Environmental impacts of Hurricane Florence flooding in eastern North Carolina: temporal analysis of contaminant distribution and potential human health risks

Human health risk assessment of heavy metals in residential soil-Houston, Texas

Human health risk assessment increasingly recognizes the need to integrate participatory-based research, geospatial analysis, and environmental epidemiology, particularly to address contamination concerns in underserved and disadvantaged communities. Here, we demonstrate the combined application of such methods within the Greater Fifth Ward neighborhood in Northeast Houston. In particular, in tandem with community members, we collected soil samples from 193 residential sites using a complete canvassing method from July to November 2021 to characterize contaminant concentrations, focusing on heavy metals and metalloids As, Ba, Cd, Cr, Pb, Se, Ag, and Hg measured using Inductively Coupled Plasma-Mass Spectrometry. Individual heavy metals as well as cumulative cancer and non-cancer risks were calculated for children and adults using the USEPA Regional Soil Screening Levels and benchmarks for specific land uses, such as crop growing. Soils from most sites had low or typical background levels expected in urban areas, but samples from several locations had significantly elevated lead levels (>1200 mg/kg) that warrant additional examination. Geospatial analysis suggested clustering of heavy metal contaminants within one geographic area of the neighborhood. This study highlights how participatory research in underserved environmental justice communities can help characterize current conditions as well as establish priorities for future investigation.

Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example

Geospatial modeling methods in population-level kidney research have not been used to full potential because few studies have completed associative spatial analyses between risk factors and exposures and kidney conditions and outcomes. Spatial modeling has several advantages over traditional modeling, including improved estimation of statistical variation and more accurate and unbiased estimation of coefficient effect direction or magnitudes by accounting for spatial data structure. Because most population-level kidney research data are geographically referenced, there is a need for better understanding of geospatial modeling for evaluating associations of individual geolocation with processes of care and clinical outcomes. In this review, we describe common spatial models, provide details to execute these analyses, and perform a case-study to display how results differ when integrating geographic structure. In our case-study, we used U.S. nationwide 2019 chronic kidney disease (CKD) data from Centers for Disease Control and Prevention's Kidney Disease Surveillance System and 2006 to 2010 U.S. Environmental Protection Agency environmental quality index (EQI) data and fit a nonspatial count model along with global spatial models (spatially lagged model [SLM]/pseudo-spatial error model [PSEM]) and a local spatial model (geographically weighted quasi-Poisson regression [GWQPR]). We found the SLM, PSEM, and GWQPR improved model fit in comparison to the nonspatial regression, and the PSEM model decreased the positive relationship between EQI and CKD prevalence. The GWQPR also revealed spatial heterogeneity in the EQI-CKD relationship. To summarize, spatial modeling has promise as a clinical and public health translational tool, and our case-study example is an exhibition of how these analyses may be performed to improve the accuracy and utility of findings.

Shifting terrains: Understanding residential contaminants after flood disasters

Flood disasters can induce the mass transport of soils and sediments. This has the potential to distribute contaminants and present novel combinations to new locations - including residential neighborhoods. Even when soil contaminants cannot be directly attributed to the disaster, data on bacterial and heavy metal(loids) can facilitate an environmentally just recovery by enabling reconstruction decisions that fill data gaps to minimize future exposure. These data-gathering interventions may be especially useful in poor, rural, and racially diverse communities where there is a high probability of exposure to multiple hazards and a potential dependency on the financial resources of disaster aid as a means of reducing chronic exposures to other environmental pollutants. At the same time, entering these post-disasters spaces is ethically complex. To acknowledge this complexity, we pilot a framework for work that gathers social-ecological hazard information while retaining a fair-minded approach to transdisciplinary work. Assembled a transdisciplinary team to recruit participants from 90 households subjected to flooding in the southeastern US. Participating households agreed to interviews to elicit flood experience and environmental health concerns, soil sampling for fecal bacteria (E. coli) and soil sampling for selected heavy metals and metalloids (Pb, As, Cd) at their flooded residence. Soil sampling found a wide range of E. coli concentrations in soil (0.4-1115.7 CFU/ dry gram). Heavy metal(loid)s were detected at most residences (As 97.9 %; Ca 25.5 %; Pb 100 %). Individually, heavy metal(loid) concentrations did not exceed regulatory thresholds. Hazard, risk, and mitigation concerns expressed during interviews reveal that integrated human-nature concepts complicate common understandings of how hazard perceptibility (smell, sight, touch, and information) affects research-action spaces. Qualitative analysis of interviews and field notes revealed that soil-related hazards addressed by our biophysical protocols were less salient than changes with direct causal associations with flooding. We conclude by discussing the potential for the social-ecological hazard information that is fair-minded and transdisciplinary (SHIFT) framework to advance environmentally just approaches to research-action spaces after disasters.

