Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error

Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review

OBJECTIVES

Climate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air pollution under different climate change scenarios.

DESIGN

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 flow checklist. A population-exposure-outcome framework was established. Population referred to the general global population of all ages, the exposure of interest was air pollution and its projection, and the outcome was the occurrence of NCDs attributable to air pollution and burden of disease (BoD) based on the health indices of mortality, morbidity, disability-adjusted life years, years of life lost and years lived with disability.

DATA SOURCES

The Web of Science, Ovid MEDLINE and EBSCOhost databases were searched for articles published from 2005 to 2023.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

The eligible articles were evaluated using the modified scale of a checklist for assessing the quality of ecological studies.

DATA EXTRACTION AND SYNTHESIS

Two reviewers searched, screened and selected the included studies independently using standardised methods. The risk of bias was assessed using the modified scale of a checklist for ecological studies. The results were summarised based on the projection of the BoD of NCDs attributable to air pollution.

RESULTS

This review included 11 studies from various countries. Most studies specifically investigated various air pollutants, specifically particulate matter <2.5 µm (PM2.5), nitrogen oxides and ozone. The studies used coupled-air quality and climate modelling approaches, and mainly projected health effects using the concentration-response function model. The NCDs attributable to air pollution included cardiovascular disease (CVD), respiratory disease, stroke, ischaemic heart disease, coronary heart disease and lower respiratory infections. Notably, the BoD of NCDs attributable to air pollution was projected to decrease in a scenario that promotes reduced air pollution, carbon emissions and land use and sustainable socioeconomics. Contrastingly, the BoD of NCDs was projected to increase in a scenario involving increasing population numbers, social deprivation and an ageing population.

CONCLUSION

The included studies widely reported increased premature mortality, CVD and respiratory disease attributable to PM2.5. Future NCD projection studies should consider emission and population changes in projecting the BoD of NCDs attributable to air pollution in the climate change era.

PROSPERO REGISTRATION NUMBER

CRD42023435288.

A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth

Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure 3 days prior to delivery. The newly developed methods are available in the R package KDExp.

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.

Short-term exposure to ambient particulate matter and outpatient visits for respiratory diseases among children: A time-series study in five Chinese cities

There was limited evidence regarding the association between short-term exposure to ambient particulate matter (PM) and respiratory outpatient visits among children at a multicity level. In this study, a time-series study was conducted among children aged 0-14 years in five Chinese cities from 2013 to 2018. City-specific effects of fine particles (PM_2.5), inhalable particles (PM₁₀) and coarse particles (PM_10-2.5) were estimated for time lags of zero up to seven previous days using the overdispersed generalized additive models after adjusting for time trends, meteorological variables, day of the week and holidays. Meta-analyses were applied to pool the overall effects, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. The overall effects of PM were significantly associated with total and cause-specific respiratory outpatients among children, even at PM_2.5 and PM₁₀ levels below the current Chinese Ambient Air Quality Standards (CAAQS) Grade II. Each 10 μg/m³ increment in PM_2.5, PM₁₀ and PM_10-2.5 at lag 07 was associated with a 1.39% (95% CI: 0.38%, 2.40%), 1.10% (95% CI: 0.38%, 1.83%) and 2.93% (95% CI: 1.05%, 4.84%) increase in total respiratory outpatients, respectively. An E-R relationship was observed except for PM_2.5 in Beijing and PM₁₀ and PM_10-2.5 in Shanghai. The effects of PM were stronger in cold season in 3 southern cities, while it was stronger in transition season in 2 northern cities. In conclusion, short-term PM exposures were dose-responsive associated with increased respiratory outpatient visits among children, even for PM_2.5 and PM₁₀ levels below current CAAQS II in certain cities.

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components

Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM_2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations (R² from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM_2.5 components could be estimated with good accuracy, especially when collocated PM_2.5 total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses.

Coarse particles (PM2.5-10) and cause-specific hospitalizations in southwestern China: Association, attributable risk and economic costs

The short-term morbidity effects of the coarse particle (diameter in 2.5-10 μm, PM_2.5-10), as well as the corresponding morbidity burden and economic costs, remain understudied, especially in developing countries. This study aimed to examine the associations of PM_2.5-10 with cause-specific hospitalizations in a multi-city setting in southwestern China and assess the attributable risk and economic costs. City-specific associations were firstly estimated using generalized additive models with quasi-poisson distribution to handle over-dispersion, and then combined to obtain the regional average association. City-specific and pooled concentration-response (C-R) associations of PM_2.5-10 with cause-specific hospitalizations were also modeled. Subgroup analyses were performed by age, sex, season and region. The health and economic burden of hospitalizations for multiple outcomes due to PM_2.5-10 were further evaluated. A total of 4,407,601 non-accidental hospitalizations were collected from 678 hospitals. The estimates of percentage change in hospitalizations per 10 μg/m³ increase in PM_2.5-10 at lag01 was 0.68% (95%CI: 0.33%-1.03%) for non-accidental causes, 0.86% (95% CI: 0.36%-1.37%) for circulatory diseases, 1.52% (95% CI: 1.00%-2.05%) for respiratory diseases, 1.08% (95% CI: 0.47%-1.69%) for endocrine diseases, 0.66% (95% CI: 0.12%-1.21%) for nervous system diseases, and 0.84% (95% CI: 0.42%-1.25%) for genitourinary diseases, respectively. The C-R associations of PM_2.5-10 with cause-specific hospitalizations suggested some evidence of nonlinearity, except for endocrine diseases. Meanwhile, the adverse effects were modified by age and season. Overall, about 0.70% (95% CI: 0.35%-1.06%) of non-accidental hospitalizations and 0.78% (95% CI: 0.38%-1.17%) of total hospitalization expenses could be attributed to PM_2.5-10. The largest morbidity burden and economic costs were observed in respiratory diseases. Our findings indicate that PM_2.5-10 exposure may increase the risk of hospitalizations for multiple outcomes, and account for considerable morbidity and economic burden.

