Download or read book Estimating the Impact of Air Pollution Using Small Area Estimation written by Mine C̜etinkaya and published by . This book was released on 2011 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimating the Health Effects of Air Pollutants written by Bart D. Ostro and published by World Bank Publications. This book was released on 1994 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does one assess the health benefits of air pollution control? Does response functions applied to data on Jakarta reveal that air quality improvements will reduce illness, premature death, and learning disabilities in children. Lead and respirable particles are the most important problems.

Download or read book Estimation of Monetary Values of Air Pollutant Emissions in Various US Areas written by and published by . This book was released on 1994 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two general methods of estimating monetary values of air pollutants are presented in this paper. The damage estimate method directly estimated, air pollutant by simulating air quality, identifying health and other welfare impacts damage values and valuing the identified impacts of air pollution, and valuing the identified impacts. Although the method is theoretically sound, many assumptions are involved in each of its estimation steps, and uncertainty exists in each step. The control cost estimate method estimates the marginal emission control cost, which represents the opportunity cost offset by emission reductions from some given control measures. Studies conducted to estimate emission values in US regions used either the damage estimate method or the control cost estimate method. Taking emission values estimated for some US air basins, this paper establishes regression relationships between emission values and total population and air pollutant concentrations. On the basis of the established relationships, both damage-based and control-cost-based emission values are estimated for 17 major US urban areas.

Download or read book Statistical Methods for Environmental Epidemiology with R written by Roger D. Peng and published by Springer Science & Business Media. This book was released on 2008-12-15 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: As an area of statistical application, environmental epidemiology and more speci cally, the estimation of health risk associated with the exposure to - vironmental agents, has led to the development of several statistical methods and software that can then be applied to other scienti c areas. The stat- tical analyses aimed at addressing questions in environmental epidemiology have the following characteristics. Often the signal-to-noise ratio in the data is low and the targets of inference are inherently small risks. These constraints typically lead to the development and use of more sophisticated (and pot- tially less transparent) statistical models and the integration of large hi- dimensional databases. New technologies and the widespread availability of powerful computing are also adding to the complexities of scienti c inves- gation by allowing researchers to t large numbers of models and search over many sets of variables. As the number of variables measured increases, so do the degrees of freedom for in uencing the association between a risk factor and an outcome of interest. We have written this book, in part, to describe our experiences developing and applying statistical methods for the estimation for air pollution health e ects. Our experience has convinced us that the application of modern s- tistical methodology in a reproducible manner can bring to bear subst- tial bene ts to policy-makers and scientists in this area. We believe that the methods described in this book are applicable to other areas of environmental epidemiology, particularly those areas involving spatial{temporal exposures.

