An examination of exposure measurement error from air pollutant spatial variability in time-series studies

The association between PM2.5 components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models

BACKGROUND

PM2.5 is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develop an analytical methodology combining traditional and novel machine learning models to evaluate PM2.5's combined effects on blood pressure (BP) and identify the most toxic components.

METHODS

We measured late-pregnancy BP of 1138 women from the Heshan cohort while simultaneously analyzing 31 PM2.5 components. We utilized multiple linear regression modeling to establish the relationship between PM2.5 components and late-pregnancy BP and applied Random Forest (RF) and generalized Weighted Quantile Sum (gWQS) regression to identify the most toxic components contributing to elevated BP and to quantitatively evaluate the cumulative effect of the PM2.5 component mixtures.

RESULTS

The results revealed that 16 PM2.5 components, such as EC, OC, Ti, Fe, Mn, Cu, Cd, Mg, K, Pb, Se, Na+, K+, Cl-, NO3-, and F-, contributed to elevated systolic blood pressure (SBP), while 26 components, including two carbon components (EC, OC), fourteen metallics (Ti, Fe, Mn, Cr, Mo, Co, Cu, Zn, Cd, Na, Mg, Al, K, Pb), one metalloid (Se), and nine water-soluble ions (Na+, K+, Mg2+, Ca2+, NH4+, Cl-, NO3-, SO42-, F-), contributed to elevated diastolic blood pressure (DBP). Mn and Cr were the most toxic components for elevated SBP and DBP, respectively, as analyzed by RF and gWQS models and verified against each other. Exposure to PM2.5 component mixtures increased SBP by 1.04 mmHg (95% CI: 0.33-1.76) and DBP by 1.13 mmHg (95% CI: 0.47-1.78).

CONCLUSIONS

Our study highlights the effectiveness of combining traditional and novel models as an analytical strategy to quantify the health effects of PM2.5 constituent mixtures.

Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

BACKGROUND

Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error.

OBJECTIVE

To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits.

METHODS

We obtained daily counts of ED visits for Atlanta, GA during 2009-2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics.

RESULTS

Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging).

SIGNIFICANCE

The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies.

IMPACT STATEMENT

This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.

A comparison of experience sampled hay fever symptom severity across rural and urban areas of the UK

Hay fever affects people differently and can change over a lifetime, but data is lacking on how environmental factors may influence this. This study is the first to combine atmospheric sensor data with real-time, geo-positioned hay fever symptom reports to examine the relationship between symptom severity and air quality, weather and land use. We study 36145 symptom reports submitted over 5 years by over 700 UK residents using a mobile application. Scores were recorded for nose, eyes and breathing. Symptom reports are labelled as urban or rural using land-use data from the UK's Office for National Statistics. Reports are compared with AURN network pollution measurements and pollen and meteorological data taken from the UK Met Office. Our analysis suggests urban areas record significantly higher symptom severity for all years except 2017. Rural areas do not record significantly higher symptom severity in any year. Additionally, symptom severity correlates with more air quality markers in urban areas than rural areas, indicating that differences in allergy symptoms may be due to variations in the levels of pollutants, pollen counts and seasonality across land-use types. The results suggest that a relationship exists between urban surroundings and hay fever symptoms.

Size-segregated particle number concentrations and outpatient-department visits for pediatric respiratory diseases in Shanghai, China

BACKGROUND

Few studies have simultaneously explored which size of particles has the greatest impact on the risk for pediatric asthma, bronchitis and upper respiratory tract infections (URTIs).

OBJECTIVES

To investigate the short-term association between size-segregated particle number concentrations (PNCs) and outpatient-department visits (ODVs) for major pediatric respiratory diseases.

METHODS

Daily counts of pediatric ODVs for asthma, bronchitis and URTIs were obtained from 66 hospitals in Shanghai, China, from 2016 to 2018. Pollutant effects were estimated using Poisson generalized additive models combined with polynomial distributed lag models. We also fitted co-pollutant cumulative effects models included six criteria air pollutants and conducted stratifying analyses by gender, age, season and geographic distances.

RESULTS

We identified a total of 430,103 patients with asthma, 1,547,013 patients with bronchitis, and 2,155,738 patients with URTIs from the hospitals. Effect estimates increased with decreasing particle size. Ultrafine particle (UFP) and PNCs of 0.10-0.40 µm particles (PNC_0.10-0.40) were associated with increased ODVs for asthma, bronchitis and URTIs at cumulative lags up to 3d. Associations tended to appear stable after adjusting for criteria air pollutants. At the cumulative lag 0-2d, each interquartile range increase in UFP was associated with increased ODVs due to asthma (relative risk 1.21, 95% CI: 1.07, 1.38), bronchitis (1.20, 95% CI: 1.07, 1.34) and URTI (1.17, 95% CI: 1.06, 1.30), whereas the associations for PNC_0.10-0.40 remained significant but attenuated in magnitude.

CONCLUSIONS

UFP may be a leading contributor to the adverse respiratory effects of particulate air pollution and the effects increased with decreasing particle size.

Neuroendocrine stress hormones associated with short-term exposure to nitrogen dioxide and fine particulate matter in individuals with and without chronic obstructive pulmonary disease: A panel study in Beijing, China

Air pollution is a major trigger of chronic obstructive pulmonary disease (COPD). Dysregulation of the neuroendocrine hypothalamic-pituitary-adrenal (HPA) and sympathetic-adrenal medullary (SAM) axes is essential in progression of COPD. However, it is not clear whether air pollution exposure is associated with neuroendocrine responses in individuals with and without COPD. Based on a panel study of 51 stable COPD patients and 78 non-COPD participants with 384 clinical visits, we measured the morning serum levels of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), cortisol, norepinephrine, and epinephrine as indicators of stress hormones released from the HPA and SAM axes. Ambient nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), and meteorological conditions were continuously monitored at the station from 2 weeks before the start of clinical visits. Linear mixed-effects models were used to estimate associations between differences in stress hormones following an average of 1-14-day exposures to NO₂ and PM_2.5. The average 1 day air pollutant levels prior to the clinical visits were 24.4 ± 14.0 ppb for NO₂ and 55.6 ± 41.5 μg/m³ for PM_2.5. We observed significant increases in CRH, ACTH, and norepinephrine, and decreases in cortisol and epinephrine with interquartile range increase in the average NO₂ and PM_2.5 concentrations in all participants. In the stratified analyses, we identified significant between-group difference in epinephrine following NO₂ exposure in individuals with and without COPD. These results may suggest the susceptibility of COPD patients to the neuroendocrine responses associated with short-term air pollution exposure.

Differential associations of particle size ranges and constituents with stroke emergency-room visits in Shanghai, China

BACKGROUND AND PURPOSE

Fine particulate matter (PM_2.5) has been associated with increased risks of stroke, but it remains unclear which specific size ranges and chemical constituents dominate the effects of PM_2.5 on stroke. We aimed to evaluate the associations of size-segregated particles and various constituents of PM_2.5 with daily emergency-room visits for stroke.

METHODS

We conducted a time-series study to investigate the associations of 5 particle size ranges from 0.01 to 2.5 µm and 35 constituents of PM_2.5 with the daily emergency-room visits for stroke in Shanghai, from 2014 to 2019. Over-dispersed generalized additive models were used to estimate the associations. The robustness of these associations was evaluated by additionally controlling for PM_2.5 mass.

RESULTS

For size ranges from 0.01 to 0.3 µm, there were significant positive associations between particle number concentrations and daily emergency-room visits for stroke with the strongest associations occurring for the size range 0.05-0.1 µm. The size-dependent pattern was not changed by adjusting for PM_2.5 and gaseous pollutants. The associations of daily emergency-room visits for stroke also varied considerably by various PM_2.5 constituents. After controlling for the simultaneous exposure to PM_2.5 and gaseous pollutants in two-pollutant models, we identified 11 out of 35 constituents that had robust associations, these being organic carbon, elemental carbon, chlorine, magnesium, ammonium, nitrate, sulfate, copper, manganese, lead and zinc.

CONCLUSION

Ultra-fine particles and some PM_2.5 constituents (i.e., carbonaceous fractions, inorganic ions and some elements) may be mainly responsible for the excess risk of stroke induced by PM_2.5.

Association of abnormal-glucose tolerance during pregnancy with exposure to PM2.5 components and sources

Maternal exposure to PM_2.5 has been associated with abnormal glucose tolerance during pregnancy, but little is known about which constituents and sources are most relevant to glycemic effects. We conducted a retrospective cohort study of 1148 pregnant women to investigate associations of PM_2.5 chemical components with gestational diabetes mellitus (GDM) and impaired glucose tolerance (IGT) and to identify the most harmful sources in Heshan, China from January 2015 to July 2016. We measured PM_2.5 using filter-based method and analyzed them for 28 constituents, including carbonaceous species, water-soluble ions and metal elements. Contributions of PM_2.5 sources were assessed by positive matrix factorization (PMF). Logistic regression model was used to estimate composition-specific and source-specific effects on GDM/IGT. Random forest algorithm was applied to evaluate the relative importance of components to GDM and IGT. PM_2.5 total mass and several chemical constituents were associated with GDM and IGT across the early to mid-gestation periods, as were the PM_2.5 sources fossil fuel/oil combustion, road dust, metal smelting, construction dust, electronic waster, vehicular emissions and industrial emissions. The trimester-specific associations differed among pollutants and sources. The third and highest quartile of elemental carbon, ammonium (NH₄⁺), iron (Fe) and manganese (Mn) across gestation were consistently associated with higher odds of GDM/IGT. Maternal exposures to zinc (Zn), titanium (Ti) and vehicular emissions during the first trimester, and vanadium (V), nickel (Ni), road dust and fossil fuel/oil combustion during the second trimester were more important for GDM/IGT. This study provides important new evidence that maternal exposure to PM_2.5 components and sources is significantly related to elevated risk for abnormal glucose tolerance during pregnancy.

Exposure to chemical components of fine particulate matter and ozone, and placenta-mediated pregnancy complications in Tokyo: a register-based study

BACKGROUND

Maternal exposure to fine particulate matter (PM_2.5) was associated with pregnancy complications. However, we still lack comprehensive evidence regarding which specific chemical components of PM_2.5 are more harmful for maternal and foetal health.

OBJECTIVE

We focused on exposure over the first trimester (0-13 weeks of gestation), which includes the early placentation period, and investigated whether PM_2.5 and its components were associated with placenta-mediated pregnancy complications (combined outcome of small for gestational age, preeclampsia, placental abruption, and stillbirth).

