Association between unemployment, income, education level, population size and air pollution in Czech cities: evidence for environmental inequality? A pilot national scale analysis

An investigation of inequalities in exposure to PM2.5 air pollution across small areas in Ireland

The link between exposure to air pollution and adverse effects on human health is well documented. Yet, in a European context, research on the spatial distribution of air pollution and the characteristics of areas is relatively scarce, and there is a need for research using different spatial scales, a wider variety of socioeconomic indicators (such as ethnicity) and new methodologies to assess these relationships. This study uses comprehensive data on a wide range of demographic and socioeconomic indicators, matched to data on PM2.5 concentrations for small areas in Ireland, to assess the relationship between social vulnerability and PM2.5 air pollution. Examining a wide range of socioeconomic indicators revealed some differentials in PM2.5 concentration levels by measure and by rural and urban classification. However, statistical modelling using concentration curves and concentration indices did not present substantial evidence of inequalities in PM2.5 concentrations across small areas. In common with other western European countries, an overall decline in the levels of PM2.5 between 2011 and 2016 was observed in Ireland, though the data indicates that almost all small areas in Ireland were found to have exceeded the World Health Organization (WHO)'s PM2.5 annual guideline (of 5 µg/m3), calling for greater policy efforts to reduce air pollution in Ireland. The recent Clean Air Strategy contains a commitment to achieve the WHO guideline limits for PM2.5 by 2040, with interim targets at various points over the next two decades. Achieving these targets will require policy measures to decarbonise home heating, promote active travel and the transition to electric vehicles, and further regulations on burning fossil fuels and enforcing environmental regulations more tightly. From a research and information-gathering perspective, installing more monitoring stations at key points could improve the quality and spatial dimension of the data collected and facilitate the assessment of the implementation of the measures in the Clean Air Strategy.

The mediating role of air pollutants in the association between education and lung function among the elderly, the HAPIEE study

BACKGROUND

Chronic exposure to air pollutants harms human health, and at a geographical level, concentrations of air pollutants are often associated with socioeconomic disadvantage.

OBJECTIVES

The aim of this study was to investigate the effects of educational attainment and air pollution on lung function in older adults, and whether air pollution may mediate the effect of education.

METHODS

The study included 6381 individuals (mean age 58.24 ± 7.14 years) who participated in the Czech HAPPIE (Health, Alcohol, and Psychosocial Factors in Eastern Europe) study. Participants' residential addresses were linked to air pollution data, including mean exposures to PM10 (particulate matter of aerodynamic diameter below 10 μm) and NO2 (nitrogen dioxide). We used path analysis to link educational attainment and air pollutants to a standardized measure of the Forced Expiratory Volume in the first second (FEV1).

RESULTS

Higher levels of participants' education were associated with lower exposures to PM10 and NO2. Individuals with tertiary education had higher standardized FEV1 than individuals with primary education (88 % vs 95 %). Path analysis revealed a direct positive effect of education on FEV1, while about 12 % of the relationship between education and lung function was mediated by PM10 and NO2. CONCLUSIONS: Education (typically completed at young ages) appeared to have a protective effect on lung function later in life, and a small part of this effect was mediated by air pollution.

Association between fine particulate matter and fecundability in Henan, China: A prospective cohort study

OBJECTIVE

To investigate the relationship between ambient fine particulate matter (PM2.5) exposure and fecundability.

METHODS

This study included 751,270 female residents from Henan Province who participated in the National Free Pre-conception Check-up Projects during 2015-2017. Ambient cycle-specific PM2.5 exposure was assessed at the county level for each participant using satellite-based PM2.5 concentration data at 1-km resolution. Cox proportional hazards models with time-varying exposure were used to estimate the association between fecundability and PM2.5 exposure, adjusted for potential individual risk factors.

RESULTS

During the study period, 568,713 participants were pregnant, monthly mean PM2.5 concentrations varied from 25.5 to 114.0 µg/m3 across study areas. For each 10 µg/m3 increase in cycle-specific PM2.5, the hazard ratio for fecundability was 0.951 (95 % confidence interval: 0.950-0.953). The association was more pronounced in women who were older, with urban household registration, history of pregnancy, higher body mass index (BMI), hypertension, without exposure to tobacco, or whose male partners were older, with higher BMI, or hypertension.

CONCLUSION

In this population-based prospective cohort, ambient cycle-specific PM2.5 exposure was associated with reduced fecundability. These findings may support the adverse implications of severe air pollution on reproductive health.

