Effect of air pollution control on life expectancy in the United States: an analysis of 545 U.S. counties for the period from 2000 to 2007

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM_2.5 and PM₁₀ linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM_2.5 and PM₁₀ connected to COVID-19: 17.4 µg/m³ (PM_2.5) and 29.6 µg/m³ (PM₁₀) for Paris; 15.6 µg/m³ (PM_2.5) and 20.6 µg/m³ (PM₁₀) for Lyon; 14.3 µg/m³ (PM_2.5) and 22.04 µg/m³ (PM₁₀) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

Inflammatory effects of particulate matter air pollution

Air pollution is an important cause of non-communicable diseases globally with particulate matter (PM) as one of the main air pollutants. PM is composed of microscopic particles that contain a mixture of chemicals and biological elements that can be harmful to human health. The aerodynamic diameter of PM facilitates their deposition when inhaled. For instance, coarse PM having a diameter of < 10 μm is deposited mainly in the large conducting airways, but PM of < 2.5 μm can cross the alveolar-capillary barrier, traveling to other organs within the body. Epidemiological studies have shown the association between PM exposure and risk of disease, namely those of the respiratory system such as lung cancer, asthma, and chronic obstructive pulmonary disease (COPD). However, cardiovascular and neurological diseases have also been reported, including hypertension, atherosclerosis, acute myocardial infarction, stroke, loss of cognitive function, anxiety, and Parkinson's and Alzheimer's diseases. Inflammation is a common hallmark in the pathogenesis of many of these diseases associated with exposure to a variety of air pollutants, including PM. This review focuses on the main effects of PM on human health, with an emphasis on the role of inflammation.

Guidance to Reduce the Cardiovascular Burden of Ambient Air Pollutants: A Policy Statement From the American Heart Association

In 2010, the American Heart Association published a statement concluding that the existing scientific evidence was consistent with a causal relationship between exposure to fine particulate matter and cardiovascular morbidity and mortality, and that fine particulate matter exposure is a modifiable cardiovascular risk factor. Since the publication of that statement, evidence linking air pollution exposure to cardiovascular health has continued to accumulate and the biological processes underlying these effects have become better understood. This increasingly persuasive evidence necessitates policies to reduce harmful exposures and the need to act even as the scientific evidence base continues to evolve. Policy options to mitigate the adverse health impacts of air pollutants must include the reduction of emissions through action on air quality, vehicle emissions, and renewable portfolio standards, taking into account racial, ethnic, and economic inequality in air pollutant exposure. Policy interventions to improve air quality can also be in alignment with policies that benefit community and transportation infrastructure, sustainable food systems, reduction in climate forcing agents, and reduction in wildfires. The health care sector has a leadership role in adopting policies to contribute to improved environmental air quality as well. There is also potentially significant private sector leadership and industry innovation occurring in the absence of and in addition to public policy action, demonstrating the important role of public-private partnerships. In addition to supporting education and research in this area, the American Heart Association has an important leadership role to encourage and support public policies, private sector innovation, and public-private partnerships to reduce the adverse impact of air pollution on current and future cardiovascular health in the United States.

Changes in Life Expectancy of Respiratory Diseases from Attaining Daily PM2.5 Standard in China: A Nationwide Observational Study

Although exposure to air pollution increases the risk of premature mortality and years of life lost (YLL), the effects of daily air quality improvement to the life expectancy of respiratory diseases remained unclear. We applied a generalized additive model (GAM) to assess the associations between daily PM_2.5 exposure and YLL from respiratory diseases in 96 Chinese cities during 2013–2016. We further estimated the avoidable YLL, potential gains in life expectancy, and the attributable fraction by assuming daily PM_2.5 concentration decrease to the air quality standards of China and World Health Organization. Regional and national results were generated by random-effects meta-analysis. A total of 861,494 total respiratory diseases and 586,962 chronic obstructive pulmonary disease (COPD) caused death from 96 Chinese cities were recorded during study period. Each 10 μg/m³ increase of PM_2.5 in 3-day moving average (lag02) was associated with 0.16 (95% CI: 0.08, 0.24) years increment in life expectancy from total respiratory diseases. The highest effect was observed in Southwest region with 0.42 (95% CI: 0.22, 0.62) years increase in life expectancy. By attaining the WHO's Air Quality Guidelines, we estimated that an average of 782.09 (95% CI: 438.29, 1125.89) YLLs caused by total respiratory death in each city could be avoided, which corresponded to 1.15% (95% CI: 0.67%, 1.64%) of the overall YLLs, and 0.12 (95% CI: 0.07, 0.17) years increment in life expectancy. The results of COPD were generally consistent with total respiratory diseases. Our findings indicate that reduction in daily PM_2.5 concentrations might lead to longer life expectancy from respiratory death.

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This is a nationwide time-series study in 96 Chinese cities

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PM_2.5 level was associated with increased risk of respiratory death

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PM_2.5 level was associated with increased years of life lost of respiratory death

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Daily PM_2.5 reduction might lead to longer life expectancy from respiratory death

Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease.

METHODS

GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden.

FINDINGS

The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51-12·1) deaths (19·2% [16·9-21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12-9·31) deaths (15·4% [14·6-16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253-350) DALYs (11·6% [10·3-13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0-9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10-24 years, alcohol use for those aged 25-49 years, and high systolic blood pressure for those aged 50-74 years and 75 years and older.

INTERPRETATION

Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public.

FUNDING

Bill & Melinda Gates Foundation.

Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease.

METHODS

GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden.

FINDINGS

The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51-12·1) deaths (19·2% [16·9-21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12-9·31) deaths (15·4% [14·6-16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253-350) DALYs (11·6% [10·3-13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0-9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10-24 years, alcohol use for those aged 25-49 years, and high systolic blood pressure for those aged 50-74 years and 75 years and older.

INTERPRETATION

Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public.

FUNDING

Bill & Melinda Gates Foundation.

Health Benefits of Air Pollution Reduction

Air pollution is a grave risk to human health that affects nearly everyone in the world and nearly every organ in the body. Fortunately, it is largely a preventable risk. Reducing pollution at its source can have a rapid and substantial impact on health. Within a few weeks, respiratory and irritation symptoms, such as shortness of breath, cough, phlegm, and sore throat, disappear; school absenteeism, clinic visits, hospitalizations, premature births, cardiovascular illness and death, and all-cause mortality decrease significantly. The interventions are cost-effective. Reducing factors causing air pollution and climate change have strong cobenefits. Although regions with high air pollution have the greatest potential for health benefits, health improvements continue to be associated with pollution decreases even below international standards. The large response to and short time needed for benefits of these interventions emphasize the urgency of improving global air quality and the importance of increasing efforts to reduce pollution at local levels.

How longer can people live by achieving the daily ambient fine particulate pollution standards in the Pearl River Delta region, China?

BACKGROUND

Previous research has reported the effects of long-term fine particulate matter (PM_2.5) pollution on years of life lost (YLL), but these effects may not represent the full impact. This study aims to estimate potential benefits in life time from adhering to daily ambient PM_2.5 concentration standards/guidelines.

METHODS

This study evaluated the relationship between daily ambient PM_2.5 level and YLL using a two-stage approach with generalized additive models and meta-analysis. Potential life expectancy gains were then estimated by presuming that daily PM_2.5 levels were in compliance with the Chinese and WHO standards. In addition, the attributable fraction of YLL due to excess PM_2.5 exposure was also calculated.

RESULTS

During 2013-2016, 459,468 non-accidental deaths were recorded in the six cities of Pearl River Delta, China. Each 10 μg/m³ increment in four-day average (lag₀₃) level of PM_2.5 was related to an increment of 13.31 [95% confidence interval (CI): 5.74, 20.87] years of life lost. Implementation of the WHO guidelines might avoid 180,980.83 YLLs (95% CI: 78,116.07, 283,845.60), which corresponded to 0.39 (95% CI: 0.17, 0.62) years of increased life time per death. Additionally, an estimated 0.15% (95% CI: 0.06%, 0.23%) or 2.04% (95% CI: 0.88%, 3.20%) of YLLs could be attributed to PM_2.5 exposures higher than the Chinese or WHO guidelines, respectively.

