Asthma and air pollution in the Bronx: methodological and data considerations in using GIS for environmental justice and health research

Access to Interpretable Data to Support Disproportionate Health Risks from Industrial Releases: A Case Study on the Environmental Protection Agency's Datasets and Their Application to the Latinx Communities of Houston, Texas

Latinx communities face disproportionate environmental injustices and are targeted due to systematic economic and political inequities. This research evaluates the ease at which links between industrial releases and risk of adverse health effects can be defined to influence policy change in Houston, TX. The Environmental Protection Agency (EPA)'s Toxic Release Inventory (TRI) is the most comprehensive public database on industrial facilities' toxic chemical releases in the US. TRI is presented within a risk-based context through the Risk Screening Environmental Indicators (RSEI) scores. TRI and RSEI datasets for Houston in 2022 were assessed in QGIS to analyze chemical release and risk in neighborhoods using Community Tabulation Areas (CTAs), identifying demographics of communities facing disproportionate industrial releases and consequent potential health risks. Geospatial visualizations reflected Latinx communities to house the heaviest polluting industrial facilities in Houston. As a result, these communities face the highest potential risk of adverse health effects due to exposure to a multitude of chemicals-particularly 1,3-butadiene, benzene, and chromium-as reflected in cumulative RSEI scores. An analysis of TRI and RSEI datasets elucidates the burden of gathering and analyzing chemical release data in a public health context, reflecting why change beginning at the local level can be difficult for under-resourced Latinx communities facing industrial pollution. Improving the accessibility and utility of the EPA resources will provide a resource to advocate for data-driven policy change.

Scarcity of medical ethics research in allergy and immunology: A review and call to action

Medical ethics is relevant to the clinical practice of allergy and immunology regardless of the type of patient, disease state, or practice setting. When engaging in clinical care, performing research, or enacting policies on the accessibility and distribution of healthcare resources, physicians regularly make and justify decisions using the fundamental principles of medical ethics. Thus, knowledge of these principles is paramount for allergists/immunologists. To date, there has been a shortage of medical ethics research in allergy and immunology. This review describes this scarcity, highlights publication trends over time, and advocates for additional support for research and training in medical ethics with a focus on topics germane to the practice of allergy and immunology.

The Impact of Climate Change on Asthma and Allergic-Immunologic Disease

PURPOSE OF REVIEW

This review discusses climate change-related impacts on asthma and allergic-immunologic disease, relevant US public health efforts, and healthcare professional resources.

RECENT FINDINGS

Climate change can impact people with asthma and allergic-immunologic disease through various pathways, including increased exposure to asthma triggers (e.g., aeroallergens, ground-level ozone). Climate change-related disasters (e.g., wildfires, floods) disrupting healthcare access can complicate management of any allergic-immunologic disease. Climate change disproportionately affects some communities, which can exacerbate disparities in climate-sensitive diseases like asthma. Public health efforts include implementing a national strategic framework to help communities track, prevent, and respond to climate change-related health threats. Healthcare professionals can use resources or tools to help patients with asthma and allergic-immunologic disease prevent climate change-related health impacts. Climate change can affect people with asthma and allergic-immunologic disease and exacerbate health disparities. Resources and tools are available to help prevent climate change-related health impacts at the community and individual level.

Effect modification of the association between fine particulate air pollution during a wildfire event and respiratory health by area-level measures of socio-economic status, race/ethnicity, and smoking prevalence

Fine particulate air pollution (PM2.5) is decreasing in most areas of the United States, except for areas most affected by wildfires, where increasing trends in PM2.5 can be attributed to wildfire smoke. The frequency and duration of large wildfires and the length of the wildfire season have all increased in recent decades, partially due to climate change, and wildfire risk is projected to increase further in many regions including the western United States. Increasingly, empirical evidence suggests differential health effects from air pollution by class and race; however, few studies have investigated such differential health impacts from air pollution during a wildfire event. We investigated differential risk of respiratory health impacts during the 2008 northern California wildfires by a comprehensive list of socio-economic status (SES), race/ethnicity, and smoking prevalence variables. Regardless of SES level across nine measures of SES, we found significant associations between PM2.5 and asthma hospitalizations and emergency department (ED) visits during these wildfires. Differential respiratory health risk was found by SES for ED visits for chronic obstructive pulmonary disease where the highest risks were in ZIP codes with the lowest SES levels. Findings for differential effects by race/ethnicity were less consistent across health outcomes. We found that ZIP codes with higher prevalence of smokers had greater risk of ED visits for asthma and pneumonia. Our study suggests that public health efforts to decrease exposures to high levels of air pollution during wildfires should focus on lower SES communities.

Environmental justice and allergic disease: A Work Group Report of the AAAAI Environmental Exposure and Respiratory Health Committee and the Diversity, Equity and Inclusion Committee

Environmental justice is the concept that all people have the right to live in a healthy environment, to be protected against environmental hazards, and to participate in decisions affecting their communities. Communities of color and low-income populations live, work, and play in environments with disproportionate exposure to hazards associated with allergic disease. This unequal distribution of hazards has contributed to health disparities and is largely the result of systemic racism that promotes segregation of neighborhoods, disinvestment in predominantly racial/ethnic minority neighborhoods, and discriminatory housing, employment, and lending practices. The AAAAI Environmental Exposure and Respiratory Health Committee and Diversity, Equity and Inclusion Committee jointly developed this report to improve allergy/immunology specialists' awareness of environmental injustice, its roots in systemic racism, and its impact on health disparities in allergic disease. We present evidence supporting the relationship between exposure to environmental hazards, particularly at the neighborhood level, and the disproportionately high incidence and poor outcomes from allergic diseases in marginalized populations. Achieving environmental justice requires investment in at-risk communities to increase access to safe housing, clean air and water, employment opportunities, education, nutrition, and health care. Through policies that promote environmental justice, we can achieve greater health equity in allergic disease.

Does the environmental inequality matter? A literature review

The environmental inequality theory reveals that the risk of environmental pollution exposure varies among regions and groups and that particular groups face a higher threat of environmental pollution. In recent years, studies on the environmental inequality issue in developed countries have been increasing, while related literature on developing countries is very scarce. It has been found that some factors, such as race and economic status, have a close relationship with the risk of environmental pollution exposure faced by individuals. For the first time, this paper provides an extensive review of existing theoretical and empirical studies on environmental inequality. We review, in detail, the evolution of the environmental inequality theory, including the definition and measurement of environmental inequality. Further, we provide a systematic refresher on the main performance of environmental inequality, including health, education, labor productivity, and real estate prices. We also identify several causes of environmental inequality, such as ethnic differences, economic status, human capital, and household registration systems. Finally, we discuss prospects for the future research on this issue.

