Spatial and temporal variability in urban fine particulate matter concentrations

The Role of Neighborhood Air Pollution in Disparate Racial and Ethnic Asthma Acute Care Use

Rationale: The share of Black or Latinx residents in a census tract remains associated with asthma-related emergency department (ED) visit rates after controlling for socioeconomic factors. The extent to which evident disparities relate to the within-city heterogeneity of long-term air pollution exposure remains unclear. Objectives: To investigate the role of intraurban spatial variability of air pollution in asthma acute care use disparity. Methods: An administrative database was used to define census tract population-based incidence rates of asthma-related ED visits. We estimate the associations between census tract incidence rates and 1) average fine and coarse particulate matter, nitrogen dioxide (NO2), and sulfur dioxide (SO2), and 2) racial and ethnic composition using generalized linear models controlling for socioeconomic and housing covariates. We also examine for the attenuation of incidence risk ratios (IRRs) associated with race/ethnicity when controlling for air pollution exposure. Measurements and Main Results: Fine and coarse particulate matter and SO2 are all associated with census tract-level incidence rates of asthma-related ED visits, and multipollutant models show evidence of independent risk associated with coarse particulate matter and SO2. The association between census tract incidence rate and Black resident share (IRR, 1.51 [credible interval (CI), 1.48-1.54]) is attenuated by 24% when accounting for air pollution (IRR, 1.39 [CI, 1.35-1.42]), and the association with Latinx resident share (IRR, 1.11 [CI, 1.09-1.13]) is attenuated by 32% (IRR, 1.08 [CI, 1.06-1.10]). Conclusions: Neighborhood-level rates of asthma acute care use are associated with local air pollution. Controlling for air pollution attenuates associations with census tract racial/ethnic composition, suggesting that intracity variability in air pollution could contribute to neighborhood-to-neighborhood asthma morbidity disparities.

Benefits of Short-term Premature Mortality Reduction Attributed to PM2.5 Pollution: A Case Study in Long an Province, Vietnam

PM2.5 pollution exposure is the leading cause of disease burden globally, especially in low- and middle-income countries, including Vietnam. Therefore, economic damage in this context must be quantified. Long An province in the Southern Key Economic (SKE) region was selected as a research area. This study aimed to evaluate PM2.5-related human health effects causing early deaths attributable to respiratory, cardiovascular, and circulatory diseases in all ages and genders. Health end-points and health impact estimation, economic loss model, groups of PM2.5 concentration data, data of exposed population, data of baseline premature mortality rate, and data of health impact functions were used. Hourly PM2.5 concentration data sets were generated specifically using the coupled Weather Research and Forecasting Model (WRF)/Community Multiscale Air Quality Modelling System (CMAQ) models. Daily PM2.5 pollution levels considered mainly in the dry season (from January to April 2018) resulted in 12.9 (95% CI - 0.6; 18.7) all-cause premature deaths per 100,000 population, of which 7.8 (95% CI 1.1; 7.1), 1.5 (95% CI - 0.2; 3.1), and 3.6 (95% CI - 1.5; 8.5) were due to respiratory diseases (RDs; 60.54%), cardiovascular diseases (CVDs; 11.81%), and circulatory system diseases (CSDs; 27.65%) per 100,000 population, respectively. The total economic losses due to acute PM2.5 exposure-related premature mortality cases reached 62.0 (95% CI - 2.7; 89.6) billion VND, equivalent to 8.3 (95% CI - 0.4; 12.0) million USD. The study outcomes contributed remarkably to the generation and development of data sources for effectively managing ambient air quality in Long An.

Assessment of PM10 and PM2.5 over Ghaziabad, an industrial city in the Indo-Gangetic Plain: spatio-temporal variability and associated health effects