CONCLUSIONS

This study suggests that people might live longer by controlling daily PM_2.5 concentration and highlights the need to adopt stricter standards in China.

The heterogeneous effect of socioeconomic driving factors on PM2.5 in China's 30 province-level administrative regions: Evidence from Bayesian hierarchical spatial quantile regression

China has become one of the most serious PM_2.5-dominated air pollution country. Despite a great deal of research has focused on analysing the influence of social and economic driving forces of PM_2.5 pollution in China, most research in existence either applying mean regression or failing to consider the spatial autocorrelation. Motivated by this, this paper utilizes a Bayesian hierarchical spatial quantile regression method to explore the effect of socioeconomic activity on PM_2.5 air pollution. By introducing spatial random effects into the model, the spatial autocorrelations of residuals are significantly reduced. The empirical study demonstrated that the PM_2.5 concentration levels were strongly correlated with total population, urbanization rate, industrialization level and energy efficiency at all quantiles. For upper quantiles, the impact of urbanization rate on the haze is the greatest among all the predictors, then followed by the total population; while for lower quantiles, industrialization has the greatest impact on the PM_2.5 concentration. The impacts of energy efficiency in the lower 15% and upper 15% quantiles are higher compared to any of the other quantiles.

Outdoor air pollution and cancer: An overview of the current evidence and public health recommendations

Outdoor air pollution is a major contributor to the burden of disease worldwide. Most of the global population resides in places where air pollution levels, because of emissions from industry, power generation, transportation, and domestic burning, considerably exceed the World Health Organization's health-based air-quality guidelines. Outdoor air pollution poses an urgent worldwide public health challenge because it is ubiquitous and has numerous serious adverse human health effects, including cancer. Currently, there is substantial evidence from studies of humans and experimental animals as well as mechanistic evidence to support a causal link between outdoor (ambient) air pollution, and especially particulate matter (PM) in outdoor air, with lung cancer incidence and mortality. It is estimated that hundreds of thousands of lung cancer deaths annually worldwide are attributable to PM air pollution. Epidemiological evidence on outdoor air pollution and the risk of other types of cancer, such as bladder cancer or breast cancer, is more limited. Outdoor air pollution may also be associated with poorer cancer survival, although further research is needed. This report presents an overview of outdoor air pollutants, sources, and global levels, as well as a description of epidemiological evidence linking outdoor air pollution with cancer incidence and mortality. Biological mechanisms of air pollution-derived carcinogenesis are also described. This report concludes by summarizing public health/policy recommendations, including multilevel interventions aimed at individual, community, and regional scales. Specific roles for medical and health care communities with regard to prevention and advocacy and recommendations for further research are also described.

Evaluation of wearing comfort of dust masks

Dust masks are widely used to prevent the inhalation of particulate matter into the human respiratory organs in polluted air environments. The filter of a dust mask inherently obstructs the natural respiratory air flows, and this flow resistance is mainly responsible for the discomfort experienced when wearing a dust mask. In atmospheric conditions seriously contaminated with fine dust, it is recommended that common citizens wear a dust mask in their everyday lives, yet many people are reluctant to wear a dust mask owing to the discomfort experienced when wearing it for a long time. Understanding of physical reasons for the discomfort is thus crucial in designing a dust mask, but remains far from clear. This study presents a technique to quantify the wearing comfort of dust masks. By developing a respiration simulator to measure the pressure loss across a dust mask, we assessed the energy costs to overcome flow resistance when breathing through various types of dust masks. The energy cost for a single inhalation varies with the mask type in a range between 0 and 10 mJ. We compared the results with the survey results of 40 people about the wearing comfort of the dust masks, which revealed that the wearing comfort crucially depends on the energy cost required for air inhalation though the dust mask. Using the measured energy cost during inhalation as a parameter to quantify the wearing comfort, we present a comprehensive evaluation of the performance of dust masks in terms of not only the filtering performance but also the wearing comfort. Our study suggests some design principles for dust mask filters, auxiliary electric fans, and check valves.

Quantitative Characterization of Uncertainty in the Concentration-Response Relationship between Long-Term PM2.5 Exposure and Mortality at Low Concentrations

Extensive epidemiologic evidence supports a linear, no-threshold concentration-response (C-R) relationship between long-term exposure to fine particles (PM_2.5) and mortality in the United States. While examinations of the C-R relationship are designed to assess the shape of the C-R curve, they do not provide the information needed to quantitatively characterize uncertainty at specific PM_2.5 concentrations, which is often needed in the context of risk assessments and benefits analyses. We developed a novel approach, using information that is typically available in published epidemiologic studies, to quantitatively characterize uncertainty at different concentrations along the PM_2.5 concentration distribution. Our approach utilizes the annual mean PM_2.5 concentration and corresponding standard deviation from a published epidemiologic study to estimate the standard deviation of hypothetical PM_2.5 concentration distributions defined at 0.1 μg/m³ increments. The hypothetical distributions are then used to derive adjusted uncertainty estimates in the reported effect estimate at low concentrations (i.e., concentrations lower than the annual mean observed in the study). We demonstrate the application of this method in six individual epidemiologic studies that examined the relationship between long-term PM_2.5 exposure and mortality and were conducted in different geographic locations worldwide and at different PM_2.5 concentrations. This new method allows for a more comprehensive quantitative evaluation of uncertainty in the shape of the C-R relationship between long-term PM_2.5 exposure and mortality at concentrations below the mean annual concentrations observed in current studies.

The Health Opportunity Index: Understanding the Input to Disparate Health Outcomes in Vulnerable and High-Risk Census Tracts

The Health Opportunity Index (HOI) is a multivariate tool that can be more efficiently used to identify and understand the interplay of complex social determinants of health (SDH) at the census tract level that influences the ability to achieve optimal health. The derivation of the HOI utilizes the data-reduction technique of principal component analysis to determine the impact of SDH on optimal health at lower census geographies. In the midst of persistent health disparities and the present COVID-19 pandemic, we demonstrate the potential utility of using 13-input variables to derive a composite metric of health (HOI) score as a means to assist in the identification of the most vulnerable communities during the current pandemic. Using GIS mapping technology, health opportunity indices were layered by counties in Ohio to highlight differences by census tract. Collectively we demonstrate that our HOI framework, principal component analysis and convergence analysis methodology coalesce to provide results supporting the utility of this framework in the three largest counties in Ohio: Franklin (Columbus), Cuyahoga (Cleveland), and Hamilton (Cincinnati). The results in this study identified census tracts that were also synonymous with communities that were at risk for disparate COVID-19 related health outcomes. In this regard, convergence analyses facilitated identification of census tracts where different disparate health outcomes co-exist at the worst levels. Our results suggest that effective use of the HOI composite score and subcomponent scores to identify specific SDH can guide mitigation/intervention practices, thus creating the potential for better targeting of mitigation and intervention strategies for vulnerable communities, such as during the current pandemic.

The Impact of Chronic Ambient Exposure to PM2.5 and Ozone on Asthma Prevalence and COPD Mortality Rates in the Southeastern United States

Respiratory diseases affect millions of people across the United States annually. Two of the most common respiratory diseases are chronic obstructive pulmonary disease (COPD) and asthma. Mortality rates due to COPD have increased by an estimated 30% between 1980 and 2014, with significant variances among geographic regions. Both acute and chronic ambient exposures to fine particulate matter (PM_2.5) and ozone have been associated with exacerbations of respiratory diseases in numerous studies, and exposure to air pollutants are considered as the largest health risk factor globally. This study adds to the current literature by reporting the results of a time series analysis of the impact of PM_2.5 and ozone on prevalence rates of asthma and mortality rates for COPD at regional and county levels across the southeastern United States for the years 2005-2014. While general reductions in levels of PM_2.5 and ozone were demonstrated across all years, a distributed lag model showed continued strong associations between PM_2.5 and prevalence of asthma and mortality due to COPD, even at relatively small increases in ambient exposure (<1 μg/m³) across the southeastern United States. The results of the study support the need for additional research that considers factors such as patient demographics, medical histories, and health disparities in combination with ambient exposures to known pollutants.

