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A process-based model for pentachlorophenol dissipation in a flooded paddy soil

Ying, Shanshan, Li, Jia, Lin, Jiajiang, He, Yan, Wu, Laosheng, Zeng, Lingzao
Environmental pollution 2018 v.243 pp. 1422-1433
Bayesian theory, chlorides, dechlorination, environmental fate, models, paddy soils, pentachlorophenol, prediction, quantitative analysis, soil profiles, sorption, space and time, uncertainty
Process-based models have been widely used for predicting environmental fate of contaminants. Nevertheless, accurate modeling of pentachlorophenol (PCP) dissipation in soils at the millimeter-scale remains a challenge due to the scarcity of observation data and uncertainty associated with model assumptions and estimation of the model parameters. To provide quantitative analysis of PCP-dissipation at the anaerobic/aerobic interface of a rhizobox experiment, this study implemented Bayesian parameter estimation for a process-based reactive chemical transport model. The model considered the main transport and transformation processes of chemicals including diffusion, sorption and degradation. The contributions of the processes to PCP dissipation were apportioned both in space and time. Using the maximum-a-posteriori (MAP) estimation of parameters, our model fitted the experimental data better compared with the previous work. Our results indicated that the most reactive zone for PCP dissipation occurred in the layer of 0–2.4 mm where degradation in solid phase dominated the PCP dissipation, while upward diffusion was the main mechanism for the reduction of PCP concentration in deeper layer (2.4–4.8 mm). By considering the coupled reactive transport of PCP and Cl−, the average degrees of PCP dechlorination in each layer were estimated from corresponding total concentrations of PCP and Cl−. The degrees of PCP dechlorination in the ponding water and the top layer of soil profile were highest, while 2,3,4,5- TeCP and 3,4,5- TCP were identified as the main dechlorination products in the soil. This study demonstrated that combining Bayesian estimation with process-based reactive chemical transport model can provide more insights of PCP dissipation at the millimeter-scale. This approach can help to understand complex dissipation mechanisms for other contaminants.