Analysis of Short-Term Effects of Air Pollution on Cardiovascular Disease Using Bayesian Spatio-temporal Models

There has been an increasing number of clinical and epidemiologic research projects providing supporting evidence that short-term exposure to ambient air pollution contributes to the exacerbation of cardiovascular disease. However, few studies consider measurement error and spatial effects in the estimate of underlying air pollution levels, and less is known about the influence of baseline air pollution levels on cardiovascular disease. We used hospital admissions data for cardiovascular diseases (CVD) collected from an inland, heavily polluted city and a coastal city in Shandong Province, China. Bayesian spatio-temporal models were applied to obtain the underlying pollution level in each city, then generalized additive models were adopted to assess the health effects. The total cardiovascular disease hospitalizations were significantly increased in the inland city by 0.401% (0.029, 0.775), 0.316% (0.086, 0.547), 0.903% (0.252, 1.559), and 2.647% (1.607, 3.697) per 10 μg/m3 increase in PM2.5, PM10, SO2, and NO2, respectively. The total cardiovascular diseases hospitalizations were increased by 6.568% (3.636, 9.584) per 10μg/m3 increase in the level of NO2. Although the air pollution overall had a more significant adverse impact on cardiovascular disease hospital admissions in the heavily polluted inland city, the short-term increases in air pollution levels in the less polluted coastal areas led to excessive exacerbations of cardiovascular disease.

Error in air pollution exposure model determinants and bias in health estimates

BACKGROUND

Land use regression (LUR) models are commonly used in environmental epidemiology to assign spatially resolved estimates of air pollution to study participants. In this setting, estimated LUR model parameters are assumed to be transportable to a main study (the ''transportability assumption''). We provide an empirical illustration of how violation of this assumption can affect exposure predictions and bias health-effect estimates.

METHODS

We based our simulation on two existing LUR models, one for nitrogen dioxide, the other for particulate matter with aerodynamic diameter <2.5 μm. We assessed the impact of error in exposure determinants used in the LUR models on resultant air pollution predictions and on bias in an exposure-health-effect estimate assessed in a hypothetical cohort. We assigned error to predictors at monitoring sites (sites used to develop the LUR model) and at prediction sites (sites for which exposure predictions were needed), allowing for different error levels between site types.

RESULTS

Realistic error in the exposure determinants of the selected LUR models did not induce large additional error in exposure predictions and resulted in only minor (<1%) bias in health-effect estimates. Bias in the health-effect estimates strongly increased (up to 13.6%) when exposure determinant errors were different for monitoring sites than for prediction sites.

CONCLUSIONS

These results suggest that only modest reductions in bias in estimated exposure health-effects are to be expected from reducing error in exposure determinants. It is important to avoid heterogeneous errors in exposure determinants between monitoring sites and prediction sites to satisfy the transportability assumption and avoid bias in estimated exposure health-effects.

Associations between Coarse Particulate Matter Air Pollution and Cause-Specific Mortality: A Nationwide Analysis in 272 Chinese Cities

BACKGROUND

Coarse particulate matter with aerodynamic diameter between 2.5 and [Formula: see text] ([Formula: see text]) air pollution is a severe environmental problem in developing countries, but its challenges to public health were rarely evaluated.

OBJECTIVE

We aimed to investigate the associations between day-to-day changes in [Formula: see text] and cause-specific mortality in China.

METHODS

We conducted a nationwide daily time-series analysis in 272 main Chinese cities from 2013 to 2015. The associations between [Formula: see text] concentrations and mortality were analyzed in each city using overdispersed generalized additive models. Two-stage Bayesian hierarchical models were used to estimate national and regional average associations, and random-effect models were used to pool city-specific concentration-response curves. Two-pollutant models were adjusted for fine particles with aerodynamic diameter [Formula: see text] ([Formula: see text]) or gaseous pollutants.

RESULTS

Overall, we observed positive and approximately linear concentration-response associations between [Formula: see text] and daily mortality. A [Formula: see text] increase in [Formula: see text] was associated with higher mortality due to nonaccidental causes [0.23%; 95% posterior interval (PI): 0.13, 0.33], cardiovascular diseases (CVDs; 0.25%; 95% PI: 0.13, 0.37), coronary heart disease (CHD; 0.21%; 95% PI: 0.05, 0.36), stroke (0.21%; 95% PI: 0.08, 0.35), respiratory diseases (0.26%; 95% PI: 0.07, 0.46), and chronic obstructive pulmonary disease (COPD; 0.34%; 95% PI: 0.12, 0.57). Associations were stronger for cities in southern vs. northern China, with significant differences for total and cardiovascular mortality. Associations with [Formula: see text] were of similar magnitude to those for [Formula: see text] in both single- and two-pollutant models with mutual adjustment. Associations were robust to adjustment for gaseous pollutants other than nitrogen dioxide and sulfur dioxide. Meta-regression indicated that a larger positive correlation between [Formula: see text] and [Formula: see text] predicted stronger city-specific associations between [Formula: see text] and total mortality.

CONCLUSIONS

This analysis showed significant associations between short-term [Formula: see text] exposure and daily nonaccidental and cardiopulmonary mortality based on data from 272 cities located throughout China. Associations appeared to be independent of exposure to [Formula: see text], carbon monoxide, and ozone. https://doi.org/10.1289/EHP2711.

Time-Series Analysis of Air Pollution and Health Accounting for Covariate-Dependent Overdispersion

Time-series studies are routinely used to estimate associations between adverse health outcomes and short-term exposures to ambient air pollutants. Use of the Poisson log-linear model with the assumption of constant overdispersion is the most common approach, particularly when estimating associations between daily air pollution concentrations and aggregated counts of adverse health events throughout a geographical region. We examined how the assumption of constant overdispersion plays a role in estimation of air pollution effects by comparing estimates derived from the standard approach with those estimated from covariate-dependent Bayesian generalized Poisson and negative binomial models that accounted for potential time-varying overdispersion. Through simulation studies, we found that while there was negligible bias in effect estimates, the standard quasi-Poisson approach can result in a larger standard error when the constant overdispersion assumption is violated. This was also observed in a time-series study of daily emergency department visits for respiratory diseases and ozone concentration in Atlanta, Georgia (1999-2009). Allowing for covariate-dependent overdispersion resulted in a reduction in the ozone effect standard error, while the ozone-associated relative risk remained robust to different model specifications. Our findings suggest that improved characterization of overdispersion in time-series modeling can result in more precise health effect estimates in studies of short-term environmental exposures.

