced in the coming decades by accelerated advances in biomedical technology that either delay the onset and age progression of major fatal diseases or that slow the aging process itself. Findings: Results indicate that the current forecasts of the U.S. Social Security Administration and U.S. Census Bureau may underestimate the rise in life expectancy at birth for men and women combined, by 2050, from 3.1 to 7.9 years. Conclusions: The cumulative outlays for Medicare and Social Security could be higher by $3.2 to $8.3 trillion relative to current government forecasts. This article discusses the implications of these results regarding the benefits and costs of an aging society and the prospect that health disparities could attenuate some of these changes. PMID:20021588

Short-term ensemble radar rainfall forecasts for hydrological applications

NASA Astrophysics Data System (ADS)

Codo de Oliveira, M.; Rico-Ramirez, M. A.

2016-12-01

Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

Investigation and analysis of medical waste generation in Enshi area of Hubei Province, China

NASA Astrophysics Data System (ADS)

Dengchao, Jin; Hongjun, Teng; Zhenbo, Bao; Yang, Li

2017-03-01

Based on medical waste collecting data of Enshi medical waste disposal center. The generation of medical waste and its change trend in Enshi area were both studied. The influencing factors and changing rules of medical waste generation were also analyzed. It can be found that the amount of medical waste in Enshi area is increasing year by year, the average annual growth rate of about 6.14% between 2011-2014. It was also found that the output of medical wastes varied regularity by seasons. February was the lowest month for medical waste, March and July were the peak months. By statistical analysis, average annual medical waste production per 10000 people was 4.5 ton and per bed average annual production was 133.58 kg.

Environmental assessment, finding of no significant impact, and response to comments. Radioactive waste storage

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

The Department of Energy`s (DOE) Rocky Flats Environmental Technology Site (the Site), formerly known as the Rocky Flats Plant, has generated radioactive, hazardous, and mixed waste (waste with both radioactive and hazardous constituents) since it began operations in 1952. Such wastes were the byproducts of the Site`s original mission to produce nuclear weapons components. Since 1989, when weapons component production ceased, waste has been generated as a result of the Site`s new mission of environmental restoration and deactivation, decontamination and decommissioning (D&D) of buildings. It is anticipated that the existing onsite waste storage capacity, which meets the criteria for low-levelmore » waste (LL), low-level mixed waste (LLM), transuranic (TRU) waste, and TRU mixed waste (TRUM) would be completely filled in early 1997. At that time, either waste generating activities must cease, waste must be shipped offsite, or new waste storage capacity must be developed.« less

Cost-Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools

NASA Astrophysics Data System (ADS)

Henley, E. M.; Pope, E. C. D.

2017-12-01

This commentary concerns recent work on solar wind forecasting by Owens and Riley (2017). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an "ensemble" forecast, and application of a "cost-loss" analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

Methodology for quantification of waste generated in Spanish railway construction works.

PubMed

de Guzmán Báez, Ana; Villoria Sáez, Paola; del Río Merino, Mercedes; García Navarro, Justo

2012-05-01

In the last years, the European Union (EU) has been focused on the reduction of construction and demolition (C&D) waste. Specifically, in 2006, Spain generated roughly 47million tons of C&D waste, of which only 13.6% was recycled. This situation has lead to the drawing up of many regulations on C&D waste during the past years forcing EU countries to include new measures for waste prevention and recycling. Among these measures, the mandatory obligation to quantify the C&D waste expected to be originated during a construction project is mandated. However, limited data is available on civil engineering projects. Therefore, the aim of this research study is to improve C&D waste management in railway projects, by developing a model for C&D waste quantification. For this purpose, we develop two equations which estimate in advance the amount, both in weight and volume, of the C&D waste likely to be generated in railway construction projects, including the category of C&D waste generated for the entire project. Copyright © 2012 Elsevier Ltd. All rights reserved.

The quality and value of seasonal precipitation forecasts for an early warning of large-scale droughts and floods in West Africa

NASA Astrophysics Data System (ADS)

Bliefernicht, Jan; Seidel, Jochen; Salack, Seyni; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

2017-04-01

Seasonal precipitation forecasts are a crucial source of information for an early warning of hydro-meteorological extremes in West Africa. However, the current seasonal forecasting system used by the West African weather services in the framework of the West African Climate Outlook forum (PRESAO) is limited to probabilistic precipitation forecasts of 1-month lead time. To improve this provision, we use an ensemble-based quantile-quantile transformation for bias correction of precipitation forecasts provided by a global seasonal ensemble prediction system, the Climate Forecast System Version 2 (CFS2). The statistical technique eliminates systematic differences between global forecasts and observations with the potential to preserve the signal from the model. The technique has also the advantage that it can be easily implemented at national weather services with low capacities. The statistical technique is used to generate probabilistic forecasts of monthly and seasonal precipitation amount and other precipitation indices useful for an early warning of large-scale drought and floods in West Africa. The evaluation of the statistical technique is done using CFS hindcasts (1982 to 2009) in a cross-validation mode to determine the performance of the precipitation forecasts for several lead times focusing on drought and flood events depicted over the Volta and Niger basins. In addition, operational forecasts provided by PRESAO are analyzed from 1998 to 2015. The precipitation forecasts are compared to low-skill reference forecasts generated from gridded observations (i.e. GPCC, CHIRPS) and a novel in-situ gauge database from national observation networks (see Poster EGU2017-10271). The forecasts are evaluated using state-of-the-art verification techniques to determine specific quality attributes of probabilistic forecasts such as reliability, accuracy and skill. In addition, cost-loss approaches are used to determine the value of probabilistic forecasts for multiple users in warning situations. The outcomes of the hindcasts experiment for the Volta basin illustrate that the statistical technique can clearly improve the CFS precipitation forecasts with the potential to provide skillful and valuable early precipitation warnings for large-scale drought and flood situations several months in ahead. In this presentation we give a detailed overview about the ensemble-based quantile-quantile-transformation, its validation and verification and the possibilities of this technique to complement PRESAO. We also highlight the performance of this technique for extremes such as the Sahel drought in the 80ties and in comparison to the various reference data sets (e.g. CFS2, PRESAO, observational data sets) used in this study.

Hospital waste management in Brazil: a case study.

PubMed

Mattoso, V D; Schalch, V

2001-12-01

The evaluation of the current definition, classification and quantification of hospital waste being carried out by hospitals in different countries is extremely important to avoid improper waste management practices. In this work, the waste management from a 400-bed Brazilian hospital which generates about 386 kg per day of hospital waste was studied. The generation rate of just over one kg per bed per day was considered small, although more than 50% of the waste from non-isolation wards consisted of food waste. It was also interesting to note that the highest generation rate per patient per day was found in private rooms and the lowest rate in the public ones. The waste practices used in this hospital are discussed in terms of current Brazilian legislation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Osmanlioglu, Ahmet Erdal

Pre-treatment of radioactive waste is the first step in waste management program that occurs after waste generation from various applications in Turkey. Pre-treatment and characterization practices are carried out in Radioactive Waste Management Unit (RWMU) at Cekmece Nuclear Research and Training Center (CNRTC) in Istanbul. This facility has been assigned to take all low-level radioactive wastes generated by nuclear applications in Turkey. The wastes are generated from research and nuclear applications mainly in medicine, biology, agriculture, quality control in metal processing and construction industries. These wastes are classified as low- level radioactive wastes. Pre-treatment practices cover several steps. In thismore » paper, main steps of pre-treatment and characterization are presented. Basically these are; collection, segregation, chemical adjustment, size reduction and decontamination operations. (author)« less

Flowsheets and source terms for radioactive waste projections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Forsberg, C.W.

1985-03-01

Flowsheets and source terms used to generate radioactive waste projections in the Integrated Data Base (IDB) Program are given. Volumes of each waste type generated per unit product throughput have been determined for the following facilities: uranium mining, UF/sub 6/ conversion, uranium enrichment, fuel fabrication, boiling-water reactors (BWRs), pressurized-water reactors (PWRs), and fuel reprocessing. Source terms for DOE/defense wastes have been developed. Expected wastes from typical decommissioning operations for each facility type have been determined. All wastes are also characterized by isotopic composition at time of generation and by general chemical composition. 70 references, 21 figures, 53 tables.

Special Analysis: 2016-001 Analysis of the Potential Under-Reporting of Am-241 Inventory for Nitrate Salt Waste at Area G

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chu, Shaoping; Stauffer, Philip H.; Birdsell, Kay Hanson

The Los Alamos National Laboratory (LANL) generates radioactive waste as a result of various activities. Operational waste is generated from a wide variety of research and development activities including nuclear weapons development, energy production, and medical research. Environmental restoration (ER), and decontamination and decommissioning (D&D) waste is generated as contaminated sites and facilities at LANL undergo cleanup or remediation. The majority of this waste is low-level radioactive waste (LLW) and is disposed of at the Technical Area 54 (TA-54), Area G disposal facility.

Strategic Minimization of High Level Waste from Pyroprocessing of Spent Nuclear Fuel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, Michael F.; Benedict, Robert W.

The pyroprocessing of spent nuclear fuel results in two high-level waste streams--ceramic and metal waste. Ceramic waste contains active metal fission product-loaded salt from the electrorefining, while the metal waste contains cladding hulls and undissolved noble metals. While pyroprocessing was successfully demonstrated for treatment of spent fuel from Experimental Breeder Reactor-II in 1999, it was done so without a specific objective to minimize high-level waste generation. The ceramic waste process uses “throw-away” technology that is not optimized with respect to volume of waste generated. In looking past treatment of EBR-II fuel, it is critical to minimize waste generation for technologymore » developed under the Global Nuclear Energy Partnership (GNEP). While the metal waste cannot be readily reduced, there are viable routes towards minimizing the ceramic waste. Fission products that generate high amounts of heat, such as Cs and Sr, can be separated from other active metal fission products and placed into short-term, shallow disposal. The remaining active metal fission products can be concentrated into the ceramic waste form using an ion exchange process. It has been estimated that ion exchange can reduce ceramic high-level waste quantities by as much as a factor of 3 relative to throw-away technology.« less

Verification of Ensemble Forecasts for the New York City Operations Support Tool

NASA Astrophysics Data System (ADS)

Day, G.; Schaake, J. C.; Thiemann, M.; Draijer, S.; Wang, L.

2012-12-01

The New York City water supply system operated by the Department of Environmental Protection (DEP) serves nine million people. It covers 2,000 square miles of portions of the Catskill, Delaware, and Croton watersheds, and it includes nineteen reservoirs and three controlled lakes. DEP is developing an Operations Support Tool (OST) to support its water supply operations and planning activities. OST includes historical and real-time data, a model of the water supply system complete with operating rules, and lake water quality models developed to evaluate alternatives for managing turbidity in the New York City Catskill reservoirs. OST will enable DEP to manage turbidity in its unfiltered system while satisfying its primary objective of meeting the City's water supply needs, in addition to considering secondary objectives of maintaining ecological flows, supporting fishery and recreation releases, and mitigating downstream flood peaks. The current version of OST relies on statistical forecasts of flows in the system based on recent observed flows. To improve short-term decision making, plans are being made to transition to National Weather Service (NWS) ensemble forecasts based on hydrologic models that account for short-term weather forecast skill, longer-term climate information, as well as the hydrologic state of the watersheds and recent observed flows. To ensure that the ensemble forecasts are unbiased and that the ensemble spread reflects the actual uncertainty of the forecasts, a statistical model has been developed to post-process the NWS ensemble forecasts to account for hydrologic model error as well as any inherent bias and uncertainty in initial model states, meteorological data and forecasts. The post-processor is designed to produce adjusted ensemble forecasts that are consistent with the DEP historical flow sequences that were used to develop the system operating rules. A set of historical hindcasts that is representative of the real-time ensemble forecasts is needed to verify that the post-processed forecasts are unbiased, statistically reliable, and preserve the skill inherent in the "raw" NWS ensemble forecasts. A verification procedure and set of metrics will be presented that provide an objective assessment of ensemble forecasts. The procedure will be applied to both raw ensemble hindcasts and to post-processed ensemble hindcasts. The verification metrics will be used to validate proper functioning of the post-processor and to provide a benchmark for comparison of different types of forecasts. For example, current NWS ensemble forecasts are based on climatology, using each historical year to generate a forecast trace. The NWS Hydrologic Ensemble Forecast System (HEFS) under development will utilize output from both the National Oceanic Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) and the Climate Forecast System (CFS). Incorporating short-term meteorological forecasts and longer-term climate forecast information should provide sharper, more accurate forecasts. Hindcasts from HEFS will enable New York City to generate verification results to validate the new forecasts and further fine-tune system operating rules. Project verification results will be presented for different watersheds across a range of seasons, lead times, and flow levels to assess the quality of the current ensemble forecasts.

Household solid waste characteristics and management in Chittagong, Bangladesh.

PubMed

Sujauddin, Mohammad; Huda, S M S; Hoque, A T M Rafiqul

2008-01-01

Solid waste management (SWM) is a multidimensional challenge faced by urban authorities, especially in developing countries like Bangladesh. We investigated per capita waste generation by residents, its composition, and the households' attitudes towards waste management at Rahman Nagar Residential Area, Chittagong, Bangladesh. The study involved a structured questionnaire and encompassed 75 households from five different socioeconomic groups (SEGs): low (LSEG), lower middle (LMSEG), middle (MSEG), upper middle (UMSEG) and high (HSEG). Wastes, collected from all of the groups of households, were segregated and weighed. Waste generation was 1.3 kg/household/day and 0.25 kg/person/day. Household solid waste (HSW) was comprised of nine categories of wastes with vegetable/food waste being the largest component (62%). Vegetable/food waste generation increased from the HSEG (47%) to the LSEG (88%). By weight, 66% of the waste was compostable in nature. The generation of HSW was positively correlated with family size (r xy=0.236, p<0.05), education level (r xy=0.244, p<0.05) and monthly income (r xy=0.671, p<0.01) of the households. Municipal authorities are usually the responsible agencies for solid waste collection and disposal, but the magnitude of the problem is well beyond the ability of any municipal government to tackle. Hence dwellers were found to take the service from the local waste management initiative. Of the respondents, an impressive 44% were willing to pay US dollars 0.3 to US dollars 0.4 per month to waste collectors and it is recommended that service charge be based on the volume of waste generated by households. Almost a quarter (22.7%) of the respondents preferred 12-1 pm as the time period for their waste to be collected. This study adequately shows that household solid waste can be converted from burden to resource through segregation at the source, since people are aware of their role in this direction provided a mechanism to assist them in this pursuit exists and the burden is distributed according to the amount of waste generated.

Household solid waste characteristics and management in Chittagong, Bangladesh

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sujauddin, Mohammad; Huda, S.M.S.; Hoque, A.T.M. Rafiqul

2008-07-01

Solid waste management (SWM) is a multidimensional challenge faced by urban authorities, especially in developing countries like Bangladesh. We investigated per capita waste generation by residents, its composition, and the households' attitudes towards waste management at Rahman Nagar Residential Area, Chittagong, Bangladesh. The study involved a structured questionnaire and encompassed 75 households from five different socioeconomic groups (SEGs): low (LSEG), lower middle (LMSEG), middle (MSEG), upper middle (UMSEG) and high (HSEG). Wastes, collected from all of the groups of households, were segregated and weighed. Waste generation was 1.3 kg/household/day and 0.25 kg/person/day. Household solid waste (HSW) was comprised of ninemore » categories of wastes with vegetable/food waste being the largest component (62%). Vegetable/food waste generation increased from the HSEG (47%) to the LSEG (88%). By weight, 66% of the waste was compostable in nature. The generation of HSW was positively correlated with family size (r{sub xy} = 0.236, p < 0.05), education level (r{sub xy} = 0.244, p < 0.05) and monthly income (r{sub xy} = 0.671, p < 0.01) of the households. Municipal authorities are usually the responsible agencies for solid waste collection and disposal, but the magnitude of the problem is well beyond the ability of any municipal government to tackle. Hence dwellers were found to take the service from the local waste management initiative. Of the respondents, an impressive 44% were willing to pay US$0.3 to US$0.4 per month to waste collectors and it is recommended that service charge be based on the volume of waste generated by households. Almost a quarter (22.7%) of the respondents preferred 12-1 pm as the time period for their waste to be collected. This study adequately shows that household solid waste can be converted from burden to resource through segregation at the source, since people are aware of their role in this direction provided a mechanism to assist them in this pursuit exists and the burden is distributed according to the amount of waste generated.« less

Audits of hazardous waste TSDFs let generators sleep easy. [Hazardous waste treatment, storage and disposal facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carr, F.H.

1990-02-01

Because of the increasingly strict enforcement of the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA) and the Resource Conservation and Recovery Act (RCRA), generators of hazardous waste are compelled to investigate the hazardous waste treatment, storage and disposal facility (TSDF) they use. This investigation must include an environmental and a financial audit. Simple audits may be performed by the hazardous waste generator, while more thorough ones such as those performed for groups of generators are more likely to be conducted by environmental consultants familiar with treatment, storage, and disposal techniques and the regulatory framework that guides them.

Energy: An annotated bibliography

NASA Technical Reports Server (NTRS)

Blow, S. J. (Compiler)

1975-01-01

This bibliography is the first update of a previous energy bibliography dated August 1974. It contains approximately 3,300 selected references on energy and energy related topics from bibliographic sources dated August 1974 through December 1974. The references are arranged by date, with the latest works first, in subject categories. (1) Energy and power - general; resources, supply/demand, and forecasting; policy, legislation, and regulation; research and development, environment; consumption and economics; conservation; and systems analysis. (2) Energy and power sources - general; fossil fuels; hydrogen and other fuels; organic wastes and waste heat; nuclear; geothermal; solar; wind; ocean/water; magnetohydrodynamics and electrohydrodynamics; and gas and steam turbines. (3) Energy and power storage and transmission.

Energy: An annotated bibliography

NASA Technical Reports Server (NTRS)

Blow, S. J. (Compiler)

1974-01-01

This bibliography is a compilation of approximately 4,300 selected references on energy and energy related topics. The references are arranged by date, with the latest works first, in the following subject categories: (1) energy and power - general; resources, supply/demand, and forecasting; policy, legislation, and regulation; research and development; environment; consumption and economics; and conservation, (2) energy and power sources - general, fossil fuels, hydrogen and methanol, organic wastes and waste heat, nuclear, geothermal, solar, wind, ocean/water, magnetohydrodynamics and electrohydrodynamics, and gas and steam turbines, and (3) energy and power storage and transmission. Literature from bibliographic sources dated January 1972 through July 1974 is covered, with some pertinent literature prior to 1972 included.

A comparative analysis of errors in long-term econometric forecasts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tepel, R.

1986-04-01

The growing body of literature that documents forecast accuracy falls generally into two parts. The first is prescriptive and is carried out by modelers who use simulation analysis as a tool for model improvement. These studies are ex post, that is, they make use of known values for exogenous variables and generate an error measure wholly attributable to the model. The second type of analysis is descriptive and seeks to measure errors, identify patterns among errors and variables and compare forecasts from different sources. Most descriptive studies use an ex ante approach, that is, they evaluate model outputs based onmore » estimated (or forecasted) exogenous variables. In this case, it is the forecasting process, rather than the model, that is under scrutiny. This paper uses an ex ante approach to measure errors in forecast series prepared by Data Resources Incorporated (DRI), Wharton Econometric Forecasting Associates (Wharton), and Chase Econometrics (Chase) and to determine if systematic patterns of errors can be discerned between services, types of variables (by degree of aggregation), length of forecast and time at which the forecast is made. Errors are measured as the percent difference between actual and forecasted values for the historical period of 1971 to 1983.« less

Intermittent Demand Forecasting in a Tertiary Pediatric Intensive Care Unit.

PubMed

Cheng, Chen-Yang; Chiang, Kuo-Liang; Chen, Meng-Yin

2016-10-01

Forecasts of the demand for medical supplies both directly and indirectly affect the operating costs and the quality of the care provided by health care institutions. Specifically, overestimating demand induces an inventory surplus, whereas underestimating demand possibly compromises patient safety. Uncertainty in forecasting the consumption of medical supplies generates intermittent demand events. The intermittent demand patterns for medical supplies are generally classified as lumpy, erratic, smooth, and slow-moving demand. This study was conducted with the purpose of advancing a tertiary pediatric intensive care unit's efforts to achieve a high level of accuracy in its forecasting of the demand for medical supplies. On this point, several demand forecasting methods were compared in terms of the forecast accuracy of each. The results confirm that applying Croston's method combined with a single exponential smoothing method yields the most accurate results for forecasting lumpy, erratic, and slow-moving demand, whereas the Simple Moving Average (SMA) method is the most suitable for forecasting smooth demand. In addition, when the classification of demand consumption patterns were combined with the demand forecasting models, the forecasting errors were minimized, indicating that this classification framework can play a role in improving patient safety and reducing inventory management costs in health care institutions.

Energy from waste in Europe: an analysis and comparison of the EU 27.

PubMed

Sommer, Manuel; Ragossnig, Arne

2011-10-01

This article focuses on analysing the development of waste-generated energy in the countries of the European Union (EU 27). Besides elaborating the relevant legal and political framework in the waste and energy sector as well as climate protection, the results from correlation analyses based on the databases of the energy statistics from Eurostat are discussed. The share of energy from waste is correlated with macro-economic, waste- and energy-sector-related data, which have been defined as potentially relevant for energy recovery from waste in the countries of the European Union. The results show that a single factor influencing the extent of waste-generated energy could not be isolated as it is being influenced not only by the state of economic development and the state of development of waste management systems in the respective countries but also by energy-sector-related factors and the individual priority settings in those countries. Nevertheless the main driving force for an increase in the utilization of waste for energy generation can be seen in the legal and political framework of the European Union leading to the consequence that market conditions influence the realization of waste management infrastructure for waste-generated energy.

Improved Modeling Tools Development for High Penetration Solar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Washom, Byron; Meagher, Kevin

2014-12-11

One of the significant objectives of the High Penetration solar research is to help the DOE understand, anticipate, and minimize grid operation impacts as more solar resources are added to the electric power system. For Task 2.2, an effective, reliable approach to predicting solar energy availability for energy generation forecasts using the University of California, San Diego (UCSD) Sky Imager technology has been demonstrated. Granular cloud and ramp forecasts for the next 5 to 20 minutes over an area of 10 square miles were developed. Sky images taken every 30 seconds are processed to determine cloud locations and cloud motionmore » vectors yielding future cloud shadow locations respective to distributed generation or utility solar power plants in the area. The performance of the method depends on cloud characteristics. On days with more advective cloud conditions, the developed method outperforms persistence forecasts by up to 30% (based on mean absolute error). On days with dynamic conditions, the method performs worse than persistence. Sky Imagers hold promise for ramp forecasting and ramp mitigation in conjunction with inverter controls and energy storage. The pre-commercial Sky Imager solar forecasting algorithm was documented with licensing information and was a Sunshot website highlight.« less

Easy to retrieve but hard to believe: metacognitive discounting of the unpleasantly possible.

PubMed

O'Brien, Ed

2013-06-01

People who recall or forecast many pleasant moments should perceive themselves as happier in the past or future than people who generate few such moments; the same principle should apply to generating unpleasant moments and perceiving unhappiness. Five studies suggest that this is not always true. Rather, people's metacognitive experience of ease of thought retrieval ("fluency") can affect perceived well-being over time beyond actual thought content. The easier it is to recall positive past experiences, the happier people think they were at the time; likewise, the easier it is to recall negative past experiences, the unhappier people think they were. But this is not the case for predicting the future. Although people who easily generate positive forecasts predict more future happiness, people who easily generate negative forecasts do not infer future unhappiness. Given pervasive tendencies to underestimate the likelihood of experiencing negative events, people apparently discount hard-to-believe metacognitive feelings (e.g., easily imagined unpleasant futures). Paradoxically, people's well-being may be maximized when they contemplate some bad moments or just a few good moments.

