The Distribution of Environmental Equity: Exploring Spatial Nonstationarity in Multivariate Models of Air Toxic Releases

Association of Environmental Injustice and Cardiovascular Diseases and Risk Factors in the United States

BACKGROUND

While the impacts of social and environmental exposure on cardiovascular risks are often reported individually, the combined effect is poorly understood.

METHODS AND RESULTS

Using the 2022 Environmental Justice Index, socio-environmental justice index and environmental burden module ranks of census tracts were divided into quartiles (quartile 1, the least vulnerable census tracts; quartile 4, the most vulnerable census tracts). Age-adjusted rate ratios (RRs) of coronary artery disease, strokes, and various health measures reported in the Prevention Population-Level Analysis and Community Estimates data were compared between quartiles using multivariable Poisson regression. The quartile 4 Environmental Justice Index was associated with a higher rate of coronary artery disease (RR, 1.684 [95% CI, 1.660-1.708]) and stroke (RR, 2.112 [95% CI, 2.078-2.147]) compared with the quartile 1 Environmental Justice Index. Similarly, coronary artery disease 1.057 [95% CI,1.043-1.0716] and stroke (RR, 1.118 [95% CI, 1.102-1.135]) were significantly higher in the quartile 4 than in the quartile 1 environmental burden module. Similar results were observed for chronic kidney disease, hypertension, diabetes, obesity, high cholesterol, lack of health insurance, sleep <7 hours per night, no leisure time physical activity, and impaired mental and physical health >14 days.

CONCLUSIONS

The prevalence of CVD and its risk factors is highly associated with increased social and environmental adversities, and environmental exposure plays an important role independent of social factors.

Assessing community-level exposure to social vulnerability and isolation: spatial patterning and urban-rural differences

BACKGROUND

Environmental health disparity research involves the use of metrics to assess exposure to community-level vulnerabilities or inequities. While numerous vulnerability indices have been developed, there is no agreement on standardization or appropriate use, they have largely been applied in urban areas, and their interpretation and utility likely vary across different geographies.

OBJECTIVE

We evaluated the spatial distribution, variability, and relationships among different metrics of social vulnerability and isolation across urban and rural settings to inform interpretation and selection of metrics for environmental disparity research.

METHODS

For all census tracts in North Carolina, we conducted a principal components analysis using 23 socioeconomic/demographic variables from the 2010 United States Census and American Community Survey. We calculated or obtained the neighborhood deprivation index (NDI), residential racial isolation index (RI), educational isolation index (EI), Gini coefficient, and social vulnerability index (SVI). Statistical analyses included Moran's I for spatial clustering, t-tests for urban-rural differences, Pearson correlation coefficients, and changes in ranking of tracts across metrics.

RESULTS

Social vulnerability metrics exhibited clear spatial patterning (Moran's I ≥ 0.30, p < 0.01). Greater educational isolation and more intense neighborhood deprivation was observed in rural areas and greater racial isolation in urban areas. Single-domain metrics were not highly correlated with each other (rho ≤ 0.36), while composite metrics (i.e., NDI, SVI, principal components analysis) were highly correlated (rho > 0.80). Composite metrics were more highly correlated with the racial isolation metric in urban (rho: 0.54-0.64) versus rural tracts (rho: 0.36-0.48). Census tract rankings changed considerably based on which metric was being applied.

SIGNIFICANCE

High correlations between composite metrics within urban and rural tracts suggests they could be used interchangeably; single domain metrics cannot. Composite metrics capture different facets of vulnerabilities in urban and rural settings, and these complexities should be examined by researchers applying metrics to areas of diverse urban and rural forms.

Multiscale Analysis of the Relationship between Toxic Chemical Hazard Risks and Racial/Ethnic and Socioeconomic Groups in Texas, USA

Although quantitative environmental (in)justice research demonstrates a disproportionate burden of toxic chemical hazard risks among racial/ethnic minorities and people in low socioeconomic positions, limited knowledge exists on how racial/ethnic and socioeconomic groups across geographic spaces experience toxic chemical hazards. This study analyzed the spatial non-stationarity in the associations between toxic chemical hazard risk and community characteristics of census block groups in Texas, USA, for 2017 using a multiscale geographically weighted regression. The results showed that the percentage of Black or Asian population has significant positive associations with toxic risk across block groups in Texas, meaning that racial minorities suffered more from toxic risk wherever they are located in the state. By contrast, the percentage of Hispanic or Latino has a positive relationship with toxic risk, and the relationship varies locally and is only significant in eastern areas of Texas. Statistical associations between toxic risk and socioeconomic variables are not stationary across the state, showing sub-state patterns of spatial variation in terms of the sign, significant level, and magnitude of the coefficient. Income has a significant negative association with toxic risk around the Dallas-Fort Worth-Arlington Metropolitan Statistical Area. Proportions of people without high school diploma and the unemployment rate both have positive relationships with toxic risk in the eastern area of Texas. Our findings highlight the importance of identifying the spatial patterns of the association between toxic chemical hazard risks and community characteristics at the census block group level for addressing environmental inequality.

