Examining the effects of air pollution composition on within region differences in PM2.5 mortality risk estimates

Investigating seasonal air quality variations consequent to the urban vegetation in the metropolis of Faisalabad, Pakistan

Urban atmospheric pollution is global problem and and have become increasingly critical in big cities around the world. Issue of toxic emissions has gained significant attention in the scientific community as the release of pollutants into the atmosphere rising continuously. Although, the Pakistani government has started the Pakistan Clean Air Program to control ambient air quality however, the desired air quality levels are yet to be reached. Since the process of mapping the dispersion of atmospheric pollutants in urban areas is intricate due to its dependence on multiple factors, such as urban vegetation and weather conditions. Therefore, present research focuses on two essential items: (1) the relationship between urban vegetation and atmospheric variables (temperature, relative humidity (RH), sound intensity (SI), CO, CO2, and particulate matter (PM0.5, PM1.0, and PM2.5) and (2) the effect of seasonal change on concentration and magnitude of atmospheric variables. A geographic Information System (GIS) was utilized to map urban atmospheric variables dispersion in the residential areas of Faisalabad, Pakistan. Pearson correlation and principal component analyses were performed to establish the relationship between urban atmospheric pollutants, urban vegetation, and seasonal variation. The results showed a positive correlation between urban vegetation, metrological factors, and most of the atmospheric pollutants. Furthermore, PM concentration showed a significant correlation with temperature and urban vegetation cover. GIS distribution maps for PM0.5, PM1.0, PM2.5, and CO2 pollutants showed the highest concentration of pollutants in poorly to the moderated vegetated areas. Therefore, it can be concluded that urban vegetation requires a rigorous design, planning, and cost-benefit analysis to maximize its positive environmental effects.

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

OBJECTIVE

To investigate potential interactive effects of fine particulate matter (PM2.5) and ozone (O3) on daily mortality at global level.

DESIGN

Two stage time series analysis.

SETTING

372 cities across 19 countries and regions.

POPULATION

Daily counts of deaths from all causes, cardiovascular disease, and respiratory disease.

MAIN OUTCOME MEASURE

Daily mortality data during 1994-2020. Stratified analyses by co-pollutant exposures and synergy index (>1 denotes the combined effect of pollutants is greater than individual effects) were applied to explore the interaction between PM2.5 and O3 in association with mortality.

RESULTS

During the study period across the 372 cities, 19.3 million deaths were attributable to all causes, 5.3 million to cardiovascular disease, and 1.9 million to respiratory disease. The risk of total mortality for a 10 μg/m3 increment in PM2.5 (lag 0-1 days) ranged from 0.47% (95% confidence interval 0.26% to 0.67%) to 1.25% (1.02% to 1.48%) from the lowest to highest fourths of O3 concentration; and for a 10 μg/m3 increase in O3 ranged from 0.04% (-0.09% to 0.16%) to 0.29% (0.18% to 0.39%) from the lowest to highest fourths of PM2.5 concentration, with significant differences between strata (P for interaction <0.001). A significant synergistic interaction was also identified between PM2.5 and O3 for total mortality, with a synergy index of 1.93 (95% confidence interval 1.47 to 3.34). Subgroup analyses showed that interactions between PM2.5 and O3 on all three mortality endpoints were more prominent in high latitude regions and during cold seasons.

CONCLUSION

The findings of this study suggest a synergistic effect of PM2.5 and O3 on total, cardiovascular, and respiratory mortality, indicating the benefit of coordinated control strategies for both pollutants.

Source sectors underlying PM2.5-related deaths among children under 5 years of age in 17 low- and middle-income countries

BACKGROUND

Fine particulate matter (PM_2.5) from different source sectors might differ in toxicity. However, data from large-scale studies on vulnerable children in low- and middle-income countries (LMICs) are insufficient.

OBJECTIVE

To analyze the association of under-five death (U5D) with long-term exposure to PM_2.5 from different sources.

