Current approaches used in epidemiologic studies to examine short-term multipollutant air pollution exposures

Air pollution epidemiology traditionally focuses on the relationship between individual air pollutants and health outcomes (e.g., mortality). To account for potential copollutant confounding, individual pollutant associations are often estimated by adjusting or controlling for other pollutants in the mixture. Recently, the need to characterize the relationship between health outcomes and the larger multipollutant mixture has been emphasized in an attempt to better protect public health and inform more sustainable air quality management decisions

Source-specific pollution exposure and associations with pulmonary response in the Atlanta Commuters Exposure Studies

Concentrations of traffic-related air pollutants are frequently higher within commuting vehicles than in ambient air. Pollutants found within vehicles may include those generated by tailpipe exhaust, brake wear, and road dust sources, as well as pollutants from in-cabin sources. Sourcespecific pollution, compared to total pollution, may represent regulation targets that can better protect human health. We estimated source-specific pollution exposures and corresponding pulmonary response in a panel study of commuters. We used constrained positive matrix factorization to estimate source-specific pollution factors and, subsequently, mixed effects models to estimate associations between source-specific pollution and pulmonary response. We identified four pollution factors that we named: crustal, primary tailpipe traffic, non-tailpipe traffic, and secondary. Among asthmatic subjects (N=48), interquartile range increases in crustal and secondary pollution were associated with changes in lung function of −1.33% (95% confidence interval (CI): −2.45, −0.22) and −2.19% (95% CI: −3.46, −0.92) relative to baseline, respectively. Among non-asthmatic subjects (N=51), non-tailpipe pollution was associated with pulmonary response only at 2.5 hours post-commute. We found no significant associations between pulmonary response and primary tailpipe pollution. Health effects associated with traffic-related pollution may vary by source, and therefore some traffic pollution sources may require targeted interventions to protect healt

Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons

Abstract Background As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity. Methods In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step. Results Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large. Conclusions There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.http://deepblue.lib.umich.edu/bitstream/2027.42/112386/1/12940_2013_Article_691.pd

HUMAN EXPOSURE TO AIR TOXICS IN URBAN ENVIRONMENTS: HEALTH RISKS, SOCIODEMOGRAPHIC DISPARITIES, AND MIXTURE PROFILES

Exposure to air toxics in urban environments may be of significant health concern because populations and emission sources are concentrated in the same geographic area. The overall objective of this study is to characterize the sources, variations, and mixture profiles of ambient air toxics in urban environments, and examine the sociodemographic disparities in exposures to air toxics in a typical U.S. metropolitan area.A model-to-monitor comparison was performed to evaluate the validity of modeling air toxics data using national datasets. Modeled concentrations in the 2011 National-scale Air Toxics Assessment (NATA) moderately agreed with monitoring measurements, and a sizable portion showed underestimation. Results warranted the need for actual monitoring data to conduct air toxics exposure assessment.Air toxics samples were collected in 106 census tracts in the Memphis area in 2014, and samples were analyzed for 71 volatile organic compounds (VOCs). Ambient VOC levels in Memphis were generally higher than the national averages in urban settings, but were mostly below the reference concentrations (RfCs). Factor analysis identified 5 major sources: manufacturing processes, vehicle exhaust, industrial solvents, refrigerants, and gasoline additives. The major non-cancer risks were from neurological, respiratory, and reproductive/developmental effects. The cumulative cancer risk was 5.9 3.3 10-4, with naphthalene and benzyl chloride as risk drivers.Sociodemographic disparities in cancer risks were examined by regressing cancer risks against socioeconomic, racial, and spatial parameters at the census tract level. We conducted separate disparity analyses using modeling data from 2011 NATA and our air toxics monitoring data. Analysis using modeling data showed strong sociodemographic disparities but that using monitoring data did not show. The discrepancy brought cautions for use of modeling air pollution data in environmental disparity research.We further assessed exposure to VOCs mixtures in five typical microenvironments (MEs): home, office, vehicle cabin, gas station, and community outdoors. The multivariate analysis of variance and pairwise analysis showed VOC profiles were distinguishingly different among MEs. The classification of profiles was achieved using the random forest. We anticipate wide applications of exposure profiles in epidemiologic research of exposure to air toxic mixtures