Determination of the best knot and bandwidth in geographically weighted truncated spline nonparametric regression using generalized cross validation

This study proposes the development of nonparametric regression for data containing spatial heterogeneity with local parameter estimates for each observation location. GWTSNR combines Truncated Spline Nonparametric Regression (TSNR) and Geographically Weighted Regression (GWR). So it is necessary to determine the optimum knot point from TSNR and determine the best geographic weighting (bandwidth) from GWR by deciding the best knot point and bandwidth using Generalized Cross Validation (GCV). The case study analyzed the Morbidity Rate in North Sumatra in 2020. This study will estimate the model using knot points 1, 2, and 3 and geographic weighting of the Kernel Function, Gaussian, Bisquare, Tricube, and Exponential. Based on data analysis, we obtained that the best model for Morbidity Rate data in North Sumatra 2020 based on the minimum GCV value is the model using knots 1 and the Kernel Function of Bisquare. Based on the GWTSNR model, the significant predictors in each district/city were grouped into eight groups. Furthermore, the GWTSNR is better at modeling morbidity rates in North Sumatra 2020 by obtaining adjusted R-square = 96.235 than the TSNR by obtaining adjusted R-squared = 70.159. Some of the highlights of the proposed approach are:•The method combines nonparametric and spatial regression in determining morbidity rate modeling.•There were three-knot points tested in the truncated spline nonparametric regression and four geographic weightings in the spatial regression and then to determine the best knot and bandwidth using Generalized Cross Validation.•This paper will determine regional groupings in North Sumatra 2020 based on significant predictors in modeling morbidity rates.

Data-driven discovery of the governing equations for transport in heterogeneous media by symbolic regression and stochastic optimization

With advances in instrumentation and the tremendous increase in computational power, vast amounts of data are becoming available for many complex phenomena in macroscopically heterogeneous media, particularly those that involve flow and transport processes, which are problems of fundamental interest that occur in a wide variety of physical systems. The absence of a length scale beyond which such systems can be considered as homogeneous implies that the traditional volume or ensemble averaging of the equations of continuum mechanics over the heterogeneity is no longer valid and, therefore, the issue of discovering the governing equations for flow and transport processes is an open question. We propose a data-driven approach that uses stochastic optimization and symbolic regression to discover the governing equations for flow and transport processes in heterogeneous media. The data could be experimental or obtained by microscopic simulation. As an example, we discover the governing equation for anomalous diffusion on the critical percolation cluster at the percolation threshold, which is in the form of a fractional partial differential equation, and agrees with what has been proposed previously.

Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy

This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21-65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures.

The Societal Impact of Sharing Economy Platform Self-Regulations—An Empirical Investigation

The rise of the sharing economy has disrupted traditional industries and has had many unforeseen societal impacts. This has sparked policy debates on whether and how the sharing economy should be regulated to promote the healthy growth of such markets. In this research, we examine the impact of platform self-regulations in the context of the home-sharing market. Using policy changes that reduce the number of Airbnb listings, we empirically test the impact of platform self-regulations on crime rates. Our results suggest that a reduction in Airbnb listings resulting from platform self-regulations leads to a reduction in crime. We further study the impact of these policy changes on different types of crime and find that these self-regulations lead to a reduction in incidents of crime such as assault, robbery, and burglary but an increase in theft incidents. In addition, we find that the impact of these policies varies based on the neighborhood’s characteristics, such as income, housing price, and population. This research contributes to our understanding of the societal impacts of the sharing economy and the impact of platform self-regulation. Our findings also provide empirical evidence to inform policy making.

The stationarity bias in research on the environmental determinants of health

An implicit assumption often made in research on the environmental determinants of health is that the relationships between environmental factors and their health effects are stable over space and time. This is the assumption of stationarity. The health impacts of environmental factors, however, may vary not only over space and time but also over the value ranges of the environmental factors under investigation. Few studies to date have examined how often the stationarity assumption is violated and when violated, to what extent findings might be misleading. Using selected studies as examples, this paper explores how the stationarity assumption can lead to misleading conclusions about health-environment relationships that may in turn have serious health consequences or policy implications. It encourages researchers to embrace nonstationarity and recognize its meaning because it helps direct our attention to the ignored factors or processes that may enhance our understanding of the phenomena under investigation.

Sociological Perspectives on Climate Change and Society: A Review

Society is at an important intersection in dealing with the challenges of climate change, and this paper is presented at a critical juncture in light of growing recognition that the natural sciences are insufficient to deal with these challenges. Critical aspects of sociological perspectives related to climate change research are brought together in this review in the hope of fostering greater interdisciplinary collaboration between the natural and social sciences. We fervently argue for the need to inculcate interdisciplinary approaches that can provide innovative perspectives and solutions to the challenges we face from the impacts of climate change. As such, some critical sociological perspectives are addressed, with two objectives: (a) to provide a foundational opening for readers seeking an introductory perspective and potential core contributions of sociological insights on climate change; and (b) to explore opportunities and obstacles that may occur with increased interdisciplinary cooperation and collaboration. We lay out fundamental ideas by assembling a loosely connected body of sociological research, hoping to develop and advance the collaborative research agenda between sociology and other disciplines for the near future.

