Air Pollution and Adverse Health Effects: Assessing Exposure

Air pollution and adverse health effects:

Assessing exposure windows and sensitivity to modeling choices

Mike Zhongyu He

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2020

Mike Zhongyu He

Abstract

Air pollution and adverse health effects:

Assessing exposure windows and sensitivity to modeling choices

Mike Zhongyu He

Air pollution is one of the leading environmental problems of the 21st century, and the rise of global urbanization has increasingly exacerbated air pollution’s public health impact. Existing epidemiologic studies have tackled the relationship between air pollution and health from a variety of perspectives, but many areas of research remain lacking, including studies originating from developing countries, the assessment of exposure windows and sensitivity of modeling choices, and a better understanding of the climate change impacts on air pollution and health. In this dissertation, I address all of the issues mentioned above. Chapter 1 formally introduces the topics of this dissertation and summarizes background information on several major air pollutants. It then provides an overview of existing research on air pollution epidemiology and describes key knowledge gaps. In Chapter 2, we conduct a systematic review of the scientific literature for data on fine particulate matter (PM2.5) in China, where historical PM2.5 data are not widely available prior to 2013. Using the 574 PM2.5 measurements we identified from the literature, we detected

differences in PM2.5 levels across both geographic and economic regions of China. In Chapter 3, we investigate the associations between short- and intermediate-term exposure of nitrogen dioxide

(NO2) and mortality in 42 counties in China from 2013 to 2015, and find evidence of significant associations up to seven days prior to exposure. In Chapter 4, we investigate the association between PM2.5 and hospitalizations in New York State using five separate exposure datasets from

2002 to 2012. We find that despite some fluctuations in effect estimates, the majority of models yielded consistently significantly harmful associations. In Chapter 5, we utilize a global chemistry- climate model to project ozone levels in 2050 under a variety of emissions scenarios and quantify the mortality impact associated with changes in ozone concentrations between 2015 and 2050 according to each scenario. We find that under climate change alone and adherence to current legislation, ozone-related deaths would increase. However, under a best-case scenario of maximum technologically feasible reductions in emissions, over 300,000 premature deaths related to ozone can be avoided. Finally, Chapter 6 summarizes the findings of this dissertation and discusses potential directions in future research. While much work remains to be done, this dissertation work takes an important step forward in closing existing knowledge gaps in the field of air pollution epidemiology. More importantly, we hope that our work sends a strong public health message on the importance of continuing research on air pollution and health.

Table of Contents

List of Tables and Figures...... iii Acknowledgments...... v Dedication ...... vii Chapter 1 ...... 1 1.1 Background ...... 1

1.2 Thesis Overview ...... 8

1.3 References ...... 10

Chapter 2: Fine particulate matter concentrations in urban Chinese cities ...... 15 2.1 Abstract ...... 15

2.2 Introduction ...... 16

2.3 Methods...... 18

2.4 Results ...... 23

2.5 Discussion ...... 25

2.6 References ...... 31

Chapter 3: Short- and intermediate-term exposure to NO2 and mortality: A multi-county analysis in China ...... 41 3.1 Abstract ...... 41

3.2 Introduction ...... 42

3.3 Methods...... 45

3.4 Results ...... 48

3.5 Discussion ...... 51

3.6 References ...... 58

3.7 Supplemental Materials ...... 62

Chapter 4: Short-term PM2.5 and cardiovascular and respiratory admissions in NY State: Assessing sensitivity to exposure model choice ...... 70 4.1 Abstract ...... 71

4.2 Introduction ...... 72

4.3 Methods...... 73

4.4 Results ...... 76

4.5 Discussion ...... 81

4.6 References ...... 87

4.7 Supplemental Materials ...... 91

Chapter 5: Mid-21st century ozone air quality and health burden in China under emissions scenarios and climate change: A health impact assessment ...... 96 5.1 Introduction ...... 97

5.2 Methods...... 97

5.3 Results ...... 98

5.4 Discussion ...... 99

5.5 References ...... 101

Chapter 6: Conclusion...... 102 6.1 Summary of Findings ...... 102

6.2 Discussion ...... 104

6.3 Future Directions ...... 110

6.4 Concluding Remarks ...... 112

6.5 References ...... 113

List of Tables and Figures

Chapter 1 Figure 1. Existing long-term air pollution and mortality studies…………………………...5 Figure 2. PM2.5 monitoring stations in New York State from AQS………………………...8

Chapter 2 Figures Figure 1. Flow diagram of the study selection process……………………………………20 Figure 2. Search strategy…………………………………………………………………21 Figure 3. Regions of China……………………………………………………………….22 Figure 4. PM2.5 levels in different regions………………………………………………..24

Tables Table 1. Descriptive statistics of geographic regions……………………………………..24 Table 2. Descriptive statistics of three economic regions………………………………...25

Chapter 3 Figures Figure 1. Geographic distribution of study counties……………………………………...45 3 Figure 2. 30-day lag-specific cumulated effect estimates per 10 µg/m NO2 increase for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality……...49 Figure S1. Lag-specific non-cumulated effect estimates per 10 µg/m3 for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality……………………...64 Figure S2. Lag-specific non-cumulated effect estimates per 10 µg/m3 for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality; sex stratified………………………………………………………………………………….65 Figure S3. 30-day lag-specific cumulated effect estimates per 10 µg/m3 for a) non- accidental mortality; b) cardiovascular mortality; and c) respiratory mortality; sex stratified………………………………………………………………………………….66 Figure S4. Forest plots of cumulated effect estimates per 10 µg/m3 stratified by county for a) lag0-1; b) lag0-7; c) lag0-30…………………………………………………………..67 Figure S5. 30-day lag-specific cumulated effect estimates per 10 µg/m3 using fixed effect models for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality………………………………………………………………………………….68 Figure S6. Lag-specific non-cumulated effect estimates per 10 µg/m3 for non-accidental mortality, comparing fixed effects and meta-analysis results……………………………69

iii

Tables Table 1. Pollutant, confounder, and outcome descriptive statistics……………………….48 Table S1. Descriptive statistics by county………………………………………………..62 Table S2. Mortality effect estimates per 10 µg/m3, Lag 0-1……………………………....63 Table S3. Mortality effect estimates per 10 µg/m3, Lag 30…………………………….....63 Table S4. Lag-specific cumulated effect estimates per 10 µg/m3, Lag 0-7………………..63 Table S5. Lag-specific cumulated effect estimates per 10 µg/m3, Lag 0-30………………63

Chapter 4 Figures Figure 1. Percent increase in a) daily CVD admissions rates and b) daily respiratory 3 admissions rates per 10 µg/m for all PM2.5 products……………………………………..78 Figure 2. Seasonal effect modification for daily CVD admissions rates using all available data……………………………………………………………………………………….79 Figure 3. Spatial effect modification for daily CVD admissions rates using all available data……………………………………………………………………………………….80 Figure S1. Percent increase in a) daily CVD admissions rates and b) daily respiratory admissions rates for subset excluding AQS………………………………………………91

Tables Table 1. Descriptive statistics for all counties………………………………………….....77 Table 2. Correlation of PM2.5 products…………………………………………………...77 Table S1. Descriptive statistics by season………………………………………………...92 Table S2. Descriptive statistics by quartiles of rural population…………………………94 Table S3. Descriptive statistics for complete-case analysis………………………………95

Chapter 5 Figure 1. a) 2010 baseline population in China; b) change in premature deaths between 2015 and 2050 under CLIM (2015CLIM); c) changes in premature deaths between 2015 and 2050 under CLE (2050CLE); d) changes in premature deaths between 2015 and 2050 under MTFR (2050MFR)………………………………………………………………...99

Acknowledgments

First and foremost, I would like to thank my advisors, Drs. Marianthi-Anna

Kioumourtzoglou and Patrick Kinney. Marianthi, you have been an incredible mentor to me over the past four years. Your passion for research is infectious, and you truly lead by example with your impeccable work ethic that continuously inspires and challenges me on a regular basis. I will be forever grateful for everything that you have taught me, and I know that I will continue to rely on your wisdom for the rest of my life. Pat, thank you for kindling my interests on air pollution research in China and for providing me with the incredible opportunity to work on what I consider to be the best set of research projects that a doctoral student could ask for. It was through your help that I found my direction in research early on, and I greatly appreciate and value your constant guidance, both in science and in life.

I would also like to thank my committee members, Drs. Jeffrey Shaman, Arlene Fiore, and

Ana Navas-Acien, all of whom made a significant impact on my graduate education. Jeff, I enjoyed

TA’ing for your classes as much as our countless conversations about our shared interests outside of research. Arlene, thank you for introducing me to air pollution modeling during my first semester at Columbia – I wouldn’t have imagined at the time that this would become my research focus as a postdoc! Ana, thank you for helping me make the decision to pursue a PhD: if it weren’t for your advice, I might very well be on a different path right now and would have never discovered my love for research.

The work in this dissertation could not have been completed without the help of numerous collaborators outside of Columbia. In particular, I would like to thank Drs. Xiaoming Shi, Tiantian

Li, and Ms. Chen Chen from the Chinese Center for Disease Control and Prevention; Drs. Shuxiao

Wang, Jia Xing, and Dian Ding from Tsinghua University; and my coauthor Xiange (Anna) Zeng for their support and contributions to my research.

Equally important are my amazing colleagues and the incredible staff in the EHS department. Sarah Kramer, Yanelli Núñez, Sebastian Rowland, Lizzy Gibson, Ahlam Abuawad, and Jenni Shearston, thank you for everything that you’ve done for me. From listening to my rambles and helping me with R code to coming to support me at my numerous performances, I truly appreciate everything that you have done for me and am so very grateful for all of it. Bernice

Ramos-Perez, thank you for your patience in assisting me with my mountain of reimbursement forms, grant submissions, and taking time out of your busy schedule just to chat with me. Nina

Kulacki, from Hopkins to Columbia, thank you for making everything outside of academics as smooth as possible.

I also want to thank my voice teacher Dr. Robert Osborne and Barnard-Columbia Chorus director Dr. Gail Archer for helping me keep music in my life over the past five years: life without singing is simply unimaginable. Of course, none of this would have been possible without the support from my friends and family. Mom and Dad, thank you for your support and putting your trust in me to pursue my dream. Jason He, thank you for being a great brother and putting up with all of my stupidity. Emil Wang, thank you for proofreading and editing every important document that I have written since freshman year of college, in addition to everything else that you have done for me. I would also like to thank my friends Chihua Li, Willis Chen, Michael Seung, and

Hansong Li for their support throughout my time as a doctoral student – I couldn’t have done it without you all.

TO MY BELOVED GRANDPARENTS

DAOLUO WANG & RONGXIN SU

vii

Chapter 1

Introduction

1.1 Background

Air pollution is one of the leading environmental problems of the 21st century. The World

Health Organization (WHO) estimates that ambient air pollution was responsible for over 7 million premature deaths worldwide in 2014 [1]. Of the various forms of ambient air pollution, particulate matter (PM) affects more people than any other pollutant [2], and other air pollutants such as nitrogen dioxide (NO2) and ozone remain persistent problems in both developing and developed countries. This chapter will provide an overview of major air pollutants, existing research in air pollution epidemiology, and critical knowledge gaps that this dissertation aims to address.

1.1.1 Particulate matter, nitrogen dioxide, and ozone

PM is defined as a complex mixture of extremely small particles and liquid droplets, made up of acids, organic chemicals, metals, and soil or dust particles [3]. PM is most commonly

1 characterized by its aerodynamic diameter, usually divided into three categories: inhalable or thoracic particles (particles with aerodynamic diameter ≤ 10μm; PM10), thoracic coarse particles

(particles with aerodynamic diameter between 2.5 and 10μm; PM10-2.5), and fine particles (particles with aerodynamic diameter ≤ 2.5μm; PM2.5). Of these three categories, public health policies have become increasingly targeted towards PM2.5, as existing literature suggests that it may play the largest role in affecting human health [4]. More specifically, relative to the coarser particles, PM2.5 can travel for much longer distances and remain suspended for longer periods of time. As a result of its small size, PM2.5 can bypass the human body’s defense mechanism and penetrate deeper into the lungs [3].

PM2.5 inhalation can result in respiratory oxidative stress, reduced lung function, and aggravation of existing respiratory symptoms. This in turn causes systemic inflammation and oxidative stress, which can affect the heart (altered cardiac autonomic function, increased myocardial ischemia), vasculature (atherosclerosis, vasoconstriction and hypertension), blood

(altered rheology, reduced oxygen saturation), and the brain (cerebrovascular ischemia, decreased cerebral brain volume) [4–6].

NO2 is a highly reactive gas and a member of the larger family of gases known as nitrogen oxides (NOx). Similar to PM, NO2 has been linked to numerous environmental and health effects.

In the presence of sunlight, NOx is a major precursor to ozone [7]. NO2 is also involved in the formation of acid rain, haze, and nutrient pollution in coastal waters [8]. As a respiratory irritant,

NO2 not only aggravates existing respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), but is also linked with increased cellular inflammation, bronchial hyperresponsiveness, and susceptibility to respiratory infections [8,9]. Fossil fuel combustion is a major source of ambient NO2, and as such NO2 is a well-established surrogate of traffic-related air

2 pollution (TRAP) [8–11]. The world is currently undergoing the largest wave of urban growth in history, and the United Nations projects that over 5 billion people will be living in cities by 2030

[12]. With the increased urbanization of populations around the world, TRAP will likely have a much larger impact on more people than ever before.

Ozone is also a highly reactive gas that exists both in the Earth’s upper atmosphere

(stratospheric ozone) and at ground level (tropospheric ozone). Stratospheric ozone is produced naturally and forms a protective layer that shields the earth from the sun’s harmful ultraviolet rays

[13]. Tropospheric ozone, on the other hand, is are not emitted directly but predominantly forms via secondary reactions between NOx and volatile organic compounds (VOC) in the presence of sunlight. At the ground level, ozone can cause constrictions of muscles in the airways and lead to a variety of respiratory symptoms, including wheezing, shortness of breath, and aggravated lung disease [14].

Ozone pollution has remained a significant problem in both the developing and developed world for a number of reasons. For example, in the developing world, PM and NOx mitigation have often been seen as priorities for air pollution control, with less or no emphasis placed on

VOC. It has been well established for decades that when ozone production is VOC-limited, decreased levels of PM and NOx actually contribute to increased ozone production [15]. Recent modeling and observational studies in China have both found that as NOx levels have declined, ozone and secondary PM2.5 levels have increased [16,17]. In the developed world, high levels of urbanization are inextricably linked to relatively high levels of NOx, and ultimately result in high levels of ozone in the summer under NOx-limited settings [18]. In the United States, 90% of non- compliance to the National Ambient Air Quality Standards (NAAQS) in 2016 were due to exceedance in ozone levels, and 10% from all other pollutants combined [18], emphasizing the

3 challenge ozone mitigation poses in developed countries. The examples above represent two simplifications of numerous potential pathways for ozone production, and ozone’s highly nonlinear chemistry is in reality far more complex [11].

1.1.2 Existing research in air pollution epidemiology

The association between air pollution and health has long been a topic of interest in epidemiology, and existing studies have covered many topics. For all pollutants, studies can generally be categorized by exposure duration (short-term vs. long-term) and outcome of interest.

The following paragraphs briefly summarize some of the existing evidence in each of these categories.

Short-term (daily to weekly) air pollution and mortality studies constitute a large proportion of the existing air pollution epidemiology literature. Early studies were conducted in the United States and Europe [19–22], but have since been replicated in many developing countries

[23–27]. Recently, a study of ambient PM and daily mortality in 652 cities found consistently harmful associations worldwide [28], an example of the robust evidence of this relationship.