Dispersion and deposition estimation of fugitive iron particles from an iron industry on nearby communities via AERMOD

Emission of fugitive iron particles from anthropogenic sources can have significant effects on the human health and the environment. In this study, a regulatory air pollutant dispersion model (AERMOD) was implemented to predict the dispersion and deposition of fugitive iron particles towards a mid-sized residential area in Sultanate of Oman. The performance of the model was validated using air, soil, and dust fall samples. PM₁₀ was found as the most abundant iron particles in the soil samples. The results showed that the maximum daily concentration level of fugitive iron particles simulated through AERMOD was 7.19 μg/m³. Statistical analysis, including fractional bias (FB), normalized mean square error (NMSE), and predicted/observed ratio (Pred./Obs.), showed a reliable agreement in accuracy and precision between the datasets (for air samples FB = 0.024, NMSE = 0.001, Pred./Obs. = 0.976; for dust fall samples FB = -0.004, NMSE = 0.000, Pred./Obs. = 1.004). However, uncertainties and differences were from the external sources, such as other industries in the region. The results presented that the concentration levels were below the national and international guidelines proposed by the US Environmental Protection Agency (USEPA) and Omani Ambient Air Quality Standards (OAAQS). The methodology followed and the developed dispersion model can be generalized to other industries from which the dispersion of fugitive metal particles need to be evaluated as a potential route for human exposure. Graphical abstract ᅟ.

The effect of urban particulate matter on cultured human nasal fibroblasts

BACKGROUND

Exposure to urban particulate matter (UPM) has been linked to aggravation of various health problems. Although the effects of UPM on the lower respiratory tract have been extensively studied, more research is required on the impact of UPM on the upper respiratory tract and the underlying mechanisms. Thus, we investigated the cytotoxic effects of UPM on cultured human nasal fibroblasts, the underlying signaling pathways involved, and changes in cytokine levels.

METHODS

Human turbinate tissue specimens were collected during partial turbinectomies performed on 6 patients, and then cultured. The effect of UPM on nasal fibroblast viability was explored. Real-time reverse transcription-polymerase chain reaction was used to measure the mRNA levels of genes encoding cytokines and chemokines (interleukin [IL]-4, IL-6, IL-8, and tumor necrosis factor-α) before and after 24 hours of UPM treatment. Enzyme-linked immunosorbent assays were employed to measure IL-6 and IL-8 levels. The status of the p38 and nuclear factor (NF)-κB signaling pathways was analyzed by Western blotting.

RESULTS

UPM reduced cell viability in a dose-dependent manner and increased IL-6 and IL-8 expression at both the mRNA and protein levels. UPM induced the phosphorylation of p38 and NF-κB p65; inhibitors of the actions of these proteins repressed phosphorylation and the expression of IL-6 and IL-8.

CONCLUSION

UPM induced IL-6 and IL-8 expression by fibroblasts via p38 and NF-κB classical signaling, suggesting that UPM can induce or aggravate allergic and/or chronic rhinitis.

Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty

The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects.

Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid

RATIONALE

Short- and long-term fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter [PM2.5]) pollution is associated with asthma development and morbidity, but there are few data on the effects of long-term exposure to coarse PM (PM10-2.5) on respiratory health.

OBJECTIVES

To understand the relationship between long-term fine and coarse PM exposure and asthma prevalence and morbidity among children.

METHODS

A semiparametric regression model that incorporated PM2.5 and PM10 monitor data and geographic characteristics was developed to predict 2-year average PM2.5 and PM10-2.5 exposure during the period 2009 to 2010 at the zip-code tabulation area level. Data from 7,810,025 children aged 5 to 20 years enrolled in Medicaid from 2009 to 2010 were used in a log-linear regression model with predicted PM levels to estimate the association between PM exposure and asthma prevalence and morbidity, adjusting for race/ethnicity, sex, age, area-level urbanicity, poverty, education, and unmeasured spatial confounding.

MEASUREMENTS AND MAIN RESULTS

Exposure to coarse PM was associated with increased asthma diagnosis prevalence (rate ratio [RR] for 1-μg/m3 increase in coarse PM level, 1.006; 95% confidence interval [CI], 1.001-1.011), hospitalizations (RR, 1.023; 95% CI, 1.003-1.042), and emergency department visits (RR, 1.017; 95% CI, 1.001-1.033) when adjusting for fine PM. Fine PM exposure was more strongly associated with increased asthma prevalence and morbidity than coarse PM. The estimates remained elevated across different levels of spatial confounding adjustment.

CONCLUSIONS

Among children enrolled in Medicaid, exposure to higher average coarse PM levels is associated with increased asthma prevalence and morbidity. These results suggest the need for direct monitoring of coarse PM and reconsideration of limits on long-term average coarse PM pollution levels.

Concentration-dependent effects of PM2.5 mass on expressions of adhesion molecules and inflammatory cytokines in nasal mucosa of rats with allergic rhinitis

Allergic rhinitis (AR) represents a clinical health issue affecting approximately 500 million people worldwide. This study aimed to explore the effects of airborne fine particulate matter (PM_2.5) on the nasal mucosa of rats with AR. Seventy-five healthy male SD rats were included and randomly divided into the normal, model, low-concentration, middle-concentration, and high-concentration groups (15 rats each group). AR rat models were established using sensitized mixture and were stimulated using different concentrations of PM_2.5. Sneeze and nose-scratching events were observed. Automatic hematology analyzer was utilized to count white blood cells (WBCs). The serum IgE, ICAM-1, and VCAM-1 expressions, eosinophil (EOS) infiltration, and IFN-γ, IL-4, IL-5, IL-33, and TSLP expressions were detected by ELISA, HE staining, and qRT-PCR. Greater numbers of WBCs, increased IgE level, elevated levels of ICAM-1, VCAM-1, EOS, IFN-γ, IL-4, IL-5, IL-33, and TSLP in the model, low-concentration, middle-concentration, and high-concentration groups than the normal group. The same trend also exhibited in rats of the middle-concentration and high-concentration groups than that of the model and low-concentration groups. Comparisons between normal rats and AR rats indicated that AR rats exhibit remarkably higher cytokine expression levels of IFN-γ, IL-4, IL-5, TSLP, and IL-33. The study revealed that as stimulation is triggered by PM_2.5, AR rats result in increased levels of adhesion molecules and inflammatory cytokine expressions in a concentration-dependent manner. Analyses of PM_2.5 as well as, its effects on AR are crucial in the continued drive for both prevention and management of the disease.

