Insights to estimate exposure to regulated and non-regulated disinfection by-products in drinking water

Knowledge about human exposure and health effects associated with non-routinely monitored disinfection by-products (DBPs) in drinking water is sparse.

To provide insights to estimate exposure to regulated and non-regulated DBPs in drinking water.

We collected tap water from homes (N = 42), bottled water (N = 10), filtered tap water with domestic activated carbon jars (N = 6) and reverse osmosis (N = 5), and urine (N = 39) samples of participants from Barcelona, Spain. We analyzed 11 haloacetic acids (HAAs), 4 trihalomethanes (THMs), 4 haloacetonitriles (HANs), 2 haloketones, chlorate, chlorite, and trichloronitromethane in water and HAAs in urine samples. Personal information on water intake and socio-demographics was ascertained in the study population (N = 39) through questionnaires. Statistical models were developed based on THMs as explanatory variables using multivariate linear regression and machine learning techniques to predict non-regulated DBPs.

Chlorate, THMs, HAAs, and HANs were quantified in 98-100% tap water samples with median concentration of 214, 42, 18, and 3.2 μg/L, respectively. Multivariate linear regression models had similar or higher goodness of fit (R2) compared to machine learning models. Multivariate linear models for dichloro-, trichloro-, and bromodichloroacetic acid, dichloroacetonitrile, bromochloroacetonitrile, dibromoacetonitrile, trichloropropnanone, and chlorite showed good predictive ability (R² = 0.8-0.9) as 80-90% of total variance could be explained by THM concentrations. Activated carbon filters reduced DBP concentrations to a variable extent (27-80%), and reverse osmosis reduced DBP concentrations ≥98%. Only chlorate was detected in bottled water samples (N = 3), with median = 13.0 µg/L. Creatinine-adjusted trichloroacetic acid was the most frequently detected HAA in urine samples (69.2%), and moderately correlated with estimated drinking water intake (r = 0.48).

Findings provide valuable insights for DBP exposure assessment in epidemiological studies. Validation of predictive models in a larger number of samples and replication in different settings is warranted.

Our study focused on assessing and describing the occurrence of several classes of DBPs in drinking water and developing exposure models of good predictive ability for non-regulated DBPs.