A benefit allocation model for the joint prevention and control of air pollution in China: In view of environmental justice

The benefit allocation of fair and justice is an inevitable guarantee for the long-term operation of the joint prevention and control of air pollution (JPCAP). The ignorance of interest demands of various governance subjects in the existing benefit allocation mechanism results in the widespread "free rider" behavior in the joint control and unsatisfactory effects of JPCAP. Given this, it is imperative to build a reasonable benefit allocation model. The innovation of this paper is proposing a benefit allocation model of JPCAP to achieve the symmetry between control costs and benefits based on environmental justice. The control objectives and total benefits of JPCAP are calculated through the adjustment of optimal removal rates. The interest demands of various control subjects and benefit compensation scheme are clarified by adopting an improved Shapley method, which comprehensively considers factors affecting environmental justice. An empirical analysis is conducted on SO₂ governance in Beijing-Tianjin-Hebei (BTH) and its surrounding areas. The results show that the benefit allocation model based on environmental justice can not only accurately evaluate the benefits of joint control, but also effectively achieve the symmetry between control costs and benefits. This study provides a scientific and reasonable theoretical basis for the benefit allocation of SO₂ control and can be extended to the researches and practices of other air pollutants control.

Characterization of fetal exposure to multiple metals among an urban population: A case study of New York City

INTRODUCTION

Metals and metalloids are ubiquitous and persistent in urban areas and are generally released into the environment as mixtures.

OBJECTIVES

The purpose of this study was to establish baseline concentrations of selected elements in meconium samples among a large urban population in the US and understand the spatial variability in concentrations. The association of metal mixtures on birth weight was also assessed.

METHODS

This cross-sectional study was conducted across five public hospitals located in New York City, NY (NYC) in four boroughs. We collected meconium sample from 116 infants during the first 24 h after delivery and quantified 11 metals using ICP-MS. Principal component analysis was used to determine metal mixtures and their association with birth weight. Spatial hot spots of each metal were calculated using the Getis-Ord (GI*).

RESULTS

Essential elements were detected in all samples with Zn in the greatest abundance (median = 274.5 μg/g) and Mo in the least (median = 0.1845 μg/g). Pb was detected in all but two samples (median = 0.0222 μg/g), while Cd levels were detected in approximately half of the samples (median = 0.0019 μg/g). Co-located hot spots were detected for Cu, Zn, and Fe in southeast Brooklyn; Cd, Cr, and Ni in eastern Queens; and Al and Mo in south Queens. There was a significant inverse relationship between Pb concentrations (beta = -1935.7; p = 0.006) and the mixture of Cr, Cu, Mo, Zn (beta = -157.7; p = 0.045) and birth weight.

CONCLUSIONS

Our findings indicate that meconium is an effective biomarker for measuring metal exposures among an urban population. We were able to quantify detectable levels of ten of the eleven metals measured in the study and characterize nutritionally necessary trace elements and metals derived from anthropogenic sources without biologic need in a cohort of NYC newborns. Further research needs to establish the change point from necessary to toxic, for the essential elements.

Environmental justice and power plant emissions in the Regional Greenhouse Gas Initiative states

Policies to reduce greenhouse gases associated with electricity generation have been a major focus of public policy in the United States, but their implications for achieving environmental justice among historically overburdened communities inappropriately remains a marginal issue. In this study we address research gaps in historical and current ambient air emissions burdens in environmental justice communities from power plants participating in the Regional Greenhouse Gases Initiative (RGGI), the country's first market-based power sector emissions reduction program. We find that in RGGI states the percentage of people of color that live within 0-6.2 miles from power plants is up to 23.5 percent higher than the percent of the white population that lives within those same distance bands, and the percentage of people living in poverty that live within 0-5 miles from power plants is up to 15.3 percent higher than the percent of the population not living in poverty within those same distance bands. More importantly, the transition from coal to natural gas underway before RGGI formally started resulted in large increases in both the number of electric-generating units burning natural gas and total net generation from natural gas in environmental justice communities hosting electric-generating units, compared to other communities. Our findings indicate that power sector carbon mitigation policies' focusing on aggregate emissions reductions have largely benefitted non-environmental justice communities and have not redressed the fundamental problem of disparities in pollutant burdens between EJ and non-EJ communities. These must be directly addressed in climate change and carbon emissions mitigation policy.

A Community-Integrated Geographic Information System Study of Air Pollution Exposure Impacts in Colfax, LA

A community-integrated geographic information systems (CIGIS) study assimilating qualitative and quantitative information about human exposures and health was conducted in Colfax, Louisiana, which hosts a commercial open burn/open detonation thermal treatment (TT) facility that destroys waste from Superfund sites, explosives, military ordnances, and propellants. Fifty-eight percent of residents identified as Black, and median annual income was $16,318, with 90% of the population living below the poverty line. We conducted oral history interviews of twenty-nine residents and mined public records to document the community's experiences. Interviews focused on themes of Colfax's history, changing community fabric, resident health, and air pollution. The oral histories and public comments by community members provided information about lived experiences, including several health conditions, toleration of noise and vibration, property damage, and resulting changes to activity levels. These statements provided insight into the extent of suffering experienced by the local community. We also ran dispersion models for dates in 2020 when the waste stream composition, mass, and burn/smoldering times were provided in the facility's public records. The dispersion models placed the air pollution at the homes of residents during some of the time, and waste stream records from the TT facility agree with community testimony about health effects based on the known health effects of those compounds. CIGIS integration of our community-based qualitative data and maps with quantitative air pollution dispersion model output illustrated alignment between community complaints of impacts to health and property, known toxicological information about waste stream compounds, and dispersion model output.

PM2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS

Assessing personal exposure risk from PM2.5 air pollution poses challenges due to the limited availability of high spatial resolution data for PM2.5 and population density. This study introduced a seasonal spatial-temporal method of modeling PM2.5 distribution characteristics at a 1-km grid level based on remote sensing data and Geographic Information Systems (GIS). The high-accuracy population density data and the relative exposure risk model were used to assess the relationship between exposure to PM2.5 air pollution and public health. The results indicated that the spatial-temporal PM2.5 concentration could be simulated by MODIS images and GIS method and could provide high spatial resolution data sources for exposure risk assessment. PM2.5 air pollution risks were most serious in spring and winter, and high risks of environmental health hazards were mostly concentrated in densely populated areas in Shanghai-Hangzhou Bay, China. Policies to control the total population and pollution discharge need follow the principle of adaptation to local conditions in high-risk areas. Air quality maintenance and ecological maintenance should be carried out in low-risk areas to reduce exposure risk and improve environmental health.

Towards emplaced understandings of risk: How caregivers of children with asthma identify and manage asthma-related risk across different places

In the United States, pediatric asthma is distributed geographically across lines of racialized segregation. We draw on emplacement, or the theory that embodied experiences and the material world are mutually informed, to situate such geographic trends within the narratives of 41 caregivers of children with asthma. Results suggest that caregivers identified and managed asthma-related risk with regard to the relational and structural conditions of three categories of locations: (1) houses, (2) neighborhoods, and (3) schools and other childhood institutions. Within each type of location, caregivers used emplaced knowledge and emplaced caregiving tactics to respond to asthma-related risk. Based on our findings, we identify critical intervention topics that are consistent with families' everyday lived experiences of place.

