Development and Application of Individual and Population-level Human Exposure Models for Fine Particles and Other Vehicle-related Air Pollutants in Southern California PDF Download

Author: Jun Wu
Publisher:
ISBN:
Category :
Languages : en
Pages : 396

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Author: Juliana A. Maantay
Publisher: Springer Science & Business Media
ISBN: 9400703295
Category : Medical
Languages : en
Pages : 500

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This book focuses on a range of geospatial applications for environmental health research, including environmental justice issues, environmental health disparities, air and water contamination, and infectious diseases. Environmental health research is at an exciting point in its use of geotechnologies, and many researchers are working on innovative approaches. This book is a timely scholarly contribution in updating the key concepts and applications of using GIS and other geospatial methods for environmental health research. Each chapter contains original research which utilizes a geotechnical tool (Geographic Information Systems (GIS), remote sensing, GPS, etc.) to address an environmental health problem. The book is divided into three sections organized around the following themes: issues in GIS and environmental health research; using GIS to assess environmental health impacts; and geospatial methods for environmental health. Representing diverse case studies and geospatial methods, the book is likely to be of interest to researchers, practitioners and students across the geographic and environmental health sciences. The authors are leading researchers and practitioners in the field of GIS and environmental health.

Author: Jonathan Zhong Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Automobile traffic has been a longstanding source of air pollution in human communities. The target of major regulations in the past few decades, the transportation sector has gone through significant changes, ranging from shifts in vehicle fleet composition to natural and artificial disruptions to traffic patterns. Both an essential form of transportation as well as a major source of air pollution, traffic, to this day, remains a human necessity and a public health challenge. As such, measuring and modeling the temporal and spatial distribution of traffic related air pollution (TRAP) is a critical necessity for exposure scientists, epidemiologists, and other public health professionals. In this dissertation, we investigate methods to measure TRAP in response to recent trends and disruptions in traffic patterns and composition, with a particular focus on California State. To this end, we employ novel combinations of big data, including regulatory air quality data in addition to traffic, land-use, and internet-of-things network data. It is divide into five chapters: an introduction (chapter 1), three chapters of original research (chapters 2-4), and a discussion of the conclusions of the work (chapter 5). Chapter 2 evaluates the near-road air quality impacts of traffic disruptions associated with the COVID-19 pandemic. Following global activity stoppages associated with stay-at-home measures, studies reported improved air quality in several cities and countries around the world. While widely observed, many studies could not properly attribute the decline of traffic in this chapter evaluates the relationship between traffic volume as reported by the California Department of Transportation and near-road NO and NO2 emissions at seven EPA monitoring sites in California state: four in Northern California and three in Southern California. Chapter 3 models the spatial distribution of non-tailpipe emissions-related PM2.5 chemical species and oxidative potential in Southern California. Combining gold-standard filter samples, land-use data, and a novel internet-of-things low-cost sensor network dataset in a spatial regression (Co-Kriging with External Drift) model, we create a set of exposure surfaces for exposures that serve as tracers of TRAP. Results indicate that compared to typical modelling techniques, namely land-use regressions, the addition of low-cost sensor data improves model accuracy and precision. Chapter 4 evaluates the associations between exposures modeled in Chapter 3 and the ischemic placental disease (IPD) in a prospectively-followed pregnancy cohort of 178 women. Air quality regulation have resulted in declines in tailpipe emissions in recent years. As stated earlier, TRAP is also generated from non-tailpipe sources, including brake and tire wear. Concerned that brake and tire wear particles contain metals and other organic compounds that could harm fetal health, this study uses a logistic regression model to estimate exposure outcome associations. Compared to conventional exposures, namely PM2.5 and black carbon, we find stronger associations between IPD and exposures more specific to brake and tire wear, such as barium, as well as oxidative potential markers. At the time of filing, Chapter 2 has been published in Environmental Science and Technology Letters, Chapter 3 has been published in Environment International, and Chapter 4 is currently in preparation for submission to an academic journal.