Disaster waste characteristics and radiation distribution as a result of the Great East Japan Earthquake.

PubMed

Shibata, Tomoyuki; Solo-Gabriele, Helena; Hata, Toshimitsu

2012-04-03

The compounded impacts of the catastrophes that resulted from the Great East Japan Earthquake have emphasized the need to develop strategies to respond to multiple types and sources of contamination. In Japan, earthquake and tsunami-generated waste were found to have elevated levels of metals/metalloids (e.g., mercury, arsenic, and lead) with separation and sorting more difficult for tsunami-generated waste as opposed to earthquake-generated waste. Radiation contamination superimposed on these disaster wastes has made it particularly difficult to manage the ultimate disposal resulting in delays in waste management. Work is needed to develop policies a priori for handling wastes from combined catastrophes such as those recently observed in Japan.

40 CFR 761.340 - Applicability.

Code of Federal Regulations, 2011 CFR

2011-07-01

... leaching characteristics for storage or disposal. (a) Existing accumulations of non-liquid, non-metal PCB bulk product waste. (b) Non-liquid, non-metal PCB bulk product waste from processes that continuously generate new waste. (c) Non-liquid PCB remediation waste from processes that continuously generate new...

Greater-than-Class C low-level radioactive waste characterization: Estimated volumes, radionuclide activities, and other characteristics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hulse, R.A.

1991-08-01

Planning for storage or disposal of greater-than-Class C low-level radioactive waste (GTCC LLW) requires characterization of that waste to estimate volumes, radionuclide activities, and waste forms. Data from existing literature, disposal records, and original research were used to estimate the characteristics and project volumes and radionuclide activities to the year 2035. GTCC LLW is categorized as: nuclear utilities waste, sealed sources waste, DOE-held potential GTCC LLW; and, other generator waste. It has been determined that the largest volume of those wastes, approximately 57%, is generated by nuclear power plants. The Other Generator waste category contributes approximately 10% of the totalmore » GTCC LLW volume projected to the year 2035. Waste held by the Department of Energy, which is potential GTCC LLW, accounts for nearly 33% of all waste projected to the year 2035; however, no disposal determination has been made for that waste. Sealed sources are less than 0.2% of the total projected volume of GTCC LLW.« less

Mixed waste management options

DOE Office of Scientific and Technical Information (OSTI.GOV)

Owens, C.B.; Kirner, N.P.

1991-12-31

Disposal fees for mixed waste at proposed commercial disposal sites have been estimated to be $15,000 to $40,000 per cubit foot. If such high disposal fees are imposed, generators may be willing to apply extraordinary treatment or regulatory approaches to properly dispose of their mixed waste. This paper explores the feasibility of several waste management scenarios and attempts to answer the question: Can mixed waste be managed out of existence? Existing data on commercially generated mixed waste streams are used to identify the realm of mixed waste known to be generated. Each waste stream is evaluated from both a regulatorymore » and technical perspective in order to convert the waste into a strictly low-level radioactive or a hazardous waste. Alternative regulatory approaches evaluated in this paper include a delisting petition, no migration petition, and a treatability variance. For each waste stream, potentially available treatment options are identified that could lead to these variances. Waste minimization methodology and storage for decay are also considered. Economic feasibility of each option is discussed broadly.« less

Guidelines for generators to meet HWHF acceptance requirements for hazardous, radioactive, and mixed wastes at Berkeley Lab. Revision 3

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, R.

1996-06-01

This document provides performance standards that one, as a generator of hazardous chemical, radioactive, or mixed wastes at the Berkeley Lab, must meet to manage their waste to protect Berkeley Lab staff and the environment, comply with waste regulations and ensure the continued safe operation of the workplace, have the waste transferred to the correct Waste Handling Facility, and enable the Environment, Health and Safety (EH and S) Division to properly pick up, manage, and ultimately send the waste off site for recycling, treatment, or disposal. If one uses and generates any of these wastes, one must establish a Satellitemore » Accumulation Area and follow the guidelines in the appropriate section of this document. Topics include minimization of wastes, characterization of the wastes, containers, segregation, labeling, empty containers, and spill cleanup and reporting.« less

Municipal solid waste management in Phnom Penh, capital city of Cambodia.

PubMed

Seng, Bunrith; Kaneko, Hidehiro; Hirayama, Kimiaki; Katayama-Hirayama, Keiko

2011-05-01

This paper presents an overview of municipal solid waste management (MSWM) for both technical and regulatory arrangements in the municipality of Phnom Penh (MPP), Cambodia. Problems with the current MSWM are identified, and challenges and recommendations for future improvement are also given in this paper. MPP is a small city with a total area of approximately 374 km(2) and an urban population of about 1.3 million in 2008. For the last 14 years, average annual municipal solid waste (MSW) generated in MPP has increased rapidly from 0.136 million tons in 1995 to 0.361 million tons in 2008. The gross generation rate of MSW per capita was 0.74 kg day(-1). However, the per capita household waste generation was 0.487 kg day(- 1). At 63.3%, food waste is the predominant portion of generated waste, followed by plastics (15.5%), grass and wood (6.8%), and paper and cardboard (6.4%). The remaining waste, including metals, glass, rubber/leather, textiles, and ceramic/ stone, accounted for less than 3%. Waste recycling through informal sectors is very active; recycled waste accounted for about 9.3% of all waste generated in 2003. Currently, the overall technical arrangement, including storage and discharge, collection and transport, and disposal, is still in poor condition, which leads to environmental and health risks. These problems should be solved by improving legislation, environmental education, solid waste management facilities, and management of the waste scavengers.

A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Zack, J. W.

2015-12-01

Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble, which is a case-matching scheme. The presentation will provide (1) an overview of each method and the experimental design, (2) performance comparisons based on standard metrics such as bias, MAE and RMSE, (3) a summary of the performance characteristics of each approach and (4) a preview of further experiments to be conducted.

Application of the LEPS technique for Quantitative Precipitation Forecasting (QPF) in Southern Italy: a preliminary study

NASA Astrophysics Data System (ADS)

Federico, S.; Avolio, E.; Bellecci, C.; Colacino, M.; Walko, R. L.

2006-03-01

This paper reports preliminary results for a Limited area model Ensemble Prediction System (LEPS), based on RAMS (Regional Atmospheric Modelling System), for eight case studies of moderate-intense precipitation over Calabria, the southernmost tip of the Italian peninsula. LEPS aims to transfer the benefits of a probabilistic forecast from global to regional scales in countries where local orographic forcing is a key factor to force convection. To accomplish this task and to limit computational time in an operational implementation of LEPS, we perform a cluster analysis of ECMWF-EPS runs. Starting from the 51 members that form the ECMWF-EPS we generate five clusters. For each cluster a representative member is selected and used to provide initial and dynamic boundary conditions to RAMS, whose integrations generate LEPS. RAMS runs have 12-km horizontal resolution. To analyze the impact of enhanced horizontal resolution on quantitative precipitation forecasts, LEPS forecasts are compared to a full Brute Force (BF) ensemble. This ensemble is based on RAMS, has 36 km horizontal resolution and is generated by 51 members, nested in each ECMWF-EPS member. LEPS and BF results are compared subjectively and by objective scores. Subjective analysis is based on precipitation and probability maps of case studies whereas objective analysis is made by deterministic and probabilistic scores. Scores and maps are calculated by comparing ensemble precipitation forecasts against reports from the Calabria regional raingauge network. Results show that LEPS provided better rainfall predictions than BF for all case studies selected. This strongly suggests the importance of the enhanced horizontal resolution, compared to ensemble population, for Calabria for these cases. To further explore the impact of local physiographic features on QPF (Quantitative Precipitation Forecasting), LEPS results are also compared with a 6-km horizontal resolution deterministic forecast. Due to local and mesoscale forcing, the high resolution forecast (Hi-Res) has better performance compared to the ensemble mean for rainfall thresholds larger than 10mm but it tends to overestimate precipitation for lower amounts. This yields larger false alarms that have a detrimental effect on objective scores for lower thresholds. To exploit the advantages of a probabilistic forecast compared to a deterministic one, the relation between the ECMWF-EPS 700 hPa geopotential height spread and LEPS performance is analyzed. Results are promising even if additional studies are required.

Bio-Medical Waste Managment in a Tertiary Care Hospital: An Overview

PubMed Central

Ahuja, Sanjiv; Madan, Molly; Asthana, Ajay Kumar

2016-01-01

Introduction Bio-Medical Waste (BMW) management is of utmost importance as its improper management poses serious threat to health care workers, waste handlers, patients, care givers, community and finally the environment. Simultaneously, the health care providers should know the quantity of waste generated in their facility and try to reduce the waste generation in day-to-day work because lesser amount of BMW means a lesser burden on waste disposal work and cost saving. Aim To have an overview of management of BMW in a tertiary care teaching hospital so that effective interventions and implementations can be carried out for better outcome. Materials and Methods The observational study was carried out over a period of five months from January 2016 to May 2016 in Chhatrapati Shivaji Subharti Hospital, Meerut by the Infection Control Team (ICT). Assessment of knowledge was carried out by asking set of questions individually and practice regarding awareness of BMW Management among the Health Care Personnel (HCP) was carried out by direct observation in the workplace. Further, the total BMW generated from the present setup in kilogram per bed per day was calculated by dividing the mean waste generated per day by the number of occupied beds. Results Segregation of BMW was being done at the site of generation in almost all the areas of the hospital in color coded polythene bags as per the hospital protocol. The different types of waste being collected were infectious solid waste in red bag, soiled infectious waste in yellow bag and sharp waste in puncture proof container and blue bag. Though awareness (knowledge) about segregation of BMW was seen in 90% of the HCP, 30%-35% did not practice. Out of the total waste generated (57912 kg.), 8686.8 kg. (15%) was infectious waste. Average infectious waste generated was 0.341 Kg per bed per day. The transport, treatment and disposal of each collected waste were outsourced and carried out by ‘Synergy’ waste management Pvt. Ltd. Conclusion The practice of BMW Management was lacking in 30-35% HCP which may lead to mixing of the 15% infectious waste with the remaining non-infectious. Therefore, training courses and awareness programs about BMW management will be carried out every month targeting smaller groups. PMID:28050362

Estimating maquiladora hazardous waste generation on the U.S./Mexico border

NASA Astrophysics Data System (ADS)

Bowen, Mace M.; Kontuly, Thomas; Hepner, George F.

1995-03-01

Maquiladoras, manufacturing plants that primarily assemble foreign components for reexport, are located in concentrations along the northern frontier of the US/Mexico border. These plants process a wide variety of materials using modern industrial technologies within the context of developing world institutions and infrastructure. Hazardous waste generation by maquiladoras represents a critical environmental management issue because of the spatial concentration of these plants in border municipalities where the infrastructure for waste management is nonexistent or poor. These border municipalities contain rapidly increasing populations, which further stress their waste handling infrastructure capacities while exposing their populations to greater contaminant risks. Limited empirical knowledge exists concerning hazardous waste types and generation rates from maquiladorsas. There is no standard reporting method for waste generation or methodology for estimating generation rates at this time. This paper presents a method that can be used for the rapid assessment of hazardous waste generation. A first approximation of hazardous waste generation is produced for maquiladoras in the three municipalities of Nogales, Sonora, Mexicali, Baja California, and Cd. Juarez, Chihuahua, using the INVENT model developed by the World Bank. In addition, our intent is to evaluate the potential of the INVENT model for adaptation to the US/Mexico border industrial situation. The press of border industrial development, especially with the recent adoption of the NAFTA, make such assessments necessary as a basis for the environmental policy formulation and management needed in the immediate future.

40 CFR 194.8 - Approval process for waste shipment from waste generator sites for disposal at the WIPP.

Code of Federal Regulations, 2010 CFR

2010-07-01

... waste generator site will be conveyed in a letter from the Administrator's authorized representative to... transmittal to the WIPP Waste Information System database of waste characterization data, in accordance with... will be conveyed in a letter from the Administrator's authorized representative to DOE. EPA will not...

40 CFR 194.8 - Approval process for waste shipment from waste generator sites for disposal at the WIPP.

Code of Federal Regulations, 2011 CFR

2011-07-01

... waste generator site will be conveyed in a letter from the Administrator's authorized representative to... transmittal to the WIPP Waste Information System database of waste characterization data, in accordance with... will be conveyed in a letter from the Administrator's authorized representative to DOE. EPA will not...

40 CFR 194.8 - Approval process for waste shipment from waste generator sites for disposal at the WIPP.

Code of Federal Regulations, 2012 CFR

2012-07-01

... waste generator site will be conveyed in a letter from the Administrator's authorized representative to... transmittal to the WIPP Waste Information System database of waste characterization data, in accordance with... will be conveyed in a letter from the Administrator's authorized representative to DOE. EPA will not...

40 CFR 194.8 - Approval process for waste shipment from waste generator sites for disposal at the WIPP.

Code of Federal Regulations, 2013 CFR

2013-07-01

... waste generator site will be conveyed in a letter from the Administrator's authorized representative to... transmittal to the WIPP Waste Information System database of waste characterization data, in accordance with... will be conveyed in a letter from the Administrator's authorized representative to DOE. EPA will not...

Impact of the 4 April 2014 Saharan dust outbreak on the photovoltaic power generation in Germany

NASA Astrophysics Data System (ADS)

Rieger, Daniel; Steiner, Andrea; Bachmann, Vanessa; Gasch, Philipp; Förstner, Jochen; Deetz, Konrad; Vogel, Bernhard; Vogel, Heike

2017-11-01

The importance for reliable forecasts of incoming solar radiation is growing rapidly, especially for those countries with an increasing share in photovoltaic (PV) power production. The reliability of solar radiation forecasts depends mainly on the representation of clouds and aerosol particles absorbing and scattering radiation. Especially under extreme aerosol conditions, numerical weather prediction has a systematic bias in the solar radiation forecast. This is caused by the design of numerical weather prediction models, which typically account for the direct impact of aerosol particles on radiation using climatological mean values and the impact on cloud formation assuming spatially and temporally homogeneous aerosol concentrations. These model deficiencies in turn can lead to significant economic losses under extreme aerosol conditions. For Germany, Saharan dust outbreaks occurring 5 to 15 times per year for several days each are prominent examples for conditions, under which numerical weather prediction struggles to forecast solar radiation adequately. We investigate the impact of mineral dust on the PV-power generation during a Saharan dust outbreak over Germany on 4 April 2014 using ICON-ART, which is the current German numerical weather prediction model extended by modules accounting for trace substances and related feedback processes. We find an overall improvement of the PV-power forecast for 65 % of the pyranometer stations in Germany. Of the nine stations with very high differences between forecast and measurement, eight stations show an improvement. Furthermore, we quantify the direct radiative effects and indirect radiative effects of mineral dust. For our study, direct effects account for 64 %, indirect effects for 20 % and synergistic interaction effects for 16 % of the differences between the forecast including mineral dust radiative effects and the forecast neglecting mineral dust.

Seasonal streamflow prediction using ensemble streamflow prediction technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar

2014-05-01

Streamflow forecasts are essential for making critical decision for optimal allocation of water supplies for various demands that include irrigation for agriculture, habitat for fisheries, hydropower production and flood warning. The major objective of this study is to explore the Ensemble Streamflow Prediction (ESP) based forecast in New Zealand catchments and to highlights the present capability of seasonal flow forecasting of National Institute of Water and Atmospheric Research (NIWA). In this study a probabilistic forecast framework for ESP is presented. The basic assumption in ESP is that future weather pattern were experienced historically. Hence, past forcing data can be used with current initial condition to generate an ensemble of prediction. Small differences in initial conditions can result in large difference in the forecast. The initial state of catchment can be obtained by continuously running the model till current time and use this initial state with past forcing data to generate ensemble of flow for future. The approach taken here is to run TopNet hydrological models with a range of past forcing data (precipitation, temperature etc.) with current initial conditions. The collection of runs is called the ensemble. ESP give probabilistic forecasts for flow. From ensemble members the probability distributions can be derived. The probability distributions capture part of the intrinsic uncertainty in weather or climate. An ensemble stream flow prediction which provide probabilistic hydrological forecast with lead time up to 3 months is presented for Rangitata, Ahuriri, and Hooker and Jollie rivers in South Island of New Zealand. ESP based seasonal forecast have better skill than climatology. This system can provide better over all information for holistic water resource management.

Performance of time-series methods in forecasting the demand for red blood cell transfusion.

PubMed

Pereira, Arturo

2004-05-01

Planning the future blood collection efforts must be based on adequate forecasts of transfusion demand. In this study, univariate time-series methods were investigated for their performance in forecasting the monthly demand for RBCs at one tertiary-care, university hospital. Three time-series methods were investigated: autoregressive integrated moving average (ARIMA), the Holt-Winters family of exponential smoothing models, and one neural-network-based method. The time series consisted of the monthly demand for RBCs from January 1988 to December 2002 and was divided into two segments: the older one was used to fit or train the models, and the younger to test for the accuracy of predictions. Performance was compared across forecasting methods by calculating goodness-of-fit statistics, the percentage of months in which forecast-based supply would have met the RBC demand (coverage rate), and the outdate rate. The RBC transfusion series was best fitted by a seasonal ARIMA(0,1,1)(0,1,1)(12) model. Over 1-year time horizons, forecasts generated by ARIMA or exponential smoothing laid within the +/- 10 percent interval of the real RBC demand in 79 percent of months (62% in the case of neural networks). The coverage rate for the three methods was 89, 91, and 86 percent, respectively. Over 2-year time horizons, exponential smoothing largely outperformed the other methods. Predictions by exponential smoothing laid within the +/- 10 percent interval of real values in 75 percent of the 24 forecasted months, and the coverage rate was 87 percent. Over 1-year time horizons, predictions of RBC demand generated by ARIMA or exponential smoothing are accurate enough to be of help in the planning of blood collection efforts. For longer time horizons, exponential smoothing outperforms the other forecasting methods.

Generation and management of waste electric vehicle batteries in China.

PubMed

Xu, ChengJian; Zhang, Wenxuan; He, Wenzhi; Li, Guangming; Huang, Juwen; Zhu, Haochen

2017-09-01

With the increasing adoption of EVs (electric vehicles), a large number of waste EV LIBs (electric vehicle lithium-ion batteries) were generated in China. Statistics showed generation of waste EV LIBs in 2016 reached approximately 10,000 tons, and the amount of them would be growing rapidly in the future. In view of the deleterious effects of waste EV LIBs on the environment and the valuable energy storage capacity or materials that can be reused in them, China has started emphasizing the management, reuse, and recycling of them. This paper presented the generation trend of waste EV LIBs and focused on interrelated management development and experience in China. Based on the situation of waste EV LIBs management in China, existing problems were analyzed and summarized. Some recommendations were made for decision-making organs to use as valuable references to improve the management of waste EV LIBs and promote the sustainable development of EVs.

Characterizing Urban Household Waste Generation and Metabolism Considering Community Stratification in a Rapid Urbanizing Area of China

PubMed Central

Xiao, Lishan; Lin, Tao; Chen, Shaohua; Zhang, Guoqin; Ye, Zhilong; Yu, Zhaowu

2015-01-01

The relationship between social stratification and municipal solid waste generation remains uncertain under current rapid urbanization. Based on a multi-object spatial sampling technique, we selected 191 households in a rapidly urbanizing area of Xiamen, China. The selected communities were classified into three types: work-unit, transitional, and commercial communities in the context of housing policy reform in China. Field survey data were used to characterize household waste generation patterns considering community stratification. Our results revealed a disparity in waste generation profiles among different households. The three community types differed with respect to family income, living area, religious affiliation, and homeowner occupation. Income, family structure, and lifestyle caused significant differences in waste generation among work-unit, transitional, and commercial communities, respectively. Urban waste generation patterns are expected to evolve due to accelerating urbanization and associated community transition. A multi-scale integrated analysis of societal and ecosystem metabolism approach was applied to waste metabolism linking it to particular socioeconomic conditions that influence material flows and their evolution. Waste metabolism, both pace and density, was highest for family structure driven patterns, followed by lifestyle and income driven. The results will guide community-specific management policies in rapidly urbanizing areas. PMID:26690056

Costs associated with the management of waste from healthcare facilities: An analysis at national and site level.

PubMed

Vaccari, Mentore; Tudor, Terry; Perteghella, Andrea

2018-01-01

Given rising spend on the provision of healthcare services, the sustainable management of waste from healthcare facilities is increasingly becoming a focus as a means of reducing public health risks and financial costs. Using data on per capita healthcare spend at the national level, as well as a case study of a hospital in Italy, this study examined the relationship between trends in waste generation and the associated costs of managing the waste. At the national level, healthcare spend as a percentage of gross domestic product positively correlated with waste arisings. At the site level, waste generation and type were linked to department type and clinical performance, with the top three highest generating departments of hazardous healthcare waste being anaesthetics (5.96 kg day -1 bed -1 ), paediatric and intensive care (3.37 kg day -1 bed -1 ) and gastroenterology-digestive endoscopy (3.09 kg day -1 bed -1 ). Annual overall waste management costs were $US5,079,191, or approximately $US2.36 kg -1 , with the management of the hazardous fraction of the waste being highest at $US3,707,939. In Italy, reduction in both waste arisings and the associated costs could be realised through various means, including improved waste segregation, and linking the TARI tax to waste generation.

Selection of human consumables for future space missions

NASA Technical Reports Server (NTRS)

Bourland, C. T.; Smith, M. C.

1991-01-01

Consumables for human spaceflight include oxygen, water, food and food packaging, personal hygiene items, and clothing. This paper deals with the requirements for food and water, and their impact on waste product generation. Just as urbanization of society has been made possible by improved food processing and packaging, manned spaceflight has benefitted from this technology. The downside of this technology is increased food package waste product. Since consumables make up a major portion of the vehicle onboard stowage and generate most of the waste products, selection of consumables is a very critical process. Food and package waste comprise the majority of the trash generated on the current shuttle orbiter missions. Plans for future missions must include accurate assessment of the waste products to be generated, and the methods for processing and disposing of these wastes.

Final Technical Report for Contract No. DE-EE0006332, "Integrated Simulation Development and Decision Support Tool-Set for Utility Market and Distributed Solar Power Generation"

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cormier, Dallas; Edra, Sherwin; Espinoza, Michael

This project will enable utilities to develop long-term strategic plans that integrate high levels of renewable energy generation, and to better plan power system operations under high renewable penetration. The program developed forecast data streams for decision support and effective integration of centralized and distributed solar power generation in utility operations. This toolset focused on real time simulation of distributed power generation within utility grids with the emphasis on potential applications in day ahead (market) and real time (reliability) utility operations. The project team developed and demonstrated methodologies for quantifying the impact of distributed solar generation on core utility operations,more » identified protocols for internal data communication requirements, and worked with utility personnel to adapt the new distributed generation (DG) forecasts seamlessly within existing Load and Generation procedures through a sophisticated DMS. This project supported the objectives of the SunShot Initiative and SUNRISE by enabling core utility operations to enhance their simulation capability to analyze and prepare for the impacts of high penetrations of solar on the power grid. The impact of high penetration solar PV on utility operations is not only limited to control centers, but across many core operations. Benefits of an enhanced DMS using state-of-the-art solar forecast data were demonstrated within this project and have had an immediate direct operational cost savings for Energy Marketing for Day Ahead generation commitments, Real Time Operations, Load Forecasting (at an aggregate system level for Day Ahead), Demand Response, Long term Planning (asset management), Distribution Operations, and core ancillary services as required for balancing and reliability. This provided power system operators with the necessary tools and processes to operate the grid in a reliable manner under high renewable penetration.« less

Process Waste Assessment, Mechanics Shop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phillips, N.M.