METHOD

We evaluated demographic and health survey data for 79,995 babies born in 2017 in 16 Asian and African LMICs (AA-LMICs) and a Latin America low-income country (i.e., Haiti). Long-term exposure to PM_2.5 was assessed by a well-established product that attributed the annual concentration to 20 source sectors in 2017. The associations of survival during < 5-year periods with each source-specific concentration of PM_2.5 were analyzed by Cox regression with multiple adjustments. We derived a multiple-pollutant ridge regression model to estimate the joint exposure-response function (JERF) between U5D and PM_2.5 mixtures. To evaluate how sources affected PM_2.5 toxicity, we evaluated the number of U5Ds attributable to PM_2.5 based on the source profiles for 88 AA-LMICs.

RESULTS

According to the single-pollutant model, the risk of U5D increased by 7% (95% confidence interval [CI]: 5%, 9%) for each 10 μg/m³ increment in total PM_2.5 concentration. The model performance was lower than that of the multiple-pollutant ridge regression model. For each 10 μg/m³ increment in PM_2.5, the excess risk of U5D ranged from 6% (95% CI: 4%, 9%) in Nepal to 10% (95% CI: 6%, 14%) in Mauritania. Based on the JERF, PM_2.5 contributed to 817,647 (95% CI: 585,729, 1,050,439), i.e., 28.0% (95% CI: 20.1%, 35.8%), of all U5Ds across the 88 AA-LMICs. The PM_2.5-related U5Ds were mostly attributable to PM_2.5 produced by desert dust, followed by solid biofuel combustion and open fires.

CONCLUSION

The average toxicity of PM_2.5 varied by source profile, which should be taken into consideration when planning public health interventions. For some AA LMICs, natural sources of PM_2.5 had the most significant health effects, and should not be ignored to ensure the protection of child health.

Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the standardised environmental predictors (a) without any transformation (b) transformed with PCA, and (c) transformed with SPCA. The comparison of findings underlined the potential of SPCA for conducting inference in scenarios where multicollinearity can increase the risk of Type II error. Our analysis unravelled a significant association between average noise pollution and increased risk of all-cause mortality. Specifically, those in the upper deciles of noise exposure have between 5 and 10% increased risk of all-cause mortality compared to the lowest decile.

Mitochondria and traffic-related air pollution linked coronary artery calcification: exploring the missing link

The continuing increase in the exposure to Traffic-related air pollution (TRAP) in the general population is predicted to result in a higher incidence of non-communicable diseases like cardiovascular disease. The chronic exposure of air particulate matter from TRAP upon the vascular system leads to the enhancement of deposition of calcium in the vasculature leading to coronary artery calcification (CAC), triggered by inflammatory reactions and endothelial dysfunction. This calcification forms within the intimal and medial layers of vasculature and the underlying mechanism that connects the trigger from TRAP is not well explored. Several local and systemic factors participate in this active process including inflammatory response, hyperlipidemia, presence of self-programmed death bodies and high calcium-phosphate concentrations. These factors along with the loss of molecules that inhibit calcification and circulating nucleation complexes influence the development of calcification in the vasculature. The loss of defense to prevent osteogenic transition linked to micro organelle dysfunction that includes deteriorated mitochondria, elevated mitochondrial oxidative stress, and defective mitophagy. In this review, we examine the contributory role of mitochondria involved in the mechanism of TRAP linked CAC development. Further we examine whether TRAP is an inducer or trigger for the enhanced progression of CAC.

Hydroxyl Radical Production by Air Pollutants in Epithelial Lining Fluid Governed by Interconversion and Scavenging of Reactive Oxygen Species