Download or read book Indoor Pollutants written by National Research Council and published by National Academies Press. This book was released on 1981-01-01 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Application of Causal Inference Methods to Estimate Single Pollutant and Multi Pollutant Health Effects in Asthmatic Children in Fresno California written by Jonathan Maclean Snowden and published by . This book was released on 2011 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: The methodological challenges associated with conducting research on air pollution mixtures are well-known: correlated co-pollutants result in unstable effect estimates and large standard errors, hindering the assignment of causality to any one exposure. There is still relatively little research in the growing multi-pollutant literature that is focused on the mixture itself as the unit of analysis. In this dissertation, I implement a statistical method from the causal inference literature to estimate the effects of ambient air pollution, as single pollutants and in a two-pollutant mixture. I analyze the effects of single-pollutant and multi-pollutant summertime ambient air pollution exposures on pulmonary function in a cohort of children with asthma living in Fresno, California. I employ a technique from the causal inference literature, the Population Intervention Model (PIM), to describe the health effects that would result from several hypothetical interventions that involve lowering concentrations of ambient air pollution. By describing the health effects of the ambient air pollutants in these terms, this approach estimates results that are relevant to real-world policy questions. Furthermore, this analytical approach permits the calculation of air pollution health effects that correspond to multiple pollutants dynamically changing within a mixture, as ambient air pollution is actually experienced by people. I interpret each of these health effects according to whether it reflects a realistic, or even a possible, exposure scenario during the study period and in the region where data were collected. I achieve this through an examination of the individual and joint distributions of the pollutants under study. This dissertation contains several analyses, corresponding to single- and multi-pollutant exposure regimens. In the first analysis, I analyze the effects of ambient summertime NO2 on FEF25-75 in a single-pollutant approach that demonstrates the methodological approach. All analyses use central-site exposure data, assigning all subjects on a given study day the same air pollution exposure values. Ambient PM10-2.5 is analyzed throughout as a summertime pollutant of secondary interest, both in a single-pollutant PM10-2.5 analysis, and in a mixture analysis. For the multi-pollutant mixture analysis, I extend the Population Intervention Model framework demonstrated in the single-pollutant analyses to a two-pollutant summer analysis of ambient NO2 and PM10-2.5, estimating health effects associated with an intervention that dynamically alters levels of one or both pollutants. In this two-pollutant analysis, I estimate the effects of lowering levels of one co-pollutant while "controlling for" the other (i.e., holding it at observed levels), as well as the effects of a joint intervention that decreases levels of both pollutants. The Background chapter presents a brief history of air pollution epidemiology and policy, and reviews the epidemiologic and statistical research upon which this dissertation builds. The Methods chapter describes the data collection protocol of the Fresno Asthmatic Children's Environment Study (FACES), the theoretical basis for the chosen methodological approach, and the details of the statistical methods employed in these analyses. In the Results section, I describe the characteristics of the FACES study sample, provide tabular and graphical descriptions of the distribution of ambient air pollution in the study, and present the results of the single- and multi-pollutant PIM analyses. In the Discussion section, I provide interpretation of the effects estimated in these various analyses, and refer back to the single- and multi-pollutant exposure distributions to situate the various health effects in appropriate context, and to speculate on potential sources of bias. All health effects calculated in these analyses were estimated relatively imprecisely; however, comparison of the magnitude and direction of the risk differences across analyses demonstrates patterns and provides information about the respiratory effects of the pollutants analyzed in this study. Furthermore, consideration of the individual and joint distributions of the two exposures yields key insight that guides the interpretation of these findings, especially as relates to parameter identifiability. In this analysis, there is ample evidence that the types of air pollution profiles described by two interventions are not realistic given the observed data, and furthermore that there is not support in the data to estimate health effects for these interventions. These parameters were defined to be comparable to standard practice in the multi-pollutant literature. The finding that they were not identifiable in the FACES data argues against giving weight to these specific findings, and also raises broader questions about parameters of this type: large, isolated single-pollutant concentration changes in a multi-pollutant exposure regimen. The work presented here emphasizes that such parameters should be scrutinized for positivity and data support before commencing analysis, regardless of the analytical approach chosen.

Download or read book Improving Exposure Response Estimation in Air Pollution Health Effects Assessments written by Bernard Sam Beckerman and published by . This book was released on 2014 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book A Simple Screening Technique for Estimating the Impact of a Point source of Air Pollution Relative to the Air Quality Standards written by D. Bruce Turner and published by . This book was released on 1973 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Assessment of Personal Exposure to Air Pollution Based on Trajectory Data written by Guixing Wei and published by . This book was released on 2018 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Proposed Methodology for Estimating the Impact of Highway Improvements on Urban Air Pollution written by Wallace E. Reed and published by . This book was released on 1971 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this methodology is to indicate the expected change in ambient air quality in the vicinity of a highway improvement and in the total background level of urban air pollution resulting from the highway improvement. Both the jurisdiction in which it is located and groups living adjacent to the proposed improvement should be made aware of the total and relative change to be expected. This change should be related to levels of air pollution which have known effects on human, animal, and crop health, property values, and activity operating costs such as cleaning and air filtering in urban areas. If the construction of a highway network will itself lend to air pollution exceeding established air quality standards, or in conjunction with the land uses it encourages will exceed such standards, the local jurisdictions should be aware of the trade offs between highway and other types of pollution needed to stay below the standards set for the area. In addition the procedure for estimating vehicle emission levels and concentrations on the improvement right-of-way can also be used to estimate the effects of air pollution on driver behavior and highway safety.