Breathing Room: Industrial Zoning and Asthma Incidence Using School District Health Records in the City of Santa Ana, California

Background: Traffic and industrial emissions are associated with increased pediatric asthma morbidity. However, few studies have examined the influence of city industrial zoning on pediatric asthma outcomes among minoritized communities with limited access to air monitoring. Methods: In this cross-sectional analysis of 39,974 school-aged students in Santa Ana, CA, we investigated the effect of proximity to areas zoned for industrial use on pediatric asthma prevalence, physical fitness, school attendance, and standardized test scores. Results: The study population was 80.6% Hispanic, with 88.2% qualifying for free/reduced lunch. Compared to students living more than 1 km away from industrial zones, those living within 0.5 km had greater odds of having asthma (adjusted OR 1.21, 95% CI 1.09 to 1.34, p < 0.001). Among children with asthma, those living between 0.5−1.0 km had greater odds of being overweight or obese (aOR 1.47, 95% CI 1.00, 2.15, p = 0.047). Industrial zone proximity was not significantly associated with worse fitness and academic outcomes for students with asthma. Conclusion: These findings suggest that industrial zone proximity is associated with increased pediatric asthma in a predominantly Latino community in Southern California.

Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County

Non-governmental air quality monitoring networks include low-cost, networked air pollution sensors hosted at homes and schools that display real-time pollutant concentration estimates on publicly accessible websites. Such networks can empower people to take health-protective actions, but their unplanned organization may produce an uneven spatial distribution of sensors. Barriers to acquiring sensors may disenfranchise particular social groups. To test this directly, we quantitatively examine if there are social inequalities in the distribution of sensors in a non-governmental air quality monitoring network (PurpleAir) in Los Angeles County, California. We paired sociodemographic data from the American Community Survey and estimates of PM_2.5 concentrations from the USEPA's Downscaler model at the census tract level (n = 2203) with a sensors per capita (SPC) variable, which is based on population proximity to PurpleAir sensors (n = 696) in Los Angeles County. Findings from multivariable generalized estimating equations (GEEs) controlling for clustering by housing age and value reveal patterns of environmental injustice in the distribution of PurpleAir sensors across Los Angeles County census tracts. Tracts with higher percentages of Hispanic/Latino/a and Black residents and lower median household income had decreased SPC. There was a curvilinear (concave) relationship between the percentage of renter-occupants and SPC. Sensors were concentrated in tracts with greater percentages of adults and seniors (vs. children), higher occupied housing density, and higher PM_2.5 pollution. Results reveal social inequalities in the self-organizing PurpleAir network, suggesting another layer of environmental injustice such that residents of low-income and minority neighborhoods have reduced access to information about local air pollution.

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.

Evaluation of a Program to Reduce Home Environment Risks for Children with Asthma Residing in Urban Areas

Pediatric asthma morbidity is often linked to challenges including poor housing quality, inability to access proper medical care, lack of medications, and poor adherence to medical regimens. Such factors also propagate known disparities, by race and income, in asthma-related outcomes. Multimodal home visits have an established evidence base in support of their use to improve such outcomes. The Collaboration to Lessen Environmental Asthma Risks (CLEAR) is a partnership between the Cincinnati Children's Hospital Medical Center and the local health department which carries out home visits to provide healthy homes education and write orders for remediation should code violations and environmental asthma triggers be identified. To assess the strengths and weaknesses of the program, we obtained qualitative feedback from health professionals and mothers of children recently hospitalized with asthma using key informant interviews. Health professionals viewed the program as a positive support system for families and highlighted the potential benefit of education on home asthma triggers and connecting families with services for home improvements. Mothers report working to correct asthma triggers in the home based on the education they received during the course of their child's recent illness. Some mothers indicated mistrust of the health department staff completing home visits, indicating a further need for research to identify the sources of this mistrust. Overall, the interviews provided insights into successful areas of the program and areas for program improvement.

Disparities in COPD Hospitalizations: A Spatial Analysis of Proximity to Toxics Release Inventory Facilities in Illinois

Disproportionate distribution of air pollution is a major burden on the health of people living in proximity to toxic facilities. There are over 1000 Toxics Release Inventory (TRI) facilities distributed across the state of Illinois. This study investigates and spatially analyzes the relationship between chronic obstructive pulmonary disease (COPD) hospitalizations and toxic emissions from TRI facilities. In addition, this study investigates the connection between COPD hospitalizations and socioeconomic variables. Accounting for dispersion of air pollution beyond the TRI facilities source was attained using the inverse distance weighting interpolation approach. Multiple statistical methods were used including principal components analysis, linear regression, and bivariate local indicators of spatial association (BiLISA). The results from the linear regression model and BiLISA clustering maps show there is a strong connection between COPD hospitalizations and socioeconomic status along with race. TRI emissions were not statistically significant, but there are three major clusters of high COPD hospitalizations with high TRI emissions. Rural areas also seem to carry a higher burden of pollution-emitting facilities and respiratory hospitalizations.

The use of non- invasive ventilation in asthma exacerbation - a two year retrospective analysis of outcomes

Background: The use of Non-Invasive Ventilation (NIV) in acute asthma exacerbation remains controversial. Comparative data on patient characteristics that benefit from NIV in asthma exacerbation to those patients that fail NIV remains limited. Our study compares some of these patient characteristics and examines if NIV is safe and effective in carefully selected patients.

Methods: Following institutional review board approval, we extracted from the electronic medical record and conducted a retrospective chart-based review of those patients who received NIV in the emergency room for a diagnosis of asthma exacerbation from January 2017 to December 2018.

Results and Conclusion: The rate of failure of NIV overall was low, at 9.17%, with younger patients more likely to fail NIV (P = 0.03) and need invasive mechanical ventilation. Surprisingly, baseline asthma severity did not impact NIV failure rate, and neither did body mass index, smoking history, and a host of clinical characteristics. Understandably, the length of stay was significantly longer in the group of patients that failed NIV. There were no adverse events, such as an increased rate of barotrauma events in either group. In conclusion, this study contributes to the growing body of evidence that NIV is a safe and effective adjunct to routine care in the management of patients with asthma exacerbation.

The Environmental and Social Determinants of Health Matter in a Pandemic: Predictors of COVID-19 Case and Death Rates in New York City

Our research objective was to determine which environmental and social factors were predictive of coronavirus disease 2019 (COVID-19) case and death rates in New York City (NYC), the original epicenter of the pandemic in the US, and any differential impacts among the boroughs. Data from various sources on the demographic, health, and environmental characteristics for NYC zip codes, neighborhoods, and boroughs were analyzed along with NYC government’s reported case and death rates by zip code. At the time of analysis, the Bronx had the highest COVID-19 case and death rates, while Manhattan had the lowest rates. Significant predictors of a higher COVID-19 case rate were determined to be proportion of residents aged 65 years plus; proportion of residents under 65 years with a disability; proportion of White residents; proportion of residents without health insurance; number of grocery stores; and a higher ozone level. For COVID-19 death rates, predictors include proportion of residents aged 65 years plus; proportion of residents who are not US citizens; proportion on food stamps; proportion of White residents; proportion of residents under 65 years without health insurance; and a higher level of ozone. Results across boroughs were mixed, which highlights the unique demographic, socioeconomic, and community characteristics of each borough. To reduce COVID-19 inequities, it is vital that the NYC government center the environmental and social determinants of health in policies and community-engaged interventions adapted to each borough.

