As health-based drinking water standards for per-and polyfluorinated alkyl substances (PFAS) continue to evolve, public health and environmental protection decision-makers must assess exposure risks associated with all public drinking water systems in the United States (US). Unfortunately, current knowledge regarding the presence of PFAS in environmental systems is limited. In this study, a screening approach was established to: (1) identify and direct attention toward potential PFAS hot spots in drinking water sources, (2) prioritize sampling locations, and ( 3) provide insights regarding the potential PFAS sources that contaminate groundwater and surface water. Our approach incorporates geospatial data from public sources, including the US Environmental Protection Agency's Toxic Release Inventory, to identify locations where PFAS may be present in drinking water sources. An indicator factor (also known as "risk factor") was developed as a function of distance between potential past and/or present PFAS users (e.g., military bases, industrial sites, and airports) and the public water system, which generates a heat map that visualizes potential exposure risks. A binomial logistic regression model indicates whether PFAS are likely to be detected in public water systems. The results obtained using the developed screening approach aligned well (with a 76% overall model accuracy) with PFAS sampling and chemical analysis data from 81 public drinking water systems in the state of Kentucky. This study proposes this screening model as an effective decision aid to assist key decision-makers in identifying and prioritizing sampling locations for potential PFAS exposure risks in the public drinking water sources in their service areas.
In the midst of the COVID-19 pandemic, United States (U.S.) educational institutions must weigh incomplete scientific evidence to inform decisions about how best to re-open schools without sacrificing public health. While many communities face surging case numbers, others are experiencing case plateaus or even decreasing numbers. Simultaneously, some U.S. school systems face immense infrastructure challenges and resource constraints, while others are better positioned to resume face-to-face instruction. In this review, we first examine potential engineering controls to reduce SARS-CoV-2 exposures; we then present processes whereby local decision-makers can identify and partner with scientists, faculty, students, parents, public health officials, and others to determine the controls most appropriate for their communities. While no solution completely eliminates risks of SARS-CoV-2 exposure and illness, this mini-review discusses engaged decision and communication processes that incorporate current scientific knowledge, school district constraints, local tolerance for health risk, and community priorities to help guide schools in selecting and implementing re-opening strategies that are acceptable, feasible, and context-specific.
Per- and polyfluoroalkyl substances (PFAS) contamination in water sources near potential PFAS users is well known. Therefore, it is useful for PFAS stakeholders to visualize hot-spot areas and bring attention to the water systems that are near to those areas. Towards this end, we extracted information about PFAS sources, drinking water information, sewer water information, and Source Water Assessment Protection Program (SWAPP) information from publicly available sources to create five different maps in ArcGIS Online that highlight PFAS contamination in relation to potential PFAS users. Following the FAIR (Findable, Accessible, Interoperable and Reusable) principles, we created a Figshare repository that includes all data and associated metadata with these five ArcGIS maps. Moreover, the Figshare repository includes a metadata description of the maps in JSON format that adheres to a draft Minimum Information about Geospatial Information System (MIAGIS) standard we have developed. We hope this MIAGIS draft will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community. We have also developed a miagis Python package that facilitates the generation of a MIAGIS-compliant JSON metadata file.
We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets.
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