U.S. census unit population exposures to ambient air pollutants

Air Pollutants and Risk of Parkinson's Disease among Women in the Sister Study

BACKGROUND

Air pollutants may contribute to the development of Parkinson's disease (PD), but empirical evidence is limited and inconsistent.

OBJECTIVES

This study aimed to prospectively investigate the associations of PD with ambient exposures to fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) and nitrogen dioxide (NO 2 ).

METHODS

We analyzed data from 47,108 US women from the Sister Study, enrolled from 2003-2009 (35-80 years of age) and followed through 2018. Exposures of interest included address-level ambient PM 2.5 and NO 2 in 2009 and their cumulative averages from 2009 to PD diagnosis with varying lag-years. The primary outcome was PD diagnosis between 2009 and 2018 (n = 163 ). We used multivariable Cox proportional hazards and time-varying Cox models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).

RESULTS

NO 2 exposure in 2009 was associated with PD risk in a dose-response manner. The HR and 95% CI were 1.22 (95% CI: 1.03, 1.46) for one interquartile [4.8 parts per billion (ppb)] increment in NO 2 , adjusting for age, race and ethnicity, education, smoking status, alcohol drinking, caffeine intake, body mass index, physical activity, census region, residential area type, area deprivation index (ADI), and self-reported health status. The association was confirmed in secondary analyses with time-varying averaged cumulative exposures. For example, the multivariable adjusted HR for PD per 4.8   ppb increment in NO 2 was 1.25 (95% CI: 1.05, 1.50) in the 2-year lag analysis using cumulative average exposure. Post hoc subgroup analyses overall confirmed the association. However, statistical interaction analyses found that the positive association of NO 2 with PD risk was limited to women in urban, rural, and small town areas and women with ≥ 50 th percentile ADI but not among women from suburban areas or areas with < 50 th percentile ADI. In contrast, PM 2.5 exposure was not associated with PD risk with the possible exception for women from the Midwest region of the US (HR interquartile -range = 2.49 , 95% CI: 1.20, 5.14) but not in other census regions.

DISCUSSION

In this nationwide cohort of US women, higher level exposure to ambient NO 2 is associated with a greater risk of PD. This finding needs to be independently confirmed and the underlying mechanisms warrant further investigation. https://doi.org/10.1289/EHP13009.

Ambient Air Pollutants and Olfaction among Women 50-79 Years of Age from the Sister Study

BACKGROUND

Poor olfaction is common in older adults and may have profound adverse implications on their health. However, little is known about the potential environmental contributors to poor olfaction.

OBJECTIVE

We investigated ambient fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] and nitrogen dioxide (NO 2 ) in relation to poor olfaction in middle-aged to older women.

METHODS

The Sister Study is a nationwide cohort of 50,884 women in the United States with annual average air pollutant exposures estimated based on participants' residences from enrollment (2003-2009) through 2017. This analysis was limited to 3,345 women, 50-79 years of age as of January 2018, who completed the Brief Smell Identification Test (B-SIT) in 2018-2019. Poor olfaction was defined as a B-SIT score of ≤ 9 in the primary analysis. We conducted multivariable logistic regressions, accounting for covariates and study sampling design.

RESULTS

Overall, we found little evidence for associations of air pollutants with poor olfaction. The odds ratio (OR) and 95% confidence interval (CI) of poor olfaction for each interquartile range (IQR) increment of air pollutants in 2006 were 1.03 (95% CI: 0.91, 1.17) for PM 2.5 (per 3.3   μ g / m 3 ) and 1.08 (95% CI: 0.96, 1.22) for NO 2 (per 5.7  ppb ). Results were similar in the analyses using the most recent (2017) or the cumulative average (2006-2017) air pollutant exposure data. Secondary analyses suggested potential association in certain subgroups. The OR per IQR was 1.35 (95% CI: 1.11, 1.65) for PM 2.5 among younger participants (< 54.2 years of age) and 1.87 (95% CI: 1.29, 2.71) for NO 2 among current smokers.