Simulated 2017 nationwide sampling at 13,940 major U.S. sewage treatment plants to assess seasonal population bias in wastewater-based epidemiology

Wastewater-based epidemiology (WBE) is an economical technique for monitoring and managing the health and behavior of human populations. Using 2017 nationwide data on geospatial population demographics as a test case, we simulated repeated sampling at all major U.S. wastewater treatment plants (WWTPs; n = 13,940) under constant biomarker loading conditions, to explore the potential sensitivity of WBE for generating skewed data. Simulation of repeated sewage sampling over all four seasons of 2017 yielded a number of expected, inter-dependent phenomena triggered by cooler wintertime temperatures compared to summertime results, including relatively (i) slower in-sewer biomarker decay, (ii) longer distal reach of WBE, (iii) larger effective sewershed monitoring areas, and (iv) an increase in the population represented. Additional important but not necessarily anticipated simulation outcomes included (v) distinct, non-random changes in demographic parameters of monitored subpopulations (e.g., by household income, educational attainment, military service, unemployment, and lack of health insurance), (vi) recurring observation of the latter demographic patterns across various geospatial scales and regions, and (vii) more evenly distributed results in the winter. In contrast, data obtainable by WBE in the summertime were dominated by households residing closest to the WWTP and subpopulations of relatively lesser wealth, educational achievement, healthcare access and employability. The analytical approach presented here should be readily applicable to other regions worldwide and may help to improve the design, robustness and interpretation of future WBE studies.

Integration of air pollution data collected by mobile measurement to derive a preliminary spatiotemporal air pollution profile from two neighboring German-Czech border villages

Generally, there are only a few fixed air quality monitoring stations installed in villages or rural areas and only a few studies on small-scale variations in air pollution have been described in detail, which make it difficult to estimate human exposure in such environments and related adverse health effects. Moreover, biomass combustion can be an important source of air pollution in rural areas, comparable to vehicle and industrial emissions in urban planning. And their air pollutants are mainly affected by local sources. For this reason, a survey on rural air pollution was carried out in this study. Therefore, portable, battery-powered monitoring devices were used to measure particulate matter (PM₁₀, PM_2.5, PM₁, particle number concentration, and black carbon) in order to study air quality in rural communities. The focus of the investigations was to explore the application of mobile monitoring equipment in small-scale environments, compare the differences in rural air pollutants between two neighboring villages in two countries, and the identification of pollution hotspots. The measurements were carried out in November 2018 in two villages on the German-Czech border. Over a period of four days, 21 mobile measurements along fixed routes were carried out simultaneously at both locations. The analysis of the data revealed significant differences in PN and PM concentrations in rural air pollutants between the two countries. The spatial and temporal distribution of air pollution hotspots in the Czech village was higher than that in the German village. The relationships between the measurement parameters were weak but highly significant and the meteorological parameters can effect air pollution. Overall, the results of this study show that mobile measurements are suitable for effectively recording and distinguishing spatial and temporal characteristics of air quality.

Exploring the Role of Incidental and Integral Compassion and Anger in Health Communication about Pollution

Scholars have examined two types of emotions, namely, incidental and integral emotions, in health communication on the basis of the source of elicitation. Despite numerous studies on the independent effects of these two types of emotions, limited research exists on how emotions may interact to influence health communication outcomes, including support for health-improving policies. To augment current knowledge, this study conducted a 2 (incidental: compassion vs. anger) × 2 (integral: compassion vs. anger) between-subjects factorial experiment in the context of the human health effects of pollution. Results showed a main effect of the incidental compassion (vs. anger) condition on protective policy support, which was mediated by self-reported compassion. In addition, this main effect was moderated by political ideology such that it was found among moderates and conservatives, but not liberals. No interaction effects were observed. These findings contribute to the literature by exploring how incidental and integral emotions may or may not interact and by complementing existing research on the moderating effect of political ideology regarding environment-related messaging.

Leveraging Existing Cohorts to Study Health Effects of Air Pollution on Cardiometabolic Disorders: India Global Environmental and Occupational Health Hub

Air pollution is a growing public health concern in developing countries and poses a huge epidemiological burden. Despite the growing awareness of ill effects of air pollution, the evidence linking air pollution and health effects is sparse. This requires environmental exposure scientist and public health researchers to work more cohesively to generate evidence on health impacts of air pollution in developing countries for policy advocacy. In the Global Environmental and Occupational Health (GEOHealth) Program, we aim to build exposure assessment model to estimate ambient air pollution exposure at a very fine resolution which can be linked with health outcomes leveraging well-phenotyped cohorts which have information on geolocation of households of study participants. We aim to address how air pollution interacts with meteorological and weather parameters and other aspects of the urban environment, occupational classification, and socioeconomic status, to affect cardiometabolic risk factors and disease outcomes. This will help us generate evidence for cardiovascular health impacts of ambient air pollution in India needed for necessary policy advocacy. The other exploratory aims are to explore mediatory role of the epigenetic mechanisms (DNA methylation) and vitamin D exposure in determining the association between air pollution exposure and cardiovascular health outcomes. Other components of the GEOHealth program include building capacity and strengthening the skills of public health researchers in India through variety of training programs and international collaborations. This will help generate research capacity to address environmental and occupational health research questions in India. The expertise that we bring together in GEOHealth hub are public health, clinical epidemiology, environmental exposure science, statistical modeling, and policy advocacy.

Industrial pollution and human health: evidence from middle-income countries

The hasty economic development in developing countries comes along with poorer air quality, which has severe toxicological effects on the environment and human health. This study is carried out to explore and empirically investigate the relationship between industrial pollution and health using the panel of middle-income countries (MIC) over 1990-2016. This study uses two indicators of health status, namely life expectancy and infant mortality, and two indicators of industrial pollution, namely carbon dioxide emissions and nitrous oxide emissions. This analysis is carried out using fixed effects (FE) technique on the grounds of the Hausman test. The empirical results suggest that industrial pollution tends to decrease life expectancy and increases infant mortality. In addition, this study suggests that the adverse impact of industrial pollution is greater in lower-middle-income countries (LMIC) in comparison with upper-middle-income countries (UMIC). This study recommends the programs to improve human health status and needs to focus on policies that mitigate industrial pollution burden.

Later-Life Exposure to Moderate PM2.5 Air Pollution and Life Loss of Older Adults in Taiwan

Background: Few studies have directly estimated expected life loss attributable to lifetime exposure to fine particulate matter (PM_2.5). Methods: We used claims data from Taiwan’s National Health Insurance to create 63 study cohorts of 1.91 million residents aged 60–79 years old residing in small areas where air quality monitoring stations are situated. The survival status of each person was followed from 2001 to 2016. We applied an extrapolation algorithm to estimate the lifetime survival function so that we could directly estimate life expectancy (LE) and the lifetime exposure to PM_2.5 of each cohort. We estimated the association between LE and lifetime exposure to PM_2.5 among the 63 cohorts. We also fit a Cox proportional hazards model to all the data combined to estimate the relative risk of mortality. Results: Older adults who lived in an area with a higher lifetime weighted average PM_2.5 of 10 μg/m3 had a shortened LE by 0.34 (95% CI: 0.22–0.46) years. The hazard ratio of mortality was 1.0245 (1.0242–1.0248) per one μg/m3 increase in lifetime average PM_2.5. Conclusion: This study provides strong evidence that later-life exposure to moderate PM_2.5 air pollution had a substantial impact on the life loss of older adults.

The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time

Major improvements in air quality since 1990, observed through reductions in fine particulate matter (PM2.5), have been associated with reduced cardiovascular mortality rates (CMR). However, it is not well understood whether the health benefit attributed to PM2.5 reductions has been similar across strata of socioeconomic deprivation (SED). Using mixed effect regression models, we estimated the PM2.5-related change in the CMR across 2,132 US counties in five SED strata between 1990 and 2010. The analysis included annual county CMR (deaths/100,000 person-year), annual county PM2.5 (μg/m3), and an index of county SED based on socioeconomic factors from the 1990 US Census. The contribution of PM2.5 reductions to decreased CMR varied by SED strata and over time. Yearly differences resulted from varying rates of PM2.5 reduction and because of the non-linear relationship between CMR and PM2.5 concentration. In early years, PM2.5-related CMR reductions were smallest in the most deprived counties compared to all other counties (range: 0.4-0.6 vs 0.7-1.6 fewer deaths/100,000 person-year), due to slower rates of PM2.5 reduction in these counties. However, in later years, PM2.5-related CMR reductions were highest counties with moderate to high deprivation, compared to counties with the least deprivation (range: 1.0-2.2 vs 0.5-0.9 fewer deaths/100,000 person-year) due to larger CMR reductions per decrease in PM2.5. We identified that CMR reductions related to air quality improvements have become more similar over time between socioeconomic strata.