Download or read book Improving Land Use Based Estimation of Traffic Related Air Pollution by Extending Models Over Space and Time written by Kerolyn Katrina Shairsingh and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Land-use regression (LUR) modeling is used to predict traffic-related air pollution (TRAP) at the urban-scale; which makes it a common tool for estimating exposure in health studies. However, these models are developed for a specific time and geographic area. This can lead to inaccurate exposure assessments when TRAP estimations are required for past or future years, or in cities beyond the model's geographic area. This research used mobile sampled data to identify ways to improve the accuracy of model estimations when predicting exposure across time and space. Mobile measurements were separated into three resolved concentrations (local, neighbourhood- and regional- background) based on the spline of minimums method, a time-series approach. Associations between concentrations and land-use data (proximity to highways, industries, parks) were stronger for resolved than unresolved concentrations. To more accurately model observed concentration patterns across a wider geographic area, LUR models were developed from these resolved concentrations. For most air pollutants (ultrafine particles, nitrogen dioxide and nitric oxide), the resolved models were better able to assess exposure than unresolved models (those developed with total ambient concentrations) when they were spatially extended to areas bordering the model's geographic area. Furthermore, these improvements in the resolved models' estimations were observed for similar and different land-use practices between the model's geographic area and bordering areas. Traditional LUR models contain only spatial predictor variables and these can be backcasted or forecasted through linear scaling for past or future exposure estimates. Spatiotemporal are another type of model that contain both spatial and temporal predictor variables; these can estimate past or future concentrations by updating the temporal predictor variables (wind speed, temperature, reference monitor concentrations) within the model. This research found that spatiotemporal models were better able to estimate observed concentration patterns in present-day and historic concentrations, but backcasting of spatial models showed more accurate historical estimates. In addition, this work developed hybrid land-use models (coupling machine learning techniques and land-use data) that were used to estimate historical concentrations. The hybrid models were better able to estimate present-day exposure than traditional LUR models, however, LUR models were better able to assess historical exposure.

Download or read book An Evaluation of Methods and Technology to Estimate Localized Environmental and Health Impacts from Air Pollution and Pesticide Use written by Margaret Isied and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans are a product of their environment - the air we breathe, the water we drink, the food we eat, are all in one way or another our "environment", which in turn, impacts our health. Air pollution has been a long-standing issue, from the time humans innovated cooking over fire stoves, to our present-day reliance on transportation, technology, and industry. Exposure to air pollution has been linked to premature deaths, respiratory diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and bronchitis, total body inflammation, and cancer. We are exposed to environmental contaminants everywhere and every day. For example, pesticides are used for farming practices to increase crop yield. With the advancements in commercial farming, any number of highly toxic, highly volatile pesticides are ubiquitously used within the same area. Many communities living near major sources of air pollution, such as freeways, industrial sites, and agricultural areas, have been demonstrated to be disproportionately burdened by these environmental contaminants. While environmental conditions have improved drastically since the 1970s, there is high variability of what communities are experiencing at the local level. Recently, there has been a rise in environmental concerns locally; communities are concerned that current environmental monitoring and assessment methods are flawed in two ways. First, these methods are focused on regional impacts not capturing local environmental conditions within smaller communities. Since the 1970s, environmental agencies have evaluated environmental contaminant levels using monitoring and modeling techniques that demonstrate how pollutant concentrations are impacting a region. Many of these methods were developed to demonstrate compliance with state and federal standards. For example, monitoring equipment is strategically placed to understand the impacts of air quality on a region, rather than a local community, and air dispersion modeling has typically been reserved for large industrial operations that are likely to exceed regional air quality standards. Second, these methods do not consider exposure to multiple environmental contaminants which, coupled with social burdens such as low income and limited access to resources, make communities more susceptible to health impacts, ultimately diminishing their quality of life. Single pollutant evaluations are not representative of real-world exposure scenarios. These concerns highlight a needed call to action to better understand and evaluate environmental pollutants. New or repurposed methods and tools would ultimately provide regulators data at a more granular scale to make decisions in the interest of specific communities, rather than over an entire region. A better understanding of pollution variation in a community would also help regulators know where to focus intervention efforts. My dissertation explores tools and methods with the goal to: (1) recommend how to use new low-cost sensor monitoring technology to successfully understand localized air quality impacts, (2) present a case study using localized air pollution data to better quantify community exposures to air pollution, and (3) explore how air dispersion modeling can be used to evaluate exposure to multiple pesticides at the local level. Results from this dissertation developed new methods for setting up low-cost air quality sensor networks, emphasize variable air pollution concentrations within communities, and demonstrated the feasibility of repurposing modeling tools to evaluate pesticide use. This research is critical to reinforcing the importance of implementing new methods and technologies to understand localized impacts and provide data to regulatory bodies who are responsible for emission control, land use decision making, and public health intervention.