Indoor air pollution and its association with poor lung function, microalbuminuria and variations in blood pressure among kitchen workers in India: a cross-sectional study

BACKGROUND

The present study is an attempt to explore the association between kitchen indoor air pollutants and physiological profiles in kitchen workers with microalbuminuria (MAU) in north India (Lucknow) and south India (Coimbatore).

METHODS

The subjects comprised 145 control subjects, 233 kitchen workers from north India and 186 kitchen workers from south India. Information related to the personal and occupational history and health of the subjects at both locations were collected using a custom-made questionnaire. Worker lung function was measured using a spirometer. Blood pressure was monitored using a sphygmomanometer. Urinary MAU was measured using a urine analyzer. Indoor air monitoring in kitchens for particulate matter (PM), total volatile organic compounds (TVOC), carbon dioxide (CO₂) and carbon monoxide (CO) was conducted using indoor air quality monitors. The size and shape of PM in indoor air was assessed using a scanning electron microscope (SEM). Fourier transform infrared (FTIR) spectroscopy was used to detect organic or inorganic compounds in the air samples.

RESULTS

Particulate matter concentrations (PM_2.5 and PM₁) were significantly higher in both north and south Indian kitchens than in non-kitchen areas. The concentrations of TVOC, CO and CO₂ were higher in the kitchens of north and south India than in the control locations (non-kitchen areas). Coarse, fine and ultrafine particles and several elements were also detected in kitchens in both locations by SEM and elemental analysis. The FTIR spectra of kitchen indoor air at both locations show the presence of organic chemicals. Significant declines in systolic blood pressure and lung function were observed in the kitchen workers with MAU at both locations compared to those of the control subjects. A higher prevalence of obstruction cases with MAU was observed among the workers in the southern region than in the controls (p < 0.01).

CONCLUSIONS

Kitchen workers in south India have lower lung capacities and a greater risk of obstructive and restrictive abnormalities than their north Indian counterparts. The study showed that occupational exposure to multiple kitchen indoor air pollutants (ultrafine particles, PM_2.5, PM₁, TVOC, CO, CO₂) and FTIR-derived compounds can be associated with a decline in lung function (restrictive and obstructive patterns) in kitchen workers with microalbuminuria. Further studies in different geographical locations in India among kitchen workers on a wider scale are required to validate the present findings.

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

BACKGROUND

Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future.

METHODS

We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP).

RESULTS

NO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations.

CONCLUSIONS

NO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.

PM2.5 as a marker of exposure to tobacco smoke and other sources of particulate matter in Cairo, Egypt

SETTING

Cairo and Giza governorates of Egypt.

BACKGROUND

Particulate matter under 2.5 μm in diameter (PM2.5) arises from diverse sources, including tobacco smoke from cigarettes and waterpipes, and is recognized as a cause of acute and chronic morbidity and mortality.

OBJECTIVE

To measure PM2.5 in workplaces with different intensities of smoking and varying levels of smoking restrictions.

DESIGN

We conducted an air sampling study to measure PM2.5 levels in a convenience sample of indoor and outdoor venues in 2005-2006.

RESULTS

Using a calibrated SidePak instrument, 3295 individual measurements were collected at 96 venues. Compared to indoor venues where tobacco smoking was banned (PM2.5 levels 72-81 μg/m(3)), places offering waterpipes to patrons of cafes (478 μg/m(3)) and Ramadan tents (612 μg/m(3)) had much higher concentrations, as did venues such as public buildings with poor enforcement of smoking restrictions (range 171-704 μg/m(3)). Both the number of waterpipe smokers and the number of cigarette smokers observed at each venue contributed significantly to the overall burden of PM2.5.

CONCLUSION

Such data will support smoke-free policies and programs aimed specifically at reducing environmental tobacco exposure and improving air quality in general, and will provide a baseline for monitoring the impact of tobacco control policies.

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.

Mitigating the effects of preferentially selected monitoring sites for environmental policy and health risk analysis

The potential effects of air pollution are a major concern both in terms of the environment and in relation to human health. In order to support both environmental and health policy there is a need for accurate estimates of the exposures that populations might experience. The information for this typically comes from environmental monitoring networks but often the locations of monitoring sites are preferentially located in order to detect high levels of pollution. Using the information from such networks has the potential to seriously affect the estimates of pollution that are obtained and that might be used in health risk analyses. In this context, we explore the topic of preferential sampling within a long-standing network in the UK that monitored black smoke due to concerns about its effect on public health, the extent of which came to prominence during the famous London fog of 1952. Abatement measures led to a decline in the levels of black smoke and a subsequent reduction in the number of monitoring locations that were thought necessary to provide the information required for policy support. There is evidence of selection bias during this process with sites being kept in the most polluted areas. We assess the potential for this to affect the estimates of risk associated air pollution and show how using Bayesian spatio-temporal exposure models may be used to attempt to mitigate the effects of preferential sampling in this case.

Spatial measurement errors in the field of spatial epidemiology

Background

Spatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological analysis of spatial data.

Methods

Google Scholar, Medline, and Scopus databases were searched using a broad set of terms for papers indexed by a term indicating location (space or geography or location or position) and measurement error (measurement error or measurement inaccuracy or misclassification or uncertainty): we reviewed all papers appearing before December 20, 2014. These papers and their citations were reviewed to identify the relevance to our review.