Local and Environmental Reservoirs of Salmonella enterica After Hurricane Florence Flooding

In many regions of the world, including the United States, human and animal fecal genetic markers have been found in flood waters. In this study, we use high-resolution whole genomic sequencing to examine the origin and distribution of Salmonella enterica after the 2018 Hurricane Florence flooding. We specifically asked whether S. enterica isolated from water samples collected near swine farms in North Carolina shortly after Hurricane Florence had evidence of swine origin. To investigate this, we isolated and fully sequenced 18 independent S. enterica strains from 10 locations (five flooded and five unflooded). We found that all strains have extremely similar chromosomes with only five single nucleotide polymorphisms (SNPs) and possessed two plasmids assigned bioinformatically to the incompatibility groups IncFIB and IncFII. The chromosomal core genome and the IncFIB plasmid are most closely related to environmental Salmonella strains isolated previously from the southeastern US. In contrast, the IncFII plasmid was found in environmental S. enterica strains whose genomes were more divergent, suggesting the IncFII plasmid is more promiscuous than the IncFIB type. We identified 65 antibiotic resistance genes (ARGs) in each of our 18 S. enterica isolates. All ARGs were located on the Salmonella chromosome, similar to other previously characterized environmental isolates. All isolates with different SNPs were resistant to a panel of commonly used antibiotics. These results highlight the importance of environmental sources of antibiotic-resistant S. enterica after extreme flood events.

Differential exposure to drinking water contaminants in North Carolina: Evidence from structural topic modeling and water quality data

To better understand water security of communities in North Carolina, this research uses structural topic modeling (STM) and geographic mapping to identify the main topics and pollutant categories being researched and the areas exposed to drinking water contaminants. The textual data derived from the journal article abstracts that examined water pollution in North Carolina is from 1964 to present. The STM analysis of textual data is paired with socio-demographic data from the 2015-2019 American Community Survey (ACS) 5-year estimates and water pollution data from North Carolina state agencies. The STM findings show that the most discussed topics relate to runoff management, wastewater from concentrated agricultural feeding operations, emerging contaminants, land development, and health impacts as a result of water contamination. The article discusses how the topics especially threaten groundwater resources used by community water systems and private wells. Those communities served by private wells are predominantly low-income and minority populations. As a result, threats to groundwater supplies exacerbate existing issues of environmental justice in North Carolina, especially in the Coastal Plains Region. The STM findings revealed that several key threats to safe drinking water are less covered by academic literature, such as poultry concentrated agricultural feeding operations and climate impacts, which may increase disparities in water access in North Carolina.

Adapting to Climate Change: Leveraging Systems-Focused Multidisciplinary Research to Promote Resilience

Approximately 2000 official and potential Superfund sites are located within 25 miles of the East or Gulf coasts, many of which will be at risk of flooding as sea levels rise. More than 60 million people across the United States live within 3 miles of a Superfund site. Disentangling multifaceted environmental health problems compounded by climate change requires a multidisciplinary systems approach to inform better strategies to prevent or reduce exposures and protect human health. The purpose of this minireview is to present the National Institute of Environmental Health Sciences Superfund Research Program (SRP) as a useful model of how this systems approach can help overcome the challenges of climate change while providing flexibility to pivot to additional needs as they arise. It also highlights broad-ranging SRP-funded research and tools that can be used to promote health and resilience to climate change in diverse contexts.

