Social, ecological, and climatic factors interact creating a heterogeneous matrix that determines the spatiotemporal distribution of mosquitoes and human risks of exposure to the diseases they transmit. We explore linkages between the social and institutional processes behind residential abandonment, urban ecology, and the interactions of socio-ecological processes with abiotic drivers of mosquito production. Specifically, we test the relative roles of infrastructure degradation and vegetation for explaining the presence of Aedes albopictus Skuse 1894 to better predict spatial heterogeneity in mosquito exposure risk within urban environments. We further examine how precipitation interacts with these socially underpinned biophysical variables. We use a hierarchical statistical modeling approach to assess how environmental and climatic conditions over 3 years influence mosquito ecology across a socioeconomic gradient in Baltimore, MD. We show that decaying infrastructure and vegetation are important determinants of Ae. albopictus infestation. We demonstrate that both precipitation and vegetation influence mosquito production in ways that are mediated by the level of infrastructural decay on a given block. Mosquitoes were more common on blocks with greater abandonment, but when precipitation was low, mosquitoes were more likely to be found in higher-income neighborhoods with managed container habitat. Likewise, although increased vegetation was a negative predictor of mosquito infestation, more vegetation on blocks with high abandonment was associated with the largest mosquito populations. These findings indicate that fine spatial scale modeling of mosquito habitat within urban areas is needed to more accurately target vector control.

Objectives. We assessed spatial disparities in the distribution of Toxic Release Inventory (TRI) facilities in Charleston, SC. Methods. We used spatial methods and regression to assess burden disparities in the study area at the block and census-tract levels by race/ethnicity and socioeconomic status (SES). Results. Results revealed an inverse relationship between distance to TRI facilities and race/ethnicity and SES at the block and census-tract levels. Results of regression analyses showed a positive association between presence of TRI facilities and high percentage non-White and a negative association between number of TRI facilities and high SES. Conclusions. There are burden disparities in the distribution of TRI facilities in Charleston at the block and census-tract level by race/ethnicity and SES. Additional research is needed to understand cumulative risk in the region.

Background: Studies have demonstrated a relationship between segregation and level of education, occupational opportunities, and risk behaviors, yet a paucity of research has elucidated the association between racial residential segregation, socioeconomic deprivation, and lifetime cancer risk. Objectives: We examined estimated lifetime cancer risk from air toxics by racial composition, segregation, and deprivation in census tracts in Metropolitan Charleston. Methods: Segregation indices were used to measure the distribution of groups of people from different races within neighborhoods. The Townsend Index was used to measure economic deprivation in the study area. Poisson multivariate regressions were applied to assess the association of lifetime cancer risk with segregation indices and Townsend Index along with several sociodemographic measures. Results: Lifetime cancer risk from all pollution sources was 28 persons/million for half of the census tracts in Metropolitan Charleston. Isolation Index and Townsend Index both showed significant correlation with lifetime cancer risk from different sources. This significance still holds after adjusting for other sociodemographic measures in a Poisson regression, and these two indices have stronger effect on lifetime cancer risk compared to the effects of sociodemographic measures. Conclusions: We found that material deprivation, measured by the Townsend Index and segregation measured by the Isolation index, introduced high impact on lifetime cancer risk by air toxics at the census tract level.

According to the US Environmental Protection Agency (EPA), Superfund is a federal government program implemented to clean up uncontrolled hazardous waste sites. Twenty-six sites in South Carolina (SC) have been included on the National Priorities List (NPL), which has serious human health and environmental implications. The purpose of this study was to assess spatial disparities in the distribution of Superfund sites in SC.

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.

Citizen science (CS) has been an increasingly utilized means by which scientists leverage members of the public to increase the amount of data collected and analyzed. However, the underrepresentation of individuals from certain socio-cultural groups can have consequences that can manifest in the scientific outcomes of those CS projects such as biases in the data. Additionally, this underrepresentation can potentially affect long-term viability and support of CS as a community of practice. CS programs that promote greater inclusivity would likely provide opportunities for communities to define, investigate, and address pressing issues in collaboration with professional scientists. In this paper we discuss a CS project that sought to include underrepresented communities in Baltimore, Maryland using Pandya's framework for inclusive CS. While the project met all of its scientific research goals, translating the CS for broader social outcomes in the community proved challenging. Here we highlight perspectives from local community members and research personnel about the barriers to CS engagement, challenges in translating scientific outcomes to social justice efforts, and opportunities to address these barriers in CS program development and design.