Air pollution is a major risk factor for human health. Chemical reactions in the epithelial lining fluid (ELF) of the human respiratory tract result in the formation of reactive oxygen species (ROS), which can lead to oxidative stress and adverse health effects. We use kinetic modeling to quantify the effects of fine particulate matter (PM2.5), ozone (O₃), and nitrogen dioxide (NO₂) on ROS formation, interconversion, and reactivity, and discuss different chemical metrics for oxidative stress, such as cumulative production of ROS and hydrogen peroxide (H₂O₂) to hydroxyl radical (OH) conversion. All three air pollutants produce ROS that accumulate in the ELF as H₂O₂, which serves as reservoir for radical species. At low PM2.5 concentrations (<10 μg m^-3), we find that less than 4% of all produced H₂O₂ is converted into highly reactive OH, while the rest is intercepted by antioxidants and enzymes that serve as ROS buffering agents. At elevated PM2.5 concentrations (>10 μg m^-3), however, Fenton chemistry overwhelms the ROS buffering effect and leads to a tipping point in H₂O₂ fate, causing a strong nonlinear increase in OH production. This shift in ROS chemistry and the enhanced OH production provide a tentative mechanistic explanation for how the inhalation of PM2.5 induces oxidative stress and adverse health effects.

Exploration of PM mass, source, and component-related factors that might explain heterogeneity in daily PM2.5-mortality associations across the United States

Multi-city epidemiologic studies examining short-term (daily) differences in fine particulate matter (PM_2.5) provide evidence of substantial spatial heterogeneity in city-specific mortality risk estimates across the United States. Because PM_2.5 is a mixture of particles, both directly emitted from sources or formed through atmospheric reactions, some of this heterogeneity may be due to regional variations in PM_2.5 toxicity. Using inverse variance weighted linear regression, we examined change in percent change in mortality in association with 24 "exposure" determinants representing three basic groupings based on potential explanations for differences in PM toxicity - size, source, and composition. Percent changes in mortality for the PM_2.5-mortality association for 313 core-based statistical areas and their metropolitan divisions over 1999-2005 were used as the outcome. Several determinants were identified as potential contributors to heterogeneity: all mass fraction determinants, vehicle miles traveled (VMT) for diesel total, VMT gas per capita, PM_2.5 ammonium, PM_2.5 nitrate, and PM_2.5 sulfate. In multivariable models, only daily correlation of PM_2.5 with PM₁₀ and long-term average PM_2.5 mass concentration were retained, explaining approximately 10% of total variability. The results of this analysis contribute to the growing body of literature specifically focusing on assessing the underlying basis of the observed spatial heterogeneity in PM_2.5-mortality effect estimates, continuing to demonstrate that this heterogeneity is multifactorial and not attributable to a single aspect of PM.

Temporal changes in short-term associations between cardiorespiratory emergency department visits and PM2.5 in Los Angeles, 2005 to 2016

BACKGROUND

Emissions control programs targeting certain air pollution sources may alter PM_2.5 composition, as well as the risk of adverse health outcomes associated with PM_2.5.

OBJECTIVES

We examined temporal changes in the risk of emergency department (ED) visits for cardiovascular diseases (CVDs) and asthma associated with short-term increases in ambient PM_2.5 concentrations in Los Angeles, California.

METHODS

Poisson log-linear models with unconstrained distributed exposure lags were used to estimate the risk of CVD and asthma ED visits associated with short-term increases in daily PM_2.5 concentrations, controlling for temporal and meteorological confounders. The models were run separately for three predefined time periods, which were selected based on the implementation of multiple emissions control programs (EARLY: 2005-2008; MIDDLE: 2009-2012; LATE: 2013-2016). Two-pollutant models with individual PM_2.5 components and the remaining PM_2.5 mass were also considered to assess the influence of changes in PM_2.5 composition on changes in the risk of CVD and asthma ED visits associated with PM_2.5 over time.

RESULTS

The relative risk of CVD ED visits associated with a 10 μg/m³ increase in 4-day PM_2.5 concentration (lag 0-3) was higher in the LATE period (rate ratio = 1.020, 95% confidence interval = [1.010, 1.030]) compared to the EARLY period (1.003, [0.996, 1.010]). In contrast, for asthma, relative risk estimates were largest in the EARLY period (1.018, [1.006, 1.029]), but smaller in the following periods. Similar temporal differences in relative risk estimates for CVD and asthma were observed among different age and season groups. No single component was identified as an obvious contributor to the changing risk estimates over time, and some components exhibited different temporal patterns in risk estimates from PM_2.5 total mass, such as a decreased risk of CVD ED visits associated with sulfate over time.