Multilevel and spatial analyses of childhood malnutrition in Uganda: examining individual and contextual factors

In this study, we examine the concepts of spatial dependence and spatial heterogeneity in the effect of macro-level and micro-level factors on stunting among children aged under five in Uganda. We conducted a cross-sectional analysis of 3624 Ugandan children aged under five, using data from the 2016 Ugandan Demographic and Health Survey. Multilevel mixed-effect analysis, spatial regression methods and multi-scale geographically weight regression (MGWR) analysis were employed to examine the association between our predictors and stunting as well as to analyse spatial dependence and variability in the association. Approximately 28% of children were stunted. In the multilevel analysis, the effect of drought, diurnal temperature and livestock per km² on stunting was modified by child, parent and household factors. Likewise, the contextual factors had a modifiable effect on the association between child’s sex, mother’s education and stunting. The results of the spatial regression models indicate a significant spatial error dependence in the residuals. The MGWR suggests rainfall and diurnal temperature had spatial varying associations with stunting. The spatial heterogeneity of rainfall and diurnal temperature as predictors of stunting suggest some areas in Uganda might be more sensitive to variability in these climatic conditions in relation to stunting than others.

The Intersection of Race, Immigration Status, and Environmental Justice

Environmental injustice occurs when marginalized groups face disproportionate environmental impacts from a range of threats. Environmental racism is a particular form of environmental injustice and frequently includes the implementation of policies, regulations, or institutional practices that target communities of color for undesirable waste sites, zoning, and industry. One example of how the United States federal and state governments are currently practicing environmental racism is in the form of building and maintaining toxic prisons and immigrant detention prisons, where people of color and undocumented persons are the majority of inmates and detainees who suffer disproportionate health risk and harms. This article discusses the historical and contemporary conditions that have shaped the present political landscape of racial and immigration conflicts and considers those dynamics in the context of the literature on environmental justice. Case studies are then presented to highlight specific locations and instances that exemplify environmental injustice and racism in the carceral sector. The article concludes with an analysis of the current political drivers and motivations contributing to these risks and injustices, and ends with a discussion of the scale and depth of analysis required to alleviate these impacts in the future, which might contribute to greater sustainability among the communities affected.

The Search for Environmental Justice: The Story of North Birmingham

Environmental justice is a rising social movement throughout the world. Research is beginning to define the movement and address the disparities that exist among communities exposed to pollution. North Birmingham, a community made up of six neighborhoods in Jefferson County, Alabama, in the United States, is a story of environmental injustice. Heavy industry, including the 35th Avenue Superfund Site, has caused significant environmental pollution over time, leaving residents concerned that their health and well-being are at risk from continued exposure. For years, pollution has impacted the community, and residents have fought and challenged industry and government. The United States (U.S.) Environmental Protection Agency (EPA), the U.S. Agency for Toxic Substances and Disease Registry (ATSDR), and the Jefferson County Department of Health (JCDH) in Alabama have historically played a role in working with the community regarding their health concerns. In this manuscript, we describe a city entrenched in environmental injustice. We provide the history of the community, the responsible parties named for the contamination, the government’s involvement, and the community’s response to this injustice. Through this manuscript, we offer insight into a global concern that challenges local communities on a daily basis.

Linking Industrial Hazards and Social Inequalities: Environmental Injustice in Gujarat, India

Industrial development in India has rarely been studied through the perspective of environmental justice (EJ) such that the association between industrial development and significant economic and social inequalities remains to be examined. Our article addresses this gap by focusing on Gujarat in western India, a leading industrial state that exemplifies the designation of India as an "emerging economy." We link the geographic concentration of industrial facilities classified as major accident hazard (MAH) units, further subdivided by size (large or medium/small) and ownership (public or private), to the socio-demographic composition of the population at the subdistrict (taluka) level. Generalized estimating equations (GEEs) are used to analyze statistical associations between MAH unit density and explanatory variables related to the economic and social status of the residential population at the subdistrict level. Our results indicate a significant relationship between presence of socially disadvantaged populations (Scheduled Castes and Scheduled Tribes) and density of all types of MAH units, except those associated with the public sector. Higher urbanization and lower home ownership are also found to be strong predictors of MAH unit density. Overall, our article represents an important step towards understanding the complexities of environmental inequalities stemming from Gujarat's industrial economy.

Impaired Water Hazard Zones: Mapping Intersecting Environmental Health Vulnerabilities and Polluter Disproportionality

This study advanced a rigorous spatial analysis of surface water-related environmental health vulnerabilities in the California Bay-Delta region, USA, from 2000 to 2006. It constructed a novel hazard indicator—“impaired water hazard zones’’—from regulatory estimates of extensive non-point-source (NPS) and point-source surface water pollution, per section 303(d) of the U.S. Clean Water Act. Bivariate and global logistic regression (GLR) analyses examined how established predictors of surface water health-hazard exposure vulnerability explain census block groups’ proximity to impaired water hazard zones in the Bay-Delta. GLR results indicate the spatial concentration of Black disadvantage, isolated Latinx disadvantage, low median housing values, proximate industrial water pollution levels, and proximity to the Chevron oil refinery—a disproportionate, “super emitter”, in the Bay-Delta—significantly predicted block group proximity to impaired water hazard zones. A geographically weighted logistic regression (GWLR) specification improved model fit and uncovered spatial heterogeneity in the predictors of block group proximity to impaired water hazard zones. The modal GWLR results in Oakland, California, show how major polluters beyond the Chevron refinery impair the local environment, and how isolated Latinx disadvantage was the lone positively significant population vulnerability factor. The article concludes with a discussion of its scholarly and practical implications.

Industry and geographic patterns of use and emission of carcinogens in Ontario, Canada, 2011-2015

Objectives

The goal of this study was to leverage data from two environmental regulatory initiatives, Ontario’s Toxics Reduction Act (TRA) and Canada’s National Pollutant Release Inventory (NPRI), to assess their ability to monitor trends in the use and emission of carcinogens by industry sector in Ontario.

