Pinnacles and pitfalls for source apportionment of potential health effects from airborne particle exposure

Source apportionment of PM10 and PM2.5 air pollution, and possible impacts of study characteristics in South Korea

INTRODUCTION

Studies of source apportionment (SA) for particulate matter (PM) air pollution have enhanced understanding of dominant pollution sources and quantification of their contribution. Although there have been many SA studies in South Korea over the last two decades, few studies provided an integrated understanding of PM sources nationwide. The aim of this study was to summarize findings of PM SA studies of South Korea and to explore study characteristics.

METHODS

We selected studies that estimated sources of PM₁₀ and PM_2.5 performed for 2000-2017 in South Korea using Positive Matrix Factorization and Chemical Mass Balance. We reclassified the original PM sources identified in each study into seven categories: motor vehicle, secondary aerosol, soil dust, biomass/field burning, combustion/industry, natural source, and others. These seven source categories were summarized by using frequency and contribution across four regions, defined by northwest, west, southeast, and southwest regions, by PM₁₀ and PM_2.5. We also computed the population-weighted mean contribution of each source category. In addition, we compared study features including sampling design, sampling and lab analysis methods, chemical components, and the inclusion of Asian dust days.

RESULTS

In the 21 selected studies, all six PM₁₀ studies identified motor vehicle, soil dust, and combustion/industry, while all 15 PM_2.5 studies identified motor vehicle and soil dust. Different from the frequency, secondary aerosol produced a large contribution to both PM₁₀ and PM_2.5. Motor vehicle contributed highly to both, whereas the contribution of combustion/industry was high for PM₁₀. The population-weighted mean contribution was the highest for the motor vehicle and secondary aerosol sources for both PM10 and PM2.5. However, these results were based on different subsets of chemical speciation data collected at a single sampling site, commonly in metropolitan areas, with short overlap and measured by different lab analysis methods.

CONCLUSION

We found that motor vehicle and secondary aerosol were the most common and influential sources for PM in South Korea. Our study, however, suggested a caution to understand SA findings from heterogeneous study features for study designs and input data.

Recent Approaches to Estimate Associations Between Source-Specific Air Pollution and Health

PURPOSE OF REVIEW

Estimating health effects associated with source-specific exposure is important for better understanding how pollution impacts health and for developing policies to better protect public health. Although epidemiologic studies of sources can be informative, these studies are challenging to conduct because source-specific exposures (e.g., particulate matter from vehicles) often are not directly observed and must be estimated. We reviewed recent studies that estimated associations between pollution sources and health to identify methodological developments designed to address important challenges.

RECENT FINDINGS

Notable advances in epidemiologic studies of sources include approaches for (1) propagating uncertainty in source estimation into health effect estimates, (2) assessing regional and seasonal variability in emissions sources and source-specific health effects, and (3) addressing potential confounding in estimated health effects. Novel methodological approaches to address challenges in studies of pollution sources, particularly evaluation of source-specific health effects, are important for determining how source-specific exposure impacts health.

Public health and components of particulate matter: the changing assessment of black carbon