Short-term air pollution and morbidity studies are also well represented in the literature.

For example, using a national database of time-series data from 1999 to 2002, Dominici et al. determined that short-term PM2.5 exposure was associated with an increase in hospital admissions for various cardiovascular and respiratory outcomes [29], and numerous similar studies have been conducted in developed countries [30–34]. In recent years, there has also been a growing number of studies coming from developing countries, including Iran [35], Egypt [36], and China [37,38].

Relative to short-term studies, long-term (annual or longer) air pollution and health studies are less numerous and almost exclusively conducted in developed regions such as the United States and Europe. Early works by Dockery et al. and Pope et al. found strong evidence of long-term

4 exposure to PM2.5 as an important environmental risk factor for cardiopulmonary and lung cancer mortality [39,40]. A substantial number of similar studies have been conducted since, largely reaching the same conclusions [41]. Aside from a limited number of studies coming from China

(Figure 1), however, there have been virtually no replication studies conducted in locations outside of the United States and Europe, a gap that will be gradually filled as monitoring stations and dedicated cohorts become available over the coming years in more countries.

Figure 1. Existing long-term air pollution and mortality studies. Reproduced with permission from C. Arden Pope III

1.1.3 Knowledge gaps

Despite the remarkable research progress that air pollution epidemiology has made in the past several decades, there are several critical knowledge gaps in the literature that still exist. First,

5 air pollution exposure data from monitoring stations are still lacking in many developing countries, and as a result, corresponding epidemiologic evidence from these countries are also lacking. While there are numerous efforts to establish new monitoring systems around the world, such systems will still take a long time to collect data before any meaningful epidemiologic analyses can be conducted. Furthermore, this does not resolve the issue of conducting studies prior to the establishment of monitoring systems. For example, in recent years, China has dedicated significant effort and resources towards combating air pollution, especially in its growing urban areas. In

2012, China updated its National Ambient Air Quality Standards (NAAQS) to include PM2.5 for the first time, resulting in the establishment of an extensive monitoring network beginning in 2013

[42]. Currently, there are over 700 cities around the country that monitor PM2.5, and the number continues to expand. The expansion of the monitoring systems has resulted in an explosion of large-scale, short-term air pollution epidemiologic studies in China since 2013 [23,24,38].

However, given the lack of exposure data, similar studies for study periods prior to 2013 remain rare.

Another knowledge gap originates from the lack of studies that investigates relationships in between that of the short- and long-term. Numerous biomarker studies have provided biological plausibility for different biological pathways of PM2.5 and NO2 at different timescales [43–45], including monthly exposures [46–48]. Given the established biological plausibility, it is therefore imperative for time series studies to look at exposures beyond that of the short- and long-term to assess the impact of these intermediate-term associations, which have been rarely studied in the literature.

Third, time series studies in air pollution have traditionally relied on air pollution monitors for exposure data. However, monitors are typically placed in more densely populated urban areas,

6 and there are large portions of rural areas that do not have any monitoring in place, even in countries with comprehensive monitoring systems such as the United States. For example, the Air

Quality System (AQS), a nationwide monitoring network established by the United States

Environmental Protection Agency (US EPA), includes thousands of monitors in its network [49].

As seen in Figure 2, however, the vast majority of monitors are located in major cities, offering little insight on actual exposure levels in rural locations.

In order to reduce exposure measurement error and include populations in areas without monitors or with sparse monitoring, there has been an increasing use of air pollution prediction models in epidemiology for exposure assessment. These prediction models provide outputs with greatly expanded coverage compared to what individual monitors can provide, and predict both spatial and temporal changes in air pollution. Many epidemiologic studies have utilized air pollution predictions from a single model to assign exposures. However, exposure-response functions generated from multiple studies that use different prediction models are not necessarily comparable, both spatially and temporally. Studies that investigate the sensitivity of exposure model choice in the same epidemiologic analysis remain scarce.

Lastly, studies investigating the climate change impacts of air pollution and health are well represented in the literature, but they have largely focused on global impacts. Several studies have quantified global premature mortality associated with climate change-induced air pollution [50–

52], but fewer studies have investigated the impacts at finer scales. There is a need for more location-specific health impact assessments of climate change on air pollution conducted using more localized data, for both exposure (i.e. downscaled air pollutant predictions) and health (i.e. location-specific exposure-response functions, detailed baseline mortality rates).

Figure 2. PM2.5 monitoring stations in New York State from AQS

1.2 Thesis Overview

In this thesis, I address the knowledge gaps mentioned above, all of which are critical to air pollution epidemiology. In Chapter 2, I complete a systematic review of the existing literature in search of scientific literature that reported PM2.5 concentrations in China from 2005 to 2016, especially focusing on data prior to 2013 which are generally unavailable. I searched for English articles using the PubMed and Embase search engines and also for Chinese articles using the China

National Knowledge Infrastructure (CNKI). I extracted all relevant data from existing studies and categorized them into three economic and six geographic regions.

In Chapter 3, I examine the association between short- and intermediate-term exposure to

NO2 and mortality in 42 counties in China from 2013 to 2015. Using a highly flexible approach

(distributed-lag non-linear models), I investigate the relationship between non-accidental, cardiovascular, and respiratory-related mortality and NO2 up to 30 days before the event for each county, then pool together the county-specific effect estimates using a random effects meta- analysis.

In Chapter 4, I examine the sensitivity of estimated effects to the choice of data from multiple air pollution prediction models for epidemiologic studies. I utilize five different air pollution data sets in New York State from 2002 to 2012 and assess the association between short- term exposure to PM2.5 and cardiovascular and respiratory hospitalizations.

In Chapter 5, I complete a set of health impact assessment calculations that quantifies mid-

21st century ozone-related mortality in China. Using output from the Geophysical Fluid Dynamics

Laboratory Atmospheric Model version 3 (GFDL-AM3), I calculate the change in premature deaths associated with ozone under three potential scenarios: climate change alone, adherence to current legislation, and maximum technologically feasible reduction. This work sets the stage for additional analyses at much finer scales that I plan to complete in the near future.

1.3 References

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Chapter 2

Fine particulate matter concentrations in urban Chinese cities

Mike Z. He1, Xiange Zeng2, Kaiyue Zhang3,4, Patrick L. Kinney5

1 Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA 2 Program in Public Health Studies, Johns Hopkins University Krieger School of Arts and Sciences, Baltimore, MD 21218, USA 3 Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210000, Jiangsu, China 4 Yangzhou Center for Disease Control and Prevention, Yangzhou 225000, Jiangsu, China 5 Department of Environmental Health, School of Public Health, Boston University, Boston, MA 02118, USA

Published: He MZ, Zeng X, Zhang K, Kinney PL: Fine Particulate Matter Concentrations in Urban Chinese Cities, 2005-2016: A Systematic Review. Int J Environ Res Public Health 14(2), 191. 2017.

2.1 Abstract

Background: Particulate matter pollution has become a growing health concern over the past few decades globally. The problem is especially evident in China, where particulate matter levels prior to 2013 are publicly unavailable. We conducted a systematic review of scientific literature that

15 reported fine particulate matter (PM2.5) concentrations in different regions of China from 2005 to

2016.

Methods: We searched for English articles in PubMed and Embase and for Chinese articles in the

China National Knowledge Infrastructure (CNKI). We evaluated the studies overall and categorized the collected data into six geographical regions and three economic regions.

Results: The mean (SD) PM2.5 concentration, weighted by the number of sampling days, was 60.64

(33.27) μg/m3 for all geographic regions and 71.99 (30.20) μg/m3 for all economic regions. A one- way ANOVA shows statistically significant differences in PM2.5 concentrations between the various geographic regions (F = 14.91, p < 0.0001) and the three economic regions (F = 4.55, p =

0.01).

Conclusions: This review identifies quantifiable differences in fine particulate matter concentrations across regions of China. The highest levels of fine particulate matter were found in the northern and northwestern regions and especially Beijing. The high percentage of data points exceeding current federal regulation standards suggests that fine particulate matter pollution remains a huge problem for China. As pre-2013 emissions data remain largely unavailable, we hope that the data aggregated from this systematic review can be incorporated into current and future models for more accurate historical PM2.5 estimates.

2.2 Introduction

Air pollution has long been considered a major environmental problem around the world, and the concern about particulate matter (PM) has been increasing in the last decades. A number of studies have measured ambient air concentrations of PM in numerous large cities in China, but no study to date has attempted to evaluate these results collectively. Furthermore, although China has officially started to monitor PM2.5 levels since 2013, pre-2013 data remains unavailable to the

16 public, making it difficult to study any historical trends of PM2.5 in China. This paper aims to gather data collected on fine particulate matter in the scientific literature using a systematic review approach. In doing so, this review has two goals: (1) to quantify and contrast ambient air

PM2.5 concentrations in six geographic and three economic regions, and (2) to provide a database of pre-2013 measured PM2.5 data useful for current and future modelers to calibrate modeling data.

Particulate matter is defined as “a complex mixture of extremely small particles and liquid droplets, made up of acids, organic chemicals, metals, and soil or dust particles” [1]. Particulate pollution is divided into several categories based on its size. Inhalable coarse particles, also known as PM10, are particulate matter that has an aerodynamic diameter less than 10 micrometers. Fine particles, or PM2.5, are particles that have an aerodynamic diameter less than 2.5 μm. Fine particles are further divided into two refined categories: ultrafine (0.01 μm to 0.1 μm) and fine (0.1 μm to

2.5 μm) particles [1]. For the purposes of this study, we will only distinguish between the two large categories of PM10 and PM2.5.

The size of particulate matter has been directly linked to its potential of causing health problems, with smaller particles posing a greater threat than larger ones do [1]. Because of its small size, PM2.5 actually bypasses the human body’s defense mechanism, and it can lodge deeply into the respiratory system and from there be absorbed into the systemic circulation. With no natural clearance mechanism, the body is unable to efficiently remove PM2.5, resulting in an increase in the risk of many cardiorespiratory disorders [2], including pulmonary and systemic oxidative stress, immunological modifications, hypoxemia, atherosclerosis, a faster progression of chronic obstructive pulmonary disease, a decrease in lung function, as well as other cardiovascular diseases

[3].

Fine particulate matter has become an increasing environmental problem in China due to the country’s face-paced economic development, rapid urbanization, and drastic increase in the use of motor vehicles. Because PM2.5 was not a pollution criterion in China’s National Ambient Air

Quality Standards (NAAQS) of 1996, so it was not mandatorily monitored and there had been no official national data on PM2.5 emissions up until 2013. China updated its NAAQS in 2012 to include PM2.5, and these new standards have become effective as of 1 January 2016 [4].

In their 2014 paper, Yao and Lu estimated particulate matter concentrations in China with remote sensing and discovered spatial differentiation of both PM10 and PM2.5 in various regions using an artificial neural network (ANN), showing that some regions suffer from more serious

PM2.5 pollution than PM10 pollution [5]. However, the ANN was partially trained using PM2.5 data from the United States, which is a large potential source of error for their predictions. Ma et al.

(2016) estimates PM2.5 data from 2004 to 2013 using a statistical model, but their model is completely reliant on monitoring data from 2013 to 2014 and does not use any actual historical data to calibrate their models, a limitation that they state in their paper [6]. Both models would benefit greatly with the incorporation of historical PM2.5 data, even if this data is only available at a small scale or for short time periods.

We emphasize here that the goal of this review is not to provide a representative overview of

PM2.5 across China. Rather, it aims to fill the gap of unavailable historical data by looking at published scientific literature that independently collected PM2.5 data and aggregate them in one place as a resource for future modelers.

2.3 Methods

We conducted an extensive search for scientific literature that measured ambient air

PM2.5 concentrations in different Chinese cities using databases PubMed, Embase, and China

National Knowledge Infrastructure (CNKI). We used the following free text and Medical Subject

Headings (MeSH): “PM2.5”, “PM10”, “air pollution”, “particulate matter”, and “aerosol”. We also used the names of the China’s 34 province-level administrative divisions. The search period was through 14 January 2016.

Only studies that collected original data on ambient PM2.5 concentrations in China were included in this review. We excluded studies that are: (1) not conducted in China, (2) unrelated to ambient air pollution levels, (3) analyzing pre-2005 data, and (4) using the same set of data as other cited studies. A flowchart of the study selection process is shown in Figure 1. Applying these exclusion criteria, we identified 98 distinct references including 575 independent measurements from various cities, mostly from provincial capitals and large urban areas. A detailed list of our search strategy is also shown in Figure 2.

Figure 1. Flow diagram of the study selection process

Figure 2. Search strategy

To facilitate data analysis and allow for adequate comparisons of results, the data were partitioned into six geographically determined regions: Northeastern, Northern, Northwestern, Eastern, South

Central, and Southwestern. All of these regions are consistent with China’s official “traditional regions,” which are the current provincial-level divisions grouped by the country’s former administrative areas from 1949 to 1952. A picture of the regions is shown in Figure 3 (adapted from work by Ericmetro, redistribution allowed under Creative Commons Public License CC BY-

SA 3.0). These regions serve as units of comparison that are fairly evenly distributed in terms of geographic size and population density, but other equally valid divisions exist as well (for example, dividing the South Central region into separate South and Central regions). Additionally, Beijing was included as a separate category due to the large number of data points, and Taiwan was included as a separate category since it did not belong to the six regions mentioned above.

Figure 3. Regions of China

For policy relevance, we also looked at three important economic regions: the Beijing-

Tianjin-Hebei Metropolitan Region, the Yangtze River Delta, and the Pearl River Delta. These regions are consistent with China’s recent “Action Plan on Prevention and Control of Air

Pollution”, labeled as the three regions of highest priority over the next five to ten years [7].

Statistical analysis was performed using Stata13 (StataCorp Inc., College Station, TX, USA).

Weighted averages of PM2.5 concentrations were obtained based on the number of days of sampling in each study. These weights were determined using the number of days of sampling in each study divided by the number of days of sampling in each region. For studies where exact sampling day was not available, sampling months and seasons were used to approximate the number of sampling days. For the purposes of the study, we categorized March, April, and May as spring; June, July, and August as summer; September, October, and November as autumn; and

December, January, and February as winter. Weighted regional and overall averages are presented

22 in the results section. The results are also compared to China’s current annual standard (35 μg/m3)

3 and 24-h standard (75 μg/m ) limits for 24-h averages PM2.5 concentrations [8]. All

3 PM2.5 measurements in this study are reported as μg/m , and measurements from references that are reported in different units are converted accordingly.

2.4 Results

Data was obtained for all 34 of China’s province-level administrative divisions, including 22 provinces, five autonomous regions, four municipalities, two special administrative regions (Hong

Kong and Macau), and Taiwan. For the purposes of this study, we will disregard the specific classifications and focus solely on province-level administrative regions. We identified a total of

574 separate measurements: 61 in the Northeastern Region [9–17], 53 in the Northern Region [14,

17–29], 42 in the Northwestern Region [14, 17, 30–35], 121 in the Eastern Region [14, 17, 30, 33,

36–51], 136 in the South Central Region [14, 17, 33–34, 52–62], 51 in the Southwestern region

[14, 17, 63–64], 98 in Beijing [10, 14, 17, 19, 22, 30, 33, 65–95], and 12 in Taiwan [96–106].

The Beijing-Tianjin-Hebei metropolitan region, Yangtze River Delta, and Pearl River Delta are all defined in the dataset based on the major cities that make up each of these regions. We identified 123 separate measurements for the Beijing-Tianjin-Hebei metropolitan region, 59 for the Yangtze River Delta, and 38 for the Pearl River Delta.