METHODS

From 2013 to 2015, we obtained information, from the Japan Perinatal Registry Network database, on 83,454 women who delivered singleton infants within 23 Tokyo wards (≈627 km²). Using daily filter sampling of PM_2.5 at one monitoring location, we analysed carbon and ion components, and assigned the first trimester average of the respective pollutant concentrations to each woman.

RESULTS

The ORs of placenta-mediated pregnancy complications were 1.14 (95% CI = 1.08-1.22) per 0.51 μg/m³ (interquartile range) increase of organic carbon and 1.11 (1.03-1.18) per 0.06 μg/m³ increase of sodium. Organic carbon was also associated with four individual complications. There was no association between ozone and outcome.

SIGNIFICANCE

There were specific components of PM_2.5 that have adverse effects on maternal and foetal health.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods

This report provides a final summary of the principal findings and key conclusions of a study supported by an HEI grant aimed at "Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution." It is the second and final report on this topic. The study was designed to advance four critical areas of inquiry and methods development. First, it focused on predicting short- and long-term exposures to ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project.

Considering our (1) massive study population, (2) numerous sensitivity analyses, and (3) transparent assessment of covariate balance indicating the quality of causal inference for simulating randomized experiments, we conclude that conditionally on the required assumptions for causal inference, our results collectively indicate that long-term PM2.5 exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses.

Our work provides comprehensive evidence of associations between exposures to PM2.5, NO2, and O3 and various health outcomes. In the current report, we report more specific results on the causal link between long-term exposure to PM2.5 and mortality, even at PM2.5 levels below or equal to 12 μg/m3, and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure.

For the period 2000-2016, we found that all statistical approaches led to consistent results: a 10-μg/m3 decrease in PM2.5 led to a statistically significant decrease in mortality rate ranging between 6% and 7% (= 1 - 1/hazard ratio [HR]) (HR estimates 1.06 [95% CI, 1.05 to 1.08] to 1.08 [95% CI, 1.07 to 1.09]). The estimated HRs were larger when studying the cohort of Medicare beneficiaries that were always exposed to PM2.5 levels lower than 12 μg/m3 (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]).

Comparing the results from multiple and single pollutant models, we found that adjusting for the other two pollutants slightly attenuated the causal effects of PM2.5 and slightly elevated the causal effects of NO2 exposure on all-cause mortality. The results for O3 remained almost unchanged.

We found evidence of a harmful causal relationship between mortality and long-term PM2.5 exposures adjusted for NO2 and O3 across the range of annual averages between 2.77 and 17.16 μg/m3 (included >98% of observations) in the entire cohort of Medicare beneficiaries across the continental United States from 2000 to 2016. Our results are consistent with recent epidemiological studies reporting a strong association between long-term exposure to PM2.5 and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards.

There is, in general, a harmful causal impact of long-term NO2 exposures to mortality adjusted for PM2.5 and O3 across the range of annual averages between 3.4 and 80 ppb (included >98% of observations). Yet within low levels (annual mean ≤53 ppb) below the current national standards, the causal impacts of NO2 exposures on all-cause mortality are nonlinear with statistical uncertainty.

The ERCs of long-term O3 exposures on all-cause mortality adjusted for PM2.5 and NO2 are almost flat below 45 ppb, which shows no statistically significant effect. Yet we observed an increased hazard when the O3 exposures were higher than 45 ppb, and the HR was approximately 1.10 when comparing Medicare beneficiaries with annual mean O3 exposures of 50 ppb versus those with 30 ppb.

institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred.

A list of abbreviations and other terms appears at the end of this volume.

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

BACKGROUND

Most epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM_2.5, NO₂, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy.

METHODS

Measurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures.

RESULTS

Mortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO₂). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO₂) yield more bias, while the co-pollutant (PM_2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias.

CONCLUSIONS

Our simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

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

BACKGROUND

Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model.

METHODS

We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM_2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM_2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM_2.5 dataset.

RESULTS

For all PM_2.5 datasets, we observed positive associations between PM_2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m³ increase in daily PM_2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available.

CONCLUSIONS

Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM_2.5 and CVD admissions, regardless of model choice.

Review of the Newly Developed, Mobile Optical Sensors for Real-Time Measurement of the Atmospheric Particulate Matter Concentration

Due to the adverse effects on human health and the environment, air quality monitoring, specifically particulate matter (PM), has received increased attention over the last decades. Most of the research and policy actions have been focused on decreasing PM pollution and the development of air monitoring technologies, resulting in a decline of total ambient PM concentrations. For these reasons, there is a continually increasing interest in mobile, low-cost, and real-time PM detection instruments in both indoor and outdoor environments. However, to the best of the authors’ knowledge, there is no recent literature review on the development of newly designed mobile and compact optical PM sensors. With this aim, this paper gives an overview of the most recent advances in mobile optical particle counters (OPCs) and camera-based optical devices to detect particulate matter concentration. Firstly, the paper summarizes the particulate matter effects on human health and the environment and introduces the major particulate matter classes, sources, and characteristics. Then, it illustrates the different theories, detection methods, and operating principles of the newly developed portable optical sensors based on light scattering (OPCs) and image processing (camera-based sensors), including their advantages and disadvantages. A discussion concludes the review by comparing different novel optical devices in terms of structures, parameters, and detection sensitivity.

Exposure to ultrafine particles and oral flora, respiratory function, and biomarkers of inflammation: A panel study in children

Particulate matter (PM) is the most important air pollution problem that leads to substantial health effects. However, very few studies focused on the effects of ultrafine particles (UFPs, particles< 0.1 μm) on children respiratory health. We performed a panel study with 3 rounds of follow-up among 65 pupils at the Elementary School Affiliated to Shanghai Normal University in China from November 2018 to June 2019. Real-time concentrations of UFPs were measured in the campus. In each visit, we detected biomarkers in saliva and microflora in buccal mucosa, fractional exhaled nitric oxide (FeNO) and lung function. We applied a linear mixed-effect (LME) model to examine the associations of UFPs and each health outcome. We found increased levels of FeNO and tumor necrosis factor-α (TNF-α) and reduced lung function in association with higher UFP exposure. For each interquartile range increase of UFPs, the largest changes were found in lag 0-72 h for forced vital capacity [-69.02 ml (95% CI: -114.20, -23.84)], TNF-α [13.41 pg/ml (95% CI: 7.08, 19.73)], and FeNO [26.85% (95% CI: 11.84%, 43.88%)]. UFP exposure was associated with reduced diversity in buccal microflora with largest reduction in lag 0-72 h [12.24 (95% CI: 7.76, 16.71) for Ace index; 8.78 (95% CI: 2.96, 14.60) for Chao1 index]. UFP exposure was also associated with increased Streptococcus, Gemella, and decreased Actinomyces. Short-term UFP exposures may impair the respiratory system by inducing inflammation, decreasing lung function and attenuating buccal microbe diversity in children.

Fine particulate matter constituents and heart rate variability: A panel study in Shanghai, China

BACKGROUND

Short-term exposure to fine particulate matter (PM_2.5) has been associated with reduced heart rate variability (HRV), an established indicator of cardiac autonomic function, but it remains uncertain which specific constituents of PM_2.5 had key impacts.

OBJECTIVE

To examine the short-term associations between various PM_2.5 constituents and HRV measures.

METHODS

We conducted a retrospective panel study among 78 participants who received repeated 24-h electrocardiogram testing in Shanghai, China from 2015 to 2019. We obtained daily concentrations of 14 main chemical constituents of PM_2.5 from a fixed-site monitor. During 3 or 4 rounds of follow-ups, we measured 6 HRV parameters, including 3 frequency-domain parameters (power in very low frequency, low frequency and high frequency) and 3 time-domain parameters (standard deviation of normal-to-normal intervals, root mean square successive difference and percent of adjacent normal R-R intervals with a difference ≥50 msec). We used linear mixed-effects models to analyze the data after controlling for time trends, environmental and individual risk factors.

RESULTS

The average daily PM_2.5 exposure was 45.8 μg/m³ during the study period. The present-day exposure to PM_2.5 had the strongest negative influences on various HRV indicators. These associations attenuated greatly on lag 1 d or lag 2 d. Elemental carbon, organic carbon, nitrate, sulfate, arsenic, cadmium, chromium and nickel were consistently associated with reduced HRV parameters in both single-constituent models and constituent-PM_2.5 models.

CONCLUSION

Our study highlighted the key roles of traffic-related components of PM_2.5 in inhibiting cardiac autonomic function.

Examining the relationship between ambient carbon monoxide, nitrogen dioxide, and mental health-related emergency department visits in California, USA

Growing evidence suggests air pollutants may harm the central nervous system, potentially impacting mental health. However, such impacts of air pollutants on mental health and the sub-populations most affected remain poorly understood, especially in California. We examined the relationship between short-term ambient carbon monoxide (CO), nitrogen dioxide (NO₂), and mental health-related emergency department (ED) visits in California from 2005 to 2013. Daily mean concentrations of the pollutants were acquired from the U.S. Environmental Protection Agency Air Quality System Data Mart ground monitoring data. Moving averages of pollutant concentrations were linked to counts of ED visits obtained from the California Office of Statewide Health Planning and Development. Seven mental health outcomes, defined by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, were studied: all mental disorders, bipolar disorder, depression, schizophrenia, substance abuse, homicide/inflicted injury, and suicide/self-harm. Monitor-level associations were estimated with quasi-Poisson regression models and combined using random-effects meta-analysis. CO and NO₂ were found to be positively associated with ED visits due to homicide/inflicted injury, with the warm season (May-October) driving the CO association. An interquartile range (IQR) (0.28 ppm) increase in two-day average CO during the warm season was associated with a 3.13% (95% confidence interval (CI): 1.43, 4.84) elevation in risk of an ED visit due to homicide/inflicted injury (n = 122,749 ED visits). An IQR (10.79 ppb) increase in two-day average NO₂ was associated with a 2.60% (95% CI: 1.17, 4.05) elevation in risk of an ED visit due to homicide/inflicted injury (n = 206,919 ED visits). Subgroup analyses indicated children, Hispanics, and males were particularly vulnerable. Except for an inverse relationship between NO₂ and substance abuse, neither pollutant was robustly associated with visits due to other mental health morbidities. Our results suggest short-term elevations in CO and NO₂ may promote violent behavior. Further investigation in other populations and ranges of air pollution exposure is warranted.

