Estimating ambient-origin PM2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis

Model misspecification, measurement error, and apparent supralinearity in the concentration-response relationship between PM2.5 and mortality

A growing number of studies have produced results that suggest the shape of the concentration-response (C-R) relationship between PM2.5 exposure and mortality is "supralinear" such that incremental risk is higher at the lowest exposure levels than at the highest exposure levels. If the C-R function is in fact supralinear, then there may be significant health benefits associated with reductions in PM2.5 below the current US National Ambient Air Quality Standards (NAAQS), as each incremental tightening of the PM2.5 NAAQS would be expected to produce ever-greater reductions in mortality risk. In this paper we undertake a series of tests with simulated cohort data to examine whether there are alternative explanations for apparent supralinearity in PM2.5 C-R functions. Our results show that a linear C-R function for PM2.5 can falsely appear to be supralinear in a statistical estimation process for a variety of reasons, such as spatial variation in the composition of total PM2.5 mass, the presence of confounders that are correlated with PM2.5 exposure, and some types of measurement error in estimates of PM2.5 exposure. To the best of our knowledge, this is the first simulation-based study to examine alternative explanations for apparent supralinearity in C-R functions.

Exposures and behavioural responses to wildfire smoke

Pollution from wildfires constitutes a growing source of poor air quality globally. To protect health, governments largely rely on citizens to limit their own wildfire smoke exposures, but the effectiveness of this strategy is hard to observe. Using data from private pollution sensors, cell phones, social media posts and internet search activity, we find that during large wildfire smoke events, individuals in wealthy locations increasingly search for information about air quality and health protection, stay at home more and are unhappier. Residents of lower-income neighbourhoods exhibit similar patterns in searches for air quality information but not for health protection, spend less time at home and have more muted sentiment responses. During smoke events, indoor particulate matter (PM2.5) concentrations often remain 3-4× above health-based guidelines and vary by 20× between neighbouring households. Our results suggest that policy reliance on self-protection to mitigate smoke health risks will have modest and unequal benefits.

Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM2.5 exposure

BACKGROUND

Due to the adverse health effects of air pollution, researchers have advocated for personal exposure measurements whereby individuals carry portable monitors in order to better characterise and understand the sources of people's pollution exposure.

OBJECTIVES

The aim of this systematic review is to assess the differences in the magnitude and sources of personal PM2.5 exposures experienced between countries at contrasting levels of income.

METHODS

This review summarised studies that measured participants personal exposure by carrying a PM2.5 monitor throughout their typical day. Personal PM2.5 exposures were summarised to indicate the distribution of exposures measured within each country income category (based on low (LIC), lower-middle (LMIC), upper-middle (UMIC), and high (HIC) income countries) and between different groups (i.e. gender, age, urban or rural residents).

RESULTS

From the 2259 search results, there were 140 studies that met our criteria. Overall, personal PM2.5 exposures in HICs were lower compared to other countries, with UMICs exposures being slightly lower than exposures measured in LMICs or LICs. 34% of measured groups in HICs reported below the ambient World Health Organisation 24-h PM2.5 guideline of 15 μg/m3, compared to only 1% of UMICs and 0% of LMICs and LICs. There was no difference between rural and urban participant exposures in HICs, but there were noticeably higher exposures recorded in rural areas compared to urban areas in non-HICs, due to significant household sources of PM2.5 in rural locations. In HICs, studies reported that secondhand smoke, ambient pollution infiltrating indoors, and traffic emissions were the dominant contributors to personal exposures. While, in non-HICs, household cooking and heating with biomass and coal were reported as the most important sources.

CONCLUSION

This review revealed a growing literature of personal PM2.5 exposure studies, which highlighted a large variability in exposures recorded and severe inequalities in geographical and social population subgroups.