Author: Sponsored by The Health Effects Institute
Publisher: National Academies Press
ISBN: 0309037263
Category : Science
Languages : en
Pages : 703

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"The combination of scientific and institutional integrity represented by this book is unusual. It should be a model for future endeavors to help quantify environmental risk as a basis for good decisionmaking." â€"William D. Ruckelshaus, from the foreword. This volume, prepared under the auspices of the Health Effects Institute, an independent research organization created and funded jointly by the Environmental Protection Agency and the automobile industry, brings together experts on atmospheric exposure and on the biological effects of toxic substances to examine what is knownâ€"and not knownâ€"about the human health risks of automotive emissions.

Author: Fatema Parvez
Publisher:
ISBN:
Category : Air
Languages : en
Pages :

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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

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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: National Research Council
Publisher: National Academies Press
ISBN:
Category : Medical
Languages : en
Pages : 553

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Discusses pollution from tobacco smoke, radon and radon progeny, asbestos and other fibers, formaldehyde, indoor combustion, aeropathogens and allergens, consumer products, moisture, microwave radiation, ultraviolet radiation, odors, radioactivity, and dirt and discusses means of controlling or eliminating them.

Author: Sarah Elisabeth Chambliss
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Exposure to air pollution causes diseases of the lungs, cardiovascular system, brain, and numerous other systems, and is a leading environmental health risk worldwide. The burden of air pollution exposure is not distributed evenly across the population of the United States, and often falls more heavily on low-income groups and people of color. An accurate understanding of how air pollution levels vary on multiple spatial scales is critical for shaping effective policies to improve air quality for the highest exposed communities. Pollutants with primary and secondary contributions like fine particulate matter (PM2.5) vary significantly within urban areas on length scales of 1 km but are influenced by emissions at scales of 100 km or more, while other pollutant categories exhibit strong near-source decay at length scales of 100 m. In this dissertation I apply two complementary approaches to assess multiscale spatial patterns for five health-relevant pollutants: PM2.5, black carbon (BC), ultrafine particles (UFP), nitrogen oxide (NO), and nitrogen dioxide (NO2). Using a reduced-complexity chemical transport model I show that current emissions patterns lead to significant PM2.5 exposure disparity among racial-ethnic groups, income categories, and other socioeconomic groupings, driven by the systematically higher proximity to emissions from on-road mobile sources, industry, natural gas and petroleum development, and other major sources. To estimate exposure disparity for pollutants that vary at very fine spatial scales and follow difficult-to-model patterns driven by complex characteristics of the urban landscape (BC, UFP, NO, and NO2), I use data collected via mobile monitoring to construct empirical air pollution maps for a variety of neighborhoods in the San Francisco Bay Area. These measurements show high exposure disparities both within and among racial-ethnic groups, with disparity in mean concentrations driven by differences in neighborhood background concentrations but higher within-group disparity driven by highly localized near-source gradients. I also assess sources of uncertainty in mobile monitoring-based mapping techniques. These complementary approaches provide a broad picture of causes of urban exposure disparity in California and can inform future mitigation measures

Author: Naihua Duan
Publisher:
ISBN:
Category : Air
Languages : en
Pages : 28

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Author: Guixing Wei
Publisher:
ISBN:
Category : Air
Languages : en
Pages : 198