Temporal and spatial effect of air pollution on hospital admissions for myocardial infarction: a case-crossover study

BACKGROUND

In studies showing associations between ambient air pollution and myocardial infarction (MI), data have been lacking on the inherent spatial variability of air pollution. The aim of this study was to determine whether the long-term spatial distribution of air pollution influences short-term temporal associations between air pollution and admission to hospital for MI.

METHODS

We identified adults living in Calgary who were admitted to hospital for an MI between 2004 and 2012. We evaluated associations between short-term exposure to air pollution (ozone [O₃], nitrogen dioxide [NO₂], sulfur dioxide [SO₂], carbon monoxide [CO], particulate matter < 10 μm in diameter [PM₁₀] and particulate matter < 2.5 μm in diameter [PM_2.5]), and hospital admissions for MI using a time-stratified, case-crossover study design. Air Quality Health Index (AQHI) scores were calculated from a composition of O₃, NO₂ and PM_2.5. Conditional logistic regression models were stratified by low, medium and high levels of neighbourhood NO₂ concentrations derived from land use regression models; results of these analyses are presented as odds ratios (ORs) with 95% confidence intervals (CIs).

RESULTS

From 2004 to 2012, 6142 MIs were recorded in Calgary. Individuals living in neighbourhoods with higher long-term air pollution concentrations were more likely to be admitted to hospital for MI after short-term elevations in air pollution (e.g., 5-day average NO₂: OR 1.20, 95% CI 1.03-1.40, per interquartile range [IQR]) as compared with regions with lower air pollution (e.g., 5-day average NO₂: OR 0.90, 95% CI 0.78-1.04, per IQR). In high NO₂ tertiles, the AQHI score was associated with MI (e.g., 5-day average OR 1.13, 95% CI 1.02-1.24, per IQR; 3-day average OR 1.13, 95% CI 1.04-1.23, per IQR).

INTERPRETATION

Our results show that the effect of air pollution on hospital admissions for MI was stronger in areas with higher NO₂ concentrations than that in areas with lower NO₂ concentrations. Individuals living in neighbourhoods with higher traffic-related pollution should be advised of the health risks and be attentive to special air quality warnings.

Size-fractionated particulate air pollution and myocardial infarction emergency hospitalization in Shanghai, China

BACKGROUND

Fine particulate matter (PM_2.5) air pollution has been associated with increased risks of acute myocardial infarction (AMI), but it remains unknown about the potentially differentiated effects of size-fractionated particulate matter on AMI risk.

OBJECTIVE

To identify the specific size ranges that dominate the effects of particulate matter on AMI onset.

METHODS

We conducted a time-series study in Shanghai, China from January 2014 to December 2018. We evaluated particle size distribution of 0.01 μm to 2.5 μm from an environmental supersite and AMI emergency hospitalizations from the largest cardiovascular hospital in Shanghai. We used over-dispersed generalized additive models to estimate the associations of size-fractionated particle number concentrations (PNC) with AMI and its types.

RESULTS

We identified a total of 4720 AMI emergency hospitalizations. PM_2.5 was significantly associated with increased AMI risk on the concurrent day. The associations were significant only for PNC < 0.3 μm. For an IQR increase of PNCs for size ranges 0.01-0.03 μm, 0.03-0.05 μm, 0.05-0.10 μm and 0.10-0.30 μm, AMI hospitalizations increased by 6.68% (95% CI: 2.77%, 10.74%), 6.53% (95% CI: 2.08%, 11.17%), 5.78% (95% CI: 0.92%, 10.88%) and 5.92% (95% CI: 1.31%, 10.74%), respectively. The associations of PNC < 0.05 μm remained significant when adjusting for other air pollutants. There were consistently much stronger associations of particles with ST-segment elevation AMI than those with non-ST-segment elevation AMI.

CONCLUSIONS

This epidemiological investigation suggested that ultrafine particles, especially those <0.05 μm, may be mainly responsible for the acute AMI risk induced by PM_2.5.

Acute Air Pollution Exposure and the Risk of Violent Behavior in the United States

BACKGROUND

Violence is a leading cause of death and an important public health threat, particularly among adolescents and young adults. However, the environmental causes of violent behavior are not well understood. Emerging evidence suggests exposure to air pollution may be associated with aggressive or impulsive reactions in people.

METHODS

We applied a two-stage hierarchical time-series model to estimate change in risk of violent and nonviolent criminal behavior associated with short-term air pollution in U.S. counties (2000-2013). We used daily monitoring data for ozone and fine particulate matter (PM2.5) from the Environmental Protection Agency and daily crime counts from the Federal Bureau of Investigation. We evaluated the exposure-response relation and assessed differences in risk by community characteristics of poverty, urbanicity, race, and age.

RESULTS

Our analysis spans 301 counties in 34 states, representing 86.1 million people and 721,674 days. Each 10 µg/m change in daily PM2.5 was associated with a 1.17% (95% confidence interval [CI] = 0.90, 1.43) and a 10 ppb change in ozone with a 0.59% (95% CI = 0.41, 0.78) relative risk increase (RRI) for violent crime. However, we observed no risk increase for nonviolent property crime due to PM2.5 (RRI: 0.11%; 95% CI = -0.09, 0.31) or ozone (RRI: -0.05%; 95% CI = -0.22, 0.12). Our results were robust across all community types, except rural regions. Exposure-response curves indicated increased violent crime risk at concentrations below regulatory standards.

CONCLUSIONS

Our results suggest that short-term changes in ambient air pollution may be associated with a greater risk of violent behavior, regardless of community type.

Linkage between Particulate Matter Properties and Lung Function in Schoolchildren: A Panel Study in Southern China

While several scientific studies have linked PM_2.5 to decreased lung function, there is still some degree of uncertainty regarding which particulate physicochemical properties are most harmful. We followed a panel of 57 healthy schoolchildren (857 person-days) to investigate the associations between a wide variety of PM_2.5 and lung function in Heshan, China in 2016 for three periods. We monitored the daily concentrations of mass, chemical composition, size, number, surface area, and volume of particulate mixture. Associations of lung function with various particle metrics were estimated using generalized estimating equations and unconstrained distributed lag models. Random forest model was used to compare the relative importance of exposure metrics. Immediate (lag 0) associations of PM_2.5 and carbonaceous aerosols with reduced FEV₁ and MMEF, and accumulation-mode particles with FEV₁ were found. Slightly delayed (lag 1, 2) effects on PEF were particularly prominent for Aitken-mode particles. Possible cumulative (lags 0-2) effects of PM_2.5 and carbonaceous aerosols on PEF and Aitken-mode particles on FEV₁, MMEF, and PEF were observed. This study provides comprehensive evidence that the physicochemical properties of particulate mixtures are associated with reduced lung function in children. Organic carbon (OC) may be an important risk factor for the decreased lung function related to PM exposure.

Necessity of personal sampling for exposure assessment on specific constituents of PM2.5: Results of a panel study in Shanghai, China

Many epidemiological studies have evaluated the health risks of ambient fine particulate matter (PM_2.5). However, few studies have investigated the potential exposure misclassification caused by using ambient PM_2.5 concentrations as proxy for individual exposure to PM_2.5 in regions with high-level of air pollution. This study aimed to compare the differences between personal and ambient PM_2.5 constituent concentrations, and to predict the personal exposure of sixteen PM_2.5 constituents. We collected 141 72-h personal exposure filter samples from a panel of 36 healthy non-smoking college students in Shanghai, China. We then used the liner mixed effects models to predict personal constituent-specific exposure using ambient observations and several possible influencing factors including time-activity patterns, temporal variables, and meteorological conditions. The final model of each component was further evaluated by determination coefficient (R²) and root mean square error (RMSE) from leave-one-out-cross-validation (LOOCV). We observed ambient concentrations were higher than personal concentrations for all PM_2.5 components except for Mn, Fe, Ca, and V. Especially, ambient NH₄⁺, As, and NO₃^- concentrations were 3.65, 5.65 and 7.33-fold higher than their corresponding personal concentrations, respectively. The ambient level was the strongest predictor of their corresponding personal PM_2.5 components with the highest marginal R² (R_M²: 0.081 ~ 0.901), meteorological conditions (R_M²: 0.000 ~ 0.357), time-activity pattern (R_M²: 0.000 ~ 0.083) and temporal indicators (R_M²: 0.031 ~ 0.562) were also important predictors. Our final models predicted at least 50% of the variance of all personal PM_2.5 constituents and even over 90% for K, Pb, and SO₄^2-. LOOCV analysis showed that R² and RMSE ranged from 0.251 to 0.907 and 0.000 to 0.092 μg/m³, respectively. Our results showed that ambient concentration of most PM_2.5 constituents along with time-activity patterns, temporal variables, and meteorological conditions, could adequately predict personal exposure concentration. Prediction models of individual PM_2.5 constituent may help to improve the accuracy of exposure measurement in future epidemiological studies.

Cyclists' exposure to air pollution: in situ evaluation with a cargo bike platform

Cyclists' exposure to air pollutants near roadways has been associated with numerous health effects. While the adverse health effects concerning aerosols have traditionally been assessed with data of particle mass concentrations, it appears that the number concentration is also another important indicator of toxicity. Thus, to holistically evaluate one's exposure to aerosol particles, assessments should be based on mass concentrations and number concentrations. In order to assess individual cyclists' exposure as they move through space and time, spatiotemporal high-resolution approaches are needed. Therefore, a mobile, fast-response monitoring platform was developed that uses a cargo bicycle as a base. Data of particle mass concentrations (PM₁, PM_2.5, PM₁₀) and particle number concentrations (PN₁₀) were collected along two different routes, one characterized by high-intensity vehicle traffic and one by low-intensity vehicle traffic. While high spatiotemporal heterogeneity was observed for all measured quantities, the PN₁₀ concentrations fluctuated the most. High concentrations of PN₁₀ could be clearly associated with vehicle traffic. For PM_2.5, this relation was less pronounced. Mean particle concentrations of all measures were significantly higher along the high-traffic route. Comparing route exposures, the inhalation of PM_2.5 was similar between both routes, whereas along the high-traffic route, cyclists were exposed to twice the particle number. We conclude that the cargo bike, featuring high-frequency mobile measurements, was useful to characterize the spatial distribution of mass concentrations and number concentrations across an urban environment. Overall, our results suggest that the choice of route is a key factor in reducing cyclists' exposure to air pollution.