CONCLUSIONS

Temporal changes in the risk of CVD and asthma ED visits associated with short-term increases in ambient PM_2.5 concentrations were observed. These changes could be related to changes in PM_2.5 composition (e.g., an increasing fraction of organic carbon and a decreasing fraction of sulfate in PM_2.5). Other factors such as improvements in healthcare and differential exposure misclassification might also contribute to the changes.

Public health effect and its economics loss of PM2.5 pollution from coal consumption in China

China's energy structure is based on coal resource and it accounts for main proportion in the primary energy consumption. Coal consumption produces PM_2.5 pollution, which seriously affects public health. Considering that there are few studies on the effect PM_2.5 pollution produced by coal consumption, this paper uses the Poisson Regression model to estimate the impacts on public health and the economic loss of PM_2.5 pollution produced by coal consumption using the data in 2015. Based on these results, the paper also predicts the impacts on public health effect and its economic loss caused by PM_2.5 pollution from coal consumption under the baseline scenario and total coal consumption control scenario in 2020 and 2030. Finally, based on the research conclusions, suggestions are proposed to reduce the public health economic loss from PM_2.5 pollution caused by coal consumption.

Source Contributions to Rural Carbonaceous Winter Aerosol in North-Eastern Poland

Concentrations of aerosol particles in Poland and their sources are rarely discussed in peer-reviewed journal articles despite serious air quality issues. A source apportionment of carbonaceous aerosol particles was performed during winter at a rural background environment field site in north-eastern Poland. Data were used of light absorption at seven wavelengths and levoglucosan concentrations along existing monitoring of PM2.5, organic carbon and elemental carbon (OC/EC) at the Diabła Góra EMEP monitoring site between January 17 and March 19 during the EMEP intensive winter campaign of 2018. Average PM2.5, OC, EC, equivalent black carbon (eBC) and levoglucosan concentrations and standard deviations amounted to 18.5 ± 9.3, 4.5 ± 2.5, 0.57 ± 0.28, 1.04 ± 0.62 and 0.134 ± 0.084 µg m−3 respectively. Various tools for source apportionment were used to obtain a source contribution to carbonaceous matter (CM) with three components. The wood combustion source component contributed 1.63 µg m−3 (21%), domestic coal combustion 3.3 µg m−3 (41%) and road transport exhaust 2.9 µg m−3 (38%). Similar levels and temporal variability were found for the nearby Lithuanian site of Preila, corroborating the Polish results.

A systematic review of cardiovascular responses associated with ambient black carbon and fine particulate matter

BACKGROUND

Exposure to fine particulate matter (PM_2.5), an ambient air pollutant with mass-based standards promulgated under the Clean Air Act, and black carbon (BC), a common component of PM_2.5, are both associated with cardiovascular health effects.

OBJECTIVES

To elucidate whether BC is associated with distinct, or stronger, cardiovascular responses compared to PM_2.5, we conducted a systematic review. We evaluated the associations of short- and long-term BC, or the related component elemental carbon (EC), with cardiovascular endpoints including heart rate variability, heart rhythm, blood pressure and vascular function, ST segment depression, repolarization abnormalities, atherosclerosis and heart function, in the context of what is already known about PM_2.5.

DATA SOURCES

We conducted a stepwise systematic literature search of the PubMed, Web of Science and TOXLINE databases and applied Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines for reporting our results.

STUDY ELIGIBILITY CRITERIA

Studies reporting effect estimates for the association of quantitative measurements of ambient BC (or EC) and PM_2.5, with relevant cardiovascular endpoints (i.e. meeting inclusion criteria) were included in the review. Included studies were evaluated for risk of bias in study design and results.

STUDY APPRAISAL AND SYNTHESIS METHODS

Risk of bias evaluations assessed aspects of internal validity of study findings based on study design, conduct, and reporting to identify potential issues related to confounding or other biases. Study results are presented to facilitate comparison of the consistency of associations with PM_2.5 and BC within and across studies.