The effect of haze pollution on rural-to-urban migrants' long-term residence intentions

Severe haze pollution in China threatens human health, and its negative effect hampers rural-to-urban migrants' settlement intentions in destination cities. Using the 2017 China Migrants Dynamic Survey Data (CMDS), the satellite data of PM2.5, and city-level data, this study investigates the impact of haze pollution on rural migrants, long-term residence intentions in Chinese context with IV-probit model, and mediating effect model. Overall, we find an inverted U-shaped relationship between the level of haze pollutants and rural migrants' long-term settlement intentions. Robustness check using multi-measures and thermal inversion as the instrumental variable supports this conclusion. The mediating effect model shows haze pollution plays its role through two opposite mechanisms: signal effect and health effect. When the size of signal effect is larger than health effect, rural migrants are inclined to settle down in their host cities; otherwise, they show lower settlement willingness. The turning point appears when PM2.5 concentration reaches 38.5 μg/m3; migrants have the highest long-term residence intentions. Currently, the national average PM2.5 concentration is 40.98 μg/m3, indicating that China is at the stage where the health effect of haze pollution holds a dominant position. Haze pollution has heterogeneous impacts on migrants' residence intentions. From the individual level, the younger generation, female, and higher-educated migrants have a higher tolerance for polluted air. From the city level, migrants who work in the city with 5 to 10 million dwellers have the highest long-term residence intention and are less sensitive to haze pollution. Thus, we propose stringent environmental regulations and more inclined public service policies to migrants.

Socioeconomic and racial disparities in source-apportioned PM2.5 levels across urban areas in the contiguous US, 2010

Fine particulate matter (PM2.5) air pollution exposure is associated with short and long-term health effects. Several studies found differences in PM2.5 exposure associated with neighborhood racial and socioeconomic composition. However, most focused on total PM2.5 mass rather than its chemical components and their sources. In this study, we describe the ZIP code characteristics that drive the disparities in exposure to PM2.5 chemical components attributed to source categories both nationally and regionally. We obtained annual mean predictions of PM2.5 and fourteen of its chemical components from spatiotemporal models and socioeconomic and racial predictor variables from the 2010 US Census, and the American Community Survey 5-year estimates. We used non-negative matrix factorization to attribute the chemical components to five source categories. We fit generalized nonlinear models to assess the associations between the neighborhood predictors and each PM2.5 source category in urban areas in the United States in 2010 (n=25,790 zip codes). We observed higher PM2.5 levels in ZIP codes with higher proportions of Black individuals and lower socioeconomic status. Racial exposure disparities were mainly attributed to Heavy Fuel, Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Economic disparities were mainly attributed to Soil and Crustal Dust, Heavy Fuel Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Upon further analysis through stratifying by regions within the United States, we found that the associations between ZIP code characteristics and source-attributed PM2.5 levels were generally greater in Western states. In conclusion, racial, socioeconomic, and geographic inequalities in exposure to PM2.5 and its components are driven by systematic differences in component sources that can inform air quality improvement strategies.

Spatial Equity of PM2.5 Pollution Exposures in High-Density Metropolitan Areas Based on Remote Sensing, LBS and GIS Data: A Case Study in Wuhan, China

In-depth studies have been conducted on the risk of exposure to air pollution in urban residents, but most of them are static studies based on the population of residential units. Ignoring the real environmental dynamics during daily activity and mobility of individual residents makes it difficult to accurately estimate the level of air pollution exposure among residents and determine populations at higher risk of exposure. This paper uses the example of the Wuhan metropolitan area, high-precision air pollution, and population spatio-temporal dynamic distribution data, and applies geographically weighted regression models, bivariate LISA analysis, and Gini coefficients. The risk of air pollution exposure in elderly, low-age, and working-age communities in Wuhan was measured and the health equity within vulnerable groups such as the elderly and children was studied. We found that ignoring the spatio-temporal behavioral activities of residents underestimated the actual exposure hazard of PM_2.5 to residents. The risk of air pollution exposure was higher for the elderly than for other age groups. Within the aging group, a few elderly people had a higher risk of pollution exposure. The high exposure risk communities of the elderly were mainly located in the central and sub-center areas of the city, with a continuous distribution characteristic. No significant difference was found in the exposure risk of children compared to the other populations, but a few children were particularly exposed to pollution. Children's high-exposure communities were mainly located in suburban areas, with a discrete distribution. Compared with the traditional static PM_2.5 exposure assessment, the dynamic assessment method proposed in this paper considers the high mobility of the urban population and air pollution. Thus, it can accurately reveal the actual risk of air pollution and identify areas and populations at high risk of air pollution, which in turn provides a scientific basis for proposing planning policies to reduce urban PM_2.5 and improve urban spatial equity.