In 2012, the WHO classified diesel emissions as carcinogenic, and its European branch suggested creating a public health standard for airborne black carbon (BC). In 2011, EU researchers found that life expectancy could be extended four to nine times by reducing a unit of BC, vs reducing a unit of PM2.5. Only recently could such determinations be made. Steady improvements in research methodologies now enable such judgments. In this Critical Review, we survey epidemiological and toxicological literature regarding carbonaceous combustion emissions, as research methodologies improved over time. Initially, we focus on studies of BC, diesel, and traffic emissions in the Western countries (where daily urban BC emissions are mainly from diesels). We examine effects of other carbonaceous emissions, e.g., residential burning of biomass and coal without controls, mainly in developing countries. Throughout the 1990s, air pollution epidemiology studies rarely included species not routinely monitored. As additional PM2.5. chemical species, including carbonaceous species, became more widely available after 1999, they were gradually included in epidemiological studies. Pollutant species concentrations which more accurately reflected subject exposure also improved models. Natural "interventions"--reductions in emissions concurrent with fuel changes or increased combustion efficiency; introduction of ventilation in highway tunnels; implementation of electronic toll payment systems--demonstrated health benefits of reducing specific carbon emissions. Toxicology studies provided plausible biological mechanisms by which different PM species, e.g, carbonaceous species, may cause harm, aiding interpretation of epidemiological studies. Our review finds that BC from various sources appears to be causally involved in all-cause, lung cancer and cardiovascular mortality, morbidity, and perhaps adverse birth and nervous system effects. We recommend that the US. EPA rubric for judging possible causality of PM25. mass concentrations, be used to assess which PM2.5. species are most harmful to public health.

IMPLICATIONS

Black carbon (BC) and correlated co-emissions appear causally related with all-cause, cardiovascular, and lung cancer mortality, and perhaps with adverse birth outcomes and central nervous system effects. Such findings are recent, since widespread monitoring for BC is also recent. Helpful epidemiological advances (using many health relevant PM2.5 species in models; using better measurements of subject exposure) have also occurred. "Natural intervention" studies also demonstrate harm from partly combusted carbonaceous emissions. Toxicology studies consistently find biological mechanisms explaining how such emissions can cause these adverse outcomes. A consistent mechanism for judging causality for different PM2.5 species is suggested.

Classification and regression trees for epidemiologic research: an air pollution example

BACKGROUND

Identifying and characterizing how mixtures of exposures are associated with health endpoints is challenging. We demonstrate how classification and regression trees can be used to generate hypotheses regarding joint effects from exposure mixtures.

METHODS

We illustrate the approach by investigating the joint effects of CO, NO2, O3, and PM2.5 on emergency department visits for pediatric asthma in Atlanta, Georgia. Pollutant concentrations were categorized as quartiles. Days when all pollutants were in the lowest quartile were held out as the referent group (n = 131) and the remaining 3,879 days were used to estimate the regression tree. Pollutants were parameterized as dichotomous variables representing each ordinal split of the quartiles (e.g. comparing CO quartile 1 vs. CO quartiles 2-4) and considered one at a time in a Poisson case-crossover model with control for confounding. The pollutant-split resulting in the smallest P-value was selected as the first split and the dataset was partitioned accordingly. This process repeated for each subset of the data until the P-values for the remaining splits were not below a given alpha, resulting in the formation of a "terminal node". We used the case-crossover model to estimate the adjusted risk ratio for each terminal node compared to the referent group, as well as the likelihood ratio test for the inclusion of the terminal nodes in the final model.

RESULTS

The largest risk ratio corresponded to days when PM2.5 was in the highest quartile and NO2 was in the lowest two quartiles (RR: 1.10, 95% CI: 1.05, 1.16). A simultaneous Wald test for the inclusion of all terminal nodes in the model was significant, with a chi-square statistic of 34.3 (p = 0.001, with 13 degrees of freedom).

CONCLUSIONS

Regression trees can be used to hypothesize about joint effects of exposure mixtures and may be particularly useful in the field of air pollution epidemiology for gaining a better understanding of complex multipollutant exposures.

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.

Assessment of heterogeneity of metal composition of fine particulate matter collected from eight U.S. counties using principal component analysis