This study examined the PM₁₀ and PM_2.5 concentration, associated mortality, and transport pathways in Ghaziabad which is an industrial city in the Indo-Gangetic Plain. To achieve this, PM (both PM₁₀ and PM_2.5) and meteorological parameters were measured from June 2018 to May 2019 at 2 locations and analyzed together with data from a 3rd location in Ghaziabad. The highest daily average PM₁₀ and PM_2.5 concentrations were ~ 1000 µg m^-3 and ~ 450 µg m^-3, respectively. At each of the three locations, the annual mean PM₁₀ concentrations were ~ 260 ± 150 µg m^-3 while the PM_2.5 concentrations were 140 ± 90 µg m^-3. Nonparametric Spearman rank correlation analysis between meteorological parameters and PM concentrations indicated that ventilation coefficient was anti-correlated with PM concentration during the post-monsoon and winter seasons (the most polluted seasons) with rank correlation values of approximately - 0.50. Multiple linear regression (MLR) revealed that the variability in local meteorological parameters account for ~ 50% variability (maximum) in PM₁₀ mass during the monsoon and PM_2.5 during the post-monsoon season. For long-range sources, cluster and concentrated weighted trajectory (CWT) analyses utilizing regional meteorology showed the impact of transported PM from sources in Arabian sea through western India in monsoon and from parts of South Asia through Northwestern IGP and neighboring cities in Uttar Pradesh in other seasons. Finally, mortality estimates show that the number of deaths attributable to ambient PM_2.5 in Ghaziabad were ~ 873 per million individuals which was ~ 70% higher than Delhi.

Ambient fine particulate matter in Latin American cities: Levels, population exposure, and associated urban factors

BACKGROUND

Exposure to particulate matter (PM_2.5) is a major risk factor for morbidity and mortality. Yet few studies have examined patterns of population exposure and investigated the predictors of PM_2.5 across the rapidly growing cities in lower- and middle-income countries.

OBJECTIVES

Characterize PM_2.5 levels, describe patterns of population exposure, and investigate urban factors as predictors of PM_2.5 levels.

METHODS

We used data from the Salud Urbana en America Latina/Urban Health in Latin America (SALURBAL) study, a multi-country assessment of the determinants of urban health in Latin America, to characterize PM_2.5 levels in 366 cities comprising over 100,000 residents using satellite-derived estimates. Factors related to urban form and transportation were explored.

RESULTS

We found that about 172 million or 58% of the population studied lived in areas with air pollution levels above the defined WHO-AQG of 10 μg/m³ annual average. We also found that larger cities, cities with higher GDP, higher motorization rate and higher congestion tended to have higher PM_2.5. In contrast cities with higher population density had lower levels of PM_2.5. In addition, at the sub-city level, higher intersection density was associated with higher PM_2.5 and more green space was associated with lower PM_2.5. When all exposures were examined adjusted for each other, higher city per capita GDP and higher sub-city intersection density remained associated with higher PM_2.5 levels, while higher city population density remained associated with lower levels. The presence of mass transit was also associated with lower PM_2.5 after adjustment. The motorization rate also remained associated with PM_2.5 and its inclusion attenuated the effect of population density.

DISCUSSION

These results show that PM_2.5 exposures remain a major health risk in Latin American cities and suggest that urban planning and transportation policies could have a major impact on ambient levels.

Hourly land-use regression models based on low-cost PM monitor data

Land-use regression (LUR) models provide location and time specific estimates of exposure to air pollution and thereby improve the sensitivity of health effects models. However, they require pollutant concentrations at multiple locations along with land-use variables. Often, monitoring is performed over short durations using mobile monitoring with research-grade instruments. Low-cost PM monitors provide an alternative approach that increases the spatial and temporal resolution of the air quality data. LUR models were developed to predict hourly PM concentrations across a metropolitan area using PM concentrations measured simultaneously at multiple locations with low-cost monitors. Monitors were placed at 23 sites during the 2015/16 heating season. Monitors were externally calibrated using co-located measurements including a reference instrument (GRIMM particle spectrometer). LUR models for each hour of the day and weekdays/weekend days were developed using the deletion/substitution/addition algorithm. Coefficients of determination for hourly PM predictions ranged from 0.66 and 0.76 (average 0.7). The hourly-resolved LUR model results will be used in epidemiological studies to examine if and how quickly, increases in ambient PM concentrations trigger adverse health events by reducing the exposure misclassification that arises from using less time resolved exposure estimates.