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Air pollution has been among the biggest environmental risks to human health. Exposure assessment to air pollution is essentially a procedure to quantify the degree to which people get exposed to hazardous air pollution. Exposure assessment is also a critical step in health-related studies exploring the relationship between personal exposure to environmental stressors and adverse health outcomes. Given the critical role of exposure assessment, it is important to accurately quantify and characterize personal exposure in geographic space and time. For years numerous exposure assessment methods have been developed with respect to a wide spectrum of air pollutants. Of all the methods, the most commonly used one is to use a representative geographic unit as the surrogate location to estimate the potential impact from hazardous air pollution from differing sources on that location. The representative unit is one person's home location in most cases. Such studies, however, have failed to recognize the significance of both the dynamics of human activities and the variation of air pollution in geographic space and time. It is believed that personal exposure is essentially a function of space and time as an individual's time-activity patterns and intensities of air pollutant in question vary over space and time. It is therefore imperative to account for the spatiotemporal dynamics of both in exposure assessment. To this end, the goal of this study is to account for the spatiotemporal dynamics of both human time-activity patterns and air pollution for assessing personal exposure. More specifically this dissertation aims to achieve three objectives as summarized below. First, in light of the deficiency of existing home-based exposure assessment methods, this study proposes an innovative trajectory-based model for assessing personal exposure to ambient air pollution. This model provides a computational framework for assessing personal exposure when trajectories, documenting human spatiotemporal activities, are modeled into a series of tours, microenvironments (MEs), and visits. A set of individual-level trajectories was simulated to test the performance of the proposed model, in conjunction with one-day air pollution (PM2.5) data in Beijing, China. The results from the test demonstrated that the trajectory-based model is capable of capturing the spatiotemporal variation of personal exposure, thus providing more accurate, detailed and enriched information to better understand personal exposure. The findings indicate that there is considerable variation in intra-microenvironment and inter-microenvironment exposure, which identified the importance of distinguishing between different MEs. Moreover, this study tested the proposed model using an empirical dataset. Second, little is known about the difference between the estimated exposure based on home locations only and that considering the locations of all human activities. To fill this gap, this study aims to test whether the exposure calculated from the home-based method is statistically significantly different from the exposure estimated by the newly developed trajectory-based model. A Dataset containing 4,000 individual-level one-day trajectories (Dataset 1) was simulated to test the aforementioned hypothesis. The exposure estimates in comparison are the average hourly exposure over a 24-hour period from two exposure assessment methods. The 4,000 trajectories were split into another two subsets (Datasets 2, 3) according to the difference between home-based exposure estimates and trajectory-based exposure estimates. The Wilcoxon Signed-rank test was used to evaluate whether the difference between the two models is significant. The results show that the statistically significant difference was found only in Dataset 3. The same test was also applied to a set of empirical trajectories. The significant difference exists in the results from the empirical data. The mixed results suggest that additional research is needed to verify the difference between the two exposure assessment methods. Third, little research has taken into consideration of hourly traffic variation and human activities simultaneously in a model for assessing personal exposure to traffic emissions. To fill this gap, this study develops a new trajectory-based model to quantify personal exposure to traffic emissions. The hourly share of daily traffic volume of each roadway in the study area was estimated by calculating the traffic allocation factors (TAFs) of each roadway. Next, the hourly traffic emission surfaces were built using the hourly shares and a kernel density algorithm. A 3-D cube representing the spatiotemporal distribution of traffic emission was constructed, which overlaid the simulated individual-level trajectory data for assessing personal exposure to traffic emissions. The results showed that people's time-activity patterns (e.g., where an individual lives/works, where an individual travels) were significant factors in exposure assessment. This study suggests that people's time activities and hourly variation of traffic emission should be simultaneously addressed when assessing personal exposure to traffic emissions. To sum up, this study has devoted a large effort in quantifying and characterizing personal exposure in geographic space and time. A few of contributions to the knowledge of exposure science are listed as follows. First, this study contributes two exposure assessment models in characterizing personal spatiotemporal exposure using trajectory data. One is developed for assessing personal exposure to ambient air pollution, and the other one is for assessing personal exposure to traffic emissions. Second, this study demonstrates the intra- and inter-microenvironment variation of personal exposure and reveals the significance of people's time-activity patterns in exposure assessment. Third, this study investigates the difference in exposure estimates between conventional home-based methods considering home locations only and trajectory-based methods accounting for the locations of all activities. The mixed findings from Wilcoxon Signed-rank tests suggest more research is needed to explore how personal exposure varies with time-activity patterns. All these contributions will have important implications in exposure science, environment science, and epidemiology.