Abstract  Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-validations of different kriging models, c) contrast three popular kriging approaches, and d) calculate SE of the kriging estimations. We used PM data for PM with aerodynamic diameter </= 10 microm (PM10) and aerodynamic diameter </= 2.5 microm (PM2.5) from the U.S. Environmental Protection Agency for the year 2000. Kriging estimations were performed at 94,135 geocoded addresses of Women's Health Initiative study participants using the ArcView geographic information system. We developed a semiautomated program to enable large-scale daily kriging estimation and assessed validity of semivariogram models using prediction error (PE) , standardized prediction error (SPE) , root mean square standardized (RMSS) , and SE of the estimated PM. National- and regional-scale kriging performed satisfactorily, with the former slightly better. The average PE, SPE, and RMSS of daily PM10 semivariograms using regular ordinary kriging with a spherical model were 0.0629, -0.0011, and 1.255 microg/m3, respectively ; the average SE of the estimated residential-level PM10 was 27.36 microg/m3. The values for PM2.5 were 0.049, 0.0085, 1.389, and 4.13 microg/m3, respectively. Lognormal ordinary kriging yielded a smaller average SE and effectively eliminated out-of-range predicted values compared to regular ordinary kriging. Semiautomated daily kriging estimations and semivariogram cross-validations are feasible on a national scale. Lognormal ordinary kriging with a spherical model is valid for estimating daily ambient PM at geocoded residential addresses.

Abstract  Background: We and others have shown that increases in particulate air pollutant (PM) concentrations in the previous hours and days have been associated with increased risks of myocardial infarction, but little is known about the relationships between air pollution and specific subsets of myocardial infarction, such as ST-elevation myocardial infarction (STEMI) and non ST-elevation myocardial infarction (NSTEMI). Methods: Using data from acute coronary syndrome patients with STEMI (n = 338) and NSTEMI (n = 339) and case-crossover methods, we estimated the risk of STEMI and NSTEMI associated with increased ambient fine particle (<2.5 um) concentrations, ultrafine particle (10-100 nm) number concentrations, and accumulation mode particle (100-500 nm) number concentrations in the previous few hours and days. Results: We found a significant 18% increase in the risk of STEMI associated with each 7.1 mu g/m(3) increase in PM2.5 concentration in the previous hour prior to acute coronary syndrome onset, with smaller, non-significantly increased risks associated with increased fine particle concentrations in the previous 3, 12, and 24 hours. We found no pattern with NSTEMI. Estimates of the risk of STEMI associated with interquartile range increases in ultrafine particle and accumulation mode particle number concentrations in the previous 1 to 96 hours were all greater than 1.0, but not statistically significant. Patients with pre-existing hypertension had a significantly greater risk of STEMI associated with increased fine particle concentration in the previous hour than patients without hypertension. Conclusions: Increased fine particle concentrations in the hour prior to acute coronary syndrome onset were associated with an increased risk of STEMI, but not NSTEMI. Patients with pre-existing hypertension and other cardiovascular disease appeared particularly susceptible. Further investigation into mechanisms by which PM can preferentially trigger STEMI over NSTEMI within this rapid time scale is needed.

Abstract  Fine particulate matter is associated with adverse health outcomes. Exposure to fine particulate matter may disproportionately affect urban communities with larger numbers of vulnerable residents. We used multilevel logistic regression models to estimate the joint effects of fine particulate matter (PM2.5) and population vulnerabilities on cardiopulmonary mortality (CPM). We estimated the health benefits of reductions in PM2.5 across census tracts in the Detroit metropolitan area with varying levels of population vulnerability, using cluster-specific odds ratios scaled to reflect PM2.5-attributable cardiopulmonary risk. PM2.5 and population vulnerability were independently associated with odds of CPM. Odds of CPM and the number of deaths attributable to PM2.5 were greatest in census tracts with both high PM2.5 exposures and population vulnerability. Reducing PM2.5 in census tracts with high PM2.5 would lead to an estimated 18% annual reduction in PM2.5-attributable CPM. Between 78⁻79% of those reductions in CPM would occur within census tracts with high population vulnerabilities. These health benefits of reductions in PM2.5 occurred at levels below current U.S. reference concentrations. Focusing efforts to reduce PM2.5 in the Detroit metropolitan area in census tracts with currently high levels would also lead to greater benefits for residents of census tracts with high population vulnerabilities.