A stochastic exposure model integrating random forest and agent-based approaches: Evaluation for PM2.5 in Jiangsu, China

This research proposes an Activity Pattern embedded Air Pollution Exposure Model (AP2EM), based on survey data of when, where, and how people spend their time and indoor/outdoor ratios for microenvironments. AP2EM integrates random forest and agent-based approaches to simulate the stochastic exposure to outdoor fine particulate matter (PM_2.5) along with indoor and in-vehicle PM_2.5 of outdoor origin. The R² of the linear regression between the model's calculations and personal measurement was 0.65, which was more accurate than the commonly-used aggregated exposure (AE) model and the outdoor exposure (OE) model. The population-weighted PM_2.5 exposure estimated by the AP2EM was 36.7 μg/m³ in Jiangsu, China, during 2014-2017. The OE model overestimated exposure by 54.0%, and the AE model underestimated exposure by 6.5%. These misestimate reflect ignorance of traditional studies on effects posed from time spent indoors (~85%) and doing low respiratory rate activities (~93%), problems of biased sampling, and neglecting low probability events. The proposed AP2EM treats activity patterns of individuals as chains and uses stochastic estimates to model activity choices, providing a more comprehensive understanding of human activity and exposure characteristics. Overall, the AP2EM is applicable for other air pollutants in different regions and benefits China's air pollution control policy designs.

A review and analysis of personal and ambient PM2.5 measurements: Implications for epidemiology studies

BACKGROUND

In epidemiology studies, ambient measurements of PM_2.5 are often used as surrogates for personal exposures. However, it is unclear the degree to which ambient PM_2.5 reflects personal exposures.

OBJECTIVE

In order to examine potential sources of bias in epidemiology studies, we conducted a review and meta-analysis of studies to determine the extent to which short-term measurements of ambient PM_2.5 levels are related to short-term measurements of personal PM_2.5 levels.

METHODS

We conducted a literature search of studies reporting both personal and ambient measurements of PM_2.5 published in the last 10 years (2009-2019) and incorporated studies published prior to 2009 from reviews.

RESULTS

Seventy-one studies were identified. Based on 17 studies reporting slopes, a meta-analysis revealed an overall slope of 0.56 μg/m³ (95% CI: [0.39, 0.73]) personal PM_2.5 per μg/m³ increase in ambient PM_2.5. Slopes for summer months were higher (slope = 0.73, 95% CI: [0.64, 0.81]) than for winter (slope = 0.46, 95% CI: [0.36, 0.57]). Based on 44 studies reporting correlations, we calculated an overall personal-ambient PM_2.5 correlation of 0.63 (95% CI: [0.55, 0.71]). Correlations were stronger in studies conducted in Canada (r = 0.86, 95% CI: [0.67, 0.94]) compared to the USA (r = 0.60, 95% CI: [0.49, 0.70]) and China (r = 0.60, 95% CI: [0.46, 0.71]). Correlations also were stronger in urban areas (r = 0.53, 95% CI: [0.43, 0.62]) vs. suburban areas (r = 0.36, 95% CI: [0.21, 0.49]).

SIGNIFICANCE

Our results suggest a large degree of variability in the personal-ambient PM_2.5 association and the potential for exposure misclassification and measurement error in PM_2.5 epidemiology studies.

Association of long-term exposure to PM2.5 with hypertension prevalence and blood pressure in China: a cross-sectional study

OBJECTIVE

Evidence of the effects of long-term fine particulate matter (PM_2.5) exposure on hypertension and blood pressure is limited for populations exposed to high levels of PM_2.5. We aim to assess associations of long-term exposure to PM_2.5 with hypertension prevalence and blood pressure, and further explore the subpopulation differences and effect modification by participant characteristics in these associations in China.

METHODS

We analysed cross-sectional data from 883 827 participants aged 35-75 years in the China Patient-Centred Evaluative Assessment of Cardiac Events Million Persons Project. Data from the monitoring station were used to estimate the 1-year average concentration of PM_2.5. The associations of PM_2.5 exposure with hypertension prevalence and blood pressure were investigated by generalised linear models, with PM_2.5 included as either linear or spline functions.

RESULTS

The 1-year PM_2.5 exposure of the study population ranged from 8.8 to 93.8 µg/m³ (mean 49.2 µg/m³). The adjusted OR of hypertension prevalence related to a 10 μg/m³ increase in 1-year PM_2.5 exposure was 1.04 (95% CI, 1.02 to 1.05). Each 10 μg/m³ increment in PM_2.5 exposure was associated with increases of 0.19 mm Hg (95% CI, 0.10 to 0.28) and 0.13 mm Hg (95% CI, 0.08 to 0.18) in systolic blood pressure and diastolic blood pressure, respectively. The concentration-response curves for hypertension prevalence and systolic blood pressure showed steeper slopes at higher PM_2.5 levels; while the curve for diastolic blood pressure was U-shaped. The elderly, men, non-current smokers and obese participants were more susceptible to the exposure of PM_2.5.

