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Author: Fatema Parvez Publisher: ISBN: Category : Air Languages : en Pages :
Book Description
Traffic related air pollution is considered one of the major challenges for a large number of urban population. The rapid growth of the world's motor-vehicle fleet due to population growth and economic improvement causes a significant negative impact on public health. As pollutants from roadway emission sources reach background concentration levels within a few hundred meters from the source, it is very challenging to implement a model that captures this behavior. Currently available air quality modeling approaches can compute the source specific pollutant fate on either a regional or a local scale but still lack effective ways to estimate the combined regional and local source contributions to exposure. Temporal variabilities in human activities and differences in pollutant dispersion pattern in stable and unstable atmospheric conditions greatly influence the exposure. Estimating air pollution exposure from local sources such as motor vehicles while considering all the variables impacting the dispersion make the process computationally intensive. We developed a hybrid modeling framework combining a regional model, CAMx - Comprehensive Air Quality Model with Extensions, and a local scale dispersion model, R-LINE, to estimate concentrations of both primary and secondary species from onroad emission sources. We utilized all chemical and physical processes available in CAMx and use the Particulate Matter Source Apportionment Technology, PSAT to quantify the concentrations from onroad and non-road emission sources. We employed R-LINE to estimate pollutant distribution from onroad emission sources at a finer resolution. Combining these two models, we estimated combined concentrations at a finer spatial resolution and at hourly temporal resolution. We have applied this modeling framework to three major cities in Connecticut and quantified human exposure to NOx, PM2.5, and elemental carbon (EC) at census block group resolution. We also estimated health risks on different demographic groups associated with PM2.5 exposures. Our approach of using a dispersion model is unique as it uses the mass fraction of the total dispersed pollutant at different receptor points and hence is not dependent on extensive roadway emissions data or extensive model runs. Overall, this modeling approach overcomes two major challenges facing hybrid modeling for near roadway exposures- double counting emissions and a lack of temporal variability in estimating concentrations.
Author: Fatema Parvez Publisher: ISBN: Category : Air Languages : en Pages :
Book Description
Traffic related air pollution is considered one of the major challenges for a large number of urban population. The rapid growth of the world's motor-vehicle fleet due to population growth and economic improvement causes a significant negative impact on public health. As pollutants from roadway emission sources reach background concentration levels within a few hundred meters from the source, it is very challenging to implement a model that captures this behavior. Currently available air quality modeling approaches can compute the source specific pollutant fate on either a regional or a local scale but still lack effective ways to estimate the combined regional and local source contributions to exposure. Temporal variabilities in human activities and differences in pollutant dispersion pattern in stable and unstable atmospheric conditions greatly influence the exposure. Estimating air pollution exposure from local sources such as motor vehicles while considering all the variables impacting the dispersion make the process computationally intensive. We developed a hybrid modeling framework combining a regional model, CAMx - Comprehensive Air Quality Model with Extensions, and a local scale dispersion model, R-LINE, to estimate concentrations of both primary and secondary species from onroad emission sources. We utilized all chemical and physical processes available in CAMx and use the Particulate Matter Source Apportionment Technology, PSAT to quantify the concentrations from onroad and non-road emission sources. We employed R-LINE to estimate pollutant distribution from onroad emission sources at a finer resolution. Combining these two models, we estimated combined concentrations at a finer spatial resolution and at hourly temporal resolution. We have applied this modeling framework to three major cities in Connecticut and quantified human exposure to NOx, PM2.5, and elemental carbon (EC) at census block group resolution. We also estimated health risks on different demographic groups associated with PM2.5 exposures. Our approach of using a dispersion model is unique as it uses the mass fraction of the total dispersed pollutant at different receptor points and hence is not dependent on extensive roadway emissions data or extensive model runs. Overall, this modeling approach overcomes two major challenges facing hybrid modeling for near roadway exposures- double counting emissions and a lack of temporal variability in estimating concentrations.
Author: Mark J. Nieuwenhuijsen Publisher: Oxford University Press, USA ISBN: 0199378789 Category : Medical Languages : en Pages : 417
Book Description
This completely updated edition of Exposure Assessment in Environmental Epidemiology offers a practical introduction to exposure assessment methodologies in environmental epidemiologic studies. In addition to methods for traditional methods -- questionnaires, biomonitoring -- this new edition is expanded to include geographic information systems, modeling, personal sensoring, remote sensing, and OMICs technologies. In addition, each of these methods is contextualized within a recent epidemiology study, maximizing illustration for students and those new to these to these techniques. With clear writing and extensive illustration, this book will be useful to anyone interested in exposure assessment, regardless of background.
