Clustering cities with similar fine particulate matter exposure characteristics based on residential infiltration and in-vehicle commuting factors

Identifying groups of children's social mobility opportunity for public health applications using k-means clustering

Background

The Opportunity Atlas project is a pioneering effort to trace social mobility and adulthood socioeconomic outcomes back to childhood residence. Half of the variation in adulthood socioeconomic outcomes was explainable by neighborhood-level socioeconomic characteristics during childhood. Clustering census tracts by Opportunity Atlas characteristics would allow for further exploration of variance in social mobility. Our objectives here are to identify and describe spatial clustering trends within Opportunity Atlas outcomes.

Methods

We utilized a k-means clustering machine learning approach with four outcome variables (individual income, incarceration rate, employment, and percent of residents living in a neighborhood with low levels of poverty) each given at five parental income levels (1st, 25th, 50th, 75th, and 100th percentiles of the national distribution) to create clusters of census tracts across the contiguous United States (US) and within each Environmental Protection Agency region.

Results

At the national level, the algorithm identified seven distinct clusters; the highest opportunity clusters occurred in the Northern Midwest and Northeast, and the lowest opportunity clusters occurred in rural areas of the Southwest and Southeast. For regional analyses, we identified between five to nine clusters within each region. PCA loadings fluctuate across parental income levels; income and low poverty neighborhood residence explain a substantial amount of variance across all variables, but there are differences in contributions across parental income levels for many components.

Conclusions

Using data from the Opportunity Atlas, we have taken four social mobility opportunity outcome variables each stratified at five parental income levels and created nationwide and EPA region-specific clusters that group together census tracts with similar opportunity profiles. The development of clusters that can serve as a combined index of social mobility opportunity is an important contribution of this work, and this in turn can be employed in future investigations of factors associated with children's social mobility.

Use of clay and organic matter contents to predict soil pH vulnerability in response to acid or alkali spills

Acid or alkali spills destroy the physicochemical properties of soils and cause irreversible damage to their ecological functions. This study examined changes in physicochemical properties (i.e., organic matter, clay content, and cation exchange capacity (CEC)) as well as pH buffering capacity (indicator of soil ecological function) of 20 field soils in response to the spills. Also, we identified the characteristics of soils vulnerable to the spills. Although the spills did not substantially change the clay content, organic matter decreased by approximately 50%, consequently resulting in a 41% decrease in pH buffering capacity. When we classified soils into three groups based on soil properties and pH buffering capacity, the extent of change in soil properties by spill differed by group. As the organic matter content increased or clay content decreased, the soil tended to be more vulnerable to spills in terms of the degree to which the soil function was changed. Considering that the protonation-deprotonation characteristics of clay sized fraction were not remarkably changed by the spills, this result was mainly attributed to the dissolution of organic matter. Together with the successful prediction of CEC and pH buffering capacity by multiple linear regression models using organic matter and clay content, our findings enable the easy classification of soils based on their vulnerability and site-specific management of areas with a high probability of spills.

Acute Effects of Ambient Air Pollution on Asthma Emergency Department Visits in Ten U.S. States

BACKGROUND

Previous studies of short-term ambient air pollution exposure and asthma morbidity in the United States have been limited to a small number of cities and/or pollutants and with limited consideration of effects across ages.

OBJECTIVES

To estimate acute age group-specific effects of fine and coarse particulate matter (PM), major PM components, and gaseous pollutants on emergency department (ED) visits for asthma during 2005-2014 across the United States.

METHODS

We acquired ED visit and air quality data in regions surrounding 53 speciation sites in 10 states. We used quasi-Poisson log-linear time-series models with unconstrained distributed exposure lags to estimate site-specific acute effects of air pollution on asthma ED visits overall and by age group (1-4, 5-17, 18-49, 50-64, and 65+ y), controlling for meteorology, time trends, and influenza activity. We then used a Bayesian hierarchical model to estimate pooled associations from site-specific associations.

RESULTS

Our analysis included 3.19 million asthma ED visits. We observed positive associations for multiday cumulative exposure to all air pollutants examined [e.g., 8-d exposure to PM2.5: rate ratio of 1.016 with 95% credible interval (CI) of (1.008, 1.025) per 6.3-μg/m3 increase, PM10-2.5: 1.014 (95% CI: 1.007, 1.020) per 9.6-μg/m3 increase, organic carbon: 1.016 (95% CI: 1.009, 1.024) per 2.8-μg/m3 increase, and ozone: 1.008 (95% CI: 0.995, 1.022) per 0.02-ppm increase]. PM2.5 and ozone showed stronger effects at shorter lags, whereas associations of traffic-related pollutants (e.g., elemental carbon and oxides of nitrogen) were generally stronger at longer lags. Most pollutants had more pronounced effects on children (<18 y old) than adults; PM2.5 had strong effects on both children and the elderly (>64 y old); and ozone had stronger effects on adults than children.

CONCLUSIONS

We reported positive associations between short-term air pollution exposure and increased rates of asthma ED visits. We found that air pollution exposure posed a higher risk for children and older populations. https://doi.org/10.1289/EHP11661.