DISCUSSION

This study did not find convincing evidence that air pollutants have lasting detrimental effects on the sense of smell of women 50-79 years of age. The subgroup analyses are exploratory, and the findings need independent confirmation. https://doi.org/10.1289/EHP12066.

Effects of Population Weighting on PM10 Concentration Estimation

Particulate matter with an aerodynamic diameter of 10 μm or less (PM₁₀) pollution poses a considerable threat to human health, and the first step in quantifying health impacts of human exposure to PM₁₀ pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provide a more refined exposure assessment as it includes the spatiotemporal distribution of the population into the pollution concentration estimation. This study assessed the population weighting effects on the estimated PM₁₀ concentrations in Malaysia for years 2000, 2008, and 2013. Estimated PM₁₀ annual mean concentrations with a spatial resolution of 5 kilometres retrieved from satellite data and population count obtained from the Gridded Population of the World version 4 (GPWv4) from the Centre for International Earth Science Information Network (CIESIN) were overlaid to generate the PWEL of PM₁₀ for each state. The calculated PWEL of PM₁₀ concentrations were then classified based on the World Health Organization (WHO) and the national Air Quality Guidelines (AQG) and interim targets (IT) for comparison. Results revealed that the annual mean PM₁₀ concentrations in Malaysia ranged from 31 to 73 µg/m³ but became generally lower, ranging from 20 to 72 µg/m³ after population weighting, suggesting that the PM₁₀ population exposure in Malaysia might have been overestimated. PWEL of PM₁₀ distribution showed that the majority of the population lived in areas that complied with the national AQG, but were vulnerable to exposure level 3 according to the WHO AQG and IT, indicating that the population was nevertheless potentially exposed to significant health effects from long-term exposure to PM₁₀ pollution.

Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data

Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM₁₀ air pollution and to investigate the population exposure to the distribution of PM₁₀, daily and monthly PM₁₀ concentrations of 379 air pollution monitoring stations were obtained for a period from 01/01/2017 to 31/12/2017. Firstly, PM₁₀ concentrations were classified using the head/tail break clustering algorithm to identify locations with elevated PM₁₀ levels. Subsequently, population exposure levels were calculated using population-weighted PM₁₀ concentrations. Finally, the power-law distribution was used to test the distribution of PM₁₀ polluted areas. Our results indicate that the head/tail break algorithm, with an appropriate segmentation threshold, can effectively identify areas with high PM₁₀ concentrations. The distribution of the population according to exposure level shows that the majority of people is living in polluted areas. The distribution of heavily PM₁₀ polluted areas in Germany follows the power-law distribution well, but their boundaries differ from the boundaries of administrative cities; some even cross several administrative cities. These classification results can guide policymakers in dividing the country into several areas for pollution control.

Socioeconomic factors and regional differences of PM2.5 health risks in China

China is a country with one of the highest concentrations of airborne particulate matter smaller than 2.5 μm (PM_2.5) in the world, and it has obvious spatial-distribution characteristics. Areas of concentrated population tend to be regions with higher PM_2.5 concentrations, which further aggravate the impact of PM_2.5 pollution on population health. Using PM_2.5-concentration and socioeconomic data for 225 cities in China in 2015, we adopted a PM_2.5-health-risk-assessment method (with simplified calculation) and applied the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to analyze the effects of socioeconomic factors on PM_2.5 health risks. The results showed that: (1) At the national level, the order of contribution degree of each socioeconomic factor in the PM_2.5-health-risk and PM_2.5-concentration model is consistent. (2) From a regional perspective, in all three regions, the industrial structure is the decisive factor affecting PM_2.5 health risks, and reduction of energy intensity increases PM_2.5 health risks, but the impact of the total amount of urban central heating on PM_2.5 health risks is very low. In the eastern region, the increased urbanization rate and length of highways significantly increase PM2.5 health risks, but the increasing effect of the extent of built-up area is the lowest. In the central region, the increasing effects of the extent of built-up area on PM_2.5 health risks are significantly greater than the decreasing effects of the urbanization rate. In the western region, economic development has the least effect on reducing PM_2.5 health risks. Our research enriches PM_2.5-health-risk theory and provides some theoretical support for PM_2.5-health-risk diversity management in China.