Figure 4 presents the PM2.5 levels in all 34 province-level administrative divisions, and Table

1 summarizes the regional averages reported with their respective standard deviations. The

“Number of Measurements” column refers to the number of data points used in each region. The

“% Above Annual Limit” column refers to the percentage of data points in each region that

3 exceeded China’s current annual PM2.5 emissions limit of 35 μg/m in urban areas. The “% Above

24-h Limit” column refers to the percentage of data points in each region that exceeded China’s

3 current 24-h PM2.5 emissions limit of 75 μg/m in urban cities. The overall average represents an arithmetic average of all 574 data points weighted by the total number of days of sampling across all regions.

Figure 4. PM2.5 levels in different regions

Table 1. Summary of geographic regions 3 Region [PM2.5] (μg/m ) Number of % Above % Above 24- Measurements Annual Limit hour Limit Northeastern 66.50±27.96 61 91.80% 34.43% Northern 76.10±38.69 53 100% 50.94% Northwestern 85.41±59.19 42 100% 14.29% Eastern 55.41±18.16 121 86.78% 20.66% South Central 50.23±21.00 136 75.74% 16.91% Southwestern 48.72±13.63 51 90.20% 11.76% Beijing 94.42±23.83 98 100% 77.55% Taiwan 30.49±1.81 12 8.33% 0% Overall Average 60.64±33.27 574 87.80% 32.06%

Table 2 provides summaries similar to that of Table 1 but for the three economic regions. Note that there is overlap between the geographic and economic regions. While the geographic regions include all data points collected from the search, the three economic regions only encompasses about 40% of this data. A one-way analysis of variance test of PM2.5 concentrations yielded a statistically significant difference (F = 14.91, p < 0.0001) among all geographic regions and all economic regions (F = 4.55, p = 0.01).

Table 2. Summary of three economic regions 3 Region [PM2.5] (μg/m ) Number of % Above % Above 24- Measurements Annual hour Limit Limit BTH 93.73±25.89 123 100% 78.05% Yangtze River 55.86±17.62 59 93.22% 28.1% Pearl River 47.23±14.86 38 65.79% 13.16% Overall Average 71.99±30.20 220 92.27% 53.64%

2.5 Discussion

2.5.1 Comments on findings

As mentioned at the beginning of the paper, the reasoning behind the choice of geographic regions is for the facilitation of data interpretation. The six regions presented in this study are frequently used categories representing the provincial-level divisions in China and this facilitates communication. We decided to report Beijing separately from the Northern Region due to the large amount of current literature we found that reported PM2.5 data for Beijing. In fact, Beijing contributed to 98 of the 151 total results from the Northern Region, which would severely bias the results in the Northern Region if analyzed together.

In contrast, the economic regions represent a more policy-relevant division for China’s most critically polluted areas. The Beijing-Tianjin-Hebei metropolitan region in particular has the

25 highest levels of PM2.5 of the three economic regions, and the three economic regions overall has higher PM2.5 levels than the average of all geographic regions.

Our findings in Figure 4 are slightly different from data available from current monitoring stations in China. Several reasons may contribute to this. First, our data is aggregated from a variety of data sources, each with different sampling methodologies. Furthermore, data collection from these sources are usually focused on select large cities, leaving hundreds of other cities in the provinces excluded due to lack of data. Lastly, many data points from this study were collected prior to 2013, back when a national PM2.5 monitoring network was not yet existent. These data points were also used for the averages provided in Figure 4 and contribute to potential discrepancies.

One of the most challenging parts of this study was standardizing the dataset for proper analysis. With numerous papers that recorded data at different times of the year using different timeframes and different instruments, it was necessary to come up with a methodology to allow

3 the maximum number of data to be interpretable. Most papers reported PM2.5 in μg/m , which is a standard measure of particulate matter and other compounds. Data with different units were converted to μg/m3 for analysis. The sample size was determined based on the number of days of sampling, which was either directly stated or implied in each paper included in this study. The presence of a sample size allowed for proper adjustment of the data, allocating more weight to studies that made measurements for a longer period of time than those with shorter timeframes.

This step was crucial for the analysis, as papers included in this study collected samples for as short as three days and as long as several years.

As seen in Table 1, the Northwestern Region had the highest mean levels of PM2.5 at 85.4

μg/m3, followed by the Northern and Northeastern regions. Beijing had the highest mean levels of

3 3 PM2.5 at 94.42 μg/m , while Taiwan had the lowest mean levels at 30.49 μg/m . The fact that

Beijing had the highest levels out of all considered categories shows that we were right to consider it as a separate category, as it had the potential to bias the results in the Northern Region if left unaddressed. In contrast, while Taiwan had the fewest number of observations (n = 12), it produced both the lowest concentrations and the smallest standard deviation, showing the consistency of the measurements that were taken in various cities.

We chose to use both the annual and the 24-h PM2.5 standards as a means to be as conservative as possible with our estimates. The vast majority of the gathered data points were taken from large urban cities, to which these standards apply. We see from Table 1 that based on the annual standard, nearly 90% of all data points exceeded the limit. Even under the most lenient standard in the nation, 32% of all data points still exceed the limit. In fact, China’s standards are already very lenient. For reference, the World Health Organization guidelines for PM2.5 is only 10

μg/m3 for the annual mean and 25 μg/m3 for the 24-h mean, approximately three times lower than that of China’s permitted levels [107]. These results are a strong indicator that most if not all large cities in China suffer from air pollution and especially particulate matter pollution. Undoubtedly,

China’s PM2.5 problem is severe, and a long-term commitment is required to resolve it.

2.5.2 Applications to current literature

There have been a number of related studies published in the literature recently.

Krzyzanowski et al. looked at annual average concentrations of PM2.5 in a number of mega-cities from around the world, including 12 cities from China [108]. Krzyzanowski used both surface monitoring and modeling used by the Global Burden of Disease 2010 [109] to estimate particulate matter exposure. GBD 2010 utilized a combination of satellite estimates and high resolution air quality models to complement surface monitoring data, allowing for estimates to be made in areas

27 even where surface monitoring data were unavailable [108]. This is an approach that should be seeing more use in the future as modeling technology continues to advance at an incredible speed.

A similar study was conducted by Yao and Lu in 2014 which focused on looking at particulate matter and population exposure in China using remote sensing techniques. Yao and Lu used three types of data: atmospheric aerosol product derived from NASA, meteorological data for atmospheric analysis, and population density data from the Center for International Earth Science

Information Network (CIESIN) [5]. Using an ANN algorithm that was suggested in a prior paper by Gupta et al. [110], Yao and Lu built a framework centered on the ANN called the ANN estimation model, which they then used to estimate particulate matter concentration. While the

methods of Yao and Lu are innovative, their ANN was partially trained using PM2.5 data from the

United States, a potential source of substantial error for their predictions. Their model would benefit from the incorporation of collected data, which our paper provides to a certain extent.

More recently (2016), Ma et al. reconstructed historical PM2.5 data from 2004 to 2013 using a combination of aerosol optical depth data and a two-stage statistical model [6]. Although their model shows promising results at the seasonal and monthly levels, it is based completely on extrapolating newer data (2013–2014) with no calibration or comparison to actual data from the earlier time periods. The authors note this as one of their major limitations and notes that historical

PM2.5 data would allow them to be able to make annual model adjustments. Once again, the data aggregated from this review, albeit mostly short-term and relatively few in numbers, may be incorporated into their model for more accurate estimates.

2.5.3 Limitations

There are several limitations to our study. Two types of variation were detected in our study: seasonal and regional (urban versus suburban areas). Seasonal variation was one of the challenges

28 we expected to face in our study. Wang et al. was one of the few papers that attempted to quantify

differences in PM2.5 fluctuations over the seasons, and noticed that PM2.5 concentrations were highest in the winter, following a trend of winter > autumn > spring > summer [59]. Using our formal definition of season, we attempted to categorize all obtained data points into a season if possible. This was not possible for annual data or data that were reported collectively over more than one season (e.g., March to August). A large portion of our dataset did come from the

wintertime, making the results an overestimate of annual PM2.5 concentration but probably an

underestimate of winter PM2.5 concentration.

Regional variation was a smaller and easier problem to manage. A number of papers separated collected data for urban and suburban areas. In general, urban areas were found to have slightly

higher concentrations of PM2.5 [56, 59, 111]. For papers that reported urban and suburban averages separately, the two numbers were reported as separate entries based on the number of sampling days.

Lastly, differing measurement methods and sampling locations pose a potential threat to the validity of our results. The data used from this review comes from a variety of studies that collected

PM2.5 for various reasons. These studies often used different instruments to collect PM2.5 and collected data from different areas of cities (e.g., residential areas versus highways), and detailed sampling locations were generally not available.

2.5.4 Conclusions

This analysis provides evidence that based on existing research, PM2.5 levels vary among different geographic and economic regions of China. More importantly, this review fills the gap of pre-2013 ambient PM2.5 concentrations in China, which is unavailable to the public, with

PM2.5 data collected independently by different studies. It is our hope that the aggregated dataset

29 from this review may be used as a starting point for calibrating and improving current and future air pollution models.

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Chapter 3

Short- and intermediate-term exposure to NO2 and mortality: A multi-county analysis in China

Mike Z. He1, Patrick L. Kinney2, Tiantian Li3, Chen Chen3, Qinghua Sun3, Jie Ban3, Jiaonan Wang3, Siliang Liu1, Jeff Goldsmith4, Marianthi-Anna Kioumourtzoglou1

1 Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA 2 Department of Environmental Health, School of Public Health, Boston University, Boston, MA 02118, USA 3 National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China 4 Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA

Published: He MZ, Kinney PL, Li T, Chen C, Sun Q, Ban J, Wang J, Liu S, Goldsmith J, Kioumourtzoglou M-A: Short- and intermediate-term exposure NO2 and mortality: A multi-county analysis in China. Environmental Pollution 261: 114165. 2020.

3.1 Abstract

Background: Nitrogen dioxide (NO2) is a well-established traffic emissions tracer and has been associated with multiple adverse health outcomes. Short- and long-term exposure to NO2 has been

41 studied and is well-documented in existing literature, but information on intermediate-term NO2 effects and mortality is lacking, despite biological plausibility.

Methods: We obtained daily NO2 and mortality data from 42 counties in China from 2013 to 2015.

Distributed-lag non-linear models were employed to investigate the relationship between non- accidental mortality and NO2 up to 30 days before the event, including PM2.5, temperature, relative humidity, and holidays as covariates in a random effects meta-analysis pooling county-specific estimates. We repeated the analysis for cardiovascular- and respiratory-related mortality, and explored sex-stratified associations.

3 Results: Per 10 µg/m increase in NO2, we estimated a 0.13% (95%CI: 0.03, 0.23%), 0.57%

(95%CI: -0.04, 1.18%), and -0.14% (95%CI: -1.63, 1.37%) change in non-accidental mortality for same-day and previous-day NO2 (lag0-1 cumulated), in the preceding 7 days (lag0-7 cumulated), and in the preceding 30 days (lag0-30 cumulated), respectively. The strongest estimate was observed for respiratory-related mortality in the lag0-30 cumulated effect for women (3.12%;

95%CI: -1.66, 8.13%).

Conclusions: We observed a trend of higher effect estimates of intermediate-term NO2 exposure on respiratory mortality compared to that of the short-term, although the differences were not statistically significant. Our results at longer lags for all-cause and cardiovascular mortality were sensitive to modeling choices. Future work should further investigate intermediate-term air pollution exposure given their potential biological relevance, but in larger scale settings.

3.2 Introduction

Ambient air pollution is one of the leading environmental problems of the 21st century. Air pollutants such as fine particulate matter (particles with aerodynamic diameter ≤ 2.5µm; PM2.5) have been consistently linked with increases in mortality and morbidity [1–5]. The Global Burden

Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) estimated that exposure to PM2.5 caused 4.2 million deaths in 2015, representing 7.6% of total global deaths [6]. Numerous existing epidemiologic studies have also linked many other air pollutants to increased mortality, including ozone (O3) [4,7–10], sulfur dioxide (SO2) [11–13], and also nitrogen dioxide (NO2) [14].

NO2 is a highly reactive gas and a primary pollutant from a variety of sources, especially from the combustion of fossil fuel [15,16]. As such, it is a well-established traffic emissions tracer

[17,18] and a regularly monitored air pollutant around the world. Existing studies conducted in

North America and Europe have linked both short-term (i.e. daily) and long-term (i.e. annual) exposure to NO2 to increased mortality [14,19,20]. In vitro studies have also reported that NO2 is associated with cellular inflammation, bronchial hyperresponsiveness, and increased risk of infection, particularly in the respiratory system [16]. Furthermore, mechanistic studies have linked intermediate-term (i.e. monthly) exposure to NO2 and black carbon (a PM2.5 component also commonly used as a traffic emissions tracer) to sub-clinical outcomes, such as increased blood pressure and decreased lung function [21,22]. However, despite reported biologic plausibility, there remains a lack of epidemiologic studies exploring the relationship between intermediate- term NO2 exposure and clinical outcomes.

China’s enormous air pollution-related health burden and high greenhouse gas emissions place it squarely at the center of attention of air pollution research. As the world’s most populated country with over 1.4 billion people, China is one of the fastest developing countries in the world and also one of the most impacted by the adverse health effects of air pollution [6]. While the effects of both short- and long-term exposure to NO2 have been well-studied in Western countries, there have been relatively few studies on this topic conducted in China. Ambient air pollution levels in China are often orders of magnitudes higher than those in Western countries, and the

43 wider exposure range fills a major gap in existing research, specifically allowing us to better characterize the exposure-response curve at a wider range of NO2 concentrations. In addition, the distribution of potential modifiers of the NO2-mortality association across countries, both at the population- and individual-level, are likely very different, greatly limiting the generalizability of studies from developed countries to China. To date, we are aware of only three multi-city studies that investigated the short-term associations of NO2 on mortality in China [23–25]. Furthermore, multi-city studies that investigate the association of NO2 and mortality at longer time-scales in

China, such as at the monthly or annual levels, are currently lacking. In fact, NO2-mortality studies using exposure metrics in between daily and annual exposure are scarce in general, and we are unaware of any studies that investigates intermediate-term NO2 exposure on cause-specific mortality around the world.

The objective of this study is to investigate the association between mortality and exposure to NO2 up to 30 days before the event in 42 Chinese counties. Existing studies provide evidence for health effects of air pollution at the monthly scale. For example, 28-day moving average exposure to black carbon, a traffic emissions tracer like NO2, has been linked increased blood pressure [21]. 28-day moving averages of various air pollutants, including black carbon, carbon monoxide, and NO2, have also been linked to decreased lung function [22]. Given that studies of sub-clinical health endpoints provide biological plausibility for different biological pathways of

NO2 at different timescales [26–28], understanding the clinical effects of monthly NO2 exposure on mortality will not only complete the spectrum of different exposure associations for the NO2- mortality association but also provide additional China-specific concentration-response functions that can be used to best inform China-specific regulatory actions.

3.3 Methods

Figure 1. Geographic distribution of study counties

3.3.1 Study location

This study utilized data from 42 counties across 11 cities in China from January 2013 to

December 2015. These cities include: Beijing (seven counties), Chengdu (six counties),

Guangzhou (three counties), Harbin (four counties), Nanjing (three counties), Shanghai (seven counties), Shijiazhuang (four counties), Suzhou (one county), Taiyuan (two counties), Wuhan

(three counties), and Xi’an (two counties). The locations of the counties are shown in Figure 1.

Note that in China, counties are administrative units similar to districts, and are thus smaller than cities.