Methods

Data reported to the TRA and NPRI by industrial facilities in Ontario were retrieved from 2011 to 2015. Twenty-six known and suspected carcinogens were identified (IARC) and the trends in the use and emission were evaluated by industry sector. The locations of industrial facilities that used and released carcinogens were mapped by Public Health Unit (PHU).

Results

Chemical manufacturing and primary metal manufacturing sectors accounted for 84% of all reported industrial use of carcinogens during the period 2011–2015. The largest source of carcinogen emissions came from facilities in the primary metal manufacturing and paper manufacturing sectors. The largest number of industrial facilities that reported the use and release of carcinogens were located in the City of Toronto and Peel Region PHUs. Overall, the use of carcinogens across all sectors appeared to decrease by 8%, while emissions increased by about 2%.

Conclusion

The results of this study show the need to reduce the use and emission of select carcinogens in priority industry sectors. Environmental reporting programs, such as the TRA and NPRI, can serve as important tools in cancer prevention by tracking potential carcinogen exposures in the environment and in the workplace.

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.

Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods

Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice.

Environmental Health Related Socio-Spatial Inequalities: Identifying "Hotspots" of Environmental Burdens and Social Vulnerability

Differential exposure to multiple environmental burdens and benefits and their distribution across a population with varying vulnerability can contribute heavily to health inequalities. Particularly relevant are areas with high cumulative burdens and high social vulnerability termed as “hotspots”. This paper develops an index-based approach to assess these multiple burdens and benefits in combination with vulnerability factors at detailed intra-urban level. The method is applied to the city of Dortmund, Germany. Using non-spatial and spatial methods we assessed inequalities and identified “hotspot” areas in the city. We found modest inequalities burdening higher vulnerable groups in Dortmund (CI = −0.020 at p < 0.05). At the detailed intra-urban level, however, inequalities showed strong geographical patterns. Large numbers of “hotspots” exist in the northern part of the city compared to the southern part. A holistic assessment, particularly at a detailed local level, considering both environmental burdens and benefits and their distribution across the population with the different vulnerability, is essential to inform environmental justice debates and to mobilize local stakeholders. Locating “hotspot” areas at this detailed spatial level can serve as a basis to develop interventions that target vulnerable groups to ensure a health conducive equal environment.

Risky Substance Use Environments and Addiction: A New Frontier for Environmental Justice Research

Substance use disorders are widely recognized as one of the most pressing global public health problems, and recent research indicates that environmental factors, including access and exposure to substances of abuse, neighborhood disadvantage and disorder, and environmental barriers to treatment, influence substance use behaviors. Racial and socioeconomic inequities in the factors that create risky substance use environments may engender disparities in rates of substance use disorders and treatment outcomes. Environmental justice researchers, with substantial experience in addressing racial and ethnic inequities in environmental risk from technological and other hazards, should consider similar inequities in risky substance use environments as an environmental justice issue. Research should aim at illustrating where, why, and how such inequities in risky substance use environments occur, the implications of such inequities for disparities in substance use disorders and treatment outcomes, and the implications for tobacco, alcohol, and drug policies and prevention and treatment programs.

A Spatial Analysis of Body Mass Index and Neighborhood Factors in Community-Dwelling Older Men and Women

The spatial distribution of obesity among the older population can yield insights into the influence of contextual factors associated with this public health problem. We tested the relationship between neighborhood-level characteristics and body mass index (BMI) using global and local spatial statistics of geographic clustering, using data derived from a random-digit-dial sample of 5,319 community-dwelling adults aged 50 to 74 residing in 1,313 census tracts in New Jersey. Geographically weighted regression modeled associations between BMI clusters and neighborhood characteristics, including metrics of structure, safety, demographics, and amenities. Across the sample panel, average BMI was 28.62 kg/m(2) for women and 28.25 kg/m(2) for men. There was significant spatial clustering of obesity by census tract, varying by gender across the state. Neighborhood characteristics were more strongly related to BMI for women than men. This research illuminates the role of neighborhood contextual factors and will assist community planners, officials, and public health practitioners as they address the rise in obesity.

Assessing the variables affecting on the rate of solid waste generation and recycling: An empirical analysis in Prespa Park

Economic development, urbanization, and improved living standards increase the quantity and complexity of generated solid waste. Comprehensive study of the variables influencing household solid waste production and recycling rate is crucial and fundamental for exploring the generation mechanism and forecasting future dynamics of household solid waste. The present study is employed in the case study of Prespa Park. A model, based on the interrelationships of economic, demographic, housing structure and waste management policy variables influencing the rate of solid waste generation and recycling is developed and employed. The empirical analysis is based on the information derived from a field questionnaire survey conducted in Prespa Park villages for the year 2014. Another feature of this study is to test whether a household's waste generation can be decoupled from its population growth. Descriptive statistics, bivariate correlation analysis and F-tests are used to know the relationship between variables. One-way and two-way fixed effects models data analysis techniques are used to identify variables that determine the effectiveness of waste generation and recycling at household level in the study area. The results reveal that households with heterogeneous characteristics, such as education level, mean building age and income, present different challenges of waste reduction goals. Numerically, an increase of 1% in education level of population corresponds to a waste reduction of 3kg on the annual per capita basis. A village with older buildings, in the case of one year older of the median building age, corresponds to a waste generation increase of 12kg. Other economic and policy incentives such as the mean household income, pay-as-you-throw, percentage of population with access to curbside recycling, the number of drop-off recycling facilities available per 1000 persons and cumulative expenditures on recycling education per capita are also found to be effective measures in waste reduction. The mean expenditure for recycling education spent on a person for years 2010 and 2014 is 12 and 14 cents, respectively and it vary from 0 to €1. For years 2010 and 2014, the mean percentage of population with access to curbside recycling services is 38.6% and 40.3%, and the mean number of drop-off recycling centers per 1000 persons in the population is 0.29 and 0.32, respectively. Empirical evidence suggests that population growth did not necessarily result in increases in waste generation. The results provided are useful when planning, changing or implementing sustainable municipal solid waste management.