Acute effects of air pollution on all-cause mortality: a natural experiment from haze control measures in Chiang Mai Province, Thailand

BACKGROUND

Serious haze episodes have been a seasonal event in Chiang Mai province for more than a decade. In 2008, local government agencies introduced comprehensive measures to control haze and limit its impacts on public health. This study assessed the acute effects of ambient air pollutants on all-cause mortality before and after the introduction of those haze control measures.

METHODS

We obtained daily mortality counts and data on mass concentrations of particulate matter <10 micron in aerodynamic diameter (PM₁₀), gaseous pollutants (SO₂, NO₂, O₃, and CO), and meteorology in Chiang Mai Province between January 2002 and December 2016. We analyzed the data using a case-crossover approach adjusting for temperature, relative humidity, seasonality, and day-of-week. We assessed change in the excess risks of all-cause mortality associated with an increase in interquartile range (IQR) of pollutant concentration before and after control measures came into force.

RESULTS

We found decreased PM₁₀ levels and markedly reduced excess risks of daily mortality associated with an IQR increase in PM₁₀ concentrations in the years after haze-control measures were implemented (2009-2016). We found mixed results for gaseous pollutants: SO₂ showed no significant change in excess risk of daily mortality throughout the study period, while NO₂ and CO showed significant excess risks only in the period 2012-2016, and 8-h maximum O₃ showed a decrease in excess risk despite an increase in its atmospheric levels after the introduction of haze control measures in 2008.

CONCLUSIONS

The findings indicate that the government haze control measures first introduced in Chiang Mai province in 2008 have successfully reduced episodic PM₁₀ concentrations, which has led to a decrease in short-term all-cause mortality.

The acute effects of fine particulate matter constituents on circulating inflammatory biomarkers in healthy adults

BACKGROUND

Systemic inflammation is considered one of the key mechanisms in the development of cardiovascular diseases induced by fine particulate matter (PM_2.5) air pollution. However, evidence concerning the effects of various PM_2.5 constituents on circulating inflammatory biomarkers were limited and inconsistent.

OBJECTIVES

To evaluate the associations of short-term exposure to a variety of PM_2.5 constituents with circulating inflammatory biomarkers.

METHODS

We conducted a panel study from May to October 2016 among 40 healthy adults in Shanghai, China. We monitored the concentrations of 27 constituents of PM_2.5. We applied linear mixed-effect models to analyze the associations of PM_2.5 and its constituents with 7 inflammatory biomarkers, and further assessed the robustness of the associations by fitting models adjusting for PM_2.5 mass and/or their collinearity. Benjamini-Hochberg false discovery rate was used to correct for multiple comparisons.

RESULTS

The associations of PM_2.5 were strongest at lag 0 d with tumor necrosis factor-α (TNF-α), at lag 1 d with interleukin-6, interleukin-8, and interleukin-17A, at lag 02 d with monocyte chemoattractant protein-1 (MCP-1) and intercellular adhesion molecule-1 (ICAM-1). After correcting for multiple comparisons in all models, Cl^-, K⁺, Si, K, As, and Pb were significantly associated with interleukin-8; SO₄^2- and Se were marginally significantly associated with interleukin-8; SO₄^2-, As, and Se were marginally significantly associated with TNF-α; and Si, K, Zn, As, Se, and Pb were marginally significantly associated with MCP-1.

CONCLUSIONS

Our results suggested that some constituents (SO₄^2-, Cl^-, K⁺, and some elements) might be mainly responsible for systemic inflammation triggered by short-term PM_2.5 exposure.

Assessment of Traffic-Related Air Pollution: Case Study of Pregnant Women in South Texas

Population groups vulnerable to adverse effects of traffic-related air pollution correspond to children, pregnant women and elderly. Despite these effects, literature is limited in terms of studies focusing on these groups and a reason often cited is the limited information on their mobility important for exposure assessment. The current study presents a method for assessing individual-level exposure to traffic-related air pollution by integrating mobility patterns tracked by global positioning system (GPS) devices with dynamics of air pollutant concentrations. The study is based on a pool of 17 pregnant women residing in Hidalgo County, Texas. The traffic-related particulate matter with diameter of less than 2.5 micrometer (PM_2.5) emissions and air pollutant concentrations are predicted using MOVES and AERMOD models, respectively. The daily average traffic-related PM_2.5 concentration was found to be 0.32 µg/m³, with the highest concentration observed in transit (0.56 µg/m³), followed by indoors (0.29 µg/m³), and outdoor (0.26 µg/m³) microenvironment. The obtained exposure levels exhibited considerable variation between time periods, with higher levels during peak commuting periods, close to the US–Mexico border region and lower levels observed during midday periods. The study also assessed if there is any difference between traffic-related dynamic exposure, based on time-varying mobility patterns, and static exposure, based solely on residential locations, and found a difference of 9%, which could be attributed to the participants’ activity patterns being focused mostly indoors.

Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM2.5 and mortality

Multi-city population-based epidemiological studies of short-term fine particulate matter (PM_2.5) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM_2.5 and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m³ increase in PM_2.5 and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R² < 1%), multivariate models began to explain some of the observed heterogeneity (R² = 13%).

Impact of air pollution control policies on cardiorespiratory emergency department visits, Atlanta, GA, 1999-2013

BACKGROUND

Air pollution control policies resulting from the 1990 Clean Air Act Amendments were aimed at reducing pollutant emissions, ambient concentrations, and ultimately adverse health outcomes.

OBJECTIVES

As part of a comprehensive air pollution accountability study, we used a counterfactual study design to estimate the impact of mobile source and electricity generation control policies on health outcomes in the Atlanta, GA, metropolitan area from 1999 to 2013.

METHODS

We identified nine sets of pollution control policies, estimated changes in emissions in the absence of these policies, and employed those changes to estimate counterfactual daily ambient pollutant concentrations at a central monitoring location. Using a multipollutant Poisson time-series model, we estimated associations between observed pollutant levels and daily counts of cardiorespiratory emergency department (ED) visits at Atlanta hospitals. These associations were then used to estimate the number of ED visits prevented due to control policies, comparing observed to counterfactual daily concentrations.

RESULTS

Pollution control policies were estimated to substantially reduce ambient concentrations of the nine pollutants examined for the period 1999-2013. We estimated that pollutant concentration reductions resulting from the control policies led to the avoidance of over 55,000 cardiorespiratory disease ED visits in the five-county metropolitan Atlanta area, with greater proportions of visits prevented in later years as effects of policies became more fully realized. During the final two years of the study period, 2012-2013, the policies were estimated to prevent 16.5% of ED visits due to asthma (95% interval estimate: 7.5%, 25.1%), 5.9% (95% interval estimate: -0.4%, 12.3%) of respiratory ED visits, and 2.3% (95% interval estimate: -1.8%, 6.2%) of cardiovascular disease ED visits.

DISCUSSION

Pollution control policies resulting from the 1990 Clean Air Act Amendments led to substantial estimated reductions in ambient pollutant concentrations and cardiorespiratory ED visits in the Atlanta area.

Estimation of personal exposure to fine particles (PM2.5) of ambient origin for healthy adults in Hong Kong

Personal exposure and ambient fine particles (PM_2.5) measurements for 13 adult subjects (ages 19-57) were conducted in Hong Kong between April 2014 and June 2015. Six to 21 personal samples (mean = 19) per subject were obtained throughout the study period. Samples were analyzed for mass by gravimetric analysis, and 19 elements (from Na to Pb) were analyzed using X-Ray Fluorescence. Higher subject-specific correlations between personal and ambient sulfur (r_s = 0.92; p < 0.001) were found as compared to PM_2.5 mass (r_s = 0.79; p < 0.001) and other elements (0.06 < r_s < 0.86). Personal vs. ambient sulfur regression yielded an average exposure factor (F_pex) of 0.73 ± 0.02, supporting the use of sulfur as a surrogate to estimate personal exposure to PM_2.5 of ambient origin (E_a). E_a accounted for 41-82% and 57-73% of total personal PM_2.5 exposures (P) by season and by subject, respectively. The importance of both E_a and non-ambient exposures (E_na, 11.2 ± 5.6 μg/m³; 32.5 ± 10.9%) are noted. Mixed-effects models were applied to estimate the relationships between ambient PM_2.5 concentrations and their corresponding exposure variables (E_a, P). Higher correlations for E_a (0.90; p < 0.001) than for P (0.58; p < 0.01) were found. A calibration coefficient < 1 suggests an attenuation of 22% (ranging 16-28%) of the true effect estimates when using average ambient concentrations at central monitoring stations as surrogates for E_a. Stationary ambient data can be used to assess population exposure only if PM exposure is dominated by E_a.

CAUSAL INFERENCE IN THE CONTEXT OF AN ERROR PRONE EXPOSURE: AIR POLLUTION AND MORTALITY

We propose a new approach for estimating causal effects when the exposure is measured with error and confounding adjustment is performed via a generalized propensity score (GPS). Using validation data, we propose a regression calibration (RC)-based adjustment for a continuous error-prone exposure combined with GPS to adjust for confounding (RC-GPS). The outcome analysis is conducted after transforming the corrected continuous exposure into a categorical exposure. We consider confounding adjustment in the context of GPS subclassification, inverse probability treatment weighting (IPTW) and matching. In simulations with varying degrees of exposure error and confounding bias, RC-GPS eliminates bias from exposure error and confounding compared to standard approaches that rely on the error-prone exposure. We applied RC-GPS to a rich data platform to estimate the causal effect of long-term exposure to fine particles (PM2.5) on mortality in New England for the period from 2000 to 2012. The main study consists of 2202 zip codes covered by 217,660 1 km × 1 km grid cells with yearly mortality rates, yearly PM2.5 averages estimated from a spatio-temporal model (error-prone exposure) and several potential confounders. The internal validation study includes a subset of 83 1 km × 1 km grid cells within 75 zip codes from the main study with error-free yearly PM2.5 exposures obtained from monitor stations. Under assumptions of noninterference and weak unconfoundedness, using matching we found that exposure to moderate levels of PM2.5 (8 < PM2.5 ≤ 10 μg/m3) causes a 2.8% (95% CI: 0.6%, 3.6%) increase in all-cause mortality compared to low exposure (PM2.5 ≤ 8 μg/m3).