Effectiveness of Using Enhanced Filters in Schools and Homes to Reduce Indoor Exposures to PM2.5 from Outdoor Sources and Subsequent Health Benefits for Children with Asthma

Filters can reduce indoor concentrations of particulate matter (PM_2.5), but their benefits have not been well-characterized. This study investigates exposure, health, and cost impacts of high efficiency filters in homes and schools, focusing on the asthma-related outcomes. Reductions in indoor exposures to PM_2.5 from outdoor sources with enhanced filters (e.g., MERV 12) are estimated using probabilistic indoor air quality models, and avoided health impacts are quantified using health impact assessment. These methods are applied using data from Detroit, Michigan, an urban region with elevated asthma rates. Replacing inefficient filters with enhanced filters in schools would reduce the PM_2.5-attributable asthma burden by 13% annually, with higher benefits for more efficient filters. Marginal costs average $63 per classroom or $32 per child with asthma per year. In homes, using efficient furnace filters or air cleaners yields 11 to 16% reductions in the asthma burden with an annualized marginal costs of $151-494 per household. Additional benefits include reductions in health risk for adults and lower exposures to other contaminants such as PM from indoor sources. On the basis of the included health outcomes, efficient filters in schools in particular is a potentially cost-efficient way to reduce the asthma-related health burden for children.

An Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10

Air pollution can endanger human health, especially in urban areas. Assessment of air quality primarily relies on ground-based measurements, but these provide only limited information on the spatial distribution of pollutants. In recent years, satellite derived Aerosol Optical Depth (AOD) has been used to approximate particulate matter (PM) with varying success. In this study, the relationship between hourly mean concentrations of particulate matter with a diameter of 10 micrometers or less (PM10) and instantaneous AOD measurements is investigated for Berlin, Germany, for 2001–2015. It is found that the relationship between AOD and PM10 is rarely linear and strongly influenced by ambient relative humidity (RH), boundary layer height (BLH), wind direction and wind speed. Generally, when a moderately dry atmosphere (30% < RH ≤ 50%) coincides with a medium BLH (600–1200 m), AOD and PM10 are in the same range on a semi-quantitative scale. AOD increases with ambient RH, leading to an overestimation of the dry particle concentration near ground. However, this effect can be compensated if a low boundary layer (<600 m) is present, which in turn significantly increases PM10, eventually leading to satellite AOD and PM10 measurements of similar magnitude. Insights of this study potentially influence future efforts to estimate near-ground PM concentrations based on satellite AOD.

Air pollutant strategies to reduce adverse health impacts and health inequalities: a quantitative assessment for Detroit, Michigan

The development of air quality management (AQM) strategies provides opportunities to improve public health and reduce health inequalities. This study evaluates health and inequality impacts of alternate SO2 control strategies in Detroit, MI, a designated non-attainment area. Control alternatives include uniform reductions across sources, ranking approaches based on total emissions and health impacts per ton of pollutant emitted, and optimizations that meet concentration and health goals. Using dispersion modeling and quantitative health impact assessment (HIA), these strategies are evaluated in terms of ambient concentrations, health impacts, and the inequality in health risks. The health burden attributable to SO2 emissions in Detroit falls primarily among children and includes 70 hospitalizations and 6,000 asthma-related respiratory symptom-days annually, equivalent to 7 disability-adjusted life years (DALYs). The health burden disproportionately falls on Hispanic/Latino residents, residents with less than a high school diploma, and foreign-born residents. Control strategies that target smaller facilities near exposed populations provide the greatest benefit in terms of the overall health burden reductions and the inequality of attributable health risk; conventional strategies that target the largest emission sources can increase inequality and provide only modest health benefits. The assessment is novel in using spatial analyses that account for urban scale gradients in exposure, demographics, vulnerability, and population health. We show that quantitative HIA methods can be used to develop AQM strategies that simultaneously meet environmental, public health, and environmental justice goals, advancing AQM beyond its current compliance-oriented focus.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

Integrated assessment of air pollution by metals and source apportionment using ryegrass (Lolium multiflorum Lam.) in southern Brazil

One of the biggest environmental problems existing today is air pollution, which is characterized by the presence of toxic gases and metal pollutants, the latter of which is generally associated with emissions of particulate matter (PM) from industries or automotive vehicles. Biomonitoring is a method that can be used to assess air pollution levels because it makes it possible to determine what effects these air pollutants cause in living organisms and their responses. The species Lolium multiflorum Lam., known as ryegrass, is considered a good bioindicator of metals, since it accumulates these substances during exposure. This study proposes to conduct an integrated assessment of air quality using two different monitoring methodologies: biomonitoring with L. multiflorum and active monitoring in areas with different levels of urbanization and industrialization. Concentrations found in ryegrass plants revealed high levels of Pb, Cr, Zn, and Cu, indicating that vehicular and industrial emissions were the main sources of pollution. Analysis of PM also revealed soot and biogenic particles, which can transport metals. Therefore, with the proposed method, the anthropogenic impact on air pollution in the investigated area could be clearly demonstrated.