Abstract  BACKGROUND: Evidence indicates that air pollution contributes to cardiopulmonary mortality. There is ongoing debate regarding the size and shape of the pollution–mortality exposure–response relationship. There are also growing appeals for estimates of pollution–mortality relationships that use public data and are based on large, representative study cohorts.

OBJECTIVES: Our goal was to evaluate fine particulate matter air pollution ([Formula: see text]) and mortality using a large cohort that is representative of the U.S. population and is based on public data. Additional objectives included exploring model sensitivity, evaluating relative effects across selected subgroups, and assessing the shape of the [Formula: see text]–mortality relationship.

METHODS: National Health Interview Surveys (1986–2014), with mortality linkage through 2015, were used to create a cohort of 1,599,329 U.S. adults and a subcohort with information on smoking and body mass index (BMI) of 635,539 adults. Data were linked with modeled ambient [Formula: see text] at the census-tract level. Cox proportional hazards models were used to estimate [Formula: see text]–mortality hazard ratios for all-cause and specific causes of death while controlling for individual risk factors and regional and urban versus rural differences. Sensitivity and subgroup analyses were conducted and the shape of the [Formula: see text]–mortality relationship was explored.

RESULTS: Estimated mortality hazard ratios, per [Formula: see text] long-term exposure to [Formula: see text], were 1.12 (95% CI: 1.08, 1.15) for all-cause mortality, 1.23 (95% CI: 1.17, 1.29) for cardiopulmonary mortality, and 1.12 (95% CI: 1.00, 1.26) for lung cancer mortality. In general, [Formula: see text]–mortality associations were consistently positive for all-cause and cardiopulmonary mortality across key modeling choices and across subgroups of sex, age, race-ethnicity, income, education levels, and geographic regions.

DISCUSSION: This large, nationwide, representative cohort of U.S. adults provides robust evidence that long-term [Formula: see text] exposure contributes to cardiopulmonary mortality risk. The ubiquitous and involuntary nature of exposures and the broadly observed effects across subpopulations underscore the public health importance of breathing clean air. https://doi.org/10.1289/EHP4438.

Abstract  Summary Background Long-term exposure to fine particulate matter less than 2·5 μm in diameter (PM2·5) and traffi c-related air pollutant concentrations are associated with cardiovascular risk. The disease process underlying these associations remains uncertain. We aim to assess association between long-term exposure to ambient air pollution and progression of coronary artery calcium and common carotid artery intima-media thickness. Methods In this prospective 10-year cohort study, we repeatedly measured coronary artery calcium by CT in 6795 participants aged 45–84 years enrolled in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) in six metropolitan areas in the USA. Repeated scans were done for nearly all participants between 2002 and 2005, for a subset of participants between 2005 and 2007, and for half of all participants between 2010 and 2012. Common carotid artery intimamedia thickness was measured by ultrasound in all participants at baseline and in 2010–12 for 3459 participants. Residence-specifi c spatio-temporal pollution concentration models, incorporating Community specific measurements, agency monitoring data, and geographical predictors, estimated concentrations of PM2·5 and nitrogen oxides (NOX) between 1999 and 2012. The primary aim was to examine the association between both progression of coronary artery calcium and mean carotid artery intima-media thickness and long-term exposure to ambient air pollutant concentrations (PM2·5, NOX, and black carbon) between examinations and within the six metropolitan areas, adjusting for baseline age, sex, ethnicity, socioeconomic characteristics, cardiovascular risk factors, site, and CT scanner technology. Findings In this population, coronary calcium increased on average by 24 Agatston units per year (SD 58), and intimamedia thickness by 12 μm per year (10), before adjusting for risk factors or air pollutant exposures. Participant specific pollutant concentrations averaged over the years 2000–10 ranged from 9·2–22·6 µg PM2·5/m³ and 7·2–139·2 parts per billion (ppb) NOX. For each 5 µg PM2·5/m³ increase, coronary calcium progressed by 4·1 Agatston units per year(95% CI 1·4–6·8) and for each 40 ppb NOX coronary calcium progressed by 4·8 Agatston units per year (0·9–8·7). Pollutant exposures were not associated with intima-media thickness change. The estimate for the effect of a 5 µg/m³ higher long-term exposure to PM2·5 in intima-media thickness was –0·9 µm per year (95% CI –3·0 to 1·3). For 40 ppb higher NOX, the estimate was 0·2 µm per year (–1·9 to 2·4). Interpretation Increased concentrations of PM2·5 and traffic-related air pollution within metropolitan areas, in ranges commonly encountered worldwide, are associated with progression in coronary calcifi cation, consistent with acceleration of atherosclerosis. This study supports the case for global eff orts of pollution reduction in prevention of cardiovascular diseases.

