Download or read book NDMA Formation During Drinking Water Treatment: Veterinary Antibiotics as Precursors, the Effect of Natural Organic Matter and the Significance of Treatment Practices written by Shannon Roback and published by . This book was released on 2015 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nitrosodimethylamine (NDMA) is a nitrosated amine that has been associated with a 10-5 increase in lifetime cancer risk at the ng/L level. NDMA may be formed from a variety of anthropogenic amine precursors during drinking water treatment utilizing chloramines as a disinfectant. In this dissertation, ten veterinary antibiotics were tested for their ability to form NDMA. The antibiotics were tested at different pH, temperature, chlorine to ammonia weight ratio (Cl2/NH3) and time to determine the impact of these factors on formation. Molar conversions ranged from 0.04 to 4.9 percent, with antibiotics containing more than one dimethylamine (DMA) functional group forming significantly more NDMA. The highest formation for most of the compounds was seen near pH 8.4. The effect of Cl2/NH3 ratio, temperature, and hold time was somewhat varied for each chemical, suggesting that the effects of these parameters were compound-specific. This suggests that large-scale farming run-off may be a new source NDMA precursors. NDMA formation is slowed by the presence of natural organic matter (NOM). It is not currently known which components of NOM are responsible for the reduction in NDMA formation. In this dissertation, water containing NOM was fractionated into different MW size groups or separated based on polarity. The high molecular weight NOM fractions (> 10 kDa), polar and charged components were shown to be the most effective in reducing the amount of NDMA formed. Some precursors have high sorption coefficients to NOM, which is the likely mechanism for reduction of NDMA formation from these compounds. Lastly, NDMA formation can be highly impacted by numerous factors relevant to drinking water treatment. In this dissertation, water samples and treatment plant data were collected from approximately 20 drinking water treatment plants in the U.S. and Canada over 2 years. Linear mixed effects models with random intercepts, which account for variability between treatment plants, were used to assess variable significance and create predictive equations. UV254 concentration in the plant influent, sucralose concentration, polyDADMAC concentration, pre-chlorination time, Cl2/NH3 ratio, use of GAC, water pH, and biofiltration were associated with NDMA concentration in the distribution system.