Local Variability in the Impacts of Residential Particulate Matter and Pest Exposure on Children's Wheezing Severity: A Geographically Weighted Regression Analysis of Environmental Health Justice

Two assumptions have underpinned environmental justice over the past several decades: 1) uneven environmental exposures yield correspondingly unequal health impacts and 2) these effects are stable across space. To test these assumptions, relationships for residential pest and PM2.5 exposures with children's wheezing severity are examined using global (ordinary least squares) and local (geographically weighted regression [GWR]) models using cross-sectional observational survey data from El Paso (Texas) children. In the global model, having pests and higher levels of PM2.5 were weakly associated with greater wheezing severity. The local model reveals two types of asthmogenic socio-environments where environmental exposures more powerfully predict greater wheezing severity. The first is a lower-income context where children are disproportionately exposed to pests and PM2.5 and the second is a higher-income socio-environment where children are exposed to lower levels of PM2.5, yet PM2.5is counterintuitively associated with more severe wheezing. Findings demonstrate that GWR is a powerful tool for understanding relationships between environmental conditions, social characteristics and health inequalities.

Toxic releases and risk disparity: a spatiotemporal model of industrial ecology and social empowerment

Information-based regulations (IBRs) are founded on the theoretical premise that public participation in accomplishing policy goals is empowered by open access to information. Since its inception in 1988, the Toxics Release Inventory (TRI) has provided the framework and regulatory impetus for the compilation and distribution of data on toxic releases associated with industrial development, following the tenets of IBR. As TRI emissions are reputed to disproportionately affect low-income communities, we investigated how demographic characteristics are related to change in TRI emissions and toxicity risks between 1989 and 2002, and we sought to identify factors that predict these changes. We used local indicators of spatial association (LISA) maps and spatial regression techniques to study risk disparity in the Los Angeles urban area. We also surveyed 203 individuals in eight communities in the same region to measure the levels of awareness of TRI, attitudes towards air pollution, and general environmental risk. We discovered, through spatial lag models, that changes in gross and toxic emissions are related to community ethnic composition, poverty level, home ownership, and base 1989 emissions (R-square=0.034-0.083). We generated a structural equation model to explain the determinants of social empowerment to act on the basis of environmental information. Hierarchical confirmatory factor analysis (HCFA) supports the theoretical model that individual empowerment is predicted by risk perception, worry, and awareness (Chi-square=63.315, p=0.022, df=42). This study provides strong evidence that spatiotemporal changes in regional-scale environmental risks are influenced by individual-scale empowerment mediated by IBRs.

Trees grow on money: urban tree canopy cover and environmental justice

This study examines the distributional equity of urban tree canopy (UTC) cover for Baltimore, MD, Los Angeles, CA, New York, NY, Philadelphia, PA, Raleigh, NC, Sacramento, CA, and Washington, D.C. using high spatial resolution land cover data and census data. Data are analyzed at the Census Block Group levels using Spearman's correlation, ordinary least squares regression (OLS), and a spatial autoregressive model (SAR). Across all cities there is a strong positive correlation between UTC cover and median household income. Negative correlations between race and UTC cover exist in bivariate models for some cities, but they are generally not observed using multivariate regressions that include additional variables on income, education, and housing age. SAR models result in higher r-square values compared to the OLS models across all cities, suggesting that spatial autocorrelation is an important feature of our data. Similarities among cities can be found based on shared characteristics of climate, race/ethnicity, and size. Our findings suggest that a suite of variables, including income, contribute to the distribution of UTC cover. These findings can help target simultaneous strategies for UTC goals and environmental justice concerns.

Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

Nonstationary disordered materials and media, those for which the probability distribution function of any property varies spatially when shifted in space, are abundant and encountered in astrophysics, oceanography, air pollution patterns, large-scale porous media, biological tissues and organs, and composite materials. Their reconstruction and modeling is a notoriously difficult and largely unsolved problem. We propose a method for reconstructing a broad class of such media based on partitioning them into locally stationary zones. Two methods are used for the partitioning. One is based on the Shannon entropy, while the second method utilizes a watershed transform. The locally stationary zones are then reconstructed based on a cross-correlation function and one-dimensional raster path that we recently introduced [P. Tahmasebi and M. Sahimi, Phys. Rev. Lett. 110, 078002 (2013)], with overlaps between the zones to ensure seamless transition from one zone to another. A large number of examples, including porous media, ecological systems, disordered materials, and biological tissues and organs, are reconstructed and analyzed to demonstrate the accuracy of the method.