Regional Differences and Spatial Convergence of Green Development in China

Green development is an important path to achieving economic, environmental, and social sustainability. Based on the comprehensive evaluation system of economy, environment, and society, this study used the entropy method, Theil index decomposition method, and spatial β-convergence model to study the differences and spatial convergence of China’s green development from 2010 to 2020. The research conclusions are as follows: First, China’s green development has an upward trend, and the eastern region is higher. Second, the regional differences in green development have the characteristic of rising first and then falling, and the differences within regions are the main source of imbalances in China’s green development. Third, China’s green development has obvious characteristics of spatial absolute β-convergence and spatial conditional β-convergence. Green innovation is conducive to narrowing the gaps in the convergence speed of regional green development. The research results comprehensively explain the characteristics of China’s green development and provide realistic evidence for China’s green development in the future.

Air pollution at the residence of Danish adults, by socio-demographic characteristics, morbidity, and address level characteristics

BACKGROUND

Exposure to outdoor air pollution is associated with adverse health effects. Previous studies have indicated higher levels of air pollution in socially deprived areas.

AIM

To investigate associations between air pollution and socio-demographic variables, comorbidity, stress, and green space at the residence in Denmark.

METHODS

We included 2,237,346 persons living in Denmark, aged 35 years or older in 2017. We used the high resolution, multi-scale DEHM/UBM/AirGIS air pollution modelling system to calculate mean concentrations of air pollution with PM_2.5, elemental carbon, ultrafine particles and NO₂ at residences held the preceding five years. We used nationwide registries to retrieve information about socio-demographic indicators at the individual and neighborhood levels. We used general linear regression models to analyze associations between socio-demographic indicators and air pollution at the residence.

RESULTS

Individuals with high SES (income, higher white-collar worker and high educational level) and of non-Danish origin were exposed to higher levels of air pollution than individuals of low SES and of Danish origin, respectively. We found comparable levels of air pollution according to sex, stress events and morbidity. For neighborhood level SES indicators, we found high air pollution levels in neighborhoods with low SES measured as proportion of social housing, sole providers, low income and unemployment. In contrast, we found higher air pollution levels in neighborhoods with higher educational level and a low proportion of manual labor. People living in an apartment and/or with little green space had higher air pollution levels.

CONCLUSION

In Denmark, high levels of residential air pollution were associated with higher individual SES and non-Danish origin. For neighborhood-level indicators of SES, no consistent pattern was observed. These results highlight the need for analyzing many different socio-demographic indicators to understand the complex associations between SES and exposure to air pollution.

Environmental impact of the shadow economy, globalisation, and human capital: Capturing spillovers effects using spatial panel data approach

Most of the environmental degradation literature evaluates the determinants of polluting gas emissions as a spatially static process. However, environmental pollution is a problem that is not limited to the borders of the countries. One way to capture temporal and spatial changes in pollutant emissions is by using the benefits of spatial panel data models. This research aims to empirically examine the environmental impact of the shadow economy, the globalisation index, and the human capital index in 101 countries during 1995-2018. We employ a set of spatial autoregressive models (SAR), Durbin spatial models (SDM), and spatial lag models (SLX) of panel data to estimate direct, indirect, and total impacts. The results are stable before changes in the econometric specification and different ways of calculating spatial weights matrix. The results show that polluting gas emissions have a high spatial dependence on all specifications. The interdependence between the countries explains the spillover effect of environmental pollution on the rest of the countries that are geographically close. The policy implications derived from our research point to achieving sustainable economic and environmental development, where coordinated actions among countries and greater regulation of the behaviors of economic agents related to the shadow economy are recommended.

Burden of dust storms on years of life lost in Seoul, South Korea: A distributed lag analysis

Although dust storms have been associated with adverse health outcomes, studies on the burden of dust storms on deaths are limited. As global warming has induced significant climate changes in recent decades, which have accelerated desertification worldwide, it is necessary to evaluate the burden of dust storm-induced premature mortality using a critical measure of disease burden, such as the years of life lost (YLL). The YLL attributable to dust storms have not been examined to date. This study investigated the association between Asian dust storms (ADS) and the YLL in Seoul, South Korea, during 2002-2013. We conducted a time-series study using a generalized additive model assuming a Gaussian distribution and applied a distributed lag model with a maximum lag of 5 days to investigate the delayed and cumulative effects of ADS on the YLL. We also conducted stratified analyses using the cause of death (respiratory and cardiovascular diseases) and sociodemographic status (sex, age, education level, occupation, and marital status). During the study period, 108 ADS events occurred, and the average daily YLL was 1511 years due to non-accidental causes. The cumulative ADS exposure over the 6-day lag period was associated with a significant increase of 104.7 (95% CI, 31.0-178.5 years) and 34.4 years (4.0-64.7 years) in the YLL due to non-accidental causes and cardiovascular mortality, respectively. Sociodemographic analyses revealed associations between ADS exposure and the YLL in males, both <65 and ≥ 65 years old, those with middle-level education, and the unemployed, unmarried, and widowed (26.5-83.8 years). This study provides new evidence suggesting that exposure to dust storms significantly increases the YLL. Our findings suggest that dust storms are a critical environmental risk affecting premature mortality. These results could contribute to the establishment of public health policies aimed at managing dust storm exposure and reducing premature deaths.

Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework

Given the serious adverse health effects associated with many pollutants, and the inequitable distribution of these effects between socioeconomic groups, air pollution is often a focus of environmental justice (EJ) research. However, EJ analyses that aim to illuminate whether and how air pollution hazards are inequitably distributed may present a unique set of requirements for estimating pollutant concentrations compared to other air quality applications. Here, we perform a scoping review of the range of data analytic and modeling methods applied in past studies of air pollution and environmental injustice and develop a guidance framework for selecting between them given the purpose of analysis, users, and resources available. We include proxy, monitor-based, statistical, and process-based methods. Upon critically synthesizing the literature, we identify four main dimensions to inform method selection: accuracy, interpretability, spatiotemporal features of the method, and usability of the method. We illustrate the guidance framework with case studies from the literature. Future research in this area includes an exploration of increasing data availability, advanced statistical methods, and the importance of science-based policy.

The construction and examination of social vulnerability and its effects on PM2.5 globally: combining spatial econometric modeling and geographically weighted regression

Fine particulate matter (PM2.5) is of widespread concern, as it poses a serious impact on economic development and human health. Although the influence of socioeconomic factors on PM2.5 has been studied, the constitution and the effect analysis of social vulnerability to PM2.5 remain unclear. In this study, a comprehensive theoretical framework with appropriate indicators for social vulnerability to PM2.5 was constructed. Using spatial autocorrelation analysis, a positive global spatial autocorrelation and notable local spatial cluster relationships were identified. Spatial econometric modeling and geographically weighted regression modeling were performed to explore the cause-effect relationship of social vulnerability to PM2.5. The spatial error model indicated that population and education inequality in the sensitivity dimension caused a significant positive impact on PM2.5, and biocapacity and social governance in the capacity dimension strongly contributed to the decrease of PM2.5 globally. The geographically weighted regression model revealed spatial heterogeneity in the effects of the social vulnerability variables on PM2.5 among countries. These empirical results can provide policymakers with a new perspective on social vulnerability as it relates to PM2.5 governance and targeted environmental pollution management.

The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong

Human mortality is in part a function of multiple socioeconomic factors that differ both spatially and temporally. Adjusting for other covariates, the human lifespan is positively associated with household wealth. However, the extent to which mortality in a geographical region is a function of socioeconomic factors in both that region and its neighbors is unclear. There is also little information on the temporal components of this relationship. Using the districts of Hong Kong over multiple census years as a case study, we demonstrate that there are differences in how wealth indicator variables are associated with longevity in (a) areas that are affluent but neighbored by socially deprived districts versus (b) wealthy areas surrounded by similarly wealthy districts. We also show that the inclusion of spatially-distributed variables reduces uncertainty in mortality rate predictions in each census year when compared with a baseline model. Our results suggest that geographic mortality models should incorporate nonlocal information (e.g., spatial neighbors) to lower the variance of their mortality estimates, and point to a more in-depth analysis of sociospatial spillover effects on mortality rates.

The association between long-term exposure to low-level PM2.5 and mortality in the state of Queensland, Australia: A modelling study with the difference-in-differences approach

BACKGROUND

To date, few studies have investigated the causal relationship between mortality and long-term exposure to a low level of fine particulate matter (PM2.5) concentrations.

METHODS AND FINDINGS

We studied 242,320 registered deaths in Queensland between January 1, 1998, and December 31, 2013, with satellite-retrieved annual average PM2.5 concentrations to each postcode. A variant of difference-in-differences (DID) approach was used to investigate the association of long-term PM2.5 exposure with total mortality and cause-specific (cardiovascular, respiratory, and non-accidental) mortality. We observed 217,510 non-accidental deaths, 133,661 cardiovascular deaths, and 30,748 respiratory deaths in Queensland during the study period. The annual average PM2.5 concentrations ranged from 1.6 to 9.0 μg/m3, which were well below the current World Health Organization (WHO) annual standard (10 μg/m3). Long-term exposure to PM2.5 was associated with increased total mortality and cause-specific mortality. For each 1 μg/m3 increase in annual PM2.5, we found a 2.02% (95% CI 1.41%-2.63%; p < 0.01) increase in total mortality. Higher effect estimates were observed in Brisbane than those in Queensland for all types of mortality. A major limitation of our study is that the DID design is under the assumption that no predictors other than seasonal temperature exhibit different spatial-temporal variations in relation to PM2.5 exposure. However, if this assumption is violated (e.g., socioeconomic status [SES] and outdoor physical activities), the DID design is still subject to confounding.