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Analyzing the association between fine particulate matter (PM_2.5) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 μg/m³ in mainland China, and the highest population exposure to PM_2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM_2.5 was positively related to GDP. An apparent upward trend of population exposure to PM_2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM_2.5 increases by 1.05 person/km² at the 80th percentile, and 1.88 person/km² at the 95th percentile, respectively.

Ambient air quality and spatio-temporal patterns of cardiovascular emergency department visits

BACKGROUND

Air pollutants have been associated with various adverse health effects, including increased rates of hospital admissions and emergency room visits. Although numerous time-series studies and case-crossover studies have estimated associations between day-to-day variation in pollutant levels and mortality/morbidity records, studies on geographic variations in emergency department use and the spatial effects in their associations with air pollution exposure are rare.

METHODS

We focused on the elderly who visited emergency room for cardiovascular related disease (CVD) in 2011. Using spatially and temporally resolved multi-pollutant exposures, we investigated the effect of short-term exposures to ambient air pollution on emergency department utilization. We developed two statistical models with and without spatial random effects within a hierarchical Bayesian framework to capture the spatial heterogeneity and spatial autocorrelation remaining in emergency department utilization.

RESULTS

Although the cardiovascular effect of spatially homogeneous pollutants, such as PM2.5 and ozone, was unchanged, we found the cardiovascular effect of NO[Formula: see text] was pronounced after accounting for the spatially correlated structure in emergency department utilization. We also identified areas with high ED utilization for CVD among the elderly and assessed the uncertainty associated with risk estimates.

CONCLUSIONS

We assessed the short-term effect of multi-pollutants on cardiovascular risk of the elderly and demonstrated the use of community multiscale air quality model-derived spatially and temporally resolved multi-pollutant exposures to an epidemiological study. Our results indicate that NO[Formula: see text] was significantly associated with the elevated ED utilization for CVD among the elderly.

Ambient Air Pollution and Asthma-Related Outcomes in Children of Color of the USA: a Scoping Review of Literature Published Between 2013 and 2017

PURPOSE OF REVIEW

Given racial disparities in ambient air pollution (AAP) exposure and asthma risk, this review offers an overview of the literature investigating the ambient air pollution-asthma relationship in children of color between 2013 and 2017.

RECENT FINDINGS

AAP is likely a key contributor to the excess burden of asthma in children of color due to pervasive exposure before birth, at home, and in school. Recent findings suggest that psychosocial stressors may modify the relationship between AAP and asthma. The effect of AAP on asthma in children of color is likely modulated by multiple unique psychosocial stressors and gene-environment interactions. Although children of color are being included in asthma studies, more research is still needed on impacts of specific criteria pollutants throughout the life course. Additionally, future studies should consider historical factors when analyzing current exposure profiles.

GIS-based analysis of population exposure to PM2.5 air pollution-A case study of Beijing

PM_2.5, formally defined as particulate matter with diameter less than 2.5μm, is one of most harmful air pollutants threatening human health. Numerous epidemiological studies have shown that both short-term and long-term exposures to PM_2.5 are strongly linked with respiratory diseases. In this study, various types of spatio-temporal data were collected and used to estimate the spatio-temporal variation of PM_2.5 exposure in Beijing in 2014. The seasonal and daily variation of the population-weighted exposure level (PWEL) in 2014 was estimated and compared. The results show that the population exposure to ambient air pollution differs significantly in the four seasons, and the exposure levels in winter and spring are notably higher than the other seasons; the exposure level changes greatly from North to South, and each sub-district maintains similarity to neighboring sub-districts.

