Validating self-reported Toxic Release Inventory data using Benford's Law: investigating toxic chemical release hazards in floodplains

IntroductionAcute and long-term health impacts from flooding related toxic chemical releases are a significant local health concern and can disproportionately impact communities with vulnerable populations; reliable release data are needed to quantify this hazard.MethodsIn this paper, we analyze US Federal Emergency Management Agency designated floodplain data and US Environmental Protection Agency Toxic Release Inventory (TRI) data to determine if geographically manipulated databases adhere to Benford's Law.ResultsWe investigated multiple variants and discovered pollution releases adhere to Benford's Law and tests which thereby validates the self-reported toxic release dataset.DiscussionWe find that Benford's Law applies to self-reported toxic chemical release and disposal data, indicating a lack of widespread data errors or manipulation

Critique of Current Social Vulnerability Indices and Opportunities for Improvement

Between 1980 and 2013, the United States (U.S.) experienced 151 weather related disasters causing approximately $1 billion in overall damages with total costs exceeding$1 trillion. Social vulnerability (SV) is a widely used concept that aims to assess the differences in the susceptibility to disasters, losses, and coping and recovery abilities of communities. The SV of populations at risk of disasters in the majority of cases is expressed as an index (SVI) which has the potential to be used for deriving proactive plans that will protect communities and assist them to rebound from emergency situations. The majority of indices aiming to assess SV are derived with a composite model based on principal component analysis or percentile ranks. Only a few studies have attempted to assess existing SVI in terms of their relation to potential losses from disasters; these assessments found a limited predictive performance in terms of identifying potentially high risk areas. We argue and demonstrate that the current methodologies for deriving SVI may not capture the qualitatively differentiating nature of vulnerability of communities in geographic areas and do not provide a practical and reliable planning tool. Our study proposes a paradigm shift by considering SV to disasters as a classification issue and, consequently, by introducing classification modeling and performance assessment techniques which are likely to provide a different perspective on attributes influencing SV as well as a reliable approach to identify potentially high risk areas. To demonstrate the potentials of this approach historical U.S. Census and hurricane loss data from the FEMA Hazus® program were used for the Houston metropolitan area

Assessment of Spatial Analysis and Decision Assistance (SADA) Potential for Clean Up

Current federal and state regulations related to brownfields promote applicable practices that contain inherent problems. The primary issue with federal and state regulations governing brownfields is that risk assessment measures and spatial distribution of contaminants are not prominently factored in brownfield redevelopment. These boundaries of the contaminants are critical for establishing proper protection of the potential exposed population such as clean-up workers. Recent public domain software developments such as the Spatial Analysis and Decision Assistance (SADA) software can provide a reliable and cost effective tool for developing a comprehensive approach to brownfield redevelopment which will account for the spatial distribution of the contaminants and provide a rational solution to critical operational issues such as hotspots, restrictive zones for the protection of workers, and prioritization of clean-up operations. Actual data from a real brownfield site in Cook County, Illinois was used in this study to evaluate SADA applicability to brownfield redevelopment. Using SADA, a sample design was established using historical data and implemented at the site. The data captured from the SADA identified site investigation was useful to identify hotspots of contaminants of concern and creation of worker restrictive zones based on future redevelopment. The results for the brownfield site classified statically significant to actual results observed and appears SADA is appropriate tool for brownfield redevelopmen

MCVD: Prioritizing Vaccinations in Cook County, Illinois

Considering the potential for widespread adoption of social vulnerability indices (SVI) to prioritize COVID-19 vaccinations, there is a need to carefully assess them, particularly for correspondence with outcomes (such as loss of life) in the context of the COVID-19 pandemic. The UIC SPH PHGIS team developed a Midwest Comprehensive Visualization Dashboard (MCVD) for prioritizing COVID-19 vaccinations that shows bivariate maps displaying COVID-19 mortality in relation to social vulnerability percentiles for census tracts in Cook County, Illinois. The information provided in the MCVD is vital for the multidimensional needs of an effective vaccination strategy which will account for population vulnerability and the realized losses within each community

Hospitalizations for heat-stress illness varies between rural and urban areas: an analysis of Illinois data, 1987–2014

