UNESCO Chair on Aflaj Studies, Archaeohydrology, University of Nizwa, Nizwa, Oman.
Department of Geography, Chandidas Mahavidyalaya, Birbhum, WB, 731215, India.
Environ Sci Pollut Res Int. 2022 Mar;29(14):20421-20436. doi: 10.1007/s11356-021-17224-9. Epub 2021 Nov 4.
Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian approaches such as Bayesian generalized linear model (BGLM), Bayesian regularized neural network (BRNN), Bayesian additive regression tree (BART), and Bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling. The results showed that the Bayesian models used in this study were all competent to model groundwater nitrate and the BART model with R = 0.83 was more efficient than the other models. The result of variable importance showed that potassium (K) has the highest importance in the models followed by rainfall, altitude, groundwater depth, and distance from the residential area. The results of the study can support the decision-making process to control and reduce the sources of nitrate pollution.
硝酸盐是地下水的主要污染物，其主要来源是城市废水和农业活动。在本研究中，采用了贝叶斯方法，如贝叶斯广义线性模型（BGLM）、贝叶斯正则化神经网络（BRNN）、贝叶斯加性回归树（BART）和贝叶斯岭回归（BRR），对伊朗法尔斯省马尔达什特流域半干旱地区的地下水硝酸盐污染进行建模。选择了 11 个地下水（GW）硝酸盐条件因素作为预测模型的输入参数。结果表明，本研究中使用的贝叶斯模型都能够很好地对地下水硝酸盐进行建模，其中 R=0.83 的 BART 模型比其他模型更有效。变量重要性的结果表明，在模型中钾（K）的重要性最高，其次是降雨量、海拔、地下水深度和与居民区的距离。研究结果可以支持决策过程，以控制和减少硝酸盐污染的来源。