Not Breathing Easy: "Disarticulated Homework" in Asthma Management

Recent health policy in the United States encourages an outsourcing of labor from professional practice into domestic spaces, where in theory, medical professionals supply the training, technologies, and guidance needed to discharge responsibility for care to patients or caregivers. Mattingly et al. (2011) term this labor "chronic homework," describing the relationship between the assigning and undertaking of medical care at the borders of professional and domestic domains. This is a system predicated on relationships between professional and caregiver. However, in our research with families and providers in two U.S. sites, we observed a "disarticulation" of asthma care from professional medicine. Caregivers may undertake routine asthma management with little physician oversight, transforming chronic homework into what we term "disarticulated homework." We argue that expanding the concept of chronic homework to theorize disarticulation processes can help elucidate how health disparities are reproduced in the gap between medical systems and domestic life. [asthma, self-management, caregiving pharmaceuticalization, health disparities].

Compounding Risks Caused by Heat Exposure and COVID-19 in New York City: A Review of Policies, Tools, and Pilot Survey Results

The Coronavirus Disease 2019 (COVID-19) pandemic changed many social, economic, environmental, and healthcare determinants of health in New York City (NYC) and worldwide. COVID-19 potentially heightened the risk of heat-related health impacts in NYC, particularly on the most vulnerable communities, who often lack equitable access to adequate cooling mechanisms such as air conditioning (AC) and good quality green space. Here, we review some of the policies and tools which have been developed to reduce vulnerability to heat in NYC. We then present results from an online pilot survey of members of the environmental justice organization WE ACT for Environmental Justice (WE ACT) between July 11 and August 8, 2020, which asked questions to evaluate how those in Northern Manhattan coped with elevated summer heat in the midst of the COVID-19 pandemic. We also make some policy recommendations based on our initial findings. Results of our pilot survey suggest that people stayed indoors more due to COVID-19 and relied more on AC units to stay cool. Survey responses also indicated that some avoided visiting green spaces due to concerns around overcrowding and did not regularly frequent them due to the distance from their homes. The responses also demonstrate a potential racial disparity in AC access; AC ownership and access was highest amongst white and lowest amongst Latino/a/x and Black respondents. The impacts of COVID-19 have highlighted the need to accelerate efforts to improve preparedness for extreme heat like the City of New York's AC and cooling center programs, heat ventilation and air conditioning (HVAC) retrofitting, equitable green space expansion, and stronger environmental justice community networks and feedback mechanisms to hear from affected residents. Conducting a survey of this kind annually may provide an additional effective component of evaluating cooling initiatives in NYC.

Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping

Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions.

Asthma-prone areas modeling using a machine learning model

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O₃), sulfur dioxide (SO₂), carbon monoxide (CO), and nitrogen dioxide (NO₂). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).

Climate change and dermatology: An introduction to a special topic, for this special issue

Anthropogenic global climate change is a well-documented phenomenon that has led to average global temperatures climbing to approximately 1 °C above preindustrial (1850-1900) levels, with even higher regional deviations in some areas and significantly increased average warming in densely populated urban centers. In 2018, the United Nations Intergovernmental Panel on Climate Change set a threshold of 1.5 °C of average warming (above the preindustrial baseline), beyond which our planet will become significantly less hospitable to human life. However, adverse human health impacts are already occurring due to current levels of global climate change, as summarized by publications such as The Lancet's annual "Countdown on Health and Climate Change," initiated in 2016. The human health impacts of climate change are truly cross-disciplinary, with nearly every medical specialty either already facing or set to face effects. The field of dermatology is not immune to these risks. This special issue of the International Journal of Women's Dermatology is dedicated to the cross section of dermatology and climate change. This initial article will serve as an overview to introduce readers to the topic and to lay the groundwork for the rest of the issue. We are delighted to work with the Women's Dermatological Society and welcome their support for this dedicated issue. Herein, you will read from up-and-coming stars in the field and established experts, including articles on the following key areas: infectious diseases, environmentally friendly office practices, sunscreens and the environment, refugee health, heat-related illness, the effect of air pollution on the skin, the impact of climate change on pediatric dermatology, implications for skin cancer, and skin issues related to flooding and extreme weather events.

Situating household management of children's asthma in the context of social, economic, and environmental injustice

BACKGROUND

Structural determinants of health are social, economic, and environmental forces that generate unequal opportunities for resources and unequally distribute exposure to risk. For example, economic constraint, racial discrimination and segregation, and environmental injustice shape population-level asthma prevalence and severity. Structural determinants are especially relevant to consider in clinical settings because they affect everyday household asthma management.

OBJECTIVE

To examine how structural determinants shape everyday household management of pediatric asthma and offer a framework for providers to understand asthma management in social context.

DESIGN

Qualitative interviews of caregivers for children with asthma.

PARTICIPANTS

Participants included 41 caregivers in two U.S. cities: St. Louis, Missouri (n = 25) and Gainesville, Florida (n = 16). Most caregivers were women (83%), Black (73%) and/or had low socioeconomic status (SES; 78%). Caregivers cared for children with asthma aged 0-4 (32%), 5-11 (68%) and 12-17 (54%).

APPROACH

We carried out narrative interviews with caregivers using an adapted McGill Illness Narrative Interview and using qualitative analysis techniques (e.g. inductive and deductive coding, constant comparison).

KEY RESULTS

Caregivers highlighted three ways that structural determinants complicated asthma management at home: 1) housing situations, 2) competing household illnesses and issues, and 3) multi-household care.

CONCLUSIONS

By connecting social, economic, and environmental injustices to the everyday circumstances of asthma management, our study can help providers understand how social contexts challenge asthma management and can open conversations about barriers to adherence and strategies for supporting asthma management at home. We offer recommendations for medical system reform, clinical interactions, and policy advocacy.

Light pollution inequities in the continental United States: A distributive environmental justice analysis

Excessive exposure to ambient light at night is a well-documented hazard to human health, yet analysts have not examined it from an environmental justice (EJ) perspective. We conducted the first EJ study of exposure to light pollution by testing for socially disparate patterns across the continental United States (US). We first calculated population-weighted mean exposures to examine whether ambient light pollution in the US differed between racial/ethnic groups. We then used multivariable generalized estimating equations (GEEs) that adjust for geographic clustering to examine whether light pollution was distributed inequitably based on racial/ethnic composition and socioeconomic status across US neighborhoods (census tracts). Finally, we conducted a stratified analysis of metropolitan core, suburban, and small city-rural tracts to determine whether patterns of inequity varied based on urban-rural context. We found evidence of disparities in exposures to light pollution based on racial/ethnic minority and low-to-mid socioeconomic statuses. Americans of Asian, Hispanic or Black race/ethnicity had population-weighted mean exposures to light pollution in their neighborhoods that were approximately two times that of White Americans. GEEs indicated that neighborhoods composed of higher proportions of Blacks, Hispanics, Asians, or renter-occupants experienced greater exposures to ambient light at night. Stratified analyses indicated that those patterns of inequity did not substantially vary based on urban-rural context. Findings have implications for understanding environmental influences on health disparities, raise concerns about the potential for a multiple environmental jeopardy situation, and highlight the need for policy actions to address light pollution.

