1 MOE Key Laboratory of Resourcces and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206, China.
2 MOE Key Laboratory of Resourcces and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206, China; Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, S4S 0A2, Canada. Electronic address: huang@iseis.org.
3 Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec, H3G 1M8, Canada.
4 Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, S4S 0A2, Canada.

Treatment of rural domestic wastewater using multi-soil-layering systems: Performance evaluation, factorial analysis and numerical modeling.

Sci Total Environ. 2018 Dec 10;644:536-546

Authors: Song P, Huang G, An C, Shen J, Zhang P, Chen X, Shen J, Yao Y, Zheng R, Sun C

Abstract

The discharge of wastewater in rural areas without effective treatment may result in contamination of surrounding surface water and groundwater resources. This study explored the wastewater treatment performance of multi-soil-layering (MSL) systems through interactive factorial analysis. MSL systems showed good performances under various operating conditions. The COD and BOD5 removal rates in MSL systems could reach 98.53 and 93.66%, respectively. The performances of MSL systems in TP removal stayed at high levels ranged from 97.97 to 100% throughout the experiments. The NH4+?-?N removal rates of the well performed MSL systems reached highest levels ranging from 89.96 to 100%. The TN removal rates of aerated MSL systems ranged from 51.11 to 64.44% after 72?days of operation. The independent effects of bottom submersion, microbial amendment and aeration, as well as most interactions were significant. The performance of MSL systems was mainly affected by bottom submersion and aeration as well as their interactions. Aeration was the most positive factor for the removal of organic matter, TP and NH4+?-?N. However, oxygenated environment was unfavorable for NO3-?-?N removal. In the submerged area with limited oxygen, the microbial transformation of NO3-?-?N still occurred. A stepwise-cluster inference model was developed for tackling the multivariate nonlinear relationships in contaminant removal processes. The results can help obtain a better understanding of the complicated processes among contaminant removal in MSL systems.

PMID: 29990903 [PubMed]