ToxPi*GIS Toolkit: creating, viewing, and sharing integrative visualizations for geospatial data using ArcGIS

BACKGROUND

Presenting a comprehensive picture of geographic data comprising multiple factors is an inherently integrative undertaking. Visualizing such data in an interactive form is essential for public sharing and geographic information systems (GIS) analysis. The Toxicological Prioritization Index (ToxPi) framework offers a visual analytic integrating data that is compatible with geographic data. ArcGIS is a predominant geospatial software available for presenting and communicating geographic data, yet to our knowledge there is no methodology for integrating ToxPi profiles into ArcGIS maps.

OBJECTIVE

We introduce an actively developed suite of software, the ToxPi*GIS Toolkit, for creating, viewing, sharing, and analyzing interactive ToxPi profiles in ArcGIS to allow for new GIS analysis and an avenue for providing geospatial results to the public.

METHODS

The ToxPi*GIS Toolkit is a collection of methods for creating interactive feature layers that contain ToxPi profiles. It currently includes an ArcGIS Toolbox (ToxPiToolbox.tbx) for drawing location-specific ToxPi profiles in a single feature layer, a collection of modular Python scripts that create predesigned layer files containing ToxPi feature layers from the command line, and a collection of Python routines for useful data manipulation and preprocessing. We present workflows documenting ToxPi feature layer creation, sharing, and embedding for both novice and advanced users looking for additional customizability.

RESULTS

Map visualizations created with the ToxPi*GIS Toolkit can be made freely available on public URLs, allowing users without ArcGIS Pro access or expertise to view and interact with them. Novice users with ArcGIS Pro access can create de novo custom maps, and advanced users can exploit additional customization options. The ArcGIS Toolbox provides a simple means for generating ToxPi feature layers. We illustrate its usage with current COVID-19 data to compare drivers of pandemic vulnerability in counties across the United States.

SIGNIFICANCE

The integration of ToxPi profiles with ArcGIS provides new avenues for geospatial analysis, visualization, and public sharing of multi-factor data. This allows for comparison of data across a region, which can support decisions that help address issues such as disease prevention, environmental health, natural disaster prevention, chemical risk, and many others. Development of new features, which will advance the interests of the scientific community in many fields, is ongoing for the ToxPi*GIS Toolkit, which can be accessed from www.toxpi.org .

Comparative assessment of blood mercury in American alligators (Alligator mississippiensis) from Coastal North Carolina and Florida

Mercury (Hg) is a widespread and harmful persistent pollutant of aquatic ecosystems. Except for the northern most populations of American alligators (Alligator Mississippiensis) found in North Carolina, the potential adverse health impacts of Hg on ecosystems and humans consuming alligator meat have been studied for over three decades. Now that alligators are being recreationally hunted and consumed across their range, it is especially important to monitor toxic contaminant levels to best understand possible adverse impacts of exposures on alligator populations and human health. In this study, we determined blood Hg concentrations in American alligators from an urbanized site in Wilmington, NC, a nearby site at Lake Waccamaw, NC, and a site on the St Johns River in Florida. Median blood total Hg (tHg) concentrations were particularly high at Lake Waccamaw (526 ng/g, range 152-946 ng/g), resulting in median muscle concentrations (0.48 mg/kg, range 0.13-0.88 mg/kg) well above US EPA screening values for fish consumption. Median concentrations at the Wilmington site (69 ng/g, range 22-336 ng/g) were generally low, and Hg concentrations from the St Johns River site (143 ng/g, range 54-244 ng/g) were comparable to those reported in previous studies. Analysis of relationships between tHg concentrations and a panel of blood chemistry biomarkers found only modest concentration-dependent impact on biomarkers of renal function. The results of this study reveal that local environmental factors greatly impact Hg bioaccumulation in alligators, findings that reaffirm local contaminant biomonitoring in alligator populations will be critical for affective management and determination of guidelines for safe consumption of harvested alligators.