3.3.2 Exposure assessment

Hourly concentrations of NO2 and PM2.5 were obtained from the National Air Pollution

Monitoring System. We calculated daily average concentrations of NO2 and PM2.5 in each county using the averages of the hourly reported values. In each county, NO2 and PM2.5 concentrations from the monitoring station closest to the county center were assigned as the exposure for that county. If there were no monitoring stations located within a county, then the closest monitoring station from a nearby county was used for exposure assessment for that county. If multiple air monitors were available for a county, the concentrations were averaged. Of the 42 counties in this study, 18 included monitoring stations within the boundaries of the county. Aside from two rural counties (Fangzheng and Yilan), where the closest monitoring station was ~200 km away, the average distance of the closest nearby monitoring station to the centroid of each county that did not have a monitoring station was 24.4 km. Daily average temperature and relative humidity of cities were obtained from the data sharing network of the China Meteorological Bureau, which were then assigned to each county that comprises the city.

3.3.3 Outcome assessment

Daily mortality data for each county were obtained from the Chinese Center for Disease

Control and Prevention’s Disease Surveillance Point System. The International Classification of

Diseases, 10th revision was used to classify non-accidental mortality (A00-R99) and cause-specific mortality for each county, including for cardiovascular disease (I00-I99) and respiratory disease

(J00-J99). Mortality counts were also stratified by sex.

3.3.4 Statistical analysis

We employed distributed-lag non-linear models (DLNMs) [29] to investigate the relationship between non-accidental mortality and NO2 exposure up to 30 days before the event.

The distributed lag model framework allows for the adjustment of exposures on other days while still estimating the temporal trend of the association, under the assumption that it varies smoothly as a function of time [29–32]. Specifically, we utilized overdispersed county-specific Poisson regression models to evaluate the NO2-mortality relationship. We selected the best fitting model and the appropriate degrees of freedom (df) for all non-linear terms included in the model based on quasi-Akaike Information Criterion (qAIC). In the model, we included smooth functions of calendar time to adjust for seasonality and long-term trends (using natural cubic splines with 4 df per year), as well as indicator variables for day of the week. We assessed potential non-linearity in the exposure-response relationships using penalized splines for NO2. We found no evidence of deviation from linearity, using the qAIC to select the best fitting linear versus non-linear models.

Based on this criterion, we adopted a linear exposure-response for the NO2-mortality relationship and used smoothing functions to model the lag constraint (natural spline, 3 df). We controlled for potential confounding bias due to PM2.5 linearly, due to weather by including smoothing functions of average temperature (natural spline, 3 df) and relative humidity (natural spline, 3 df) all as 30- day distributed-lag terms, and from holidays as an indicator variable. We pooled the county- specific model results using a random effects meta-analysis [33,34]. We tested for calendar time df from 4 to 7 per year, and for NO2, PM2.5, temperature, and relative humidity, we evaluated various lag constraint structures, including polynomials and natural splines, from 3 to 6 df. We then repeated the analysis using both cause-specific (cardiovascular and respiratory) and sex- stratified mortality.

Finally, as a sensitivity analysis we also fit a fixed effects model using information on all counties in the same quasi-Poisson model with county-specific intercepts. We adjusted for the same variables as in the county-specific models. This model assumes the same confounding structure across all counties. However, county-specific DLNMs with 30 lags could become quite unstable, especially if the number of cases in some counties is small, and the fixed effects model using all the available information provides larger stability in the estimates.

3 All results are presented as percent changes per 10 µg/m increase in NO2 concentrations for comparability with other studies. All statistical analyses were performed using the R Statistical

Software, version 3.5.1 (Foundation for Statistical Computing, Vienna, Austria).

3.4 Results

Table 1. Pollutant, confounder, and outcome descriptive statistics. (2013-2015; N = 42,248 days) Variable Mean Min 25% 50% 75% Max 3 NO2 (µg/m ) 50.2 0.8 32.0 46.0 63.4 327.8 3 PM2.5 (µg/m ) 73.1 3.1 33.4 55.1 92.0 1009.6 Mean Temperature (°C) 15.3 -25.7 7.4 17.6 23.9 35.5 Relative Humidity (%) 66.8 2.1 56.0 71.0 81.0 100.0 Daily Mortality Counts Non-accidental 11.5 0.0 6.0 10.0 15.0 53.0 Cardiovascular 5.0 0.0 3.0 4.0 7.0 36.0 Respiratory 1.5 0.0 0.0 1.0 2.0 19.0

Table 1 shows the descriptive statistics of the variables used for the daily model. The

3 average daily and monthly NO2 levels for the study period across all counties were 50.2 µg/m and

50.6 µg/m3, respectively, both of which are below China’s current 24-hour regulatory standard of

3 80 µg/m [35]. The correlation between NO2 and PM2.5 in our data was 0.60. On average, there were around 12 non-accidental, 5 cardiovascular, and 2 respiratory deaths per county per day.

3 Figure 2. 30-day lag-specific cumulated effect estimates per 10 µg/m NO2 increase for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality

Figures 2a, 2b, and 2c show the 30-day lag-specific cumulated mortality effect estimates

3 per 10 µg/m increase in NO2 for non-accidental, cardiovascular-, and respiratory-related mortality, respectively. Sex-stratified lag-specific cumulated estimates and lag-specific non- cumulated effect estimates are presented in the supplemental materials (Figures S1-S3).

Specifically, we estimated an increase of 0.13% (95%CI: 0.03, 0.23%) in non-accidental mortality for same-day and previous day NO2 (lag0-1 cumulated), and an overall decrease of 0.14% (95%CI:

-1.63, 1.37%) for the lag-specific cumulated effect of NO2 30-days before the event (lag0-30 cumulated). For cardiovascular mortality, we observed an increase of 0.10% (95%CI: -0.06,

0.25%) for lag0-1 cumulated NO2, and a decrease of 0.87% (95%CI: -3.26, 1.58%) for lag0-30 cumulated NO2. For respiratory mortality, we observed an increase of 0.03% (95%CI: -0.23,

0.30%) for lag0-1 cumulated NO2, and an increase of 2.13% (95%CI: -1.21, 5.58%) for lag0-30 cumulated NO2.

In the sex-stratified analyses, effect estimates for women were generally higher than those of men at lag0-1 cumulated and at lag0-30 cumulated (Figure S2 and S3). In women, a 10 µg/m3 increase in 30-day cumulated NO2 resulted in a 0.83% (95%CI: -1.23, 2.93%) increase in non-

3 accidental mortality. In comparison, for men, a 10 µg/m increase in 30-day cumulated NO2 resulted in a 1.10% (95%CI: -2.82, 0.65%) decrease in non-accidental mortality. Similar trends were observed for both cardiovascular and respiratory-related mortality, although the confidence intervals overlapped for sex-stratified results (Figure S3). Figure S4 presents the county-specific and pooled cumulated effect estimates for lag0-1, lag0-7, and lag0-30.

In sensitivity analyses we observed that these results were sensitive to modeling choices.

Specifically, in the fixed effects analysis we found similar effect estimates at lag0-1 and statistically significant positive cumulated lag0-30 effect estimates (Figure S5). In general, while

50 lag-specific effect estimates from the meta-analysis displayed the same trend, they resulted in attenuated estimates compared to the fixed effects analysis (Figure S6).

3.5 Discussion

Using data from 42 counties spanning from northern to southern China, we investigated the association between NO2 and mortality for up to 30 days exposure before the event. We found increases in non-accidental mortality across China for up to 7 days exposure before the event, as well as higher albeit insignificant effect estimates for respiratory mortality for up to 30 days exposure before the event. To our best knowledge, this is the first study to investigate the association between intermediate-term NO2 exposure and mortality, and it adds to the already rich database of research looking at similar relationships between short-term NO2 exposure and mortality [14,19,23–25,36].

Our results for the estimated effects of short-term NO2 exposure are smaller in magnitude compared to existing studies conducted in North America and Europe. In their single-pollutant models, Chiusolo and colleagues found a 2.09% (95%CI: 0.96, 3.24%) increase in all-natural mortality, a 2.63% (95%CI: 1.53, 3.75%) increase in cardiac mortality, and a 3.48% (95%CI: 0.75,

3 6.29%) increase in respiratory mortality per 10 µg/m increase in NO2 [14]. Crouse and colleagues reported a 5.2% (95%CI: 4.5, 5.9%) increase in non-accidental mortality, a 4.1% (95%CI: 2.8,

5.3%) increase in cardiovascular mortality, and a 3.6% (95%CI: 1.2, 6.1%) increase in diseases of the respiratory system per 8.1ppb increase in NO2 [20]. We believe that these smaller effect estimates may be explained by a plateauing exposure-response relationship. A recent study by

Burnett et al. found that the long-term PM2.5-mortality relationship levels off at higher concentrations of PM2.5 [37], and a similar phenomenon may be observed here in NO2 as well.

Please note that this would not contradict our findings of a linear association in the NO2

51 concentration range included in our analyses. It is possible that we are only able to observe the linear portion at the high NO2 levels of the full non-linear exposure-response curve. In comparison, our results were more similar to those of studies from China. Wong and colleagues using data from three cities in China found a 1.19% (95%CI: 0.71, 1.66%) increase in natural mortality, a 1.32%

(95%CI: 0.79, 1.86%) increase in cardiovascular mortality, and a 1.63% (95%CI: 0.62, 2.64%)

3 increase in respiratory mortality per 10 µg/m increase in NO2 [23]. In a more recent paper, Chen and colleagues conducted a nationwide time-series across 272 Chinese cities and found a 0.9%

(95%PI: 0.7, 1.1%) increase in non-accidental mortality, a 0.9% (95%PI: 0.7, 1.2%) increase in cardiovascular mortality, and a 1.2% (95%PI: 0.9, 1.5%) increase in respiratory mortality per 10

3 µg/m increase in 2-day average concentrations of NO2 [25].

In our sex-stratified analysis, we observed stronger effect estimates in women than in men for lag0-30 cumulated exposure and all-cause mortality, albeit with overlapping confidence intervals. Stronger effect estimates in women have been reported previously in air pollution research [38]. While the exact reason remains unclear, existing research has linked this disparity to sex differences in toxicity, metabolic rate, and sex steroid hormones [39,40]. Additional research is necessary to elucidate the biological mechanisms behind this phenomenon. Nonetheless, these differences in our analyses were not statistically significant and could be due to chance.

As mentioned earlier, to our best knowledge, this is the first study that investigated the association between intermediate-term NO2 exposure and mortality. Numerous biomarker studies have provided biological plausibility for different biological pathways of NO2 sub-clinical effects at different timescales [26–28]. In terms of short-term exposure, one generally accepted mechanism is that air pollution is linked to pulmonary inflammation, which then results in the production of local inflammatory mediators that can then lead to systemic inflammation. As

52 mentioned previously, intermediate-term (e.g. 28-day moving average) exposure to black carbon, a traffic emissions tracer like NO2, has been linked increased blood pressure [21]. 28-day moving averages of various air pollutants, including black carbon, carbon monoxide, and NO2, have also been linked to decreased lung function [22]. Long-term (e.g. annual) exposure to air pollution, on the other hand, have been linked to DNA damage, epigenetic alterations, and biological aging [28].

For NO2 specifically, some of the major proposed mechanisms of toxicity include lipid peroxidation in cell membranes and reactions of free radicals on both structural and functional molecules [26]. Depending on the molecules of interest, the time it takes for a reaction to occur may differ drastically. However, despite strong biological plausibility, we did not observe elevated cumulated lag0-30 NO2 effect estimates for mortality, with the exception of respiratory mortality.

Given the reported biological plausibility and the limited evidence for intermediate-term exposures, it is therefore of interest for time-series studies to look at the effect of exposures beyond that of the short- and long-term on clinical outcomes.

Although the effect estimates for short-term exposures are similar in our main and sensitivity analyses, we obtained different results for the cumulated lag0-30 exposures in the random effects meta-analysis vs. the fixed effects analysis. Specifically, we observed null associations when we ran county-specific models and then pooled in a meta-analysis and significantly positive associations in the fixed effects models. This discrepancy in results could indicate that the assumption of a similar confounding structure across counties in the fixed effects model may not be valid. However, it is also likely that the county-specific DLNMs with 30 lags, especially in some of the smaller counties in our analyses, may not have been stable enough to accommodate the high NO2 day-to-day autocorrelation. This is evident by the highly sensitive lag structure to the change in df observed in our sensitivity analysis. We are therefore not confident in

53 our reported cumulated lag0-30 results and further research in settings with longer study periods is warranted. Nonetheless, we observed good agreement in the cumulated lag0-1 and lag0-7 effect estimates in both the main and sensitivity analyses.

Ambient air quality has been regulated in China since 1982, and NO2 is one of the originally regulated air pollutants. Since then, the standards have been revised three times, with the most recent standards released in 2012 (GB 3095-2012) that took effect in 2016. Under current

3 standards, annual, 24-hour, and hourly limits for NO2 are set at 40, 80, and 200 µg/m , respectively

[35]. In addition to the ambient air quality standards, China’s twelfth Five-Year Plan (2011-2015) specifically set a goal of reducing national emissions of nitrogen oxides (NOx) by 10% relative to

2010 levels [41]. The most recent thirteenth Five-Year Plan (2016-2020) further tightened air quality regulations, requiring prefecture-level and larger cities to have an air quality index of 100 or below in at least 80% of the days per year [42]. Combined with China’s concerted effort in reducing traffic in major urban areas through road space rationing (such as the even-odd license plate policy) and the drastic expansion of the public transportation system, NOx reduction will remain one of China’s top priorities over the next several years. We recommend that future policies should formally incorporate the health effects of different time-metrics of air pollution exposure as considered criteria. Specifically, given the current lack of existing standards for NO2 levels between daily and annual time-metrics, we believe that it is important to consider additional NO2 standards at more intermediate timescales, such as the monthly level. It is possible that NO2 concentrations slightly lower than the daily standard—i.e., in compliance with existing standards—are observed on multiple days within a month. However, neither the daily nor the annual standards would address this issue, highlighting the need to also consider intermediate-term standards.

Our study has numerous strengths. First, this is the first study on the intermediate-term associations of NO2 on mortality to date. Second, our dataset has coverage over 42 counties in 11 cities from northern to southern China, and provides a representative sample of China’s urban population in some of its most densely populated cities. Third, the use of DLNMs allows us to simultaneously evaluate short-term and longer-term NO2-mortality associations. Compared to a monthly-only model (i.e. average monthly exposure, aggregated monthly outcomes), the distributed lag models allow us to be more confident that the estimated intermediate-term effect is actually an intermediate-term effect, and not just an aggregate of short-term effects or an indicator for long-term effects. Lastly, this work coincides with priorities in China’s recent Five-Year Plans, and adds to the growing body of literature that policy makers can reference when formulating future regulations.

Our study also has a few limitations. First, although the existing dataset provides a representative spatial coverage of China, it is lacking in temporal coverage and only includes the time period of 2013 to 2015. Although this relatively short time period does not provide us with enough statistical power to explore longer-term associations of NO2 (e.g. annual exposures), the already large sample size allows us to be confident with all of our existing conclusions. Second,

NO2 exposure data are obtained from the National Air Pollution Monitoring System, which provides hourly NO2 concentrations for each county and may be prone to measurement error since point estimates from monitors are used to represent county-level ambient concentrations. Third, we only had three years of data in each county, potentially limiting our power for the county- specific DLNMs and the stability of our results at longer lags. Lastly, there are a number of counties that did not have monitoring stations within its vicinity, and the closest monitoring station from a nearby county had to be used as the exposure assignment, which is another potential source

55 of measurement error. However, only two counties were assigned monitors that were ~200 km away—their exclusion from the meta-analysis did not impact our results—and the centroids of the remaining 16 counties without a monitoring station were on average < 25 km away from the assigned monitoring station. A sensitivity analysis comparing the meta-analysis of all counties versus the meta-analysis of a subset of counties that only contained monitoring stations within its vicinity did not show significant differences. There is no reason, furthermore, to believe that the above errors would be differential, and non-differential exposure measurement error in time series studies has been shown to bias estimates towards the null [43,44], making the results of our study more likely to be conservative.