Interventions to reduce ambient air pollution and their effects on health: An abridged Cochrane systematic review

BACKGROUND

A broad range of interventions have been implemented to improve ambient air quality, and many of these have been evaluated. Yet to date no systematic review has been conducted to identify and synthesize these studies. In this systematic review, we assess the effectiveness of interventions in reducing ambient particulate matter air pollution and improving adverse health outcomes.

METHODS

We searched a range of electronic databases across multiple disciplines, as well as grey literature databases, trial registries, reference lists of included studies and the contents of relevant journals, through August 2016. Eligible for inclusion were randomized and cluster randomized controlled trials, as well as several non-randomized study designs often used for evaluating air quality interventions. We included studies that evaluated interventions targeting industrial, residential, vehicular and multiple sources, with respect to their effect on mortality, morbidity and the concentrations of particulate matter (PM - including PM₁₀, PM_2.5, coarse particulate matter and combustion-related PM), as well as several criteria pollutants, including ozone, carbon monoxide, nitrogen oxides, nitrogen dioxide, nitric oxide and sulphur dioxide. We did not restrict studies based on the population, setting or comparison. Two authors independently assessed studies for inclusion, extracted data and assessed risk of bias. We assessed risk of bias using the Graphic Appraisal Tool for Epidemiological studies (GATE) for correlation studies, as modified and employed by the UK National Institute for Health and Care Excellence. We synthesized evidence narratively, as well as graphically using harvest plots. We assessed the certainty of evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system.

RESULTS

We included 42 studies assessing 38 unique interventions. These comprised a heterogeneous mix of interventions, including those aiming to address industrial sources (n = 5; e.g. the closure of a factory), residential sources (n = 7; e.g. coal ban), vehicular sources (n = 22; e.g. low emission zones), and multiple sources (n = 4; e.g. tailored measures that target both local traffic and industrial polluters). Evidence for effectiveness was mixed. Most included studies observed either no significant association or an association favoring the intervention, with little evidence that the assessed interventions might be harmful.

CONCLUSIONS

Given the heterogeneity across interventions, outcomes, and methods, it was difficult to derive overall conclusions regarding the effectiveness of interventions in terms of improved air quality or health. Some evidence suggests that interventions are associated with improvements in air quality and human health, with very little evidence suggesting interventions were harmful. The evidence base highlights the challenges related to establishing the effectiveness of specific air pollution interventions on outcomes. It also points to the need for improved study design and analysis methods, as well as more uniform evaluations. The prospective planning of evaluations and an evaluation component built into the design and implementation of interventions may also be particularly beneficial.

Potential gains in life expectancy by attaining daily ambient fine particulate matter pollution standards in mainland China: A modeling study based on nationwide data

BACKGROUND

Ambient fine particulate matter pollution (PM2.5) is one leading cause of disease burden, but no study has quantified the association between daily PM2.5 exposure and life expectancy. We aimed to assess the potential benefits in life expectancy by attaining the daily PM2.5 standards in 72 cities of China during 2013-2016.

METHODS AND FINDINGS

We applied a two-stage approach for the analysis. At the first stage, we used a generalized additive model (GAM) with a Gaussian link to examine the city-specific short-term association between daily PM2.5 and years of life lost (YLL); at the second stage, a random-effects meta-analysis was used to generate the regional and national estimations. We further estimated the potential gains in life expectancy (PGLE) by assuming that ambient PM2.5 has met the Chinese National Ambient Air Quality Standard (NAAQS, 75 μg/m3) or the ambient air quality guideline (AQG) of the World Health Organization (WHO) (25 μg/m3). We also calculated the attributable fraction (AF), which denoted the proportion of YLL attributable to a higher-than-standards daily mean PM2.5 concentration. During the period from January 18, 2013 to December 31, 2016, we recorded 1,226,849 nonaccidental deaths in the study area. We observed significant associations between daily PM2.5 and YLL: each 10 μg/m3 increase in three-day-averaged (lag02) PM2.5 concentrations corresponded to an increment of 0.43 years of life lost (95% CI: 0.29-0.57). We estimated that 168,065.18 (95% CI: 114,144.91-221,985.45) and 68,684.95 (95% CI: 46,648.79-90,721.11) years of life lost can be avoided by achieving WHO's AQG and Chinese NAAQS in the study area, which corresponded to 0.14 (95% CI: 0.09-0.18) and 0.06 (95% CI: 0.04-0.07) years of gain in life expectancy for each death in these cities. We observed differential regional estimates across the 7 regions, with the highest gains in the Northwest region (0.28 years of gain [95% CI: 0.06-0.49]) and the lowest in the North region (0.08 [95% CI: 0.02-0.15]). Furthermore, using WHO's AQG and Chinese NAAQS as the references, we estimated that 1.00% (95% CI: 0.68%-1.32%) and 0.41% (95% CI: 0.28%-0.54%) of YLL could be attributable to the PM2.5 exposure at the national level. Findings from this study were mainly limited by the unavailability of data on individual PM2.5 exposure.

CONCLUSIONS

This study indicates that significantly longer life expectancy could be achieved by a reduction in the ambient PM2.5 concentrations. It also highlights the need to formulate a stricter ambient PM2.5 standard at both national and regional levels of China to protect the population's health.

Impact of Reductions in Emissions from Major Source Sectors on Fine Particulate Matter-Related Cardiovascular Mortality

BACKGROUND

Reductions in ambient concentrations of fine particulate matter (PM2.5) have contributed to reductions in cardiovascular (CV) mortality.

OBJECTIVES

We examined changes in CV mortality attributed to reductions in emissions from mobile, point, areal, and nonroad sources through changes in concentrations of PM2.5 and its major components [nitrates, sulfates, elemental carbon (EC), and organic carbon (OC)] in 2,132 U.S. counties between 1990 and 2010.

METHODS

Using Community Multiscale Air Quality model estimated PM2.5 total and component concentrations, we calculated population-weighted annual averages for each county. We estimated PM2.5 total- and component-related CV mortality, adjusted for county-level population characteristics and baseline PM2.5 concentrations. Using the index of Emission Mitigation Efficiency for primary emission-to-particle pathways, we expressed changes in particle-related mortality in terms of precursor emissions by each sector.

RESULTS

PM2.5 reductions represented 5.7% of the overall decline in CV mortality. Large point source emissions of sulfur dioxide accounted for 6.685 [95% confidence interval (CI): 5.703, 7.667] fewer sulfate-related CV deaths per 100,000 people. Mobile source emissions of primary EC and nitrous oxides accounted for 3.396 (95% CI: 2.772, 4.020) and 3.984 (95% CI: 2.472, 5.496) fewer CV deaths per 100,000 people respectively. Increased EC and OC emissions from areal sources increased carbon-related CV mortality by 0.788 (95% CI: -0.540, 2.116) and 0.245 (95% CI: -0.697, 1.187) CV deaths per 100,000 people.

DISCUSSION

In a nationwide epidemiological study of emission sector contribution to PM2.5-related mortality, we found that reductions in sulfur-dioxide emissions from large point sources and nitrates and EC emissions from mobile sources contributed the largest reduction in particle-related mortality rates respectively. https://doi.org/10.1289/EHP5692.