1993-05-01

This Process Waste Assessment was conducted to evaluate hazardous wastes generated in the Mechanics Shop. The Mechanics Shop maintains and repairs motorized vehicles and equipment on the SNL/California site, to include motorized carts, backhoes, street sweepers, trash truck, portable emergency generators, trencher, portable crane, and man lifts. The major hazardous waste streams routinely generated by the Mechanics Shop are used oil, spent off filters, oily rags, and spent batteries. The used off and spent off filters make up a significant portion of the overall hazardous waste stream. Waste oil and spent batteries are sent off-site for recycling. The rags andmore » spent on filters are not recycled. They are disposed of as hazardous waste. Mechanics Shop personnel continuously look for opportunities to minimize hazardous wastes.« less

Assessment of alternative disposal methods to reduce greenhouse gas emissions from municipal solid waste in India.

PubMed

Yedla, Sudhakar; Sindhu, N T

2016-06-01

Open dumping, the most commonly practiced method of solid waste disposal in Indian cities, creates serious environment and economic challenges, and also contributes significantly to greenhouse gas emissions. The present article attempts to analyse and identify economically effective ways to reduce greenhouse gas emissions from municipal solid waste. The article looks at the selection of appropriate methods for the control of methane emissions. Multivariate functional models are presented, based on theoretical considerations as well as the field measurements to forecast the greenhouse gas mitigation potential for all the methodologies under consideration. Economic feasibility is tested by calculating the unit cost of waste disposal for the respective disposal process. The purpose-built landfill system proposed by Yedla and Parikh has shown promise in controlling greenhouse gas and saving land. However, these studies show that aerobic composting offers the optimal method, both in terms of controlling greenhouse gas emissions and reducing costs, mainly by requiring less land than other methods. © The Author(s) 2016.

40 CFR 262.213 - Laboratory clean-outs.

Code of Federal Regulations, 2010 CFR

2010-07-01

... eligible academic entity is not required to count a hazardous waste that is an unused commercial chemical..., subpart C) generated solely during the laboratory clean-out toward its hazardous waste generator status... out, the date the laboratory clean-out begins and ends, and the volume of hazardous waste generated...

Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China.

PubMed

Fu, Hui-zhen; Li, Zhen-shan; Wang, Rong-hua

2015-07-01

The quantities and composition of municipal solid waste (MSW) are important factors in the planning and management of MSW. Daily human activities were classified into three groups: maintenance activities (meeting the basic needs of food, housing and personal care, MA); subsistence activities (providing the financial support requirements, SA); and leisure activities (social and recreational pursuits, LA). A model, based on the interrelationships of expenditure on consumer goods, time distribution, daily activities, residents groups, and waste generation, was employed to estimate MSW generation by different activities and resident groups in five provinces (Zhejiang, Guangdong, Hebei, Henan and Sichuan) of China. These five provinces were chosen for this study and the distribution patterns of MSW generated by different activities and resident groups were revealed. The results show that waste generation in SA and LA fluctuated slightly from 2003 to 2008. For general waste generation in the five provinces, MA accounts for more than 70% of total MSW, SA approximately 10%, and LA between 10% and 16% by urban residents in 2008. Females produced more daily MSW than males in MA. Males produced more daily MSW than females in SA and LA. The wastes produced at weekends in MA and LA were far greater than on weekdays, but less than on weekdays for SA wastes. Furthermore, one of the model parameters (the waste generation per unit of consumer expenditure) is inversely proportional to per-capita disposable income of urban residents. A significant correlation between gross domestic product (GDP) and waste generation by SA was observed with a high coefficient of determination. Copyright © 2015 Elsevier Ltd. All rights reserved.

Production of Biogas from wastes Blended with CowDung for Electricity generation-A Case study

NASA Astrophysics Data System (ADS)

Muthu, D.; Venkatasubramanian, C.; Ramakrishnan, K.; Sasidhar, Jaladanki

2017-07-01

The country’s production of solid waste generation is piling up year after year and the generation of Bio-Gas finds a fruitful solution to overcome this problem. This technology can contribute to energy conservation if the economic viability and social acceptance of this technology are favorable. Our campus has a number of hostel buildings which generates large quantum of kitchen waste and sewage per day. This research will have process ofcarrying out survey, characterization of kitchen waste from several kitchens & Canteens and knowing the potential for biogas production. The waste generated from kitchen and sewage from the hostels is given as feedstock to produce 600 m3 of biogas per day with cow dung as byproduct. The methane gas generated from Biogas is purified and this is used for power generation. Two biogas engine generators of 30 kVA and 50 kVA were installed. This power is used for backup power for girl’s hostel lighting load. From this study it is concluded that the generation of Biogas production and its usage for power production is the best option to handle these large quantum of sewage, kitchen waste generated from various buildings and also treated effluent from biogas plant and the biomass generated is a wealth for doing agriculture for any community ultimately it protects the environment.

Recycled agricultural wastes: biochars multifunctional role in agriculture and environment

USDA-ARS?s Scientific Manuscript database

The rapid population growth, urbanization and modernization worldwide have resulted in the significant increase of waste generated. Waste production is a major environmental problem in our society. In fact, recycling and using raw materials from the waste we generate are some of the environmental ch...

Cost Effective Analysis of Recycled Products for Use in Highway Construction.

DOT National Transportation Integrated Search

1998-04-01

Over 4.5 billion of non-hazardous wastes are generated in the United States each year. Out of these wastes over 200 million tons of post consumer waste is generated. The disposal of post consumer waste is the responsibility of municipality and societ...

Flow Distribution Control Characteristics in Marine Gas Turbine Waste- Heat Recovery Systems. Phase 2. Flow Distribution Control in Waste-Heat Steam Generators

DTIC Science & Technology

1982-07-01

waste-heat steam generators. The applicable steam generator design concepts and general design consideration were reviewed and critical problems...a once-through forced-circulation steam generator design should be selected because of stability, reliability, compact- ness and lightweight...consists of three sections and one appendix. In Section I, the applicable steam generator design conccpts and general design * considerations are reviewed

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elicio, Andy U.

My ERM 593 applied project will provide guidance for the Los Alamos National Laboratory Waste Stream Profile reviewer (i.e. RCRA reviewer) in regards to Reviewing and Approving a Waste Stream Profile in the Waste Compliance and Tracking System. The Waste Compliance and Tracking system is called WCATS. WCATS is a web-based application that “supports the generation, characterization, processing and shipment of LANL radioactive, hazardous, and industrial waste.” The LANL generator must characterize their waste via electronically by filling out a waste stream profile (WSP) in WCATS. Once this process is completed, the designated waste management coordinator (WMC) will perform amore » review of the waste stream profile to ensure the generator has completed their waste stream characterization in accordance with applicable state, federal and LANL directives particularly P930-1, “LANL Waste Acceptance Criteria,” and the “Waste Compliance and Tracking System User's Manual, MAN-5004, R2,” as applicable. My guidance/applied project will describe the purpose, scope, acronyms, definitions, responsibilities, assumptions and guidance for the WSP reviewer as it pertains to each panel and subpanel of a waste stream profile.« less

[Management of hazardous waste in a hospital].

PubMed

Neveu C, Alejandra; Matus C, Patricia

2007-07-01

An inadequate management of hospital waste, that have toxic, infectious and chemical wastes, is a risk factor for humans and environment. To identify, quantify and assess the risk associated to the management of hospital residues. A cross sectional assessment of the generation of hazardous waste from a hospital, between June and August 2005, was performed. The environmental risk associated to the management of non-radioactive hospital waste was assessed and the main problems related to solid waste were identified. The rate of generation of hazardous non-radioactive waste was 1.35 tons per months or 0.7 kg/bed/day. Twenty five percent of hazardous liquid waste were drained directly to the sewage system. The drug preparation unit of the pharmacy had the higher environmental risk associated to the generation of hazardous waste. The internal transport of hazardous waste had a high risk due to the lack of trip planning. The lack of training of personnel dealing with these waste was another risk factor. Considering that an adequate management of hospital waste should minimize risks for patients, the hospital that was evaluated lacks an integral management system for its waste.

Global Impacts and Regional Actions: Preparing for the 1997-98 El Niño.

NASA Astrophysics Data System (ADS)

Buizer, James L.; Foster, Josh; Lund, David

2000-09-01

It has been estimated that severe El Niño-related flooding and droughts in Africa, Latin America, North America, and Southeast Asia resulted in more than 22 000 lives lost and in excess of $36 billion in damages during 1997-98. As one of the most severe events this century, the 1997-98 El Niño was unique not only in terms of physical magnitude, but also in terms of human response. This response was made possible by recent advances in climate-observing and forecasting systems, creation and dissemination of forecast information by institutions such as the International Research Institute for Climate Prediction and NOAA's Climate Prediction Center, and individuals in climate-sensitive sectors willing to act on forecast information by incorporating it into their decision-making. The supporting link between the forecasts and their practical application was a product of efforts by several national and international organizations, and a primary focus of the United States National Oceanic and Atmospheric Administration Office of Global Programs (NOAA/OGP).NOAA/OGP over the last decade has supported pilot projects in Latin America, the Caribbean, the South Pacific, Southeast Asia, and Africa to improve transfer of forecast information to climate sensitive sectors, study linkages between climate and human health, and distribute climate information products in certain areas. Working with domestic and international partners, NOAA/OGP helped organize a total of 11 Climate Outlook Fora around the world during the 1997-98 El Niño. At each Outlook Forum, climatologists and meteorologists created regional, consensus-based, seasonal precipitation forecasts and representatives from climate-sensitive sectors discussed options for applying forecast information. Additional ongoing activities during 1997-98 included research programs focused on the social and economic impacts of climate change and the regional manifestations of global-scale climate variations and their effect on decision-making in climate-sensitive sectors in the United States.The overall intent of NOAA/OGP's activities was to make experimental forecast information broadly available to potential users, and to foster a learning process on how seasonal-to-interannual forecasts could be applied in sectors susceptible to climate variability. This process allowed users to explore the capabilities and limitations of climate forecasts currently available, and forecast producers to receive feedback on the utility of their products. Through activities in which NOAA/OGP and its partners were involved, it became clear that further application of forecast information will be aided by improved forecast accuracy and detail, creation of common validation techniques, continued training in forecast generation and application, alternate methods for presenting forecast information, and a systematic strategy for creation and dissemination of forecast products.The overall intent of NOAA/OGP's activities was to make experimental forecast information broadly available to potential users, and to foster a learning process on how seasonal-to-interannual forecasts could be applied in sectors susceptible to climate variability. This process allowed users to explore the capabilities and limitations of climate forecasts currently available, and forecast producers to receive feedback on the utility of their products. Through activities in which NOAA/OGP and its partners were involved, it became clear that further application of forecast information will be aided by improved forecast accuracy and detail, creation of common validation techniques, continued training in forecast generation and application, alternate methods for presenting forecast information, and a systematic strategy for creation and dissemination of forecast products.

40 CFR 761.340 - Applicability.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Accordance With § 761.62, and Sampling PCB Remediation Waste Destined for Off-Site Disposal, in Accordance... generate new waste. (c) Non-liquid PCB remediation waste from processes that continuously generate new...

The effect of gender and age structure on municipal waste generation in Poland

DOE Office of Scientific and Technical Information (OSTI.GOV)

Talalaj, Izabela Anna, E-mail: izabela.tj@gmail.com; Walery, Maria, E-mail: m.walery@pb.edu.pl

Highlights: • An effect of gender and age structure on municipal waste generation was presented. • The waste accumulation index is influenced by a number of unemployed women. • Greater share of women in society contributes to greater waste production. • A model describing the analyzed dependences was determined. - Abstract: In this study the effect of gender and age structure on municipal waste generation was investigated. The data from 10-year period, from 2001 to 2010 year, were taken into consideration. The following parameters of gender and age structure were analyzed: men and woman quantity, female to male ratio, numbermore » of working, pre-working and post-working age men/women, number of unemployed men/women. The results have showed a strong correlation of annual per capita waste generation rate with number of unemployed women (r = 0.70) and female to male ratio (r = 0.81). This indicates that waste generation rate is more depended on ratio of men and women that on quantitative size of each group. Using the regression analysis a model describing the dependence between female to male ratio, number of unemployed woman and waste quantity was determined. The model explains 70% of waste quantity variation. Obtained results can be used both to improve waste management and to a fuller understanding of gender behavior.« less

Development potential of e-waste recycling industry in China.

PubMed

Li, Jinhui; Yang, Jie; Liu, Lili

2015-06-01

Waste electrical and electronic equipment (WEEE or e-waste) recycling industries in China have been through several phases from spontaneous informal family workshops to qualified enterprises with treatment fund. This study attempts to analyse the development potential of the e-waste recycling industry in China from the perspective of both time and scale potential. An estimation and forecast of e-waste quantities in China shows that, the total e-waste amount reached approximately 5.5 million tonnes in 2013, with 83% of air conditioners, refrigerators, washing machines, televisions sand computers. The total quantity is expected to reach ca. 11.7 million tonnes in 2020 and 20 million tonnes in 2040, which indicates a large increase potential. Moreover, the demand for recycling processing facilities, the optimal service radius of e-waste recycling enterprises and estimation of the profitability potential of the e-waste recycling industry were analysed. Results show that, based on the e-waste collection demand, e-waste recycling enterprises therefore have a huge development potential in terms of both quantity and processing capacity, with 144 and 167 e-waste recycling facilities needed, respectively, by 2020 and 2040. In the case that e-waste recycling enterprises set up their own collection points to reduce the collection cost, the optimal collection service radius is estimated to be in the range of 173 km to 239 km. With an e-waste treatment fund subsidy, the e-waste recycling industry has a small economic profit, for example ca. US$2.5/unit for television. The annual profit for the e-waste recycling industry overall was about 90 million dollars in 2013. © The Author(s) 2015.

77 FR 41720 - Hazardous Waste Management System; Identification and Listing of Hazardous Waste; Proposed Exclusion

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-16

... delist? B. How does IBM generate the waste? C. How did IBM sample and analyze the petitioned waste? D..., thickened/conditioned, and pressed to generate the F006 waste stream. C. How did IBM sample and analyze the... the volatiles and semi-volatiles samples were non- detect. E. How did EPA evaluate the risk of...

Waste generated in high-rise buildings construction: a quantification model based on statistical multiple regression.

PubMed

Parisi Kern, Andrea; Ferreira Dias, Michele; Piva Kulakowski, Marlova; Paulo Gomes, Luciana

2015-05-01

Reducing construction waste is becoming a key environmental issue in the construction industry. The quantification of waste generation rates in the construction sector is an invaluable management tool in supporting mitigation actions. However, the quantification of waste can be a difficult process because of the specific characteristics and the wide range of materials used in different construction projects. Large variations are observed in the methods used to predict the amount of waste generated because of the range of variables involved in construction processes and the different contexts in which these methods are employed. This paper proposes a statistical model to determine the amount of waste generated in the construction of high-rise buildings by assessing the influence of design process and production system, often mentioned as the major culprits behind the generation of waste in construction. Multiple regression was used to conduct a case study based on multiple sources of data of eighteen residential buildings. The resulting statistical model produced dependent (i.e. amount of waste generated) and independent variables associated with the design and the production system used. The best regression model obtained from the sample data resulted in an adjusted R(2) value of 0.694, which means that it predicts approximately 69% of the factors involved in the generation of waste in similar constructions. Most independent variables showed a low determination coefficient when assessed in isolation, which emphasizes the importance of assessing their joint influence on the response (dependent) variable. Copyright © 2015 Elsevier Ltd. All rights reserved.

Impact of Aquarius and SMAP Sea Surface Salinity Observations on Seasonal Predictions of the 2015 El Nino

NASA Technical Reports Server (NTRS)

Hackert, E.; Kovach, R.; Marshak, J.; Borovikov, A.; Molod, A.; Vernieres, G.

2018-01-01

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts for the big 2015 El Nino event. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP (Soil Moisture Active Passive Mission), allows a unique opportunity to compare and contrast forecasts generated with the benefit of these two satellite SSS observation types. Four distinct experiments are presented that include 1) freely evolving model SSS (i.e. no satellite SSS), relaxation to 2) climatological SSS (i.e. WOA13 SSS), 3) Aquarius, and 4) SMAP initialization. Coupled hindcasts are then generated from these initial conditions for March 2015. These forecasts are then validated against observations and evaluated with respect to the observed El Nino development.

Generation rates and chemical compositions of waste streams in a typical crewed space habitat

NASA Technical Reports Server (NTRS)

Wydeven, Theodore; Golub, Morton A.

1990-01-01

A judicious compilation of generation rates and chemical compositions of potential waste feed streams in a typical crewed space habitat was made in connection with the waste-management aspect of NASA's Physical/Chemical Closed-Loop Life Support Program. Waste composition definitions are needed for the design of waste-processing technologies involved in closing major life support functions in future long-duration human space missions. Tables of data for the constituents and chemical formulas of the following waste streams are presented and discussed: human urine, feces, hygiene (laundry and shower) water, cleansing agents, trash, humidity condensate, dried sweat, and trace contaminants. Tables of data on dust generation and pH values of the different waste streams are also presented and discussed.

Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

NASA Astrophysics Data System (ADS)

Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

2017-12-01

A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

NCAR's Experimental Real-time Convection-allowing Ensemble Prediction System

NASA Astrophysics Data System (ADS)

Schwartz, C. S.; Romine, G. S.; Sobash, R.; Fossell, K.

2016-12-01

Since April 2015, the National Center for Atmospheric Research's (NCAR's) Mesoscale and Microscale Meteorology (MMM) Laboratory, in collaboration with NCAR's Computational Information Systems Laboratory (CISL), has been producing daily, real-time, 10-member, 48-hr ensemble forecasts with 3-km horizontal grid spacing over the conterminous United States (http://ensemble.ucar.edu). These computationally-intensive, next-generation forecasts are produced on the Yellowstone supercomputer, have been embraced by both amateur and professional weather forecasters, are widely used by NCAR and university researchers, and receive considerable attention on social media. Initial conditions are supplied by NCAR's Data Assimilation Research Testbed (DART) software and the forecast model is NCAR's Weather Research and Forecasting (WRF) model; both WRF and DART are community tools. This presentation will focus on cutting-edge research results leveraging the ensemble dataset, including winter weather predictability, severe weather forecasting, and power outage modeling. Additionally, the unique design of the real-time analysis and forecast system and computational challenges and solutions will be described.

The Art and Science of Long-Range Space Weather Forecasting

NASA Technical Reports Server (NTRS)

Hathaway, David H.; Wilson, Robert M.

2006-01-01

Long-range space weather forecasts are akin to seasonal forecasts of terrestrial weather. We don t expect to forecast individual events but we do hope to forecast the underlying level of activity important for satellite operations and mission pl&g. Forecasting space weather conditions years or decades into the future has traditionally been based on empirical models of the solar cycle. Models for the shape of the cycle as a function of its amplitude become reliable once the amplitude is well determined - usually two to three years after minimum. Forecasting the amplitude of a cycle well before that time has been more of an art than a science - usually based on cycle statistics and trends. Recent developments in dynamo theory -the theory explaining the generation of the Sun s magnetic field and the solar activity cycle - have now produced models with predictive capabilities. Testing these models with historical sunspot cycle data indicates that these predictions may be highly reliable one, or even two, cycles into the future.

Anaerobic codigestion of dairy manure and food manufacturing waste for renewable energy generation in New York State

NASA Astrophysics Data System (ADS)

Rankin, Matthew J.

Anaerobic digestion is a microbiological process that converts biodegradable organic material into biogas, consisting primarily of methane and carbon dioxide. Anaerobic digestion technologies have been integrated into wastewater treatment facilities nationwide for many decades to increase the economic viability of the treatment process by converting a waste stream into two valuable products: biogas and fertilizer. Thus, anaerobic digestion offers potential economic and environmental benefits of organic waste diversion and renewable energy generation. The use of biogas has many applications, including cogeneration, direct combustion, upgrading for conversion to feed a fuel cell, and compression for injection into the natural gas grid or for vehicular use. The potential benefits of waste diversion and renewable energy generation are now being realized by major organic waste generators in New York State, in particular the food manufacturing and dairy industries, thus warranting an analysis of the energy generation potential for these waste products. Anaerobic codigestion of dairy manure and food-based feedstocks reflects a cradle-to- cradle approach to organic waste management. Given both of their abundance throughout New York State, waste-to-energy processes represent promising waste management strategies. The objective of this thesis was to evaluate the current technical and economic feasibility of anaerobically codigesting existing dairy manure and food manufacturing waste feedstocks in New York State to produce high quality biogas for renewable energy generation. The first element to determining the technical feasibility of anaerobic codigestion potential in New York State was to first understand the feedstock availability. A comprehensive survey of existing organic waste streams was conducted. The key objective was to identify the volume and composition of dairy manure and liquid-phase food manufacturing waste streams available in New York State to make codigestion of multiple feedstocks in centralized anaerobic codigestion facilities an economically attractive alternative to traditional waste disposal pathways (e.g. landfill and wastewater treatment facilities). A technical and environmental assessment of processing food manufacturing wastes and dairy manure for production of electricity via cogeneration, while dependent on biogas quantity and quality as well as the proximity of the waste generators to the centralized codigestion facility, suggests that a real possibility exists for integrating dairy operations with food manufacturing facilities, dependent on the values of the parameters indicated in this thesis. The results of the environmental analysis show that considerable electricity generation and greenhouse gas emissions reductions are possible, depending primarily on feedstock availability and proximity to the centralized anaerobic digester. The initial results are encouraging and future work is warranted for analyzing the site-specific technical and economic viability of codigesting dairy manure and food manufacturing wastes to produce high quality biogas for renewable energy generation in New York State.

Hazardous Wastes and the Consumer Connection. A Guide for Educators and Citizens Concerned with the Role of Consumers in the Generation of Hazardous Wastes.

ERIC Educational Resources Information Center

Assaff, Edith

Many consumers do not see a strong connection between our lifestyles and buying decisions, and the amount of hazardous wastes generated in the United States. This guide was developed to be used by educators and citizens concerned with the role of consumers in the generation of hazardous wastes. It examines several products in terms of their…

Decomposition analysis of the waste generation and management in 30 European countries.

PubMed

Korica, Predrag; Cirman, Andreja; Žgajnar Gotvajn, Andreja

2016-11-01

An often suggested method for waste prevention is substitution of currently-used materials with materials which are less bulky, contain less hazardous components or are easier to recycle. For policy makers it is important to have tools available that provide information on the impact of this substitution on the changes in total amounts of waste generated and managed. The purpose of this paper is to see how much changes in the mix of 15 waste streams generated in eight economic sectors from 30 European countries have influenced the amounts of waste generated and managed in the period 2004-2012. In order to determine these impacts, two variations of the logarithmic mean Divisia index (LMDI) analysis model were developed and applied. The results show that the changes in the mix of waste streams in most cases did not have a considerable influence on the changes in the amounts of generated waste. In the analyses of waste sent for landfill, incineration without energy recovery, incineration with energy recovery and recovery other than energy recovery, the results also show that the changes in the mix of waste streams in most cases did not have the expected/desired influence on the changes in the amounts of managed waste. This paper provides an example on the possibilities of applying the LMDI analysis as a tool for quantifying the potential of effects which implemented or planned measures could have on the changes in waste management systems. © The Author(s) 2016.