Download or read book Links Between Air Quality and Economic Growth written by Shanthi Nataraj and published by Rand Corporation. This book was released on 2013-12-20 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report assesses what evidence exists for the ways in which local air quality could influence local economic growth and how those effects might be relevant to the Pittsburgh region.

Download or read book Targeted Maximum Likelihood Estimation for Evaluation of the Health Impacts of Air Pollution written by Varada Sarovar and published by . This book was released on 2017 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: The adverse effects of air pollution on human life is of serious concern for today's society. Two population groups that are especially vulnerable to air pollution are pregnant women and their growing fetuses, and the focus of this thesis is to study the effects of air pollution on these populations. In order to address the methodological limitations in prior research, we quantify the impact of air pollution on various adverse pregnancy outcomes, utilizing machine learning and novel causal inference methods. Specifically, we utilize two semi-parametric, double robust, asymptotically efficient substitution estimators to estimate the causal attributable risk of various pregnancy outcomes of interest. Model fitting via machine learning algorithms helps to avoid reliance on misspecified parametric models and thereby improve both the robustness and precision of our estimates, ensuring meaningful statistical inference. Under assumptions, the causal attributable risk that we estimate translates to the absolute change in adverse pregnancy outcome risk that would be observed under a hypothetical intervention to change pollution levels, relative to currently observed levels. The estimated causal attributable risk provides a quantitative estimate of a quantity with more immediate public health and policy relevance.

Download or read book Estimating Health Effects of Air Pollution written by Bart Ostro and published by . This book was released on 1994 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Workbook of Atmospheric Dispersion Estimates written by D. Bruce Turner and published by . This book was released on 1969 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Costs Benefits and Efficiency of Air Quality Regulation written by Joshua Murphy and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Air pollution poses serious risks to public health. Combatting it sensibly requires credible empirical estimates of the relevant costs and benefits. The intent of this thesis is to provide such estimates. Chapter 1 examines the value of reducing emissions from power plants, an important source of air pollution in several countries. Within a model of health, consumption, production, power generation, and resource extraction, I derive a 'sufficient statistics' formula for the change in social welfare due to a small reduction in emissions. The formula simplifies to a comparison of marginal benefits (in terms of reduced mortality risk, monetized using the value of a statistical life) and the marginal cost of abatement. I estimate these inputs using quasi-experimental variation induced by the Clean Air Interstate Rule, a policy that capped power plant emissions in the United States. Results indicate that further reducing those emissions would be worthwhile. Chapter 2 re-examines the effect of a county's regulatory status under the US Clean Air Act on the change in its air pollution concentration, the 'first-stage' underlying causal estimates of the benefits of reducing air pollution in several studies. Using data thought no longer to exist, I find that one of the commonly-used measurement approaches -- a regression-discontinuity estimator -- is invalid. The other commonly-used approach -- a difference-in-differences estimator -- delivers inflated estimates of the effects of regulation on air pollution. These findings suggest that the literature substantially understates the benefits of reducing air pollution. Chapter 3, joint with Robert McMillan, provides causal estimates of the effects of sustained exposure to severe air pollution on mortality risk. Our research design is based on the 'smoke control' provisions of the UK's Clean Air Act of 1956, which granted local authorities power to address emissions from the domestic chimney. We find that smoke control caused significant reductions in air pollution in areas that implemented it relative to those that did not. Our 2SLS estimates, which combine this exogenous variation in air quality improvements with local mortality data, indicate a significant reduction in probability of death. They are relevant when calculating air pollution's costs in coal-burning middle-income countries today.