Environmental justice implications of arsenic contamination in California's San Joaquin Valley: a cross-sectional, cluster-design examining exposure and compliance in community drinking water systems

Abstract

Background: Few studies of environmental justice examine inequities in drinking water contamination. Those studies that have done so usually analyze either disparities in exposure/harm or inequitable implementation of environmental policies. The US EPA's 2001 Revised Arsenic Rule, which tightened the drinking water standard for arsenic from 50 μg/L to 10 μg/L, offers an opportunity to analyze both aspects of environmental justice.

Methods: We hypothesized that Community Water Systems (CWSs) serving a higher proportion of minority residents or residents of lower socioeconomic status (SES) have higher drinking water arsenic levels and higher odds of non-compliance with the revised standard. Using water quality sampling data for arsenic and maximum contaminant level (MCL) violation data for 464 CWSs actively operating from 2005-2007 in California's San Joaquin Valley we ran bivariate tests and linear regression models.

Results: Higher home ownership rate was associated with lower arsenic levels (ß-coefficient= -0.27 μg As/L, 95% (CI), -0.5, -0.05). This relationship was stronger in smaller systems (ß-coefficient = -0.43, CI, -0.84, -0.03). CWSs with higher rates of homeownership had lower odds of receiving an MCL violation (OR, 0.33; 95% CI, 0.16, 0.67); those serving higher percentages of minorities had higher odds (OR, 2.6; 95% CI, 1.2, 5.4) of an MCL violation.

Conclusions: We found that higher arsenic levels and higher odds of receiving an MCL violation were most common in CWSs serving predominantly socio-economically disadvantaged communities. Our findings suggest that communities with greater proportions of low SES residents not only face disproportionate arsenic exposures, but unequal MCL compliance challenges.

Figure 1

Average arsenic concentration^a of community water systems (CWS ^b,c) in study sample, (n = 464), 2005–2007.^a Estimate based on average of each point-of-entry source’s average concentration; ^b Sources of data: CDPH Water Quality Monitoring and PICME databases (CDPH 2008a,b); ^c Approximate location of CWSs are depicted, but not true boundaries. Due to close proximity of some CWSs, map partially covers some CWSs.