Abstract  This report describes the results of a study of long-term effects of PM components in the American Cancer Society's Cancer Prevention Study II cohort; a time-series study of short-term effects of PM components on cardiovascular and other diseases in people living in 150 U.S. cities; and two toxicologic studies in animals exposed by inhalation to concentrated ambient particles, and in animals and human cells exposed to particles collected on filters from five different airsheds across the United States. This report, along with Research Report 178 (Vedal et al.), is one of HEI's National Particle Component Toxicity (NPACT) studies, which describe the most systematic multidisciplinary studies to date to investigate the health effects of PM components in humans and animal models at locations across the United States where the effects of PM sources and components may differ. The report includes a Commentary and a Synthesis by the NPACT Review Panel.

Abstract  Webcams and automated, color photography cameras have been routinely operated in many U.S. national parks and other federal lands as far back as 1988, with a general goal of meeting interpretive needs within the public lands system and communicating effects of haze on scenic vistas to the general public, policy makers, and scientists. Additionally, it would be desirable to extract quantifiable information from these images to document how visibility conditions change over time and space and to further reflect the effects of haze on a scene, in the form of atmospheric extinction, independent of changing lighting conditions due to time of day, year, or cloud cover. Many studies have demonstrated a link between image indexes and visual range or extinction in urban settings where visibility is significantly degraded and where scenes tend to be gray and devoid of color. In relatively clean, clear atmospheric conditions, clouds and lighting conditions can sometimes affect the image radiance field as much or more than the effects of haze. In addition, over the course of many years, cameras have been replaced many times as technology improved or older systems wore out, and therefore camera image pixel density has changed dramatically. It is shown that gradient operators are very sensitive to image resolution while contrast indexes are not. Furthermore, temporal averaging and time of day restrictions allow for developing quantitative relationships between atmospheric extinction and contrast-type indexes even when image resolution has varied over time. Temporal averaging effectively removes the variability of visibility indexes associated with changing cloud cover and weather conditions, and changes in lighting conditions resulting from sun angle effects are best compensated for by restricting averaging to only certain times of the day.

Abstract  Objectives. To investigate potential changes in burdens from coal-fired electricity-generating units (EGUcfs) that emit fine particulate matter (PM2.5, defined as matter with a nominal mean aerodynamic diameter of ≤ 2.5 µm) among racial/ethnic and economic groups after reduction of operations in 92 US EGUcfs.Methods. PM2.5 burdens calculated for EGUs listed in the 2008, 2011, and 2014 National Emissions Inventory were recalculated for 2017 after omitting emissions from 92 EGUcfs. The combined influence of race/ethnicity and poverty on burden estimates was characterized.Results. Omission of 92 EGUcfs decreased PM2.5 burdens attributable to EGUs by 8.6% for the entire population and to varying degrees for every population subgroup. Although the burden decreased across all subgroups, the decline was not equitable. After omission of the 92 EGUcfs, burdens were highest for the below-poverty and non-White subgroups. Proportional disparities between White and non-White subgroups increased. In our combined analysis, the burden was highest for the non-White-high-poverty subgroup.Conclusions. Our results indicate that subgroups living in poverty experience the greatest absolute burdens from EGUcfs. Changes as a result of EGUcf closures suggest a shift in burden from White to non-White subgroups. Policymakers could use burden analyses to jointly promote equity and reduce emissions.

Abstract  Decreasing ambient fine particulate matter (PM2.5) concentrations over time together with increasing life expectancy raise concerns about temporal confounding of associations between PM2.5 and mortality. To address this issue, we examined PM2.5-associated mortality risk ratios (MRRs) estimated for approximately 20,000,000 US Medicare beneficiaries, who lived within six miles of an Environmental Protection Agency air quality monitoring site, between December 2000 and December 2012. We assessed temporal confounding by examining whether PM2.5-associated MRRs vary by study period length. We then evaluated three approaches to control for temporal confounding: (1) assessing exposures using the residual of PM2.5 regressed on time; (2) adding a penalized spline term for time to the health model; and (3) including a term that describes temporal variability in PM2.5 into the health model, with this term estimated using decomposition approaches. We found a 10 μg/m3 increase in PM2.5 exposure to be associated with a 1.20 times (95% confidence interval [CI] = 1.20, 1.21) higher risk of mortality across the 13-year study period, with the magnitude of the association decreasing with shorter study periods. MRRs remained statistically significant but were attenuated when models adjusted for long-term time trends in PM2.5. The residual-based, time-adjusted MRR equaled 1.12 (95% CI = 1.11, 1.12) per 10 μg/m3 for the 13-year study period and did not change when shorter study periods were examined. Spline- and decomposition-based approaches produced similar but less-stable MRRs. Our findings suggest that epidemiological studies of long-term PM2.5 can be confounded by long-term time trends, and this confounding can be controlled using the residuals of PM2.5 regressed on time.