Enhancing models and measurements of traffic-related air pollutants for health studies using dispersion modeling and Bayesian data fusion

Research Report 202 describes a study led by Dr. Stuart Batterman at the University of Michigan, Ann Arbor and colleagues. The investigators evaluated the ability to predict traffic-related air pollution using a variety of methods and models, including a line source air pollution dispersion model and sophisticated spatiotemporal Bayesian data fusion methods. Exposure assessment for traffic-related air pollution is challenging because the pollutants are a complex mixture and vary greatly over space and time. Because extensive direct monitoring is difficult and expensive, a number of modeling approaches have been developed, but each model has its own limitations and errors. Dr. Batterman and colleagues sought to improve model estimations by applying and systematically comparing the performance of different statistical models. The study made extensive use of data collected in the Near-road EXposures and effects of Urban air pollutants Study (NEXUS), a cohort study designed to examine the relationship between near-roadway pollutant exposures and respiratory outcomes in children with asthma who live close to major roadways in Detroit, Michigan

Statistical methods for linking the chemical composition of particulate matter to health outcomes

Short-term exposure to particulate matter air pollution less than 2.5 micrometers in aerodynamic diameter (PM2.5) has been associated with mortality and morbidity in epidemiologic studies. PM2.5 is a complex mixture of many different chemicals that are emitted from both natural and anthropogenic sources. Recent studies have found that the toxicity of PM2.5 depends on its chemical composition, however estimating ambient PM2.5 constituent concentrations is challenging in part for several reasons. First, the network of ambient monitors that measure PM2.5 chemical constituents is spatially sparse and many communities have only one monitor. Additionally, some constituents of PM2.5 are spatially heterogeneous and their concentrations observed at ambient monitors may not be representative of surrounding areas. Last, PM2.5 constituents that contribute minimally to PM2.5 total mass frequently have censored concentrations that fall below minimum detection limits. Together, these attributes of the available data make estimating ambient PM2.5 constituent concentrations difficult and complicate subsequent health effects analyses. We could also estimate health effects of PM2.5 sources, which emit combinations of chemical constituents. PM2.5 sources are not directly measured and are frequently inferred from PM2.5 constituent concentrations. The challenge of estimating PM2.5 sources is magnified by the measurement error and data limitations of observed PM2.5 constituent concentrations. This dissertation developed methods to address measurement issues in estimating ambient concentrations of PM2.5 constituents and PM2.5 sources. Then, these methods were used to estimate associations between mortality and short-term exposure to PM2.5 constituents and PM2.5 sources