Supervised mixture of experts models for population health

We propose a machine learning driven approach to derive insights from observational healthcare data to improve public health outcomes. Our goal is to simultaneously identify patient subpopulations with differing health risks and to find those risk factors within each subpopulation. We develop two supervised mixture of experts models: a Supervised Gaussian Mixture model (SGMM) for general features and a Supervised Bernoulli Mixture model (SBMM) tailored to binary features. We demonstrate the two approaches on an analysis of high cost drivers of Medicaid expenditures for inpatient stays. We focus on the three diagnostic categories that accounted for the highest percentage of inpatient expenditures in New York State (NYS) in 2016. When compared with state-of-the-art learning methods (random forests, boosting, neural networks), our approaches provide comparable prediction performance while also extracting insightful subpopulation structure and risk factors. For problems with binary features the proposed SBMM provides as good or better performance than alternative methods while offering insightful explanations. Our results indicate the promise of such approaches for extracting population health insights from electronic health care records.

Air pollution and children's respiratory health: a scoping review of socioeconomic status as an effect modifier

OBJECTIVES

Air pollution is a leading environmental risk, and socioeconomic status (SES) is postulated as an effect modifier, especially in children. There is a growing interest in exploring this modifier. The present manuscript reviews SES as an effect modifier in children's respiratory health.

METHODS

A search in the PubMed and SCOPUS databases was conducted in September 2017 to identify studies with the inclusion criteria of being centred on children, respiratory outcomes, air pollutants and SES measurement.

RESULTS

A total of 17 studies were included. Twelve used single SES variables, and the remaining studies included composite SES indices. Household income (9) and parental education (8) were frequently evaluated. The significance of the effect modifier was found in nine studies that demonstrated a higher risk for individuals living in a lower SES. Sources of heterogeneity included SES measurement, health outcomes and geographical aggregation.

CONCLUSIONS

The results suggest a higher modification in the effect of SES, generally indicating greater risk for children in lower SES. Children's characteristics need to be more carefully theorized and measured in this area, including the use of transdisciplinary approaches.

Cholera deaths in Soho, London, 1854: Risk Terrain Modeling for epidemiological investigations

Risk Terrain Modeling (RTM) is a spatial analysis technique used to diagnose environmental conditions that lead to hazardous outcomes. Originally developed for applications to violent crime analysis, RTM is utilized here to analyze Dr. John Snow’s data from the 1854 cholera outbreak in London to demonstrate its potential value to contemporary epidemiological investigations. Dr. Snow saved countless lives when he traced the source of the cholera outbreak to a specific water pump through inductive reasoning, which he communicated through maps and spatial evidence. His methods have since inspired several fields of scientific inquiry. Informed by the extant research on RTM, we speculated that it could have helped test Dr. Snow’s hypothesis about cholera and empirically identified the sole source of contaminated well water. We learned that it could and, although it was not available to Dr. Snow in the 1800s, we discuss RTM’s implications for present-day research and practice as it relates to the analysis, prevention and treatment of cholera and other public health threats around the world.

Review of the effect of air pollution exposure from industrial point sources on asthma-related effects in childhood

We reviewed epidemiologic studies of the association between exposure to air pollution from industries and asthma-related outcomes in childhood. We searched bibliographic databases and reference lists of relevant articles to identify studies examining the association between children's exposure to air pollution from industrial point-sources and asthma-related outcomes, including asthma, asthma-like symptoms, wheezing, and bronchiolitis. We extracted key characteristics of each study and when appropriate we performed a random-effects meta-analysis of results and quantified heterogeneity (I ²). Thirty-six studies were included in this review. Meta-analysis was generally not possible and limited to a few studies because of substantial variation across design characteristics and methodologies. In case-crossover studies using administrative health data, pooled odds ratio (OR) of hospitalization for asthma and bronchiolitis in children <5 years were 1.02 [95% confidence intervals (CI): 0.96, 1.08; I ² = 56%] and 1.01 (95% CI: 0.97, 1.05; I ² = 64%) per 10 ppb increase in the daily mean and hourly maximum concentration of sulfur dioxide (SO₂), respectively. For PM_2.5, pooled ORs were 1.02 (95% CI: 0.93, 1.10; I ² = 56%) and 1.01 (95% CI: 0.98, 1.03 I ² = 33%) per 10 μg/m³ increment in the daily mean and hourly maximum concentration. In cross-sectional studies using questionnaires, pooled ORs for the prevalence of asthma and wheezing in relation to residential proximity to industry were 1.98 (95% CI: 0.87, 3.09; I ² =71%) and 1.33 (95% CI: 0.86, 1.79; I ²= 65%), respectively. In conclusion, this review showed substantial heterogeneity across study designs and methods. Meta-analysis results suggested no evidence of an association for short-term asthma-related effects and an indication for long-term effects, but heterogeneity between results and limitations in terms of design and exposure assessment preclude drawing definite conclusions. Further well-conducted studies making use of a longitudinal design and of refined exposure assessment methods are needed to improve risk estimates.

Mobile monitoring of air and noise pollution in Philadelphia neighborhoods during summer 2017

Mobile monitoring is a useful approach for measuring intra-urban variation of air pollution in urban environments. In this study, we used a mobile monitoring approach to study the spatial-temporal variability of air and noise pollution in urban neighborhoods of Philadelphia. During summer 2017, we used portable instruments to measure PM_2.5, black carbon (BC), and noise levels along 5 km paths in four residential neighborhoods (Tioga, Mill Creek, Chestnut Hill, and Northern Liberties) and one commercial district (Center City) in Philadelphia, Pennsylvania, USA. A total of 62 sets of measurements were made at three different times of day (during morning rush hour, mid-afternoon, and during afternoon rush hour) from June 5 to July 7, 2017. Spatially, there was a significant difference in PM_2.5 concentrations among the four residential neighborhoods. Overall, the Chestnut Hill neighborhood had the highest PM_2.5 concentrations (13.25 ± 6.89 μg/m³), followed by Tioga (9.58 ± 4.83 μg/m³), Northern Liberties (7.02 ± 4.17 μg/m³), and Mill Creek (3.9 ± 4.5 μg/m³). There was temporal variability of pollutants depending on the neighborhood; Northern Liberties demonstrated the highest temporal variability in these data. The highest PM_2.5 (18.86 ± 3.17 mg/m³) was measured in the Chestnut Hill neighborhood during mid-afternoon. Mean PM_2.5, BC, and noise levels based on mobile measurements at Philadelphia during summer 2017 were 8.41 ± 4.31 μg/m³, 0.99 ± 0.44 μg C/m³, and 62.01 ± 3.20 dBA, respectively. Environmental noise showed the highest temporal variation of the monitored components for 3 time periods. In general, tree cover showed a weak and inconclusive association with particulate pollution levels.