Abstract  Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

Abstract  The Interagency Monitoring of Protected Visual Environments (IMPROVE) network is the basis for monitoring visibility in Class I Areas throughout the United States. Monitoring is conducted by collecting PM2.5 and PM10 samples every third day at over 150 remote and rural sites nationwide, with PM2.5 samples being analyzed for chemical composition. Light extinction is reconstructed using an algorithm that relates speciated mass to light extinction based on aerosol mass and size (second IMPROVE equation). In addition, the National Park Service directly measures light scattering at a subset of IMPROVE sites using Optec NGN-2 integrating nephelometers. The optical measurements serve as a check of the reconstructed scattering, in that measured scattering from the nephelometers and reconstructed scattering from the second IMPROVE equation should be equivalent. During its development, the second IMPROVE equation was shown to accurately estimate light scattering for a broad range of aerosol compositions and loadings for samples collected through 2003. Here reconstructed scattering is assessed from the second IMPROVE equation by comparing measured light scattering from nephelometers to reconstructed light scattering at 11 collocated sites over a 16 year period (2001–2016). The comparisons suggest that the relationship between measured and reconstructed light scattering has changed over time and that in recent years the second IMPROVE equation has underestimated light scattering at many sites. This shift toward poorer agreement corresponds to periods with relatively large decreases in sulfate and organic mass concentrations. These decreases lead to biases in reconstructed scattering calculated with the second IMPROVE equation, due to the assumed relationship between mass concentration and size distribution. This relationship, referred to as the split component algorithm, appears to be flawed as currently implemented. A potential approach is explored that scales the split component algorithm each year and at each site, based on measured mass concentrations at the site. The proposed approach appears to reduce the biases in the second IMPROVE equation.

Abstract  BACKGROUND: Previous studies have reported that fine particle (PM2.5) concentrations triggered ST elevation myocardial infarctions (STEMI). In Rochester, NY, multiple air quality policies and economic changes/influences from 2008 to 2013 led to decreased concentrations of PM2.5 and its major constituents (SO42-, NO3-, elemental and primary organic carbon). This study examined whether the rate of STEMI associated with increased ambient gaseous and PM component concentrations was different AFTER these air quality policies and economic changes (2014-2016), compared to DURING (2008-2013) and BEFORE these polices and changes (2005-2007).

METHODS: Using 921 STEMIs treated at the University of Rochester Medical Center (2005-2016) and a case-crossover design, we examined whether the rate of STEMI associated with increased PM2.5, ultrafine particles (UFP, < 100 nm), accumulation mode particles (AMP, 100-500 nm), black carbon, SO2, CO, and O3 concentrations in the previous 1-72 h was modified by the time period related to these pollutant source changes (BEFORE, DURING, AFTER).

RESULTS: Each interquartile range (3702 particles/cm3) increase in UFP concentration in the previous 1 h was associated with a 12% (95% CI = 3%, 22%) increase in the rate of STEMI. The effect size was larger in the AFTER period (26%) than the DURING (5%) or BEFORE periods (9%). There were similar patterns for black carbon and SO2.

CONCLUSIONS: An increased rate of STEMI associated with UFP and other pollutant concentrations was higher in the AFTER period compared to the BEFORE and DURING periods. This may be due to changes in PM composition (e.g. higher secondary organic carbon and particle bound reactive oxygen species) following these air quality policies and economic changes.

Abstract  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 [PM2.5]), 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 PM2.5. 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.

Abstract  BACKGROUND: Epidemiological studies have consistently demonstrated that exposure to fine particulate matter (PM2.5) is associated with increased risks of mortality. To a lesser extent, a series of studies suggest that living in greener areas is associated with reduced risks of mortality. Only a handful of studies have examined the interplay between PM2.5, greenness, and mortality.