Characterization of the concentration-response curve for ambient ozone and acute respiratory morbidity in 5 US cities

Although short-term exposure to ambient ozone (O₃) can cause poor respiratory health outcomes, the shape of the concentration-response (C-R) between O₃ and respiratory morbidity has not been widely investigated. We estimated the effect of daily O₃ on emergency department (ED) visits for selected respiratory outcomes in 5 US cities under various model assumptions and assessed model fit. Population-weighted average 8-h maximum O₃ concentrations were estimated in each city. Individual-level data on ED visits were obtained from hospitals or hospital associations. Poisson log-linear models were used to estimate city-specific associations between the daily number of respiratory ED visits and 3-day moving average O₃ levels controlling for long-term trends and meteorology. Linear, linear-threshold, quadratic, cubic, categorical, and cubic spline O₃ C-R models were considered. Using linear C-R models, O₃ was significantly and positively associated with respiratory ED visits in each city with rate ratios of 1.02-1.07 per 25 ppb. Models suggested that O₃-ED C-R shapes were linear until O₃ concentrations of roughly 60 ppb at which point risk continued to increase linearly in some cities for certain outcomes while risk flattened in others. Assessing C-R shape is necessary to identify the most appropriate form of the exposure for each given study setting.

Indoor Exposure to Ambient Particles and Its Estimation Using Fixed Site Monitors

Ambient PM_2.5 concentrations measured at fixed site monitors (FSM) are often biased with respect to exposure concentrations because of spatial variability and infiltration. Based on comparison of ambient concentrations from 14 FSMs and of exposure concentrations measured indoors and outdoors at two schools in Hong Kong for winter and summer seasons, the magnitude and sources of exposure error based on using FSMs as a surrogate for exposure are quantified. An approach for bias correcting surrogate exposure estimates from FSMs is demonstrated. The approach is based on a proximity factor (PF) that accounts for differences in spatial locations, proximity to emissions and deviation from dominant wind direction, and an infiltration factor (IF) that varies by season. The combination of the PF and IF reduce bias in mean school exposure estimates from ±90% to ±20%. Bias in exposure estimates from using FSMs as surrogates tend to be smaller for which the exposure site and FSM are aligned with wind direction, have similar sampling height, and are in close proximity. The methodology demonstrated to assess concordance between FSMs and exposure measurement sites can be applied more broadly to help reduce exposure error, which may help to interpret seasonal variations in health estimates.

Hourly land-use regression models based on low-cost PM monitor data

Land-use regression (LUR) models provide location and time specific estimates of exposure to air pollution and thereby improve the sensitivity of health effects models. However, they require pollutant concentrations at multiple locations along with land-use variables. Often, monitoring is performed over short durations using mobile monitoring with research-grade instruments. Low-cost PM monitors provide an alternative approach that increases the spatial and temporal resolution of the air quality data. LUR models were developed to predict hourly PM concentrations across a metropolitan area using PM concentrations measured simultaneously at multiple locations with low-cost monitors. Monitors were placed at 23 sites during the 2015/16 heating season. Monitors were externally calibrated using co-located measurements including a reference instrument (GRIMM particle spectrometer). LUR models for each hour of the day and weekdays/weekend days were developed using the deletion/substitution/addition algorithm. Coefficients of determination for hourly PM predictions ranged from 0.66 and 0.76 (average 0.7). The hourly-resolved LUR model results will be used in epidemiological studies to examine if and how quickly, increases in ambient PM concentrations trigger adverse health events by reducing the exposure misclassification that arises from using less time resolved exposure estimates.

Assessing the associations of daily respiratory symptoms and lung function in schoolchildren using an Air Quality Index for ozone: Results from the RESPOZE panel study in Athens, Greece

BACKGROUND

Air Quality indicators or indices (AQIs) are mainly used for communicating the air pollution levels and risk to the general population. However, very few epidemiological studies have used AQIs for characterizing exposure.

OBJECTIVE

In the framework of the RESPOZE panel study we evaluated the association of daily ozone AQI levels with the daily occurrence of respiratory symptoms and Peak Expiratory Flow (PEF) and compared the effects with those estimated using measurements from fixed outdoor monitoring sites, in the city of Athens, Greece.

MATERIALS AND METHODS

A panel of 97 children, aged 10-11years, was followed intensively for 35days (5weeks) during the academic year 2013-14. PEF and symptoms were recorded daily by each child. Two ozone AQIs classifying the air quality into 7 categories of increasing severity, were calculated; one characterizing the whole Athens area and one the local area around the child's residence and school. Measurements from fixed sites were also used. Mixed effects models for repeated measurements were applied, adjusting for several confounders.

RESULTS

Increasing ozone levels were associated with increased incidence of symptoms, but the strongest and most statistically significant associations were found with the local air quality characterization with the AQI. Specifically, an increase in AQI-local by one category was associated with 34% (95% CI: 9%, 64%) increased odds of stuffy nose. When the AQI categories were "Bad" and "Severe", an increase in the incidence of cough was observed (OR 3.05 (95% CI: 1.29, 7.22) and 6.42 (95% CI: 1.47, 28.03) respectively). We did not observe a statistically significant association between AQI and PEF.

CONCLUSION

Our results show that the use of an AQI based on local conditions may be advantageous over the use of only measurements when investigating the effects of air pollution on health outcomes for improving communication of risk to the public.

Errors associated with the use of roadside monitoring in the estimation of acute traffic pollutant-related health effects

Near-road monitoring creates opportunities to provide direct measurement on traffic-related air pollutants and to better understand the changing near-road environment. However, how such observations represent traffic-related air pollution exposures for estimating adverse health effect in epidemiologic studies remains unknown. A better understanding of potential exposure measurement error when utilizing near-road measurement is needed for the design and interpretation of the many observational studies linking traffic pollution and adverse health. The Dorm Room Inhalation to Vehicle Emission (DRIVE) study conducted near-road measurements of several single traffic indicators at six indoor and outdoor sites ranging from 0.01 to 2.3 km away from a heavily-trafficked (average annual daily traffic over 350,000) highway artery between September 2014 to January 2015. We examined spatiotemporal variability trends and assessed the potential for bias and errors when using a roadside monitor as a primary traffic pollution exposure surrogate, in lieu of more spatially-refined, proximal exposure indicators. Pollutant levels measured during DRIVE showed a low impact of this highway hotspot source. Primary pollutant species, including NO, CO, and BC declined to near background levels by 20-30 m from the highway source. Patterns of correlation among the sites also varied by pollutant and time of day. NO₂, specifically, exhibited spatial trends that differed from other single-pollutant primary traffic indicators. This finding provides some indication of limitations in the use of NO₂ as a primary traffic exposure indicator in panel-based health effect studies. Interestingly, roadside monitoring of NO, CO, and BC tended to be more strongly correlated with sites, both near and far from the road, during morning rush hour periods, and more weakly correlated during other periods of the day. We found pronounced attenuation of observed changes in health effects when using measured pollutant from the near-road monitor as a surrogate for true exposure, and the magnitude varied substantially over the course of the day. Caution should be taken when using near-road monitoring network observations, alone, to investigate health effects of traffic pollutants.

Determinants of personal exposure to fine particulate matter (PM2.5) in adult subjects in Hong Kong

Personal monitoring for fine particulate matter (PM_2.5) was conducted for adults (48 subjects, 18-63years of age) in Hong Kong during the summer and winter of 2014-2015. All filters were analyzed for PM_2.5 mass and constituents (including carbonaceous aerosols, water-soluble ions, and elements). We found that season (p=0.02) and occupation (p<0.001) were significant factors affecting the strength of the personal-ambient PM_2.5 associations. We applied mixed-effects models to investigate the determinants of personal exposure to PM_2.5 mass and constituents, along with within- and between-individual variance components. Ambient PM_2.5 was the dominant predictor of (R²=0.12-0.59, p<0.01) and the largest contributor (>37.3%) to personal exposures for PM_2.5 mass and most components. For all subjects, a one-unit (2.72μg/m³) increase in ambient PM_2.5 was associated with a 0.75μg/m³ (95% CI: 0.59-0.94μg/m³) increase in personal PM_2.5 exposure. The adjusted mixed-effects models included information extracted from individual's activity diaries as covariates. The results showed that season, occupation, time indoors at home, in transit, and cleaning were significant determinants for PM_2.5 components in personal exposure (R²_β=0.06-0.63, p<0.05), contributing to 3.0-70.4% of the variability. For one-hour extra time spent at home, in transit, and cleaning an average increase of 1.7-3.6% (ammonium, sulfate, nitrate, sulfur), 2.7-12.3% (elemental carbon, ammonium, titanium, iron), and 8.7-19.4% (ammonium, magnesium ions, vanadium) in components of personal PM_2.5 were observed, respectively. In this research, the within-individual variance component dominated the total variability for all investigated exposure data except PM_2.5 and EC. Results from this study indicate that performing long-term personal monitoring is needed for examining the associations of mass and constituents of personal PM_2.5 with health outcomes in epidemiological studies by describing the impacts of individual-specific data on personal exposures.

The relationship between fine particulate matter (PM2.5) and schizophrenia severity

PURPOSE

Although particulate matter (PM) is reported to affect the rate of emergency admissions for schizophrenia, no study has examined the relationship between particulate matter less than 2.5 μm in diameter (PM_2.5) and the severity of schizophrenia.

METHODS

We obtained data on patients with schizophrenia at a psychiatric hospital, and on air pollution in Sakai, Japan between Feb 1, 2013 and April 30, 2016. Multivariate logistic regression analyses were used to estimate the relationship between PM_2.5 concentrations and scores on the Brief Psychiatric Rating Scale (BPRS) of schizophrenia patients at admission, with a lag of up to 7 days.

RESULTS

During the study period, there were 1193 schizophrenia cases. The odds ratio (OR) for a BPRS score ≥ 50 at admission was 1.05 [95% confidence interval 1.00-1.10] and the effect of PM_2.5 concentration was significant for lag period of 2 days. The ORs associated with PM_2.5 concentration increased substantially for patients over 65 years of age.

CONCLUSIONS

Ambient PM_2.5 concentration was associated with exacerbation of schizophrenia. Our results suggest that protection for several days should be considered for controlling PM_2.5-related schizophrenia, especially among elderly patients.

Developing Multipollutant Exposure Indicators of Traffic Pollution: The Dorm Room Inhalation to Vehicle Emissions (DRIVE) Study

Introduction

The Dorm Room Inhalation to Vehicle Emissions (DRIVE2) study was conducted to measure traditional single-pollutant and novel multipollutant traffic indicators along a complete emission-to-exposure pathway. The overarching goal of the study was to evaluate the suitability of these indicators for use as primary traffic exposure metrics in panel-based and small-cohort epidemiological studies.