Abstract Background The disease burden due to heat-stress illness (HSI), which can result in significant morbidity and mortality, is expected to increase as the climate continues to warm. In the United States (U.S.) much of what is known about HSI epidemiology is from analyses of urban heat waves. There is limited research addressing whether HSI hospitalization risk varies between urban and rural areas, nor is much known about additional diagnoses of patients hospitalized for HSI. Methods Hospitalizations in Illinois for HSI (ICD-9-CM codes 992.x or E900) in the months of May through September from 1987 to 2014 (n = 8667) were examined. Age-adjusted mean monthly hospitalization rates were calculated for each county using U.S. Census population data. Counties were categorized into five urban-rural strata using Rural Urban Continuum Codes (RUCC) (RUCC1, most urbanized to RUCC5, thinly populated). Average maximum monthly temperature (°C) was calculated for each county using daily data. Multi-level linear regression models were used, with county as the fixed effect and temperature as random effect, to model monthly hospitalization rates, adjusting for the percent of county population below the poverty line, percent of population that is Non-Hispanic Black, and percent of the population that is Hispanic. All analyses were stratified by county RUCC. Additional diagnoses of patients hospitalized for HSI and charges for hospitalization were summarized. Results Highest rates of HSI hospitalizations were seen in the most rural, thinly populated stratum (mean annual summer hospitalization rate of 1.16 hospitalizations per 100,000 population in the thinly populated strata vs. 0.45 per 100,000 in the metropolitan urban strata). A one-degree Celsius increase in maximum monthly average temperature was associated with a 0.34 increase in HSI hospitalization rate per 100,000 population in the thinly populated counties compared with 0.02 per 100,000 in highly urbanized counties. The most common additional diagnoses of patients hospitalized with HSI were dehydration, electrolyte abnormalities, and acute renal disorders. Total and mean hospital charges for HSI cases were $167.7 million and$20,500 (in 2014 US dollars). Conclusion Elevated temperatures appear to have different impacts on HSI hospitalization rates as function of urbanization. The most rural and the most urbanized counties of Illinois had the largest increases in monthly hospitalization rates for HSI per unit increase in the average monthly maximum temperature. This suggests that vulnerability of communities to heat is complex and strategies to reduce HSI may need to be tailored to the degree of urbanization of a county

Visualizing environmental justice issues in urban areas with a community input approach

According to environmental justice, environmental degradation and benefits should not be disproportionately shared between communities. Identifying disparities in the spatial distribution of environmental degradation is therefore a prerequisite for validating the state of environmental justice in a geographic region. Under ideal circumstances, environmental risk assessment is a preferred metric, but only when exposure levels have been quantified reliably after estimating the risk. In this study, we adopt a proximity burden metric caused by adjacent hazardous sources, allowing us to evaluate the environmental burden distribution and vulnerability to pollution sources. In close collaboration with a predominantly Latinx community in Chicago, we highlight the usefulness of our approach through a case study that shows how certain community areas in the city are likely to bear a disproportionate burden of environmental pollution caused by industrial roads

A New Approach to the Social Vulnerability Indices: Decision Tree-Based Vulnerability Classification Model

The Social Vulnerability Index (SVI), a composite score identifying populations at risk from disasters, is often used to predict vulnerability and plan for community-based disaster prevention and emergency response. Our study introduces a decision tree based approach to developing an SVI that captures the heterogeneity of both vulnerable populations and disasters and we demonstrate the importance of incorporating a disaster loss classification into estimating social vulnerability to increase the predictive performance of the model. Findings suggest that the SVI based on the decision tree approach dramatically increased the accuracy of predicting high vulnerability areas

A second wave of COVID-19 in Cook County: What lessons can be applied?

During the ongoing public health crisis, many agencies are reporting COVID-19 health outcome information based on the overall population. This practice can lead to misleading results and underestimation of high risk areas. To gain a better understanding of spatial and temporal distribution of COVID-19 deaths; the long term care facility (LTCF) and household population (HP) deaths must be used. This approach allows us to better discern high risk areas and provides policy makers with reliable information for community engagement and mitigation strategies. By focusing on high-risk LTCFs and residential areas, protective measures can be implemented to minimize COVID-19 spread and subsequent mortality.  These areas should be a high priority target when COVID-19 vaccines become available