CONCLUSIONS

Long-term exposure to PM_2.5 is associated with higher blood pressure and increased risk of hypertension prevalence. The effects of PM_2.5 on hypertension prevalence become more pronounced at higher PM_2.5 levels. These findings emphasise the need to reduce air pollution, especially in areas with severe air pollution.

Cumulative exposure amount of PM2.5 in the ambient air is associated with coronary atherosclerosis - Serial coronary CT angiography study

BACKGROUND

We investigated the change of coronary atherosclerosis with long-term exposure to fine particulate matter of aerodynamic diameter <2.5 ​μm (PM_2.5) using coronary computed tomography angiography (CCTA).

METHODS

Subjects undergoing serial CCTAs between January 2007 and December 2017 (n ​= ​3,127) were analyzed. Each individual's cumulative amount of PM_2.5 exposure between the two CCTAs was evaluated by Kriging interpolation and zonal analysis, considering the time interval between the two CCTAs. The main outcome was progression of coronary artery calcium (CAC) with additional semiquantitative analysis on the changes in the severity and composition of atherosclerotic plaques.

RESULTS

The CAC scores increased by 30.8 Agatston units per-year under a median PM_2.5 concentration 24.9 ​μg/m³ and tended to increase with the cumulative amount of PM_2.5 exposure (r ​= ​0.321, p ​<0.001). The CAC progressed in 1,361 (43.5%) subjects during a median 53 months follow-up. The cumulative amount of PM_2.5 exposure was independently associated with CAC progression (adjusted OR 1.09, p ​<0.001). By random forest analysis, the relative impact of cumulative amount of PM_2.5 exposure on CAC progression was higher than that of traditional cardiovascular risk factors and the average concentration of PM_2.5. The extent of coronary atherosclerosis and newly developed calcified plaque on follow-up were also significantly associated with the cumulative amount of PM_2.5 exposure.

CONCLUSIONS

Cumulative exposure to air pollution is associated with the progression of diffuse coronary calcification, the importance of which may be more significant than other traditional cardiovascular risk factors. Further investigations into the causality between PM_2.5 and coronary atherosclerosis are warranted to improve global cardiovascular health.

Characterizing outdoor infiltration and indoor contribution of PM2.5 with citizen-based low-cost monitoring data

Epidemiological research on the adverse health outcomes due to PM_2.5 exposure frequently relies on measurements from regulatory air quality monitors to provide ambient exposure estimates, whereas personal PM_2.5 exposure may deviate from ambient concentrations due to outdoor infiltration and contributions from indoor sources. Research in quantifying infiltration factors (F_inf), the fraction of outdoor PM_2.5 that infiltrates indoors, has been historically limited in space and time due to the high costs of monitor deployment and maintenance. Recently, the growth of openly accessible, citizen-based PM_2.5 measurements provides an unprecedented opportunity to characterize F_inf at large spatiotemporal scales. In this analysis, 91 consumer-grade PurpleAir indoor/outdoor monitor pairs were identified in California (41 residential houses and 50 public/commercial buildings) during a 20-month period with around 650000 h of paired PM_2.5 measurements. An empirical method was developed based on local polynomial regression to estimate site-specific F_inf. The estimated site-specific F_inf had a mean of 0.26 (25^th, 75^th percentiles: [0.15, 0.34]) with a mean bootstrap standard deviation of 0.04. The F_inf estimates were toward the lower end of those reported previously. A threshold of ambient PM_2.5 concentration, approximately 30 μg/m³, below which indoor sources contributed substantially to personal exposures, was also identified. The quantified relationship between indoor source contributions and ambient PM_2.5 concentrations could serve as a metric of exposure errors when using outdoor monitors as an exposure proxy (without considering indoor-generated PM_2.5), which may be of interest to epidemiological research. The proposed method can be generalized to larger geographical areas to better quantify PM_2.5 outdoor infiltration and personal exposure.