Methods

Intensive field sampling was conducted on the campus of the Georgia Institute of Technology (GIT) between September 2014 and January 2015 at 8 monitoring sites (2 indoors and 6 outdoors) ranging from 5 m to 2.3 km from the busiest and most congested highway artery in Atlanta. In addition, 54 GIT students living in one of two dormitories either near (20 m) or far (1.4 km) from the highway were recruited to conduct personal exposure sampling and weekly biomonitoring. The pollutants measured were selected to provide information about the heterogeneous particulate and gaseous composition of primary traffic emissions, including the traditional traffic-related species (e.g., carbon monoxide [CO], nitrogen dioxide [NO2], nitric oxide [NO], fine particulate matter [PM2.5], and black carbon [BC]), and of secondary species (e.g., ozone [O3] and sulfate as well as organic carbon [OC], which is both primary and secondary) from traffic and other sources. Along with these pollutants, we also measured two multipollutant traffic indicators: integrated mobile source indicators (IMSIs) and fine particulate matter oxidative potential (FPMOP). IMSIs are derived from elemental carbon (EC), CO, and nitrogen oxide (NOx) concentrations, along with the fractions of these species emitted by gasoline and diesel vehicles, to construct integrated estimates of gasoline and diesel vehicle impacts. Our FPMOP indicator was based on an acellular assay involving the depletion of dithiothreitol (DTT), considering both water-soluble and insoluble components (referred to as FPMOPtotal-DTT). In addition, a limited assessment of 18 low-cost sensors was added to the study to supplement the four original aims.

Results

Pollutant levels measured during the study showed a low impact by this highway hotspot source on its surrounding vicinity. These findings are broadly consistent with results from other studies throughout North America showing decreased relative contributions to urban air pollution from primary traffic emissions. We view these reductions as an indication of a changing near-road environment, facilitated by the effectiveness of mobile source emission controls. Many of the primary pollutant species, including NO, CO, and BC, decreased to near background levels by 20 to 30 m from the highway source. Patterns of correlation among the sites also varied by pollutant and time of day. NO2 exhibited spatial trends that differed from those of the other single-pollutant primary traffic indicators. We believe this was caused by kinetic limitations in the photochemical chemistry, associated with primary emission reductions, required to convert the NO-dominant primary NOx, emitted from automobiles, to NO2. This finding provides some indication of limitations in the use of NO2 as a primary traffic exposure indicator in panel-based health effect studies. Roadside monitoring of NO, CO, and BC tended to be more strongly correlated with sites, both near and far from the road, during morning rush hour periods and often weakly to moderately correlated during other time periods of the day. This pattern was likely associated with diurnal changes in mixing and chemistry and their impact on spatial heterogeneity across the campus. Among our candidate multipollutant primary traffic indicators, we report several key findings related to the use of oxidative potential (OP)-based indicators. Although earlier studies have reported elevated levels of FPMOP in direct exhaust emissions, we found that atmospheric processing further enhanced FPMOPtotal-DTT, likely associated with the oxidation of primary polycyclic aromatic hydrocarbons (PAHs) to quinones and hydroxyquinones and with the oxidization and water solubility of metals. This has important implications in terms both of the utility of FPMOPtotal-DTT as a marker for exhaust emissions and of the importance of atmospheric processing of particulate matter (PM) being tied to potential health outcomes. The results from the personal exposure monitoring also point to the complexity and diversity of the spatiotemporal variability patterns among the study monitoring sites and the importance of accounting for location and spatial mobility when estimating exposures in panel-based and small-cohort studies. This was most clearly demonstrated with the personal BC measurements, where ambient roadside monitoring was shown to be a poor surrogate for exposures to BC. Alternative surrogates, including ambient and indoor BC at the participants' respective dorms, were more strongly associated with personal BC, and knowledge of the participants' mean proximity to the highway was also shown to explain a substantial level of the variability in corresponding personal exposures to both BC and NO2. In addition, untargeted metabolomic indicators measured in plasma and saliva, which represent emerging methods for measuring exposure, were used to extract approximately 20,000 and 30,000 features from plasma and saliva, respectively. Using hydrophilic interaction liquid chromatography (HILIC) in the positive ion mode, we identified 221 plasma features that differed significantly between the two dorm cohorts. The bimodal distribution of these features in the HILIC column was highly idiosyncratic; one peak consisted of features with elevated intensities for participants living in the near dorm; the other consisted of features with elevated intensities for participants in the far dorm. Both peaks were characterized by relatively short retention times, indicative of the hydrophobicity of the identified features. The results from the metabolomics analyses provide a strong basis for continuing this work toward specific chemical validation of putative biomarkers of traffic-related pollution. Finally, the study had a supplemental aim of examining the performance of 18 low-cost CO, NO, NO2, O3, and PM2.5 pollutant sensors. These were colocated alongside the other study monitors and assessed for their ability to capture temporal trends observed by the reference monitoring instrumentation. Generally, we found the performance of the low-cost gas-phase sensors to be promising after extensive calibration; the uncalibrated measurements alone, however, would likely not have led to reliable results. The low-cost PM sensors we evaluated had poor accuracy, although PM sensor technology is evolving quickly and warrants future attention.

Conclusions

An immediate implication of the changing near-road environment is that future studies aimed at characterizing hotspots related to mobile sources and their impacts on health will need to consider multiple approaches for characterizing spatial gradients and exposures. Specifically and most directly, the mobile source contributions to ambient concentrations of single-pollutant indicators of traffic exposure are not as distinguishable to the degree that they have been in the past. Collectively, the study suggests that characterizing exposures to traffic-related pollutants, which is already difficult, will become more difficult because of the reduction in traffic-related emissions. Additional multi-tiered approaches should be considered along with traditional measurements, including the use of alternative OP measures beyond those based on DTT assays, metabolomics, low-cost sensors, and air quality modeling.

Impacts of Regulations on Air Quality and Emergency Department Visits in the Atlanta Metropolitan Area, 1999-2013

INTRODUCTION

The United States and Western Europe have seen great improvements in air quality, presumably in response to various regulations curtailing emissions from the broad range of sources that have contributed to local, regional, and global pollution. Such regulations, and the ensuing controls, however, have not come without costs, which are estimated at tens of billions of dollars per year. These costs motivate accountability-related questions such as, to what extent do regulations lead to emissions changes? More important, to what degree have the regulations provided the expected human health benefits?

Here, the impacts of specific regulations on both electricity generating unit (EGU) and on-road mobile sources are examined through the classical accountability process laid out in the 2003 Health Effects Institute report linking regulations to emissions to air quality to health effects, with a focus on the 1999-2013 period. This analysis centers on regulatory actions in the southeastern United States and their effects on health outcomes in the 5-county Atlanta metropolitan area. The regulations examined are largely driven by the 1990 Clean Air Act Amendments (C). This work investigates regulatory actions and controls promulgated on EGUs: the Acid Rain Program (ARP), the NOx Budget Trading Program (NBP), and the Clean Air Interstate Rule (CAIR) - and mobile sources: Tier 2 Gasoline Vehicle Standards and the 2007 Heavy Duty Diesel Rule.

METHODS

Each step in the classic accountability process was addressed using one or more methods. Linking regulations to emissions was accomplished by identifying major federal regulations and the associated state regulations, along with analysis of individual facility emissions and control technologies and emissions modeling (e.g., using the U.S. Environmental Protection Agency's [U.S. EPA's] MOtor Vehicle Emissions Simulator [MOVES] mobile-source model). Regulators, including those from state environmental and transportation agencies, along with the public service commissions, play an important role in implementing federal rules and were involved along with other regional stakeholders in the study. We used trend analysis, air quality modeling, satellite data, and a ratio-of-ratios technique to investigate a critical current issue, a potential large bias in mobile-source oxides of nitrogen (NOx) emissions estimates.

The second link, emissions-air quality relationships, was addressed using both empirical analyses as well as chemical transport modeling employing the Community Multiscale Air Quality (CMAQ) model. Kolmogorov-Zurbenko filtering accounting for day of the year was used to separate the air quality signal into long-term, seasonal, weekday-holiday, and short-term meteorological signals. Regression modeling was then used to link emissions and meteorology to ambient concentrations for each of the species examined (ozone [O3], particulate matter ≤ 2.5 μm in aerodynamic diameter [PM2.5], nitrogen dioxide [NO2], sulfur dioxide [SO2], carbon monoxide [CO], sulfate [SO4-2], nitrate [NO3-], ammonium [NH4+], organic carbon [OC], and elemental carbon [EC]). CMAQ modeling was likewise used to link emissions changes to air quality changes, as well as to further establish the relative roles of meteorology versus emissions change impacts on air quality trends. CMAQ and empirical modeling were used to investigate aerosol acidity trends, employing the ISORROPIA II thermodynamic equilibrium model to calculate pH based on aerosol composition. The relationships between emissions and meteorology were then used to construct estimated counterfactual air quality time series of daily pollutant concentrations that would have occurred in the absence of the regulations. Uncertainties in counterfactual air quality were captured by the construction of 5,000 pollutant time series using a Monte Carlo sampling technique, accounting for uncertainties in emissions and model parameters.

Health impacts of the regulatory actions were assessed using data on cardiorespiratory emergency department (ED) visits, using patient-level data in the Atlanta area for the 1999-2013 period. Four outcome groups were chosen based on previous studies identifying associations with ambient air pollution: a combined respiratory disease (RD) category; the subgroup of RD presenting with asthma; a combined cardiovascular disease (CVD) category; and the subgroup of CVD presenting with congestive heart failure (CHF).

Models were fit to estimate the joint effects of multiple pollutants on ED visits in a time-series framework, using Poisson generalized linear models accounting for overdispersion, with a priori model formulations for temporal and meteorological covariates and lag structures. Several parameterizations were considered for the joint-effects models, including different sets of pollutants and models with nonlinear pollutant terms and first-order interactions among pollutants. Use of different periods for parameter estimates was assessed, as associations between pollutant levels and ED visits varied over the study period. A 7-pollutant, nonlinear model with pollutant interaction terms was chosen as the baseline model and fitted using pollutant and outcome data from 1999-2005 before regulations might have substantially changed the toxicity of pollutant mixtures. In separate analyses, these models were fitted using pollutant and outcome data from the entire 1999-2013 study period. Daily counterfactual time series of pollutant concentrations were then input into the health models, and the differences between the observed and counterfactual concentrations were used to estimate the impacts of the regulations on daily counts of ED visits. To account for the uncertainty in both the estimation of the counterfactual time series of ambient pollutant levels and the estimation of the health model parameters, we simulated 5,000 sets of parameter estimates using a multivariate normal distribution based on the observed variance-covariance matrix, allowing for uncertainty at each step of the chain of accountability. Sensitivity tests were conducted to assess the robustness of the results.

