Combining PM2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures

Effects of using different exposure data to estimate changes in premature mortality attributable to PM2.5 and O3 in China

The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM_2.5) and ozone (O₃) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM_2.5 and O₃ in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM_2.5 and O₃ using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM_2.5-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142-159) to 308 (CI95: 213-241) thousands from 2014 to 2018 by using different concentration data, while O₃-related premature mortality increases by 67 (CI95: 26-104) to 103 (CI95: 40-163) thousands. The estimated mean changes are up to 40% different for the PM_2.5-related mortality, and up to 30% for the O₃-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 10³ people/km², mostly located in Central China, for both PM_2.5 and O₃. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.

Explore Regional PM2.5 Features and Compositions Causing Health Effects in Taiwan

Chemical compositions of atmospheric fine particles like PM2.5 prove harmful to human health, particularly to cardiopulmonary functions. Multifaceted health effects of PM2.5 have raised broader, stronger concerns in recent years, calling for comprehensive environmental health-risk assessments to offer new insights into air-pollution control. However, there have been few studies adopting local air-quality-monitoring datasets or local coefficients related to PM2.5 health-risk assessment. This study aims to assess health effects caused by PM2.5 concentrations and metal toxicity using epidemiological and toxicological methods based on long-term (2007-2017) hourly monitoring datasets of PM2.5 concentrations in four cities of Taiwan. The results indicated that (1) PM2.5 concentrations and hazardous substances varied substantially from region to region, (2) PM2.5 concentrations significantly decreased after 2013, which benefited mainly from two actions against air pollution, i.e., implementing air-pollution-control strategies and raising air-quality standards for certain emission sources, and (3) under the condition of low PM2.5 concentrations, high health risks occurred in eastern Taiwan on account of toxic substances adsorbed on PM2.5 surface. It appears that under the condition of low PM2.5 concentrations, the results of epidemiological and toxicological health-risk assessments may not agree with each other. This raises a warning that air-pollution control needs to consider toxic substances adsorbed in PM2.5 and region-oriented control strategies are desirable. We hope that our findings and the proposed transferable methodology can call on domestic and foreign authorities to review current air-pollution-control policies with an outlook on the toxicity of PM2.5.

Autophagy changes in lung tissues of mice at 30 days after carbon black-metal ion co-exposure

OBJECTIVES

Accumulating studies have investigated the PM2.5-induced pulmonary toxicity, while gaps still remain in understanding its toxic mechanism. Due to its high specific surface area and adsorption capacity similar to nanoparticles, PM2.5 acts as a significant carrier of metals in air and then leads to altered toxic effects. In this study, we aimed to use CBs and Ni as model materials to investigate the autophagy changes and pulmonary toxic effects at 30 days following intratracheal instillation of CBs-Ni mixture.

MATERIALS AND METHODS

Groups of mice were instilled with 100 µL normal saline (NS), 20 µg CBs, and 4 µg Ni or CBs-Ni mixture, respectively. At 7 and 30 days post-instillation, all the mice were weighed and then sacrificed. The evaluation system was composed of the following: (a) autophagy and lysosomal function assessment, (b) trace element biodistribution observation in lungs, (c) pulmonary lavage biomedical analysis, (d) lung histopathological evaluation, (e) coefficient analysis of major organs and (f) CBs-Ni interaction and cell proliferation assessment.

RESULTS

We found that after CBs-Ni co-exposure, no obvious autophagy and lysosomal dysfunction or pulmonary toxicity was detected, along with complete clearance of Ni from lung tissues as well as recovery of biochemical indexes to normal range.

CONCLUSIONS

We conclude that the damaged autophagy and lysosomal function, as well as physiological function, was repaired at 30 days after exposure of CBs-Ni. Our findings provide a new idea for scientific assessment of the impact of fine particles on environment and human health, and useful information for the comprehensive treatment of air pollution.

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.

Ultrafine particle libraries for exploring mechanisms of PM2.5-induced toxicity in human cells

Air pollution worldwide, especially in China and India, has caused serious health issues. Because PM_2.5 particles consist of solid particles of diverse properties with payloads of inorganic, organic and biological pollutants, it is still not known what the major toxic components are and how these components induce toxicities. To explore this complex issue, we apply reductionism principle and an ultrafine particle library approach in this work. From investigation of 63 diversely functionalized ultrafine particles (FUPs) with adsorbed key pollutants, our findings indicate that 1) only certain pollutants in the payloads of PM_2.5 are responsible for causing cellular oxidative stress, cell apoptosis, and cytotoxicity while the particle carriers are much less toxic; 2) pollutant-induced cellular oxidative stress and oxidative stress-triggered apoptosis are identified as one of the dominant mechanisms for PM_2.5-induced cytotoxicity; 3) each specific toxic component on PM_2.5 (such as As, Pb, Cr or BaP) mainly affects its specific target organ(s) and, adding together, these pollutants may cause synergistic or just additive effects. Our findings demonstrate that reductionism concept and model PM_2.5 particle library approach are very effective in our endeavor to search for a better understanding of PM_2.5-induced health effects.

Advances in Understanding Air Pollution and CVD

The MESA Air (Multi-Ethnic Study of Atherosclerosis and Air Pollution) leveraged the platform of the MESA cohort into a prospective longitudinal study of relationships between air pollution and cardiovascular health. MESA Air researchers developed fine-scale, state-of-the-art air pollution exposure models for the MESA Air communities, creating individual exposure estimates for each participant. These models combine cohort-specific exposure monitoring, existing monitoring systems, and an extensive database of geographic and meteorological information. Together with extensive phenotyping in MESA-and adding participants and health measurements to the cohort-MESA Air investigated environmental exposures on a wide range of outcomes. Advances by the MESA Air team included not only a new approach to exposure modeling, but also biostatistical advances in addressing exposure measurement error and temporal confounding. The MESA Air study advanced our understanding of the impact of air pollutants on cardiovascular disease and provided a research platform for advances in environmental epidemiology.

Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort

Although cohort studies of the health effects of PM2.5 have developed exposure prediction models to represent spatial variability across participant residences, few models exist for PM2.5 components. We aimed to develop a city-specific spatio-temporal prediction approach to estimate long-term average concentrations of four PM2.5 components including sulfur, silicon, and elemental and organic carbon for the Multi-Ethnic Study of Atherosclerosis cohort, and to compare predictions to those from a national spatial model. Using 2-week average measurements from a cohort-focused monitoring campaign, the spatio-temporal model employed selected geographic covariates in a universal kriging framework with the data-driven temporal trend. Relying on long-term means of daily measurements from regulatory monitoring networks, the national spatial model employed dimension-reduced predictors using universal kriging. For the spatio-temporal model, the cross-validated and temporally-adjusted R(2) was relatively higher for EC and OC, and in the Los Angeles and Baltimore areas. The cross-validated R(2)s for both models across the six areas were reasonably high for all components except silicon. Predicted long-term concentrations at participant homes from the two models were generally highly correlated across cities but poorly correlated within cities. The spatio-temporal model may be preferred for city-specific health analyses, whereas both models could be used for multi-city studies.