Health and Environmental Justice Implications of Retiring Two Coal-Fired Power Plants in the Southern Front Range Region of Colorado

Despite improvements in air quality over the past 50 years, ambient air pollution remains an important public health issue in the United States. In particular, emissions from coal-fired power plants still have a substantial impact on both nearby and regional populations. Of particular concern is the potential for this impact to fall disproportionately on low-income communities and communities of color. We conducted a quantitative health impact assessment to estimate the health benefits of the proposed decommissioning of two coal-fired electricity generating stations in the Southern Front Range region of Colorado. We estimated changes in exposures to fine particulate matter and ozone using the Community Multiscale Air Quality model and predicted avoided health impacts and related economic values. We also quantitatively assessed the distribution of these benefits by population-level socioeconomic status. Across the study area, decommissioning the power plants would result in 2 (95% CI: 1-3) avoided premature deaths each year due to reduced PM2.5 exposures and greater reductions in hospitalizations and other morbidities. Health benefits resulting from the modeled shutdowns were greatest in areas with lower educational attainment and other economic indicators. Our results suggest that decommissioning these power plants and replacing them with zero-emissions sources could have broad public health benefits for residents of Colorado, with larger benefits for those that are socially disadvantaged. Our results also suggested that researchers and decision makers need to consider the unique demographics of their study areas to ensure that important opportunities to reduce health disparities associated with point-source pollution.

Acute effect of daily fine particulate matter pollution on cerebrovascular mortality in Shanghai, China: a population-based time series study

Numerous studies have investigated the impacts of ambient fine particulate matter (PM_2.5) on human health. In this study, we examined the association of daily PM_2.5 concentrations with the number of deaths for the cerebrovascular disease on the same day, using the generalized additive model (GAM) controlling for temporal trend and meteorological variables. We used the data between 2012 and 2014 from Shanghai, China, where the adverse health effects of PM_2.5 have been of particular concern. Three different approaches (principal component analysis, shrinkage smoothers, and the least absolute shrinkage and selection operator regularization) were used in GAM to handle multicollinear meteorological variables. Our results indicate that the average daily concentration of PM_2.5 in Shanghai was high, 55 μg/m³, with an average daily death for cerebrovascular disease (CVD) of 62. There was 1.7% raised cerebrovascular disease deaths per 10 μg/m³ increase in PM_2.5 concentration in the unadjusted model. However, PM_2.5 concentration was no longer associated with CVD deaths after controlling for meteorological variables. The results were consistent in the three modelling techniques that we used. As a large number of people are exposed to air pollution, further investigation with longer time period including individual-level information is needed to examine the association.

Relationship between fine particulate air pollution exposure and human adult life expectancy in Taiwan

Among the air pollutants, particulate matter with an aerodynamic diameter less than 2.5 um (PM_2.5) is of particular interest to environmental medicine as epidemiologic studies consistently reported that long-term exposure to PM_2.5 is associated with increased risk of premature death in adults. Life expectancy is a well-documented and important measure of overall public health policy. However, few investigators examined the relationship between PM_2.5 levels and adult life expectancy. In this Taiwan-wide study, county-level annual mean PM_2.5 concentrations data were collected concomitantly with potential confounding variables including demographic and socioeconomic status, as well as smoking prevalence. Subsequently, these PM_2.5 data were analyzed with respect to county-level adult life expectancy data for the period 2010 to 2017. Linear regression was used to determine the relationship between PM_2.5 and life expectancy in adults. Residents residing in the counties characterized as containing higher levels of PM_2.5 exhibited significantly reduced life expectancy after controlling for potential confounders. For each 10 ug/m³ increase in PM_2.5 there was an estimated mean decrease in life expectancy in adults of 0.3 years. The results of this study shed light on the relationship between fine particulate air pollution exposure and risk to human health in Taiwan.

Change in the number of PM2.5-attributed deaths in China from 2000 to 2010: Comparison between estimations from census-based epidemiology and pre-established exposure-response functions

Long-term exposure to ambient fine particulate matter (PM_2.5) has been identified as a major contributor to disease burden in many countries, including China. The effects of long-term PM_2.5 exposure have been evaluated by risk assessment studies, which are based on an exposure-response function (ERF) derived from a specific epidemiological study or multiple studies. To explore whether generalization from the pre-established ERFs (including the integrated exposure-response [IER] model and the global exposure mortality model [GEMM]) introduces biases into risk assessment of a specific local population, we conducted the first census-based epidemiological study of PM_2.5, aimed at the entire population of mainland China. Using a difference-in-difference analysis at the county level, we associated mortality changes from 2000 to 2010 to corresponding PM_2.5 changes, with adjustments made for multiple socioeconomic factors. Based on the epidemiological linkage between PM_2.5 and total mortality, we calculated the change in PM_2.5-attributed deaths and compared this value with the results derived from the pre-established models (IER and GEMM). According to the epidemiological model, a 10-μg/m³ increment in PM_2.5 was associated with a 4.3% (95% confidence interval [CI]: 1.9%-6.7%) increment in total mortality, and the change in PM_2.5-attributed deaths from 2000 to 2010 was estimated as 561,000 (95% CI: 539,000-581,000). The census-based estimation was in better agreement with the GEMM results (545,000-612,000) than was the IER result (354,000 [95% CI: 286,000-421,000]). In sensitivity analyses of the epidemiological model, the association between PM_2.5 exposure and mortality was stronger among younger adults, consistent with the pre-established models. Due to the potential limitations of our findings, we cannot conclusively state that GEMM is more reliable than IER in China. Future studies are warranted to confirm or refute our findings.

Living Well with Pollution? The Impact of the Concentration of PM2.5 on the Quality of Life of Patients with Asthma

While the negative influence of environmental pollution on the respiratory system is well established, especially for people with bronchial hyper-reactivity, the impact of particulate matter on quality of life in asthma patients is not well understood. Three hundred adult asthma patients were recruited for a study; for each patient, the daily concentrations of particulate matter of 2.5 µm or less in diameter (PM_2.5) were recorded from air quality monitoring stations. The study was conducted over two weeks. After two weeks, the patients filled out the Asthma Quality of Life Questionnaire (AQLQ), evaluating the quality of their lives throughout the monitored period. Patients exposed to a higher concentration of PM_2.5 had significantly lower AQLQ scores. Every 10 µg/m³ of an increase in the concentration of PM_2.5 resulted in a decrease of the AQLQ score by 0.16. All domains of quality of life (symptoms, activity limitations, emotional functioning, and environmental stimuli) assessed in the questionnaire were negatively affected by PM_2.5. These findings provide an important argument in favor of educating physicians and patients and raising awareness about the detrimental health effects of air pollution. Improving the quality of life of people with asthma requires an immediate and substantial reduction of air pollution.

Accountability Assessment of Health Improvements in the United States Associated with Reduced Coal Emissions Between 2005 and 2012

BACKGROUND

National, state, and local policies contributed to a 65% reduction in sulfur dioxide emissions from coal-fired power plants between 2005 and 2012 in the United States, providing an opportunity to directly quantify public health benefits attributable to these reductions under an air pollution accountability framework.

METHODS

We estimate ZIP code-level changes in two different-but related-exposure metrics: total PM2.5 concentrations and exposure to coal-fired power plant emissions. We associate changes in 10 health outcome rates among approximately 30 million US Medicare beneficiaries with exposure changes between 2005 and 2012 using two difference-in-difference regression approaches designed to mitigate observed and unobserved confounding.

RESULTS

Rates per 10,000 person-years of six cardiac and respiratory health outcomes-all cardiovascular disease, chronic obstructive pulmonary disorder, cardiovascular stroke, heart failure, ischemic heart disease, and respiratory tract infections-decreased by between 7.89 and 1.95 per (Equation is included in full-text article.)decrease in PM2.5, with comparable decreases in coal exposure leading to slightly larger rate decreases. Results for acute myocardial infarction, heart rhythm disorders, and peripheral vascular disease were near zero and/or mixed between the various exposure metrics and analyses. A secondary analysis found that nonlinearities in relationships between changing health outcome rates and coal exposure may explain differences in their associations.

CONCLUSIONS

The direct analyses of emissions reductions estimate substantial health benefits via coal power plant emission and PM2.5 concentration reductions. Differing responses associated with changes in the two exposure metrics underscore the importance of isolating source-specific impacts from those due to total PM2.5 exposure.