While China has rapidly expanded the number of air monitoring stations over the past few years, relatively few monitors exist in western China, which is traditionally less populated and less urbanized than eastern China. Most existing air pollution epidemiological studies in China are therefore better representations of urban China rather than China as a whole. As the number of air monitors in China continue to increase or predictions from highly spatiotemporally resolved NO2 models become available, more work needs to explore the air pollution-mortality relationship in understudied regions, such as Qinghai province, Xinjiang Autonomous Region, and Tibet

Autonomous Region. As this is a county-level analysis, future studies in China should also explore intermediate- and long-term effects of air pollution on mortality and morbidity at the individual level, preferably using dedicated air pollution cohorts.

In conclusion, we investigated the association between mortality and NO2 exposure up to

30 days before the event and found significant cumulated associations up to seven days for non- accidental mortality, as well as an increase (albeit insignificant) of over 3% in respiratory mortality

3 per 10 µg/m increase in ambient NO2 for cumulated lag0-30. Our all-cause and cardiovascular

56 mortality cumulated lag0-30 effect estimates, however, were sensitive to modeling choices. Given the above and the established biological relevance, we believe that there is still a need to investigate intermediate-term exposure to air pollutants using data available for longer periods as these become available, and we would recommend sufficiently powered studies that are interested in intermediate-term health effects to further explore this topic.

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3.7 Supplemental Materials

Table S1. Descriptive statistics by county Mean (SD) Number of Distance to Total City County N Size Population Daily NO2 Monitors Nearest Deaths (km2) (µg/m3) Monitor (km)* Beijing Changping 1,094 1,352 467,680 13,003 36.59 ± 22.81 2 0 Dongcheng 1,091 41 1,660,501 16,785 55.59 ± 26.17 1 0 Fengtai 1,094 306 573,180 25,677 71.54 ± 25.12 2 0 Mentougou 1,089 1,321 2,112,162 5,670 45.07 ± 24.83 1 0 Miyun 1,094 2,227 1,184,256 8,064 26.88 ± 17.17 2 0 Tongzhou 1,094 906 290,476 14,413 54.79 ± 24.90 2 0 Yanqing 1,084 1,992 317,426 5,048 41.81 ± 19.37 2 0 Chengdu Jintang 1,030 1,156 828,140 16,140 55.70 ± 20.46 0 41.8 Pengzhou 1,024 1,420 1,279,930 15,960 21.99 ± 10.42 0 28.4 Qingyang 1,029 68 717,227 11,342 48.89 ± 18.80 1 0 Qionglai 970 1,384 1,375,699 12,497 55.43 ± 18.44 0 69.47 Shuangliu 920 1,032 612,753 11,706 55.43 ± 18.44 1 0 Wuhou 1,030 77 762,887 9,624 55.43 ± 18.44 0 8.78 Guangzhou Liwan 1,028 62 1,764,828 17,179 58.44 ± 25.56 1 0 Panyu 1,016 530 1,157,666 11,688 47.44 ± 23.39 1 0 Yuexiu 1,033 33 898,200 21,038 56.01 ± 20.85 1 0 Harbin Daowai 983 619 906,421 17,350 64.11 ± 29.32 2 0 Fangzheng 963 2,969 1,343,857 3,751 64.11 ± 29.32 0 178.6 Nangang 1,036 183 1,088,345 21,714 64.11 ± 29.32 0 14.1 Yilan 981 4,616 203,853 7,255 64.11 ± 29.32 0 244.3 Nanjing Lishui 1,026 1,067 710,298 8,156 54.99 ± 22.63 0 52.3 Pukou 1,028 913 388,319 9,547 42.83 ± 21.01 1 0 Yuhuatai 950 135 421,323 3,962 54.99 ± 22.63 0 12.0 Shanghai Changning 952 37 1,083,463 15,041 49.05 ± 23.18 0 5.1 Fengxian 940 705 1,582,398 11,662 50.85 ± 22.03 0 31.1 Huangpu 974 20 391,293 25,371 50.94 ± 24.14 0 2.3 Jinshan 940 586 2,429,372 11,337 50.85 ± 22.03 0 40.4 Minhang 940 372 732,438 22,652 50.85 ± 22.03 0 8.3 Putuo 886 55 429,891 24,751 49.05 ± 23.18 1 0 Songjiang 911 606 1,288,881 12,340 50.85 ± 22.03 0 24.1 Shijiazhuang Gaocheng 1,009 836 244,799 12,710 57.65 ± 31.90 0 17.5 Shenze 986 296 775,110 4,632 57.65 ± 31.90 0 56.1 Xinji 1,010 951 615,919 10,821 57.65 ± 31.90 0 61.4 Zanhuang 966 1,210 250,264 4,246 37.09 ± 27.73 0 33.1 Suzhou Zhangjiagang 1,059 772 1,246,762 17,579 39.48 ± 18.60 2 0 Taiyuan Qingxu 1,019 609 643,584 5,010 38.08 ± 17.15 0 16.5 Xinghualing 973 170 343,861 8,803 40.16 ± 16.76 1 0 Wuhan Caidian 1,010 1,108 829,665 8,254 22.01 ± 14.07 1 0 Jiang’an 1,038 64 618,994 13,212 58.58 ± 24.86 2 0 Qiaokou 988 46 895,957 11,247 50.60 ± 26.37 0 4.5 Xi’an Lianhu 1,011 38 698,513 11,632 58.65 ± 30.60 1 0 Lintong 949 916 655,875 9,010 42.80 ± 26.46 1 0 *If a county has monitors, then distance = 0

Table S2. Mortality effect estimates per 10 µg/m3, Lag 0-1 All Men Women Non-Accidental Mortality (%) 0.13 (0.03, 0.23) 0.06 (-0.07, 0.19) 0.20 (0.06, 0.34) Cardiovascular Mortality (%) 0.10 (-0.06, 0.25) 0.00 (-0.20, 0.20) 0.19 (-0.01, 0.39) Respiratory Mortality (%) 0.03 (-0.23, 0.30) -0.05 (-0.43, 0.32) 0.18 (-0.19, 0.55)

Table S3. Mortality effect estimates per 10 µg/m3, Lag 30 All Men Women Non-Accidental Mortality (%) 0.03 (-0.08, 0.13) 0.00 (-0.16, 0.16) 0.04 (-0.10, 0.18) Cardiovascular Mortality (%) 0.07 (-0.07, 0.22) 0.05 (-0.17, 0.26) 0.06 (-0.14, 0.27) Respiratory Mortality (%) 0.00 (-0.30, 0.30) 0.06 (-0.32, 0.45) 0.05 (-0.35, 0.45)

Table S4. Lag-specific cumulated effect estimates per 10 µg/m3, Lag 0-7 All Men Women Non-Accidental Mortality (%) 0.57 (-0.04, 1.18) 0.12 (-0.64, 0.87) 1.04 (0.24, 1.85) Cardiovascular Mortality (%) 0.25 (-0.66, 1.18) -0.13 (-1.13, 0.89) 0.69 (-0.46, 1.85) Respiratory Mortality (%) 0.45 (-0.96, 1.89) -0.04 (-2.03, 1.99) 1.25 (-0.75, 3.30)

Table S5. Lag-specific cumulated effect estimates per 10 µg/m3, Lag 0-30 All Men Women Non-Accidental Mortality (%) -0.14 (-1.63, 1.37) -1.10 (-2.82, 0.65) 0.83 (-1.23, 2.93) Cardiovascular Mortality (%) -0.87 (-3.26, 1.58) -0.73 (-3.31, 1.93) -1.01 (-4.01, 2.07) Respiratory Mortality (%) 2.13 (-1.21, 5.58) 2.14 (-2.59, 7.11) 3.12 (-1.66, 8.13)

Figure S1. Lag-specific non-cumulated effect estimates per 10 µg/m3 for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality

Figure S2. Lag-specific non-cumulated effect estimates per 10 µg/m3 for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality; sex stratified

Figure S3. 30-day lag-specific cumulated effect estimates per 10 µg/m3 for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality; sex stratified

Figure S4. Forest plots of cumulated effect estimates per 10 µg/m3 stratified by county for a) lag0-1; b) lag0-7; c) lag0-30

Figure S5. 30-day lag-specific cumulated effect estimates per 10 µg/m3 using fixed effect models for a) non-accidental mortality; b) cardiovascular mortality; and c) respiratory mortality

Figure S6. Lag-specific non-cumulated effect estimates per 10 µg/m3 for non-accidental mortality, comparing fixed effects and meta-analysis results

Chapter 4

Short-term PM2.5 and cardiovascular and respiratory admissions in NY State: Assessing sensitivity to exposure model choice

Mike Z. He1, Vivian Do1, Siliang Liu1, Patrick L. Kinney2, Arlene M. Fiore3,4, Xiaomeng Jin3,4, Nicholas DeFelice5, Jianzhao Bi6, Yang Liu6, Tabassum Z. Insaf7,8, Marianthi-Anna Kioumourtzoglou1

1 Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA 2 Department of Environmental Health, School of Public Health, Boston University, Boston, MA 02118, USA 3 Department of Earth and Environmental Sciences, Columbia University, New York, NY 10027, USA 4 Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA 5 Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 6 Gangarosa Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, GA 30322, USA 7 New York State Department of Health, Albany, NY 12203, USA 8 School of Public Health, University at Albany, Rensselaer, NY 12144, USA

Under Review: He MZ, Do V, Liu S, Kinney PL, Fiore AM, Jin X, DeFelice N, Bi J, Liu Y, Insaf TZ, Kioumourtzoglou M-A: Short-term PM2.5 and cardiovascular and admissions in NY State: assessing sensitivity to exposure model choice. Environmental Research.

4.1 Abstract

Background: In recent years, there has been an increasing use of air pollution prediction models in epidemiologic studies for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single prediction model is correct. However, an evaluation of the sensitivity of the estimated effects to the choice of exposure model is lacking.

Methods: We obtained county-level daily inpatient cardiovascular (CVD) and respiratory admission counts from the New York (NY) Statewide Planning and Resources Cooperative

System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions

(2002-2012), including the Community Multi-scale Air Quality Model (CMAQ) and data from the

Centers for Disease Control and Prevention’s Wide-ranging Online Data for Epidemiologic

Research (CDC WONDER), among others. We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 levels and CVD and respiratory admissions, adjusting for potential confounders, separately for each state-wide PM2.5 dataset. We also repeated our analyses using PM2.5 measured at monitoring stations available in 18 of 62 counties.

Results: We observed positive, significant associations between PM2.5 and CVD for three out of the four PM2.5 datasets. Across the modeled exposure estimates, effect estimates ranged from

0.23% (95%CI: -0.06, 0.53%) to 0.96% (95%CI: 0.70, 1.21%) per 10 µg/m3 increase in daily

PM2.5. We observed positive, significant associations between PM2.5 and respiratory admissions for all four PM2.5 datasets. Across the modeled exposure estimates, effect estimates ranged from

0.68% (95%CI: -0.04, 1.41%) to 1.48% (95%CI: 1.08, 1.87%) per 10 µg/m3 increase in daily

PM2.5. In general, CMAQ PM2.5 yielded the highest effect estimates for both outcomes, while the

CDC dataset yielded the lowest estimates.

Conclusions: Effect estimates varied by a factor of almost four across methods to model exposures for CVD and almost two for respiratory admissions, likely due to varying degrees of exposure measurement error. This highlights the need for improved exposure assessment that minimizes error and includes uncertainty characterization – including uncertainty due to exposure model choice – and propagation into the health models. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD and respiratory admissions, regardless of model choice.

4.2 Introduction

The association between air pollution and adverse health is one of the most well-researched topics in epidemiology, with studies spanning different pollutants [1–3], timescale of exposure [4–

6], and outcomes of interest [7–9]. Historically, time-series studies in air pollution epidemiology have primarily utilized data from monitoring stations for exposure assignment. In the United

States, this is primarily accomplished using data from the Environmental Protection Agency’s

(EPA) Air Quality System (AQS) database [10]. Although monitors provide information on air pollutants, there are strong assumptions when working with such data for health studies. For example, Bell et al. noted that because the location of monitor systems depends on regulatory compliance rather than population density, monitor data are not necessarily best suited for public health research [11]. Furthermore, monitor locations are by definition points in space and, thus, may not adequately capture population exposures in a pre-specified area in the time series analysis

(e.g., a city) [12,13].

To reduce exposure measurement error and, further, include populations in areas without monitors, there has been an increasing use of prediction models in air pollution epidemiology for the purposes of exposure assignment. These prediction models provide outputs with full coverage at a much finer spatial resolution than a spatial point which monitors represent, and predict both

72 spatial and temporal changes in air pollution. Initially, such models were simple, and early efforts used largely statistical approaches, such as land use regression models and generalized additive mixed models [14–16]. With increased computation capacity and demand for higher spatial and temporal resolution, the prediction models have grown increasingly sophisticated. Examples include the use of remote sensing data, predictions from chemical transport models, and more robust methods for higher predictive accuracy (e.g. random forests, neural networks, ensemble models) [17–20].

Many research groups are currently developing and improving prediction models for exposure assessment in epidemiologic studies. However, most epidemiologic studies to date use air pollution predictions from a single model to assign exposures. In fact, to date, we are aware of just two studies that uses multiple air pollution predictions for the exposure of interest [21,22].

This is of critical importance because the results from these epidemiologic studies are often used to inform regulations, but the exposure-response functions that are generated from such studies are not necessarily comparable, both spatially and temporally. Our study aims to address this critical knowledge gap by assessing the sensitivity of fine particle (PM2.5; particles with aerodynamic diameter ≤ 2.5 µm) health effect estimates to the choices of different models for exposure assessment in a time series setting. As a case study, we focus on the associations between daily

PM2.5 concentrations and cardiovascular disease (CVD)- and respiratory-related hospitalizations in New York State (NYS).

4.3 Methods

4.3.1 Exposure Assessment

We obtained five publicly available, daily PM2.5 exposure products over NYS. These include data from the United States EPA’s AQS database, which provides PM2.5 monitoring data

73 in 18 of 62 counties in NYS [10]; daily output from the Community Multiscale Air Quality

Modeling System (CMAQ), an atmospheric chemical transport model also developed by the EPA to simulate regional air pollution [23]; the Fused Air Quality Surface Using Downscaling

(FAQSD), which uses a Bayesian space-time downscaler model to fuse the AQS measurements with CMAQ estimates [23]; the United States Centers for Disease Control and Prevention’s Wide- ranging Online Data for Epidemiologic Research (CDC WONDER), which links satellite-derived and spatially interpolated ground-based PM2.5 using linear regression [24]; and a product from

Emory University, which integrates satellite aerosol optical depth (AOD), land use data, and meteorological variables in a machine learning model [19]. More details regarding the PM2.5 products can be found in an existing publication [25].

Exposure data were available from 2002-2012, except the CDC WONDER data, which was available from 2003-2011. Daily average temperature and relative humidity were obtained from the North American Land Data Assimilation Systems (NLDAS), which provides the meteorological data at 1/8th degree grids over the study area [26]. For the purposes of this study, we averaged all grids within a county to obtain daily county-level averages for all PM2.5 products and the corresponding meteorological variables.