RESULTS

Our results demonstrate similar associations for BC (or EC) and PM_2.5 with the cardiovascular endpoints examined. Across studies, associations for BC and PM_2.5 varied in their magnitude and precision, and confidence intervals were generally overlapping within studies. Where differences in the magnitude of the association between BC or EC and PM_2.5 within a study could be discerned, no consistent pattern across the studies examined was apparent.

LIMITATIONS

We were unable to assess the independence of the effect of BC, relative the effect of PM_2.5, on the cardiovascular system, nor was information available to understand the impact of differential exposure misclassification.

CONCLUSIONS

Overall, the evidence indicates that both BC (or EC) and PM_2.5 are associated with cardiovascular effects but the available evidence is not sufficient to distinguish the effect of BC (or EC) from that of PM_2.5 mass.

Current State of Research on the Risk of Morbidity and Mortality Associated with Air Pollution in Korea

PURPOSE

The effects of air pollution on health can vary regionally. Our goal was to comprehensively review previous epidemiological studies on air pollution and health conducted in Korea to identify future areas of potential study.

MATERIALS AND METHODS

We systematically searched all published epidemiologic studies examining the association between air pollution and occurrence of death, diseases, or symptoms in Korea. After classifying health outcomes into mortality, morbidity, and health impact, we summarized the relationship between individual air pollutants and health outcomes.

RESULTS

We analyzed a total of 27 studies that provided 104 estimates of the quantitative association between risk of mortality and exposure to air pollutants, including particulate matter with aerodynamic diameter less than 10 μm, particulate matter with aerodynamic diameter less than 2.5 μm, sulfur dioxide, nitrogen dioxide, ozone, and carbon monoxide in Korea between January 1999 and July 2018. Regarding the association with morbidity, there were 38 studies, with 98 estimates, conducted during the same period. Most studies examined the short-term effects of air pollution using a time series or case-crossover study design; only three cohort studies that examined long-term effects were found. There were four health impact studies that calculated the attributable number of deaths or disability-adjusted life years due to air pollution.

CONCLUSION

There have been many epidemiologic studies in Korea regarding air pollution and health. However, the present review shows that additional studies, especially cohort and experimental studies, are needed to provide more robust and accurate evidence that can be used to promote evidence-based policymaking.

Short-term effects of fine particulate matter on non-accidental and circulatory diseases mortality: A time series study among the elder in Changchun

Background and objectives

Fine particulate matter (PM_2.5, particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) has multiple adverse effects on human health, especially on the respiratory and circulatory system. The purpose of this study was to evaluate the short-term effect of PM_2.5 on the mortality risk of non-accidental and circulatory diseases, and to explore the potential effect modification by sex, education and death location.

Methods

We collected daily mortality counts of Changchun (China) residents, daily meteorology and air pollution data, from January 1, 2014, to January 1, 2017. We focused on the elderly (≥65 years old) population who died from non-accidental causes and circulatory diseases, and stratified them by sex, education, and death location. A generalized additive Poisson regression model (GAM) was used to analyse the impact of air pollutants on mortality. We fit single pollutant models to examine PM_2.5 effects with different lag structures of single-day (distributed lag:lag0-lag3) and multi-day (moving average lag: lag01-lag03). To test the sensitivity of the model, a multi-pollutant model was established when the PM_2.5 effect was strongest.

Results

In the single pollutant models, an increment of PM_2.5 by 10 μg/m³ at lag0-3 was associated with a 0.385% (95% CI: 0.069% to 0.702%) increase in daily non-accidental mortality and a 0.442% (95% CI: 0.038% to 0.848%) increase in daily circulatory disease mortality. NO₂ (lag1) and O₃ (lag0, lag1, lag2, lag01,lag02, lag03) were associated with daily non-accidental death and NO₂ (lag1, lag3, lag03) and O₃ (lag0, lag1, lag01,lag02, lag03) were associated with daily circulatory disease mortality. In the co-pollutant models, the risk estimates for PM_2.5 changed slightly. The excess mortality risk of non-accidental and circulatory diseases was higher for women, people with low education, and died outside hospital.