Author: Carlos Borrego Publisher: Springer Science & Business Media ISBN: 144198867X Category : Science Languages : en Pages : 633
Book Description
This volume covers the latest scientific developments in the real world applications of pollution modeling. Topics covered include: the role of atmospheric models in air pollution policy and abatement strategies; integrated regional modelling; global and long-range transport; aerosols as atmospheric contaminants; model assessment and verification; and application of new concepts in different regions of the world.
Author: Clemens Mensink Publisher: Springer Nature ISBN: 3030220559 Category : Science Languages : en Pages : 490
Book Description
Current developments in air pollution modeling are explored as a series of contributions from researchers at the forefront of their field. This newest contribution on air pollution modeling and its application is focused on local, urban, regional and intercontinental modeling; emission modeling and processing; data assimilation and air quality forecasting; model assessment and evaluation; atmospheric aerosols. Additionally, this work also examines the relationship between air quality and human health and the effects of climate change on air quality. This work is a collection of selected papers presented at the 36th International Technical Meeting on Air Pollution Modeling and its Application, held in Ottawa, Canada, May 14-18, 2018. The book is intended as reference material for students and professors interested in air pollution modeling at the graduate level as well as researchers and professionals involved in developing and utilizing air pollution models.
Author: Bernard Sam Beckerman Publisher: ISBN: Category : Languages : en Pages : 115
Book Description
Of the 3.7 million deaths attributed to outdoor air pollution, ischemic heart disease (IHD) represents 40% of the total deaths, or approximately 1.48 million deaths, which occur mainly in older adults. IHD is the largest single causes of death attributable to ambient air pollution. Research on the progression and incidence of IHD are pointing to ambient fine particulate matter (PM) as a major contributor to morbidity and mortality outcomes. In this context, improvements in air pollution exposure assessment methods and health effects assessments are developed and investigated in this thesis. With the exposure assessment, methods and tools were created that had utility for improving air pollution exposure assessment. Two exposure assessment chapters are presented. The first of these is focused on the creation of a national-level spatio-temporal air pollution exposure model. In the second exposure chapter, emphasis is placed on the development and evaluation of methods used to estimate annual average daily traffic - a local source of ambient particulates and other air pollutants thought to have heightened toxicity. A model was created to predict ambient fine particulate matter less than 2.5 microns in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling (Chapter 2). We developed a novel hybrid approach that combine a land use regression model (LUR) and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals,. The PM2.5 dataset included observations at 1,464 monitoring locations with approximately 10% of locations reserved for cross-validation across the contiguous United States. In the LUR, variables based on remote sensing estimates of PM2.5, land use and traffic indicators were made available to the Deletion/Substitution/Addition machine learning algorithm used to select predictive models describing local variability in PM2.5. Two modeling configurations were tested. The first included all of the available covariates; and the second did not include the remote sensing. The remote sensing variable was not based on any ground information. Specific results showed that normalized cross-validated R2 values for LUR were 0.63 and 0.11 with and without remote sensing, respectively; suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R2 were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework effectively predicts ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S. The network interpolation tool used to estimate traffic is described in Chapter 3. The program was created using free open-source software, namely Python 2.7 and its related libraries. It was applied to two county study areas in California, USA (Alameda and Los Angeles), where inverse distance weighted (IDW) and kriging annual average daily traffic (AADT) models were estimated. These estimates were compared to: each other; to an entirely independent dataset; and against a traffic model using similar methods to those used in the traffic estimates employed in the exposure model in Chapter 2. Results show different levels of predictive agreement. Using cross-validation methods, the R2 for these models were 0.36 and 0.32 in Alameda and 0.46 and 0.47 in Los Angeles, for IDW and Kriging, respectively. Differences in model performance seen between and within the study area suggest that data issues may have materially contributed; these include: temporal discordance in the measurements and mischaracterization of road types. A comparison of network interpolation methods to those used to estimate traffic in Chapter 2 found the network methods to be superior. For the health effects analysis that that estimated an exposure response curve describing the effect of PM2.5 on ischemic heart disease mortality, monthly ambient PM2.5 estimates (from the model outlined in Chapter 2) were averaged to represent long-term exposure at the home. Super Learner evaluated 14 models that fell within the classes of parametric, semi-parametric, and non-parametric models. A generalized additive model with splined terms was identified as being most predictive of life expectancy. Over the range of exposure 3-27 μg/m3 the estimated years of life lost over this interval was 0.6 years. This relationship, however, was not linear. It followed the pattern reported in previous studies with increased risk rates at lower exposures and a flattening out of the curve at higher exposures. An inflection point appeared to occur near 10 μg/m3. These estimates failed to reach significance at the 95% confidence criteria but were close enough to be suggestive of a relationship. Results from a complementary simulation showed that left truncation characteristics of the cohort likely biased to results towards the null. In addition, the use of inverse probability of censoring weights to control for bias induced by right censoring added variability to the estimator that likely reduced the power to detect and effect. This research has shown the utility of machine-learning algorithms for improving health effects assessments in the field of air pollution epidemiology. In exposure science, they have proven their utility in creating estimates of exposure that can be used to characterize multiple scales of variability. In health effects assessments, in combination with causal inference methods, this work has shown the utility of these methods to detect non-linear effects in novel parameter estimates in individual cohort studies. In addition to the methodological contribution, the health effects results contribute to the discussion about the burden of disease attributable to particulate matter.