4.3.2 Outcome Assessment

Daily total cardiovascular and respiratory inpatient hospital admission counts for each county were obtained from NYS Department of Health’s Statewide Planning and Research

Cooperative System (SPARCS). SPARCS is a comprehensive data reporting system that collects information on hospital admissions and emergency department visits within NYS, and includes approximately 98% of all hospitalizations in non-federal acute care facilities, regardless of insurance status [27]. The International Classification of Diseases, 9th revision (ICD-9) was used

74 to classify cardiovascular (ICD-9 codes 390-459) and respiratory (ICD-9 codes 460-519) hospitalizations.

4.3.3 Statistical Analysis

We employed overdispersed Poisson regression models to investigate the relationship between PM2.5 and same-day cardiovascular and respiratory hospitalizations, separately for each set of PM2.5 concentrations, using all available data. In the models, we included smooth functions of calendar time to adjust for seasonality and long-term trends (using natural cubic splines with 4 df per year for the CVD model, 5 df per year for the respiratory model), as well as indicator variables for day of the week. To control for potential spatial confounding, we included indicator variables for all counties used in the analyses. We controlled for potential confounding effects by weather by including smoothing functions for average temperature (natural spline, 3 df) and relative humidity (natural spline, 3 df) in all models.

We selected the best fitting model and appropriate dfs for all non-linear terms included in the model based on the quasi-Akaike Information Criterion. Specifically, we tested for calendar time df from 4 to 7 per year, and for PM2.5, temperature, and relative humidity, from 3 to 6 df.

We first ran analyses with all available information for each prediction model. To ensure comparability across all PM2.5 products, we then restricted the analysis to only counties where

AQS data were available (“AQS only”), and finally to a dataset only with overlapping observations across all prediction models (“complete-case analysis”). We recognize that the results from this last set of analyses may have limited generalizability; however, our aim with this last analysis was to facilitate direct comparison across models.

It is likely that different exposure models perform differently in space and time. To assess the impact of varying prediction model performance in space and time, thus, as a secondary

75 analysis we evaluated potential spatio-temporal effect modification using all available exposure data and CVD hospitalizations. To assess effect modification by urban density, we obtained data on the urban and rural populations by county in NYS from the 2010 United States Census, and included in each model an interaction term between PM2.5 and number of individuals living in rural areas within each county. For effect modification varying by season, we broke each year up into four three-month increments to define seasons: spring (March – May), summer (June – August), autumn (September – November), and winter (December – February) and included interaction terms between PM2.5 and season. We assessed statistical significance of the continuous interaction term (rural population) directly in the model, and of the interaction with the categorical season variable using a likelihood ratio test by comparing it to a model without the interaction term with season.

We present all results as percentage change in hospital admission rates per 10 µg/m3 increase in PM2.5. All statistical analyses were performed using the R Statistical Software, version

3.5.1 (Foundation for Statistical Computing, Vienna, Austria).

4.4 Results

Table 1 shows the descriptive statistics of the variables used for the daily models for all available data. The average daily PM2.5 levels in the AQS, CMAQ, FAQSD, CDC WONDER, and

Emory datasets are 10.7, 8.7, 9.8, 9.5, and 8.2 µg/m3, respectively. On average, each county had

6.8 inpatient CVD admissions and 1.6 respiratory admissions per day. Additional descriptive statistics by season, quartiles of rural population, and those used for our complete-case analysis can be found in the supplemental material (Tables S1-S3).

Table 1. Descriptive statistics for all counties (unless otherwise noted; N = 249,116 days) Variable Mean Min 25% 50% 75% Max Daily CVD admission counts 6.8 0.0 1.0 2.0 5.0 115.0 per county Daily respiratory admission 1.6 0.0 0.0 0.0 2.0 39.0 counts per county 3 PM2.5 (µg/m ) AQS† 10.7 0.0 6.0 9.1 13.6 97.1 CMAQ 8.7 0.0 4.2 6.9 11.4 98.2 Fused 9.8 0.2 5.6 8.2 12.3 99.7 CDC 9.5 0.0 5.5 8.2 12.1 58.7 Emory 8.2 0.4 4.3 6.5 10.1 81.4 Mean temperature (°C) 9.0 -25.3 0.8 9.5 18.1 31.5 Relative humidity (%) 79.3 24.6 73.8 80.5 86.4 100.9 †Monitoring sites were only available in 18 of the 62 counties in NYS.

Table 2 displays the correlation across the different PM2.5 products at the county level. In general, the AQS, Fused, CDC, and Emory products are all highly correlated with each other, with correlations ranging from 0.83 to 0.92. The CMAQ product, however, is not very highly correlated with any of the other four products, with correlations only ranging from 0.49 to 0.61.

Table 2. Correlation of PM2.5 products AQS CMAQ Fused CDC Emory AQS 1.00 CMAQ 0.52 1.00 Fused 0.89 0.61 1.00 CDC 0.83 0.49 0.86 1.00 Emory 0.90 0.52 0.92 0.85 1.00

3 Figure 1 shows the effect estimates per 10 µg/m increase in PM2.5 across the different

PM2.5 products and types of analyses. Effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.96% (95%CI: 0.70, 1.21%) for CVD and from 0.68% (95%CI: -0.04, 1.41%) to 1.48%

(95%CI: 1.08, 1.87%). In general, CMAQ data produced the highest effect estimates with the tightest confidence intervals, while the CDC WONDER data yielded the lowest effect estimates.

To maximize spatial coverage, we performed a sensitivity analysis on a subset excluding AQS, and it did not change our results. Details are reported in our supplemental materials (Figure S1).

Figure 1. Percent increase in a) daily CVD admissions rates and b) daily respiratory 3 admissions rates per 10 µg/m for all PM2.5 products

Using likelihood ratio tests, we detected statistically significant effect modification by season for all PM2.5 products. Figure 2 shows the results from models assessing effect modification by season using all available data for each model. For most models, we generally observed higher effect estimates in the autumn and winter seasons, reaching as high as a 1.87% increase in CVD

3 admissions per 10 µg/m increase in PM2.5 in the autumn (for AQS). In comparison, the lowest effect estimates were observed in the spring, some of which are even negative (albeit not statistically significant).

Figure 2. Seasonal effect modification for daily CVD admissions rates using all available data

Figure 3 shows the results from the spatial effect modification models. Results are

3 displayed as the percent increase in CVD admissions for a 10 µg/m increase in PM2.5 for each

1000 person increase in the rural population of each county. In all but the CMAQ model, we detected statistically significant decreases in the effect estimates as rural population increased. In other words, the highest effect estimates were observed in urban areas.

Figure 3. Spatial effect modification for daily CVD admissions rates using all available data

4.5 Discussion

Using five different sets of PM2.5 predictions spanning from 2002 to 2012, we investigated the relationship between PM2.5 and CVD and respiratory hospital admissions in NYS and found consistently harmful associations across all exposure metrics, with effect estimates quantitatively varying by a factor of almost four in CVD and almost two in respiratory admissions. In subsequent analyses, we explored potential spatial and temporal effect modification using CVD hospital admissions. We found higher effect estimates in the autumn and winter, and higher effect estimates in more urban areas. These results were also largely consistent across exposure metrics.

There are a few papers in the literature that evaluate performance across different air pollution models, though most of which focus on only one or two models. Bravo et al. compared

PM2.5 predictions from a CMAQ simulation to that of ground-based monitors and found that

CMAQ underestimated PM2.5, with substantial variations seasonally [28]. Lee et al. developed a space-time geostatistical kriging model to predict PM2.5, and compared their predictions to satellite-based PM2.5 estimates directly from AOD retrievals; they found that the kriging model provided more accurate estimates within 100 km of a monitoring station, while satellite estimates were more accurate for locations greater than 100 km from a monitoring station [29]. Jin et al. compared seven publicly available PM2.5 products over NYS from 2002 to 2012, including information from ground-based observations, remote sensing, and chemical transport models, and found that while the products differed in spatial patterns, all showed consistent decreases in PM2.5 over the observed time period. Of the five daily PM2.5 products that we utilized in this study, Jin et al. found that the Emory product had the best agreement with ground-based observations in urban areas [25].

To date, most existing air pollution epidemiologic studies that assign exposure based on prediction models typically only use data from a single model to assign exposures [17,18,30]. We are aware of two epidemiologic studies that incorporate more than one exposure models. Weber et al. conducted a case-crossover study over New York City from 2004-2006 looking at the association between short-term exposure to PM2.5 and heart failure, utilizing five different exposure models that combine air pollution monitors, AOD, and CMAQ. They found that effect estimates across the models were similar, and incorporating AOD did not significantly increase model performance [22]. McGuinn et al. investigated the association between long-term exposure to PM2.5 and cardiovascular disease using different exposure assessment methods. Utilizing station monitoring data, two different CMAQ configurations, and two satellite-based models from 2002 to 2009 for a cohort of patients who had undergone a cardiac catherization residing in North

Carolina, they found nearly equivalent results for all exposure assessment methods. [21]. Our work expands on this currently limited body of literature, and systematically compares various types of existing prediction models and their utility for health studies of acute events.

In our main analysis, we found positive and significant associations across all but one of the models. However, effect estimates fluctuated by up to a factor of four across different health models. These differences are likely due to the fact that different models are built on different sets of assumptions, uses different input data, and employ different statistical methods and computational algorithms which collectively leads to differences in their predicted PM2.5 distributions. However, there is no reason to believe that such differences across models are dependent on our outcome, and non-differential exposure measurement error in time series studies has been shown to bias effect estimates towards the null [13]. The predictive accuracy of all these models likely varies in space and time in different ways, which could best explain the differences

82 in our results. We can see in the most comparable analyses (i.e. “AQS only” and “complete-case analyses”) that the confidence intervals for all effect estimates widely overlap. While the results from this analysis decrease the generalizability of our findings because it focuses on predominantly urban areas, it is the only analysis that allows direct comparison of the effect estimates across the five PM2.5 products. Therefore, we cannot conclude that these models truly yield statistically significantly different estimates.

In our secondary analysis of temporal effect modification, we found higher effect estimates in the autumn and winter seasons. With the available information that we have, it is not possible to attribute this finding to varying impacts of exposure measurement error across seasons or to a biological mechanism. One possible explanation for these findings could be that all prediction models perform better during the autumn and winter months; smaller amounts of exposure measurement error would result in de-attenuation of effect estimates [25]. A second explanation could be that NYS estimated PM2.5 effects on CVD admissions are worse during fall and winter months. Existing literature has had mixed findings regarding this topic. Studies by Bell et al. and

Hsu et al. that investigated the effects of PM2.5 on CVD morbidity found highest effect estimates in the winter [31–33]. On the other hand, studies by Peng et al. and Dai et al., which investigated the effects of PM2.5 and mortality, found higher effect estimates in the spring and summer [34,35].

In the case of our study, the higher effect estimates may indicate a higher contribution from more localized particles, such as traffic, during the colder months. Traffic has been consistently identified as a particularly toxic source of particulate matter, full of combustion products such as black carbon and heavy metals [36–38]. Since the mixing height in the winter is lower, this will likely result in higher near-surface PM2.5 concentrations even if emissions remain the same as they dilute within a smaller near-surface volume.

In comparison to the results for seasonal effect modification, the results for spatial effect modification are much more consistent: effect estimates were highest in urbanized areas, and decreased as rural population increased. The decrease is least pronounced in the CMAQ model, where the interaction term was actually not significant, and most in the CDC model. Again, this finding could be due to two possible explanations. First, it is possible that all models except CMAQ have higher predictive accuracy in more densely populated areas. This would not be surprising, as

AQS monitors are located near urban centers or more densely populated areas and all models except CMAQ were trained on or fused with PM2.5 concentrations measured at monitoring stations

[25]. Given that our results for spatial effect modification were very similar across all models except CMAQ (Figure 3), we would expect these models to perform similarly in NYS counties with limited or no monitoring. Second, our findings of effect modification by urbanicity indicate that particle composition in urban areas may be more toxic than that of rural areas, which is consistent with our interpretation of seasonality as discussed above. Similar findings have been found in the literature [39,40], likely because that the distribution of potential effect modifiers is different in urban versus rural areas.

Our study has several limitations. First and foremost, the results from our analyses may have some comparability issues. As mentioned previously, only 18 of 62 NYS counties include monitors reporting to the AQS database. Consequently, the results using all data available are not directly comparable to each other. We ran additional analyses restricting to counties with monitoring sites and to no missingness across all prediction models. While these results are more directly comparable to each other, their overall generalizability is lower, as they primarily reflect predominantly urban areas. Furthermore, in the analyses with restricted observations, the comparability problem still remains: even by looking at only counties with AQS monitors,

84 oftentimes these counties may only include a single monitor, and its measurements are then uniformly assigned as the exposure for the entire county. In comparison, the PM2.5 output we obtain from any of the prediction models gives us the capacity to model spatial gradients that are not evident from limited and sparse point measurements.

Second, none of the modeled PM2.5 data we use are perfect: each was built and optimized for different reasons and, thus, could overpredict in certain areas and underpredict in others. For example, CMAQ was originally designed to address regional air pollution problems across the

United States, while the Emory product focuses on providing accurate PM2.5 predictions over NYS only. All PM2.5 products in this analysis (with the exception of CMAQ) utilized AQS monitors as part of their modeling process, which means that these PM2.5 products are likely to provide more accurate estimates in urban areas. Our previous work has attempted to evaluate these different

PM2.5 products using three major criteria: resolution, availability, and accuracy. We found that no single product stood out for all three criteria, and the choice of PM2.5 product for the purposes of epidemiologic studies should depend on the research question of interest [25].

Nevertheless, our study has numerous strengths. We were able to systematically investigate the sensitivity of exposure model choice in short-term epidemiologic studies. Given the increasing use of modeled air pollution data in health studies, our work is critical as an example and reference that evaluates the performance of various modeled air pollution products for use in future studies.

Second, our work provides insight for the modeling community to produce air pollution products that would be best tailored to health studies. Previously, our work has already evaluated how multiple PM2.5 products perform differently in a health impacts assessment [25], and our current work takes this a step further by evaluating the impact of multiple PM2.5 products on the effect estimates that are used in such health impacts assessments. Our work can be of interest to modeling

85 communities around the world, many of which have taken a keen enthusiasm in providing outputs that would be optimized for health studies. Last but not least, the results of our study send a strong public health message: increased PM2.5 exposure results in an increase in CVD and respiratory hospitalizations, regardless of the choice of exposure model.

In conclusion, we investigated the relationship between PM2.5 and cardiovascular and respiratory admissions in NYS from 2002-2012 using five different PM2.5 products, and found consistent, harmful associations regardless of exposure metric. However, uncertainty related with the exposure model selection is not captured in the individual estimated effects. Methods are needed for improved exposure assessment that minimizes error and includes uncertainty characterization and propagation into the health models.