Results

We were able to define and classify spatial measurement errors into four groups: (1) pure spatial location measurement errors, including both non-instrumental errors (multiple addresses, geocoding errors, outcome aggregations, and covariate aggregation) and instrumental errors; (2) location-based outcome measurement error (purely outcome measurement errors and missing outcome measurements); (3) location-based covariate measurement errors (address proxies); and (4) Covariate-Outcome spatial misaligned measurement errors. We propose how these four classes of errors can be unified within an integrated theoretical model and possible solutions were discussed.

Conclusion

Spatial measurement errors are ubiquitous threat to the validity of spatial epidemiological studies. We propose a systematic framework for understanding the various mechanisms which generate spatial measurement errors and present practical examples of such errors.

Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health

Performing studies on the risks of environmental hazards on human health requires accurate estimates of exposures that might be experienced by the populations at risk. Often there will be missing data and in many epidemiological studies, the locations and times of exposure measurements and health data do not match. To a large extent this will be due to the health and exposure data having arisen from completely different data sources and not as the result of a carefully designed study, leading to problems of both ‘change of support’ and ‘misaligned data’. In such cases, a direct comparison of the exposure and health outcome is often not possible without an underlying model to align the two in the spatial and temporal domains. The Bayesian approach provides the natural framework for such models; however, the large amounts of data that can arise from environmental networks means that inference using Markov Chain Monte Carlo might not be computationally feasible in this setting. Here we adapt the integrated nested Laplace approximation to implement spatio–temporal exposure models. We also propose methods for the integration of large-scale exposure models and health analyses. It is important that any model structure allows the correct propagation of uncertainty from the predictions of the exposure model through to the estimates of risk and associated confidence intervals. The methods are demonstrated using a case study of the levels of black smoke in the UK, measured over several decades, and respiratory mortality.

Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.

Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts.

Ambient Coarse Particulate Matter and Hospital Admissions in the Medicare Cohort Air Pollution Study, 1999-2010

BACKGROUND

In recent years a number of studies have examined the short-term association between coarse particulate matter (PM(10-2.5)) and mortality and morbidity outcomes. These studies, however, have produced inconsistent conclusions.

OBJECTIVES

We estimated both the national- and regional-level associations between PM(10-2.5) and emergency hospitalizations for both cardiovascular and respiratory disease among Medicare enrollees ≥ 65 years of age during the 12-year period 1999 through 2010.

METHODS

Using air pollution data obtained from the U.S. Environmental Protection Agency air quality monitoring network and daily emergency hospitalizations for 110 large urban U.S. counties assembled from the Medicare Cohort Air Pollution Study (MCAPS), we estimated the association between short-term exposure to PM(10-2.5) and hospitalizations using a two-stage Bayesian hierarchical model and Poisson log-linear regression models.

RESULTS

A 10-μg/m3 increase in PM(10-2.5) was associated with a significant increase in same-day cardiovascular hospitalizations [0.69%; 95% posterior interval (PI): 0.45, 0.92]. After adjusting for PM2.5, this association remained significant (0.63%; 95% PI: 0.38, 0.88). A 10-μg/m3 increase in PM(10-2.5) was not associated with a significant increase in respiratory-related hospitalizations.

CONCLUSIONS

We found statistically significant evidence that daily variation in PM(10-2.5) is associated with emergency hospitalizations for cardiovascular diseases among Medicare enrollees ≥ 65 years of age. This association was robust to adjustment for concentrations of PM2.5.

CITATION

Powell H, Krall JR, Wang Y, Bell ML, Peng RD. 2015. Ambient coarse particulate matter and hospital admissions in the Medicare Cohort Air Pollution Study, 1999-2010. Environ Health Perspect 123:1152-1158; http://dx.doi.org/10.1289/ehp.1408720.

Zinc compound air releases from Toxics Release Inventory facilities and cardiovascular disease mortality rates

BACKGROUND

Inhaled zinc has been found in association with cardiopulmonary toxicity. However, limited human epidemiologic studies are available. This study analyzed the association between covariate-adjusted cardiovascular (CVD) mortality rates and zinc compound air releases in the United States.

METHODS

We conducted an ecological analysis on the association between zinc compound air releases for 1991-2000 using the Toxics Release Inventory database and average age-adjusted CVD mortality for 2006-2010, adjusting for race/ethnicity composition and several health and socioeconomic factors. Models were estimated for males and females and for metropolitan and nonmetropolitan counties.

RESULTS

Zinc compound air releases were positively associated with increased adjusted CVD mortality rates in all four models (β=0.0085, p<0.0001 for males in nonmetropolitan counties; β=0.0093, p<0.0001 for males in metropolitan counties; β=0.0145, p<0.0001 for females in nonmetropolitan counties; and β=0.0098, p<0.0001 for females in metropolitan counties). Results were largely robust to various sensitivity analyses.

CONCLUSION

This study provides epidemiological evidence for possible CVD health impacts of inhaled zinc in the United States. Although the strongest effect was found for females in nonmetropolitan counties, the associations were consistent in nonmetropolitan or metropolitan counties for both genders.

Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications

BACKGROUND

Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

OBJECTIVE

Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

METHODS

First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

RESULTS

Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the null.

CONCLUSIONS

We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

The characteristics of coarse particulate matter air pollution associated with alterations in blood pressure and heart rate during controlled exposures

Although fine particulate matter (PM) air pollution <2.5 μm in aerodynamic diameter (PM2.5) is a leading cause of global morbidity and mortality, the potential health effects of coarse PM (2.5-10 μm in aerodynamic diameter; PM10-2.5) remain less clearly understood. We aimed to elucidate the components within coarse PM most likely responsible for mediating these hemodynamic alterations. Thirty-two healthy adults (25.9 ± 6.6 years) were exposed to concentrated ambient coarse PM (CAP) (76.2 ± 51.5 μg/m(3)) and filtered air (FA) for 2 h in a rural location in a randomized double-blind crossover study. The particle constituents (24 individual elements, organic and elemental carbon) were analyzed from filter samples and associated with the blood pressure (BP) and heart rate (HR) changes occurring throughout CAP and FA exposures in mixed model analyses. Total coarse PM mass along with most of the measured elements were positively associated with similar degrees of elevations in both systolic BP and HR. Conversely, total PM mass was unrelated, whereas only two elements (Cu and Mo) were positively associated with and Zn was inversely related to diastolic BP changes during exposures. Inhalation of coarse PM from a rural location rapidly elevates systolic BP and HR in a concentration-responsive manner, whereas the particulate composition does not appear to be an important determinant of these responses. Conversely, exposure to certain PM elements may be necessary to trigger a concomitant increase in diastolic BP. These findings suggest that particulate mass may be an adequate metric of exposure to predict some, but not all, hemodynamic alterations induced by coarse PM mass.