CONCLUSIONS

Long-term exposure to PM2.5 was associated with total, non-accidental, cardiovascular, and respiratory mortality in Queensland, Australia, where PM2.5 levels were measured well below the WHO air quality standard.

A model for estimating the lifelong exposure to PM2.5 and NO2 and the application to population studies

Numerous epidemiological studies have confirmed the negative influences of air pollutants on human health, where fine particles (PM2.5) and nitrogen dioxide (NO₂) cause the highest health risks. However, the traditional studies have only involved the ambient concentration for a short to medium time period, which ignores the influence of indoor sources, the individual time-activity pattern, and the fact that the health status is impacted by the long-term accumulated exposure. The aim of this paper is to develop a methodology to simulate the lifelong exposure (rather than outdoor concentration) to PM2.5 and NO₂ for individuals in Europe. This method is realized by developing a probabilistic model that integrates an outdoor air quality model, a model estimating indoor air pollution, an exposure model, and a life course trajectory model for predicting retrospectively the employment status. This approach has been applied to samples of two population studies in the frame of the European Commission FP7-ENVIRONMENT research project HEALS (Health and Environment-wide Associations based on Large Population Surveys), where socioeconomic data of the participants have been collected. Results show that the simulated exposures to both pollutants for the samples are influenced by socio-demographic characteristics, including age, gender, residential location, employment status and smoking habits. Both outdoor concentrations and indoor sources play an important role in the total exposure. Moreover, large variances have been observed among countries and cities. The application of this methodology provides valuable insights for the exposure modelling, as well as important input data for exploring the correlation between exposure and health impacts.

Methodology for Estimating the Lifelong Exposure to PM2.5 and NO2—The Application to European Population Subgroups

Health impacts of air pollutants, especially fine particles (PM2.5) and NO2, have been documented worldwide by epidemiological studies. Most of the existing studies utilised the concentration measured at the ambient stations to represent the pollutant inhaled by individuals. However, these measurement data are in fact not able to reflect the real concentration a person is exposed to since people spend most of their time indoors and are also affected by indoor sources. The authors developed a probabilistic methodology framework to simulate the lifelong exposure to PM2.5 and NO2 simultaneously for population subgroups that are characterised by a number of indicators such as age, gender and socio-economic status. The methodology framework incorporates the methods for simulating the long-term outdoor air quality, the pollutant concentration in different micro-environments, the time-activity pattern of population subgroups and the retrospective life course trajectories. This approach was applied to the population in the EU27 countries plus Norway and Switzerland and validated with the measurement data from European multi-centre study, EXPOLIS. Results show that the annual average exposure to PM2.5 and NO2 at European level kept increasing from the 1950s to a peak between the 1980s and the 1990s and showed a decrease until 2015 due to the implementation of a series of directives. It is also revealed that the exposure to both pollutants was affected by geographical location, gender and income level. The average annual exposure over the lifetime of an 80-year-old European to PM2.5 and NO2 amounted to 23.86 (95% CI: 2.95–81.86) and 13.49 (95% CI: 1.36–43.84) µg/m3. The application of this methodology provides valuable insights and novel tools for exposure modelling and environmental studies.

Air Quality Standards and Extreme Ozone Events in the São Paulo Megacity

Ozone events in South America might be triggered by increasing air temperatures and dry conditions, leading to vulnerable population exposure. The current air quality standards and attention levels in São Paulo state, Brazil, are 40% higher and 25% higher, respectively, than the limits recommended by the World Health Organization (WHO). We simulated an extreme ozone event in the São Paulo megacity using the Weather Research and Forecast/Chemistry model during an extreme event characterized by positive anomalies of air temperature and solar radiation. Results were evaluated using the different air quality limits from São Paulo state and the WHO, also with socioeconomic vulnerability data from the Brazilian census and cost analysis for the public health system from the extreme episode. More than 3 million people in vulnerability conditions, such as low income and families with an above-average percentage of children, live in areas where ozone concentrations exceeded the attention levels of the WHO during the episode, which is ignored by the lenient SP state environmental laws. WHO air quality guidelines must be adopted urgently in developing nations in order to provide a more accurate basis for cost analysis and population exposure, particularly the for vulnerable population groups.

Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure

Environmental burdens such as air pollution are inequitably distributed with groups of lower socioeconomic statuses, which tend to comprise of large proportions of racial minorities, typically bearing greater exposure. Such groups have also been shown to present more severe health outcomes which can be related to adverse pollution exposure. Air pollution exposure, especially in urban areas, is usually impacted by the built environment, such as major roadways, which can be a significant source of air pollution. This study aims to examine inequities in prevalence of cardiovascular and respiratory diseases in the Atlanta metropolitan region as they relate to exposure to air pollution and characteristics of the built environment. Census tract level data were obtained from multiple sources to model health outcomes (asthma, chronic obstructive pulmonary disease, coronary heart disease, and stroke), pollution exposure (particulate matter and nitrogen oxides), demographics (ethnicity and proportion of elderly residents), and infrastructure characteristics (tree canopy cover, access to green space, and road intersection density). Conditional autoregressive models were fit to the data to account for spatial autocorrelation among census tracts. The statistical model showed areas with majority African-American populations had significantly higher exposure to both air pollutants and higher prevalence of each disease. When considering univariate associations between pollution and health outcomes, the only significant association existed between nitrogen oxides and COPD being negatively correlated. Greater percent tree canopy cover and green space access were associated with higher prevalence of COPD, CHD, and stroke. Overall, in considering health outcomes in connection with pollution exposure infrastructure and ethnic demographics, demographics remained the most significant explanatory variable.

Air Pollution and Cardiovascular Disease: A Focus on Vulnerable Populations Worldwide

Purpose of review

Certain subgroups defined by sociodemographics (race/ethnicity, age, sex and socioeconomic status [SES]), geographic location (rural vs. urban), comorbid conditions and country economic conditions (developed vs. developing) may disproportionately suffer the adverse cardiovascular effects of exposure to ambient air pollution. Yet, previous reviews have had a broad focus on the general population without consideration of these potentially vulnerable populations.

Recent findings

Over the past decade, a wealth of epidemiologic studies have linked air pollutants including particulate matter, oxides of nitrogen, and carbon monoxide to cardiovascular disease (CVD) risk factors, subclinical CVD, clinical cardiovascular outcomes and cardiovascular mortality in certain susceptible populations. Highest risk for poor CVD outcomes from air pollution exist in racial/ethnic minorities, especially in blacks compared to whites in the U.S, those at low SES, elderly populations, women, those with certain comorbid conditions and developing countries compared to developed countries. However, findings are less consistent for urban compared to rural populations.

Summary

Vulnerable subgroups including racial/ethnic minorities, women, the elderly, smokers, diabetics and those with prior heart disease had higher risk for adverse cardiovascular outcomes from exposure to air pollution. There is limited data from developing countries where concentrations of air pollutants are more extreme and cardiovascular event rates are higher than that of developed countries. Further epidemiologic studies are needed to understand and address the marked disparities in CVD risk conferred by air pollution globally, particularly among these vulnerable subgroups.

Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables

BACKGROUND

Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors.

OBJECTIVES

We systematically reviewed the evidence on the association between air pollution and cardiorespiratory diseases (hospital admissions and mortality), including variability by energy, transportation, socioeconomic status, and air quality.

SEARCH METHODS

We conducted a literature search (PubMed and Web of Science) for studies published between 2006 and May 11, 2016.

SELECTION CRITERIA

We included studies if they met all of the following criteria: (1) considered at least 1 of these air pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, or particulate matter (PM2.5 or PM10); (2) reported risk for hospital admissions, mortality, or both; (3) presented individual results for respiratory diseases, cardiovascular diseases, or both; (4) considered the age groups younger than 5 years, older than 65 years, or all ages; and (5) did not segregate the analysis by gender.

DATA COLLECTION AND ANALYSIS

We extracted data from each study, including location, health outcome, and risk estimates. We performed a meta-analysis to estimate the overall effect and to account for both within- and between-study heterogeneity. Then, we applied a model selection (least absolute shrinkage and selection operator) to assess the modifier variables, and, lastly, we performed meta-regression analyses to evaluate the modifier variables contributing to heterogeneity among studies.

MAIN RESULTS

We assessed 2183 studies, of which we selected 529 for in-depth review, and 70 articles fulfilled our study inclusion criteria. The 70 studies selected for meta-analysis encompass more than 30 million events across 28 countries. We found positive associations between cardiorespiratory diseases and different air pollutants. For example, when we considered only the association between PM2.5 and respiratory diseases ( Figure 1 , we observed a risk equal to 2.7% (95% confidence interval = 0.9%, 7.7%). Our results showed statistical significance in the test of moderators for all pollutants, suggesting that the modifier variables influence the average cardiorespiratory disease risk and may explain the varying effects of air pollution.