Comparing disproportionate exposure to acute and chronic pollution risks: a case study in Houston, Texas

While environmental justice (EJ) research in the United States has focused primarily on the social distribution of chronic pollution risks, previous empirical studies have not analyzed disparities in exposure to both chronic (long-term) and acute (short-term) pollution in the same study area. Our article addresses this limitation though a case study that compares social inequities in exposure to chronic and acute pollution risks in the Greater Houston Metropolitan Statistical Area, Texas. The study integrates estimates of chronic cancer risk associated with ambient exposure to hazardous air pollutants from the Environmental Protection Agency's National-Scale Air Toxics Assessment (2005), hazardous chemical accidents from the National Response Center's Emergency Response Notification System (2007-2011), and sociodemographic characteristics from the American Community Survey (2007-2011). Statistical analyses are based on descriptive comparisons, bivariate correlations, and locally derived spatial regression models that account for spatial dependence in the data. Results indicate that neighborhoods with a higher percentage of Hispanic residents, lower percentage of homeowners, and higher income inequality are facing significantly greater exposure to both chronic and acute pollution risks. The non-Hispanic black percentage is significantly higher in neighborhoods with greater chronic cancer risk, but lower in areas exposed to acute pollution events. Households isolated by language--those highly likely to face evacuation problems during an actual chemical disaster--tend to reside in areas facing significantly greater exposure to high-impact acute events. Our findings emphasize the growing need to examine social inequities in exposure to both chronic and acute pollution risks in future EJ research and policy.

Unequal Trajectories: Racial and Class Differences in Residential Exposure to Industrial Hazard

The unequal exposure to industrial hazards via differential residential attainment and/or differential sitings of toxic facilities is a long-standing environmental justice issue. This study examines individual trajectories of residential exposure to the risk of industrial hazard over nearly two decades. Using a latent class growth analysis on longitudinal geocoded data merged with the neighborhood-level pollution measures, we discover large racial differences in trajectories of pollution exposure. A majority of individuals are exposed to above-average pollution levels at some point during the study period, but blacks are more likely than whites to experience persistent exposure to high pollution. These differences are only partially explained by racial differences in suburban neighborhood attainment, socioeconomic status, and the frequency of inter-neighborhood moves. Immobile blacks also saw their exposure increase.

Race, socioeconomic status, and air pollution exposure in North Carolina

BACKGROUND

Although studies suggest that exposure to pollutants is associated with race/ethnicity and socio-economic status (SES), many studies are limited to the geographic regions where monitoring stations are located.

OBJECTIVES

This study uses modeled predictive surfaces to examine the relationship between air pollution exposure, race/ethnicity, and measures of SES across the entire State of North Carolina.

METHODS

The daily predictions of particulate matter <2.5 µm in aerodynamic diameter (PM2.5) and ozone (O3) were determined using a spatial model that fused data from two sources: point air monitoring data and gridded numerical output. These daily predicted pollution levels for 2002 were linked with Census data. We examine the relationship between the census-tract level predicted concentration measures, SES, and racial composition.

RESULTS

SES and race/ethnicity were related to predicted concentrations of both PM2.5 and O3 for census tracts in North Carolina. Lower SES and higher proportion minority population were associated with higher levels of PM2.5. An interquartile range (IQR) increase of median household income reduced the predicted average PM2.5 level by 0.10 µg/m3. The opposite relationship was true for O3. An IQR increase of median household income increased the predicted average O3 measure by 0.11 ppb.

CONCLUSIONS

The analyses demonstrate that SES and race/ethnicity are related to predicted estimates of PM2.5 and O3 for census tracts in North Carolina. These findings offer a baseline for future exposure modeling work involving SES and air pollution for the entire state and not just among the populations residing near monitoring networks.

Spatiotemporal distributions of ambient oxides of nitrogen, with implications for exposure inequality and urban design

Intra-urban differences in concentrations of oxides of nitrogen (NO(x)) and exposure disparities in the Tampa area were investigated across temporal scales through emissions estimation, dispersion modeling, and analysis of residential subpopulation exposures. A hybrid estimation method was applied to provide link-level hourly on-road mobile source emissions. Ambient concentrations in 2002 at 1 km resolution were estimated using the CALPUFF dispersion model. Results were combined with residential demographic data at the block-group level, to investigate exposures and inequality for select racioethnic, age, and income population subgroups. Results indicate that on-road mobile sources contributed disproportionately to ground-level concentrations and dominated the spatial footprint across temporal scales (annual average to maximum hour). The black, lower income (less than $40K annually), and Hispanic subgroups had higher estimated exposures than the county average; the white and higher income (greater than $60K) subgroups had lower than average exposures. As annual average concentration increased, the disparity between groups generally increased. However for the highest 1-hr concentrations, reverse disparities were also found.

IMPLICATIONS

Current studies of air pollution exposure inequality have not fully considered differences by time scale and are often limited in spatial resolution. The modeling methods and the results presented here can be used to improve understanding of potential impacts of urban growth form on health and to improve urban sustainability. Results suggest focusing urban design interventions on reducing on-road mobile source emissions in areas with high densities of minority and low income groups.

Progress in Spatial Demography

BACKGROUND

Demography is an inherently spatial science, yet the application of spatial data and methods to demographic research has tended to lag that of other disciplines. In recent years, there has been a surge in interest in adding a spatial perspective to demography. This sharp rise in interest has been driven in part by rapid advances in geospatial data, new technologies, and methods of analysis.