4.6 References

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4.7 Supplemental Materials

Figure S1. Percent increase in a) daily CVD admissions rates and b) daily respiratory admissions rates for subset excluding AQS (Note that the AQS only results are presented here for reference)

Table S1. Descriptive statistics by season Variable Mean Min 25% 50% 75% Max Spring (N=62,744) Daily CVD admission counts 7.1 0.0 1.0 2.0 6.0 115.0 per county Daily respiratory admission 1.6 0.0 0.0 0.0 2.0 30.0 counts per county 3 PM2.5 (µg/m ) AQS† 9.4 0.1 5.5 8.2 12.0 68.9 CMAQ 8.2 0.0 4.0 6.6 10.5 88.2 Fused 8.5 0.3 5.1 7.4 10.7 51.2 CDC 8.7 0.0 5.4 7.7 10.7 58.7 Emory 8.2 0.4 4.3 6.5 10.1 53.1 Mean temperature (°C) 7.6 -22.1 2.8 7.9 12.6 27.2 Relative humidity (%) 75.8 34.5 68.3 77.4 84.9 98.4

Summer (N=62,744) Daily CVD admission counts 6.7 0.0 1.0 2.0 5.0 102.0 per county Daily respiratory admission 1.2 0.0 0.0 0.0 1.0 33.0 counts per county 3 PM2.5 (µg/m ) AQS† 13.0 0.0 6.2 10.6 17.6 97.1 CMAQ 7.5 0.1 3.7 6.0 9.9 53.4 Fused 12.2 0.3 6.8 10.2 15.6 84.1 CDC 12.2 0.0 7.8 10.7 15.4 58.7 Emory 11.0 1.0 5.3 8.8 15.0 81.3 Mean temperature (°C) 20.4 5.9 18.2 20.6 22.9 31.5 Relative humidity (%) 79.2 49.6 74.5 79.5 84.2 98.2

Autumn (N=62,062) Daily CVD admission counts 6.7 0.0 1.0 2.0 5.0 115.0 per county Daily respiratory admission 1.5 0.0 0.0 0.0 1.0 31.0 counts per county 3 PM2.5 (µg/m ) AQS† 9.5 0.0 5.2 8.1 12.2 51.5 CMAQ 8.3 0.0 3.7 6.4 10.9 98.2 Fused 8.4 0.3 4.8 7.1 10.7 64.8 CDC 8.0 0.0 4.8 7.1 9.9 51.3 Emory 6.9 0.7 3.6 5.5 8.7 51.2 Mean temperature (°C) 10.9 -9.4 5.9 11.0 16.3 27.8 Relative humidity (%) 79.3 24.6 73.5 79.6 86.0 98.3

Winter (N=61,566) Daily CVD admission counts 6.8 0.0 1.0 2.0 5.0 113.0 per county Daily respiratory admission 2.1 0.0 0.0 1.0 2.0 39.0 counts per county

3 PM2.5 (µg/m ) AQS† 10.9 0.2 7.2 9.8 13.4 44.8 CMAQ 10.9 0.2 5.7 9.1 13.9 93.6 Fused 9.8 1.2 6.0 8.7 12.4 99.7 CDC 9.2 0.0 4.7 7.6 12.0 56.5 Emory 7.8 1.3 4.9 6.7 9.5 47.5 Mean temperature (°C) -3.0 -25.3 -6.3 -2.6 0.5 16.4 Relative humidity (%) 83.1 27.4 78.9 84.9 89.5 100.9 †Monitoring sites were only available in 18 of the 62 counties in NYS.

Table S2. Descriptive statistics by quartiles of rural population (least to most rural) Variable Mean Min 25% 50% 75% Max 1st quartile (N=64,288) Daily CVD admission counts 14.7 0.0 1.0 3.0 28.0 115.0 per county Daily respiratory admission 3.6 0.0 0.0 1.0 6.0 39.0 counts per county 3 PM2.5 (µg/m ) AQS† 12.5 0.5 7.4 10.8 15.6 86.1 CMAQ 11.0 0.0 5.0 8.4 14.1 98.3 Fused 10.7 0.3 6.0 10.7 13.6 84.1 CDC 10.3 0.0 5.8 8.6 13.2 58.7 Emory 9.3 0.4 4.9 7.4 11.8 81.3 Mean temperature (°C) 10.1 -25.0 1.9 10.5 18.9 31.5 Relative humidity (%) 78.4 24.6 72.2 79.4 86.0 99.4

2nd quartile (N=60,270) Daily CVD admission counts 2.5 0.0 0.0 1.0 2.0 43.0 per county Daily respiratory admission 0.6 0.0 0.0 0.0 1.0 16.0 counts per county 3 PM2.5 (µg/m ) AQS† 7.9 0.0 4.0 6.3 10.0 97.1 CMAQ 7.7 0.0 3.7 6.2 10.1 65.2 Fused 9.1 0.3 5.2 7.7 11.4 76.1 CDC 9.2 0.0 5.4 8.0 11.6 49.9 Emory 7.4 0.7 3.9 5.8 9.1 78.7 Mean temperature (°C) 8.4 -25.3 0.0 8.8 17.6 30.3 Relative humidity (%) 79.3 32.4 73.9 80.4 86.3 100.1

3rd quartile (N=64,288) Daily CVD admission counts 4.9 0.0 1.0 2.0 4.0 75.0 per county Daily respiratory admission 1.0 0.0 0.0 0.0 1.0 28.0 counts per county 3 PM2.5 (µg/m ) AQS† 10.1 0.4 6.0 8.6 12.6 78.8 CMAQ 8.2 0.0 4.2 6.8 10.8 68.2 Fused 9.7 0.3 5.6 8.3 12.2 99.7 CDC 9.3 0.0 5.5 8.2 11.9 55.5 Emory 8.1 0.7 4.4 6.5 10.0 70.5 Mean temperature (°C) 8.7 -21.3 0.6 9.1 17.8 29.8 Relative humidity (%) 80.1 26.2 74.9 81.2 86.9 100.9

4th quartile (N=60,270) Daily CVD admission counts 4.9 0.0 2.0 3.0 6.0 43.0 per county Daily respiratory admission 1.2 0.0 0.0 1.0 2.0 17.0 counts per county

3 PM2.5 (µg/m ) AQS† 9.8 0.0 5.5 8.4 12.4 55.8 CMAQ 8.0 0.0 3.9 6.5 10.6 61.1 Fused 9.5 0.3 5.5 8.0 11.8 74.3 CDC 9.2 0.0 5.5 8.1 11.6 47.9 Emory 7.8 0.9 4.2 6.2 9.6 72.2 Mean temperature (°C) 8.8 -24.0 0.5 9.2 18.1 30.5 Relative humidity (%) 79.5 27.4 74.1 80.7 86.5 99.7 †Monitoring sites were only available in 18 of the 62 counties in NYS.

Table S3. Descriptive statistics for complete-case analysis (N = 49,533 days) Variable Mean Min 25% 50% 75% Max Daily CVD admission counts 20.9 0.0 5.0 15.0 33.0 115.0 per county Daily respiratory admission 4.9 0.0 1.0 3.0 7.0 39.0 counts per county 3 PM2.5 (µg/m ) AQS 10.8 0.0 6.1 9.1 13.8 76.0 CMAQ 8.4 0.0 5.0 8.4 14.0 98.2 Fused 11.0 0.3 6.1 9.2 14.0 99.7 CDC 10.6 0.0 6.0 8.9 13.5 58.7 Emory 9.7 1.0 5.2 7.9 12.5 56.1 Mean temperature (°C) 10.2 -24.8 2.3 10.6 18.9 31.5 Relative humidity (%) 78.9 30.1 72.8 80.1 86.6 99.3

Chapter 5

Mid-21st century ozone air quality and health burden in China under emissions scenarios and climate change: A health impact assessment

Daniel M. Westervelt1,2, Clara T. Ma3, Mike Z. He4, Arlene M. Fiore1,5, Patrick L. Kinney6, Marianthi-Anna Kioumourtzoglou4, Shuxiao Wang7, Jia Xing7, Dian Ding7, Gustavo Correa1

1 Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA 2 NASA Goddard Institute for Space Studies, New York, NY 10025 3 Department of Geology and Geophysics, Yale University, New Haven, CT 06511, USA 4 Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA 5 Department of Earth and Environmental Sciences, Columbia University, New York, NY 10027, USA 6 Department of Environmental Health, School of Public Health, Boston University, Boston, MA 02118, USA 7 School of Environment, Tsinghua University, Beijing 100084, China

Published: Westervelt DM, Ma C, He MZ*, Fiore AM, Kinney PL, Kioumourtzoglou M-A, Wang S, Xing J, Ding D, Correa G: Mid-21st century ozone air quality and health burden in China under emissions scenarios and climate change. Environ Res Lett 14 074030. 2019.

* Responsible for all health impact calculations in this study, which is the portion of the manuscript included in this chapter of the dissertation

5.1 Introduction

Despite modest emissions reductions of air pollutants in recent years, China still suffers from poor air quality, and the outlook for future air quality in China is uncertain. In this chapter, we explore the impact of two disparate 2050 emissions scenarios relative to 2015 in the context of a changing climate. Using the Geophysical Fluid Dynamics Laboratory Atmospheric Model version 3 (GFDL-AM3) chemistry-climate model to simulate surface-level ozone concentrations, we estimate the number of ozone-related excess or prevented deaths under three future scenarios and discuss their potential implications.

5.2 Methods

Ozone-related mortality in China were computed using differences in model simulations for 2050 versus 2015 in three scenarios: climate change only (CLIM), current legislation (CLE), and maximum technologically feasible reduction (MTFR). The CLIM scenario holds anthropogenic emissions of aerosols and ozone precursors at 2015 levels, and uses sea surface temperatures and sea ice cover taken from a three-member ensemble of GFDL-CM3 transient simulations under RCP8.5 (Representative Concentration Pathway), a greenhouse gas concentration trajectory adopted by the Intergovernmental Panel on Climate Change (IPCC) and typically taken as the basis for “worst case scenarios” for climate change. In contrast, the CLE and

MTFR scenarios builds upon the CLIM scenario and utilize present-day and future emissions provided by the ECLPISE v5a inventory. The CLE scenario refers to emission projections under adherence to all existing regulations, and can be thought of as a baseline scenario. The MTFR scenario explore to what extent emissions of various pollutants can be further reduced beyond what is required by the current legislation, through full application of available technical measures, without changes in the energy structures or consumers’ behaviors. In this sense, it could be thought

97 of as a “best case scenario”. Details regarding the atmospheric models, emissions inventories, and emissions scenarios can be found in an existing publication [1].

Mortality calculations are completed using the equation below:

−퐶푅퐹×퐶 푀 = 푀표 × 푃 × (1 − 푒 ), where M is the change in mortality, M0 is the county-level baseline mortality rate in China, P is the population from the 2010 Chinese census, CRF is the concentration-response function, or the estimated slope of the log-linear relationship between concentration and mortality, and C is the change in the running three-month mean of daily 1-hour maximum values of surface ozone concentration between 2015 and 2050 in each scenario. Provincial-level baseline mortality rates for all-cause mortality were obtained from the Chinese Center for Disease Control and Prevention.

We adopted the CRF from a recent long-term ozone mortality study by Turner et al. [2], since this is the most recent estimate based on an updated cohort with a longer follow-up period compared to previous studies.

5.3 Results

In Figure 1, we present the change in annual all-cause mortality between 2015 and 2050 from the CLIM (1b), CLE (1c), and MTFR (1d) scenarios. Baseline 2010 population is shown in

Figure 1a. The change in ozone concentrations due to climate change alone results in an additional

61,884 annual premature deaths in China by 2050. In the CLE scenario, which projects emissions in China to roughly stay the same or slightly increase, an additional 79,786 premature deaths are expected. Under the MTFR scenario, which projects modest reductions in ozone concentrations across most of the country, we estimate that 334,822 premature deaths can be avoided.

Figure 1. a) 2010 baseline population in China; b) change in premature deaths between 2015 and 2050 under CLIM (2015CLIM); c) change in premature deaths between 2015 and 2050 under CLE (2050CLE); d) change in premature deaths between 2015 and 2050 under MTFR (2050MFR). Units for b), c), and d) are number of deaths.

5.4 Discussion

This analysis provides preliminary insight on the potential impacts of climate change on mid-21st century ozone levels and, subsequently, on mortality in a health impact assessment framework. While the number of avoided deaths are modest relative to China’s total population, it is a significant fraction of the total number of annual premature deaths attributable to ozone in present-day worldwide, as calculated by both Cohen et al. (97,000 – 422,000) and Malley et al.

(1.04 – 1.23 million) [3,4]. The seeming discrepancy in these estimates is due to the fact that the two studies utilized different CRFs: Malley et al. [4] adopted the same Turner et al. [2] functions that we used in this work, a hazard ratio of 1.02 per 10 parts per billion (ppb) ozone increase, much larger than the CRF of 1.001 per 10 ppb estimated in Jerrett et al. [5], which is what Cohen et al.

[3] used for their calculations.

Under both the CLIM and CLE scenarios, we observed the largest mortality burdens in

Eastern China. This is not surprising given that this is the region where both population and ozone concentrations are among the highest in the country. Since climate change is also a factor in the

CLE scenario, this suggests that the ozone climate penalty, i.e., the amount of ozone concentration increase due to climate change alone, makes up a significant portion of the human health burden associated with the CLE simulation. In the MTFR scenario, where we find a reduction in mortality instead of an increase, we observe benefits in both the Sichuan Basin as well as South Central

China, in addition to Eastern China.

This work has some limitations. First, the CRF we adopted for our calculations was developed using information from cohorts in the US. However, CRFs for the association between long-term ozone exposure and mortality from China are not currently available. Second, the relatively coarse model resolution may not be able to fully resolve health burden impacts at local scale. Finally, we did not include population aging in our mortality estimates, which was recently shown to play a significant role in future mortality in China [6]. In future work, we plan on addressing these shortcomings by obtaining dynamically downscaled results using meteorological data and emissions inventories provided by Tsinghua University and county-specific, age- stratified baseline mortality rates, which will be sufficient for detecting county-level differences.

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5.5 References

[1] Westervelt, D.M.; Ma, C.T.; He, M.Z.; Fiore, A.M.; Kinney, P.L.; Kioumourtzoglou, M.A.; Wang, S.; Xing, J.; Ding, D.; Correa, G. Mid-21st century ozone air quality and health burden in China under emissions scenarios and climate change. Environ. Res. Lett. 2019, 14, doi:10.1088/1748-9326/ab260b.

[2] Turner, M.C.; Jerrett, M.; Pope, C.A.; Krewski, D.; Gapstur, S.M.; Diver, W.R.; Beckerman, B.S.; Marshall, J.D.; Su, J.; Crouse, D.L.; et al. Long-Term Ozone Exposure and Mortality in a Large Prospective Study. Am. J. Respir. Crit. Care Med. 2016, 193, 1134–42, doi:10.1164/rccm.201508-1633OC.

[3] Cohen, A.J.; Brauer, M.; Burnett, R.; Anderson, H.R.; Frostad, J.; Estep, K.; Balakrishnan, K.; Brunekreef, B.; Dandona, L.; Dandona, R.; et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. Lancet 2017, 389, 1907–1918, doi:10.1016/S0140-6736(17)30505-6.

[4] Malley, C.S.; Henze, D.K.; Kuylenstierna, J.C.I.; Vallack, H.W.; Davila, Y.; Anenberg, S.C.; Turner, M.C.; Ashmore, M.R. Updated global estimates of respiratory mortality in adults ≥ 30 years of age attributable to long-term ozone exposure. Environ. Health Perspect. 2017, 125, 1–9, doi:10.1289/EHP1390.

[5] Jerrett, M.; Burnett, R.T.; Pope, C.A.; Ito, K.; Thurston, G.; Krewski, D.; Shi, Y.; Calle, E.; Thun, M. Long-Term Ozone Exposure and Mortality. N. Engl. J. Med. 2009, 360, 1085–1095, doi:10.1056/NEJMoa0803894.

[6] Chen, K.; Fiore, A.M.; Chen, R.; Jiang, L.; Jones, B.; Schneider, A.; Peters, A.; Bi, J.; Kan, H.; Kinney, P.L. Future ozone-related acute excess mortality under climate and population change scenarios in China: A modeling study. PLoS Med. 2018, 15, 1–23, doi:10.1371/journal.pmed. 1002598.