CONCLUSIONS

Variables related to aspects of energy, transportation, and socioeconomic status may explain the varying effect size of the association between air pollution and cardiorespiratory diseases. Public Health Implications. Our study provides a transferable model to estimate the health effects of air pollutants to support the creation of environmental health public policies for national and international intervention.

OMI and Ground-Based In-Situ Tropospheric Nitrogen Dioxide Observations over Several Important European Cities during 2005-2014

In this work we present the evolution of tropospheric nitrogen dioxide (NO₂) content over several important European cities during 2005–2014 using space observations and ground-based in-situ measurements. The NO₂ content was derived using the daily observations provided by the Ozone Monitoring Instrument (OMI), while the NO₂ volume mixing ratio measurements were obtained from the European Environment Agency (EEA) air quality monitoring stations database. The European cities selected are: Athens (37.98° N, 23.72° E), Berlin (52.51° N, 13.41° E), Bucharest (44.43° N, 26.10° E), Madrid (40.38° N, 3.71° W), Lisbon (38.71° N, 9.13° W), Paris (48.85° N, 2.35° E), Rome (41.9° N, 12.50° E), and Rotterdam (51.91° N, 4.46° E). We show that OMI NO₂ tropospheric column data can be used to assess the evolution of NO₂ over important European cities. According to the statistical analysis, using the seasonal variation, we found good correlations (R > 0.50) between OMI and ground-based in-situ observations for all of the cities presented in this work. Highest correlation coefficients (R > 0.80) between ground-based monitoring stations and OMI observations were calculated for the cities of Berlin, Madrid, and Rome. Both types of observations, in-situ and remote sensing, show an NO₂ negative trend for all of locations presented in this study.

Association of PM2.5 with diabetes, asthma, and high blood pressure incidence in Canada: A spatiotemporal analysis of the impacts of the energy generation and fuel sales

Numerous studies have reported an association between fine particulate matter (PM_2.5) and human health. Often these relationships are influenced by environmental factor that varies spatially and/or temporally. To our knowledge, there are no studies in Canada that have considered energy generation and fuel sales as PM_2.5 effects modifiers. Determining exposure and disease-specific risk factors over space and time is crucial for disease prevention and control. In this study, we evaluated the association of PM_2.5 with diabetes, asthma, and High Blood Pressure (HBP) incidence in Canada. Then we explored the impact of the energy generation and fuel sales on association changes. We fit an age-period-cohort as the study design, and we applied an over-dispersed Poisson regression model to estimate the risk. We conducted a sensitivity analysis to explore the impact of variation in clean energy rates and fuel sales on outcomes changes. The study included 117 health regions in Canada between 2007 and 2014. Our findings showed strong association of PM_2.5 with diabetes, asthma, and HBP incidence. A two-year increase of 10μg/m³ in PM_2.5 was associated with an increased risk of 5.34% (95% CI: 2.28%; 12.53%) in diabetes incidence, 2.24% (95% CI: 0.93%; 5.38%) in asthma incidence, and 8.29% (95% CI: 3.44%; 19.98%) in HBP incidence. Our sensitivity analysis findings suggest higher risks of diabetes, asthma and HBP incidence when there is low clean energy generation. On the other hand, we found lower risk when we considered high rate of clean energy generation. For example, considering only diabetes incidence, we found that the risk in health regions with low rates of clean electricity is approximately 700% higher than the risk in health regions with high rates of clean electricity. Furthermore, our analysis suggested that the risk in regions with low fuel sales is 66% lower than the risk is health regions with low rates of clean electricity. Our study provides support for the creation of effective environmental health public policies that take into account the risk factors present in Canadians health regions.

Mapping distance-decay of cardiorespiratory disease risk related to neighborhood environments

Neighborhood characteristics affect an individual's quality of life. Although several studies have examined the relationship between neighborhood environments and human health, we are unaware of studies that have examined the distance-decay of this effect and then presented the risk results spatially. Our study is unique in that is explores the health effects in a less developed country compared to most studies that have focused on developed countries. The objective of our study is to quantify the distance-decay cardiorespiratory diseases risk related to 28 neighborhood aspects in the Federal District, Brazil and present this information spatially through risk maps of the region. Toward this end, we used a quantile regression model to estimate risk and GIS modeling techniques to create risk maps. Our analysis produced the following findings: i) a 2500 m increase in highway length was associated with a 46% increase in cardiorespiratory diseases; ii) 46,000 light vehicles in circulation (considering a buffer of ≤500 m from residences) was associated with 6 hospital admissions (95% CI: 2.6, 14.6) per cardiorespiratory diseases; iii) 74,000 m² of commercial areas (buffer ≤1700 m) was associated with 12 hospital admissions (95% CI: 2.2, 20.8); iv) 1km² increase in green areas intra urban was associated with less two hospital admissions, and; vi) those who live ≤500 m from the nearest point of wildfire are more likely to have cardiorespiratory diseases that those living >500 m. Our findings suggest that the approach used in this study can be an option to improve the public health policies.