OBJECTIVES

We offer a brief introduction to four of the advanced spatial analytic methods: spatial econometrics, geographically weighted regression, multilevel modeling, and spatial pattern analysis. We look at both the methods used and the insights that can be gained by applying a spatial perspective to demographic processes and outcomes. To help illustrate these substantive insights, we introduce six papers that are included in a Special Collection on Spatial Demography. We close with some predictions for the future, as we anticipate that spatial thinking and the use of geospatial data, technology, and analytical methods will change how many demographers address important demographic research questions.

CONCLUSION

Many important demographic questions can be studied and framed using spatial approaches. This will become even more evident as changes in the volume, source, and form of available demographic data-much of it geocoded-further alter the data landscape, and ultimately the conceptual models and analytical methods used by demographers. This overview provides a brief introduction to a rapidly changing field.

Local Hedonic House-Price Modelling for Urban Planners: Advantages of Using Local Regression Techniques

Hedonic house-price models have long been used in urban studies to investigate important factors characterizing cities (eg, the demand for amenities or housing submarkets). Traditionally, the formulation of hedonic models has been solved using global spatial econometric techniques. The development of local regression methods brought new insights into urban planning as the relationships between house prices and their determinants can be estimated locally and therefore mapped across space. Such maps provide planners and policy makers with valuable location-specific information to support their decision-making processes. A feature that is frequently overlooked when performing spatial local analysis is testing the statistical significance of local parameter estimates over space. This can be done by mapping the t-value of parameter estimates ( t-surfaces). In this study we propose the use of a mixed geographically weighted regression (mixed-GWR) technique to estimate a hedonic house-price model in Zurich. Mixed-GWR is an extended version of GWR by which some parameters are allowed to vary over space, while others remained fixed. To obtain spatially explicit results in a more meaningful way, we propose the use of t-surfaces to explore the statistical significance of selected local parameter estimates over space. We also follow the Bonferroni correction to overcome the problem of multiple hypothesis testing in local regression modelling. Results reveal interesting patterns in the spatial variability of local estimates for planners. For instance, areas are identified over which public policies such as house taxing have little or no effect on house pricing. Similarly, economic distortions in the housing market can be examined through the variability of residents' willingness to pay for larger dwellings. Also, urban development processes such as densification of cities can be supported by spatially exploring relevant socioeconomic variables.

Neighborhood characteristics and depressive symptoms of older people: local spatial analyses

Depressive symptoms in community-dwelling older people significantly increase the risk of developing clinically diagnosable depressive disorders. Knowledge of the spatial distribution of depressive symptoms in the older population can add important information to studies of neighborhood contextual factors and mental health outcomes, but analysis of spatial patterns is rarely undertaken. This study uses spatial statistics to explore patterns of clustering in depressive symptoms using data from a statewide survey of community-dwelling older people in New Jersey from 2006 to 2008. A significant overall pattern of clustering in depressive symptoms was observed at the state level. In a subsequent local clustering analysis, places with high levels of depressive symptoms near to other places with high levels of depressive symptoms were identified. The relationships between the level of depressive symptoms in a place and poverty, residential stability and crime were analyzed using geographically weighted regression. Significant local parameter estimates for the three independent variables were observed. Local parameters for the poverty variable were positive and significant almost everywhere in the state. The significant local parameters for residential stability and crime varied in their association with depressive symptoms in different regions of the state. This study is among the first to examine spatial patterns in depressive symptoms among community-dwelling older people, and it demonstrates the importance of exploring spatial variations in the relationships between neighborhood contextual factors and health outcomes.

Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data

In recent years, techniques have been developed to explore spatial non-stationarity and to model the entire distribution of a regressand. The former is mainly addressed by geographically weighted regression (GWR), and the latter by quantile regression (QR). However, little attention has been paid to combining these analytical techniques. The goal of this article is to fill this gap by introducing geographically weighted quantile regression (GWQR). This study briefly reviews GWR and QR, respectively, and then outlines their synergy and a new approach, GWQR. The estimations of GWQR parameters and their standard errors, the cross-validation bandwidth selection criterion, and the non-stationarity test are discussed. We apply GWQR to U.S. county data as an example, with mortality as the dependent variable and five social determinants as explanatory covariates. Maps summarize analytic results at the 5, 25, 50, 75, and 95 percentiles. We found that the associations between mortality and determinants vary not only spatially, but also simultaneously across the distribution of mortality. These new findings provide insights into the mortality literature, and are relevant to public policy and health promotion. Our GWQR approach bridges two important statistical approaches, and facilitates spatial quantile-based statistical analyses.

Mapping the results of local statistics: Using geographically weighted regression

The application of geographically weighted regression (GWR) - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity.

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation data is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.

Environmental injustices of children's exposure to air pollution from road-transport within the model British multicultural city of Leicester: 2000-09

The significant contribution of road-transport to air pollution within the urban arena is widely acknowledged, and traditionally explored in relation to health outcomes across a temporal scale. However, the structure of the urban environment is also of importance in dictating the existence of extremely variable traffic pollutant levels, which often tend to be linked with social disparities. Nevertheless 'Environmental Justice' studies have rarely tackled the adverse health implications of exposures from mobile sources (Chakraborty, 2009), or have applied statistical techniques that are appropriate for such spatial data (Gilbert and Chakraborty, 2011). This article addresses these gaps by spatially examining the distribution of respiratory hospitalisation incidents of children aged 0-15 years in relation to social circumstances and residential exposures of annual PM(10) road-transport emissions within Leicester during 2000-09. Continuing upon the theme of 'Environmental Justice', the research explores the intra-urban spatial distribution of those who produce and residentially experience the majority of road-transport emissions. The findings indicate significant global relationships to exist between children's hospitalisation rates and social-economic-status, ethnic minorities, and PM(10) road-transport emissions within Leicester. Local Indicators of Spatial Association (LISA) and Geographically Weighted Regression (GWR) identified important localised variations within the dataset, specifically relating to a double-burden of residentially experienced road-transport emissions and deprivation effecting inner city children's respiratory health. Furthermore, affluent intra-urban communities tended to contribute the highest levels of emission from private transport, while residentially experiencing relatively low exposure of transport emissions. This would suggest that environmental injustices prevail across the model British multicultural city of Leicester.