기계학습과 수용모델을 이용한 초미세먼지 오염원 및 기여도의 시공간 분포 분석

학위논문(박사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2023. 2. 김재영.직경 2.5 µm 이하의 입자상 물질인 초미세먼지는 대기중에 존재하며, 건강에 미치는 악영향으로 인해 수십 년 동안 세계적으로 관심의 대상이 되고 있는 대기오염물질이다. 초미세먼지를 효과적으로 관리하기 위해서는 다양한 시간과 공간에 대해 초미세먼지의 오염원 유형을 파악하고, 각 유형별 기여도를 정량화하는 것이 중요하다. 따라서, 초미세먼지의 오염원 추정은 핵심 과제로 다뤄져 왔으며, 통계학적 방법론을 적용해 오염원을 추정하는 수용모델이 많이 활용되고 있다. 본 연구에서는 초미세먼지의 세부 특성을 파악하기 위해 오염원 추정과 추정된 오염원의 시공간 분석을 수행하였으며, 이를 통해 효과적인 초미세먼지 관리 방안 마련에 중요한 정보를 제공하는 것을 목적으로 하였다. 오염원 유형 추정 연구를 위해, 두 가지 모델링이 수행되었다. 첫번째는 양행렬 인자 분석(Positive matrix factorization, PMF) 모델링으로, 이는 한 장소에서 초미세먼지의 오염원 유형을 구체적으로 추정하기 위해 활용되었다. 두번째는 베이지안 다변량 수용 모델링(Bayesian spatial multivariate receptor modelingm, BSMRM)으로, 이는 다수의 측정 지점으로부터 넓은 범위의 면적에 대해 주요 오염원 유형을 추정하기 위해 활용되었다. 또한, 기계학습 모델들을 활용하여 초미세먼지 오염원 유형 추정에 가장 중요한 자료로 활용되는 초미세먼지 화학성분 농도를 예측하였다. 기계학습 모델을 초미세먼지 화학성분 자료에 대해 활용가능한지를 검토하였고, 이를 통해 초미세먼지 화학성분 자료의 무결성을 향상시키고자 하였다. PMF 모델링을 통해, 대한민국 시흥시의 초미세먼지 오염원 유형 10가지를 도출하였다. 이는 각각 2차 생성 질산염(24.3%), 2차 생성 황산염(18.8%), 이동 오염원(18.8%), 난방연소(12.6%), 생물체 연소(11.8%), 석탄 연소(3.6%), 중유 관련 산업 오염원(1.8%), 제련 관련 산업 오염원(4.0%), 해염 입자(2.7%), 토양(1.7%)였다. 도출된 오염원 유형별로, 초미세먼지 호흡에 따른 건강 영향을 평가하였다. 석탄 연소, 중유 관련 산업 오염원, 이동 오염원의 초미세먼지 기여도는 낮았지만, 이로 인한 발암 위해도는 10E-6 이상으로 나타났다. 따라서, 초미세먼지의 질량농도 감축 중심의 대응만이 아닌, 오염원별 건강영향 중심의 대응이 요구된다. 기계학습 모델의 초미세먼지 화학성분 예측 능력을 평가하기 위해 4가지 기계학습 모델에 대해 입력 자료 수준, 예측 대상 성분, 입력 자료 기간, 입력 자료의 결측 비율, 자료 대상 지역을 변화하며 예측 정확도를 비교 평가하였다. GAIN(Generative Adversarial Imputation Network), FCDNN(Fully Connected Deep Neural Network), Random forest(RF), kNN(k-nearest neighboring) 모델의 4가지 기계학습 모델을 한국의 3개 지역(서울, 울산, 백령)의 2016년부터 2018년까지의 초미세먼지 화학 성분 자료에 대해 적용하여 농도를 예측하였다. 예측값과 관측값 사이의 결정계수를 통해 정확도를 비교한 결과, 예측 정확도는 GAIN이 가장 높았고, FCDNN, RF 또는 kNN 순서로 나타났다. 입력 자료의 결측률이 20%에서 80%까지 증가함에 따라 예측 정확도는 모든 모델에서 감소하였으나, 비지도 기계학습 모델인 GAIN과 kNN에서 감소 폭이 더 크게 나타났다. 입력 자료의 기간이 길어질수록, 딥러닝 모델인 GAIN과 FCDNN이 다른 두 모델인 RF와 kNN보다 예측 정확도 증가 폭이 더 컸다. 예측 대상 지역별로는, 자체 배출원이 많은 울산의 경우가 예측 정확도가 가장 낮게 나타났고, 자체 배출원의 영향이 거의 없는 백령도의 경우 예측 정확도가 가장 높게 나타났다. 대상 성분별로는 이온 성분이 예측 정확도가 높게 나타났고, 미량원소 성분은 예측 정확도가 낮았다. 본 연구는 기계학습 모델의 예측 정확도를 다양한 실험 조건에 따라 평가하여 대기오염 분야에서의 기계학습 모델의 적용 가능성을 평가했다. 베이지안 다변량 수용 모델링(BSMRM)을 통해서는 8개의 관측 지점 자료를 통해 우리나라의 주요 초미세먼지 오염원 5가지를 도출하고, 각각 오염원 유형별 기여도를 우리나라 전체에 대한 공간 분포를 추정하였다. 5가지 오염원은 각각 2차 질산염, 2차 황산염, 자동차 배출, 산업 오염원, 해염 입자였다. 각 오염원 유형별 일평균 기여도 농도를 지도에 공간적으로 표현할 수 있었다. 또한, BSMRM을 통해 예측한 오염원 유형별 기여도의 타당성 검토를 위해 테스트 사이트(안산, 대전, 광주)의 자료는 각각 제외된 모델링을 수행하여 결과를 서로 비교하여 모델의 정확도를 확인하였다. 이처럼 공간적으로 추정된 오염원 유형 기여도는 초미세먼지 화학성분을 측정하지 않는 도시에서 초미세먼지 대응 방안을 수립하는데 큰 도움이 될 수 있다. 즉, 8개의 측정 자료만으로 우리나라 전체에 대해 예측한 결과를 통해, 측정 지점이 없는 모든 도시에 대해 추정이 가능하였으며, 이 결과는 건강 영향 평가와 같은 추가 연구에도 활용될 수 있다.Particulate matter less than 2.5 micrometers (PM2.5) has been a pollutant of interest globally for more than decades, owing to its adverse health effects. For developing effective PM2.5 management strategies, it is crucial to identify their sources and quantify how much they contribute to ambient PM2.5 concentrations in time and space. Source apportionment is the key to identifying the characteristics of PM2.5. Receptor modeling is widely used to identify PM2.5 sources as a statistical method of source apportionment. The chemical constituents of PM2.5 were used as input data for receptor modeling. Therefore, this study aimed to investigate the characteristics of PM2.5 using models of source apportionment and spatiotemporal analysis for effective management strategies. Two types of modeling were performed for the source apportionment study. The first is positive matrix factorization modeling, which identifies a specific source type and its contributions to PM2.5 from one site. The second is Bayesian spatial multivariate receptor modeling, which derives