The main objectives of this study are to (1) characterize chemical constituents of particulate matter (PM) and (2) compare overall differences in PM collected from eight US. counties. This project was undertaken as a part of a larger research program conducted by the Johns Hopkins Particulate Matter Research Center (JHPMRC). The goal of the JHPMRC is to explore the relationship between health effects and exposure to ambient PM of differing composition. The JHPMRC collected weekly filter-based ambient fine particle samples from eight US. counties between January 2008 and January 2010. Each sampling effort consisted of a 5-6-week sampling period. Filters were analyzed for 25 metals using inductively coupled plasma mass spectrometry (ICP-MS). Overall compositional differences were ranked by principal component analysis (PCA). The results showed that weekly concentrations of each element varied 3-40 times between the eight counties. PCA showed that the first five principal components explained 85% of the total variance. The authors found significant overall compositional differences in PM as the average of standardized principal component scores differed between the counties. These findings demonstrate PCA is a useful tool to identify the differences in PM compositional mixtures by county. These differences will be helpful for epidemiological and toxicological studies to help explain why health risks associated with PM exposure are different in locations with similar mass concentrations of PM.

Spatial and seasonal distribution of aerosol chemical components in New York City: (1) Incineration, coal combustion, and biomass burning

We describe spatial and temporal patterns of fine particulate matter (PM(2.5)) and of 12 of its constituent chemical elements commonly observed in measurements at residential locations in New York City (NYC). These elements, that is, Ni, V, As, Se, S, Cl, Na, K, Pb, Cu, Zn, and Mn, had significant spatial and temporal variability at 10 PM(2.5) sampling locations during our winter and summer sampling campaigns. By grouping the elements into traditional source apportionment categories, we show that specific chemical components of PM(2.5) considered to have a common source category, such as As and Se for coal combustion, do not always follow the same temporal or spatial pattern. PM(2.5) mass had only limited spatial variability and a slight summertime concentration enhancement. Measurements at residential locations were, on average, consistent with EPA sampling network measurements, although we found that during times of low regional concentration, EPA measurements underestimated the PM(2.5) concentration at residential locations. These results have implications for improved understanding of exposures to specific sources of PM(2.5), and raise some concerns about source profiles used in source-receptor modeling tracer input selection.

Cardiovascular health and particulate vehicular emissions: a critical evaluation of the evidence

A major public health goal is to determine linkages between specific pollution sources and adverse health outcomes. This paper provides an integrative evaluation of the database examining effects of vehicular emissions, such as black carbon (BC), carbonaceous gasses, and ultrafine PM, on cardiovascular (CV) morbidity and mortality. Less than a decade ago, few epidemiological studies had examined effects of traffic emissions specifically on these health endpoints. In 2002, the first of many studies emerged finding significantly higher risks of CV morbidity and mortality for people living in close proximity to major roadways, vs. those living further away. Abundant epidemiological studies now link exposure to vehicular emissions, characterized in many different ways, with CV health endpoints such as cardiopulmonary and ischemic heart disease and circulatory-disease-associated mortality; incidence of coronary artery disease; acute myocardial infarction; survival after heart failure; emergency CV hospital admissions; and markers of atherosclerosis. We identify numerous in vitro, in vivo, and human panel studies elucidating mechanisms which could explain many of these cardiovascular morbidity and mortality associations. These include: oxidative stress, inflammation, lipoperoxidation and atherosclerosis, change in heart rate variability (HRV), arrhythmias, ST-segment depression, and changes in vascular function (such as brachial arterial caliber and blood pressure). Panel studies with accurate exposure information, examining effects of ambient components of vehicular emissions on susceptible human subjects, appear to confirm these mechanisms. Together, this body of evidence supports biological mechanisms which can explain the various CV epidemiological findings. Based upon these studies, the research base suggests that vehicular emissions are a major environmental cause of cardiovascular mortality and morbidity in the United States. As a means to reduce the public health consequences of such emissions, it may be desirable to promulgate a black carbon (BC) PM(2.5) standard under the National Ambient Air Quality Standards, which would apply to both on and off-road diesels. Two specific critical research needs are identified. One is to continue research on health effects of vehicular emissions, gaseous as well as particulate. The second is to utilize identical or nearly identical research designs in studies using accurate exposure metrics to determine whether other major PM pollutant sources and types may also underlie the specific health effects found in this evaluation for vehicular emissions.