METHODS: We investigated the role of residential greenness in modifying associations between long-term exposures to PM2.5 and non-accidental and cardiovascular mortality in a national cohort of non-immigrant Canadian adults (i.e., the 2001 Canadian Census Health and Environment Cohort). Specifically, we examined associations between satellite-derived estimates of PM2.5 exposure and mortality across quintiles of greenness measured within 500 m of individual's place of residence during 11 years of follow-up. We adjusted our survival models for many personal and contextual measures of socioeconomic position, and residential mobility data allowed us to characterize annual changes in exposures.

RESULTS: Our cohort included approximately 2.4 million individuals at baseline, 194,270 of whom died from non-accidental causes during follow-up. Adjustment for greenness attenuated the association between PM2.5 and mortality (e.g., hazard ratios (HRs) and 95% confidence intervals (CIs) per interquartile range increase in PM2.5 in models for non-accidental mortality decreased from 1.065 (95% CI: 1.056-1.075) to 1.041 (95% CI: 1.031-1.050)). The strength of observed associations between PM2.5 and mortality decreased as greenness increased. This pattern persisted in models restricted to urban residents, in models that considered the combined oxidant capacity of ozone and nitrogen dioxide, and within neighbourhoods characterised by high or low deprivation. We found no increased risk of mortality associated with PM2.5 among those living in the greenest areas. For example, the HR for cardiovascular mortality among individuals in the least green areas was 1.17 (95% CI: 1.12-1.23) compared to 1.01 (95% CI: 0.97-1.06) among those in the greenest areas.

CONCLUSIONS: Studies that do not account for greenness may overstate the air pollution impacts on mortality. Residents in deprived neighbourhoods with high greenness benefitted by having more attenuated associations between PM2.5 and mortality than those living in deprived areas with less greenness. The findings from this study extend our understanding of how living in greener areas may lead to improved health outcomes.

Abstract  Importance: Air pollution is associated with cardiovascular outcomes. Specifically, fine particulate matter measuring 2.5 μm or less (PM2.5) is associated with thrombosis, stroke, and myocardial infarction. Few studies have examined particulate matter and stroke risk in individuals with atrial fibrillation (AF).

Objective: To assess the association of residential-level pollution exposure in 1 year and ischemic stroke in individuals with AF.

Design, Setting, and Participants: This cohort study included 31 414 individuals with AF from a large regional health care system in an area with historically high industrial pollution. All participants had valid residential addresses for geocoding and ascertainment of neighborhood-level income and educational level. Participants were studied from January 1, 2007, through September 30, 2015, with prospective follow-up through December 1, 2017. Data analysis was performed from March 14, 2018, to October 9, 2019.

Exposures: Exposure to PM2.5 ascertained using geocoding of addresses and fine-scale air pollution exposure surfaces derived from a spatial saturation monitoring campaign and land-use regression modeling. Exposure to PM2.5 was estimated annually across the study period at the residence level.

Main Outcomes and Measures: Multivariable-adjusted stroke risk by quartile of residence-level and annual PM2.5 exposure.

Results: The cohort included 31 414 individuals (15 813 [50.3%] female; mean [SD] age, 74.4 [13.5] years), with a median follow-up of 3.5 years (interquartile range, 1.6-5.8 years). The mean (SD) annual PM2.5 exposure was 10.6 (0.7) μg/m3. A 1-SD increase in PM2.5 was associated with a greater risk of stroke after both adjustment for demographic and clinical variables (hazard ratio [HR], 1.08; 95% CI, 1.03-1.14) and multivariable adjustment that included neighborhood-level income and educational level (HR, 1.07; 95% CI, 1.00-1.14). The highest quartile of PM2.5 exposure had an increased risk of stroke relative to the first quartile (HR, 1.36; 95% CI, 1.18-1.58). After adjustment for clinical covariates, income, and educational level, risk of stroke remained greater for the highest quartile of exposure relative to the first quartile (HR, 1.21; 95% CI, 1.01-1.45).

Conclusions and Relevance: This large cohort study of individuals with AF identified associations between PM2.5 and risk of ischemic stroke. The results suggest an association between fine particulate air pollution and cardiovascular disease and outcomes.