Particulate matter air pollution and national and county life expectancy loss in the USA: A spatiotemporal analysis

BACKGROUND

Exposure to fine particulate matter pollution (PM2.5) is hazardous to health. Our aim was to directly estimate the health and longevity impacts of current PM2.5 concentrations and the benefits of reductions from 1999 to 2015, nationally and at county level, for the entire contemporary population of the contiguous United States.

METHODS AND FINDINGS

We used vital registration and population data with information on sex, age, cause of death, and county of residence. We used four Bayesian spatiotemporal models, with different adjustments for other determinants of mortality, to directly estimate mortality and life expectancy loss due to current PM2.5 pollution and the benefits of reductions since 1999, nationally and by county. The covariates included in the adjusted models were per capita income; percentage of population whose family income is below the poverty threshold, who are of Black or African American race, who have graduated from high school, who live in urban areas, and who are unemployed; cumulative smoking; and mean temperature and relative humidity. In the main model, which adjusted for these covariates and for unobserved county characteristics through the use of county-specific random intercepts, PM2.5 pollution in excess of the lowest observed concentration (2.8 μg/m3) was responsible for an estimated 15,612 deaths (95% credible interval 13,248-17,945) in females and 14,757 deaths (12,617-16,919) in males. These deaths would lower national life expectancy by an estimated 0.15 years (0.13-0.17) for women and 0.13 years (0.11-0.15) for men. The life expectancy loss due to PM2.5 was largest around Los Angeles and in some southern states such as Arkansas, Oklahoma, and Alabama. At any PM2.5 concentration, life expectancy loss was, on average, larger in counties with lower income and higher poverty rate than in wealthier counties. Reductions in PM2.5 since 1999 have lowered mortality in all but 14 counties where PM2.5 increased slightly. The main limitation of our study, similar to other observational studies, is that it is not guaranteed for the observed associations to be causal. We did not have annual county-level data on other important determinants of mortality, such as healthcare access and quality and diet, but these factors were adjusted for with use of county-specific random intercepts.

CONCLUSIONS

According to our estimates, recent reductions in particulate matter pollution in the USA have resulted in public health benefits. Nonetheless, we estimate that current concentrations are associated with mortality impacts and loss of life expectancy, with larger impacts in counties with lower income and higher poverty rate.

Interventions to reduce ambient particulate matter air pollution and their effect on health

BACKGROUND

Ambient air pollution is associated with a large burden of disease in both high-income countries (HICs) and low- and middle-income countries (LMICs). To date, no systematic review has assessed the effectiveness of interventions aiming to reduce ambient air pollution.

OBJECTIVES

To assess the effectiveness of interventions to reduce ambient particulate matter air pollution in reducing pollutant concentrations and improving associated health outcomes.

SEARCH METHODS

We searched a range of electronic databases with diverse focuses, including health and biomedical research (CENTRAL, Cochrane Public Health Group Specialised Register, MEDLINE, Embase, PsycINFO), multidisciplinary research (Scopus, Science Citation Index), social sciences (Social Science Citation Index), urban planning and environment (Greenfile), and LMICs (Global Health Library regional indexes, WHOLIS). Additionally, we searched grey literature databases, multiple online trial registries, references of included studies and the contents of relevant journals in an attempt to identify unpublished and ongoing studies, and studies not identified by our search strategy. The final search date for all databases was 31 August 2016.

SELECTION CRITERIA

Eligible for inclusion were randomized and cluster randomized controlled trials, as well as several non-randomized study designs, including controlled interrupted time-series studies (cITS-EPOC), interrupted time-series studies adhering to EPOC standards (ITS-EPOC), interrupted time-series studies not adhering to EPOC standards (ITS), controlled before-after studies adhering to EPOC standards (CBA-EPOC), and controlled before-after studies not adhering to EPOC standards (CBA); these were classified as main studies. Additionally, we included uncontrolled before-after studies (UBA) as supporting studies. We included studies that evaluated interventions to reduce ambient air pollution from industrial, residential, vehicular and multiple sources, with respect to their effect on mortality, morbidity and several air pollutant concentrations. We did not restrict studies based on the population, setting or comparison.

DATA COLLECTION AND ANALYSIS

After a calibration exercise among the author team, two authors independently assessed studies for inclusion, extracted data and assessed risk of bias. We conducted data extraction, risk of bias assessment and evidence synthesis only for main studies; we mapped supporting studies with regard to the types of intervention and setting. To assess risk of bias, we used the Graphic Appraisal Tool for Epidemiological studies (GATE) for correlation studies, as modified and employed by the Centre for Public Health Excellence at the UK National Institute for Health and Care Excellence (NICE). For each intervention category, i.e. those targeting industrial, residential, vehicular and multiple sources, we synthesized evidence narratively, as well as graphically using harvest plots.

MAIN RESULTS

We included 42 main studies assessing 38 unique interventions. These were heterogeneous with respect to setting; interventions were implemented in countries across the world, but most (79%) were implemented in HICs, with the remaining scattered across LMICs. Most interventions (76%) were implemented in urban or community settings.We identified a heterogeneous mix of interventions, including those aiming to address industrial (n = 5), residential (n = 7), vehicular (n = 22), and multiple sources (n = 4). Some specific interventions, such as low emission zones and stove exchanges, were assessed by several studies, whereas others, such as a wood burning ban, were only assessed by a single study.Most studies assessing health and air quality outcomes used routine monitoring data. Studies assessing health outcomes mostly investigated effects in the general population, while few studies assessed specific subgroups such as infants, children and the elderly. No identified studies assessed unintended or adverse effects.The judgements regarding the risk of bias of studies were mixed. Regarding health outcomes, we appraised eight studies (47%) as having no substantial risk of bias concerns, five studies (29%) as having some risk of bias concerns, and four studies (24%) as having serious risk of bias concerns. Regarding air quality outcomes, we judged 11 studies (31%) as having no substantial risk of bias concerns, 16 studies (46%) as having some risk of bias concerns, and eight studies (23%) as having serious risk of bias concerns.The evidence base, comprising non-randomized studies only, was of low or very low certainty for all intervention categories and primary outcomes. The narrative and graphical synthesis showed that evidence for effectiveness was mixed across the four intervention categories. For interventions targeting industrial, residential and multiple sources, a similar pattern emerged for both health and air quality outcomes, with essentially all studies observing either no clear association in either direction or a significant association favouring the intervention. The evidence base for interventions targeting vehicular sources was more heterogeneous, as a small number of studies did observe a significant association favouring the control. Overall, however, the evidence suggests that the assessed interventions do not worsen air quality or health.

AUTHORS' CONCLUSIONS

Given the heterogeneity across interventions, outcomes, and methods, it was difficult to derive overall conclusions regarding the effectiveness of interventions in terms of improved air quality or health. Most included studies observed either no significant association in either direction or an association favouring the intervention, with little evidence that the assessed interventions might be harmful. The evidence base highlights the challenges related to establishing a causal relationship between specific air pollution interventions and outcomes. In light of these challenges, the results on effectiveness should be interpreted with caution; it is important to emphasize that lack of evidence of an association is not equivalent to evidence of no association.We identified limited evidence for several world regions, notably Africa, the Middle East, Eastern Europe, Central Asia and Southeast Asia; decision-makers should prioritize the development and implementation of interventions in these settings. In the future, as new policies are introduced, decision-makers should consider a built-in evaluation component, which could facilitate more systematic and comprehensive evaluations. These could assess effectiveness, but also aspects of feasibility, fidelity and acceptability.The production of higher quality and more uniform evidence would be helpful in informing decisions. Researchers should strive to sufficiently account for confounding, assess the impact of methodological decisions through the conduct and communication of sensitivity analyses, and improve the reporting of methods, and other aspects of the study, most importantly the description of the intervention and the context in which it is implemented.