Author: Carlos Borrego Publisher: Springer Science & Business Media ISBN: 1402084536 Category : Technology & Engineering Languages : en Pages : 766
Book Description
In 1969, the North Atlantic Treaty Organization (NATO) established the C- mittee on Challenges of Modern Society (CCMS). The subject of air pollution was from the start one of the priority problems under study within the framework of various pilot studies undertaken by this committee. The organization of a periodic conference dealing with air pollution modelling and its application has become one of the main activities within the pilot study relating to air pollution. The first five international conferences were organized by the United States as the pilot country, the second five by the Federal Republic of Germany, the third five by Belgium, the fourth four by The Netherlands, the next five by Denmark and the last five by Portugal. This volume contains the abstracts of papers and posters presented at the 29th NATO/CCMS International Technical Meeting on Air Pollution Modelling and Its Application, held in Aveiro, Portugal, during September 24–28, 2007. This ITM was organized by the University of Aveiro, Portugal (Pilot Country and Host Organization). The key topics distinguished at this ITM included: Local and urban scale modelling; Regional and intercontinental modelling; Data assimilation and air quality forecasting; Model assessment and verification; Aerosols in the atmosphere; Interactions between climate change and air quality; Air quality and human health.
Author: National Research Council Publisher: National Academies Press ISBN: 0309042844 Category : Science Languages : en Pages : 338
Book Description
Most people in the United States spend far more time indoors than outdoors. Yet, many air pollution regulations and risk assessments focus on outdoor air. These often overlook contact with harmful contaminants that may be at their most dangerous concentrations indoors. A new book from the National Research Council explores the need for strategies to address indoor and outdoor exposures and examines the methods and tools available for finding out where and when significant exposures occur. The volume includes: A conceptual framework and common terminology that investigators from different disciplines can use to make more accurate assessments of human exposure to airborne contaminants. An update of important developments in assessing exposure to airborne contaminants: ambient air sampling and physical chemical measurements, biological markers, questionnaires, time-activity diaries, and modeling. A series of examples of how exposure assessments have been applied-properly and improperly-to public health issues and how the committee's suggested framework can be brought into practice. This volume will provide important insights to improve risk assessment, risk management, pollution control, and regulatory programs.
Author: Ranjeet S. Sokhi Publisher: Anthem Press ISBN: 1783088281 Category : Science Languages : en Pages : 453
Book Description
‘Mesoscale Modelling for Meteorological and Air Pollution Applications’ combines the fundamental and practical aspects of mesoscale air pollution and meteorological modelling. Providing an overview of the fundamental concepts of air pollution and meteorological modelling, including parameterization of key atmospheric processes, the book also considers equally important aspects such as model integration, evaluation concepts, performance evaluation, policy relevance and user training.
Author: Fatema Parvez
Author: Fatema Parvez
Author: Mark J. Nieuwenhuijsen
Author: Carlos Borrego
Author: Clemens Mensink
Author: Jun Wu
Author: Bernard Sam Beckerman
Author: Naihua Duan
Author: Carlos Borrego
Author: National Research Council
Author: Ranjeet S. Sokhi