Abstract  It has been posited that populations being exposed to long-term air pollution are more susceptible to COVID-19. Evidence is emerging that long-term exposure to ambient PM2.5 (particulate matter with aerodynamic diameter 2.5 μm or less) associates with higher COVID-19 mortality rates, but whether it also associates with the speed at which the disease is capable of spreading in a population is unknown. Here, we establish the association between long-term exposure to ambient PM2.5 in the United States (US) and COVID-19 basic reproduction ratio R0- a dimensionless epidemic measure of the rapidity of disease spread through a population. We inferred state-level R0 values using a state-of-the-art susceptible, exposed, infected, and recovered (SEIR) model initialized with COVID-19 epidemiological data corresponding to the period March 2-April 30. This period was characterized by a rapid surge in COVID-19 cases across the US states, implementation of strict social distancing measures, and a significant drop in outdoor air pollution. We find that an increase of 1 μg/m3 in PM2.5 levels below current national ambient air quality standards associates with an increase of 0.25 in R0 (95% CI: 0.048-0.447). A 10% increase in secondary inorganic composition, sulfate-nitrate-ammonium, in PM2.5 associates with ≈10% increase in R0 by 0.22 (95% CI: 0.083-0.352), and presence of black carbon (soot) in the ambient environment moderates this relationship. We considered several potential confounding factors in our analysis, including gaseous air pollutants and socio-economical and meteorological conditions. Our results underscore two policy implications - first, regulatory standards need to be better guided by exploring the concentration-response relationships near the lower end of the PM2.5 air quality distribution; and second, pollution regulations need to be continually enforced for combustion emissions that largely determine secondary inorganic aerosol formation.

Abstract  BACKGROUND: The temporal and spatial scales of exposure assessment may influence observed associations between fine particulate air pollution (PM2.5) and mortality, but few studies have systematically examined this question.

METHODS: We followed 2.4 million adults in the 2001 Canadian Census Health and Environment Cohort for nonaccidental and cause-specific mortality between 2001 and 2011. We assigned PM2.5 exposures to residential locations using satellite-based estimates and compared three different temporal moving averages (1, 3, and 8 years) and three spatial scales (1, 5, and 10 km) of exposure assignment. In addition, we examined different spatial scales based on age, employment status, and urban/rural location, and adjustment for O3, NO2, or their combined oxidant capacity (Ox).

RESULTS: In general, longer moving averages resulted in stronger associations between PM2.5 and mortality. For nonaccidental mortality, we observed a hazard ratio of 1.11 (95% CI = 1.08, 1.13) for the 1-year moving average compared with 1.23 (95% CI = 1.20, 1.27) for the 8-year moving average. Respiratory and lung cancer mortality were most sensitive to the spatial scale of exposure assessment with stronger associations observed at smaller spatial scales. Adjustment for oxidant gases attenuated associations between PM2.5 and cardiovascular mortality and strengthened associations with lung cancer. Despite these variations, PM2.5 was associated with increased mortality in nearly all of the models examined.

CONCLUSIONS: These findings support a relationship between outdoor PM2.5 and mortality at low concentrations and highlight the importance of longer-exposure windows, more spatially resolved exposure metrics, and adjustment for oxidant gases in characterizing this relationship.

Abstract  Background: Fine particulate air pollution has been linked to cardiovascular disease, but previous studies have assessed only mortality and differences in exposure between cities. We examined the association of long-term exposure to particulate matter of less than 2.5 micro m in aerodynamic diameter (PM2.5) with cardiovascular events. Methods: We studied 65,893 postmenopausal women without previous cardiovascular disease in 36 U.S. metropolitan areas from 1994 to 1998, with a median follow-up of 6 years. We assessed the women's exposure to air pollutants using the monitor located nearest to each woman's residence. Hazard ratios were estimated for the first cardiovascular event, adjusting for age, race or ethnic group, smoking status, educational level, household income, body-mass index, and presence or absence of diabetes, hypertension, or hypercholesterolemia. Results: A total of 1816 women had one or more fatal or nonfatal cardiovascular events, as confirmed by a review of medical records, including death from coronary heart disease or cerebrovascular disease, coronary revascularization, myocardial infarction, and stroke. In 2000, levels of PM2.5 exposure varied from 3.4 to 28.3 Ág per cubic meter (mean, 13.5). Each increase of 10 microg per cubic meter was associated with a 24% increase in the risk of a cardiovascular event (hazard ratio, 1.24; 95% confidence interval [CI], 1.09 to 1.41) and a 76% increase in the risk of death from cardiovascular disease (hazard ratio, 1.76; 95% CI, 1.25 to 2.47). For cardiovascular events, the between-city effect appeared to be smaller than the within-city effect. The risk of cerebrovascular events was also associated with increased levels of PM2.5 (hazard ratio, 1.35; 95% CI, 1.08 to 1.68). Conclusions: Long-term exposure to fine particulate air pollution is associated with the incidence of cardiovascular disease and death among postmenopausal women. Exposure differences within cities are associated with the risk of cardiovascular disease.