Elemental analysis of hair provides biomarkers of maternal hardship linked to adverse behavioural outcomes in 4-year-old children: The QF2011 Queensland Flood Study

BACKGROUND

Exposure to adverse experiences during pregnancy, such as a natural disaster, can modify development of the child with potential long-term consequences. Elemental hair analysis may provide useful indicators of cellular homeostasis and child health. The present study investigated (1) if flood-induced prenatal maternal stress is associated with altered hair elemental profiles in 4-year-old children, and (2) if hair elemental profiles are associated with behavioural outcomes in children.

METHODS

Participants were 75 children (39 boys; 36 girls) whose mothers were exposed to varying levels of stress due to a natural disaster (2011 Queensland Flood, Australia) during pregnancy. At 4 years of age, language development, attention and internalizing and externalizing problems were assessed and scalp hair was collected. Hair was analyzed by inductively coupled plasma mass spectrometry (ICP-MS) for 28 chemical elements.

RESULTS

A significant curvilinear association was found between maternal objective hardship and copper levels in boys, as low and high maternal objective hardship levels were associated with the highest hair copper levels. Mediation analysis revealed that low levels of maternal objective hardship and high levels of copper were associated with lower vocabulary scores. Higher levels of maternal objective hardship were associated with higher magnesium levels, which in turn were associated with attention problems and aggression in boys. In girls, high and low maternal objective hardship levels were associated with high calcium/potassium ratios.

CONCLUSION

Elemental hair analysis may provide a sensitive biomonitoring tool for early identification of health risks in vulnerable children.

Spatial and Temporal Analysis of Impacts of Hurricane Florence on Criteria Air Pollutants and Air Toxics in Eastern North Carolina

Natural and anthropogenic disasters are associated with air quality concerns due to the potential redistribution of pollutants in the environment. Our objective was to conduct a spatiotemporal analysis of air concentrations of benzene, toluene, ethylbenzne, and xylene (BTEX) and criteria air pollutants in North Carolina during and after Hurricane Florence. Three sampling campaigns were carried out immediately after the storm (September 2018) and at four-month intervals. BTEX were measured along major roads. Concurrent criteria air pollutant concentrations were predicted from modeling. Correlation between air pollutants and possible point sources was conducted using spatial regression. Exceedances of ambient air criteria were observed for benzene (in all sampling periods) and PM2.5 (mostly immediately after Florence). For both, there was an association between higher concentrations and fueling stations, particularly immediately after Florence. For other pollutants, concentrations were generally below levels of regulatory concern. Through characterization of air quality under both disaster and "normal" conditions, this study demonstrates spatial and temporal variation in air pollutants. We found that only benzene and PM2.5 were present at levels of potential concern, and there were localized increases immediately after the hurricane. These substances warrant particular attention in future disaster response research (DR2) investigations.

Utilizing ion mobility spectrometry-mass spectrometry for the characterization and detection of persistent organic pollutants and their metabolites

Persistent organic pollutants (POPs) are xenobiotic chemicals of global concern due to their long-range transport capabilities, persistence, ability to bioaccumulate, and potential to have negative effects on human health and the environment. Identifying POPs in both the environment and human body is therefore essential for assessing potential health risks, but their diverse range of chemical classes challenge analytical techniques. Currently, platforms coupling chromatography approaches with mass spectrometry (MS) are the most common analytical methods employed to evaluate both parent POPs and their respective metabolites and/or degradants in samples ranging from d rinking water to biofluids. Unfortunately, different types of analyses are commonly needed to assess both the parent and metabolite/degradant POPs from the various chemical classes. The multiple time-consuming analyses necessary thus present a number of technical and logistical challenges when rapid evaluations are needed and sample volumes are limited. To address these challenges, we characterized 64 compounds including parent per- and polyfluoroalkyl substances (PFAS), pesticides, polychlorinated biphenyls (PCBs), industrial chemicals, and pharmaceuticals and personal care products (PPCPs), in addition to their metabolites and/or degradants, using ion mobility spectrometry coupled with MS (IMS-MS) as a potential rapid screening technique. Different ionization sources including electrospray ionization (ESI) and atmospheric pressure photoionization (APPI) were employed to determine optimal ionization for each chemical. Collectively, this study advances the field of exposure assessment by structurally characterizing the 64 important environmental pollutants, assessing their best ionization sources, and evaluating their rapid screening potential with IMS-MS.