A combination of HARMONIE short time direct normal irradiance forecasts and machine learning: The #hashtdim procedure

NASA Astrophysics Data System (ADS)

Gastón, Martín; Fernández-Peruchena, Carlos; Körnich, Heiner; Landelius, Tomas

2017-06-01

The present work describes the first approach of a new procedure to forecast Direct Normal Irradiance (DNI): the #hashtdim that treats to combine ground information and Numerical Weather Predictions. The system is centered in generate predictions for the very short time. It combines the outputs from the Numerical Weather Prediction Model HARMONIE with an adaptive methodology based on Machine Learning. The DNI predictions are generated with 15-minute and hourly temporal resolutions and presents 3-hourly updates. Each update offers forecasts to the next 12 hours, the first nine hours are generated with 15-minute temporal resolution meanwhile the last three hours present hourly temporal resolution. The system is proved over a Spanish emplacement with BSRN operative station in south of Spain (PSA station). The #hashtdim has been implemented in the framework of the Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies (DNICast) project, under the European Union's Seventh Programme for research, technological development and demonstration framework.

Monthly mean forecast experiments with the GISS model

NASA Technical Reports Server (NTRS)

Spar, J.; Atlas, R. M.; Kuo, E.

1976-01-01

The GISS general circulation model was used to compute global monthly mean forecasts for January 1973, 1974, and 1975 from initial conditions on the first day of each month and constant sea surface temperatures. Forecasts were evaluated in terms of global and hemispheric energetics, zonally averaged meridional and vertical profiles, forecast error statistics, and monthly mean synoptic fields. Although it generated a realistic mean meridional structure, the model did not adequately reproduce the observed interannual variations in the large scale monthly mean energetics and zonally averaged circulation. The monthly mean sea level pressure field was not predicted satisfactorily, but annual changes in the Icelandic low were simulated. The impact of temporal sea surface temperature variations on the forecasts was investigated by comparing two parallel forecasts for January 1974, one using climatological ocean temperatures and the other observed daily ocean temperatures. The use of daily updated sea surface temperatures produced no discernible beneficial effect.

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, B. M.; Florita, A.; Orwig, K.

2012-07-01

The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent Systemmore » Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.« less

Forecasting seasonal outbreaks of influenza.

PubMed

Shaman, Jeffrey; Karspeck, Alicia

2012-12-11

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.

Forecasting seasonal outbreaks of influenza

PubMed Central

Shaman, Jeffrey; Karspeck, Alicia

2012-01-01

Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003–2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza. PMID:23184969

The effect of physical parameterizations and initial data on the numerical prediction of the President's Day cyclone

NASA Technical Reports Server (NTRS)

Atlas, R.

1984-01-01

Results are presented from a series of forecast experiments which were conducted to assess the importance of large-scale dynamical processes, diabatic heating, and initial data to the prediction of the President's Day cyclone. The synoptic situation and NMC model forecasts for this case are summarized, and the analysis/forecast system and experiments are described. The GLAS Model forecast from the GLAS analysis at 0000 GMT 18 February is found to have correctly predicted intense coastal cyclogenesis and heavy precipitation. A forecast with surface heat and moisture fluxes eliminated failed to predict any cyclogenesis while a similar forecast with only the surface moisture flux excluded showed weak development. Diabatic heating resulting from oceanic fluxes significantly contributed to the generation of low-level cyclonic vorticity and the intensification and slow rate of movement of an upper level ridge over the western Atlantic.

Sound Waste Management Plan environmental operations, and used oil management system: Restoration project 97115. Exxon Valdez oil spill restoration project final report: Volumes 1 and 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1998-06-01

This project constitutes Phase 2 of the Sound Waste Management Plan and created waste oil collection and disposal facilities, bilge water collection and disposal facilities, recycling storage, and household hazardous waste collection and storage, and household hazardous waste collection and storage facilities in Prince William Sound. A wide range of waste streams are generated within communities in the Sound including used oil generated from vehicles and vessels, and hazardous wastes generated by households. This project included the design and construction of Environmental Operations Stations buildings in Valdez, Cordova, Whittier, Chenega Bay and Tatitlek to improve the overall management of oilymore » wastes. They will house new equipment to facilitate oily waste collection, treatment and disposal. This project also included completion of used oil management manuals.« less

Forecasting daily streamflow using online sequential extreme learning machines

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.

2016-06-01

While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continually, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this problem, an online sequential learning algorithm for single hidden layer feedforward neural networks - the online sequential extreme learning machine (OSELM) - is automatically updated inexpensively as new data arrive (and the new data can then be discarded). OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1-3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates. More frequent updating gave smaller forecast errors, including errors for data above the 90th percentile. Larger datasets used in the initial training of OSELM helped to find better parameters (number of hidden nodes) for the model, yielding better predictions. With the online sequential multiple linear regression (OSMLR) as benchmark, we concluded that OSELM is an attractive approach as it easily outperformed OSMLR in forecast accuracy.

Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability-A Review

NASA Astrophysics Data System (ADS)

Holmukhe, R. M.; Dhumale, Mrs. Sunita; Chaudhari, Mr. P. S.; Kulkarni, Mr. P. P.

2010-10-01

Load forecasting is very essential to the operation of Electricity companies. It enhances the energy efficient and reliable operation of power system. Forecasting of load demand data forms an important component in planning generation schedules in a power system. The purpose of this paper is to identify issues and better method for load foecasting. In this paper we focus on fuzzy logic system based short term load forecasting. It serves as overview of the state of the art in the intelligent techniques employed for load forecasting in power system planning and reliability. Literature review has been conducted and fuzzy logic method has been summarized to highlight advantages and disadvantages of this technique. The proposed technique for implementing fuzzy logic based forecasting is by Identification of the specific day and by using maximum and minimum temperature for that day and finally listing the maximum temperature and peak load for that day. The results show that Load forecasting where there are considerable changes in temperature parameter is better dealt with Fuzzy Logic system method as compared to other short term forecasting techniques.

Carbon abatement via treating the solid waste from the Australian olive industry in mobile pyrolysis units: LCA with uncertainty analysis.

PubMed

El Hanandeh, Ali

2013-04-01

The olive oil industry in Australia has been growing at a rapid rate over the past decade. It is forecast to continue growing due to the steady increase in demand for olive oil and olive products in the local and regional market. However, the olive oil extraction process generates large amounts of solid waste called olive husk which is currently underutilized. This paper uses life-cycle methodology to analyse the carbon emission reduction potential of utilizing olive husk as a feedstock in a mobile pyrolysis unit. Four scenarios, based on different combinations of pyrolysis technologies (slow versus fast) and end-use of products (land application versus energy utilization), are constructed. The performance of each scenario under conditions of uncertainty was also investigated. The results show that all scenarios result in significant carbon emission abatement. Processing olive husk in mobile fast pyrolysis units and the utilization of bio-oil and biochar as substitutes for heavy fuel oil and coal is likely to realize a carbon offset greater than 32.3 Gg CO2-eq annually in 90% of the time. Likewise, more than 3.2 Gg-C (11.8 Gg CO2-eq) per year could be sequestered in the soil in the form of fixed carbon if slow mobile pyrolysis units were used to produce biochar.

Impacts of Short-Term Solar Power Forecasts in System Operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ibanez, Eduardo; Krad, Ibrahim; Hodge, Bri-Mathias

2016-05-05

Solar generation is experiencing an exponential growth in power systems worldwide and, along with wind power, is posing new challenges to power system operations. Those challenges are characterized by an increase of system variability and uncertainty across many time scales: from days, down to hours, minutes, and seconds. Much of the research in the area has focused on the effect of solar forecasting across hours or days. This paper presents a methodology to capture the effect of short-term forecasting strategies and analyzes the economic and reliability implications of utilizing a simple, yet effective forecasting method for solar PV in intra-daymore » operations.« less

Fennec dust forecast intercomparison over the Sahara in June 2011

NASA Astrophysics Data System (ADS)

Chaboureau, Jean-Pierre; Flamant, Cyrille; Dauhut, Thibaut; Kocha, Cécile; Lafore, Jean-Philippe; Lavaysse, Chistophe; Marnas, Fabien; Mokhtari, Mohamed; Pelon, Jacques; Reinares Martínez, Irene; Schepanski, Kerstin; Tulet, Pierre

2016-06-01

In the framework of the Fennec international programme, a field campaign was conducted in June 2011 over the western Sahara. It led to the first observational data set ever obtained that documents the dynamics, thermodynamics and composition of the Saharan atmospheric boundary layer (SABL) under the influence of the heat low. In support to the aircraft operation, four dust forecasts were run daily at low and high resolutions with convection-parameterizing and convection-permitting models, respectively. The unique airborne and ground-based data sets allowed the first ever intercomparison of dust forecasts over the western Sahara. At monthly scale, large aerosol optical depths (AODs) were forecast over the Sahara, a feature observed by satellite retrievals but with different magnitudes. The AOD intensity was correctly predicted by the high-resolution models, while it was underestimated by the low-resolution models. This was partly because of the generation of strong near-surface wind associated with thunderstorm-related density currents that could only be reproduced by models representing convection explicitly. Such models yield emissions mainly in the afternoon that dominate the total emission over the western fringes of the Adrar des Iforas and the Aïr Mountains in the high-resolution forecasts. Over the western Sahara, where the harmattan contributes up to 80 % of dust emission, all the models were successful in forecasting the deep well-mixed SABL. Some of them, however, missed the large near-surface dust concentration generated by density currents and low-level winds. This feature, observed repeatedly by the airborne lidar, was partly forecast by one high-resolution model only.

Fennec dust forecast intercomparison over the Sahara in June 2011

NASA Astrophysics Data System (ADS)

Chaboureau, J. P.; Flamant, C.; Dauhut, T.; Lafore, J. P.; Lavaysse, C.; Pelon, J.; Schepanski, K.; Tulet, P.

2016-12-01

In the framework of the Fennec international programme, a field campaign was conducted in June 2011 over the western Sahara. It led to the first observational data set ever obtained that documents the dynamics, thermodynam-ics and composition of the Saharan atmospheric boundary layer (SABL) under the influence of the heat low. In support to the aircraft operation, four dust forecasts were run daily at low and high resolutions with convection-parameterizing and convection-permitting models, respectively. The unique airborne and ground-based data sets allowed the first ever intercomparison of dust forecasts over the western Sahara. At monthly scale, large aerosol optical depths (AODs) were forecast over the Sahara, a feature observed by satellite retrievals but with different magnitudes. The AOD intensity was correctly predicted by the high-resolution models, while it was underestimated by the low-resolution models. This was partly because of the generation of strong near-surface wind associated with thunderstorm-related density currents that could only be reproduced by models representing convection explicitly. Such models yield emissions mainly in the afternoon that dominate the total emission over the western fringes of the Adrar des Iforas and the Aïr Mountains in the high-resolution forecasts. Over the western Sahara, where the harmattan contributes up to 80 % of dust emission, all the models were successful in forecasting the deep well-mixed SABL. Some of them, however, missed the large near-surface dust concentration generated by density currents and low-level winds. This feature, observed repeatedly by the airborne lidar, was partly forecast by one high-resolution model only.

Similarity-based multi-model ensemble approach for 1-15-day advance prediction of monsoon rainfall over India

NASA Astrophysics Data System (ADS)

Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati

2018-04-01

The southwest (SW) monsoon season (June, July, August and September) is the major period of rainfall over the Indian region. The present study focuses on the development of a new multi-model ensemble approach based on the similarity criterion (SMME) for the prediction of SW monsoon rainfall in the extended range. This approach is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional MME approaches. In this approach, the training dataset has been selected by matching the present day condition to the archived dataset and days with the most similar conditions were identified and used for training the model. The coefficients thus generated were used for the rainfall prediction. The precipitation forecasts from four general circulation models (GCMs), viz. European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom Meteorological Office (UKMO), National Centre for Environment Prediction (NCEP) and China Meteorological Administration (CMA) have been used for developing the SMME forecasts. The forecasts of 1-5, 6-10 and 11-15 days were generated using the newly developed approach for each pentad of June-September during the years 2008-2013 and the skill of the model was analysed using verification scores, viz. equitable skill score (ETS), mean absolute error (MAE), Pearson's correlation coefficient and Nash-Sutcliffe model efficiency index. Statistical analysis of SMME forecasts shows superior forecast skill compared to the conventional MME and the individual models for all the pentads, viz. 1-5, 6-10 and 11-15 days.

HEPA Filter Disposal Write-Up 10/19/16

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loll, C.

Process knowledge (PK) collection on HEPA filters is handled via the same process as other waste streams at LLNL. The Field technician or Characterization point of contact creates an information gathering document (IGD) in the IGD database, with input provided from the generator, and submits it for electronic approval. This document is essentially a waste generation profile, detailing the physical, chemical as well as radiological characteristics, and hazards, of a waste stream. It will typically contain a general, but sometimes detailed, description of the work processes which generated the waste. It will contain PK as well as radiological and industrialmore » hygiene analytical swipe results, and any other analytical or other supporting knowledge related to characterization. The IGD goes through an electronic approval process to formalize the characterization and to ensure the waste has an appropriate disposal path. The waste generator is responsible for providing initial process knowledge information, and approves the IGD before it routed to chemical and radiological waste characterization professionals. This is the standard characterization process for LLNL-generated HEPA Filters.« less

Greater-than-Class C low-level radioactive waste characterization: Estimated volumes, radionuclide activities, and other characteristics. Revision 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1994-09-01

The Department of Energy`s (DOE`s) planning for the disposal of greater-than-Class C low-level radioactive waste (GTCC LLW) requires characterization of the waste. This report estimates volumes, radionuclide activities, and waste forms of GTCC LLW to the year 2035. It groups the waste into four categories, representative of the type of generator or holder of the waste: Nuclear Utilities, Sealed Sources, DOE-Held, and Other Generator. GTCC LLW includes activated metals (activation hardware from reactor operation and decommissioning), process wastes (i.e., resins, filters, etc.), sealed sources, and other wastes routinely generated by users of radioactive material. Estimates reflect the possible effect thatmore » packaging and concentration averaging may have on the total volume of GTCC LLW. Possible GTCC mixed LLW is also addressed. Nuclear utilities will probably generate the largest future volume of GTCC LLW with 65--83% of the total volume. The other generators will generate 17--23% of the waste volume, while GTCC sealed sources are expected to contribute 1--12%. A legal review of DOE`s obligations indicates that the current DOE-Held wastes described in this report will not require management as GTCC LLW because of the contractual circumstances under which they were accepted for storage. This report concludes that the volume of GTCC LLW should not pose a significant management problem from a scientific or technical standpoint. The projected volume is small enough to indicate that a dedicated GTCC LLW disposal facility may not be justified. Instead, co-disposal with other waste types is being considered as an option.« less

Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

NASA Astrophysics Data System (ADS)

Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

2015-12-01

Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

Children's School Readiness: Implications for Eliminating Future Disparities in Health and Education

ERIC Educational Resources Information Center

Pagani, Linda S.; Fitzpatrick, Caroline

2014-01-01

Background: School-entry characteristics predict adult educational attainment, which forecasts dispositions toward disease prevention. Health and education risks can also be transmitted from one generation to the next. As such, school readiness forecasts a set of intertwined biopsychosocial trajectories that can influence the developmental…

Tropical Cyclone Prediction Using COAMPS-TC

DTIC Science & Technology

2014-09-01

landfalling hurricanes with the advanced hurricane WRF model. Monthly Weather Review 136:1,990–2,005, http://dx.doi.org/10.1175/2007MWR2085.1. DeMaria, M...Weisman. 2004. The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecast ( WRF ) Model. Atmospheric Science

The Impacts of Climate Variations on Military Operations in the Horn of Africa

DTIC Science & Technology

2006-03-01

variability in a region. Climate forecasts are predictions of the future state of the climate , much as we think of weather forecasts but at longer...arrive at accurate characterizations of the future state of the climate . Many of the civilian organizations that generate reanalysis data also

NOAA announces significant investment in next generation of supercomputers

Science.gov Websites

provide more timely, accurate weather forecasts. (Credit: istockphoto.com) Today, NOAA announced the next phase in the agency's efforts to increase supercomputing capacity to provide more timely, accurate turn will lead to more timely, accurate, and reliable forecasts." Ahead of this upgrade, each of

Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

NASA Astrophysics Data System (ADS)

Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

2013-12-01

To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most informative climate indices for the region of interest.

An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index

NASA Astrophysics Data System (ADS)

Ali, Mumtaz; Deo, Ravinesh C.; Downs, Nathan J.; Maraseni, Tek

2018-07-01

Forecasting drought by means of the World Meteorological Organization-approved Standardized Precipitation Index (SPI) is considered to be a fundamental task to support socio-economic initiatives and effectively mitigating the climate-risk. This study aims to develop a robust drought modelling strategy to forecast multi-scalar SPI in drought-rich regions of Pakistan where statistically significant lagged combinations of antecedent SPI are used to forecast future SPI. With ensemble-Adaptive Neuro Fuzzy Inference System ('ensemble-ANFIS') executed via a 10-fold cross-validation procedure, a model is constructed by randomly partitioned input-target data. Resulting in 10-member ensemble-ANFIS outputs, judged by mean square error and correlation coefficient in the training period, the optimal forecasts are attained by the averaged simulations, and the model is benchmarked with M5 Model Tree and Minimax Probability Machine Regression (MPMR). The results show the proposed ensemble-ANFIS model's preciseness was notably better (in terms of the root mean square and mean absolute error including the Willmott's, Nash-Sutcliffe and Legates McCabe's index) for the 6- and 12- month compared to the 3-month forecasts as verified by the largest error proportions that registered in smallest error band. Applying 10-member simulations, ensemble-ANFIS model was validated for its ability to forecast severity (S), duration (D) and intensity (I) of drought (including the error bound). This enabled uncertainty between multi-models to be rationalized more efficiently, leading to a reduction in forecast error caused by stochasticity in drought behaviours. Through cross-validations at diverse sites, a geographic signature in modelled uncertainties was also calculated. Considering the superiority of ensemble-ANFIS approach and its ability to generate uncertainty-based information, the study advocates the versatility of a multi-model approach for drought-risk forecasting and its prime importance for estimating drought properties over confidence intervals to generate better information for strategic decision-making.

Assessment of Folsom Lake Watershed response to historical and potential future climate scenarios

USGS Publications Warehouse

Carpenter, Theresa M.; Georgakakos, Konstantine P.

2000-01-01

An integrated forecast-control system was designed to allow the profitable use of ensemble forecasts for the operational management of multi-purpose reservoirs. The system ingests large-scale climate model monthly precipitation through the adjustment of the marginal distribution of reservoir-catchment precipitation to reflect occurrence of monthly climate precipitation amounts in the extreme terciles of their distribution. Generation of ensemble reservoir inflow forecasts is then accomplished with due account for atmospheric- forcing and hydrologic- model uncertainties. These ensemble forecasts are ingested by the decision component of the integrated system, which generates non- inferior trade-off surfaces and, given management preferences, estimates of reservoir- management benefits over given periods. In collaboration with the Bureau of Reclamation and the California Nevada River Forecast Center, the integrated system is applied to Folsom Lake in California to evaluate the benefits for flood control, hydroelectric energy production, and low flow augmentation. In addition to retrospective studies involving the historical period 1964-1993, system simulations were performed for the future period 2001-2030, under a control (constant future greenhouse-gas concentrations assumed at the present levels) and a greenhouse-gas- increase (1-% per annum increase assumed) scenario. The present paper presents and validates ensemble 30-day reservoir- inflow forecasts under a variety of situations. Corresponding reservoir management results are presented in Yao and Georgakakos, A., this issue. Principle conclusions of this paper are that the integrated system provides reliable ensemble inflow volume forecasts at the 5-% confidence level for the majority of the deciles of forecast frequency, and that the use of climate model simulations is beneficial mainly during high flow periods. It is also found that, for future periods with potential sharp climatic increases of precipitation amount and to maintain good reliability levels, operational ensemble inflow forecasting should involve atmospheric forcing from appropriate climatic periods.

An Operational System for Surveillance and Ecological Forecasting of West Nile Virus Outbreaks

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Davis, J. K.; Vincent, G.; Hess, A.; Hildreth, M. B.

2017-12-01

Mosquito-borne disease surveillance has traditionally focused on tracking human cases along with the abundance and infection status of mosquito vectors. For many of these diseases, vector and host population dynamics are also sensitive to climatic factors, including temperature fluctuations and the availability of surface water for mosquito breeding. Thus, there is a potential to strengthen surveillance and predict future outbreaks by monitoring environmental risk factors using broad-scale sensor networks that include earth-observing satellites. The South Dakota Mosquito Information System (SDMIS) project combines entomological surveillance with gridded meteorological data from NASA's North American Land Data Assimilation System (NLDAS) to generate weekly risk maps for West Nile virus (WNV) in the north-central United States. Critical components include a mosquito infection model that smooths the noisy infection rate and compensates for unbalanced sampling, and a human infection model that combines the entomological risk estimates with lagged effects of meteorological variables from the North American Land Data Assimilation System (NLDAS). Two types of forecasts are generated: long-term forecasts of statewide risk extending through the entire WNV season, and short-term forecasts of the geographic pattern of WNV risk in the upcoming week. Model forecasts are connected to public health actions through decision support matrices that link predicted risk levels to a set of phased responses. In 2016, the SDMIS successfully forecast an early start to the WNV season and a large outbreak of WNV cases following several years of low transmission. An evaluation of the 2017 forecasts will also be presented. Our experiences with the SDMIS highlight several important lessons that can inform future efforts at disease early warning. These include the value of integrating climatic models with recent observations of infection, the critical role of automated workflows to facilitate the timely integration of multiple data streams, the need for effective synthesis and visualization of forecasts, and the importance of linking forecasts to specific public health responses.

A Case Study of the Impact of AIRS Temperature Retrievals on Numerical Weather Prediction

NASA Technical Reports Server (NTRS)

Reale, O.; Atlas, R.; Jusem, J. C.

2004-01-01

Large errors in numerical weather prediction are often associated with explosive cyclogenesis. Most studes focus on the under-forecasting error, i.e. cases of rapidly developing cyclones which are poorly predicted in numerical models. However, the over-forecasting error (i.e., to predict an explosively developing cyclone which does not occur in reality) is a very common error that severely impacts the forecasting skill of all models and may also present economic costs if associated with operational forecasting. Unnecessary precautions taken by marine activities can result in severe economic loss. Moreover, frequent occurrence of over-forecasting can undermine the reliance on operational weather forecasting. Therefore, it is important to understand and reduce the prdctions of extreme weather associated with explosive cyclones which do not actually develop. In this study we choose a very prominent case of over-forecasting error in the northwestern Pacific. A 960 hPa cyclone develops in less than 24 hour in the 5-day forecast, with a deepening rate of about 30 hPa in one day. The cyclone is not versed in the analyses and is thus a case of severe over-forecasting. By assimilating AIRS data, the error is largely eliminated. By following the propagation of the anomaly that generates the spurious cyclone, it is found that a small mid-tropospheric geopotential height negative anomaly over the northern part of the Indian subcontinent in the initial conditions, propagates westward, is amplified by orography, and generates a very intense jet streak in the subtropical jet stream, with consequent explosive cyclogenesis over the Pacific. The AIRS assimilation eliminates this anomaly that may have been caused by erroneous upper-air data, and represents the jet stream more correctly. The energy associated with the jet is distributed over a much broader area and as a consequence a multiple, but much more moderate cyclogenesis is observed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jie; Cui, Mingjian; Hodge, Bri-Mathias

The large variability and uncertainty in wind power generation present a concern to power system operators, especially given the increasing amounts of wind power being integrated into the electric power system. Large ramps, one of the biggest concerns, can significantly influence system economics and reliability. The Wind Forecast Improvement Project (WFIP) was to improve the accuracy of forecasts and to evaluate the economic benefits of these improvements to grid operators. This paper evaluates the ramp forecasting accuracy gained by improving the performance of short-term wind power forecasting. This study focuses on the WFIP southern study region, which encompasses most ofmore » the Electric Reliability Council of Texas (ERCOT) territory, to compare the experimental WFIP forecasts to the existing short-term wind power forecasts (used at ERCOT) at multiple spatial and temporal scales. The study employs four significant wind power ramping definitions according to the power change magnitude, direction, and duration. The optimized swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental WFIP forecasts improve the accuracy of the wind power ramp forecasting. This improvement can result in substantial costs savings and power system reliability enhancements.« less

Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yannan; Hou, Zhangshuan; Meng, Da

2016-07-17

In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

Current status of solid waste management in small island developing states: A review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mohee, Romeela; Mauthoor, Sumayya, E-mail: sumayya.mauthoor@umail.uom.ac.mu; Bundhoo, Zumar M.A.