Conclusions

We found that short-term exposure to PM_2.5 increased the mortality risk of non-accidental and circulatory diseases among the elderly in Changchun. Women, people with low education and died outside hospital are more susceptible to PM_2.5. NO₂ and O₃ were also associated with an increase in mortality from non-accidental and circulatory diseases and the O₃ is a high effect.

Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection

INTRODUCTION

Advances in the development of high-resolution metabolomics (HRM) have provided new opportunities for their use in characterizing exposures to environmental air pollutants and air pollution-related disease etiologies. Exposure assessment studies have considered blood, breath, and saliva as biological matrices suitable for measuring responses to air pollution exposures. The current study examines comparability among these three matrices using HRM and explores their potential for measuring mobile-source air toxics.

METHODS

Four participants provided saliva, exhaled breath concentrate (EBC), and plasma before and after a 2 h road traffic exposure. Samples were analyzed on a Thermo Scientific QExactive MS system in positive electrospray ionization mode and resolution of 70 000 full-width at half-maximum with C18 chromatography. Data were processed using an apLCMS and xMSanalyzer on the R statistical platform.

RESULTS

The analysis yielded 7110, 6019, and 7747 reproducible features in plasma, EBC, and saliva, respectively. Correlations were moderate-to-strong (R = 0.41-0.80) across all pairwise comparisons of feature intensity within profiles, with the strongest between EBC and saliva. The associations of mean intensities between matrix pairs were positive and significant, controlling for subject and sampling time effects. Six out of 20 features shared in all three matrices putatively matched a list of known mobile-source air toxics.

CONCLUSIONS

Plasma, saliva, and EBC have largely comparable metabolic profiles measurable through HRM. These matrices have the potential to be used in identification and measurement of exposures to mobile-source air toxics, though further, targeted study is needed.

Influence of exposure differences on city-to-city heterogeneity in PM2.5-mortality associations in US cities

BACKGROUND

Multi-city population-based epidemiological studies have observed heterogeneity between city-specific fine particulate matter (PM_2.5)-mortality effect estimates. These studies typically use ambient monitoring data as a surrogate for exposure leading to potential exposure misclassification. The level of exposure misclassification can differ by city affecting the observed health effect estimate.

METHODS

The objective of this analysis is to evaluate whether previously developed residential infiltration-based city clusters can explain city-to-city heterogeneity in PM_2.5 mortality risk estimates. In a prior paper 94 cities were clustered based on residential infiltration factors (e.g. home age/size, prevalence of air conditioning (AC)), resulting in 5 clusters. For this analysis, the association between PM_2.5 and all-cause mortality was first determined in 77 cities across the United States for 2001-2005. Next, a second stage analysis was conducted evaluating the influence of cluster assignment on heterogeneity in the risk estimates.

RESULTS

Associations between a 2-day (lag 0-1 days) moving average of PM_2.5 concentrations and non-accidental mortality were determined for each city. Estimated effects ranged from -3.2 to 5.1% with a pooled estimate of 0.33% (95% CI: 0.13, 0.53) increase in mortality per 10 μg/m³ increase in PM_2.5. The second stage analysis determined that cluster assignment was marginally significant in explaining the city-to-city heterogeneity. The health effects estimates in cities with older, smaller homes with less AC (Cluster 1) and cities with newer, smaller homes with a large prevalence of AC (Cluster 3) were significantly lower than the cluster consisting of cities with older, larger homes with a small percentage of AC.

CONCLUSIONS

This is the first study that attempted to examine whether multiple exposure factors could explain the heterogeneity in PM_2.5-mortality associations. The results of this study were found to explain a small portion (6%) of this heterogeneity.

Chemical constituents and sources of ambient particulate air pollution and biomarkers of endothelial function in a panel of healthy adults in Beijing, China

BACKGROUND

Exposure to ambient air pollution has been associated with endothelial dysfunction as reflected by short-term alterations in circulating biomarkers, but the chemical constituents and pollution sources behind the association has been unclear.