Putting 'place' in its place: Comparing place-based factors in interurban analyses of life expectancy in the United States

Extant interurban research in life expectancy greatly suffers from an underestimation of the role of place. Place is often conceptualized as a level of geography; this view ignores categorical differences between types of places. In addition, despite advances in theory and research that support their use, many important place-based factors remain under-utilized as control variables. We use multivariate analyses of life expectancy for the top and bottom quartiles of household income by sex in 148 US counties to compare the strengths of seventeen diverse variables. We find that cities' built, natural, and social environments play strong roles in life expectancy disparity among cities; many place-based variables consistently compare in strength to well-known control variables such as race, education, and behaviors. Furthermore, we find that place impacts men and women differently, even within the same income quartile. Indeed, some factors are associated with higher life expectancy in some demographic groups, and lower life expectancy in others. Researchers can protect against omitted variable bias when investigating public health outcomes by using a wider range of control variables. Researchers should also use better measures of place, and consider selecting specific cases to study.

Impacts of air pollution on human and ecosystem health, and implications for the National Emission Ceilings Directive: Insights from Italy

Across the 28 EU member states there were nearly half a million premature deaths in 2015 as a result of exposure to PM_2.5, O₃ and NO₂. To set the target for air quality levels and avoid negative impacts for human and ecosystems health, the National Emission Ceilings Directive (NECD, 2016/2284/EU) sets objectives for emission reduction for SO₂, NOx, NMVOCs, NH₃ and PM_2.5 for each Member State as percentages of reduction to be reached in 2020 and 2030 compared to the emission levels into 2005. One of the innovations of NECD is Article 9, that mentions the issue of "monitoring air pollution impacts" on ecosystems. We provide a clear picture of what is available in term of monitoring network for air pollution impacts on Italian ecosystems, summarizing what has been done to control air pollution and its effects on different ecosystems in Italy. We provide an overview of the impacts of air pollution on health of the Italian population and evaluate opportunities and implementation of Article 9 in the Italian context, as a case study beneficial for all Member States. The results showed that SO₄^2- deposition strongly decreased in all monitoring sites in Italy over the period 1999-2017, while NO₃^- and NH₄⁺ decreased more slightly. As a consequence, most of the acid-sensitive sites which underwent acidification in the 1980s partially recovered. The O₃ concentration at forest sites showed a decreasing trend. Consequently, AOT40 (the metric identified to protect vegetation from ozone pollution) showed a decrease, even if values were still above the limit for forest protection (5000 ppb h^-1), while PODy (flux-based metric under discussion as new European legislative standard for forest protection) showed an increase. National scale studies pointed out that PM10 and NO2 induced about 58,000 premature deaths (year 2005), due to cardiovascular and respiratory diseases. The network identified for Italy contains a good number of monitoring sites (6 for terrestrial ecosystem monitoring, 4 for water bodies monitoring and 11 for ozone impact monitoring) distributed over the territory and will produce a high number of monitored parameters for the implementation of the NECD.

Losing Years of Human Life in Heavy Polluted Cities in Macedonia

INTRODUCTION:

The urban air pollution will deteriorate globally, by 2050 outdoor particulate air pollution and ground-level ozone is projected to become the top cause of environmentally related deaths worldwide.

AIM:

To assess the burden of diseases due to particulate air pollution and health benefits form the pollution reduction policies.

METHODS:

Environmental burden of disease methodology has been applied. Environmental data for population exposure, total years of life lost from all causes, and relevant concentration-response functions have been used in estimation.

RESULTS:

The estimated disease burden from all causes (excl. external) is 30,256 YLL (19,436-40,625 95% CI) in Skopje Region, and 10,343 YLL (6,224-14,785 95% CI) in Tetovo. In terms of cardiopulmonary mortality, the estimated disease burden is 9,282/100,000 in Tetovo, in the Skopje Region 3,784/100,000 respectively. Annually in Tetovo 1,645 years of healthy life are lost, while in the Skopje Region 3,936 due to lung cancer premature mortality. The estimated health gain is significant, for the three selected health outcomes if EU limit values are reached, 41-42% of the estimated burden in Skopje Region will be eliminated, and 74-77% in Tetovo.

CONCLUSION:

the estimated impact of particulate air pollution on mortality is significant and not negligible. The same applies to the health and well-being of the population if the EU or WHO limit levels are reached.

Protective effects of α-lipoic acid on cultured human nasal fibroblasts exposed to urban particulate matter

BACKGROUND

Exposure to urban particulate matter (UPM) has been studied as a cause of various health problems. Although the association between UPM and the respiratory tract has been well studied, further research is required to characterize the effects of UPM on the upper respiratory tract. We investigated the effects of UPM-induced reactive oxygen species (ROS) production on cultured human nasal fibroblasts, as well as the protective effects of α-lipoic acid (ALA) on ROS production and the underlying signaling pathways involved in ROS inhibition.

METHODS

Human turbinate tissue specimens were collected from 6 patients. The effects of UPM on the viability of cultured nasal fibroblasts were determined. A fluorescent malondialdehyde assay was used to measure ROS levels. Real-time reverse transcription polymerase chain reaction was used to measure the messenger RNA levels of genes encoding Nrf2, the antioxidant response elements (AREs) (HO-1, NQO1), and the proinflammatory cytokines (interleukin-6 and interleukin-8) before and after ALA treatment. Western blotting analyses were used to measure nuclear and cytosolic Nrf2 and AREs.

RESULTS

UPM reduced cell viability and increased ROS expression in nasal fibroblasts. ALA treatment decreased ROS production in UPM-exposed fibroblasts via the Nrf2, HO-1, and NQO-1 pathways. Also, ALA treatment abrogated increases in the interleukin-6 and -8 levels induced by UPM in nasal fibroblasts.

CONCLUSION

UPM exposure resulted in increased ROS production in nasal fibroblasts. ALA treatment inhibited this increase via the Nrf2 pathway, suggesting that ALA may have a protective effect against rhinitis caused by ROS expression induced by exposure to UPM.

Neighborhood sociodemographic effects on the associations between long-term PM2.5 exposure and cardiovascular outcomes and diabetes

Supplemental Digital Content is available in the text.

Exposure to PM_2.5 air pollution and neighborhood-level sociodemographic characteristics are associated with cardiovascular disease and possibly diabetes mellitus. However, the joint effect of sociodemographics and PM_2.5 on these outcomes is uncertain.

High-Level PM2.5/PM10 Exposure Is Associated With Alterations in the Human Pharyngeal Microbiota Composition

Previous studies showed that high concentration of particulate matter (PM) 2.5 and PM10 carried a large number of bacterial and archaeal species, including pathogens and opportunistic pathogens. In this study, pharyngeal swabs from 83 subjects working in an open air farmer’s market were sampled before and after exposure to smog with PM2.5 and PM10 levels up to 200 and 300 μg/m³, respectively. Their microbiota were investigated using high-throughput sequencing targeting the V3–V4 regions of the 16S rRNA gene. The genus level phylotypes was increased from 649 to 767 in the post-smog pharyngeal microbiota, of which 142 were new and detected only in the post-smog microbiota. The 142 new genera were traced to sources such as soil, marine, feces, sewage sludge, freshwater, hot springs, and saline lakes. The abundance of the genera Streptococcus, Haemophilus, Moraxella, and Staphylococcus increased in the post-smog pharyngeal microbiota. All six alpha diversity indices and principal component analysis showed that the taxonomic composition of the post-smog pharyngeal microbiota was significantly different to that of the pre-smog pharyngeal microbiota. Redundancy analysis showed that the influences of PM2.5/PM10 exposure and smoking on the taxonomic composition of the pharyngeal microbiota were statistically significant (p < 0.001). Two days of exposure to high concentrations of PM2.5/PM10 changed the pharyngeal microbiota profiles, which may lead to an increase in respiratory diseases. Wearing masks could reduce the effect of high-level PM2.5/PM10 exposure on the pharyngeal microbiota.

Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India

Fine particulate matter (PM2.5, diameter ≤2.5 μm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 μg/m³, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7⁻35.2, regional range: 8.5⁻42.0), compared to an average of 53.7 months (95% CI: 46.3⁻61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality.

Estimating the Effects of PM2.5 on Life Expectancy Using Causal Modeling Methods

BACKGROUND

Many cohort studies have reported associations between PM2.5 and the hazard of dying, but few have used formal causal modeling methods, estimated marginal effects, or directly modeled the loss of life expectancy.