A framework for estimating the US mortality burden of fine particulate matter exposure attributable to indoor and outdoor microenvironments

Exposure to fine particulate matter (PM_2.5) is associated with increased mortality. Although epidemiology studies typically use outdoor PM_2.5 concentrations as surrogates for exposure, the majority of PM_2.5 exposure in the US occurs in microenvironments other than outdoors. We develop a framework for estimating the total US mortality burden attributable to exposure to PM_2.5 of both indoor and outdoor origin in the primary non-smoking microenvironments in which people spend most of their time. The framework utilizes an exposure-response function combined with adjusted mortality effect estimates that account for underlying exposures to PM_2.5 of outdoor origin that likely occurred in the original epidemiology populations from which effect estimates are derived. We demonstrate the framework using several different scenarios to estimate the potential magnitude and bounds of the US mortality burden attributable to total PM_2.5 exposure across all non-smoking environments under a variety of assumptions. Our best estimates of the US mortality burden associated with total PM_2.5 exposure in the year 2012 range from ~230,000 to ~300,000 deaths. Indoor exposure to PM_2.5 of outdoor origin is typically the largest total exposure, accounting for ~40-60% of total mortality, followed by residential exposure to indoor PM_2.5 sources, which also drives the majority of variability in each scenario.

Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM2.5 and mortality

Multi-city population-based epidemiological studies of short-term fine particulate matter (PM_2.5) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM_2.5 and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m³ increase in PM_2.5 and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R² < 1%), multivariate models began to explain some of the observed heterogeneity (R² = 13%).

Higher-order Network Analysis of Fine Particulate Matter (PM 2.5) Transport in China at City Level

Specification of PM 2.5 transmission characteristics is important for pollution control and policymaking. We apply higher-order organization of complex networks to identify major potential PM 2.5 contributors and PM 2.5 transport pathways of a network of 189 cities in China. The network we create in this paper consists of major cities in China and contains information on meteorological conditions of wind speed and wind direction, data on geographic distance, mountains, and PM 2.5 concentrations. We aim to reveal PM 2.5 mobility between cities in China. Two major conclusions are revealed through motif analysis of complex networks. First, major potential PM 2.5 pollution contributors are identified for each cluster by one motif, which reflects movements from source to target. Second, transport pathways of PM 2.5 are revealed by another motif, which reflects transmission routes. To our knowledge, this is the first work to apply higher-order network analysis to study PM 2.5 transport.

Influence of exposure differences on city-to-city heterogeneity in PM2.5-mortality associations in US cities

BACKGROUND

Multi-city population-based epidemiological studies have observed heterogeneity between city-specific fine particulate matter (PM_2.5)-mortality effect estimates. These studies typically use ambient monitoring data as a surrogate for exposure leading to potential exposure misclassification. The level of exposure misclassification can differ by city affecting the observed health effect estimate.

METHODS

The objective of this analysis is to evaluate whether previously developed residential infiltration-based city clusters can explain city-to-city heterogeneity in PM_2.5 mortality risk estimates. In a prior paper 94 cities were clustered based on residential infiltration factors (e.g. home age/size, prevalence of air conditioning (AC)), resulting in 5 clusters. For this analysis, the association between PM_2.5 and all-cause mortality was first determined in 77 cities across the United States for 2001-2005. Next, a second stage analysis was conducted evaluating the influence of cluster assignment on heterogeneity in the risk estimates.

RESULTS

Associations between a 2-day (lag 0-1 days) moving average of PM_2.5 concentrations and non-accidental mortality were determined for each city. Estimated effects ranged from -3.2 to 5.1% with a pooled estimate of 0.33% (95% CI: 0.13, 0.53) increase in mortality per 10 μg/m³ increase in PM_2.5. The second stage analysis determined that cluster assignment was marginally significant in explaining the city-to-city heterogeneity. The health effects estimates in cities with older, smaller homes with less AC (Cluster 1) and cities with newer, smaller homes with a large prevalence of AC (Cluster 3) were significantly lower than the cluster consisting of cities with older, larger homes with a small percentage of AC.

CONCLUSIONS

This is the first study that attempted to examine whether multiple exposure factors could explain the heterogeneity in PM_2.5-mortality associations. The results of this study were found to explain a small portion (6%) of this heterogeneity.

Spatial and Temporal Variations of PM2.5 and Its Relation to Meteorological Factors in the Urban Area of Nanjing, China

The serious air pollution problem has aroused widespread public concerns in China. Nanjing city, as one of the famous cities of China, is faced with the same situation. This research aims to investigate spatial and temporal distribution characteristics of fine particulate matter (PM2.5) and the influence of weather factors on PM2.5 in Nanjing using Spearman-Rank analysis and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method. Hourly PM2.5 observation data and daily meteorological data were collected from 1 April 2013 to 31 December 2015. The spatial distribution result shows that the Maigaoqiao site suffered the most serious pollution. Daily PM2.5 concentrations in Nanjing varied from 7.3 μg/m³ to 336.4 μg/m³. The highest concentration was found in winter and the lowest in summer. The diurnal variation of PM2.5 increased greatly from 6 to 10 a.m. and after 6 p.m., while the concentration exhibited few variations in summer. In addition, the concentration was slightly higher on weekends compared to weekdays. PM2.5 was found to exhibit a reversed relation with wind speed, relative humidity, and precipitation. Although temperature had a positive association with PM2.5 in most months, a negative correlation was observed during the whole period. Additionally, a high concentration was mainly brought with the wind with a southwest direction and several relevant factors are discussed to explain the difference of the impacts of diverse wind directions.