Quantifying the benefits of stormwater harvesting for pollution mitigation

WATER RESEARCH

卷:171

Article

Zhang, Kefeng¹  Bach, Peter M.^2,3,4  Mathios, John^4,5  Deletic, Ana¹ 

[1] UNSW, Sch Civil & Environm Engn, UNSW Water Res Ctr, Sydney, NSW 2052, Australia

[2] Swiss Fed Inst Aquat Sci & Technol Eawag, Uberlandstr 133, Dubendorf, Switzerland

[3] Swiss Fed Inst Technol, Inst Environm Engn, CH-8093 Zurich, Switzerland

[4] Monash Univ, Dept Civil Engn, Monash Infrastruct Res Inst, Clayton, Vic 3800, Australia

[5] DPM Consulting PTY LTD, 22 Business Pk Dr, Notting Hill, Vic 3168, Australia

关键词: Pollution loads;    Catchment imperviousness;    Water sensitive urban design (WSUD);    Low impact development;    Sponge city;   

DOI  :  10.1016/j.watres.2019.115395

来源: Elsevier

【 摘 要 】

Stormwater harvesting (SWH) provides multiple benefits to urban water management. Other than providing water for human use, it also reduces the volume of polluted stormwater discharge to the environment. There are currently no methods available to quantify the additional environmental benefits, which could encourage greater uptake of the practice. This paper investigates a number of factors (climate and catchment characteristics, pollutant reduction targets, etc.) that could impact upon the benefits of SWH for pollution reduction through sensitivity analyses. A method was developed and tested for quantification of the pollution mitigation benefits by SWH under different scenarios. A novel indicator, Impervious Area Offset (IAO), was proposed to reflect the additional impervious area that can be left untreated to achieve the equivalent pollution load reduction targets due to the introduction of SWH. Results indicate significant correlations (p < 0.01) between IAO values and extraction rate (proportion of total annual runoff removed due to the harvesting system and water use substitution), system type, and pollutant reduction targets. The proposed linear empirical relationships between IAO values and extraction rate for different types of system configurations and pollution reduction targets were well represented by observed linear regression (average R-2 = 0.98 for all tested scenarios). Empirical relationships were validated successfully against different scenarios, with differences between predicted IAO and baseline IAO values being only +/- 4.5% for the majority of the validation scenarios. Using this simple and reliable method to rapidly quantify SWH benefits can further add to the growing business case of adopting SWH practices. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free