Abstract  We propose a method for diagnosing confounding bias under a model that links a spatially and temporally varying exposure and health outcome. We decompose the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We show that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates. We use this approach to estimate the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality rates in 113 US counties from 2000 to 2002. We decompose the association between PM2.5 and mortality into 2 components: (1) the association between "national trends" in PM2.5 and mortality; and (2) the association between "local trends," defined as county-specific deviations from national trends. This second component provides evidence as to whether counties having steeper declines in PM2.5 also have steeper declines in mortality relative to their national trends. We find that the exposure effect estimates are different at these 2 spatiotemporal scales, which raises concerns about confounding bias. We believe that the association between trends in PM2.5 and mortality at the national scale is more likely to be confounded than is the association between trends in PM2.5 and mortality at the local scale. If the association at the national scale is set aside, there is little evidence of an association between 12-month exposure to PM2.5 and mortality.

Abstract  Ozone and particulate matter PM(2.5) are co-pollutants that have long been associated with increased public health risks. Information on concentration levels for both pollutants come from two sources: monitoring sites and output from complex numerical models that produce concentration surfaces over large spatial regions. In this paper, we offer a fully-model based approach for fusing these two sources of information for the pair of co-pollutants which is computationally feasible over large spatial regions and long periods of time. Due to the association between concentration levels of the two environmental contaminants, it is expected that information regarding one will help to improve prediction of the other. Misalignment is an obvious issue since the monitoring networks for the two contaminants only partly intersect and because the collection rate for PM(2.5) is typically less frequent than that for ozone.Extending previous work in Berrocal et al. (2009), we introduce a bivariate downscaler that provides a flexible class of bivariate space-time assimilation models. We discuss computational issues for model fitting and analyze a dataset for ozone and PM(2.5) for the ozone season during year 2002. We show a modest improvement in predictive performance, not surprising in a setting where we can anticipate only a small gain.

Abstract  There is substantial observational evidence that long-term exposure to particulate air pollution is associated with premature death in urban populations. Estimates of the magnitude of these effects derive largely from cross-sectional comparisons of adjusted mortality rates among cities with varying pollution levels. Such estimates are potentially confounded by other differences among the populations correlated with air pollution, for example, socioeconomic factors. An alternative approach is to study covariation of particulate matter and mortality across time within a city, as has been done in investigations of short-term exposures. In either event, observational studies like these are subject to confounding by unmeasured variables. Therefore the ability to detect such confounding and to derive estimates less affected by confounding are a high priority.

In this article, we describe and apply a method of decomposing the exposure variable into components with variation at distinct temporal, spatial, and time by space scales, here focusing on the components involving time. Starting from a proportional hazard model, we derive a Poisson regression model and estimate two regression coefficients: the "global" coefficient that measures the association between national trends in pollution and mortality; and the "local" coefficient, derived from space by time variation, that measures the association between location-specific trends in pollution and mortality adjusted by the national trends. Absent unmeasured confounders and given valid model assumptions, the scale-specific coefficients should be similar; substantial differences in these coefficients constitute a basis for questioning the model.

We derive a backfitting algorithm to fit our model to very large spatio-temporal datasets. We apply our methods to the Medicare Cohort Air Pollution Study (MCAPS), which includes individual-level information on time of death and age on a population of 18.2 million for the period 2000-2006.

Results based on the global coefficient indicate a large increase in the national life expectancy for reductions in the yearly national average of PM2.5. However, this coefficient based on national trends in PM2.5 and mortality is likely to be confounded by other variables trending on the national level. Confounding of the local coefficient by unmeasured factors is less likely, although it cannot be ruled out. Based on the local coefficient alone, we are not able to demonstrate any change in life expectancy for a reduction in PM2.5. We use additional survey data available for a subset of the data to investigate sensitivity of results to the inclusion of additional covariates, but both coefficients remain largely unchanged.