Highlights: • Waste management is a matter of great concern for small island developing states. • On average, waste generation rate in these islands amounts to 1.29 kg/capita/day. • Illegal dumping and landfilling prevail in most small island developing states. • Sustainable waste management practices, previously absent, are now emerging. • However, many challenges still hinder the implementation of these practices. - Abstract: This article reviews the current status of waste management in Small Island Developing States (SIDS) and the challenges that are faced in solid waste management. The waste generation rates of SIDS were compared within the three geographicmore » regions namely Caribbean SIDS, Pacific SIDS and Atlantic, Indian Ocean, Mediterranean and South China (AIMS) SIDS and with countries of the Organisation for Economic Co-Operation and Development (OECD). Only Pacific SIDS had a waste generation rate less than 1 kg/capita/day. The waste generation rates for the three SIDS regions averaged 1.29 kg/capita/day while that for OECD countries was at a mean value of 1.35 kg/capita/day. The waste compositions in the different SIDS regions were almost similar owing to comparable consumption patterns while these differed to a large extent with wastes generated in OECD countries. In SIDS, the major fraction of MSW comprised of organics (44%) followed by recyclables namely paper, plastics, glass and metals (total: 43%). In contrast, MSW in OECD countries consisted mainly of recyclables (43%) followed by organics (37%). This article also reviewed the other functional elements of the waste management systems in SIDS. Several shortcomings were noted in the process of waste collection, transfer and transport namely the fact of having outdated collection vehicles and narrow roads which are inaccessible. Among the waste management practices in SIDS, waste disposal via landfilling, illegal dumping and backyard burning were favoured most of the time at the expense of sustainable waste treatment technologies such as composting, anaerobic digestion and recycling.« less

SITE GENERATED RADIOLOGICAL WASTE HANDLING SYSTEM DESCRIPTION DOCUMENT

DOE Office of Scientific and Technical Information (OSTI.GOV)

S. C. Khamankar

2000-06-20

The Site Generated Radiological Waste Handling System handles radioactive waste products that are generated at the geologic repository operations area. The waste is collected, treated if required, packaged for shipment, and shipped to a disposal site. Waste streams include low-level waste (LLW) in solid and liquid forms, as-well-as mixed waste that contains hazardous and radioactive constituents. Liquid LLW is segregated into two streams, non-recyclable and recyclable. The non-recyclable stream may contain detergents or other non-hazardous cleaning agents and is packaged for shipment. The recyclable stream is treated to recycle a large portion of the water while the remaining concentrated wastemore » is packaged for shipment; this greatly reduces the volume of waste requiring disposal. There will be no liquid LLW discharge. Solid LLW consists of wet solids such as ion exchange resins and filter cartridges, as-well-as dry active waste such as tools, protective clothing, and poly bags. Solids will be sorted, volume reduced, and packaged for shipment. The generation of mixed waste at the Monitored Geologic Repository (MGR) is not planned; however, if it does come into existence, it will be collected and packaged for disposal at its point of occurrence, temporarily staged, then shipped to government-approved off-site facilities for disposal. The Site Generated Radiological Waste Handling System has equipment located in both the Waste Treatment Building (WTB) and in the Waste Handling Building (WHB). All types of liquid and solid LLW are processed in the WTB, while wet solid waste from the Pool Water Treatment and Cooling System is packaged where received in the WHB. There is no installed hardware for mixed waste. The Site Generated Radiological Waste Handling System receives waste from locations where water is used for decontamination functions. In most cases the water is piped back to the WTB for processing. The WTB and WHB provide staging areas for storing and shipping LLW packages as well as any mixed waste packages. The buildings house the system and provide shielding and support for the components. The system is ventilated by and connects to the ventilation systems in the buildings to prevent buildup and confine airborne radioactivity via the high efficiency particulate air filters. The Monitored Geologic Repository Operations Monitoring and Control System will provide monitoring and supervisory control facilities for the system.« less

ELECTRICITY GENERATION FROM LANDFILL GAS IN TURKEY.

PubMed

Salihoglu, Nezih Kamil

2018-05-08

Landfill gas (LFG)-to-energy plants in Turkey were investigated, and the LFG-to-energy plant of a metropolitan municipal landfill was monitored for 3 years. Installed capacities and actual gas engine working hours were determined. An equation was developed to estimate the power capacity for LFG-to-energy plants for a given amount of landfilled waste. Monitoring the actual gas generation rates enabled determination of LFG generation factors for Turkish municipal waste. A significant relationship (R = 0.524, p < 0.01, 2-tailed) was found between the amounts of landfilled waste and the ambient temperature, which can be attributed to food consumption and kitchen waste generation behaviors influenced by the ambient temperature. However, no significant correlation was found between the ambient temperature and the generated LFG. A temperature buffering capacity was inferred to exist within the landfill, which enables the anaerobic reactions to continue functioning even during cold seasons. The average LFG and energy generation rates were 45 m 3 LFG/ton waste landfilled and 0.08 MWh/ton waste landfilled, respectively. The mean specific LFG consumption for electricity generation was 529 ± 28 m 3 /MWh.

Waste minimization/pollution prevention study of high-priority waste streams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ogle, R.B.

1994-03-01

Although waste minimization has been practiced by the Metals and Ceramics (M&C) Division in the past, the effort has not been uniform or formalized. To establish the groundwork for continuous improvement, the Division Director initiated a more formalized waste minimization and pollution prevention program. Formalization of the division`s pollution prevention efforts in fiscal year (FY) 1993 was initiated by a more concerted effort to determine the status of waste generation from division activities. The goal for this effort was to reduce or minimize the wastes identified as having the greatest impact on human health, the environment, and costs. Two broadmore » categories of division wastes were identified as solid/liquid wastes and those relating to energy use (primarily electricity and steam). This report presents information on the nonradioactive solid and liquid wastes generated by division activities. More specifically, the information presented was generated by teams of M&C staff members empowered by the Division Director to study specific waste streams.« less

Low-level radioactive waste management: transitioning to off-site disposal at Los Alamos National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dorries, Alison M

2010-11-09

Facing the closure of nearly all on-site management and disposal capability for low-level radioactive waste (LLW), Los Alamos National Laboratory (LANL) is making ready to ship the majority of LLW off-site. In order to ship off-site, waste must meet the Treatment, Storage, and Disposal Facility's (TSDF) Waste Acceptance Criteria (WAC). In preparation, LANL's waste management organization must ensure LANL waste generators characterize and package waste compliantly and waste characterization documentation is complete and accurate. Key challenges that must be addressed to successfully make the shift to off-site disposal of LLW include improving the detail, accuracy, and quality of process knowledgemore » (PK) and acceptable knowledge (AK) documentation, training waste generators and waste management staff on the higher standard of data quality and expectations, improved WAC compliance for off-site facilities, and enhanced quality assurance throughout the process. Certification of LANL generators will allow direct off-site shipping of LLW from their facilities.« less

Evaluation and application of site-specific data to revise the first-order decay model for estimating landfill gas generation and emissions at Danish landfills.

PubMed

Mou, Zishen; Scheutz, Charlotte; Kjeldsen, Peter

2015-06-01

Methane (CH₄) generated from low-organic waste degradation at four Danish landfills was estimated by three first-order decay (FOD) landfill gas (LFG) generation models (LandGEM, IPCC, and Afvalzorg). Actual waste data from Danish landfills were applied to fit model (IPCC and Afvalzorg) required categories. In general, the single-phase model, LandGEM, significantly overestimated CH₄generation, because it applied too high default values for key parameters to handle low-organic waste scenarios. The key parameters were biochemical CH₄potential (BMP) and CH₄generation rate constant (k-value). In comparison to the IPCC model, the Afvalzorg model was more suitable for estimating CH₄generation at Danish landfills, because it defined more proper waste categories rather than traditional municipal solid waste (MSW) fractions. Moreover, the Afvalzorg model could better show the influence of not only the total disposed waste amount, but also various waste categories. By using laboratory-determined BMPs and k-values for shredder, sludge, mixed bulky waste, and street-cleaning waste, the Afvalzorg model was revised. The revised model estimated smaller cumulative CH₄generation results at the four Danish landfills (from the start of disposal until 2020 and until 2100). Through a CH₄mass balance approach, fugitive CH₄emissions from whole sites and a specific cell for shredder waste were aggregated based on the revised Afvalzorg model outcomes. Aggregated results were in good agreement with field measurements, indicating that the revised Afvalzorg model could provide practical and accurate estimation for Danish LFG emissions. This study is valuable for both researchers and engineers aiming to predict, control, and mitigate fugitive CH₄emissions from landfills receiving low-organic waste. Landfill operators use the first-order decay (FOD) models to estimate methane (CH₄) generation. A single-phase model (LandGEM) and a traditional model (IPCC) could result in overestimation when handling a low-organic waste scenario. Site-specific data were important and capable of calibrating key parameter values in FOD models. The comparison study of the revised Afvalzorg model outcomes and field measurements at four Danish landfills provided a guideline for revising the Pollutants Release and Transfer Registers (PRTR) model, as well as indicating noteworthy waste fractions that could emit CH₄at modern landfills.

A global flash flood forecasting system

NASA Astrophysics Data System (ADS)

Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

2016-04-01

The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial resolution appropriate to the NWP system. We then demonstrate how these warning areas could eventually complement existing global systems such as the Global Flood Awareness System (GloFAS), to give warnings of flash floods. This work demonstrates the possibility of creating a global flash flood forecasting system based on forecasts from existing global NWP systems. Future developments, in post-processing for example, will need to address an under-prediction bias, for extreme point rainfall, that is innate to current-generation global models.

Quantifying household waste of fresh fruit and vegetables in the EU.

PubMed

De Laurentiis, Valeria; Corrado, Sara; Sala, Serenella

2018-04-11

According to national studies conducted in EU countries, fresh fruit and vegetables contribute to almost 50% of the food waste generated by households. This study presents an estimation of this waste flow, differentiating between unavoidable and avoidable waste. The calculation of these two flows serves different purposes. The first (21.1 kg per person per year) provides a measure of the amount of household waste intrinsically linked to the consumption of fresh fruit and vegetables, and which would still be generated even in a zero-avoidable waste future scenario. The second (14.2 kg per person per year) is a quantity that could be reduced/minimised by applying targeted prevention strategies. The unavoidable waste was assessed at product level, by considering the inedible fraction and the purchased amounts of the fifty-one most consumed fruits and vegetables in Europe. The avoidable waste was estimated at commodity group level, based on the results of national studies conducted in six EU member states. Significant differences in the amounts of avoidable and unavoidable waste generated were found across countries, due to different levels of wasteful behaviours (linked to cultural and economic factors) and different consumption patterns (influencing the amount of unavoidable waste generated). The results of this study have implications for policies both on the prevention and the management of household food waste. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

An empirical investigation of construction and demolition waste generation rates in Shenzhen city, South China.

PubMed

Lu, Weisheng; Yuan, Hongping; Li, Jingru; Hao, Jane J L; Mi, Xuming; Ding, Zhikun

2011-04-01

The construction and demolition waste generation rates (C&D WGRs) is an important factor in decision-making and management of material waste in any construction site. The present study investigated WGRs by conducting on-site waste sorting and weighing in four ongoing construction projects in Shenzhen city of South China. The results revealed that WGRs ranged from 3.275 to 8.791 kg/m(2) and miscellaneous waste, timber for formwork and falsework, and concrete were the three largest components amongst the generated waste. Based on the WGRs derived from the research, the paper also discussed the main causes of waste in the construction industry and attempted to connect waste generation with specific construction practices. It was recommended that measures mainly including performing waste sorting at source, employing skilful workers, uploading and storing materials properly, promoting waste management capacity, replacing current timber formwork with metal formwork and launching an incentive reward program to encourage waste reduction could be potential solutions to reducing current WGRs in Shenzhen. Although these results were derived from a relatively small sample and so cannot justifiably be generalized, they do however add to the body of knowledge that is currently available for understanding the status of the art of C&D waste management in China. Copyright © 2010 Elsevier Ltd. All rights reserved.

Valuing hydrological forecasts for a pumped storage assisted hydro facility

NASA Astrophysics Data System (ADS)

Zhao, Guangzhi; Davison, Matt

2009-07-01

SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

Dynamics of analyst forecasts and emergence of complexity: Role of information disparity

PubMed Central

Ahn, Kwangwon

2017-01-01

We report complex phenomena arising among financial analysts, who gather information and generate investment advice, and elucidate them with the help of a theoretical model. Understanding how analysts form their forecasts is important in better understanding the financial market. Carrying out big-data analysis of the analyst forecast data from I/B/E/S for nearly thirty years, we find skew distributions as evidence for emergence of complexity, and show how information asymmetry or disparity affects financial analysts’ forming their forecasts. Here regulations, information dissemination throughout a fiscal year, and interactions among financial analysts are regarded as the proxy for a lower level of information disparity. It is found that financial analysts with better access to information display contrasting behaviors: a few analysts become bolder and issue forecasts independent of other forecasts while the majority of analysts issue more accurate forecasts and flock to each other. Main body of our sample of optimistic forecasts fits a log-normal distribution, with the tail displaying a power law. Based on the Yule process, we propose a model for the dynamics of issuing forecasts, incorporating interactions between analysts. Explaining nicely empirical data on analyst forecasts, this provides an appealing instance of understanding social phenomena in the perspective of complex systems. PMID:28498831

New Measurements and Modeling Capability to Improve Real-time Forecast of Cascadia Tsunamis along U.S. West Coast

NASA Astrophysics Data System (ADS)

Wei, Y.; Titov, V. V.; Bernard, E. N.; Spillane, M. C.

2014-12-01

The tragedies of 2004 Sumatra and 2011 Tohoku tsunamis exposed the limits of our knowledge in preparing for devastating tsunamis, especially in the near field. The 1,100-km coastline of the Pacific coast of North America has tectonic and geological settings similar to Sumatra and Japan. The geological records unambiguously show that the Cascadia fault had caused devastating tsunamis in the past and this geological process will cause tsunamis in the future. Existing observational instruments along the Cascadia Subduction Zone are capable of providing tsunami data within minutes of tsunami generation. However, this strategy requires separation of the tsunami signals from the overwhelming high-frequency seismic waves produced during a strong earthquake- a real technical challenge for existing operational tsunami observational network. A new-generation of nano-resolution pressure sensors can provide high temporal resolution of the earthquake and tsunami signals without loosing precision. The nano-resolution pressure sensor offers a state-of the-science ability to separate earthquake vibrations and other oceanic noise from tsunami waveforms, paving the way for accurate, early warnings of local tsunamis. This breakthrough underwater technology has been tested and verified for a couple of micro-tsunami events (Paros et al., 2011). Real-time forecast of Cascadia tsunamis is becoming a possibility with the development of nano-tsunameter technology. The present study provides an investigation on optimizing the placement of these new sensors so that the forecast time can be shortened.. The presentation will cover the optimization of an observational array to quickly detect and forecast a tsunami generated by a strong Cascadia earthquake, including short and long rupture scenarios. Lessons learned from the 2011 Tohoku tsunami will be examined to demonstrate how we can improve the local forecast using the new technology We expect this study to provide useful guideline for future siting and deployment of the new-generation tsunameters. Driven by the new technology, we demonstrate scenarios of real-time forecast of Cascadia tsunami impact along the Pacific Northwest, as well as in the Puget Sound.

Correlates of domestic waste management and related health outcomes in Sunyani, Ghana: a protocol towards enhancing policy.

PubMed

Addo, Henry O; Dun-Dery, Elvis J; Afoakwa, Eugenia; Elizabeth, Addai; Ellen, Amposah; Rebecca, Mwinfaug

2017-07-03

Domestic waste generation has contributed significantly to hampering national waste management efforts. It poses serious threat to national development and requires proper treatment and management within and outside households. The problem of improper waste management has always been a challenge in Ghana, compelling several national surveys to report on the practice of waste management. However, little is known about how much waste is generated and managed within households and there is a serious dearth of information for national policy and planning. This paper seeks to document the handling and practice of waste management, including collection, storage, transportation and disposal along with the types and amount of waste generated by Households and their related health outcome. The study was a descriptive cross-sectional study and used a multi-stage sampling technique to sample 700 households. The study was planned and implemented from January to May 2015. It involved the use of structured questionnaires in the data collection over the period. Factors such as demographic characteristics, amount of waste generated, types of waste bins used within households, waste recycling, cost of disposing waste, and distance to dumpsite were all assessed. The paper shows that each surveyed household generated 0.002 t of waste per day, of which 29% are both organic and inorganic. Though more than half of the respondents (53.6%) had positive attitude towards waste management, only 29.1% practiced waste management. The study reveals that there is no proper management of domestic waste except in few households that segregate waste. The study identified several elements as determinants of waste management practice. Female respondents were less likely to practice waste management (AOR 0.45; 95% Cl 0.29, 0.79), household size also determined respondents practice (AOR 0.26; Cl 0.09, 0.77). Practice of recycling (AOR 0.03; Cl 0.02, 0.08), distance to dumpsite (AOR 0.45; Cl 0.20, 0.99), were all significant predictors of waste management practice. Cholera which is a hygiene related disease was three times more likely to determine households' waste management practice (AOR 3.22; Cl 1.33, 7.84). Considering the low waste management practice among households, there is the need for improved policy and enhanced education on proper waste management practice among households.

A model for estimation of potential generation of waste electrical and electronic equipment in Brazil

DOE Office of Scientific and Technical Information (OSTI.GOV)

Araujo, Marcelo Guimaraes, E-mail: marcel_g@uol.com.br; Magrini, Alessandra; Mahler, Claudio Fernando

2012-02-15

Highlights: Black-Right-Pointing-Pointer Literature of WEEE generation in developing countries is reviewed. Black-Right-Pointing-Pointer We analyse existing estimates of WEEE generation for Brazil. Black-Right-Pointing-Pointer We present a model for WEEE generation estimate. Black-Right-Pointing-Pointer WEEE generation of 3.77 kg/capita year for 2008 is estimated. Black-Right-Pointing-Pointer Use of constant lifetime should be avoided for non-mature market products. - Abstract: Sales of electrical and electronic equipment are increasing dramatically in developing countries. Usually, there are no reliable data about quantities of the waste generated. A new law for solid waste management was enacted in Brazil in 2010, and the infrastructure to treat this waste mustmore » be planned, considering the volumes of the different types of electrical and electronic equipment generated. This paper reviews the literature regarding estimation of waste electrical and electronic equipment (WEEE), focusing on developing countries, particularly in Latin America. It briefly describes the current WEEE system in Brazil and presents an updated estimate of generation of WEEE. Considering the limited available data in Brazil, a model for WEEE generation estimation is proposed in which different methods are used for mature and non-mature market products. The results showed that the most important variable is the equipment lifetime, which requires a thorough understanding of consumer behavior to estimate. Since Brazil is a rapidly expanding market, the 'boom' in waste generation is still to come. In the near future, better data will provide more reliable estimation of waste generation and a clearer interpretation of the lifetime variable throughout the years.« less

NEVADA TEST SITE WASTE ACCEPTANCE CRITERIA

DOE Office of Scientific and Technical Information (OSTI.GOV)

U.S. DEPARTMENT OF ENERGY, NATIONAL NUCLEAR SECURITY ADMINISTRATION, NEVADA SITE OFFICE

This document establishes the U. S. Department of Energy, National Nuclear Security Administration Nevada Site Office (NNSA/NSO) waste acceptance criteria (WAC). The WAC provides the requirements, terms, and conditions under which the Nevada Test Site will accept low-level radioactive and mixed waste for disposal. Mixed waste generated within the State of Nevada by NNSA/NSO activities is accepted for disposal. It includes requirements for the generator waste certification program, characterization, traceability, waste form, packaging, and transfer. The criteria apply to radioactive waste received at the Nevada Test Site Area 3 and Area 5 Radioactive Waste Management Site for storage or disposal.

Evaluating the biochemical methane potential (BMP) of low-organic waste at Danish landfills.

PubMed

Mou, Zishen; Scheutz, Charlotte; Kjeldsen, Peter

2014-11-01

The biochemical methane potential (BMP) is an essential parameter when using first order decay (FOD) landfill gas (LFG) generation models to estimate methane (CH4) generation from landfills. Different categories of waste (mixed, shredder and sludge waste) with a low-organic content and temporarily stored combustible waste were sampled from four Danish landfills. The waste was characterized in terms of physical characteristics (TS, VS, TC and TOC) and the BMP was analyzed in batch tests. The experiment was set up in triplicate, including blank and control tests. Waste samples were incubated at 55°C for more than 60 days, with continuous monitoring of the cumulative CH4 generation. Results showed that samples of mixed waste and shredder waste had similar BMP results, which was in the range of 5.4-9.1 kg CH4/ton waste (wet weight) on average. As a calculated consequence, their degradable organic carbon content (DOCC) was in the range of 0.44-0.70% of total weight (wet waste). Numeric values of both parameters were much lower than values of traditional municipal solid waste (MSW), as well as default numeric values in current FOD models. The sludge waste and temporarily stored combustible waste showed BMP values of 51.8-69.6 and 106.6-117.3 kg CH4/ton waste on average, respectively, and DOCC values of 3.84-5.12% and 7.96-8.74% of total weight. The same category of waste from different Danish landfills did not show significant variation. This research studied the BMP of Danish low-organic waste for the first time, which is important and valuable for using current FOD LFG generation models to estimate realistic CH4 emissions from modern landfills receiving low-organic waste. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantifying and analysing food waste generated by Indonesian undergraduate students

NASA Astrophysics Data System (ADS)

Mandasari, P.

2018-03-01

Despite the fact that environmental consequences derived from food waste have been widely known, studies on the amount of food waste and its influencing factors have relatively been paid little attention. Addressing this shortage, this paper aimed to quantify monthly avoidable food waste generated by Indonesian undergraduate students and analyse factors influencing the occurrence of avoidable food waste. Based on data from 106 undergraduate students, descriptive statistics and logistic regression were applied in this study. The results indicated that 4,987.5 g of food waste was generated in a month (equal to 59,850 g yearly); or 47.05 g per person monthly (equal to 564.62 g per person per a year). Meanwhile, eating out frequency and gender were found to be significant predictors of food waste occurrence.

40 CFR 436.31 - Specialized definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... deposits. (e) The term “process generated waste water” shall mean any waste water used in the slurry... rainfall and ground water seepage. However, if a mine is also used for treatment of process generated waste... waste water. (c) The term “10-year 24-hour precipitation event” shall mean the maximum 24 hour...

Towards an Australian ensemble streamflow forecasting system for flood prediction and water management

NASA Astrophysics Data System (ADS)

Bennett, J.; David, R. E.; Wang, Q.; Li, M.; Shrestha, D. L.