Chemical characterization of outdoor and subway fine (PM(2.5-1.0)) and coarse (PM(10-2.5)) particulate matter in Seoul (Korea) by computer-controlled scanning electron microscopy (CCSEM)

Outdoor and indoor (subway) samples were collected by passive sampling in urban Seoul (Korea) and analyzed with computer-controlled scanning electron microscopy coupled with energy dispersive x-ray spectroscopy (CCSEM-EDX). Soil/road dust particles accounted for 42%–60% (by weight) of fine particulate matter larger than 1 µm (PM_2.5–1.0) in outdoor samples and 18% of PM_2.5–1.0 in subway samples. Iron-containing particles accounted for only 3%–6% in outdoor samples but 69% in subway samples. Qualitatively similar results were found for coarse particulate matter (PM_10–2.5) with soil/road dust particles dominating outdoor samples (66%–83%) and iron-containing particles contributing most to subway PM_10–2.5 (44%). As expected, soil/road dust particles comprised a greater mass fraction of PM_10–2.5 than PM_2.5–1.0. Also as expected, the mass fraction of iron-containing particles was substantially less in PM_10–2.5 than in PM_2.5–1.0. Results of this study are consistent with known emission sources in the area and with previous studies, which showed high concentrations of iron-containing particles in the subway compared to outdoor sites. Thus, passive sampling with CCSEM-EDX offers an inexpensive means to assess PM_2.5–1.0 and PM_10-2.5 simultaneously and by composition at multiple locations.

An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models

BACKGROUND

Using multipollutant models to understand combined health effects of exposure to multiple pollutants is becoming more common. However, complex relationships between pollutants and differing degrees of exposure error across pollutants can make health effect estimates from multipollutant models difficult to interpret.

OBJECTIVES

We aimed to quantify relationships between multiple pollutants and their associated exposure errors across metrics of exposure and to use empirical values to evaluate potential attenuation of coefficients in epidemiologic models.

METHODS

We used three daily exposure metrics (central-site measurements, air quality model estimates, and population exposure model estimates) for 193 ZIP codes in the Atlanta, Georgia, metropolitan area from 1999 through 2002 for PM2.5 and its components (EC and SO4), as well as O3, CO, and NOx, to construct three types of exposure error: δspatial (comparing air quality model estimates to central-site measurements), δpopulation (comparing population exposure model estimates to air quality model estimates), and δtotal (comparing population exposure model estimates to central-site measurements). We compared exposure metrics and exposure errors within and across pollutants and derived attenuation factors (ratio of observed to true coefficient for pollutant of interest) for single- and bipollutant model coefficients.

RESULTS

Pollutant concentrations and their exposure errors were moderately to highly correlated (typically, > 0.5), especially for CO, NOx, and EC (i.e., "local" pollutants); correlations differed across exposure metrics and types of exposure error. Spatial variability was evident, with variance of exposure error for local pollutants ranging from 0.25 to 0.83 for δspatial and δtotal. The attenuation of model coefficients in single- and bipollutant epidemiologic models relative to the true value differed across types of exposure error, pollutants, and space.

CONCLUSIONS

Under a classical exposure-error framework, attenuation may be substantial for local pollutants as a result of δspatial and δtotal with true coefficients reduced by a factor typically < 0.6 (results varied for δpopulation and regional pollutants).

Spatial variable selection methods for investigating acute health effects of fine particulate matter components

Multi-site time series studies have reported evidence of an association between short term exposure to particulate matter (PM) and adverse health effects, but the effect size varies across the United States. Variability in the effect may partially be due to differing community level exposure and health characteristics, but also due to the chemical composition of PM which is known to vary greatly by location and time. The objective of this article is to identify particularly harmful components of this chemical mixture. Because of the large number of highly-correlated components, we must incorporate some regularization into a statistical model. We assume that, at each spatial location, the regression coefficients come from a mixture model with the flavor of stochastic search variable selection, but utilize a copula to share information about variable inclusion and effect magnitude across locations. The model differs from current spatial variable selection techniques by accommodating both local and global variable selection. The model is used to study the association between fine PM (PM <2.5μm) components, measured at 115 counties nationally over the period 2000-2008, and cardiovascular emergency room admissions among Medicare patients.

No effect of acute exposure to coarse particulate matter air pollution in a rural location on high-density lipoprotein function

CONTEXT

High-density lipoprotein (HDL) particles perform numerous vascular-protective functions. Animal studies demonstrate that exposure to fine or ultrafine particulate matter (PM) can promote HDL dysfunction. However, the impact of PM on humans remains unknown.

OBJECTIVE

We aimed to determine the effect of exposure to coarse concentrated ambient particles (CAP) on several metrics of HDL function in healthy humans.

METHODS

Thirty-two adults (25.9 ± 6.6 years) were exposed to coarse CAP [76.2 ± 51.5 µg·m(-3)] in a rural location and filtered air (FA) for 2 h in a randomized double-blind crossover study. Venous blood collected 2- and 20-h post-exposures was measured for HDL-mediated efflux of [(3)H]-cholesterol from cells and 20-h exposures for HDL anti-oxidant capacity by a fluorescent assay and paraoxonase activity. The changes [median (first, third quartiles)] between exposures among 29 subjects with available results were compared by matched Wilcoxon tests.

RESULTS

HDL-mediated cholesterol efflux capacity did not differ between exposures at either time point [16.60% (15.17, 19.19) 2-h post-CAP versus 17.56% (13.43, 20.98) post-FA, p  =  0.768 and 14.90% (12.47, 19.15) 20-h post-CAP versus 17.75% (13.22, 23.95) post-FA, p  =  0.216]. HOI [0.26 (0.24, 0.35) versus 0.28 (0.25, 0.40), p = 0.198] and paraoxonase activity [0.54 (0.39, 0.82) versus 0.60 μmol·min(-1 )ml plasma(-1) (0.40, 0.85), p = 0.137] did not differ 20-h post-CAP versus FA, respectively.

