Workshop analysis finds small positive link between pesticide intensity and failing water systems around Lamont, Kern County

A webinar session in a training series on racial-equity data analysis examined why Lamont and nearby areas in Kern County have failing drinking-water systems and how pesticide use may relate to those failures.

The presenters, Hannah, partner at DataMaid, and Anna, workshop co-presenter and organizer, led participants through mapping and a tract-level correlation test using CalEnviroScreen pesticide-intensity percentiles and SAFER risk-assessment data for water systems. Hannah said, "racial equity is a practice, not a destination," as a framing for combining data with community input.

Why it matters: Lamont received SAFER funding to address its drinking-water system after tests found wells exceeding maximum contaminant levels. Participants noted that public-water reliance, the town's majority Hispanic population, and the presence of agricultural land use likely shaped exposure patterns discussed in the session.

What the analysis did: In R, presenters joined SAFER system records (point data) to Census tracts from CalEnviroScreen, aggregated system counts and failing-system counts to the tract level, and regressed the tract pesticide-intensity percentile against the proportion of systems in a tract that were failing. The model produced an R² of about 0.049 (roughly 5%), and a p-value effectively 0, indicating a low probability the observed association was due to chance. Presenters described the relationship as small but statistically detectable.

Caveats and context: Presenters and attendees repeatedly emphasized that correlation does not imply causation. Anna warned researchers to ask why multiple times in a root-cause analysis and to pair numerical findings with community knowledge. Participants raised several alternative or complementary explanations tested or suggested during the workshop: historical pesticide use and legacy contamination, the age and depth of wells, groundwater-level declines that require drilling deeper wells, oil and gas and other ground disturbance, and disparities in funding and regulatory attention in predominantly agricultural and majority‑Hispanic communities.

Workshop suggestions and next steps discussed: attendees and presenters discussed practical follow-ups that fall within regulatory and programmatic practice rather than new legislation. Nicholas, who identified himself as working with a groundwater-monitoring office, described pending probationary hearings with mitigation plans that include notifying residents within a radius of contaminated monitoring wells and offering mitigation such as point-of-use reverse-osmosis systems. Presenters suggested using the SAFER funding dataset to identify candidate systems (failing systems with no SAFER funding since 2017) located in high pesticide-intensity tracts, and then prioritizing outreach or technical support to those systems.

Decisions and formal actions: No formal vote or board decision was taken during the webinar. The session was instructional and exploratory; participants were given code and a GitHub link to reproduce and extend the analysis.

What remains unclear: Workshop discussion referenced a SAFER award to Lamont of about $25,000,000 to fix its drinking-water system in 2023 or 2024 (amount and year cited in the session; not independently confirmed in the transcript). The maximum contaminant level for 1,2,3‑trichloropropane (TCP) was described in the session as having been established in 2017. Several participants noted that pesticide‑use layers may undercount urban pesticide applications and recommended comparing pesticide-intensity maps with land‑use datasets such as the National Land Cover Database.

Ending: Presenters emphasized the analysis as a starting point for targeted engagement and further study, not as proof of causation. They recommended pairing mapped and statistical findings with community outreach, local monitoring data, and multivariable analyses (including race, income, well age and depth) before prioritizing interventions.