Exploring spatial heterogeneity and environmental injustices in exposure to flood hazards using geographically weighted regression

Environmental justice issues in drinking water contaminant exposure in a European context

Previous studies have documented ethnic and sociodemographic disparities in exposure to drinking water (DW) contaminants. A majority were conducted in the U.S., with fewer studies conducted in other regions. This research aims to assess available evidence regarding environmental justice (EJ) issues in Europe, identify contaminants and potential drivers. A Scoping Review was conducted, exploring the existing European studies from 1990 to 2022. The review encompasses types of DW contaminants studied in relation to EJ, research designs, and potential drivers contributing to inequalities in exposure to specific contaminants. In addition, a case study was conducted in Ille-et-Vilaine, France, focusing on contaminants identified in the review and using a national monitoring database. Inequalities in contaminants' exposure were assessed using a composite deprivation index, FDep, at the census tract level (IRIS) applied in multilevel models and geographically weighted regression models, accounting for the rural-urban heterogeneity. Results show a limited number of primary studies focusing on EJ and DW contaminants exposure in Europe (n = 16). Various chemical contaminants such as nitrates, trihalomethanes (THMs), heavy metals, fluoride and pesticides have been assessed. Case study findings suggest some association between FDep and contaminants, with a different level of correlation depending on the contaminant. THMs show a negative correlation with deprivation, while lead displays a positive correlation related to the FDep. Disparities in exposure were also found according to the spatial scale of analysis. In rural areas, higher deprivation levels were associated with higher levels of nitrate (OR: 1.47; 95%CI: 1.02, 2.15) and lower level of fluoride (OR: 0.16; 95%CI: 0.07, 0.30) or THMs (OR: 0.73; 95%CI: 0.55, 0.98) in tap water. This study emphasizes the need for comprehensive research on EJ and DW contaminants exposure on a larger scale. Understanding complex interactions between contaminant distribution, socioeconomic factors, and exposure is essential for addressing EJ in drinking water.

Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists' mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists' sampling effort is focused more in Barcelona's lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city's upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists' sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Urban Flood Resilience Evaluation Based on Heterogeneous Data and Group Decision-Making

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state-response model, and social-economic-natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses.

Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices

Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.

Spatial variations in the associations of surface water quality with roads and traffic across an urbanization gradient in northern Georgia, USA

Roads and traffic are important elements of urbanization, but their spatial associations with surface water quality in watersheds have been seldom studied. In this study, the spatially varying associations of three urbanization indicators, including road density, traffic density, and percentages of urban land, with twenty water quality indicators, including dissolved oxygen (DO), specific conductance (SC), dissolved solids (DS), suspended solids (SS), biochemical oxygen demand (BOD), dissolved nutrients, dissolved ions, heavy metals, and coliform bacteria, across the watersheds in the northern part of the state of Georgia, USA, have been examined by a conventional statistical method, ordinary least squares regression (OLS), and a spatial statistical method, geographically weighted regression (GWR). The results from OLS show that the urbanization indicators all have significant positive associations with the majority of the studied water pollutants, indicating that water pollution is significantly contributed by human activities related to urbanization in northern Georgia. In contrast, GWR results show that the associations vary across the watersheds affected by their urbanization levels. Significant positive associations are found between each urbanization indicator and each of the studied water pollutants, but not in all watersheds. The associations of suspended solids, nitrogen nutrients, and coliform bacteria with all three urbanization indicators are more significant in less-urbanized watersheds, while the associations of dissolved ions, BOD, and orthophosphate (PO4) with road density and traffic density are more significant than those with urban land in more-urbanized watersheds, indicating that those water pollutants are more contributed by human activities associated with roads and traffic than other activities in more-urbanized areas. As a pilot study to explore how and why the associations of surface water quality with roads and traffic change across watersheds with different urbanization levels, its findings suggest that the policies of watershed management, land-use planning, and transportation planning should be tailored in local areas based on the locally important water pollutants and their associated urbanization indicators.

Assessing social vulnerability and identifying spatial hotspots of flood risk to inform socially just flood management policy

This study presents the first nationwide spatial assessment of flood risk to identify social vulnerability and flood exposure hotspots that support policies aimed at protecting high-risk populations and geographical regions of Canada. The study used a national-scale flood hazard dataset (pluvial, fluvial, and coastal) to estimate a 1-in-100-year flood exposure of all residential properties across 5721 census tracts. Residential flood exposure data were spatially integrated with a census-based multidimensional social vulnerability index (SoVI) that included demographic, racial/ethnic, and socioeconomic indicators influencing vulnerability. Using Bivariate Local Indicators of Spatial Association (BiLISA) cluster maps, the study identified geographic concentration of flood risk hotspots where high vulnerability coincided with high flood exposure. The results revealed considerable spatial variations in tract-level social vulnerability and flood exposure. Flood risk hotspots belonged to 410 census tracts, 21 census metropolitan areas, and eight provinces comprising about 1.7 million of the total population and 51% of half-a-million residential properties in Canada. Results identify populations and the geographic regions near the core and dense urban areas predominantly occupying those hotspots. Recognizing priority locations is critically important for government interventions and risk mitigation initiatives considering socio-physical aspects of vulnerability to flooding. Findings reinforce a better understanding of geographic flood-disadvantaged neighborhoods across Canada, where interventions are required to target preparedness, response, and recovery resources that foster socially just flood management strategies.

Spatial and temporal patterns of supply and demand risk for ecosystem services in the Weihe River Main Stream, NW China

The rapid development of society and economy in the post-industrial era has exacerbated the spatial matching contradiction between the demand of humans and the supply of the natural environment, while ecosystem service (ES) as a bridge linking nature and society, identifying and assessing its supply-demand risk, was beneficial to ecosystem management and promoted regional high-quality development. Based on the data of multi-source remote sensing and statistics, the supply and demand levels of four ESs, which contain food supply, carbon storage, soil conservation, and water yield in the main stem of the Weihe River in 2000, 2010, and 2020, were quantitatively measured. The spatial and temporal analysis of the supply, demand, and supply-demand ratio of each service was carried out using spatial mapping. The spatio-temporal pattern of the supply-demand risk was recognized by the method of spatial overlay, which means overlaying the supply and demand for material quality, ratio, trend, and the degree of trade-off coordination together between each service. The results showed that (1) the demand for water yield decreased slightly while the demand for food and the supply of carbon storage remained stable. In addition, the supply and demand of other services showed an upward trend. (2) The spatial distribution of the supply-demand ratio of each service shows "high in the south and low in the north" and "high in the east and low in the west," among which the supply-demand ratio of carbon storage is decreasing. (3) The overall supply-demand risk of soil conservation in the study area is low with characteristics of small range and high degree, the risk distribution characteristics of the other services are high in the east and low in the west, and the risk is high in the city center and low around. Otherwise, the supply-demand risk of food supply showed a downward trend, the risk of carbon storage showed an upward trend, the risk of soil conservation remained stable, and the risk of water yield showed a significant downward trend. Based on static supply-demand risk identification, this study assesses supply-demand risk over two periods and analyzes the trend of supply-demand risk changes over time. It clarifies the extent and direction of supply-demand risk shifts, as well as provides improved theoretical support for ecosystem service management.

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.