RESULTS

EGU NOx and SO2 emissions in the Southeast decreased by 82% and 83%, respectively, between 1999 and 2013, while mobile-source emissions controls led to estimated decreases in Atlanta-area pollutant emissions of between 61% and 93%, depending on pollutant. While EGU emissions were measured, mobile-source emissions were modeled. Our results are supportive of a potential high bias in mobile-source NOx and CO emissions estimates. Air quality benefits from regulatory actions have increased as programs have been fully implemented and have had varying impacts over different seasons. In a scenario that accounted for all emissions reductions across the period, observed Atlanta central monitoring site maximum daily 8-hour (MDA8h) O3 was estimated to have been reduced by controls in the summertime and increased in the wintertime, with a change in mean annual MDA8h O3 from 39.7 ppb (counterfactual) to 38.4 ppb (observed). PM2.5 reductions were observed year-round, with average 2013 values at 8.9 μg/m3 (observed) versus 19.1 μg/m3 (counterfactual). Empirical and CMAQ analyses found that long-term meteorological trends across the Southeast over the period examined played little role in the distribution of species concentrations, while emissions changes explained the decreases observed. Aerosol pH, which plays a key role in aerosol formation and dynamics and may have health implications, was typically very low (on the order of 1-2, but sometimes much lower), with little trend over time despite the stringent SO2 controls and SO42- reductions.

Using health models fit from 1999-2005, emissions reductions from all selected pollution-control policies led to an estimated 55,794 cardiorespiratory disease ED visits prevented (i.e., fewer observed ED visits than would have been expected under counterfactual scenarios) - 52,717 RD visits, of which 38,038 were for asthma, and 3,057 CVD visits, of which 2,104 were for CHF - among the residents of the 5-county area over the 1999-2013 period, an area with approximately 3.5 million people in 2013. During the final two years of the study (2012-2013), when pollution-control policies were most fully implemented and the associated benefits realized, these policies were estimated to prevent 5.9% of the RD ED visits that would have occurred in the absence of the policies (95% interval estimate: -0.4% to 12.3%); 16.5% of the asthma ED visits (95% interval estimate: 7.5% to 25.1%); 2.3% of the CVD ED visits (95% interval estimate: -1.8% to 6.2%); and -.6% of the CHF ED visits (95% interval estimate: 26.3% to 10.4%). Estimates of ED visits prevented were generally lower when using health models fit for the entire 1999-2013 study period.

Sensitivity analyses were conducted to show the impact of the choice of parameterization of the health models and to assess alternative definitions of the study area. When impacts were assessed for separate policy interventions, policies affecting emissions from EGUs, especially the ARP and the NBP, appeared to have had the greatest effect on prevention of RD and asthma ED visits.

CONCLUSIONS

This study demonstrates the effectiveness of regulations on improving air quality and health in the southeastern United States. It also demonstrates the complexities of accountability assessments as uncertainties are introduced in each step of the classic accountability process. While accounting for uncertainties in emissions, air quality-emissions relationships, and health models does lead to relatively large uncertainties in the estimated outcomes due to specific regulations, overall the benefits of regulations have been substantial.

Acute effects of fine particulate matter constituents on mortality: A systematic review and meta-regression analysis

BACKGROUND

The link between PM_2.5 exposure and adverse health outcomes is well documented from studies across the world. However, the reported effect estimates vary across studies, locations and constituents. We aimed to conduct a meta-analysis on associations between short-term exposure to PM_2.5 constituents and mortality using city-specific estimates, and explore factors that may explain some of the observed heterogeneity.

METHODS

We systematically reviewed epidemiological studies on particle constituents and mortality using PubMed and Web of Science databases up to July 2015.We included studies that examined the association between short-term exposure to PM_2.5 constituents and all-cause, cardiovascular, and respiratory mortality, in the general adult population. Each study was summarized based on pre-specified study key parameters (e.g., location, time period, population, diagnostic classification standard), and we evaluated the risk of bias using the Office of Health Assessment and Translation (OHAT) Method for each included study. We extracted city-specific mortality risk estimates for each constituent and cause of mortality. For multi-city studies, we requested the city-specific risk estimates from the authors unless reported in the article. We performed random effects meta-analyses using city-specific estimates, and examined whether the effects vary across regions and city characteristics (PM_2.5 concentration levels, air temperature, elevation, vegetation, size of elderly population, population density, and baseline mortality).

RESULTS

We found a 0.89% (95% CI: 0.68, 1.10%) increase in all-cause, a 0.80% (95% CI: 0.41, 1.20%) increase in cardiovascular, and a 1.10% (95% CI: 0.59, 1.62%) increase in respiratory mortality per 10μg/m³ increase in PM_2.5. Accounting for the downward bias induced by studies of single days, the all-cause mortality estimate increased to 1.01% (95% CI: 0.81, 1.20%). We found significant associations between mortality and several PM_2.5 constituents. The most consistent and stronger associations were observed for elemental carbon (EC) and potassium (K). For most of the constituents, we observed high variability of effect estimates across cities.

CONCLUSIONS

Our meta-analysis suggests that (a) combustion elements such as EC and K have a stronger association with mortality, (b) single lag studies underestimate effects, and (c) estimates of PM_2.5 and constituents differ across regions. Accounting for PM mass in constituent's health models may lead to more stable and comparable effect estimates across different studies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO: CRD42017055765.

Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure

BACKGROUND

Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods.

METHODS

We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002-2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12km spatial resolutions, and satellite-based models at 10km and 1km resolutions. Case status was either a coronary artery disease (CAD) index >23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (μg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status.

RESULTS

We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12km-CMAQ models.

CONCLUSIONS

Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data.

Associations between Ambient Fine Particulate Oxidative Potential and Cardiorespiratory Emergency Department Visits

BACKGROUND

Oxidative potential (OP) has been proposed as a measure of toxicity of ambient particulate matter (PM).

OBJECTIVES

Our goal was to address an important research gap by using daily OP measurements to conduct population-level analysis of the health effects of measured ambient OP.

METHODS

A semi-automated dithiothreitol (DTT) analytical system was used to measure daily average OP (OP^DTT) in water-soluble fine PM at a central monitor site in Atlanta, Georgia, over eight sampling periods (a total of 196 d) during June 2012-April 2013. Data on emergency department (ED) visits for selected cardiorespiratory outcomes were obtained for the five-county Atlanta metropolitan area. Poisson log-linear regression models controlling for temporal confounders were used to conduct time-series analyses of the relationship between daily counts of ED visits and either the 3-d moving average (lag 0-2) of OP^DTT or same-day OP^DTT. Bipollutant regression models were run to estimate the health associations of OP^DTT while controlling for other pollutants.

RESULTS

OP^DTT was measured for 196 d (mean=0.32 nmol/min/m³, interquartile range=0.21). Lag 0-2 OP^DTT was associated with ED visits for respiratory disease (RR=1.03, 95% confidence interval (CI): 1.00, 1.05 per interquartile range increase in OP^DTT), asthma (RR=1.12, 95% CI: 1.03, 1.22), and ischemic heart disease (RR=1.19, 95% CI: 1.03, 1.38). Same-day OP^DTT was not associated with ED visits for any outcome. Lag 0-2 OP^DTT remained a significant predictor of asthma and ischemic heart disease in most bipollutant models.

CONCLUSIONS

Lag 0-2 OP^DTT was associated with ED visits for multiple cardiorespiratory outcomes, providing support for the utility of OP^DTT as a measure of fine particle toxicity. https://doi.org/10.1289/EHP1545.

Comparison between air pollution concentrations measured at the nearest monitoring station to the delivery hospital and those measured at stations nearest the residential postal code regions of pregnant women in Fukuoka

BACKGROUND

Numerous earlier studies examining the association of air pollution with maternal and foetal health estimated maternal exposure to air pollutants based on the women's residential addresses. However, residential addresses, which are personally identifiable information, are not always obtainable. Since a majority of pregnant women reside near their delivery hospitals, the concentrations of air pollutants at the respective delivery hospitals may be surrogate markers of pollutant exposure at home. We compared air pollutant concentrations measured at the nearest monitoring station to Kyushu University Hospital with those measured at the closest monitoring stations to the respective residential postal code regions of pregnant women in Fukuoka.

METHODS

Aggregated postal code data for the home addresses of pregnant women who delivered at Kyushu University Hospital in 2014 was obtained from Kyushu University Hospital. For each of the study's 695 women who resided in Fukuoka Prefecture, we assigned pollutant concentrations measured at the nearest monitoring station to Kyushu University Hospital and pollutant concentrations measured at the nearest monitoring station to their respective residential postal code regions.

RESULTS

Among the 695 women, 584 (84.0%) resided in the proximity of the nearest monitoring station to hospital or one of the four other stations (as the nearest stations to their respective residential postal code region) in Fukuoka city. Pearson's correlation for daily mean concentrations among the monitoring stations in Fukuoka city was strong for fine particulate matter (PM_2.5), suspended particulate matter (SPM), and photochemical oxidants (Ox) (coefficients ≥0.9), but moderate for coarse particulate matter (the result of subtracting the PM_2.5 from the SPM concentrations), nitrogen dioxide, and sulphur dioxide. Hospital-based and residence-based concentrations of PM_2.5, SPM, and Ox were comparable.

CONCLUSIONS

For PM_2.5, SPM, and Ox, exposure estimation based on the delivery hospital is likely to approximate that based on the home of pregnant women.

Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach

BACKGROUND

Ground-level ozone is a potent airway irritant and a determinant of respiratory morbidity. Susceptibility to the health effects of ambient ozone may be influenced by both intrinsic and extrinsic factors, such as neighborhood socioeconomic status (SES). Questions remain regarding the manner and extent that factors such as SES influence ozone-related health effects, particularly across different study areas.