Impact of Outdoor Air Pollution on Indoor Air Quality in Low-Income Homes during Wildfire Seasons

Indoor and outdoor number concentrations of fine particulate matter (PM2.5), black carbon (BC), carbon monoxide (CO), and nitrogen dioxide (NO2) were monitored continuously for two to seven days in 28 low-income homes in Denver, Colorado, during the 2016 and 2017 wildfire seasons. In the absence of indoor sources, all outdoor pollutant concentrations were higher than indoors except for CO. Results showed that long-range wildfire plumes elevated median indoor PM2.5 concentrations by up to 4.6 times higher than outdoors. BC, CO, and NO2 mass concentrations were higher indoors in homes closer to roadways compared to those further away. Four of the homes with mechanical ventilation systems had 18% higher indoor/outdoor (I/O) ratios of PM2.5 and 4% higher I/O ratios of BC compared to other homes. Homes with exhaust stove hoods had PM2.5 I/O ratios 49% less than the homes with recirculating hoods and 55% less than the homes with no stove hoods installed. Homes with windows open for more than 12 hours a day during sampling had indoor BC 2.4 times higher than homes with windows closed. This study provides evidence that long-range wildfire plumes, road proximity, and occupant behavior have a combined effect on indoor air quality in low-income homes.

Using high-resolution residential greenspace measures in an urban environment to assess risks of allergy outcomes in children

Despite reported health benefits of urban greenspace (gs), the epidemiological evidence is less clear for allergic disease. To address a limitation of previous research, we examined the associations of medium- and high-resolution residential gs measures and tree and/or grass canopies with allergic outcomes for children enrolled in the longitudinal cincinnati childhood allergy and air pollution study (ccaaps). We estimated residential gs based on 400 m radial buffers around participant addresses (n = 478) using the normalized differential vegetation index (ndvi) and land cover-derived urban greenspace (ugs) (tree and grass coverage, combined and separate) at 30 m and 1.5-2.5 m resolution, respectively. Associations between outdoor aeroallergen sensitization and allergic rhinitis at age 7 and residential gs measures at different exposure windows were examined using multivariable logistic regression models. A 10% increase in ugs-derived grass coverage was associated with an increased risk of sensitization to grass pollens (adjusted odds ratio [aor]: 1.27; 95% confidence interval = 1.02-1.58). For each 10% increase in ugs-derived tree canopy coverage, nonstatistically significant decreased odds were found for grass pollen sensitization, tree pollen sensitization, and sensitization to either (aor range = 0.87-0.94). Results similar in magnitude to ugs-tree canopy coverage were detected for ndvi and allergic sensitizations. High-resolution (down to 1.5 m) gs measures of grass- and tree-covered areas showed associations in opposite directions for different allergy outcomes. These data suggest that measures strongly correlated with tree canopy (e.g., ndvi) may be insufficient to detect health effects associated with proximity to different types of vegetation or help elucidate mechanisms related to specific gs exposure pathways.

On the Application of Clustering and Classification Techniques to Analyze Metabolic Syndrome Severity Distribution Area and Critical Factors

In recent years, metabolic syndrome has become one of the leading causes of death in Taiwan. This study proposes a classification and clustering method specific to the administrative regions of New Taipei City to explore the incidence and corresponding risk factors for metabolic syndrome in various geographic areas. We used integrated community health screening data and survey results obtained from people aged ≥40 years in each of the administrative regions of New Taipei City as study samples. Using a combination of Ward’s method, multivariate analysis of variance, and k-means, we identified administrative regions of New Taipei City with metabolic syndrome incidences of a similar nature. Classification and regression tree methods were used to discover the key causes of metabolic syndrome in each region based on lifestyles and dietary habits. The administrative regions were divided into four groups: high-risk, slightly high-risk, normal-risk, and low-risk. The results showed that the severity of metabolic syndrome varies by region and the risk factors for metabolic syndrome vary by region. It has also been found that regions with a higher incidence of metabolic syndrome have relatively fewer medical resources.

Spatial Enablement to Support Environmental, Demographic, Socioeconomics, and Health Data Integration and Analysis for Big Cities: A Case Study With Asthma Hospitalizations in New York City

The percentage of the world's population living in urban areas is projected to increase in the next decades. Big cities are heterogeneous environments in which socioeconomic and environmental differences among the neighborhoods are often very pronounced. Each individual, during his/her life, is constantly subject to a mix of exposures that have an effect on their phenotype but are frequently difficult to identify, especially in an urban environment. Studying how the combination of environmental and socioeconomic factors which the population is exposed to influences pathological outcomes can help transforming public health from a reactive to a predictive system. Thanks to the application of state-of-the-art spatially enabled methods, patients can be stratified according to their characteristics and the geographical context they live in, optimizing healthcare processes and the reducing its costs. Some public health studies focusing specifically on urban areas have been conducted, but they usually consider a coarse spatial subdivision, as a consequence of scarce availability of well-integrated data regarding health and environmental exposure at a sufficient level of granularity to enable meaningful statistical analyses. In this paper, we present an application of highly fine-grained spatial resolution methods to New York City data. We investigated the link between asthma hospitalizations and a combination of air pollution and other environmental and socioeconomic factors. We first performed an explorative analysis using spatial clustering methods that shows that asthma is related to numerous factors whose level of influence varies considerably among neighborhoods. We then performed a Geographically Weighted Regression with different covariates and determined which environmental and socioeconomic factors can predict hospitalizations and how they vary throughout the city. These methods showed to be promising both for visualization and analysis of demographic and epidemiological urban dynamics, that can be used to organize targeted intervention and treatment policies to address the single citizens considering the factors he/she is exposed to. We found a link between asthma and several factors such as PM2.5, age, health insurance coverage, race, poverty, obesity, industrial areas, and recycling. This study has been conducted within the PULSE project, funded by the European Commission, briefly presented in this paper.

Mapping inequality: Childhood asthma and environmental injustice, a case study of St. Louis, Missouri

As a geographic continuum of poverty and affluence has emerged, so too has a geographic continuum of good and poor health. Asthma is currently one of the most prevalent chronic childhood diseases. Over the past three decades, the greatest increases in asthma rates have been in urban areas and have disproportionately affected youth in poverty and those in racial and ethnic minority groups. Neighborhoods serve as a mechanism fostering environmental injustice and perpetuating these disparities in health outcomes and life opportunity for our most vulnerable populations. Using Geographic Information Systems (GIS) methods in a case study of St. Louis Missouri, this study examines local environmental risk by identifying 'hotspots,' or statistically significant spatial clustering of high or low levels of childhood asthma, and associations with neighborhood characteristics, socio-demographic characteristics, and access to healthcare resources within these hotspots. Results revealed statistically significant clustering of high asthma rates in areas with more non-White and poor residents, higher rates of public housing, deteriorating housing, and violent crime. High asthma hotspots were also located in areas with limited physical access to healthcare resources, such as physicians and medication, and lower school attendance rates. Residents of these high asthma hotspots experience greater environmental risk, and significant disparities in health and education outcomes, physical and financial healthcare resources, and overall well-being. This study demonstrates these place-based inequalities and presents clear evidence of environmental injustice, supporting the need for investments and interventions to improve the environments, health, and economic resources of our most vulnerable youth.

The Influence of Tobacco Retailer Density and Poverty on Tobacco Use in a Densely Populated Urban Environment

OBJECTIVES

Researchers have identified associations between neighborhood-level factors (eg, income level, tobacco retailer density) and smoking behavior, but few studies have assessed these factors in urban environments. We explored the effect of tobacco retailer density, neighborhood poverty, and housing type (multiunit and public) on smoking in a large urban environment (New York City).

