A meta-analysis of the impacts of best management practices on nonpoint source pollutant concentration

被引:1
作者
Schramm, Michael [1 ]
Kikoyo, Duncan [1 ]
Wright, Janelle [1 ]
Jain, Shubham [1 ]
机构
[1] Texas A&M AgriLife Res, Texas Water Resources Inst, College Stn, TX 77843 USA
来源
FRONTIERS IN WATER | 2024年 / 6卷
关键词
best management practice; water quality; nonpoint source pollution; fecal indicator bacteria; nutrients; suspended sediment; WATER-QUALITY; ESCHERICHIA-COLI; STORMWATER; NUTRIENT; PERFORMANCE; RETENTION; SUCCESSION; REMOVAL; ECOLOGY; MODELS;
D O I
10.3389/frwa.2024.1397615
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Introduction: Best management practices (BMPs) are important tools for mitigating the impact of non-point source pollutants on water quality. Drivers of the high variance observed in BMP performance field tests are not well documented and present challenges for planning BMP construction and forecasting water quality improvements. Methods: We conducted a systematic review of published nonpoint source water quality BMP studies conducted in the United States and used a meta-analysis approach to describe variance in pollutant removal performance. We used meta-regression to explore how much BMP pollutant removal process, influent pollutant concentration, and aridity effected BMP performance. Results: Despite high variance, we found the BMPs on average were effective at reducing fecal indicator bacteria (FIB), total nitrogen (TN), total phosphorus (TP), and total suspended sediment (TSS) concentrations. We found that influent concentration and interaction effect between the BMP pollutant removal process and aridity explained a substantial amount of variance in BMP performance in FIB removal. Influent concentration explained a small amount of variability in BMP removal of TP and orthophosphate (PO4). We did not find evidence that any of our chosen variables moderated BMP performance in nitrogen or TSS removal. Through our systematic review, we found inadequate spatial representation of BMP studies to capture the underlying variability in climate, soil, and other conditions that could impact BMP performance.
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页数:15
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