Association between vehicular emissions and cardiorespiratory disease risk in Brazil and its variation by spatial clustering of socio-economic factors

Many studies have suggested that socio-economic factors are strong modifiers of human vulnerability to air pollution effects. Most of these studies were performed in developed countries, specifically in the US and Europe. Only a few studies have been performed in developing countries, and analyzed small regions (city level) with no spatial disaggregation. The aim of this study was to assess the association between vehicle emissions and cardiorespiratory disease risk in Brazil and its modification by spatial clustering of socio-economic conditions. We used a quantile regression model to estimate the risk and a geostatistical approach (K means) to execute spatial cluster analysis. We performed the risk analysis in three stages. First, we analyzed the entire study area (primary analysis), and then we conducted a spatial cluster analysis based on various municipal-level socio-economic factors, followed by a sensitivity analysis. We studied 5444 municipalities in Brazil between 2008 and 2012. Our findings showed a significant association between cardiorespiratory disease risk and vehicular emissions. We found that a 15% increase in air pollution is associated with a 6% increase in hospital admissions rates. The results from the spatial cluster analysis revealed two groups of municipalities with distinct sets of socio-economic factors and risk levels of cardiorespiratory disease related to exposure to vehicular emissions. For example, for vehicle emissions of PM in 2008, we found a relative risk of 4.18 (95% CI: 3.66, 4.93) in the primary analysis; in Group 1, the risk was 0.98 (95% CI: 0.10, 2.05) while in Group 2, the risk was 5.56 (95% CI: 4.46, 6.25). The risk in Group 2 was 480% higher than the risk in Group 1, and 35% higher than the risk in the primary analysis. Group 1 had higher values (3rd quartile) for urbanization rate, highway density, and GDP; very high values (≥3rd quartile) for population density; median values for distance from the capital; and lower values (1st quartile) for rural population density. Group 2 had lower values (1st quartile) urbanization rate; median values for highway density, GDP, and population density; between median and third quartile values for distance from the capital; and higher values (3rd quartile) for rural population density. Our findings suggest that socio-economic factors are important modifiers of the human risk of cardiorespiratory disease due to exposure to vehicle emissions in Brazil. Our study provides support for creating effective public policies related to environmental health that are targeted to high-risk populations.

Socioeconomic Disparities and Air Pollution Exposure: a Global Review

The existing reviews and meta-analyses addressing unequal exposure of environmental hazards on certain populations have focused on several environmental pollutants or on the siting of hazardous facilities. This review updates and contributes to the environmental inequality literature by focusing on ambient criteria air pollutants (including NOx), by evaluating studies related to inequality by socioeconomic status (as opposed to race/ethnicity) and by providing a more global perspective. Overall, most North American studies have shown that areas where low-socioeconomic-status (SES) communities dwell experience higher concentrations of criteria air pollutants, while European research has been mixed. Research from Asia, Africa, and other parts of the world has shown a general trend similar to that of North America, but research in these parts of the world is limited.

Air quality and social deprivation in four French metropolitan areas--a localized spatio-temporal environmental inequality analysis

Several studies have documented that more deprived populations tend to live in areas characterized by higher levels of environmental pollution. Yet, time trends and geographic patterns of this disproportionate distribution of environmental burden remain poorly assessed, especially in Europe. We investigated the spatial and temporal relationship between ambient air nitrogen dioxide (NO2) concentrations and socioeconomic and demographic data in four French metropolitan areas (Lille in the North, Lyon in the center, Marseille in the South, and Paris) during two different time periods. The geographical unit used was the census block. The dependent variable was the NO2 annual average concentration (μg/m(3)) per census block, and the explanatory variables were a neighborhood deprivation index and socioeconomic and demographic data derived from the national census. Generalized additive models were used to account for spatial autocorrelation. We found that the strength and direction of the association between deprivation and NO2 estimates varied between cities. In Paris, census blocks with the higher social categories are exposed to higher mean concentrations of NO2. However, in Lille and Marseille, the most deprived census blocks are the most exposed to NO2. In Lyon, the census blocks in the middle social categories were more likely to have higher concentrations than in the lower social categories. Despite a general reduction in NO2 concentrations over the study period in the four metropolitan areas, we found contrasting results in the temporal trend of environmental inequalities. There is clear evidence of city-specific spatial and temporal environmental inequalities that relate to the historical socioeconomic make-up of the cities and its evolution. Hence, general statements about environmental and social inequalities can be made.