The causal links between long-term exposure to major PM2.5 components and the burden of tuberculosis in China

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Abstract

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d3093416e158">We aimed to estimate the causal impacts of long-term exposure to major PM2.5 components - including black carbon, organic matter, sulfate, nitrate, and ammonium - on the incidence and mortality of tuberculosis in China. </p>

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Health Effects of Fine Particulate Air Pollution: Lines that Connect

C. Pope, Douglas Dockery (2006)

Efforts to understand and mitigate thehealth effects of particulate matter (PM) air pollutionhave a rich and interesting history. This review focuseson six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There hasbeen substantial progress in the evaluation of PM health effects at different time-scales of exposure and in the exploration of the shape of the concentration-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiological pathways that link PM exposure with cardiopulmonary morbidiity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonaryhealth. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality standards.

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Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions

Bo Zheng, Dan Tong, Meng Li … (2018)

Abstract. To tackle the problem of severe air pollution, China has implemented active clean air policies in recent years. As a consequence, the emissions of major air pollutants have decreased and the air quality has substantially improved. Here, we quantified China's anthropogenic emission trends from 2010 to 2017 and identified the major driving forces of these trends by using a combination of bottom-up emission inventory and index decomposition analysis (IDA) approaches. The relative change rates of China's anthropogenic emissions during 2010–2017 are estimated as follows: −62 % for SO 2 , −17 % for NO x , +11 % for nonmethane volatile organic compounds (NMVOCs), +1 % for NH 3 , −27 % for CO, −38 % for PM 10 , −35 % for PM 2.5 , −27 % for BC, −35 % for OC, and +16 % for CO 2 . The IDA results suggest that emission control measures are the main drivers of this reduction, in which the pollution controls on power plants and industries are the most effective mitigation measures. The emission reduction rates markedly accelerated after the year 2013, confirming the effectiveness of China's Clean Air Action that was implemented since 2013. We estimated that during 2013–2017, China's anthropogenic emissions decreased by 59 % for SO 2 , 21 % for NO x , 23 % for CO, 36 % for PM 10 , 33 % for PM 2.5 , 28 % for BC, and 32 % for OC. NMVOC emissions increased and NH 3 emissions remained stable during 2010–2017, representing the absence of effective mitigation measures for NMVOCs and NH 3 in current policies. The relative contributions of different sectors to emissions have significantly changed after several years' implementation of clean air policies, indicating that it is paramount to introduce new policies to enable further emission reductions in the future.

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Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting

Pam Factor-Litvak, Chris Gennings, Caroline Carrico … (2015)

In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies "bad actors" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set.