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Chapter 6

Conclusion

This dissertation aimed to address several critical gaps in the air pollution epidemiology literature with regards to data availability, exposure windows, sensitivity of modeling choices, and climate change impacts on air pollution and health. This chapter provides a summary of key findings from previous chapters, a discussion of conclusions, larger questions and existing challenges both in and beyond air pollution and health research, and suggestions for potential future research directions.

6.1 Summary of Findings

In Chapter 2, after conducting a comprehensive search of the existing literature, we identified 574 separate PM2.5 measurements from 2005 to 2016, 180 of which were from data collected prior to 2013. In addition, we detected statistically significant differences in measured

PM2.5 levels both across different geographic regions and the different economic regions. Because

102 pre-2013 PM2.5 data for China are largely unavailable, efforts have been mainly focused on using either global-level satellite data [1] or historically reconstructed prediction models [2] as ways of estimating pre-2013 PM2.5 concentrations, both of which do not incorporate pre-2013 ground- based measurements in China. The aggregated dataset from this review, included as a publicly available file online in the supplementary materials of our publication, may be used as a starting point for calibrating and improving current and future air pollution models.

In Chapter 3, we considered both the short- and intermediate-term associations between

NO2 and mortality in 42 counties in China from 2013 to 2015. We found significant cumulated associations up to seven days for non-accidental mortality, as well as associations of over 3% with respiratory mortality up to 30 days. Although the study had limitations in statistical power given the relatively short length and limited locations, we emphasize the need to continue investigation on potential effects of intermediate-term exposure to air pollution using sufficiently powered studies, given established biological relevance of exposure in this window.

In Chapter 4, we used five independently obtained datasets to assign PM2.5 exposures and assessed their associations with cardiovascular and respiratory hospitalizations in New York State from 2002 to 2012. The effect estimates fluctuated by as much as a factor of four for CVD and up to a factor of two for respiratory admissions across methods to model exposures, but confidence intervals of the effect estimates were largely overlapping. As exposure-response functions from epidemiologic studies are frequently used in risk assessment, these differences in effect estimates can potentially lead to large differences in reported results from corresponding risk assessment studies. This can be especially problematic under the existing framework where the uncertainties from epidemiologic estimates are not considered in risk assessment. However, it is also important to note that the vast majority of models yielded positive and significant associations. The consistent

103 results across models send an important public health message: the associations between PM2.5 and cardiovascular and respiratory admissions are harmful, regardless of model choice.

In Chapter 5, we calculated the ozone-related mortality in 2050 under three separate scenarios and found that under climate change alone and current legislation, we would see an increase in ozone-related deaths. However, under maximum technologically feasible reduction, we would see a significant number of deaths prevented, especially in the most densely populated areas of China. The methods used in this work set the stage for additional analyses to be completed with finer-scale data.

6.2 Discussion

While the research presented in this dissertation fills several existing knowledge gaps in the current literature, it also highlights a number of critical limitations and challenges, some of which are specific to the context of the topics explored here and others more general to the entire field of public health research. In the following section, we will discuss conclusions drawn from the entire dissertation, larger questions that need to be addressed by the field of air pollution and health research, and existing statistical and data limitations and potential solutions.

6.2.1 Conclusions

Across the various chapters of this dissertation, we can draw three main conclusions that constantly come up as common themes. First—and perhaps unsurprisingly—air pollution remains a serious problem in many parts of the world, regardless of development or economic status. As mentioned in Chapter 1, air pollution and health research has been investigated and reported in the scientific literature since the 1950s [3]. There has since been a plethora of studies conducted in high-income regions such as the United States and Europe. However, these regions continue to struggle with the same issues that have been studied for decades, as the harmful effects of air

104 pollution are observed even at very low levels of ambient air concentrations [4]. In developing countries, the problem is even more serious. China and India are home to nearly 40% of the global population and currently experiences some of the world’s worst air pollution. As other low- and middle-income countries continue to develop, the resulting increased energy consumption and urbanization may further exacerbate both the levels of air pollution and the number of people impacted.

Second, there is a need for better exposure assessment globally, preferably with the use of fixed monitoring sites, typically considered the gold standard of exposure assessment in air pollution. The monitoring network reporting to AQS in the United States is one of the most advanced and dense air pollution monitoring systems in the world, with coverage spanning across the entire country. However, even a monitoring system such as AQS has sparse coverage in rural areas, as seen in Figure 2 from Chapter 1. Monitoring systems in developing countries, in comparison, are generally even less dense, and some countries still lack monitoring systems completely. In Chapter 4, we discussed air pollution prediction models as a potential solution to including populations in epidemiologic studies where exposure data may not be readily available.

However, most existing air pollution prediction models utilize existing monitoring data generally located in urban locations, and the predictive accuracy of these models would further benefit from additional monitoring systems in rural areas, which would allow predictions in these rural areas to be validated.

Third, there is a need for more region- and/or country-specific air pollution epidemiologic studies, even if similar evidence already exists elsewhere. There are several reasons from both a scientific and policy perspective why we believe this is necessary. First, because the levels of PM2.5 in many existing studies from developed countries are much lower than that of developing

105 countries, we cannot be certain that conclusions from existing studies are equally applicable elsewhere, especially if the effects are non-linear [5]. Furthermore, the distribution of potential modifiers of the PM2.5-health association, both at the population- and individual-level, are likely very different, thus greatly limiting the generalizability of U.S. and European studies to developing countries. There are already efforts of establishing dedicated cohorts for the purposes of studying the effects of long-term air pollution in China [6]. However, cohorts are expensive to manage and can take a long time before any meaningful results could be reported. Similar efforts should be taken in other developing countries for the purposes of elucidating the localized health effects of air pollution; time-series studies could be the starting point, as these are relatively easy to conduct, often only rely on existing administrative data, and are, thus, faster and more cost efficient. Lastly, country-specific studies are ultimately one of the most effective tools to drive policy. From a policy maker’s perspective, he or she is much more likely to act on an issue if there is direct evidence that the issue is a problem in his or her own country. Even in areas of research where we have very high confidence of the investigated scientific relationship (e.g. short-term air pollution and mortality studies), there is merit in replicating such studies in specific locations if it could convince policy makers to act.

6.2.2 Larger Questions

In my opinion, there are two questions that air pollution and health researchers should always keep in the front of their minds. The first one is: “What studies will have the most direct impact on policy?” and the second one is “What is the next most urgent research question?”. The first question is one that should not only be on the minds of air pollution and health researchers but of the entire public health community. By nature, public health is a highly interdisciplinary field focusing on applied research, with the goal of making a direct impact on the physical,

106 psychological, and social well-being of the world. From this perspective, research in our subfield of air pollution and health should all be conducted with a clear intention of contributing to this goal.

One of the most effective ways for this goal to be implemented is through changes in policy. For example, health impact assessments, such as the one presented in Chapter 5 of this dissertation, are a tool that policy makers frequently use in making decisions on new policies and regulations. On the other hand, evaluating the extent to which air quality regulations have been successful in protecting public health, termed accountability research, is one of the most direct ways of evaluating the effectiveness of implemented policies. These are just two examples of types of research that has the most direct links to policy and consequently ones that can have a big potential impact on public well-being.

The second question is one that undoubtedly many fields struggle with. In this dissertation, we highlighted two areas of air pollution epidemiology that require further research: exposure window assessment and the use of exposure models for epidemiologic studies. In Chapter 3, we highlighted the importance of additional research on the intermediate-term effects of air pollution and health, especially given the established biological relationships and our sample size shortcomings. It is important to distinguish the fact that our study represents one of many possible interpretations of “intermediate-term”, which we defined as a month of exposure. There are additional time metrics in between day and year (e.g., bimonthly, seasonal) that may be associated with other biological mechanisms. Furthermore, the observed differences across time metrics may also vary depending on the health outcome investigated, both topics that requires further investigation.

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Similarly, there is a growing number of air pollution prediction models now being used for epidemiologic research, the vast majority of which were not developed for the purposes of health studies. For example, the Community Multiscale Air Quality Model (CMAQ), one of the prediction models that we utilized in Chapter 4, was originally developed to address regional air pollution problems in the United States, irrespective of health [7]. Future studies should continue to evaluate the strengths and shortcomings in different prediction models, and ideally combine information from multiple prediction models for more accurate predictions and uncertainty characterization in exposures due to between-model variability and overall predictive ability of the final product. Ultimately, the decision to prioritize one topic of research over another may be due to more pragmatic issues, such as sources of funding and contemporaneous events (e.g., the

COVID-19 pandemic), but we should always be mindful of why we want to pursue a certain topic of interest, rather than to conduct another study just for the sake of conducting another study.

6.2.3 Existing Challenges

There exist many methodological, computational, and practical challenges that impede our ability to further advance air pollution and health research. Specific concerns include uncertainties in health impact assessments, computational limitations of existing methods, and issues with data access and availability.

Two key challenges exist for health impact assessments of air pollution: quantifying uncertainties and providing accurate localized results. One frequent criticism of health impact assessments is that they often provide only a point estimate and no corresponding confidence intervals. This is because the calculation of these estimates relies on numerous pieces of information, some of which do not have an associated measurement of uncertainty and others where uncertainty is very difficult to quantify. Fortunately, research is already underway to provide

108 spatio-temporal uncertainty of prediction model outputs, which may allow us to quantify the uncertainty in health impact assessments in the near future. Second, existing health impact assessments are frequently done at a fairly crude level, and even if they are done on localized scales, they often rely on extrapolations, whether by assigning the same exposure to multiple locations when the prediction model resolution is too coarse or by using concentration-response functions from studies conducted in other locations. To address some of these shortcomings, preparation is already underway to conduct a refined health impact assessment as described in

Chapter 5 for PM2.5. The study will utilize dynamically downscaled PM2.5 products with more localized inputs at a resolution of 27km × 27km, approximately 8 to 10 times finer than the output used in our previous study. It will also use concentration-response functions from a recently published study on long-term PM2.5 and mortality in China [8], making the results of our study more applicable to its target population.

One of the biggest impediments for providing fine-scale exposure data from air pollution prediction models is the limitation of existing computational capacity. Simulations from air quality models and coupled climate models, such as the some of the modeled PM2.5 exposures in Chapter

4 and ozone concentrations in Chapter 5, are generated from computationally intensive processes and, under existing conditions, a single set of simulations may take months to complete. This has greatly hindered the speed of all related research that requires the use of such models. This concern is not easily addressable in the short-term as it involves either a restructuring of existing resources or a major breakthrough in computational efficiency, but will almost certainly be resolved as technology in this area of research continues to develop.

Finally, there exist a number of practical concerns regarding data accessibility that can potentially hinder research progress and make the entire process less transparent. The health data

109 used in Chapter 3 of this dissertation were all obtained from the Chinese Center for Disease Control and Prevention (Chinese CDC). This dataset is not publicly available and I received a processed version of this dataset based on the needs of my research. The health data used in Chapter 4 of this dissertation were obtained from the New York State Department of Health. While this dataset is publicly available, consent is required for individual-level data, and so I also received a processed version of this dataset including only count data. Similarly, while the monitoring data used in

Chapters 3 and 4 are both publicly available, the data I received were already processed to some degree by our collaborators. In scenarios under which we do not have access to primary data, we need to make the assumption that these data were processed properly. Whenever possible, I advocate for researchers to take part in fieldwork for primary data collection. While it is often impossible for a single researcher to collect all data for a large study, taking part in at least a portion of data collection will often offer critical insight on the quality of the data and make researchers feel more comfortable making decisions during data analysis.

6.3 Future Directions

Air pollution and health research is progressing at an unprecedented rate around the world.

As more and more countries become increasingly aware of the magnitude of the health impact associated with ambient air pollution, future research will likely continue to be plentiful, with more creative methods to address existing challenges. In the near future, I anticipate three areas of research in the field to be of particular interest: air pollution and health studies in India, the expanded use of personal monitors and low-cost fixed monitors and sensors, and the role that air pollution plays in facilitating transmission and increasing susceptibility to infectious diseases, such as COVID-19.

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Similar to China prior to 2013, India is currently experiencing some of the highest levels of ambient air pollution in the world. The most recent Global Burden of Disease (GBD) for India estimated that in 2017, 1.24 million deaths were attributable to air pollution, contributing to 12.5% of total deaths [9]. However, the study used the GBD-developed integrated exposure-response

(IER) function, and air pollution epidemiologic studies conducted in India remain extremely limited. With air pollution exposure data becoming more widely available via satellite-based measurements and prediction models, we will likely see an explosion of new time-series studies focused on the short-term health effects of air pollution from India initially, followed by more studies of longer-term effects of air pollution and health over time.

There are two potential possibilities to further improve exposure assessment: an expansion of existing fixed monitoring sites or increased use of personal monitors. In an ideal world, both of these would be pursued, each respectively representing the gold standard of ambient and personal air pollution exposure. However, it would be unrealistic to have either option for extended time periods due to the potential invasion of privacy and prohibitively high costs. There have been numerous studies that have measured personal PM2.5 exposure [10–12], and similar studies would likely continue to be on the rise. Because it is unrealistic to construct permanent fixed monitoring sites at a density ideal for air pollution researchers at a national level (e.g., every 10 or 20 km across the entire contiguous United States), there has been growing interest in low-cost sensors as a way to supplement the existing monitoring network [13–16]. Low-cost sensors may serve as a promising alternative to expand the critically needed coverage in rural areas.

The emergence of COVID-19 has changed the focus of academic research in many ways; air pollution research is no exception. A recent study has found that air pollution patterns have changed significantly pre-COVID-19 versus post-COVID-19 [17]. In addition, the COVID-19

111 outbreak has given rise a number of new potential research questions. In particular, identifying the epidemiologic relationship between air pollution and COVID-19 may provide crucial insight on more effective methods to mitigate transmission. Assessing the health impact of the numerous policies that were enacted as a result of COVID-19 can effectively quantify the magnitude of the problem for policy makers and inform potential future pandemics.

6.4 Concluding Remarks

This dissertation addressed several known knowledge gaps in air pollution and health research. However, as discussed in this chapter, many issues still remain. We currently face a number of key challenges, including methodological limitations, computational efficiency, and data accessibility, that are impeding our abilities to address additional knowledge gaps in the field; future research should attempt to overcome these issues. As an aspiring researcher in this field, I look forward to the day when we will be able to reduce air pollution globally to “safe” levels, whatever they may be, and that the right to breathing clean air becomes accessible to everyone around the world.

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[13] Zikova, N.; Hopke, P.K.; Ferro, A.R. Evaluation of new low-cost particle monitors for PM2.5 concentrations measurements. J. Aerosol Sci. 2017, 105, 24–34, doi:10.1016/j.jaerosci.2016.11. 010.

[14] Zikova, N.; Masiol, M.; Chalupa, D.C.; Rich, D.Q.; Ferro, A.R.; Hopke, P.K. Estimating hourly concentrations of PM2.5 across a metropolitan area using low-cost particle monitors. Sensors (Switzerland) 2017, 17, 1–19, doi:10.3390/s17081922.

[15] Bi, J.; Stowell, J.; Seto, E.Y.W.; English, P.B.; Al-Hamdan, M.Z.; Kinney, P.L.; Freedman, F.R.; Liu, Y. Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA. Environ. Res. 2020, 180, 108810, doi:10.1016/j.envres.2019.108810.

[16] Tryner, J.; L’Orange, C.; Mehaffy, J.; Miller-Lionberg, D.; Hofstetter, J.C.; Wilson, A.; Volckens, J. Laboratory evaluation of low-cost PurpleAir PM monitors and in-field correction using co-located portable filter samplers. Atmos. Environ. 2020, 220, 117067, doi:10.1016/j.atmosenv.2019.117067.

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