METHODS

We investigated the associations between various ambient air pollutants including gases and 31 chemical constituents and seven sources of fine particles (PM2.5) and biomarkers of endothelial function, including endothelin-1 (ET-1), E-selectin, soluble intercellular adhesion molecule 1 (ICAM-1) and vascular cell adhesion molecule 1 (VCAM-1), based on 462 repeated measurements in a panel of 40 college students who were followed for three study periods before and after relocating from a suburban area to an urban area in Beijing, China in 2010-2011. Air pollution data were obtained from central air-monitoring stations. Linear mixed-effects models were used to estimate the changes in biomarkers associated with exposures.

RESULTS

Total PM2.5 mass showed few appreciable associations with examined biomarkers. However, several PM2.5 constituents and related sources showed significant associations with examined biomarkers. PM2.5 from dust/soil and several crustal and transition metals, including strontium, iron, titanium, cobalt and magnesium, were significantly associated with increases in ET-1 at 1-day average; manganese and potassium were significantly associated with increases in ICAM-1 at 2-day average; and PM2.5 from industry and metal cadmium were significantly associated with decreases in VCAM-1 at 1-day average. In addition, carbon monoxide was significantly associated with increasing ICAM-1 at 1-day and 2-day averages, whereas nitric oxide was significantly associated with decreasing ICAM-1 at 1-day and 3-day averages.

CONCLUSIONS

Our results suggest that certain PM2.5 metal constituents were more closely associated with circulating biomarkers of endothelial function than PM2.5, and therefore highlight the research necessity to examine pollution chemical constituents in future studies.

Chemical Composition of Fine Particulate Matter and Life Expectancy: In 95 US Counties Between 2002 and 2007

BACKGROUND

In a previous study, we provided evidence that a decline in fine particulate matter (PM2.5) air pollution during the period between 2000 and 2007 was associated with increased life expectancy in 545 counties in the United States. In this article, we investigated which chemical constituents of PM2.5 were the main drivers of the observed association.

METHODS

We estimated associations between temporal changes in seven major components of PM2.5 (ammonium, sulfate, nitrate, elemental carbon matter, organic carbon matter, sodium, and silicon) and temporal changes in life expectancy in 95 counties between 2002 and 2007. We included US counties that had adequate chemical components of PM2.5 mass data across all seasons. We fitted single pollutant and multiple pollutant linear models, controlling for available socioeconomic, demographic, and smoking variables and stratifying by urban and nonurban counties.

RESULTS

In multiple pollutant models, we found that: (1) a reduction in sulfate was associated with an increase in life expectancy; and (2) reductions in ammonium and sodium ion were associated with increases in life expectancy in nonurban counties only.

CONCLUSIONS

Our findings suggest that recent reductions in long-term exposure to sulfate, ammonium, and sodium ion between 2002 and 2007 are associated with improved public health.

Comparing multipollutant emissions-based mobile source indicators to other single pollutant and multipollutant indicators in different urban areas

A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NO_x) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31–0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80–0.98). NO_x correlations with PMF factors varied across cities (r = 0.29–0.67), while correlations with IMSIs were relatively consistent (r = 0.61–0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58–0.98) than with PMF-derived factors (r = 0.07–0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data.

Using self-organizing maps to develop ambient air quality classifications: a time series example

BACKGROUND

Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies.

OBJECTIVE

Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles.

METHODS

Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques.

RESULTS

Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships.

CONCLUSION

We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.

Clustering cities with similar fine particulate matter exposure characteristics based on residential infiltration and in-vehicle commuting factors