METHODS

Using a 2-stage modeling approach we evaluated neighborhood SES as a modifier of ozone-related pediatric respiratory morbidity in Atlanta, Dallas, & St. Louis. We acquired multi-year data on emergency department (ED) visits among 5-18 year olds with a primary diagnosis of respiratory disease in each city. Daily concentrations of 8-h maximum ambient ozone were estimated for all ZIP Code Tabulation Areas (ZCTA) in each city by fusing observed concentration data from available network monitors with simulations from an emissions-based chemical transport model. In the first stage, we used conditional logistic regression to estimate ZCTA-specific odds ratios (OR) between ozone and respiratory ED visits, controlling for temporal trends and meteorology. In the second stage, we combined ZCTA-level estimates in a Bayesian hierarchical model to assess overall associations and effect modification by neighborhood SES considering categorical and continuous SES indicators (e.g., ZCTA-specific levels of poverty). We estimated ORs and 95% posterior intervals (PI) for a 25 ppb increase in ozone.

RESULTS

The hierarchical model combined effect estimates from 179 ZCTAs in Atlanta, 205 ZCTAs in Dallas, and 151 ZCTAs in St. Louis. The strongest overall association of ozone and pediatric respiratory disease was in Atlanta (OR = 1.08, 95% PI: 1.06, 1.11), followed by Dallas (OR = 1.04, 95% PI: 1.01, 1.07) and St. Louis (OR = 1.03, 95% PI: 0.99, 1.07). Patterns of association across levels of neighborhood SES in each city suggested stronger ORs in low compared to high SES areas, with some evidence of non-linear effect modification.

CONCLUSIONS

Results suggest that ozone is associated with pediatric respiratory morbidity in multiple US cities; neighborhood SES may modify this association in a non-linear manner. In each city, children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying rates of respiratory morbidity.

Characterization of criteria air pollutants in Beijing during 2014-2015

One year-long criteria air pollutants data collected in Beijing were analyzed in this paper, which can support the research on formation, transport and human health effects of air pollutants. This is the first time to study the spatial and temporal variations of criteria pollutants in Beijing using hourly observational data from 12 sites between June 2014 and May 2015 released by the Ministry of Environmental Protection (MEP) of China. Beijing is facing tremendous air pollution as the daily averaged PM_2.5 (particulate matter with aerodynamic diameter less than 2.5µm) concentrations in all sites exceeding the Chinese Ambient Air Quality Standards (CAAQS) Grade I & II standards (15 and 35µg/m³). Slightly differences in PM_2.5 and ozone (O₃) were observed between sites at the urban and rural areas. Pearson correlation coefficients show that most pollutants are temporally correlated in Beijing except for O₃. The coefficients of divergence (COD) indicate that PM_2.5 is associated at most sites with only one rural site (Dingling) having observable difference and one site may be insufficient for monitoring surrounding area. The 8h peak O₃ (O₃-8h) also correlates at different sites but with one urban site (Haidianquwanliu) different from others. In addition, an extreme PM_2.5 event (hourly average concentration exceeding 300μg/m³ for ~40h) was examined with the consideration of meteorological conditions. Southerly wind with low speed and high relative humidity allow the accumulation of pollutants while northerly wind with high speed and low relative humidity result in good air quality.

Socioeconomic Status and Non-Fatal Adult Injuries in Selected Atlanta (Georgia USA) Hospitals

BACKGROUND

Injury mortality data for adults in the United States and other countries consistently show higher mortality for those with lower socioeconomic status (SES). Data are sparse regarding the role of SES among adult, non-fatal US injuries. The current study estimated non-fatal injury risk by household income using hospital emergency department (ED) visits.

METHODS

A total of 1,308,892 ED visits at 10 Atlanta (Georgia USA) hospitals from 2001-2004 (347,866 injuries) were studied. The SES was based on US census-block group income, with subjects assigned to census blocks based on reported residence. Logistic regression was used to determine risk by SES for injuries versus all other ED visits, adjusting for demographics, hospital, and weather. Supplemental analyses using hospital data from 2010-2013, without data on SES, were conducted to determine whether earlier patterns by race, age, and gender persisted.

RESULTS

Risk for many injury categories increased with higher income. Odds ratio by quartiles of increasing income (lowest quartile as referent, 95% confidence interval [CI] given for upper most quartile) were 1.00, 1.23, 1.34, 1.40 (95% CI 1.36-1.45) for motor vehicle accidents; 1.00, 1.03, 1.11, 1.24 (95% CI 1.20-1.29) for being struck by objects; 1.00. 0.99, 1.04, 1.12 (95% CI 1.00-1.25) for suicide; and 1.00, 1.03, 1.05, 1.12 (95% CI 1.09-1.15) for falls. In contrast, decreased injury risk with increased household income was seen for assaults (1.00, 0.83, 0.73, 0.67 [95% CI 0.63-0.72], by increasing quartiles). These trends by income did not differ markedly by race and gender. Whites generally had less risk of injuries, with the exception of assaults and motor vehicle accidents. Males had higher risk of injury than females, with the exception of falls and suicide attempts. Patterns of risk for race, age, and gender were consistent between 2001-2004 and 2010-2013.

CONCLUSION

For most non-fatal injuries, those with higher income had more risk of ED visits, although the opposite was true for assault. Hulland E , Chowdhury R , Sarnat S , Chang HH , Steenland K . Socioeconomic status and non-fatal adult injuries in selected Atlanta (Georgia USA) hospitals. Prehosp Disaster Med. 2017;32(4):403-413.

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

BACKGROUND

Short-term exposure to ambient fine particulate matter (PM2.5) concentrations has been associated with increased mortality and morbidity. Determining which sources of PM2.5 are most toxic can help guide targeted reduction of PM2.5. However, conducting multicity epidemiologic studies of sources is difficult because source-specific PM2.5 is not directly measured, and source chemical compositions can vary between cities.

OBJECTIVES

We determined how the chemical composition of primary ambient PM2.5 sources varies across cities. We estimated associations between source-specific PM2.5 and respiratory disease emergency department (ED) visits and examined between-city heterogeneity in estimated associations.

METHODS

We used source apportionment to estimate daily concentrations of primary source-specific PM2.5 for four U.S. cities. For sources with similar chemical compositions between cities, we applied Poisson time-series regression models to estimate associations between source-specific PM2.5 and respiratory disease ED visits.

RESULTS

We found that PM2.5 from biomass burning, diesel vehicle, gasoline vehicle, and dust sources was similar in chemical composition between cities, but PM2.5 from coal combustion and metal sources varied across cities. We found some evidence of positive associations of respiratory disease ED visits with biomass burning PM2.5; associations with diesel and gasoline PM2.5 were frequently imprecise or consistent with the null. We found little evidence of associations with dust PM2.5.

CONCLUSIONS

We introduced an approach for comparing the chemical compositions of PM2.5 sources across cities and conducted one of the first multicity studies of source-specific PM2.5 and ED visits. Across four U.S. cities, among the primary PM2.5 sources assessed, biomass burning PM2.5 was most strongly associated with respiratory health. Citation: Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 125:97-103; http://dx.doi.org/10.1289/EHP271.

Differential health effects of short-term exposure to source-specific particles in London, U.K

BACKGROUND

There is ample evidence of adverse associations between short-term exposure to ambient particle mass concentrations and health but little is known about the relative contribution from various sources.

METHODS

We used air particle composition and number networks in London between 2011 and 2012 to derive six source-related factors for PM₁₀ and four factors for size distributions of ultrafine particles (NSD). We assessed the associations of these factors, at pre-specified lags, with daily total, cardiovascular (CVD) and respiratory mortality and hospitalizations using Poisson regression. Relative risks and 95% confidence intervals (CI) were expressed as percentage change per interquartile range increment in source-factor mass or number concentration. We evaluated the sensitivity of associations to adjustment for multiple other factors and by season.

RESULTS

We found no evidence of associations between PM₁₀ or NSD source-related factors and daily mortality, as the direction of the estimates were variable with 95% CI spanning 0%. Traffic-related PM₁₀ and NSD displayed consistent associations with CVD admissions aged 15-64years (1.01% (95%CI: 0.03%, 2.00%) and 1.04% (95%CI: -0.62%, 2.72%) respectively) as did particles from background urban sources (0.36% for PM₁₀ and 0.81% for NSD). Most sources were positively associated with pediatric (0-14years) respiratory hospitalizations, with stronger evidence for fuel oil PM₁₀ (3.43%, 95%CI: 1.26%, 5.65%). Our results did not suggest associations with cardiovascular admissions in 65+ or respiratory admissions in 15+ age groups. Effect estimates were generally robust to adjustment for other factors and by season.

CONCLUSIONS

Our findings are broadly consistent with the growing evidence of the toxicity of traffic and combustion particles, particularly in relation to respiratory morbidity in children and cardiovascular morbidity in younger adults.

Pediatric emergency department visits and ambient Air pollution in the U.S. State of Georgia: a case-crossover study

BACKGROUND

Estimating the health effects of ambient air pollutant mixtures is necessary to understand the risk of real-life air pollution exposures.

METHODS

Pediatric Emergency Department (ED) visit records for asthma or wheeze (n = 148,256), bronchitis (n = 84,597), pneumonia (n = 90,063), otitis media (n = 422,268) and upper respiratory tract infection (URI) (n = 744,942) were obtained from Georgia hospitals during 2002-2008. Spatially-contiguous daily concentrations of 11 ambient air pollutants were estimated from CMAQ model simulations that were fused with ground-based measurements. Using a case-crossover study design, odds ratios for 3-day moving average air pollutant concentrations were estimated using conditional logistic regression, matching on ZIP code, day-of-week, month, and year.

RESULTS

In multipollutant models, the association of highest magnitude observed for the asthma/wheeze outcome was with "oxidant gases" (O₃, NO₂, and SO₂); the joint effect estimate for an IQR increase of this mixture was OR: 1.068 (95% CI: 1.040, 1.097). The group of "secondary pollutants" (O₃ and the PM_2.5 components SO₄^2-, NO^3-, and NH⁴⁺) was strongly associated with bronchitis (OR: 1.090, 95% CI: 1.050, 1.132), pneumonia (OR: 1.085, 95% CI: 1.047, 1.125), and otitis media (OR: 1.059, 95% CI: 1.042, 1.077). ED visits for URI were strongly associated with "oxidant gases," "secondary pollutants," and the "criteria pollutants" (O₃, NO₂, CO, SO₂, and PM_2.5).

CONCLUSIONS

Short-term exposures to air pollution mixtures were associated with ED visits for several different pediatric respiratory diseases.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models

BACKGROUND

Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health.

METHODS

ZIP-code level estimates of exposure for six pollutants (CO, NO_x, EC, PM_2.5, SO₄, O₃) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs.

RESULTS

Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NO_x or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate.

CONCLUSIONS

The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.