Development of Metrics for Individual Exposure Assessment to Traffic Related Air Pollution PDF Download

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

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Book Description
This project has explored the use of a variety of methods for assigning exposure to traffic related air pollution. The work has been conducted simultaneously with the collection of health outcome data for an epidemiological study investigating the respiratory and irritant effects of changes in traffic related air pollution. Epidemiological studies investigating the effects of environmental exposures often suffer from poor or inadequate exposure assessment.

Author: Haneen Khreis
Publisher: Elsevier
ISBN: 0128181230
Category : Transportation
Languages : en
Pages : 650

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Book Description
Traffic-Related Air Pollution synthesizes and maps TRAP and its impact on human health at the individual and population level. The book analyzes mitigating standards and regulations with a focus on cities. It provides the methods and tools for assessing and quantifying the associated road traffic emissions, air pollution, exposure and population-based health impacts, while also illuminating the mechanisms underlying health impacts through clinical and toxicological research. Real-world implications are set alongside policy options, emerging technologies and best practices. Finally, the book recommends ways to influence discourse and policy to better account for the health impacts of TRAP and its societal costs. Overviews existing and emerging tools to assess TRAP’s public health impacts Examines TRAP’s health effects at the population level Explores the latest technologies and policies--alongside their potential effectiveness and adverse consequences--for mitigating TRAP Guides on how methods and tools can leverage teaching, practice and policymaking to ameliorate TRAP and its effects

Author: Mark J. Nieuwenhuijsen
Publisher: Oxford University Press, USA
ISBN: 0199378789
Category : Medical
Languages : en
Pages : 417

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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: Michal Krzyzanowski
Publisher: WHO Regional Office Europe
ISBN: 9289013737
Category : Business & Economics
Languages : en
Pages : 205

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Book Description
Diseases related to the air pollution caused by road transport affect tens of thousands of people in the WHO Europe region each year. This publication considers the policy challenges involved in the need to reduce the related risks to public health and the environment, whilst meeting socio-economic requirements for effective transport systems. It sets out a systematic review of the literature and a comprehensive evaluation of the health hazards of transport-related air pollution, including factors determining emissions, the contribution of traffic to pollution levels, human exposure and the results of epidemiological and toxicological studies to identify and measure the health effects, and suggestions for policy actions and further research.

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

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Book Description
"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: Guixing Wei
Publisher:
ISBN:
Category : Air
Languages : en
Pages : 198

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Book Description
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.

Author: Nicola Masey
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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This research developed efficient applications of portable measurement systems to assess human exposure to traffic-related air pollution through direct measurement, and evaluation of exposure models.Passive NO2 samplers are deployed at large numbers of sites in epidemiological studies to estimate typical concentrations over 1-4 weeks. I found that deployment time could be reduced to 2 days with limited impact on the accuracy and precision of exposure estimates. This shorter measurement time enabled observation of wind-speed effects leading to overestimation of ambient concentrations by passive samplers. Through development of a post-processing technique and/or inclusion of a membrane I improved sampler accuracy. Portable sensors can provide detailed estimates of personal exposures to air pollution. Many sensor-based monitors have not been subject to rigorous testing procedures to quantify their accuracy. I observed that the most accurate estimates of concentrations from NO2 and O3 sensor-based monitors required regular, intermittent calibration against reference analysers under similar environmental conditions to field measurements. I also found deterioration in BC monitor accuracy and precison when the attenuation of the collection filter exceeded 40 and no improvement in monitor accuracy was observed when filter darkness correction algorithms were applied. Portable sensors can be used to identify locations with higher concentrations, which may require more detailed monitoring. I established that repeated 6-minute measurements of BC and particle number concentrations estimated similar spatial trends to 1-week NO2 measurements using passive samplers. Dispersion models can be used to estimate pollution exposure at multiple locations over a study area. I found that initial user parameterisation in a weather model had limited effect on pollution estimates from a dispersion model. I evaluated a new GIS-based dispersion model (5 x 5 m NO2 estimates for a 3,500 km2 area, with model run times of under 10 minutes). I demonstrated that inclusion of discrete street canyon models and geospatial surrogates (accounting for urban morphology) improved model accuracy. The measurement and modelling evaluation research in this thesis complimented each other by providing efficient ways to directly measure population exposures.

Author: Bernard Sam Beckerman
Publisher:
ISBN:
Category :
Languages : en
Pages : 115

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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: World Health Organization
Publisher: World Health Organization
ISBN: 9289021926
Category : Medical
Languages : en
Pages : 497

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Book Description
This book presents revised guideline values for the four most common air pollutants - particulate matter, ozone, nitrogen dioxide and sulfur dioxide - based on a recent review of the accumulated scientific evidence. The rationale for selection of each guideline value is supported by a synthesis of information emerging from research on the health effects of each pollutant. As a result, these guidelines now also apply globally. They can be read in conjunction with Air quality guidelines for Europe, 2nd edition, which is still the authority on guideline values for all other air pollutants. As well as revised guideline values, this book makes a brief yet comprehensive review of the issues affecting the application of the guidelines in risk assessment and policy development. Further, it summarizes information on: . pollution sources and levels in various parts of the world, . population exposure and characteristics affecting sensitivity to pollution, . methods for quantifying the health burden of air pollution, and . the use of guidelines in developing air quality standards and other policy tools. Finally, the special case of indoor air pollution is explored. Prepared by a large team of renowned international experts who considered conditions in various parts of the globe, these guidelines are applicable throughout the world. They provide reliable guidance for policy-makers everywhere when considering the various options for air quality management.

Author: National Research Council
Publisher: National Academies Press
ISBN: 0309264685
Category : Nature
Languages : en
Pages : 210

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Book Description
From the use of personal products to our consumption of food, water, and air, people are exposed to a wide array of agents each day-many with the potential to affect health. Exposure Science in the 21st Century: A Vision and A Strategy investigates the contact of humans or other organisms with those agents (that is, chemical, physical, and biologic stressors) and their fate in living systems. The concept of exposure science has been instrumental in helping us understand how stressors affect human and ecosystem health, and in efforts to prevent or reduce contact with harmful stressors. In this way exposure science has played an integral role in many areas of environmental health, and can help meet growing needs in environmental regulation, urban and ecosystem planning, and disaster management. Exposure Science in the 21st Century: A Vision and A Strategy explains that there are increasing demands for exposure science information, for example to meet needs for data on the thousands of chemicals introduced into the market each year, and to better understand the health effects of prolonged low-level exposure to stressors. Recent advances in tools and technologies-including sensor systems, analytic methods, molecular technologies, computational tools, and bioinformatics-have provided the potential for more accurate and comprehensive exposure science data than ever before. This report also provides a roadmap to take advantage of the technologic innovations and strategic collaborations to move exposure science into the future.