major sources and their contributions to PM2.5 from multiple monitoring sites. In addition, machine learning models were used to predict the concentrations of PM2.5, which are important data for receptor modeling. Machine learning models that can be used to increase data integrity and applicability to PM2.5 data were assessed. The sources of PM2.5 and their contributions in Siheung, South Korea, were identified using positive matrix factorization modeling. These 10 sources were secondary nitrate (24.3%), secondary sulfate (18.8%), traffic (18.8%), combustion for heating (12.6%), biomass burning (11.8%), coal combustion (3.6%), heavy oil industry (1.8%), smelting industry (4.0%), sea salt (2.7%), and soil (1.7%). Based on the derived sources, the carcinogenic and non-carcinogenic health risks due to PM2.5 inhalation were estimated. The contribution to PM2.5 mass concentration was low for coal combustion, heavy oil industry, and traffic sources but exceeded the benchmark carcinogenic health risk value (1E-06). Therefore, countermeasures on PM2.5 emission sources should be performed based on the PM2.5 mass concentration and health risks. The feature extraction capabilities of the four machine learning models to predict the chemical constituents of PM2.5 were assessed by comparing the prediction accuracy depending on input variables, target constituents for prediction, available period, missing ratios of input data, and study sites. The concentrations of PM2.5 constituents were predicted at three sites (Seoul, Ulsan, and Baengnyeong) in South Korea between 2016 and 2018, using four machine learning models: generative adversarial imputation network (GAIN), fully connected deep neural network (FCDNN), random forest (RF), and k-nearest neighbor (kNN). The prediction accuracy identified by the coefficient of determination (R2) between the prediction and observation was highest in GAIN, followed by FCDNN, RF, and kNN. As the missing ratios (20, 40, 60, and 80%) of the input data increased, the prediction accuracy decreased in the four models and was more noticeable in GAIN and kNN, which are unsupervised models. As the input data period increased, the two deep learning models, GAIN and DNN, had better applicability than the other models, RF and kNN. The study sites with more emission sources exhibited lower prediction accuracy, resulting in the highest R2 in the BR island and the lowest in Ulsan. Among the target constituent groups, ions and trace elements were predicted to have the highest and lowest R2, respectively. This study demonstrated that machine learning models can be extended for further air pollution studies depending on model features, required performance, and experimental conditions, such as data availability and time constraints. The spatial distributions of five PM2.5 sources in South Korea were estimated using Bayesian spatial multivariate receptor modeling. Secondary nitrate, secondary sulfate, motor vehicle emissions, industry, and sea salts were determined to be significant contributors to ambient PM2.5 concentrations in South Korea. The spatial surface of the daily average contribution for each source in South Korea was derived from measurement data from the eight monitoring sites. The source contributions predicted by the BSMRM were also validated using held-out data from a test site (such as Ansan, Daejeon, and Gwangju). These predicted source contributions can aid in developing effective PM2.5 control strategies in cities where no speciated PM2.5 monitoring stations are available. They can also be utilized as source-specific exposures in health effect studies, even in cities where no monitoring stations are available.CHAPTER 1. INTRODUCTION 1 