Effluent temporal collective behaviors of a wastewater treatment plant community

被引:2
|
作者
Lu, Meng [1 ]
Li, Zhi-Hua [1 ,2 ]
Jiang, Yang [1 ]
机构
[1] Xian Univ Architecture & Technol, Key Lab Northwest Water Resource Environm & Ecol, MOE, Xian 710055, Peoples R China
[2] Xian Univ Architecture & Technol, Shaanxi Key Lab Environm Engn, Xian 710055, Peoples R China
基金
中国国家自然科学基金;
关键词
Activated sludge; Effluent concentration; Online data; Quasi-biweekly; Temperature; ACTIVATED-SLUDGE; SEASONAL-VARIATIONS; REMOVAL; PERFORMANCE; EFFICIENCY; OPERATION; DYNAMICS; QUALITY; ILLICIT; LOADS;
D O I
10.1016/j.scitotenv.2021.147694
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The effluent temporal characteristics of a plant community, which is composed of wastewater treatment plants (WWTPs) treating similar influent under similar conditions, contribute to understanding the performance of WWTPs. However, studies on this topic have rarely been reported. In this study, effluent data from 92 municipal plants in the same province of China collected during 2014-2019 were analyzed as time series to elucidate col-lective behaviors and to identify specific plants with abnormal effluent. The most significant periodic cycles for ammonia and chemical oxygen demand (COD) were 183 days and 91 days, respectively, suggesting that the tem-poral variation of effluent ammonia was mainly dependent on weather conditions such as temperature and the effluent COD was primarily dependent on loading rates. Interestingly, both ammonia and COD exhibited quasi-biweekly variation with a period of approximately 13 days, which is a weather-dependent collective feature, showing a synchronous variation of a plant community. Therefore, a difference in the quasi-biweekly variation pattern of a specific plant from a plant community is proposed to identify the outliers and further help to tune these plants. This study's findings suggest that the temporal collective behaviors of a plant community should also be considered for the evaluation of WWTPs, especially when online data are readily available. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:9
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