METHODS

We used data on smoking prevalence and individual sociodemographic characteristics from the 2011-2013 New York City Community Health Survey, data on tobacco retailers from the 2012 New York City Department of Consumer Affairs, data on neighborhood sociodemographic characteristics and population density from the 2009-2013 American Community Survey, and data on multiunit and public housing from the 2012 New York City Primary Land Use Tax Lot Output data set. We used aggregate neighborhood-level variables and ordinary least squares regression, geographic weighted regression, and multilevel models to assess the effects of tobacco retailer density and neighborhood poverty on smoking prevalence, adjusting for sociodemographic characteristics (age, sex, race/ethnicity, and education) and neighborhood population density. We also assessed interactions between tobacco retailer density and poverty and each housing type on smoking.

RESULTS

Neighborhood poverty positively and significantly modified the association between tobacco retailer density and prevalence of neighborhood smoking ( β = 0.003, P = .01) when we controlled for population density, sociodemographic characteristics, and types of housing. Neighborhood poverty was positively associated with the prevalence of individual smoking ( β = 0.0099, P < .001) when we adjusted for population density, sociodemographic characteristics, and type of housing.

CONCLUSION

More research is needed to determine all the environmental factors associated with smoking prevalence in a densely populated urban environment.

Relationships between home ventilation rates and respiratory health in the Colorado Home Energy Efficiency and Respiratory Health (CHEER) study

BACKGROUND

As societies adopt green building practices to reduce energy expenditures and emissions that contribute to climate change, it is important to consider how such building design changes influence health. These practices typically focus on reducing air exchange rates between the building interior and the outdoor environment to minimize energy loss, the health effects of which are not well characterized. This study aims to evaluate the relationship between air exchange rates and respiratory health in a multi-ethnic population living in low-income, urban homes.

METHODS

The Colorado Home Energy Efficiency and Respiratory Health (CHEER) study is a cross-sectional study that enrolled 302 people in 216 non-smoking, low-income single-family homes, duplexes and town-homes from Colorado's Northern Front Range. A blower door test was conducted and the annual average air exchange rate (AAER) was estimated for each home. Respiratory health was assessed using a structured questionnaire based on standard instruments. We estimated the association between AAER and respiratory symptoms, adjusting for relevant confounders.

RESULTS

Air exchange rates in many homes were high compared to prior studies (median 0.54 air changes per hour, range 0.10, 2.17). Residents in homes with higher AAER were more likely to report chronic cough, asthma and asthma-like symptoms, including taking medication for wheeze, wheeze that limited activities and dry cough at night. Allergic symptoms were not associated with AAER in any models. The association between AAER and asthma-like symptoms was stronger for households located in areas with high potential exposure to traffic related pollutants, but this was not consistent across all health outcomes.

CONCLUSIONS

While prior studies have highlighted the potential hazards of low ventilation rates in residences, this study suggests high ventilation rates in single-family homes, duplexes and town-homes in urban areas may also have negative impacts on respiratory health, possibly due to the infiltration of outdoor pollutants.

The impact of urban street tree species on air quality and respiratory illness: A spatial analysis of large-scale, high-resolution urban data

Urban trees play a key role in reducing greenhouse gas emissions, cleaning air, promoting physical activity, and improving mental health. However, it is still largely unknown how the density and species of urban street trees may impact local public health. This study demonstrates how open data mining and large-scale spatial data integration can contribute to deeper insights into the effects of urban forestry. We analyze the impact of the spatial distribution of street trees by species in New York City by combining crowd-sourced tree census data - which includes geolocation, species, size, and condition for each of 652,169 street trees - with pollen activity, allergen severity, land use, housing conditions, and neighborhood demographic data. We further integrate neighborhood asthma hospitalization and emergency department visit rates and air quality data (PM2.5) to investigate how street trees impact local air quality and the prevalence of acute respiratory illness. Using a geographically weighted regression model, the results indicate that although a greater concentration of trees contributes to better local air quality, species with severe allergenicity can increase local asthma hospitalization rates in vulnerable populations, controlling for other covariates.

Utilization of the Maryland Environmental Justice Screening Tool: A Bladensburg, Maryland Case Study

Maryland residents' knowledge of environmental hazards and their health effects is limited, partly due to the absence of tools to map and visualize distribution of risk factors across sociodemographic groups. This study discusses the development of the Maryland EJSCREEN (MD EJSCREEN) tool by the National Center for Smart Growth in partnership with faculty at the University of Maryland School of Public Health. The tool assesses environmental justice risks similarly to the U.S. Environmental Protection Agency's (USEPA) EJSCREEN tool and California's tool, CalEnviroScreen 3.0. We discuss the architecture and functionality of the tool, indicators of importance, and how it compares to USEPA's EJSCREEN and CalEnviroScreen. We demonstrate the use of MD EJSCREEN through a case study on Bladensburg, Maryland, a town in Prince George's County (PG) with several environmental justice concerns including air pollution from traffic and a concrete plant. Comparison reveals that environmental and demographic indicators in MD EJSCREEN most closely resemble those in EPA EJSCREEN, while the scoring is most similar to CalEnviroScreen. Case study results show that Bladensburg has a Prince George's environmental justice score of 0.99, and that National Air Toxics Assessment (NATA) air toxics cancer risk is concentrated in communities of color.

Air pollution, urgent asthma medical visits and the modifying effect of neighborhood asthma prevalence

BACKGROUND

Social and environmental stressors may modify associations between environmental pollutants and asthma symptoms. We examined if neighborhood asthma prevalence (higher: HAPN vs. lower: LAPN), a surrogate for underlying risk factors for asthma, modified the relationship between pollutants and urgent asthma visits.

METHODS

Through zip code, home addresses were linked to New York City Community Air Survey's land use regression model for street-level, annual average nitrogen dioxide (NO2), particulate matter (PM2.5), elemental carbon (EC), summer average ozone (O3), winter average sulfur dioxide (SO2) concentrations. Poisson regression models were fit to estimate the association (prevalence ratio, PR) between pollutant exposures and seeking urgent asthma care.

RESULTS

All pollutants, except O3 were higher in HAPN than LAPN (P < 0.01). Neighborhood asthma prevalence modified the relationship between pollutants and urgent asthma (P-interaction < 0.01, for NO2 and SO3). Associations between pollutants and urgent asthma were observed only in LAPN (NO2: PR = 1.38, P = 0.01; SO3: PR = 1.85, P = 0.04). No association was observed between pollutants and urgent asthma among children in HAPN (P > 0.05).

CONCLUSIONS

Relationships between modeled street-level pollutants and urgent asthma were stronger in LAPN compared to HAPN. Social stressors that may be more prevalent in HAPN than LAPN, could play a greater role in asthma exacerbations in HAPN vs. pollutant exposure alone.

The Use of Geographic Data to Improve Asthma Care Delivery and Population Health

The authors examine uses of geographic data to improve asthma care delivery and population health and describe potential practice changes and areas for future research.