Disproportionate proximity to environmental health hazards: methods, models, and measurement

We sought to provide a historical overview of methods, models, and data used in the environmental justice (EJ) research literature to measure proximity to environmental hazards and potential exposure to their adverse health effects. We explored how the assessment of disproportionate proximity and exposure has evolved from comparing the prevalence of minority or low-income residents in geographic entities hosting pollution sources and discrete buffer zones to more refined techniques that use continuous distances, pollutant fate-and-transport models, and estimates of health risk from toxic exposure. We also reviewed analytical techniques used to determine the characteristics of people residing in areas potentially exposed to environmental hazards and emerging geostatistical techniques that are more appropriate for EJ analysis than conventional statistical methods. We concluded by providing several recommendations regarding future research and data needs for EJ assessment that would lead to more reliable results and policy solutions.

Estimation of exposure to toxic releases using spatial interaction modeling

BACKGROUND

The United States Environmental Protection Agency's Toxic Release Inventory (TRI) data are frequently used to estimate a community's exposure to pollution. However, this estimation process often uses underdeveloped geographic theory. Spatial interaction modeling provides a more realistic approach to this estimation process. This paper uses four sets of data: lung cancer age-adjusted mortality rates from the years 1990 through 2006 inclusive from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) database, TRI releases of carcinogens from 1987 to 1996, covariates associated with lung cancer, and the EPA's Risk-Screening Environmental Indicators (RSEI) model.

RESULTS

The impact of the volume of carcinogenic TRI releases on each county's lung cancer mortality rates was calculated using six spatial interaction functions (containment, buffer, power decay, exponential decay, quadratic decay, and RSEI estimates) and evaluated with four multivariate regression methods (linear, generalized linear, spatial lag, and spatial error). Akaike Information Criterion values and P values of spatial interaction terms were computed. The impacts calculated from the interaction models were also mapped. Buffer and quadratic interaction functions had the lowest AIC values (22298 and 22525 respectively), although the gains from including the spatial interaction terms were diminished with spatial error and spatial lag regression.

CONCLUSIONS

The use of different methods for estimating the spatial risk posed by pollution from TRI sites can give different results about the impact of those sites on health outcomes. The most reliable estimates did not always come from the most complex methods.

Interneighborhood migration, race, and environmental hazards: modeling microlevel processes of environmental inequality

This study combines longitudinal individual-level data with neighborhood-level industrial hazard data to examine the extent and sources of environmental inequality. Results indicate that profound racial and ethnic differences in proximity to industrial pollution persist when differences in individual education, household income, and other microlevel characteristics are controlled. Examination of underlying migration patterns further reveals that black and Latino householders move into neighborhoods with significantly higher hazard levels than do comparable whites and that racial differences in proximity to neighborhood pollution are maintained more by these disparate mobility destinations than by differential effects of pollution on the decision to move.

RACE, INCOME, AND ENVIRONMENTAL INEQUALITY IN THE UNITED STATES

This article asks whether the relationship between neighborhood and household income levels and neighborhood hazard levels varies according to neighborhood and household racial composition. Using a national, census tract-level data set, the authors find that black, white, and Hispanic households with similar incomes live in neighborhoods of dissimilar environmental quality, that the association between neighborhood and household income levels and neighborhood hazard levels varies according to neighborhood and household racial composition, and that increases in neighborhood and household income levels are more strongly associated with declining hazard levels in black neighborhoods and households than in white neighborhoods and households. These findings contradict Wilson's claim that the significance of race has declined in the modern industrial period and demonstrate that environmental racial inequality is not the product of racial income inequality. In addition, these findings suggest that the impact of higher incomes on black/white proximity to environmental hazards has less to do with increases in white geographic mobility (relative to black geographic mobility) than with the ability of higher income blacks to escape the highly polluted, disorganized, and deteriorated neighborhoods to which so many low-income blacks are confined.

US Metropolitan-area Variation in Environmental Inequality Outcomes

Over the past 20 years a steadily increasing number of researchers have investigated the relationship between neighbourhood demographic composition and environmental hazard presence. However, relatively few researchers have attempted to determine why the distribution of social groups around environmental hazards takes the form that it does or why some studies find strong evidence of environmental racial inequality while others do not. One possible explanation for this is that environmental racial inequality levels vary from one location to another. In order to see if this is the case, the article compares environmental racial inequality levels in the 61 largest metropolitan areas in the continental US, holding the unit of analysis, type of hazard, type of region and comparison population constant across metropolitan areas. Analyses demonstrate that environmental racial inequality levels do vary across metropolitan areas. Thus, after presenting these analyses, hypotheses are tested that make predictions about the determinants of this variation. These hypothesis tests show that neither residential segregation nor racial income inequality does a good job of explaining metropolitan-area variation in environmental inequality outcomes in the US.