Particulate Matter and Cardiovascular Risk in Adults with Chronic Obstructive Pulmonary Disease

Rationale: People with chronic obstructive pulmonary disease (COPD) have an increased risk of cardiovascular disease and may be more susceptible to air pollution exposure. However, no study has examined the association between long-term fine particulate matter exposure (≤2.5 μm in aerodynamic diameter) and risk of cardiovascular events in this potentially vulnerable population. Objectives: To estimate the association between long-term fine particulate matter and risk of cardiovascular events among adults with COPD. Methods: This retrospective cohort study included 169,714 adults with COPD who were members of the Kaiser Permanente Northern California health plan during 2007-2016. Electronic health record data were linked to 1 km modeled particulate matter ≤2.5 μm in aerodynamic diameter exposure estimates. We fit Cox proportional hazard models, adjusting for age, sex, race/ethnicity, calendar year, smoking, body mass index, comorbidities, medications, and socioeconomic status. In low exposure analyses, we examined effects below the current regulation limit (12 μg/m³). Measurements and Main Results: Among adults with COPD, a 10-μg/m³ increase in 1-year mean fine particulate matter exposure was associated with an elevated risk of cardiovascular mortality (hazard ratio, 1.10; 95% confidence interval [CI], 1.01-1.20). Effects were stronger in low exposure analyses (hazard ratio, 1.88; 95% CI, 1.56-2.27). Fine particulate matter exposure was not associated with acute myocardial infarction or stroke in overall analyses. Conclusions: Long-term fine particulate matter exposure was associated with an increased risk of cardiovascular mortality among adults with COPD. Current regulations may not sufficiently protect those with COPD.

Association of fine particulate matter with glucose and lipid metabolism: a longitudinal study in young adults

OBJECTIVES

This study aimed to evaluate whether PM2.5 exposure in a highly polluted area (>100 µg/m3) affects glucose and lipid metabolism in healthy adults.

METHODS

We recruited 110 healthy adults in Baoding city, Hebei, China, and followed them up between 2017 and 2018. Personal air samplers were used to monitor personal PM2.5 levels. Eight glucose and lipid metabolism parameters were quantified. We performed the linear mixed-effect models to investigate the relationships between PM2.5 and glucose and lipid metabolism parameters. Stratified analyses were further performed according to sex and body mass index (BMI).

RESULTS

The concentration of PM2.5 was the highest in spring, with a median of 232 μg/m3 and the lowest in autumn (139 μg/m3). After adjusting for potential confounders, we found that for each twofold increase in PM2.5, the median of insulin concentration decreased by 5.89% (95% CI -10.91% to -0.58%; p<0.05), and ox-LDL increased by 6.43% (95% CI 2.21% to 10.82%; p<0.05). Stratified analyses indicated that the associations were more pronounced in females, overweight and obese participants.

CONCLUSIONS

Exposure to high PM2.5 may have deleterious effects on glucose and lipid metabolism. Females, overweight and obese participants are more vulnerable.

PM2.5 exposure of various microenvironments in a community: Characteristics and applications

While the measurement of particulate matter (PM) with a diameter of less than 2.5 μm (PM_2.5) has been conducted for personal exposure assessment, it remains unclear how models that integrate microenvironmental levels with resolved activity and location information predict personal exposure to PM. We comprehensively investigated PM_2.5 concentrations in various microenvironments and estimated personal exposure stratified by the microenvironment. A variety of microenvironments (>200 places and locations, divided into 23 components according to indoor, outdoor, and transit modes) in a community were selected to characterize PM_2.5 concentrations. Infiltration factors calculated from microenvironmental/central-site station (M/S) monitoring campaigns with time-activity patterns were used to estimate time-weighted exposure to PM_2.5 for university students. We evaluated exposures using a four-stage modeling approach and quantified the performance of each component. It was found that the SidePak monitor overestimated the concentration by 3.5 times as compared with the filter-based measurements. Higher mean concentrations of PM_2.5 were observed in the Taoist temple and night market microenvironments; in contrast, lower concentrations were observed in air-conditioned offices and car microenvironments. While the exposure model incorporating detailed time-location information and infiltration factors achieved the highest prediction (R² = 0.49) of personal exposure to PM_2.5, the use of indoor, outdoor, and transit components for modeling also generated a consistent result (R² = 0.44).

Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence

Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM_2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM_2.5 personal exposure were 55.1(82.8) µg/m³ for men and 58.5(58.8) µg/m³ for women; corresponding figures for black carbon were 4.6(7.0) µg/m³ and 6.1(9.6) µg/m³. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R_spearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (R_spearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.