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

Redondo-Hasselerharm, Paula E.; Cserbik, Dora; Flores, Cintia; Farré, Maria José; Sanchís, Josep; Alcolea, Jose A.; Planas, Carles; Caixach, Josep; Villanueva Belmonte, Cristina

doi:http://dx.doi.org/10.1038/s41370-022-00453-6

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

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Redondo-Hasselerharm PE, Cserbik D, Flores C, Farré MJ, Sanchís J, Alcolea JA, et al. Insights to estimate exposure to regulated and non-regulated disinfection by-products in drinking water. J Expo Sci Environ Epidemiol. 2022 Jun 29: 1–11. DOI: 10.1038/s41370-022-00453-6

Abstract

Background: knowledge about human exposure and health effects associated with non-routinely monitored disinfection by-products (DBPs) in drinking water is sparse. Objective: to provide insights to estimate exposure to regulated and non-regulated DBPs in drinking water. Methods: 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. Results: 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 (R 2 = 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). Significance: 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. Impact statement: 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.