1.1. Background 1 1.2. Objectives 4 1.3. Dissertation structure 5 References 7 CHAPTER 2. LITERATURE REVIEW 10 2.1. Source apportionment and receptor modeling of PM2.5 10 2.2. Toxicity and health risk of assessment PM2.5 21 2.3. Machine learning approaches in prediction of PM2.5 31 2.4. Bayesian approach in source apportionment 41 References 54 CHAPTER 3. SOURCE APPORTIONMENT OF PM2.5 USING PMF MODEL AND HEALTH RISK ASSESSMENT BY INHALATION 69 3.1. Introduction 69 3.2. Materials and methods 72 3.2.1 Study site, sampling, and analysis 72 3.2.2 Positive matrix factorization (PMF) modeling and combined analysis with meteorological data 76 3.2.3 Health risk assessment 80 3.3. Results and discussion 85 3.3.1 PM2.5 mass concentration and chemical speciation 85 3.3.2 Source apportionment of PM2.5 by PMF modeling 89 3.3.3 Carcinogenic and non-carcinogenic health risks 94 3.3.4 Probable source areas or directions 103 3.4. Summary 106 References 107 CHAPTER 4. FEATURE EXTRACTION AND PREDICTION OF PM2.5 CHEMICAL CONSTITUENTS USING MACHINE LEARNING MODELS 120 4.1. Introduction 120 4.2. Materials and methods 124 4.2.1. Study Sites and Data Collection 124 4.2.2. Machine Learning Models and Hyperparameter Optimization 127 4.2.3. Prediction Scenarios 131 4.2.4. Model Validation and Error Estimation 133 4.3. Results and discussion 134 4.3.1. Hyperparameter Optimization 134 4.3.2. Prediction Results for Scenario #1 135 4.3.3. Prediction Results for Scenario #2 157 4.3.4. Features and Performance of Four ML Models 164 4.4. Summary 166 Data Availability 167 Code Availability 167 References 168 CHAPTER 5. BAYESIAN SPATIAL MULTIVARIATE RECEPTOR MODELING FOR SPATIOTEMPORAL ANALYSIS OF PM2.5 SOURCES 175 5.1. Introduction 175 5.2. Materials and methods 180 5.2.1 Air pollution data 180 5.2.2 Bayesian spatial multivariate receptor modeling (BSMRM) 183 5.2.3 Application of BSMRM to Korea PM2.5 speciation data 185 5.3. Results and discussion 189 5.3.1 Bayesian spatial multivariate receptor modeling (BSMRM) results 189 5.3.2 Model validation 196 5.3.3 Spatial distribution of each source in South Korea 204 5.4. Summary 207 References 208 CHAPTER 6. CONCLUSIONS AND FUTURE WORK 214 6.1. Conclusions 214 6.2. Future work 218 국문 초록(ABSTRACT IN KOREAN) 219박

A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology

Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environmental exposures and health effects, which take into account exposure measurement errors introduced by uncertainty in the estimated exposure as well as spatial misalignment between the exposure and health outcome data. While two-stage Bayesian analyses are often regarded as a good alternative to fully Bayesian analyses when joint estimation is not feasible, there has been minimal research on how to properly propagate uncertainty from the first-stage exposure model to the second-stage health model, especially in the case of a large number of participant locations along with spatially correlated exposures. We propose a scalable two-stage Bayesian approach, called a sparse multivariate normal (sparse MVN) prior approach, based on the Vecchia approximation for assessing associations between exposure and health outcomes in environmental epidemiology. We compare its performance with existing approaches through simulation. Our sparse MVN prior approach shows comparable performance with the fully Bayesian approach, which is a gold standard but is impossible to implement in some cases. We investigate the association between source-specific exposures and pollutant (nitrogen dioxide (NO$_2$))-specific exposures and birth outcomes for 2012 in Harris County, Texas, using several approaches, including the newly developed method.Comment: 34 pages, 8 figure