2016-12-01

Flood forecasting in Australia has historically relied on deterministic forecasting models run only when floods are imminent, with considerable forecaster input and interpretation. These now co-existed with a continually available 7-day streamflow forecasting service (also deterministic) aimed at operational water management applications such as environmental flow releases. The 7-day service is not optimised for flood prediction. We describe progress on developing a system for ensemble streamflow forecasting that is suitable for both flood prediction and water management applications. Precipitation uncertainty is handled through post-processing of Numerical Weather Prediction (NWP) output with a Bayesian rainfall post-processor (RPP). The RPP corrects biases, downscales NWP output, and produces reliable ensemble spread. Ensemble precipitation forecasts are used to force a semi-distributed conceptual rainfall-runoff model. Uncertainty in precipitation forecasts is insufficient to reliably describe streamflow forecast uncertainty, particularly at shorter lead-times. We characterise hydrological prediction uncertainty separately with a 4-stage error model. The error model relies on data transformation to ensure residuals are homoscedastic and symmetrically distributed. To ensure streamflow forecasts are accurate and reliable, the residuals are modelled using a mixture-Gaussian distribution with distinct parameters for the rising and falling limbs of the forecast hydrograph. In a case study of the Murray River in south-eastern Australia, we show ensemble predictions of floods generally have lower errors than deterministic forecasting methods. We also discuss some of the challenges in operationalising short-term ensemble streamflow forecasts in Australia, including meeting the needs for accurate predictions across all flow ranges and comparing forecasts generated by event and continuous hydrological models.

Added value of dynamical downscaling of winter seasonal forecasts over North America

NASA Astrophysics Data System (ADS)

Tefera Diro, Gulilat; Sushama, Laxmi

2017-04-01

Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.

Complications Associated with Long-Term Disposition of Newly-Generated Transuranic Waste: A National Laboratory Perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

B.J. Orchard; L.A. Harvego; T.L. Carlson

The Idaho National Laboratory (INL) is a multipurpose national laboratory delivering specialized science and engineering solutions for the U.S. Department of Energy (DOE). Sponsorship of INL was formally transferred to the DOE Office of Nuclear Energy, Science and Technology (NE) by Secretary Spencer Abraham in July 2002. The move to NE, and designation as the DOE lead nuclear energy laboratory for reactor technology, supports the nation’s expanding nuclear energy initiatives, placing INL at the center of work to develop advanced Generation IV nuclear energy systems; nuclear energy/hydrogen coproduction technology; advanced nuclear energy fuel cycle technologies; and providing national security answersmore » to national infrastructure needs. As a result of the Laboratory’s NE mission, INL generates both contact-handled and remote-handled transuranic (TRU) waste from ongoing operations. Generation rates are relatively small and fluctuate based on specific programs and project activities being conducted; however, the Laboratory will continue to generate TRU waste well into the future in association with the NE mission. Currently, plans and capabilities are being established to transfer INL’s contact-handled TRU waste to the Advanced Mixed Waste Treatment Plant (AMWTP) for certification and disposal to the Waste Isolation Pilot Plant (WIPP). Remote-handled TRU waste is currently placed in storage at the Materials and Fuels Complex (MFC). In an effort to minimize future liabilities associated with the INL NE mission, INL is evaluating and assessing options for the management and disposition of all its TRU waste on a real-time basis at time of generation. This paper summarizes near-term activities to minimize future re handling of INL’s TRU waste, as well as, potential complications associated with the long-term disposition of newly-generated TRU waste. Potential complications impacting the disposition of INL newly-generated TRU waste include, but are not limited to: 1) required remote-handled TRU packaging configuration(s) vs. current facility capabilities, 2) long-term NE mission activities, 3) WIPP certification requirements, and 4) budget considerations.« less

Waste minimization for commercial radioactive materials users generating low-level radioactive waste. Revision 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fischer, D.K.; Gitt, M.; Williams, G.A.

1991-07-01

The objective of this document is to provide a resource for all states and compact regions interested in promoting the minimization of low-level radioactive waste (LLW). This project was initiated by the Commonwealth of Massachusetts, and Massachusetts waste streams have been used as examples; however, the methods of analysis presented here are applicable to similar waste streams generated elsewhere. This document is a guide for states/compact regions to use in developing a system to evaluate and prioritize various waste minimization techniques in order to encourage individual radioactive materials users (LLW generators) to consider these techniques in their own independent evaluations.more » This review discusses the application of specific waste minimization techniques to waste streams characteristic of three categories of radioactive materials users: (1) industrial operations using radioactive materials in the manufacture of commercial products, (2) health care institutions, including hospitals and clinics, and (3) educational and research institutions. Massachusetts waste stream characterization data from key radioactive materials users in each category are used to illustrate the applicability of various minimization techniques. The utility group is not included because extensive information specific to this category of LLW generators is available in the literature.« less

Waste minimization for commercial radioactive materials users generating low-level radioactive waste

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fischer, D.K.; Gitt, M.; Williams, G.A.

1991-07-01

The objective of this document is to provide a resource for all states and compact regions interested in promoting the minimization of low-level radioactive waste (LLW). This project was initiated by the Commonwealth of Massachusetts, and Massachusetts waste streams have been used as examples; however, the methods of analysis presented here are applicable to similar waste streams generated elsewhere. This document is a guide for states/compact regions to use in developing a system to evaluate and prioritize various waste minimization techniques in order to encourage individual radioactive materials users (LLW generators) to consider these techniques in their own independent evaluations.more » This review discusses the application of specific waste minimization techniques to waste streams characteristic of three categories of radioactive materials users: (1) industrial operations using radioactive materials in the manufacture of commercial products, (2) health care institutions, including hospitals and clinics, and (3) educational and research institutions. Massachusetts waste stream characterization data from key radioactive materials users in each category are used to illustrate the applicability of various minimization techniques. The utility group is not included because extensive information specific to this category of LLW generators is available in the literature.« less

Quantification of construction waste prevented by BIM-based design validation: Case studies in South Korea.

PubMed

Won, Jongsung; Cheng, Jack C P; Lee, Ghang

2016-03-01

Waste generated in construction and demolition processes comprised around 50% of the solid waste in South Korea in 2013. Many cases show that design validation based on building information modeling (BIM) is an effective means to reduce the amount of construction waste since construction waste is mainly generated due to improper design and unexpected changes in the design and construction phases. However, the amount of construction waste that could be avoided by adopting BIM-based design validation has been unknown. This paper aims to estimate the amount of construction waste prevented by a BIM-based design validation process based on the amount of construction waste that might be generated due to design errors. Two project cases in South Korea were studied in this paper, with 381 and 136 design errors detected, respectively during the BIM-based design validation. Each design error was categorized according to its cause and the likelihood of detection before construction. The case studies show that BIM-based design validation could prevent 4.3-15.2% of construction waste that might have been generated without using BIM. Copyright © 2015 Elsevier Ltd. All rights reserved.

Type- and Subtype-Specific Influenza Forecast.

PubMed

Kandula, Sasikiran; Yang, Wan; Shaman, Jeffrey

2017-03-01

Prediction of the growth and decline of infectious disease incidence has advanced considerably in recent years. As these forecasts improve, their public health utility should increase, particularly as interventions are developed that make explicit use of forecast information. It is the task of the research community to increase the content and improve the accuracy of these infectious disease predictions. Presently, operational real-time forecasts of total influenza incidence are produced at the municipal and state level in the United States. These forecasts are generated using ensemble simulations depicting local influenza transmission dynamics, which have been optimized prior to forecast with observations of influenza incidence and data assimilation methods. Here, we explore whether forecasts targeted to predict influenza by type and subtype during 2003-2015 in the United States were more or less accurate than forecasts targeted to predict total influenza incidence. We found that forecasts separated by type/subtype generally produced more accurate predictions and, when summed, produced more accurate predictions of total influenza incidence. These findings indicate that monitoring influenza by type and subtype not only provides more detailed observational content but supports more accurate forecasting. More accurate forecasting can help officials better respond to and plan for current and future influenza activity. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

NASA Astrophysics Data System (ADS)

O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

2015-04-01

Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

Estimation of construction waste generation and management in Thailand.

PubMed

Kofoworola, Oyeshola Femi; Gheewala, Shabbir H

2009-02-01

This study examines construction waste generation and management in Thailand. It is estimated that between 2002 and 2005, an average of 1.1 million tons of construction waste was generated per year in Thailand. This constitutes about 7.7% of the total amount of waste disposed in both landfills and open dumpsites annually during the same period. Although construction waste constitutes a major source of waste in terms of volume and weight, its management and recycling are yet to be effectively practiced in Thailand. Recently, the management of construction waste is being given attention due to its rapidly increasing unregulated dumping in undesignated areas, and recycling is being promoted as a method of managing this waste. If effectively implemented, its potential economic and social benefits are immense. It was estimated that between 70 and 4,000 jobs would have been created between 2002 and 2005, if all construction wastes in Thailand had been recycled. Additionally it would have contributed an average savings of about 3.0 x 10(5) GJ per year in the final energy consumed by the construction sector of the nation within the same period based on the recycling scenario analyzed. The current national integrated waste management plan could enhance the effective recycling of construction and demolition waste in Thailand when enforced. It is recommended that an inventory of all construction waste generated in the country be carried out in order to assess the feasibility of large scale recycling of construction and demolition waste.

Comparing the greenhouse gas emissions from three alternative waste combustion concepts.

PubMed

Vainikka, Pasi; Tsupari, Eemeli; Sipilä, Kai; Hupa, Mikko

2012-03-01

Three alternative condensing mode power and combined heat and power (CHP) waste-to-energy concepts were compared in terms of their impacts on the greenhouse gas (GHG) emissions from a heat and power generation system. The concepts included (i) grate, (ii) bubbling fluidised bed (BFB) and (iii) circulating fluidised bed (CFB) combustion of waste. The BFB and CFB take advantage of advanced combustion technology which enabled them to reach electric efficiency up to 35% and 41% in condensing mode, respectively, whereas 28% (based on the lower heating value) was applied for the grate fired unit. A simple energy system model was applied in calculating the GHG emissions in different scenarios where coal or natural gas was substituted in power generation and mix of fuel oil and natural gas in heat generation by waste combustion. Landfilling and waste transportation were not considered in the model. GHG emissions were reduced significantly in all of the considered scenarios where the waste combustion concepts substituted coal based power generation. With the exception of condensing mode grate incinerator the different waste combustion scenarios resulted approximately in 1 Mton of fossil CO(2)-eq. emission reduction per 1 Mton of municipal solid waste (MSW) incinerated. When natural gas based power generation was substituted by electricity from the waste combustion significant GHG emission reductions were not achieved. Copyright © 2011 Elsevier Ltd. All rights reserved.

Radioactive waste storage issues

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kunz, Daniel E.

1994-08-15

In the United States we generate greater than 500 million tons of toxic waste per year which pose a threat to human health and the environment. Some of the most toxic of these wastes are those that are radioactively contaminated. This thesis explores the need for permanent disposal facilities to isolate radioactive waste materials that are being stored temporarily, and therefore potentially unsafely, at generating facilities. Because of current controversies involving the interstate transfer of toxic waste, more states are restricting the flow of wastes into - their borders with the resultant outcome of requiring the management (storage and disposal)more » of wastes generated solely within a state`s boundary to remain there. The purpose of this project is to study nuclear waste storage issues and public perceptions of this important matter. Temporary storage at generating facilities is a cause for safety concerns and underscores, the need for the opening of permanent disposal sites. Political controversies and public concern are forcing states to look within their own borders to find solutions to this difficult problem. Permanent disposal or retrievable storage for radioactive waste may become a necessity in the near future in Colorado. Suitable areas that could support - a nuclear storage/disposal site need to be explored to make certain the health, safety and environment of our citizens now, and that of future generations, will be protected.« less

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

NASA Astrophysics Data System (ADS)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Environmental factor(tm) system: RCRA hazardous waste handler information (on CD-ROM). Data file

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1995-11-01

Environmental Factor(trademark) RCRA Hazardous Waste Handler Information on CD-ROM unleashes the invaluable information found in two key EPA data sources on hazardous waste handlers and offers cradle-to-grave waste tracking. It`s easy to search and display: (1) Permit status, design capacity, and compliance history for facilities found in the EPA Research Conservation and Recovery Information System (RCRIS) program tracking database; (2) Detailed information on hazardous wastes generation, management, and minimization by companies who are large quantity generators; and (3) Data on the waste management practices of treatment, storage, and disposal (TSD) facilities from the EPA Biennial Reporting System which is collectedmore » every other year. Environmental Factor`s powerful database retrieval system lets you: (1) Search for RCRA facilities by permit type, SIC code, waste codes, corrective action, or violation information, TSD status, generator and transporter status, and more. (2) View compliance information - dates of evaluation, violation, enforcement, and corrective action. (3) Lookup facilities by waste processing categories of marketing, transporting, processing, and energy recovery. (4) Use owner/operator information and names, titles, and telephone numbers of project managers for prospecting. (5) Browse detailed data on TSD facility and large quantity generators` activities such as onsite waste treatment, disposal, or recycling, offsite waste received, and waste generation and management. The product contains databases, search and retrieval software on two CD-ROMs, an installation diskette and User`s Guide. Environmental Factor has online context-sensitive help from any screen and a printed User`s Guide describing installation and step-by-step procedures for searching, retrieving, and exporting.« less

A comparative study on per capita waste generation according to a waste collecting system in Korea.

PubMed

Oh, Jung Hwan; Lee, Eui-Jong; Oh, Jeong Ik; Kim, Jong-Oh; Jang, Am

2016-04-01

As cities are becoming increasingly aware of problems related to conventional mobile collection systems, automated pipeline-based vacuum collection (AVAC) systems have been introduced in some densely populated urban areas. The reasons are that in addition to cost savings, AVAC systems can be efficient, hygienic, and environmentally friendly. Despite difficulties in making direct comparisons of municipal waste between a conventional mobile collection system and an AVAC system, it is meaningful to measure the quantities in each of these collection methods either in total or on a per capita generation of waste (PCGW, g/(day*capita)) basis. Thus, the aim of this study was to assess the difference in per capita generation of household waste according to the different waste collection methods in Korea. Observations on household waste show that there were considerable differences according to waste collection methods. The value of per capita generation of food waste (PCGF) indicates that a person in a city using AVAC produces 60 % of PCGF (109.58 g/(day*capita)), on average, compared with that of a truck system (173.10 g/(day*capita)) as well as 23 %p less moisture component than that with trucks. The value of per capita generation of general waste (PCGG) in a city with an AVAC system showed 147.73 g/(day*capita), which is 20 % less than that with trucks delivered (185 g/(day*capita)). However, general waste sampled from AVAC showed a 35 %p increased moisture content versus truck delivery.

Linking seasonal climate forecasts with crop models in Iberian Peninsula

NASA Astrophysics Data System (ADS)

Capa, Mirian; Ines, Amor; Baethgen, Walter; Rodriguez-Fonseca, Belen; Han, Eunjin; Ruiz-Ramos, Margarita

2015-04-01

Translating seasonal climate forecasts into agricultural production forecasts could help to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. In this study, we use seasonal rainfall forecasts and crop models to improve predictability of wheat yield in the Iberian Peninsula (IP). Additionally, we estimate economic margins and production risks associated with extreme scenarios of seasonal rainfall forecast. This study evaluates two methods for disaggregating seasonal climate forecasts into daily weather data: 1) a stochastic weather generator (CondWG), and 2) a forecast tercile resampler (FResampler). Both methods were used to generate 100 (with FResampler) and 110 (with CondWG) weather series/sequences for three scenarios of seasonal rainfall forecasts. Simulated wheat yield is computed with the crop model CERES-wheat (Ritchie and Otter, 1985), which is included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at two locations in northeastern Spain where the crop model was calibrated and validated with independent field data. Once simulated yields were obtained, an assessment of farmer's gross margin for different seasonal climate forecasts was accomplished to estimate production risks under different climate scenarios. This methodology allows farmers to assess the benefits and risks of a seasonal weather forecast in IP prior to the crop growing season. The results of this study may have important implications on both, public (agricultural planning) and private (decision support to farmers, insurance companies) sectors. Acknowledgements Research by M. Capa-Morocho has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM) and MULCLIVAR project (CGL2012-38923-C02-02) References Hoogenboom, G. et al., 2010. The Decision Support System for Agrotechnology Transfer (DSSAT).Version 4.5 [CD-ROM].University of Hawaii, Honolulu, Hawaii. Ritchie, J.T., Otter, S., 1985. Description and performanceof CERES-Wheat: a user-oriented wheat yield model. In: ARS Wheat Yield Project. ARS-38.Natl Tech Info Serv, Springfield, Missouri, pp. 159-175.

Seasonal forecasting for water resource management: the example of CNR Genissiat dam on the Rhone River in France

NASA Astrophysics Data System (ADS)

Dommanget, Etienne; Bellier, Joseph; Ben Daoud, Aurélien; Graff, Benjamin

2014-05-01

Compagnie Nationale du Rhône (CNR) has been granted the concession to operate the Rhone River from the Swiss border to the Mediterranean Sea since 1933 and carries out three interdependent missions: navigation, irrigation and hydropower production. Nowadays, CNR generates one quarter of France's hydropower electricity. The convergence of public and private interests around optimizing the management of water resources throughout the French Rhone valley led CNR to develop hydrological models dedicated to discharge seasonal forecasting. Indeed, seasonal forecasting is a major issue for CNR and water resource management, in order to optimize long-term investments of the produced electricity, plan dam maintenance operations and anticipate low water period. Seasonal forecasting models have been developed on the Genissiat dam. With an installed capacity of 420MW, Genissiat dam is the first of the 19 CNR's hydropower plants. Discharge forecasting at Genissiat dam is strategic since its inflows contributes to 20% of the total Rhone average discharge and consequently to 40% of the total Rhone hydropower production. Forecasts are based on hydrological statistical models. Discharge on the main Rhone River tributaries upstream Genissiat dam are forecasted from 1 to 6 months ahead thanks to multiple linear regressions. Inputs data of these regressions are identified depending on river hydrological regimes and periods of the year. For the melting season, from spring to summer, snow water equivalent (SWE) data are of major importance. SWE data are calculated from Crocus model (Météo France) and SLF's model (Switzerland). CNR hydro-meteorological forecasters assessed meteorological trends regarding precipitations for the next coming months. These trends are used to generate stochastically precipitation scenarios in order to complement regression data set. This probabilistic approach build a decision-making supports for CNR's water resource management team and provides them with seasonal forecasts and their confidence interval. After a presentation of CNR methodology, results for the years 2011 and 2013 will illustrate CNR's seasonal forecasting models ability. These years are of particular interest regarding water resource management seeing that they are, respectively, unusually dry and snowy. Model performances will be assessed in comparison with historical climatology thanks to CRPS skill score.

Performance assessment of deterministic and probabilistic weather predictions for the short-term optimization of a tropical hydropower reservoir

NASA Astrophysics Data System (ADS)

Mainardi Fan, Fernando; Schwanenberg, Dirk; Alvarado, Rodolfo; Assis dos Reis, Alberto; Naumann, Steffi; Collischonn, Walter

2016-04-01

Hydropower is the most important electricity source in Brazil. During recent years, it accounted for 60% to 70% of the total electric power supply. Marginal costs of hydropower are lower than for thermal power plants, therefore, there is a strong economic motivation to maximize its share. On the other hand, hydropower depends on the availability of water, which has a natural variability. Its extremes lead to the risks of power production deficits during droughts and safety issues in the reservoir and downstream river reaches during flood events. One building block of the proper management of hydropower assets is the short-term forecast of reservoir inflows as input for an online, event-based optimization of its release strategy. While deterministic forecasts and optimization schemes are the established techniques for the short-term reservoir management, the use of probabilistic ensemble forecasts and stochastic optimization techniques receives growing attention and a number of researches have shown its benefit. The present work shows one of the first hindcasting and closed-loop control experiments for a multi-purpose hydropower reservoir in a tropical region in Brazil. The case study is the hydropower project (HPP) Três Marias, located in southeast Brazil. The HPP reservoir is operated with two main objectives: (i) hydroelectricity generation and (ii) flood control at Pirapora City located 120 km downstream of the dam. In the experiments, precipitation forecasts based on observed data, deterministic and probabilistic forecasts with 50 ensemble members of the ECMWF are used as forcing of the MGB-IPH hydrological model to generate streamflow forecasts over a period of 2 years. The online optimization depends on a deterministic and multi-stage stochastic version of a model predictive control scheme. Results for the perfect forecasts show the potential benefit of the online optimization and indicate a desired forecast lead time of 30 days. In comparison, the use of actual forecasts with shorter lead times of up to 15 days shows the practical benefit of actual operational data. It appears that the use of stochastic optimization combined with ensemble forecasts leads to a significant higher level of flood protection without compromising the HPP's energy production.

Sources of information for tsunami forecasting in New Zealand

NASA Astrophysics Data System (ADS)

Barberopoulou, A.; Ristau, J. P.; D'Anastasio, E.; Wang, X.

2013-12-01

Tsunami science has evolved considerably in the last two decades due to technological advancements which also helped push for better numerical modelling of the tsunami phases (generation to inundation). The deployment of DART buoys has also been a considerable milestone in tsunami forecasting. Tsunami forecasting is one of the parts that tsunami modelling feeds into and is related to response, preparedness and planning. Usually tsunami forecasting refers to short-term forecasting that takes place in real-time after a tsunami has or appears to have been generated. In this report we refer to all types of forecasting (short-term or long-term) related to work in advance of a tsunami impacting a coastline that would help in response, planning or preparedness. We look at the standard types of data (seismic, GPS, water level) that are available in New Zealand for tsunami forecasting, how they are currently being used, other ways to use these data and provide recommendations for better utilisation. The main findings are: -Current investigations of the use of seismic parameters quickly obtained after an earthquake, have potential to provide critical information about the tsunamigenic potential of earthquakes. Further analysis of the most promising methods should be undertaken to determine a path to full implementation. -Network communication of the largest part of the GPS network is not currently at a stage that can provide sufficient data early enough for tsunami warning. It is believed that it has potential, but changes including data transmission improvements may have to happen before real-time processing oriented to tsunami early warning is implemented on the data that is currently provided. -Tide gauge data is currently under-utilised for tsunami forecasting. Spectral analysis, modal analysis based on identified modes and arrival times extracted from the records can be useful in forecasting. -The current study is by no means exhaustive of the ways the different types of data can be used. We are only presenting an overview of what can be done. More extensive studies with each one of the types of data collected by GeoNet and other relevant networks will help improve tsunami forecasting in New Zealand.

An empirical model for prediction of household solid waste generation rate - A case study of Dhanbad, India.

PubMed

Kumar, Atul; Samadder, S R

2017-10-01

Accurate prediction of the quantity of household solid waste generation is very much essential for effective management of municipal solid waste (MSW). In actual practice, modelling methods are often found useful for precise prediction of MSW generation rate. In this study, two models have been proposed that established the relationships between the household solid waste generation rate and the socioeconomic parameters, such as household size, total family income, education, occupation and fuel used in the kitchen. Multiple linear regression technique was applied to develop the two models, one for the prediction of biodegradable MSW generation rate and the other for non-biodegradable MSW generation rate for individual households of the city Dhanbad, India. The results of the two models showed that the coefficient of determinations (R 2 ) were 0.782 for biodegradable waste generation rate and 0.676 for non-biodegradable waste generation rate using the selected independent variables. The accuracy tests of the developed models showed convincing results, as the predicted values were very close to the observed values. Validation of the developed models with a new set of data indicated a good fit for actual prediction purpose with predicted R 2 values of 0.76 and 0.64 for biodegradable and non-biodegradable MSW generation rate respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Waste minimization charges up recycling of spent lead-acid batteries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Queneau, P.B.; Troutman, A.L.

Substantial strides are being made to minimize waste generated form spent lead-acid battery recycling. The Center for Hazardous Materials Research (Pittsburgh) recently investigated the potential for secondary lead smelters to recover lead from battery cases and other materials found at hazardous waste sites. Primary and secondary lead smelters in the U.S. and Canada are processing substantial tons of lead wastes, and meeting regulatory safeguards. Typical lead wastes include contaminated soil, dross and dust by-products from industrial lead consumers, tetraethyl lead residues, chemical manufacturing by-products, leaded glass, china clay waste, munitions residues and pigments. The secondary lead industry also is developingmore » and installing systems to convert process inputs to products with minimum generation of liquid, solid and gaseous wastes. The industry recently has made substantial accomplishments that minimize waste generation during lead production from its bread and butter feedstock--spent lead-acid batteries.« less

Pathways for Disposal of Commercially-Generated Tritiated Waste

DOE Office of Scientific and Technical Information (OSTI.GOV)

Halverson, Nancy V.