Epidemiological studies have observed between city heterogeneity in PM2.5-mortality risk estimates. These differences could potentially be due to the use of central-site monitors as a surrogate for exposure which do not account for an individual's activities or ambient pollutant infiltration to the indoor environment. Therefore, relying solely on central-site monitoring data introduces exposure error in the epidemiological analysis. The amount of exposure error produced by using the central-site monitoring data may differ by city. The objective of this analysis was to cluster cities with similar exposure distributions based on residential infiltration and in-vehicle commuting characteristics. Factors related to residential infiltration and commuting were developed from the American Housing Survey (AHS) from 2001 to 2005 for 94 Core-Based Statistical Areas (CBSAs). We conducted two separate cluster analyses using a k-means clustering algorithm to cluster CBSAs based on these factors. The first only included residential infiltration factors (i.e. percent of homes with central air conditioning (AC) mean year home was built, and mean home size) while the second incorporated both infiltration and commuting (i.e. mean in-vehicle commuting time and mean in-vehicle commuting distance) factors. Clustering on residential infiltration factors resulted in 5 clusters, with two having distinct exposure distributions. Cluster 1 consisted of cities with older, smaller homes with less central AC while homes in Cluster 2 cities were newer, larger, and more likely to have central AC. Including commuting factors resulted in 10 clusters. Clusters with shorter in-vehicle commuting times had shorter in-vehicle commuting distances. Cities with newer homes also tended to have longer commuting times and distances. This is the first study to employ cluster analysis to group cities based on exposure factors. Identifying cities with similar exposure distributions may help explain city-to-city heterogeneity in PM2.5 mortality risk estimates.

Spatial and seasonal variability of the mass concentration and chemical composition of PM2.5 in Poland

The seasonal changes in ambient mass concentrations and chemical composition of fine particulate matter (PM_2.5) were investigated in three locations in Poland. The analyses included PM_2.5-bound hazardous benzo(a)pyrene (BaP), As, Ni, Cd, and Pb. The samples of PM_2.5 were collected daily in Katowice (southern Poland, urban background site), Gdańsk, and Diabla Góra (northern Poland, urban and regional background sites, respectively) during 1-year-long campaign in 2010. Based on monthly ambient concentrations of PM_2.5-bound carbon (organic and elemental), water-soluble ions (Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺, Cl^-, NO₃^-, SO₄^2-), and elements As, Ni, Cd, Pb, Ti, Al, Fe, the chemical mass closure of PM_2.5 was checked for each of the four seasons of the year and for the heating and non-heating periods at each site. Also, the annual concentrations of PM_2.5 were determined and the annual PM_2.5 mass closure checked. At each measuring point, the PM_2.5 concentrations were high compared to its Polish yearly permissible value, 25 μg/m³, and its concentrations elsewhere in Europe. The highest annual PM_2.5 concentration, 43 μg/m³, occurred in Katowice; it was twice the annual PM_2.5 concentration in Gdańsk, and thrice the one in Diabla Góra. The high annual averages were due to very high monthly concentrations in the heating period, which were highest in the winter. PM_2.5 consisted mainly of carbonaceous matter (elemental carbon (EC) + organic matter (OM), the sum of elemental carbon, EC, and organic matter, OM; its annual mass contributions to PM_2.5 were 43, 31, and 33 % in Katowice, Gdansk, and Diabla Góra, respectively), secondary inorganic aerosol (SIA), the Na_Cl group, and crustal matter (CM)-in the decreasing order of their yearly mass contributions to PM_2.5. OM, EC, SIA, Na_Cl, and CM accounted for almost 81 % of the PM_2.5 mass in Katowice, 74 % in Gdańsk, and 90 % in Diabla Góra. The annual average toxic metal contribution to the PM_2.5 mass was not greater than 0.2 % at each site. In Katowice and Gdańsk, the yearly ambient BaP concentrations were high (15.4 and 3.2 ng/m³, respectively); in rural Diabla Góra, the concentrations of BaP were almost equal to 1 ng/m³, the Polish BaP annual limit. The great seasonal fluctuations of the shares of the component groups in PM_2.5 and of the concentrations of PM_2.5 and its components are due to the seasonal fluctuations of the emissions of PM and its precursors from hard and brown coal combustion for energy production, growing in a heating season, reaching maximum in winter, and decreasing in a non-heating period. In Gdańsk, northern Poland, especially in the spring and autumn, sea spray might have affected the chemical composition of PM_2.5. The greatest hazard from PM_2.5 occurs in Katowice, southern Poland, in winter, when very high concentrations of PM_2.5 and PM_2.5-related carbonaceous matter, including BaP, are maintained by poor natural ventilation in cities, weather conditions, and the highest level of industrialization in Poland. In less industrialized northern Poland, where the aeration in cities is better and rather gaseous than solid fuels are used, the health hazard from ambient PM_2.5 is much lower.