Multiple linear regression model for bromate formation based on the survey data of source waters from geographically different regions across China


A total of 86 source water samples from 38 cities across major watersheds of China were collected for a bromide (Br−) survey, and the bromate (BrO3−) formation potentials (BFPs) of 41 samples with Br− concentration >20 $μ$g L−1 were evaluated using a batch ozonation reactor. Statistical analyses indicated that higher alkalinity, hardness, and pH of water samples could lead to higher BFPs, with alkalinity as the most important factor. Based on the survey data, a multiple linear regression (MLR) model including three parameters (alkalinity, ozone dose, and total organic carbon (TOC)) was established with a relatively good prediction performance (model selection criterionþinspace=þinspace2.01, R2þinspace=þinspace0.724), using logarithmic transformation of the variables. Furthermore, a contour plot was used to interpret the influence of alkalinity and TOC on BrO3− formation with prediction accuracy as high as 71 %, suggesting that these two parameters, apart from ozone dosage, were the most important ones affecting the BFPs of source waters with Br− concentration >20 $μ$g L−1. The model could be a useful tool for the prediction of the BFPs of source water.

Environmental Science and Pollution Research
Jianwei Yu
Professor of Environmental Engineering
Ming Su
Ming Su
Associate Professor of Environmental Engneering

My research interest is water quality problems in drinking water bodies, with a focus on harmful algal blooms and associated taste & odor problems.

Min Yang
Min Yang
Professor of Environmental Engneering, Vice Director of RCEES, CAS