From a waste disposal standpoint, tritium is a major challenge. Because it behaves like hydrogen, tritium exchanges readily with hydrogen in the ground water and moves easily through the ground. Land disposal sites must control the tritium activity and mobility of incoming wastes to protect human health and the environment. Consequently, disposal of tritiated low-level wastes is highly regulated and disposal options are limited. The United States has had eight operating commercial facilities licensed for low-level radioactive waste disposal, only four of which are currently receiving waste. Each of these is licensed and regulated by its state. Only two ofmore » these sites accept waste from states outside of their specified regional compact. For waste streams that cannot be disposed directly at one of the four active commercial low-level waste disposal facilities, processing facilities offer various forms of tritiated low-level waste processing and treatment, and then transport and dispose of the residuals at a disposal facility. These processing facilities may remove and recycle tritium, reduce waste volume, solidify liquid waste, remove hazardous constituents, or perform a number of additional treatments. Waste brokers also offer many low-level and mixed waste management and transportation services. These services can be especially helpful for small-quantity tritiated-waste generators, such as universities, research institutions, medical facilities, and some industries. The information contained in this report covers general capabilities and requirements for the various disposal/processing facilities and brokerage companies, but is not considered exhaustive. Typically, each facility has extensive waste acceptance criteria and will require a generator to thoroughly characterize their wastes. Then a contractual agreement between the waste generator and the disposal/processing/broker entity must be in place before waste is accepted. Costs for tritiated waste transportation, processing and disposal vary based a number of factors. In many cases, wastes with very low radioactivity are priced primarily based on weight or volume. For higher activities, costs are based on both volume and activity, with the activity-based charges usually being much larger than volume-based charges. Other factors affecting cost include location, waste classification and form, other hazards in the waste, etc. Costs may be based on general guidelines used by an individual disposal or processing site, but final costs are established by specific contract with each generator. For this report, seven hypothetical waste streams intended to represent commercially-generated tritiated waste were defined in order to calculate comparative costs. Ballpark costs for disposition of these hypothetical waste streams were calculated. These costs ranged from thousands to millions of dollars. Due to the complexity of the cost-determining factors mentioned above, the costs calculated in this report should be understood to represent very rough cost estimates for the various hypothetical wastes. Actual costs could be higher or could be lower due to quantity discounts or other factors.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuniyal, Jagdish C.; Jain, Arun P.; Shannigrahi, Ardhendu S

Solid waste generation in sensitive tourist areas of the Indian Himalayan region is approaching that of some metro cities of the country. The present study showed {approx}288 g waste generation visitor{sup -1} day{sup -1} compared with the nation-wide average of 350 g capita{sup -1} day{sup -1}. About 29 metric tonnes (MT) solid waste is generated along a distance of about 19-km trek (a stretch of land or distance between two or more places covered by a walk) during a 4-month tourist season every year. Treks and trek stalls are the two major places where the visitors generate solid waste. Wastemore » estimated from stalls accounted for about 51% by weight of the total waste generation in the trekking region. The native villagers generally construct stalls every year to meet the requirement of visitors going to Valley of Flowers (VOF) and Hemkund Sahib. The average annual results of 2 years (or equivalent to the average of one, 4-month tourist season for the region) showed non-biodegradable waste (NBW) to be 96.3% by weight whereas biodegradable waste (BW) amounted to merely 3.7%. From management point of view of the government, 96% NBW could easily be reused and recycled. Nevertheless, the need is to manage this waste by bringing it from the trekking areas to the road head (Govind Ghat) first and then to transport it to adjacent recycling centers. Cold drink glass bottles (68%), plastic (26%) and metal (2%) were the major items contributing to non-biodegradable waste. The remaining organic waste could be used as feedstock for composting. A well coordinated effort of public participation is necessary at all the levels for managing waste. There is a need to educate the visitors to instill in them the habit of considering discarded waste as potentially valuable and manageable.« less

Batching alternatives for Phase I retrieval wastes to be processed in WRAP Module 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayancsik, B.A.

1994-10-13

During the next two decades, the transuranic (TRU) waste now stored in the 200 Area burial trenches and storage buildings is to be retrieved, processed in the Waste Receiving and Processing (WRAP) Module 1 facility, and shipped to a final disposal facility. The purpose of this document is to identify the criteria that can be used to batch suspect TRU waste, currently in retrievable storage, for processing through the WRAP Module 1 facility. These criteria are then used to generate a batch plan for Phase 1 Retrieval operations, which will retrieve the waste located in Trench 4C-04 of the 200more » West Area burial ground. The reasons for batching wastes for processing in WRAP Module 1 include reducing the exposure of workers and the environment to hazardous material and ionizing radiation; maximizing the efficiency of the retrieval, processing, and disposal processes by reducing costs, time, and space throughout the process; reducing analytical sampling and analysis; and reducing the amount of cleanup and decontamination between process runs. The criteria selected for batching the drums of retrieved waste entering WRAP Module 1 are based on the available records for the wastes sent to storage as well as knowledge of the processes that generated these wastes. The batching criteria identified in this document include the following: waste generator; type of process used to generate or package the waste; physical waste form; content of hazardous/dangerous chemicals in the waste; radiochemical type and quantity of waste; drum weight; and special waste types. These criteria were applied to the waste drums currently stored in Trench 4C-04. At least one batching scheme is shown for each of the criteria listed above.« less

Koopman Operator Framework for Time Series Modeling and Analysis

NASA Astrophysics Data System (ADS)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

Forecasting techno-social systems: how physics and computing help to fight off global pandemics

NASA Astrophysics Data System (ADS)

Vespignani, Alessandro

2010-03-01

The crucial issue when planning for adequate public health interventions to mitigate the spread and impact of epidemics is risk evaluation and forecast. This amount to the anticipation of where, when and how strong the epidemic will strike. In the last decade advances in performance in computer technology, data acquisition, statistical physics and complex networks theory allow the generation of sophisticated simulations on supercomputer infrastructures to anticipate the spreading pattern of a pandemic. For the first time we are in the position of generating real time forecast of epidemic spreading. I will review the history of the current H1N1 pandemic, the major road-blocks the community has faced in its containment and mitigation and how physics and computing provide predictive tools that help us to battle epidemics.

Good Bye Traditional Budgeting, Hello Rolling Forecast: Has the Time Come?

ERIC Educational Resources Information Center

Zeller, Thomas L.; Metzger, Lawrence M.

2013-01-01

This paper argues for a new approach to accounting textbook budgeting material. The business environment is not stable. Change is continuous, for large and small business alike. A business must act and react to generate shareholder value. The rolling forecast provides the necessary navigational insight. The traditional annual static budget does…

Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system

USGS Publications Warehouse

Bera, Maitreyee; Ortel, Terry W.

2018-01-12

The U.S. Geological Survey, in cooperation with DuPage County Stormwater Management Department, is testing a near real-time streamflow simulation system that assists in the management and operation of reservoirs and other flood-control structures in the Salt Creek and West Branch DuPage River drainage basins in DuPage County, Illinois. As part of this effort, the U.S. Geological Survey maintains a database of hourly meteorological and hydrologic data for use in this near real-time streamflow simulation system. Among these data are next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data, which are retrieved from the North Central River Forecasting Center of the National Weather Service. The DuPage County streamflow simulation system uses these quantitative precipitation forecast data to create streamflow predictions for the two simulated drainage basins. This report discusses in detail how these data are processed for inclusion in the Watershed Data Management files used in the streamflow simulation system for the Salt Creek and West Branch DuPage River drainage basins.

Infectious waste management in Japan: A revised regulation and a management process in medical institutions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miyazaki, M.; Une, H.

In Japan, the waste management practice is carried out in accordance with the Waste Disposal Law of 1970. The first rule of infectious waste management was regulated in 1992, and infectious wastes are defined as the waste materials generated in medical institutions as a result of medical care or research which contain pathogens that have the potential to transmit infectious diseases. Revised criteria for infectious waste management were promulgated by the Ministry of Environment in 2004. Infectious waste materials are divided into three categories: the form of waste; the place of waste generation; the kind of infectious diseases. A reductionmore » of infectious waste is expected. We introduce a summary of the revised regulation of infectious waste management in this article.« less

A study on the attitudes and behavioural influence of construction waste management in occupied Palestinian territory.

PubMed

Al-Sari, Majed I; Al-Khatib, Issam A; Avraamides, Marios; Fatta-Kassinos, Despo

2012-02-01

As a step towards comprehending what drives the management of construction waste in the occupied Palestinian territory, this paper quantifies construction waste generation and examines how the local contractors' waste management attitudes and behaviour are influenced. Collection of data was based on a survey, carried out in the southern part of the West Bank between April and May 2010. The survey targeted contractors who specialized in the construction of buildings. A logistic regression model was used to investigate the relationship between various attributes and the attitudes and behaviour that the local contractors demonstrate towards waste management. The results showed that during the construction of buildings, 17 to 81 kg of construction waste are generated per square metre of building floor. Although the area of a building is the key factor determining 74.8% of the variation of construction waste generation, the employment of labour-intensive techniques in the study area means that human factors such as the contractor's attitude and behaviour towards waste management, exert a key influence on waste generation. Attitudes towards the 3Rs of waste minimization and behaviour towards waste disposal are generally positive with smaller contractors exhibiting more positive attitudes and more satisfactory behaviour towards waste management. Overall, while contractors' behaviour towards waste sorting and disposal tends to be more satisfactory among contractors who are more conscious about the potential environmental impacts of construction waste, it was generally observed that in the absence of a regulatory framework, the voluntary attitudes and behaviour among the local contractors are mostly driven by direct economic considerations.

Assessing the value of post-processed state-of-the-art long-term weather forecast ensembles for agricultural water management mediated by farmers' behaviours

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea

2016-04-01

Recent advances in modelling of coupled ocean-atmosphere dynamics significantly improved skills of long-term climate forecast from global circulation models (GCMs). These more accurate weather predictions are supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping and watering time) and for more effectively coping with the adverse impacts of climate variability. Yet, assessing how actually valuable this information can be to a farmer is not straightforward and farmers' response must be taken into consideration. Indeed, in the context of agricultural systems potentially useful forecast information should alter stakeholders' expectation, modify their decisions, and ultimately produce an impact on their performance. Nevertheless, long-term forecast are mostly evaluated in terms of accuracy (i.e., forecast quality) by comparing hindcast and observed values and only few studies investigated the operational value of forecast looking at the gain of utility within the decision-making context, e.g. by considering the derivative of forecast information, such as simulated crop yields or simulated soil moisture, which are essential to farmers' decision-making process. In this study, we contribute a step further in the assessment of the operational value of long-term weather forecasts products by embedding these latter into farmers' behavioral models. This allows a more critical assessment of the forecast value mediated by the end-users' perspective, including farmers' risk attitudes and behavioral patterns. Specifically, we evaluate the operational value of thirteen state-of-the-art long-range forecast products against climatology forecast and empirical prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of the farmers' decision-making process. Raw ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in order to address the mismatch of the spatio-temporal scale between forecast data from GCMs and our model. For each product, the experiment is composed by two cascade simulations: 1) an ex-ante simulation using forecast data, and 2) an ex-post simulation with observations. Multi-year simulations are performed to account for climate variability, and the operational value of the different forecast products is evaluated against the perfect foresight on the basis of expected crop productivity as well as the final decisions under different decision-making criterions. Our results show that not all products generate beneficial effects to farmers' performance, and the forecast errors might be amplified due to farmers' decision-making process and risk attitudes, yielding little or even worse performance compared with the empirical approaches.

A statistical data assimilation method for seasonal streamflow forecasting to optimize hydropower reservoir management in data-scarce regions

NASA Astrophysics Data System (ADS)

Arsenault, R.; Mai, J.; Latraverse, M.; Tolson, B.

2017-12-01

Probabilistic ensemble forecasts generated by the ensemble streamflow prediction (ESP) methodology are subject to biases due to errors in the hydrological model's initial states. In day-to-day operations, hydrologists must compensate for discrepancies between observed and simulated states such as streamflow. However, in data-scarce regions, little to no information is available to guide the streamflow assimilation process. The manual assimilation process can then lead to more uncertainty due to the numerous options available to the forecaster. Furthermore, the model's mass balance may be compromised and could affect future forecasts. In this study we propose a data-driven approach in which specific variables that may be adjusted during assimilation are defined. The underlying principle was to identify key variables that would be the most appropriate to modify during streamflow assimilation depending on the initial conditions such as the time period of the assimilation, the snow water equivalent of the snowpack and meteorological conditions. The variables to adjust were determined by performing an automatic variational data assimilation on individual (or combinations of) model state variables and meteorological forcing. The assimilation aimed to simultaneously optimize: (1) the error between the observed and simulated streamflow at the timepoint where the forecasts starts and (2) the bias between medium to long-term observed and simulated flows, which were simulated by running the model with the observed meteorological data on a hindcast period. The optimal variables were then classified according to the initial conditions at the time period where the forecast is initiated. The proposed method was evaluated by measuring the average electricity generation of a hydropower complex in Québec, Canada driven by this method. A test-bed which simulates the real-world assimilation, forecasting, water release optimization and decision-making of a hydropower cascade was developed to assess the performance of each individual process in the reservoir management chain. Here the proposed method was compared to the PF algorithm while keeping all other elements intact. Preliminary results are encouraging in terms of power generation and robustness for the proposed approach.

Hanford Site annual dangerous waste report: Volume 2, Generator dangerous waste report, radioactive mixed waste

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1994-12-31

This report contains information on radioactive mixed wastes at the Hanford Site. Information consists of shipment date, physical state, chemical nature, waste description, waste number, waste designation, weight, and waste designation.

40 CFR 436.21 - Specialized definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... However, if a mine is also used for treatment of process generated waste water, discharges of commingled water from the facilities shall be deemed discharges of process generated waste water. (c) The term “10... treatment of such waste water. ...

Fact Sheet About the Hazardous Waste Generator Improvements Final Rule

EPA Pesticide Factsheets

October 28, 2016, EPA finalized a rule that revises the hazardous waste generator regulations by making them easier to understand and providing greater flexibility in how hazardous waste is managed to better fit today's business operations.

New forecasting methodology indicates more disease and earlier mortality ahead for today's younger Americans.

PubMed

Reither, Eric N; Olshansky, S Jay; Yang, Yang

2011-08-01

Traditional methods of projecting population health statistics, such as estimating future death rates, can give inaccurate results and lead to inferior or even poor policy decisions. A new "three-dimensional" method of forecasting vital health statistics is more accurate because it takes into account the delayed effects of the health risks being accumulated by today's younger generations. Applying this forecasting technique to the US obesity epidemic suggests that future death rates and health care expenditures could be far worse than currently anticipated. We suggest that public policy makers adopt this more robust forecasting tool and redouble efforts to develop and implement effective obesity-related prevention programs and interventions.

Projected Salt Waste Production from a Commercial Pyroprocessing Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpson, Michael F.

Pyroprocessing of used nuclear fuel inevitably produces salt waste from electrorefining and/or oxide reduction unit operations. Various process design characteristics can affect the actual mass of such waste produced. This paper examines both oxide and metal fuel treatment, estimates the amount of salt waste generated, and assesses potential benefit of process options to mitigate the generation of salt waste. For reference purposes, a facility is considered in which 100 MT/year of fuel is processed. Salt waste estimates range from 8 to 20 MT/year from considering numerous scenarios. It appears that some benefit may be derived from advanced processes for separatingmore » fission products from molten salt waste, but the degree of improvement is limited. Waste form production is also considered but appears to be economically unfavorable. Direct disposal of salt into a salt basin type repository is found to be the most promising with respect to minimizing the impact of waste generation on the economic feasibility and sustainability of pyroprocessing.« less

Process Waste Assessment Machine and Fabrication Shop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phillips, N.M.

1993-03-01

This Process Waste Assessment was conducted to evaluate hazardous wastes generated in the Machine and Fabrication Shop at Sandia National Laboratories, Bonding 913, Room 119. Spent machine coolant is the major hazardous chemical waste generated in this facility. The volume of spent coolant generated is approximately 150 gallons/month. It is sent off-site to a recycler, but a reclaiming system for on-site use is being investigated. The Shop`s line management considers hazardous waste minimization very important. A number of steps have already been taken to minimize wastes, including replacement of a hazardous solvent with biodegradable, non-caustic solution and filtration unit; wastemore » segregation; restriction of beryllium-copper alloy machining; and reduction of lead usage.« less

Utilization of Aluminum Waste with Hydrogen and Heat Generation

NASA Astrophysics Data System (ADS)

Buryakovskaya, O. A.; Meshkov, E. A.; Vlaskin, M. S.; Shkolnokov, E. I.; Zhuk, A. Z.

2017-10-01

A concept of energy generation via hydrogen and heat production from aluminum containing wastes is proposed. The hydrogen obtained by oxidation reaction between aluminum waste and aqueous solutions can be supplied to fuel cells and/or infrared heaters for electricity or heat generation in the region of waste recycling. The heat released during the reaction also can be effectively used. The proposed method of aluminum waste recycling may represent a promising and cost-effective solution in cases when waste transportation to recycling plants involves significant financial losses (e.g. remote areas). Experiments with mechanically dispersed aluminum cans demonstrated that the reaction rate in alkaline solution is high enough for practical use of the oxidation process. In theexperiments aluminum oxidation proceeds without any additional aluminum activation.

Integrating Satellite Measurements from Polar-orbiting instruments into Smoke Disperson Forecasts

NASA Astrophysics Data System (ADS)

Smith, N.; Pierce, R. B.; Barnet, C.; Gambacorta, A.; Davies, J. E.; Strabala, K.

2015-12-01

The IDEA-I (Infusion of Satellite Data into Environmental Applications-International) is a real-time system that currently generates trajectory-based forecasts of aerosol dispersion and stratospheric intrusions. Here we demonstrate new capabilities that use satellite measurements from the Joint Polar Satellite System (JPSS) Suomi-NPP (S-NPP) instruments (operational since 2012) in the generation of trajectory-based predictions of smoke dispersion from North American wildfires. Two such data products are used, namely the Visible Infrared Imaging Radiometer Suite (VIIRS) Aerosol Optical Depth (AOD) and the combined Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) NOAA-Unique CrIS-ATMS Processing System (NUCAPS) carbon monoxide (CO) retrievals. The latter is a new data product made possible by the release of full spectral-resolution CrIS measurements since December 2014. Once NUCAPS CO becomes operationally available it will be used in real-time applications such as IDEA-I along with VIIRS AOD and meteorological forecast fields to support National Weather Service (NWS) Incident Meteorologist (IMET) and air quality management decision making. By combining different measurements, the information content of the IDEA-I transport and dispersion forecast is improved within the complex terrain features that dominate the Western US and Alaska. The primary user community of smoke forecasts is the Western regions of the National Weather Service (NWS) and US Environmental Protection Agency (EPA) due to the significant impacts of wildfires in these regions. With this we demonstrate the quality of the smoke dispersion forecasts that can be achieved by integrating polar-orbiting satellite measurements with forecast models to enable on-site decision support services for fire incident management teams and other real-time air quality agencies.

Evaluating Snow Data Assimilation Framework for Streamflow Forecasting Applications Using Hindcast Verification

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2012-12-01

Snow water equivalent (SWE) estimation is a key factor in producing reliable streamflow simulations and forecasts in snow dominated areas. However, measuring or predicting SWE has significant uncertainty. Sequential data assimilation, which updates states using both observed and modeled data based on error estimation, has been shown to reduce streamflow simulation errors but has had limited testing for forecasting applications. In the current study, a snow data assimilation framework integrated with the National Weather System River Forecasting System (NWSRFS) is evaluated for use in ensemble streamflow prediction (ESP). Seasonal water supply ESP hindcasts are generated for the North Fork of the American River Basin (NFARB) in northern California. Parameter sets from the California Nevada River Forecast Center (CNRFC), the Differential Evolution Adaptive Metropolis (DREAM) algorithm and the Multistep Automated Calibration Scheme (MACS) are tested both with and without sequential data assimilation. The traditional ESP method considers uncertainty in future climate conditions using historical temperature and precipitation time series to generate future streamflow scenarios conditioned on the current basin state. We include data uncertainty analysis in the forecasting framework through the DREAM-based parameter set which is part of a recently developed Integrated Uncertainty and Ensemble-based data Assimilation framework (ICEA). Extensive verification of all tested approaches is undertaken using traditional forecast verification measures, including root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), volumetric bias, joint distribution, rank probability score (RPS), and discrimination and reliability plots. In comparison to the RFC parameters, the DREAM and MACS sets show significant improvement in volumetric bias in flow. Use of assimilation improves hindcasts of higher flows but does not significantly improve performance in the mid flow and low flow categories.

Decomposition of Sources of Errors in Seasonal Streamflow Forecasts in a Rainfall-Runoff Dominated Basin

NASA Astrophysics Data System (ADS)

Sinha, T.; Arumugam, S.

2012-12-01

Seasonal streamflow forecasts contingent on climate forecasts can be effectively utilized in updating water management plans and optimize generation of hydroelectric power. Streamflow in the rainfall-runoff dominated basins critically depend on forecasted precipitation in contrast to snow dominated basins, where initial hydrological conditions (IHCs) are more important. Since precipitation forecasts from Atmosphere-Ocean-General Circulation Models are available at coarse scale (~2.8° by 2.8°), spatial and temporal downscaling of such forecasts are required to implement land surface models, which typically runs on finer spatial and temporal scales. Consequently, multiple sources are introduced at various stages in predicting seasonal streamflow. Therefore, in this study, we addresses the following science questions: 1) How do we attribute the errors in monthly streamflow forecasts to various sources - (i) model errors, (ii) spatio-temporal downscaling, (iii) imprecise initial conditions, iv) no forecasts, and (iv) imprecise forecasts? and 2) How does monthly streamflow forecast errors propagate with different lead time over various seasons? In this study, the Variable Infiltration Capacity (VIC) model is calibrated over Apalachicola River at Chattahoochee, FL in the southeastern US and implemented with observed 1/8° daily forcings to estimate reference streamflow during 1981 to 2010. The VIC model is then forced with different schemes under updated IHCs prior to forecasting period to estimate relative mean square errors due to: a) temporally disaggregation, b) spatial downscaling, c) Reverse Ensemble Streamflow Prediction (imprecise IHCs), d) ESP (no forecasts), and e) ECHAM4.5 precipitation forecasts. Finally, error propagation under different schemes are analyzed with different lead time over different seasons.

Waste management/waste certification plan for the Oak Ridge National Laboratory Environmental Restoration Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clark, C. Jr.; Hunt-Davenport, L.D.; Cofer, G.H.