CONCLUSIONS

Brief inhalation of coarse PM from a rural location did not acutely impair several facets of HDL functionality. Whether coarse PM derived from urban sites, fine particles or longer term PM exposures can promote HDL dysfunction warrant future investigations.

Ambient Coarse Particulate Matter and Human Health: A Systematic Review and Meta-Analysis

Airborne particles have been linked to increased mortality and morbidity. As most research has focused on fine particles (PM2.5), the health implications of coarse particles (PM10-2.5) are not well understood. We conducted a systematic review and meta-analysis of associations for short- and long-term PM10-2.5 concentrations with mortality and hospital admissions. Using 23 mortality and 10 hospital admissions studies, we documented suggestive evidence of increased morbidity and mortality in relation to higher short-term PM10-2.5 concentrations, with stronger relationships for respiratory than cardiovascular endpoints. Reported associations were highly heterogeneous, however, especially by geographic region and average PM10-2.5 concentrations. Adjustment for PM2.5 and publication bias resulted in weaker and less precise effect estimates, although positive associations remained for short-term PM10-2.5 concentrations. Inconsistent relationships between effect estimates for PM10-2.5 and correlations between PM10-2.5 and PM2.5 concentrations, however, indicate that PM10-2.5 associations cannot be solely explained by co-exposure to PM2.5. While suggestive evidence was found of increased mortality with long-term PM10-2.5 concentrations, these associations were not robust to control for PM2.5. Additional research is required to better understand sources of heterogeneity of associations between PM10-2.5 and adverse health outcomes.

A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices.

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial-temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003-2005. Via cross-validation experiments, our model had an out-of-sample prediction R(2) of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m(3) between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial-temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined.

Hemodynamic, autonomic, and vascular effects of exposure to coarse particulate matter air pollution from a rural location

BACKGROUND

Fine particulate matter (PM) air pollution is associated with numerous adverse health effects, including increased blood pressure (BP) and vascular dysfunction. Coarse PM substantially contributes to global air pollution, yet differs in characteristics from fine particles and is currently not regulated. However, the cardiovascular (CV) impacts of coarse PM exposure remain largely unknown.

OBJECTIVES

Our goal was to elucidate whether coarse PM, like fine PM, is itself capable of eliciting adverse CV responses.

METHODS

We performed a randomized double-blind crossover study in which 32 healthy adults (25.9 ± 6.6 years of age) were exposed to concentrated ambient coarse particles (CAP; 76.2 ± 51.5 μg/m(3)) in a rural location and filtered air (FA) for 2 hr. We measured CV outcomes during, immediately after, and 2 hr postexposures.

RESULTS

Both systolic (mean difference = 0.32 mmHg; 95% CI: 0.05, 0.58; p = 0.021) and diastolic BP (0.27 mmHg; 95% CI: 0.003, 0.53; p = 0.05) linearly increased per 10 min of exposure during the inhalation of coarse CAP when compared with changes during FA exposure. Heart rate was on average higher (4.1 bpm; 95% CI: 3.06, 5.12; p < 0.0001) and the ratio of low-to-high frequency heart rate variability increased (0.24; 95% CI: 0.07, 0.41; p = 0.007) during coarse particle versus FA exposure. Other outcomes (brachial flow-mediated dilatation, microvascular reactive hyperemia index, aortic hemodynamics, pulse wave velocity) were not differentially altered by the exposures.

CONCLUSIONS

Inhalation of coarse PM from a rural location is associated with a rapid elevation in BP and heart rate during exposure, likely due to the triggering of autonomic imbalance. These findings add mechanistic evidence supporting the biological plausibility that coarse particles could contribute to the triggering of acute CV events.

Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas

This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urban environment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared several spatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. We considered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction of particles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primary component of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which site-specific effect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site, daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are not considered in local-scale dispersion models and day of the week to account for time-varying emission rates not available in emissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed that concentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transport component with covariates that account for the residual spatiotemporal variation in the pollution process.

Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods

In studies of coarse particulate matter (PM10-2.5), mass concentrations are often estimated through the subtraction of PM2.5 from collocated PM10 tapered element oscillating microbalance (TEOM) measurements. Though all field instruments have yet to be updated, the Filter Dynamic Measurement System (FDMS) was introduced to account for the loss of semivolatile material from heated TEOM filters. To assess errors in PM10-2.5 estimation when using the possible combinations of PM10 and PM2.5 TEOM units with and without FDMS, data from three monitoring sites of the Colorado Coarse Rural-Urban Sources and Health (CCRUSH) study were used to simulate four possible subtraction methods for estimating PM10-2.5 mass concentrations. Assuming all mass is accounted for using collocated TEOMs with FDMS, the three other subtraction methods were assessed for biases in absolute mass concentration, temporal variability, spatial correlation, and homogeneity. Results show collocated units without FDMS closely estimate actual PM10-2.5 mass and spatial characteristics due to the very low semivolatile PM10-2.5 concentrations in Colorado. Estimation using either a PM2.5 or PM10 monitor without FDMS introduced absolute biases of 2.4 microg/m3 (25%) to -2.3 microg/m3 (-24%), respectively. Such errors are directly related to the unmeasured semivolatile mass and alter measures of spatiotemporal variability and homogeneity, all of which have implications for the regulatory and epidemiology communities concerned about PM10-2.5. Two monitoring sites operated by the state of Colorado were considered for inclusion in the CCRUSH acute health effects study, but concentrations were biased due to sampling with an FDMS-equipped PM2.5 TEOM and PM10 TEOM not corrected for semivolatile mass loss. A regression-based model was developed for removing the error in these measurements by estimating the semivolatile concentration of PM2.5 from total PM2.5 concentrations. By estimating nonvolatile PM2.5 concentrations from this relationship, PM10-2.5 was calculated as the difference between nonvolatile PM10 and PM2.5 concentrations.

Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta

Exposure error in studies of ambient air pollution and health that use city-wide measures of exposure may be substantial for pollutants that exhibit spatiotemporal variability. Alternative spatiotemporal metrics of exposure for traffic-related and regional pollutants were applied in a time-series study of ambient air pollution and cardiorespiratory emergency department visits in Atlanta, GA, USA. Exposure metrics included daily central site monitoring for particles and gases; daily spatially refined ambient concentrations obtained from regional background monitors, local-scale dispersion, and hybrid air quality models; and spatially refined ambient exposures from population exposure models. Health risk estimates from Poisson models using the different exposure metrics were compared. We observed stronger associations, particularly for traffic-related pollutants, when using spatially refined ambient concentrations compared with a conventional central site exposure assignment approach. For some relationships, estimates of spatially refined ambient population exposures showed slightly stronger associations than corresponding spatially refined ambient concentrations. Using spatially refined pollutant metrics, we identified socioeconomic disparities in concentration-response functions that were not observed when using central site data. In some cases, spatially refined pollutant metrics identified associations with health that were not observed using measurements from the central site. Complexity and challenges in incorporating modeled pollutant estimates in time-series studies are discussed.

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

BACKGROUND

As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity.

METHODS

In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step.

RESULTS

Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large.

CONCLUSIONS

There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.

Short-term exposure to particulate matter constituents and mortality in a national study of U.S. urban communities

BACKGROUND

Although the association between PM2.5 mass and mortality has been extensively studied, few national-level analyses have estimated mortality effects of PM2.5 chemical constituents. Epidemiologic studies have reported that estimated effects of PM2.5 on mortality vary spatially and seasonally. We hypothesized that associations between PM2.5 constituents and mortality would not vary spatially or seasonally if variation in chemical composition contributes to variation in estimated PM2.5 mortality effects.

OBJECTIVES

We aimed to provide the first national, season-specific, and region-specific associations between mortality and PM2.5 constituents.

METHODS

We estimated short-term associations between nonaccidental mortality and PM2.5 constituents across 72 urban U.S. communities from 2000 to 2005. Using U.S. Environmental Protection Agency (EPA) Chemical Speciation Network data, we analyzed seven constituents that together compose 79-85% of PM2.5 mass: organic carbon matter (OCM), elemental carbon (EC), silicon, sodium ion, nitrate, ammonium, and sulfate. We applied Poisson time-series regression models, controlling for time and weather, to estimate mortality effects.

RESULTS

Interquartile range increases in OCM, EC, silicon, and sodium ion were associated with estimated increases in mortality of 0.39% [95% posterior interval (PI): 0.08, 0.70%], 0.22% (95% PI: 0.00, 0.44), 0.17% (95% PI: 0.03, 0.30), and 0.16% (95% PI: 0.00, 0.32), respectively, based on single-pollutant models. We did not find evidence that associations between mortality and PM2.5 or PM2.5 constituents differed by season or region.

CONCLUSIONS

Our findings indicate that some constituents of PM2.5 may be more toxic than others and, therefore, regulating PM total mass alone may not be sufficient to protect human health.

The effect of acute exposure to coarse particulate matter air pollution in a rural location on circulating endothelial progenitor cells: results from a randomized controlled study

CONTEXT

Fine particulate matter (PM) air pollution has been associated with alterations in circulating endothelial progenitor cell (EPC) levels, which may be one mechanism whereby exposures promote cardiovascular diseases. However, the impact of coarse PM on EPCs is unknown.

OBJECTIVE

We aimed to determine the effect of acute exposure to coarse concentrated ambient particles (CAP) on circulating EPC levels.

METHODS

Thirty-two adults (25.9 ± 6.6 years) were exposed to coarse CAP (76.2 ± 51.5 μg m(-3)) in a rural location and filtered air (FA) for 2 h in a randomized double-blind crossover study. Peripheral venous blood was collected 2 and 20 h post-exposures for circulating EPC (n = 21), white blood cell (n = 24) and vascular endothelial growth factor (VEGF) (n = 16-19) levels. The changes between exposures were compared by matched Wilcoxon signed-rank tests.

RESULTS

Circulating EPC levels were elevated 2 [108.29 (6.24-249.71) EPC mL(-1); median (25th-75th percentiles), p = 0.052] and 20 h [106.86 (52.91-278.35) EPC mL(-1), p = 0.008] post-CAP exposure compared to the same time points following FA [38.47 (0.00-84.83) and 50.16 (0.00-104.79) EPC mL(-1)]. VEGF and white blood cell (WBC) levels did not differ between exposures.

CONCLUSIONS

Brief inhalation of coarse PM from a rural location elicited an increase in EPCs that persisted for at least 20 h. The underlying mechanism responsible may reflect a systemic reaction to an acute "endothelial injury" and/or a circulating EPC response to sympathetic nervous system activation.

Coarse particles and respiratory emergency department visits in California

Although respiratory disease has been strongly connected to fine particulate air pollution (particulate matter <2.5 μm in diameter (PM2.5)), evidence has been mixed regarding the effects of coarse particles (particulate matter from 2.5 to 10 μm in diameter), possibly because of the greater spatial heterogeneity of coarse particles. In this study, we evaluated the relationship between coarse particles and respiratory emergency department visits, including common subdiagnoses, from 2005 to 2008 in 35 California counties. A time-stratified case-crossover design was used to help control for time-invariant confounders and seasonal influences, and the study population was limited to those residing within 20 km of pollution monitors to mitigate the influence of spatial heterogeneity. Significant associations between respiratory emergency department visits and coarse particle levels were observed. Asthma visits showed associations (for 2-day lag, excess risk per 10 μg/m³ = 3.3%, 95% confidence interval: 2.0, 4.6) that were robust to adjustment by other common air pollutants (particles <2.5 μm in diameter, ozone, nitrogen dioxide, carbon monoxide, and sulfur dioxide). Pneumonia and acute respiratory infection visits were not associated, although some suggestion of a relationship with chronic obstructive pulmonary disease visits was present. Our results indicate that coarse particle exposure may trigger asthma exacerbations requiring emergency care, and reducing exposures among asthmatic persons may provide benefits.