In North Carolina, approximately 2.4 million individuals, or 24% of the population, rely on drinking water from private wells. Up to 46% of private wells sampled contained health-based contaminants highlighting the potential for widespread degradation of drinking water quality in these unregulated systems. Drinking water in North Carolina has historically been an issue of environmental justice, with access to clean and reliable water varying based on community demographics. There is a need to evaluate limitations and biases in existing geospatial well testing data. In this study we used aggregated well testing data to: (i) identify temporal, spatial, and demographic trends in well-usage and testing, (ii) investigate demographic biases in reported testing, and (iii) explore how different aggregation levels influence data interpretation and outcomes on the relationship between demographics and water quality. Between 1998 and 2010, 63,000 private wells were sampled by health departments for 28 constituents. The tests recorded in this period represent less than 1% of the quantity of tests that would meet the recommendations for biannual metallic contaminant testing. Although the well population is typically thought of as a rural white population, 26% of well users were from BIPOC communities. Testing rates were skewed towards white populations, with BIPOC well users 2.4 times less likely to receive a well test. In general, data aggregation was shown to hide demographic variability and mask issues of testing discrimination. Discussion of environmental justice surrounding drinking water should involve recognition of data biases to address policy and decision-making concerns.

Recent decades have seen the integration of data analytics – such as machine learning (ML) - into various dimensions of modern socioecological systems, commerce, and countless sectors that leverage advanced technologies. The surfacing of sophisticated data techniques has unlocked new potential for innovation and novel research applications, but those leveraging these techniques must acknowledge the biases and flawed outcomes that can be produced when using data-centered tools and models. To address this matter, I overview relevant bias and inequity circumstances in socioecological systems. Then, I present an equity-centered framework (i.e., the Systemic Equity Framework) and tool (i.e., the Wells-Du Bois Protocol) which can be used to mitigate inequity in model outcomes of a socioecological food system. Specifically, the Wells-Du Bois Protocol was applied to a ML-clustering application, using food expenditure data from the United States Consumer Expenditure Survey, to show how equity-centered practices can help achieve systemic equity. Our findings suggest that more than simply applying ML methodology is needed when sociodemographic factors (e.g., race, ethnicity, and socioeconomic status) are embedded. Furthermore, these findings exemplify why equity-centered frameworks and tools are so important to employ in socioecological settings whether bias is apparent or not. The main takeaway is that equity-centered frameworks and tools must be systematically integrated into routine data analytic practices regardless of how benevolent the dataset and model components seem.

Unsheltered homelessness is rapidly becoming a critical issue in many cities worldwide. The worsening situation not only highlights the socioeconomic plight, but it also raises awareness of ancillary issues such as the potential implications for urban water quality. The objective of this study was to simultaneously leverage diverse source tracking tools to develop a chemical and microbial fingerprint describing the relative contribution of direct human inputs into Las Vegas' tributary washes. By evaluating a wide range of urban water matrices using general water quality parameters, fecal indicator bacteria (FIB), human-associated microbial markers [e.g., HF183, crAssphage, and pepper mild mottle virus (PMMoV)], 16S rRNA gene sequencing data, and concentrations of 52 anthropogenic trace organic compounds (TOrCs), this study was able to differentiate principal sources of these constituents, including contributions from unsheltered homelessness. For example, HF183 (31% vs. 0%), crAssphage (61% vs. 5%), and PMMoV (72% vs. 55%) were more frequently detected in tributary washes with higher homeless census counts vs. ‘control’ tributary washes. Illicit drugs or their metabolites (e.g., heroin, acetylmorphine, amphetamine, and cocaine) and select TOrCs (e.g., acetaminophen, caffeine, ibuprofen, and naproxen) were also detected more frequently and at higher concentrations in the more anthropogenically-impacted washes. These data can be used to raise awareness of the shared interests between the broader community and those who are experiencing homelessness, notably the importance of protecting environmental health and water quality. Ultimately, this may lead to more rapid adoption of proven strategies for achieving functional zero homelessness, or at least additional resources for unsheltered individuals.

Background: Exposure to waterborne lead during the Flint Water Crisis during April 2014-October 2015 is believed to have caused increased special education enrollment in Flint children.

Methods: This retrospective population-based cohort study utilized de-identified data for children under six years of age who had their blood lead tested during 2011 to 2019, and special education outcomes data for children enrolled in public schools for corresponding academic years (2011-12 to 2019-20) in Flint, Detroit (control city) and the State of Michigan. Trends in the following crisis-related covariates were also evaluated: waterborne contaminants, poverty, nutrition, city governance, school district policies, negative community expectations, media coverage and social media interactions.

Results: Between 2011 and 2019, including the 2014-15 crisis period, the incidence of elevated blood lead in Flint children (≥ 5µg/dL) was always at least 47% lower than in the control city of Detroit (P<0.0001) and was also never significantly higher than that for all children tested in Michigan (P=0.33). Nonetheless, special education enrollment in Flint spiked relative to Detroit and Michigan (P<0.0001). There is actually an inverse relationship between childhood blood lead and special education enrollment in Flint.

Conclusion: This study failed to confirm any positive association between actual childhood blood lead levels and special education enrollment in Flint. Results are more consistent with a hypothesis that negative psychological effects associated with media predictions of brain damage, created a self-fulfilling prophecy via a nocebo effect. The findings demonstrate a need for improved media coverage of complex environmental events like the Flint Crisis.

The Mobile Lead Testing Unit (MLTU) coordinated by the Newark Water Coalition (NWC) and University of California, Berkeley student sought to measure and educate community members on the sources of lead exposure within the home. We conducted field analysis on lead in paint, water, soil, and dust. Throughout our project spanning design, outreach, education and training, methodological design, analysis, and evaluations; the mobile lead testing unit instilled, executed, and expanded participatory action research toward community-owned and managed research projects. We present our reflections on the nuanced, unforeseen, and complexities of community-driven science and attempts to forge a path towards democratizing knowledge and science while fighting for environmental justice.

Our findings reveal the extent and concentration of lead exposure sources in 315 homes throughout Newark. We also captured qualitative opinions and understanding of lead exposure sources, protective behaivors, and perceptions of lead poisoning. We will also present how we are incorporating primary data collected and stakeholder perspectives will be used to build a quantitative model and to support the NWC in their advocacy.