Occupational Groups and Environmental Justice: A Case Study in the Bronx, New York

We used spatial analyses to examine exposure of people in vulnerable occupational groups to neighborhood-level environmental pollutants in the Bronx borough of New York City. Five-year estimates of environmental ambient exposures (derived from land use regression models for PM2.5 [particulate matter with an aerodynamic diameter ≤2.5 µm] and black carbon) and demographic and occupational variables were harmonized at the census tract level. Correlations revealed that areas with high environmental exposures also had high proportions of people in service industries and manufacturing and high proportions of socioeconomically vulnerable populations. This combination of vulnerabilities may be cumulative, suggesting residents could have high occupational and residential exposures in addition to sociodemographic-related inequity.

The combined effect of ambient ozone exposure and toxic air releases on hospitalization for asthma among children in Harris County, Texas

This study represents an analysis of the effect of exposure to ambient ozone and toxic air releases on hospitalization for asthma among children in Harris County, Texas. Our study identified temporal and spatial variations in asthma hospitalization across the study region and explored the combined effect of exposure to ambient ozone and air toxics on asthma hospitalization. Asthma hospitalization hot spots and clusters were mostly not located on zip codes with reported high quantities of total air releases of chemical pollutants. There was no significant interaction between ambient ozone exposure and toxic air releases relative to asthma hospitalization. The major predictor of asthma hospitalization was season, with hospitalization rate per 10,000 people for asthma being highest in winter period when ozone levels are usually lowest.

Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area

Few studies have linked social factors to air pollution exposure in China. Unlike the race or minority concepts in western countries, the Hukou system (residential registration system) is a fundamental reason for the existence of social deprivation in China. To assess the differences in ozone (O₃) exposure among social groups, especially groups divided by Hukou status, we assigned estimates of O₃ exposure to the latest census data of the Beijing urban area using a kriging interpolation model. We developed simultaneous autoregressive (SAR) models that account for spatial autocorrelation to identify the associations between O₃ exposure and social factors. Principal component regression was used to control the multicollinearity bias as well as explore the spatial structure of the social data. The census tracts (CTs) with higher proportions of persons living alone and migrants with non-local Hukou were characterized by greater exposure to ambient O₃. The areas with greater proportions of seniors had lower O₃ exposure. The spatial distribution patterns were similar among variables including migrants, agricultural population and household separation (population status with separation between Hukou and actual residences), which fit the demographic characteristics of the majority of migrants. Migrants bore a double burden of social deprivation and O₃ pollution exposure due to city development planning and the Hukou system.

Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status

OBJECTIVES

To quantify nationwide disparities in the location of particulate matter (PM)-emitting facilities by the characteristics of the surrounding residential population and to illustrate various spatial scales at which to consider such disparities.

METHODS

We assigned facilities emitting PM in the 2011 National Emissions Inventory to nearby block groups across the 2009 to 2013 American Community Survey population. We calculated the burden from these emissions for racial/ethnic groups and by poverty status. We quantified disparities nationally and for each state and county in the country.

RESULTS

For PM of 2.5 micrometers in diameter or less, those in poverty had 1.35 times higher burden than did the overall population, and non-Whites had 1.28 times higher burden. Blacks, specifically, had 1.54 times higher burden than did the overall population. These patterns were relatively unaffected by sensitivity analyses, and disparities held not only nationally but within most states and counties as well.

CONCLUSIONS

Disparities in burden from PM-emitting facilities exist at multiple geographic scales. Disparities for Blacks are more pronounced than are disparities on the basis of poverty status. Strictly socioeconomic considerations may be insufficient to reduce PM burdens equitably across populations.

The Peñalosa Principle of Transportation Democracy: Lessons from Bogotá on the Morality of Urban Mobility

The mayor of Bogotá, Enrique Peñalosa strives to deliver transit services that promote social equity through bicycle lanes, improved sidewalks, and a world-famous Bus Rapid Transit (BRT) system, "TransMilenio." Through examining the principles that guide his planning, we can flesh out a starting point for socially just transit systems. While such measures can alleviate several harms that transit systems cause, they rest on an incomplete foundation due to their top-down nature. To amend this situation, the author argues for a restorative justice approach to transportation democracy, using examples from Peñalosa's mayoral tenure. In turn, lessons from Bogotá's transportation history reveal how to develop transit systems that strongly favor justice.

Allergic disease associations with regional and localized estimates of air pollution

BACKGROUND

Exposure to multiple types of air pollution may contribute to and exacerbate allergic diseases including asthma and wheezing. However, few studies have examined chronic air pollution exposure and allergic disease outcomes among an adult population. Associations between potential estimates of annual average fine particulate matter (PM2.5), traffic related air pollution, and industrial source air emissions and three allergic disease outcomes (asthma, allergies and wheezing) were examined in a state-wide general population of adults.

METHODS

The study includes a representative sample of 3381 adult Wisconsin residents who participated in the 2008-2013 Survey of the Health of Wisconsin (SHOW) program. Participant data were geographically linked to The United States Environmental Protection Agency (USEPA) Baysian space-time downscaler air pollution model for PM2.5, the United States Census roadway, and USEPA's Toxic Release Inventory data. Self-report and lung function (FEV1) estimates were used to define prevalence of asthma, allergies and wheezing symptoms.

RESULTS

Annual mean exposure to fine particulate matter (PM2.5) was between 6.59 and 15.14μg/m3. An increase of 5μg/m3 in the annual mean PM2.5 resulted in a 3.58 (2.36, 5.43) increase in the adjusted odds (95% CI) of having asthma. Exposure to vehicle traffic increased the odds of both current allergies [OR (95% CI)=1.35 (1.07, 1.35)] and current asthma [OR (95% CI)=1.51 (1.14, 2.00)]. Living within 300m of an Interstate roadway was associated with a 3-fold increase in the odds of asthma. Those living within 800m of an industrial site were 47% more likely to have asthma. No significant associations were seen with wheezing.

CONCLUSIONS

Within this population exposed to overall annual average levels of estimated low level chronic exposure to fine particulate matter (PM2.5) at or near 12μg/m3, the USEPA standard for air quality, significant association between both modeled PM2.5 exposure and proximity to roadways with asthma and allergies but not wheezing were found. Industrial source emissions were not associated with any allergic disease outcomes.

Environmental Inequality and Pollution Advantage among Immigrants in the United States

Environmental inequality scholarship has paid little attention to the disproportional exposure of immigrants in the United States (U.S.) to unfavorable environmental conditions. This study investigates whether new international migrants in the U.S. are exposed to environmental hazards and how this pattern varies among immigrant subpopulations (e.g., Hispanics, Asian, European). We combine sociodemographic information from the American Community Survey with toxicity-weighted chemical concentrations (Toxics Release Inventory) to model the relationship between toxin exposure and the relative population of recent immigrants across Public Use Microdata Areas (PUMAs, n=2,054) during 2005-2011. Results from spatial panel models show that immigrants tend to be less exposed to toxins, suggesting resilience instead of vulnerability. This pattern was pronounced among immigrants from Europe and Latin America (excluding Mexico). However, our results revealed that Mexican immigrants are disproportionately exposed to environmental hazards in wealthy regions.