The importance of toxicity in determining the impact of hazardous air pollutants on the respiratory health of children in Tennessee

Respiratory diseases, exacerbated through point source pollution, are currently among the leading causes of hospitalization of children in the United States. This paper investigates the relationship between the proximity of hazardous air pollutants (HAPs) emitted from Toxic Release Inventory (TRI) facilities and the number of children diagnosed in hospitals with a respiratory disease in Tennessee. The importance of controlling for toxicity of those HAPs is of particular interest. Hospital discharge, socioeconomic, TRI emission, and HAP toxicity data are used to estimate, via Generalized Linear Methods, a logistic regression model describing the relationship between the percent of children living in a zip code area treated for respiratory illness and the average annual emissions over the previous 10 years of HAPs from TRI sites in that area. Controlling for area socioeconomic characteristics, we find that accounting for toxicity is important in uncovering the relationship between HAP emissions and respiratory health of children. A one standard deviation increase in toxicity-weighted emissions per 100 square miles is associated with an increase in the number of children diagnosed with asthma (chronic bronchitis) by about 1205 (260). The evidence suggests that, with a goal to improving children's respiratory health, monitoring the toxicity of chemicals being emitted is at least as important as simply monitoring total emission levels. This suggests that the EPA should consider making efforts toward establishing toxicity adjusted emission guidelines.

Comparison of Hourly PM2.5 Observations Between Urban and Suburban Areas in Beijing, China

Hourly PM_2.5 observations collected at 12 stations over a 1-year period are used to identify variations between urban and suburban areas in Beijing. The data demonstrates a unique monthly variation form, as compared with other major cities. Urban areas suffer higher PM_2.5 concentration (about 92 μg/m³) than suburban areas (about 77 μg/m³), and the average PM_2.5 concentration in cold season (about 105 μg/m³) is higher than warm season (about 78 μg/m³). Hourly PM_2.5 observations exhibit distinct seasonal, diurnal and day-of-week variations. The diurnal variation of PM_2.5 is observed with higher concentration at night and lower value at daytime, and the cumulative growth of nighttime (22:00 p.m. in winter) PM_2.5 concentration maybe due to the atmospheric stability. Moreover, annual average PM_2.5 concentrations are about 18 μg/m³ higher on weekends than weekdays, consistent with driving restrictions on weekdays. Additionally, the nighttime peak in weekdays (21:00 p.m.) is one hour later than weekends (20:00 p.m.) which also shows the evidence of human activity. These observed facts indicate that the variations of PM_2.5 concentration between urban and suburban areas in Beijing are influenced by complex meteorological factors and human activities.

The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh, North Carolina

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM2.5 ) concentrations in ambient air and associated health risks from PM2.5 exposure in North Carolina's Raleigh-Durham-Chapel Hill area. We integrate transportation demand, land-use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (-0.2%) point estimates of regional annual average PM2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (-33%) PM2.5-attributable mortality. Furthermore, compactness increases local variation in PM2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM2.5 in local hotspots and increase population exposure to PM2.5 . Health effects may be magnified if compact neighborhoods and PM2.5 hotspots are spatially co-located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile-dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions.

Spatiotemporal analysis of particulate air pollution and ischemic heart disease mortality in Beijing, China

BACKGROUND

Few studies have used spatially resolved ambient particulate matter with an aerodynamic diameter of <10 μm (PM10) to examine the impact of PM10 on ischemic heart disease (IHD) mortality in China. The aim of our study is to evaluate the short-term effects of PM10 concentrations on IHD mortality by means of spatiotemporal analysis approach.