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

Assessment of Population Exposure to Coarse and Fine Particulate Matter in the Urban Areas of Chennai, India

Research outcomes from the epidemiological studies have found that the course (PM₁₀) and the fine particulate matter (PM_2.5) are mainly responsible for various respiratory health effects for humans. The population-weighted exposure assessment is used as a vital decision-making tool to analyze the vulnerable areas where the population is exposed to critical concentrations of pollutants. Systemic sampling was carried out at strategic locations of Chennai to estimate the various concentration levels of particulate pollution during November 2013–January 2014. The concentration of the pollutants was classified based on the World Health Organization interim target (IT) guidelines. Using geospatial information systems the pollution and the high-resolution population data were interpolated to study the extent of the pollutants at the urban scale. The results show that approximately 28% of the population resides in vulnerable locations where the coarse particulate matter exceeds the prescribed standards. Alarmingly, the results of the analysis of fine particulates show that about 94% of the inhabitants live in critical areas where the concentration of the fine particulates exceeds the IT guidelines. Results based on human exposure analysis show the vulnerability is more towards the zones which are surrounded by prominent sources of pollution.

Spatial modeling of PM2.5 concentrations with a multifactoral radial basis function neural network

Accurate measurements of PM2.5 concentration over time and space are especially critical for reducing adverse health outcomes. However, sparsely stationary monitoring sites considerably hinder the ability to effectively characterize observed concentrations. Utilizing data on meteorological and land-related factors, this study introduces a radial basis function (RBF) neural network method for estimating PM2.5 concentrations based on sparse observed inputs. The state of Texas in the USA was selected as the study area. Performance of the RBF models was evaluated by statistic indices including mean square error, mean absolute error, mean relative deviation, and the correlation coefficient. Results show that the annual PM2.5 concentrations estimated by the RBF models with meteorological factors and/or land-related factors were markedly closer to the observed concentrations. RBF models with combined meteorological and land-related factors achieved best performance relative to ones with either type of these factors only. It can be concluded that meteorological factors and land-related factors are useful for articulating the variation of PM2.5 concentration in a given study area. With these covariate factors, the RBF neural network can effectively estimate PM2.5 concentrations with acceptable accuracy under the condition of sparse monitoring stations. The improved accuracy of air concentration estimation would greatly benefit epidemiological and environmental studies in characterizing local air pollution and in helping reduce population exposures for areas with limited availability of air quality data.

Traffic, air pollution, minority and socio-economic status: addressing inequities in exposure and risk

Higher levels of nearby traffic increase exposure to air pollution and adversely affect health outcomes. Populations with lower socio-economic status (SES) are particularly vulnerable to stressors like air pollution. We investigated cumulative exposures and risks from traffic and from MNRiskS-modeled air pollution in multiple source categories across demographic groups. Exposures and risks, especially from on-road sources, were higher than the mean for minorities and low SES populations and lower than the mean for white and high SES populations. Owning multiple vehicles and driving alone were linked to lower household exposures and risks. Those not owning a vehicle and walking or using transit had higher household exposures and risks. These results confirm for our study location that populations on the lower end of the socio-economic spectrum and minorities are disproportionately exposed to traffic and air pollution and at higher risk for adverse health outcomes. A major source of disparities appears to be the transportation infrastructure. Those outside the urban core had lower risks but drove more, while those living nearer the urban core tended to drive less but had higher exposures and risks from on-road sources. We suggest policy considerations for addressing these inequities.

Diseases Burden of Chronic Obstructive Pulmonary Disease (COPD) Attributable to Ground-Level Ozone in Thailand: Estimates Based on Surface Monitoring Measurements Data

BACKGROUND

Ambient ozone (O3) pollution has increased globally since preindustrial times. At present, O3 is one of the major air pollution concerns in Thailand, and is associated with health impacts such as chronic obstructive pulmonary disease (COPD). The objective of our study is to estimate the burden of disease attributed to O3 in 2009 in Thailand based on empirical evidence.