Abstract  Background: Although the association between exposure to particulate matter (PM) mass and mortality is well established, there remains uncertainty about which chemical components of PM are most harmful to human health. Methods: A hierarchical approach was used to determine how the association between daily PM2.5 mass and mortality was modified by PM2.5 composition in 25 US communities. First, the association between daily PM2.5 and mortality was determined for each community and season using Poisson regression. Second, we used meta-regression to examine how the pooled association was modified by community and season-specific particle composition. Results: There was a 0.74% (95% confidence interval = 0.41%-1.07%) increase in nonaccidental deaths associated with a 10 ?g/m3 increase in 2-day averaged PM2.5 mass concentration. This association was smaller in the west (0.51% [0.10%-0.92%]) than in the east (0.92% [0.23%-1.36%]), and was highest in spring (1.88% [0.23%-1.36%]). It was increased when PM2.5 mass contained a higher proportion of aluminum (interquartile range = 0.58%), arsenic (0.55%), sulfate (0.51%), silicon (0.41%), and nickel (0.37%). The combination of aluminum, sulfate, and nickel also modified the effect. These species proportions explained residual variability between the community-specific PM2.5 mass effect estimates. Conclusions: This study shows that certain chemical species modify the association between PM2.5 and mortality and illustrates that mass alone is not a sufficient metric when evaluating health effects of PM exposure.

Abstract  Time-series, cross-sectional, and prospective cohort studies have observed associations between mortality and particulate air pollution but have been limited by ecologic design or small number of subjects or study areas. The present study evaluates effects of particulate air pollution on mortality using data from a large cohort drawn from many study areas. We linked ambient air pollution data from 151 U.S. metropolitan areas in 1980 with individual risk factor on 552,138 adults who resided in these areas when enrolled in a prospective study in 1982. Deaths were ascertained through December, 1989. Exposure to sulfate and fine particulate air pollution, which is primarily from fossil fuel combustion, was estimated from national data bases. The relationships of air pollution to all-cause, lung cancer, and cardiopulmonary mortality was examined using multivariate analysis which controlled for smoking, education, and other risk factors. Although small compared with cigarette smoking, an association between mortality and particulate air pollution was observed. Adjusted relative risk ratios (and 95% confidence intervals) of all-cause mortality for the most polluted areas compared with the least polluted equaled 1.15 (1.09 to 1.22) and 1.17 (1.09 to 1.26) when using sulfate and fine particulate measures respectively. Particulate air pollution was associated with cardiopulmonary and lung cancer mortality but not with mortality due to other causes. Increased mortality is associated with sulfate and fine particulate air pollution at levels commonly found in U.S. cities. The increase in risk is not attributable to tobacco smoking, although other unmeasured correlates of pollution cannot be excluded with certainty.

Abstract  OBJECTIVES: Little is known about the potential health effects of the coarse fraction of ambient particles. The aim of this study is to estimate the links between fine (PM(2.5)) and coarse particle (PM(2.5-10)) levels and cardiorespiratory hospitalisations in six French cities during 2000-2003.

METHODS: Data on the daily numbers of hospitalisations for respiratory, cardiovascular, cardiac and ischaemic heart diseases were collected. Associations between exposure indicators and hospitalisations were estimated in each city using a Poisson regression model, controlling for confounding factors (seasons, days of the week, holidays, influenza epidemics, pollen counts, temperature) and temporal trends. City-specific findings were combined to obtain excess relative risks (ERRs) associated with a 10 mug/m(3) increase in PM(2.5) and PM(2.5-10 )levels.

RESULTS: We found positive associations between indicators of particulate pollution and hospitalisations for respiratory infection, with an ERR of 4.4% (95% CI 0.9 to 8.0) for PM(2.5-10 )and 2.5% (95% CI 0.1 to 4.8) for PM(2.5). Concerning respiratory diseases, no association was observed with PM(2.5), whereas positive trends were found with PM(2.5-10), with a significant association for the 0-14-year-old age group (ERR 6.2%, 95% CI 0.4 to 12.3). Concerning cardiovascular diseases, positive associations were observed between PM(2.5) levels and each indicator, although some did not reach significance; trends with PM(2.5-10 )were weaker and non-significant except for ischaemic heart disease in the elderly (ERR 6.4%, 95% CI 1.6 to 11.4).

CONCLUSIONS: In accordance with other studies, our results indicate that the coarse fraction may have a stronger effect than the fine fraction on some morbidity endpoints, especially respiratory diseases.