METHODS

We collected daily data on air pollution, weather conditions and IHD mortality in Beijing, China during 2008 and 2009. Ordinary kriging (OK) was used to interpolate daily PM10 concentrations at the centroid of 287 township-level areas based on 27 monitoring sites covering the whole city. A generalized additive mixed model was used to estimate quantitatively the impact of spatially resolved PM10 on the IHD mortality. The co-effects of the seasons, gender and age were studied in a stratified analysis. Generalized additive model was used to evaluate the effects of averaged PM10 concentration as well.

RESULTS

The averaged spatially resolved PM10 concentration at 287 township-level areas was 120.3 ± 78.1 μg/m3. Ambient PM10 concentration was associated with IHD mortality in spatiotemporal analysis and the strongest effects were identified for the 2-day average. A 10 μg/m3 increase in PM10 was associated with an increase of 0.33% (95% confidence intervals: 0.13%, 0.52%) in daily IHD mortality. The effect estimates using spatially resolved PM10 were larger than that using averaged PM10. The seasonal stratification analysis showed that PM10 had the statistically stronger effects on IHD mortality in summer than that in the other seasons. Males and older people demonstrated the larger response to PM10 exposure.

CONCLUSIONS

Our results suggest that short-term exposure to particulate air pollution is associated with increased IHD mortality. Spatial variation should be considered for assessing the impacts of particulate air pollution on mortality.

Pediatric asthma and ambient pollutant levels in industrializing nations

Asthma is one of the most common chronic diseases in childhood and its prevalence has been increasing within industrializing nations. The contribution of ambient pollutants to asthma symptomatology has been explored in some countries through epidemiological investigations, molecular analysis and monitoring functional outcomes. The health effects of rising environmental pollution have been of increasing concern in industrializing nations with rising urbanization patterns. This review article provides an overview of the link between pediatric asthma and exposure to rising sources of urban air pollution. It primarily focuses on the asthma-specific effects of sulfur dioxide, nitrogen dioxide, ozone and particulate matter. Worldwide trends of asthma prevalence are also provided which detail the prominent rise in asthma symptoms in many urban areas of Africa, Latin America and Asia. The molecular and functional correlation of ambient pollutants with asthma-specific airway inflammation in the pediatric population are also highlighted. The final aspect of the review considers the correlation of motor vehicle, industrial and cooking energy sources, ascribed as the major emitters among the pollutants in urban settings, with asthma epidemiology in children.

Spatial and temporal variability of source contributions to ambient PM10 during winter in Augsburg, Germany using organic and inorganic tracers

Daily PM10 samples were collected during a one-month sampling campaign from February 13 to March 12, 2008 at eight different sampling sites in Augsburg, Southern Germany. Source apportionment was performed to identify the main sources and related contributions by analysis of organic and inorganic tracers. Nine factors were separated comprising: solid fuel combustion, traffic-related emissions, secondary inorganics, and mixed sources. Spatiotemporal variation of the source contributions was evaluated using the Pearson correlation coefficient (r) and coefficient of divergence (COD). All factors (except hopanes and mixed sources) showed moderate to high (0.6<r>0.8) correlation coefficients between the eight sites and were distributed heterogeneously. Secondary sulfate and secondary nitrate factors were relatively more uniformly distributed (compared to other factors) with lower medians of COD value (0.47 and 0.56, respectively) and higher correlation values (r=0.97 and 0.85, respectively). The maximum daily average contribution for coal & wood combustion factor was observed at the LfU suburban site (4.0 μg m(-3)); wood combustion factor at the LSW residential site (5.1 μg m(-3)) ; diesel & fuel oil consumption factor at the Bifa suburban and BP urban sites (both 2.5 μg m(-3)); road dust & tram factor at the KP traffic site (16.2 μg m(-3)) and the BP urban site (6.6 μg m(-3)); hopanes factor at the BP urban and Bifa suburban sites (both 0.7 μg m(-3)); and de-icing NaCl factor at the KP traffic site (4.8 μg m(-3)). Secondary sulfate and secondary nitrate factors had approximately similar contributions (6.2 μg m(-3) and 4.3 μg m(-3), respectively) at all sites. Mixed sources factor had the highest daily average contribution to PM10 mass at the KP traffic site (7.0 μg m(-3)).

Spatial scales of pollution from variable resolution satellite imaging

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM(2.5) as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM(2.5) and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM(2.5) ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM(2.5) levels and wind speed.