1995-03-01

This Waste Management/Waste Certification (C) Plan, written for the Environmental Restoration (ER) Program at Oak Ridge National Laboratory (ORNL), outlines the criteria and methodologies to be used in the management of waste generated during ORNL ER field activities. Other agreed upon methods may be used in the management of waste with consultation with ER and Waste Management Organization. The intent of this plan is to provide information for the minimization, handling, and disposal of waste generated by ER activities. This plan contains provisions for the safe and effective management of waste consistent with the U.S. Environmental Protection Agency`s (EPA`s) guidance.more » Components of this plan have been designed to protect the environment and the health and safety of workers and the public. It, therefore, stresses that investigation derived waste (IDW) and other waste be managed to ensure that (1) all efforts be made to minimize the amount of waste generated; (2) costs associated with sampling storage, analysis, transportation, and disposal are minimized; (3) the potential for public and worker exposure is not increased; and (4) additional contaminated areas are not created.« less

Challenges and opportunities associated with waste management in India

PubMed Central

Kumar, Sunil; Smith, Stephen R.; Fowler, Geoff; Velis, Costas; Kumar, S. Jyoti; Arya, Shashi; Rena; Kumar, Rakesh

2017-01-01

India faces major environmental challenges associated with waste generation and inadequate waste collection, transport, treatment and disposal. Current systems in India cannot cope with the volumes of waste generated by an increasing urban population, and this impacts on the environment and public health. The challenges and barriers are significant, but so are the opportunities. This paper reports on an international seminar on ‘Sustainable solid waste management for cities: opportunities in South Asian Association for Regional Cooperation (SAARC) countries’ organized by the Council of Scientific and Industrial Research-National Environmental Engineering Research Institute and the Royal Society. A priority is to move from reliance on waste dumps that offer no environmental protection, to waste management systems that retain useful resources within the economy. Waste segregation at source and use of specialized waste processing facilities to separate recyclable materials has a key role. Disposal of residual waste after extraction of material resources needs engineered landfill sites and/or investment in waste-to-energy facilities. The potential for energy generation from landfill via methane extraction or thermal treatment is a major opportunity, but a key barrier is the shortage of qualified engineers and environmental professionals with the experience to deliver improved waste management systems in India. PMID:28405362

Development of sustainable waste management toward zero landfill waste for the petrochemical industry in Thailand using a comprehensive 3R methodology: A case study.

PubMed

Usapein, Parnuwat; Chavalparit, Orathai

2014-06-01

Sustainable waste management was introduced more than ten years ago, but it has not yet been applied to the Thai petrochemical industry. Therefore, under the philosophy of sustainable waste management, this research aims to apply the reduce, reuse, and recycle (3R) concept at the petrochemical factory level to achieve a more sustainable industrial solid waste management system. Three olefin plants in Thailand were surveyed for the case study. The sources and types of waste and existing waste management options were identified. The results indicate that there are four sources of waste generation: (1) production, (2) maintenance, (3) waste treatment, and (4) waste packaging, which correspond to 45.18%, 36.71%, 9.73%, and 8.37% of the waste generated, respectively. From the survey, 59 different types of industrial wastes were generated from the different factory activities. The proposed 3R options could reduce the amount of landfill waste to 79.01% of the amount produced during the survey period; this reduction would occur over a period of 2 years and would result in reduced disposal costs and reduced consumption of natural resources. This study could be used as an example of an improved waste management system in the petrochemical industry. © The Author(s) 2014.

A Basic Accounting of Variation in Municipal Solid-Waste Generation at the County Level in Texas, 2006: Groundwork for Applying Metabolic-Rift Theory to Waste Generation

ERIC Educational Resources Information Center

Clement, Matthew Thomas

2009-01-01

Environmental social scientists debate whether or not modern development reduces society's impact on the biosphere. The empirical research informing the discussion has not yet adequately examined the social determinants of municipal solid-waste (MSW) generation, an increasingly relevant issue, both ecologically and sociologically. A primary…

Getting a taste for food waste: a mixed methods ethnographic study into hospital food waste before patient consumption conducted at three New Zealand foodservice facilities.

PubMed

Goonan, Sarah; Mirosa, Miranda; Spence, Heather

2014-01-01

Foodservice organizations, particularly those in hospitals, are large producers of food waste. To date, research on waste in hospitals has focused primarily on plate waste and the affect of food waste on patient nutrition outcomes. Less focus has been placed on waste generation at the kitchen end of the hospital food system. We used a novel approach to understand reasons for hospital food waste before consumption and offer recommendations on waste minimization within foodservices. A mixed methods ethnographic research approach was adopted. Three New Zealand hospital foodservices were selected as research sites, all of which were contracted to an external foodservice provider. Data collection techniques included document analyses, observations, focus groups with kitchen staff, and one-on-one interviews with managers. Thematic analysis was conducted to generate common themes. Most food waste occurred during service and as a result of overproduction. Attitudes and habits of foodservice personnel were considered influential factors of waste generation. Implications of food waste were perceived differently by different levels of staff. Whereas managers raised discussion from a financial perspective, kitchen staff drew upon social implications. Organizational plans, controls, and use of pre-prepared ingredients assisted in waste minimization. An array of factors influenced waste generation in hospital foodservices. Exploring attitudes and practices of foodservice personnel allowed an understanding of reasons behind hospital food waste and ways in which it could be minimized. This study provides a foundation for further research on sustainable behavior within the wider foodservice sector and dietetics practice. Copyright © 2014 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Management of immunization solid wastes in Kano State, Nigeria

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oke, I.A.

Inadequate management of waste generated from injection activities can have a negative impact on the community and environment. In this paper, a report on immunization wastes management in Kano State (Nigeria) is presented. Eight local governments were selected randomly and surveyed by the author. Solid wastes generated during the Expanded Programme on Immunization were characterised using two different methods: one by weighing the waste and the other by estimating the volume. Empirical data was obtained on immunization waste generation, segregation, storage, collection, transportation, and disposal; and waste management practices were assessed. The study revealed that immunization offices were accommodated inmore » either in local government buildings, primary health centres or community health care centres. All of the stations demonstrated a high priority for segregation of the infectious wastes. It can be deduced from the data obtained that infectious waste ranged from 67.6% to 76.7% with an average of 70.1% by weight, and 36.0% to 46.1% with an average of 40.1% by volume. Non-infectious waste generated ranged from 23.3% to 32.5% with an average of 29.9% by weight and 53.9% to 64.0% with an average of 59.9% by volume. Out of non-infectious waste (NIFW) and infectious waste (IFW), 66.3% and 62.4% by weight were combustible and 33.7% and 37.6% were non-combustible respectively. An assessment of the treatment revealed that open pit burning and burial and small scale incineration were the common methods of disposal for immunization waste, and some immunization centres employed the services of the state or local government owned solid waste disposal board for final collection and disposal of their immunization waste at government approved sites.« less

Seasonal analysis of the generation and composition of solid waste: potential use--a case study.

PubMed

Aguilar-Virgen, Quetzalli; Taboada-González, Paul; Ojeda-Benítez, Sara

2013-06-01

Ensenada health officials lack pertinent information on the sustainable management of solid waste, as do health officials from other developing countries. The aims of this research are: (a) to quantify and analyze the household solid wastes generated in the city of Ensenada, Mexico, and (b) to project biogas production and estimate generation of electrical energy. The characterization study was conducted by socioeconomic stratification in two seasonal periods, and the biogas and electrical energy projections were performed using the version 2.0 Mexico Biogas Model. Per capita solid waste generation was 0.779 ± 0.019 kg per person per day within a 98 % confidence interval. Waste composition is composed mainly of food scraps at 36.25 %, followed by paper and cardboard at 21.85 %, plastic at 12.30 %, disposable diapers at 6.26 %, and textiles at 6.28 %. The maximum capacity for power generation is projected to be 1.90 MW in 2019. Waste generated could be used as an intermediate in different processes such as recycling (41.04 %) and energy recovery (46.63 %). The electrical energy that could be obtained using the biogas generated at the Ensenada sanitary landfill would provide roughly 60 % of the energy needed for street lighting.

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste

2010-01-01

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.

Generalization of information-based concepts in forecast verification

NASA Astrophysics Data System (ADS)

Tödter, J.; Ahrens, B.

2012-04-01

This work deals with information-theoretical methods in probabilistic forecast verification. Recent findings concerning the Ignorance Score are shortly reviewed, then the generalization to continuous forecasts is shown. For ensemble forecasts, the presented measures can be calculated exactly. The Brier Score (BS) and its generalizations to the multi-categorical Ranked Probability Score (RPS) and to the Continuous Ranked Probability Score (CRPS) are the prominent verification measures for probabilistic forecasts. Particularly, their decompositions into measures quantifying the reliability, resolution and uncertainty of the forecasts are attractive. Information theory sets up the natural framework for forecast verification. Recently, it has been shown that the BS is a second-order approximation of the information-based Ignorance Score (IGN), which also contains easily interpretable components and can also be generalized to a ranked version (RIGN). Here, the IGN, its generalizations and decompositions are systematically discussed in analogy to the variants of the BS. Additionally, a Continuous Ranked IGN (CRIGN) is introduced in analogy to the CRPS. The applicability and usefulness of the conceptually appealing CRIGN is illustrated, together with an algorithm to evaluate its components reliability, resolution, and uncertainty for ensemble-generated forecasts. This is also directly applicable to the more traditional CRPS.

40 CFR 761.340 - Applicability.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) TOXIC SUBSTANCES CONTROL ACT... Accordance With § 761.62, and Sampling PCB Remediation Waste Destined for Off-Site Disposal, in Accordance... generate new waste. (c) Non-liquid PCB remediation waste from processes that continuously generate new...

40 CFR 761.340 - Applicability.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) TOXIC SUBSTANCES CONTROL ACT... Accordance With § 761.62, and Sampling PCB Remediation Waste Destined for Off-Site Disposal, in Accordance... generate new waste. (c) Non-liquid PCB remediation waste from processes that continuously generate new...

40 CFR 761.340 - Applicability.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) TOXIC SUBSTANCES CONTROL ACT... Accordance With § 761.62, and Sampling PCB Remediation Waste Destined for Off-Site Disposal, in Accordance... generate new waste. (c) Non-liquid PCB remediation waste from processes that continuously generate new...

Hanford Site annual dangerous waste report: Volume 1, Part 1, Generator dangerous waste report, dangerous waste

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

This report contains information on hazardous wastes at the Hanford Site. Information consists of shipment date, physical state, chemical nature, waste description, waste number, weight, and waste designation.

Incident Waste Decision Support Tool - Waste Materials ...

EPA Pesticide Factsheets

Report This is the technical documentation to the waste materials estimator module of I-WASTE. This document outlines the methodology and data used to develop the Waste Materials Estimator (WME) contained in the Incident Waste Decision Support Tool (I-WASTE DST). Specifically, this document reflects version 6.4 of the I-WASTE DST. The WME is one of four primary features of the I-WASTE DST. The WME is both a standalone calculator that generates waste estimates in terms of broad waste categories, and is also integrated into the Incident Planning and Response section of the tool where default inventories of specific waste items are provided in addition to the estimates for the broader waste categories. The WME can generate waste estimates for both common materials found in open spaces (soil, vegetation, concrete, and asphalt) and for a vast array of items and materials found in common structures.

Quantification and probabilistic modeling of CRT obsolescence for the State of Delaware.

PubMed

Schumacher, Kelsea A; Schumacher, Thomas; Agbemabiese, Lawrence

2014-11-01

The cessation of production and replacement of cathode ray tube (CRT) displays with flat screen displays have resulted in the proliferation of CRTs in the electronic waste (e-waste) recycle stream. However, due to the nature of the technology and presence of hazardous components such as lead, CRTs are the most challenging of electronic components to recycle. In the State of Delaware it is due to this challenge and the resulting expense combined with the large quantities of CRTs in the recycle stream that electronic recyclers now charge to accept Delaware's e-waste. Therefore it is imperative that the Delaware Solid Waste Authority (DSWA) understand future quantities of CRTs entering the waste stream. This study presents the results of an assessment of CRT obsolescence in the State of Delaware. A prediction model was created utilizing publicized sales data, a variety of lifespan data as well as historic Delaware CRT collection rates. Both a deterministic and a probabilistic approach using Monte Carlo Simulation (MCS) were performed to forecast rates of CRT obsolescence to be anticipated in the State of Delaware. Results indicate that the peak of CRT obsolescence in Delaware has already passed, although CRTs are anticipated to enter the waste stream likely until 2033. Copyright © 2014 Elsevier Ltd. All rights reserved.

Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers

NASA Astrophysics Data System (ADS)

Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.

2015-12-01

The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast. The period of analysis is from 1985-2010 (over which the reforecasts of GEFS is available) and the focus season is October-November-December. We examine the improvement (if any) in long-term skill, and present results for several recent drought events in the region.

Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system.

PubMed

Hou, Xianlong; Hodges, Ben R; Feng, Dongyu; Liu, Qixiao

2017-03-15

As oil transport increasing in the Texas bays, greater risks of ship collisions will become a challenge, yielding oil spill accidents as a consequence. To minimize the ecological damage and optimize rapid response, emergency managers need to be informed with how fast and where oil will spread as soon as possible after a spill. The state-of-the-art operational oil spill forecast modeling system improves the oil spill response into a new stage. However uncertainty due to predicted data inputs often elicits compromise on the reliability of the forecast result, leading to misdirection in contingency planning. Thus understanding the forecast uncertainty and reliability become significant. In this paper, Monte Carlo simulation is implemented to provide parameters to generate forecast probability maps. The oil spill forecast uncertainty is thus quantified by comparing the forecast probability map and the associated hindcast simulation. A HyosPy-based simple statistic model is developed to assess the reliability of an oil spill forecast in term of belief degree. The technologies developed in this study create a prototype for uncertainty and reliability analysis in numerical oil spill forecast modeling system, providing emergency managers to improve the capability of real time operational oil spill response and impact assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

A system dynamics approach for hospital waste management in a city in a developing country: the case of Nablus, Palestine.

PubMed

Al-Khatib, Issam A; Eleyan, Derar; Garfield, Joy

2016-09-01

Hospitals and health centers provide a variety of healthcare services and normally generate hazardous waste as well as general waste. General waste has a similar nature to that of municipal solid waste and therefore could be disposed of in municipal landfills. However, hazardous waste poses risks to public health, unless it is properly managed. The hospital waste management system encompasses many factors, i.e., number of beds, number of employees, level of service, population, birth rate, fertility rate, and not in my back yard (NIMBY) syndrome. Therefore, this management system requires a comprehensive analysis to determine the role of each factor and its influence on the whole system. In this research, a hospital waste management simulation model is presented based on the system dynamics technique to determine the interaction among these factors in the system using a software package, ithink. This model is used to estimate waste segregation as this is important in the hospital waste management system to minimize risk to public health. Real data has been obtained from a case study of the city of Nablus, Palestine to validate the model. The model exhibits wastes generated from three types of hospitals (private, charitable, and government) by considering the number of both inpatients and outpatients depending on the population of the city under study. The model also offers the facility to compare the total waste generated among these different types of hospitals and anticipate and predict the future generated waste both infectious and non-infectious and the treatment cost incurred.

Current status of solid waste management in small island developing states: A review.

PubMed

Mohee, Romeela; Mauthoor, Sumayya; Bundhoo, Zumar M A; Somaroo, Geeta; Soobhany, Nuhaa; Gunasee, Sanjana

2015-09-01

This article reviews the current status of waste management in Small Island Developing States (SIDS) and the challenges that are faced in solid waste management. The waste generation rates of SIDS were compared within the three geographic regions namely Caribbean SIDS, Pacific SIDS and Atlantic, Indian Ocean, Mediterranean and South China (AIMS) SIDS and with countries of the Organisation for Economic Co-Operation and Development (OECD). Only Pacific SIDS had a waste generation rate less than 1kg/capita/day. The waste generation rates for the three SIDS regions averaged 1.29kg/capita/day while that for OECD countries was at a mean value of 1.35kg/capita/day. The waste compositions in the different SIDS regions were almost similar owing to comparable consumption patterns while these differed to a large extent with wastes generated in OECD countries. In SIDS, the major fraction of MSW comprised of organics (44%) followed by recyclables namely paper, plastics, glass and metals (total: 43%). In contrast, MSW in OECD countries consisted mainly of recyclables (43%) followed by organics (37%). This article also reviewed the other functional elements of the waste management systems in SIDS. Several shortcomings were noted in the process of waste collection, transfer and transport namely the fact of having outdated collection vehicles and narrow roads which are inaccessible. Among the waste management practices in SIDS, waste disposal via landfilling, illegal dumping and backyard burning were favoured most of the time at the expense of sustainable waste treatment technologies such as composting, anaerobic digestion and recycling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparing the greenhouse gas emissions from three alternative waste combustion concepts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vainikka, Pasi, E-mail: pasi.vainikka@vtt.fi; Tsupari, Eemeli; Sipilae, Kai

2012-03-15

Highlights: Black-Right-Pointing-Pointer Significant GHG reductions are possible by efficient WtE technologies. Black-Right-Pointing-Pointer CHP and high power-to-heat ratio provide significant GHG savings. Black-Right-Pointing-Pointer N{sub 2}O and coal mine type are important in LCA GHG emissions of FBC co-combustion. Black-Right-Pointing-Pointer Substituting coal and fuel oil by waste is beneficial in electricity and heat production. Black-Right-Pointing-Pointer Substituting natural gas by waste may not be reasonable in CHP generation. - Abstract: Three alternative condensing mode power and combined heat and power (CHP) waste-to-energy concepts were compared in terms of their impacts on the greenhouse gas (GHG) emissions from a heat and power generation system.more » The concepts included (i) grate, (ii) bubbling fluidised bed (BFB) and (iii) circulating fluidised bed (CFB) combustion of waste. The BFB and CFB take advantage of advanced combustion technology which enabled them to reach electric efficiency up to 35% and 41% in condensing mode, respectively, whereas 28% (based on the lower heating value) was applied for the grate fired unit. A simple energy system model was applied in calculating the GHG emissions in different scenarios where coal or natural gas was substituted in power generation and mix of fuel oil and natural gas in heat generation by waste combustion. Landfilling and waste transportation were not considered in the model. GHG emissions were reduced significantly in all of the considered scenarios where the waste combustion concepts substituted coal based power generation. With the exception of condensing mode grate incinerator the different waste combustion scenarios resulted approximately in 1 Mton of fossil CO{sub 2}-eq. emission reduction per 1 Mton of municipal solid waste (MSW) incinerated. When natural gas based power generation was substituted by electricity from the waste combustion significant GHG emission reductions were not achieved.« less

IN31A-1734 Development and Evaluation of a Gridded CrIS/ATMS Visualization for Operational Forecasting

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Smith, Nadia; Dostalek, Jack; Stevens, Eric; Nelson, Kristine; Weisz, Elisabeth; Berndt, Emily; Line, Bill; Barnet, Chris; Gambacorta, Antonia;

Environmental Factor(tm) system: RCRA hazardous waste handler information (on cd-rom). Database

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1996-04-01

Environmental Factor(tm) RCRA Hazardous Waste Handler Information on CD-ROM unleashes the invaluable information found in two key EPA data sources on hazardous waste handlers and offers cradle-to-grave waste tracking. It`s easy to search and display: (1) Permit status, design capacity and compliance history for facilities found in the EPA Resource Conservation and Recovery Information System (RCRIS) program tracking database; (2) Detailed information on hazardous wastes generation, management and minimization by companies who are large quantity generators, and (3) Data on the waste management practices of treatment, storage and disposal (TSD) facilities from the EPA Biennial Reporting System which is collectedmore » every other year. Environmental Factor`s powerful database retrieval system lets you: (1) Search for RCRA facilities by permit type, SIC code, waste codes, corrective action or violation information, TSD status, generator and transporter status and more; (2) View compliance information - dates of evaluation, violation, enforcement and corrective action; (3) Lookup facilities by waste processing categories of marketing, transporting, processing and energy recovery; (4) Use owner/operator information and names, titles and telephone numbers of project managers for prospecting; and (5) Browse detailed data on TSD facility and large quantity generators` activities such as onsite waste treatment, disposal, or recycling, offsite waste received, and waste generation and management. The product contains databases, search and retrieval software on two CD-ROMs, an installation diskette and User`s Guide. Environmental Factor has online context-sensitive help from any screen and a printed User`s Guide describing installation and step-by-step procedures for searching, retrieving and exporting. Hotline support is also available for no additional charge.« less

Environmental Factor{trademark} system: RCRA hazardous waste handler information

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1999-03-01

Environmental Factor{trademark} RCRA Hazardous Waste Handler Information on CD-ROM unleashes the invaluable information found in two key EPA data sources on hazardous waste handlers and offers cradle-to-grave waste tracking. It`s easy to search and display: (1) Permit status, design capacity and compliance history for facilities found in the EPA Resource Conservation and Recovery Information System (RCRIS) program tracking database; (2) Detailed information on hazardous wastes generation, management and minimization by companies who are large quantity generators, and (3) Data on the waste management practices of treatment, storage and disposal (TSD) facilities from the EPA Biennial Reporting System which is collectedmore » every other year. Environmental Factor`s powerful database retrieval system lets you: (1) Search for RCRA facilities by permit type, SIC code, waste codes, corrective action or violation information, TSD status, generator and transporter status and more; (2) View compliance information -- dates of evaluation, violation, enforcement and corrective action; (3) Lookup facilities by waste processing categories of marketing, transporting, processing and energy recovery; (4) Use owner/operator information and names, titles and telephone numbers of project managers for prospecting; and (5) Browse detailed data on TSD facility and large quantity generators` activities such as onsite waste treatment, disposal, or recycling, offsite waste received, and waste generation and management. The product contains databases, search and retrieval software on two CD-ROMs, an installation diskette and User`s Guide. Environmental Factor has online context-sensitive help from any screen and a printed User`s Guide describing installation and step-by-step procedures for searching, retrieving and exporting. Hotline support is also available for no additional charge.« less

Tracking Expected Improvements of Decadal Prediction in Climate Services

NASA Astrophysics Data System (ADS)

Suckling, E.; Thompson, E.; Smith, L. A.

2013-12-01

Physics-based simulation models are ultimately expected to provide the best available (decision-relevant) probabilistic climate predictions, as they can capture the dynamics of the Earth System across a range of situations, situations for which observations for the construction of empirical models are scant if not nonexistent. This fact in itself provides neither evidence that predictions from today's Earth Systems Models will outperform today's empirical models, nor a guide to the space and time scales on which today's model predictions are adequate for a given purpose. Empirical (data-based) models are employed to make probability forecasts on decadal timescales. The skill of these forecasts is contrasted with that of state-of-the-art climate models, and the challenges faced by each approach are discussed. The focus is on providing decision-relevant probability forecasts for decision support. An empirical model, known as Dynamic Climatology is shown to be competitive with CMIP5 climate models on decadal scale probability forecasts. Contrasting the skill of simulation models not only with each other but also with empirical models can reveal the space and time scales on which a generation of simulation models exploits their physical basis effectively. It can also quantify their ability to add information in the formation of operational forecasts. Difficulties (i) of information contamination (ii) of the interpretation of probabilistic skill and (iii) of artificial skill complicate each modelling approach, and are discussed. "Physics free" empirical models provide fixed, quantitative benchmarks for the evaluation of ever more complex climate models, that is not available from (inter)comparisons restricted to only complex models. At present, empirical models can also provide a background term for blending in the formation of probability forecasts from ensembles of simulation models. In weather forecasting this role is filled by the climatological distribution, and can significantly enhance the value of longer lead-time weather forecasts to those who use them. It is suggested that the direct comparison of simulation models with empirical models become a regular component of large model forecast intercomparison and evaluation. This would clarify the extent to which a given generation of state-of-the-art simulation models provide information beyond that available from simpler empirical models. It would also clarify current limitations in using simulation forecasting for decision support. No model-based probability forecast is complete without a quantitative estimate if its own irrelevance; this estimate is likely to increase as a function of lead time. A lack of decision-relevant quantitative skill would not bring the science-based foundation of anthropogenic warming into doubt. Similar levels of skill with empirical models does suggest a clear quantification of limits, as a function of lead time, for spatial and temporal scales on which decisions based on such model output are expected to prove maladaptive. Failing to clearly state such weaknesses of a given generation of simulation models, while clearly stating their strength and their foundation, risks the credibility of science in support of policy in the long term.

Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.

2012-08-15

We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 daysmore » of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.« less