OBJECTIVE

Our goal was to directly estimate the effect of PM2.5 on the distribution of life span using causal modeling techniques.

METHODS

We derived nonparametric estimates of the distribution of life expectancy as a function of PM2.5 using data from 16,965,154 Medicare beneficiaries in the Northeastern and mid-Atlantic region states (129,341,959 person-years of follow-up and 6,334,905 deaths). We fit separate inverse probability-weighted logistic regressions for each year of age to estimate the risk of dying at that age given the average PM2.5 concentration at each subject's residence ZIP code in the same year, and we used Monte Carlo simulations to estimate confidence intervals.

RESULTS

The estimated mean age at death for a population with an annual average PM2.5 exposure of 12 μg/m3 (the 2012 National Ambient Air Quality Standard) was 0.89 y less (95% CI: 0.88, 0.91) than estimated for a counterfactual PM2.5 exposure of 7.5 μg/m3. In comparison, life expectancy at 65 y of age increased by 0.9 y between 2004 and 2013 in the United States. We estimated that 23.5% of the Medicare population would die before 76 y of age if exposed to PM2.5 at 12 μg/m3 compared with 20.1% if exposed to an annual average of 7.5 μg/m3.

CONCLUSIONS

We believe that this is the first study to directly estimate the effect of PM2.5 on the distribution of age at death using causal modeling techniques to control for confounding. We find that reducing PM2.5 concentrations below the 2012 U.S. annual standard would substantially increase life expectancy in the Medicare population. https://doi.org/10.1289/EHP3130.

Association between fertility rate reduction and pre-gestational exposure to ambient fine particles in the United States, 2003-2011

Ambient pollutants are associated with clinical and sub-clinical indicators of infertility, such as poor sperm quality. However, the link between the ambient fine particle (PM_2.5) concentration and the fertility rate (FR) is unclear. In this epidemiological study, we examined the association between PM_2.5 concentration and childlessness in the United States (US). We conducted a nationwide spatiotemporal study of ~29 million births in 520 US counties from 2003 to 2011. We obtained monthly numbers and demographic data of newborns from the Centers for Disease Control and Prevention, and PM_2.5 estimates from a downscaling model of in situ observations and outputs from the Community Multiscale Air Quality Model. We evaluated the association between the mean PM_2.5 concentration and the FR during the gestational (0-8 months before birth) and pre-gestational (9-11 months before birth) periods using a Poisson model with demographic and socioeconomic covariates. We found a significant association between the FR and PM_2.5 exposure during pre-gestation but not gestation. Each 5 μg/m³ increase in pre-gestational PM_2.5 exposure was associated with a 0.7% (0.0%, 1.4%) reduction in the FR. Nonlinear analysis suggested a sublinear association between the reduction in the FR and PM_2.5 concentration without a safety threshold. Additionally, an annual mean reduction of 1.16 (1.15, 1.17) births per 1000 females aged 15-44 years was attributable to PM_2.5. This study established, for the first time, an association in the US between the FR and PM_2.5 concentration, a finding that adds to the extant epidemiological evidence of the effects of ambient pollutants on fertility, and extends the scope of the impact of low air quality on health.

Using the 13C/12C carbon isotope ratio to characterise the emission sources of airborne particulate matter: a review of literature

Particulate matter (PM) from atmospheric aerosols contains carbons that are harmful for living organisms and the environment. PM can originate from vehicle emissions, wearing of vehicle components, and dust. Size and composition determine PM transport and penetration depth into the respiratory system. Understanding PM emission characteristics is essential for developing strategies to improve air quality. The number of studies on carbon isotope composition (¹³C/¹²C) of PM samples to characterise emission factors has increased. The goal of this review is to integrate and interpret the findings from ¹³C/¹²C carbon isotope ratio (δ¹³C, ‰) analyses for the most common types of emission sources. The review integrates data from 25 studies in 13 countries. The range of δ¹³C of PM from vehicle emissions was from -28.3 to -24.5 ‰ and for non-vehicle anthropogenic emissions from -27.4 to -23.3 ‰. In contrast, PM ranges for δ¹³C from biomass burning sources differed markedly. For C₃ plants, δ¹³C ranged from -34.7 to -25.4 ‰ and for C₄ plants from -22.2 to -13.0 ‰. The ¹³C/¹²C isotope analysis of PM is valuable for understanding the sources of pollutants and distinguishing vehicle emissions from biomass burning. However, additional markers are needed to further distinguish other anthropogenic sources.

Disability Adjusted Life Years (DALYs) in Terms of Years of Life Lost (YLL) Due to Premature Adult Mortalities and Postneonatal Infant Mortalities Attributed to PM2.5 and PM10 Exposures in Kuwait

Ambient air pollution in terms of fine and coarse particulate matter (PM_2.5 and PM₁₀) has been shown to increase adult and infant mortalities. Most studies have estimated the risk of mortalities through attributable proportions and number of excess cases with no reference to the time lost due to premature mortalities. Disability adjusted life years (DALYs) are necessary to measure the health impact of Ambient particulate matter (PM) over time. In this study, we used life-tables for three years (2014⁻2016) to estimate the years of life lost (YLL), a main component of DALYs, for adult mortalities (age 30+ years) and postneonatal infant mortalities (age 28+ days⁻1 year) associated with PM_2.5 exposure and PM₁₀ exposure, respectively. The annual average of PM_2.5 and PM₁₀ concentrations were recorded as 87.9 μg/m³ and 167.5 μg/m³, which are 8 times greater than the World Health Organization (WHO) air quality guidelines of 10 μg/m³ and 20 μg/m³, respectively. Results indicated a total of 252.18 (95% CI: 170.69⁻322.92) YLL for all ages with an increase of 27,474.61 (95% CI: 18,483.02⁻35,370.58) YLL over 10 years. The expected life remaining (ELR) calculations showed that 30- and 65-year-old persons would gain 2.34 years and 1.93 years, respectively if the current PM_2.5 exposure levels were reduced to the WHO interim targets (IT-1 = 35 μg/m³). Newborns and 1-year old children may live 79.81 and 78.94 years, respectively with an increase in average life expectancy of 2.65 years if the WHO PM₁₀ interim targets were met (IT-1 = 70 μg/m³). Sensitivity analyses for YLL were carried out for the years 2015, 2025, and 2045 and showed that the years of life would increase significantly for age groups between 30 and 85. Life expectancy, especially for the elderly (≥60 years), would increase at higher rates if PM_2.5 levels were reduced further. This study can be helpful for the assessment of poor air quality represented by PM_2.5 and PM₁₀ exposures in causing premature adult mortalities and postneonatal infant mortalities in developing countries with high ambient air pollution. Information in this article adds insights to the sustainable development goals (SDG 3.9.1 and 11.6.2) related to the reduction of mortality rates attributed to ambient air levels of coarse and fine particulate matter.

Increment of ambient exposure to fine particles and the reduced human fertility rate in China, 2000-2010

Epidemiological and toxicological studies suggest that exposure to ambient fine particles (PM_2.5) can reduce human reproductive capacity. We previously reported, based on spatial epidemiology, that higher levels of PM_2.5 exposure were associated with a lower fertility rate (FR) in China. However, that study was limited by a lack of temporal variation. Using first-difference regression, we linked temporal changes in FR and PM_2.5 with adjustment for ecological covariates across 2806 counties in China during 2000-2010. Next, we performed a sensitivity analysis of the variation in the PM_2.5-FR association according to (1) geographic region, (2) indicators of the level of development, and (3) PM_2.5 concentrations. Also, we quantified the reduction in the FR attributable to ambient PM_2.5 in China for the first time. The FR decreased by 3.3% (1.2%, 5.3%) for each 10 μg/m³ increment in PM_2.5. The association varied significantly among the geographic regions, but not with the level of development. Nonlinearity analysis suggested a linear exposure-response function with an effect threshold of ~8 μg/m³. We also found that comparing to the 2000 scenario, increment of PM_2.5 in 2010 might result in a reduction of 2.50 (2.44, 2.60) infants per 1000 women aged 15-44 years per year in China. Our results confirm the statistical association between ambient particles and FR and suggest that poor air quality may contribute to childlessness in China.