METHODS

We estimated disability-adjusted life years (DALYs) attributable to O3 using the comparative risk assessment framework in the Global Burden of Diseases (GBD) study. We quantified the population attributable fraction (PAF), integrated from Geographic Information Systems (GIS)-based spatial interpolation, the population distribution of exposure, and the exposure-response coefficient to spatially characterize exposure to ambient O3 pollution on a national scale. Exposure distribution was derived from GIS-based spatial interpolation O3 exposure model using Pollution Control Department Thailand (PCD) surface air pollution monitor network sources. Relative risk (RR) and population attributable fraction (PAF) were determined using health impact function estimates for O3.

RESULT

PAF (%) of COPD attributable to O3 were determined by region: at approximately, Northern=2.1, Northeastern=7.1, Central=9.6, Eastern=1.75, Western=1.47 and Southern=1.74. The total COPD burden attributable to O3 for Thailand in 2009 was 61,577 DALYs. Approximately 0.6% of the total DALYs in Thailand is male: 48,480 DALYs; and female: 13,097 DALYs.

CONCLUSION

This study provides the first empirical evidence on the health burden (DALYs) attributable to O3 pollution in Thailand. Varying across regions, the disease burden attributable to O3 was 0.6% of the total national burden in 2009. Better empirical data on local specific sites, e.g. urban and rural areas, alternative exposure assessment, e.g. land use regression (LUR), and a local concentration-response coefficient are required for future studies in Thailand.

A decade of environmental public health tracking (2002-2012): progress and challenges

BACKGROUND

The creation of the Centers for Disease Control and Prevention Environmental Public Health Tracking Program spawned an invigorating and challenging approach toward implementing the nation's first population-based, environmental disease tracking surveillance system. More than 10 years have passed since its creation and an abundance of peer-reviewed articles have been published spanning a broad variety of public health topics related primarily to the goal of reducing diseases of environmental origin.

OBJECTIVE

To evaluate peer-reviewed literature related to Environmental Public Health Tracking during 2002-2012, recognize major milestones and challenges, and offer recommendations.

DESIGN

A narrative overview was conducted using titles and abstracts of peer-reviewed articles, key word searches, and science-based search engine databases.

MAIN OUTCOMES

Eighty published articles related to "health tracking" were identified and categorized according to 4 crossed-central themes. The Science and Research theme accounted for the majority of published articles, followed by Policy and Practice, Collaborations Among Health and Environmental Programs, and Network Development.

CONCLUSIONS

Overall, progress was reported in the areas of data linkage, data sharing, surveillance methods, and network development. Ongoing challenges included formulating better ways to establish the connections between health and the environment, such as using biomonitoring, public water systems, and private well water data. Recommendations for future efforts include use of data to inform policy and practice and use of electronic health records data for environmental health surveillance.

Population exposure to PM2.5 in the urban area of Beijing

The air quality in Beijing, especially its PM_2.5 level, has become of increasing public concern because of its importance and sensitivity related to health risks. A set of monitored PM_2.5 data from 31 stations, released for the first time by the Beijing Environmental Protection Bureau, covering 37 days during autumn 2012, was processed using spatial interpolation and overlay analysis. Following analyses of these data, a distribution map of cumulative exceedance days of PM_2.5 and a temporal variation map of PM_2.5 for Beijing have been drawn. Computational and analytical results show periodic and directional trends of PM_2.5 spreading and congregating in space, which reveals the regulation of PM_2.5 overexposure on a discontinuous medium-term scale. With regard to the cumulative effect of PM_2.5 on the human body, the harm from lower intensity overexposure in the medium term, and higher overexposure in the short term, are both obvious. Therefore, data of population distribution were integrated into the aforementioned PM_2.5 spatial spectrum map. A spatial statistical analysis revealed the patterns of PM_2.5 gross exposure and exposure probability of residents in the Beijing urban area. The methods and conclusions of this research reveal relationships between long-term overexposure to PM_2.5 and people living in high-exposure areas of Beijing, during the autumn of 2012.