Factors affecting the ability of extensive green roofs to reduce nutrient pollutants in rainfall runoff

2020 ◽  
Vol 732 ◽  
pp. 139248 ◽  
Author(s):  
Yongwei Gong ◽  
Xianwei Zhang ◽  
Junqi Li ◽  
Xing Fang ◽  
Dingkun Yin ◽  
...  
Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1217 ◽  
Author(s):  
Yongwei Gong ◽  
Dingkun Yin ◽  
Xing Fang ◽  
Junqi Li

The runoff retention effectiveness of 10 extensive green roof (EGR) modules (100 mm substrate planted Sedum lineare Thunb.) were analyzed in Beijing for 22 rainfall events (2.4–46.4 mm) from 1 July to 30 September 2017. Differences between minimum inter-event dry periods, module scales, substrate hydraulic conductivity and depths, drainage layer types and rainfall characteristics were examined to study their correlation to the retention performance of EGRs. In general, EGRs with lower substrate hydraulic conductivity, deeper substrate and lower rainfall depth had higher runoff retention performance. By comparsion, no siginificant correlation was found between rainfall duration, prior dry period, average rainfall intensity, drainage layer type and EGR runoff retention rate. Analyses of variance (ANOVA) and Tukey tests supported these results. Low or moderate rainfall (<15 mm) may or may not have an effect, but heavy rainfall (>25 mm) definitely affects the EGR retention performance of the next rainfall event.


2021 ◽  
Author(s):  
Chen Xu ◽  
Zaohong Liu ◽  
Guanjun Cai ◽  
Jian Zhan

Abstract Due to substrate layers with different substrate configurations, extensive green roofs (EGRs) exhibit different rainfall runoff retention and pollution interception effects. In the rainfall runoff scouring process, nutrient leaching often occurs in the substrate layer, which becomes a pollution source for rainwater runoff. In this study, six EGR devices with different substrate layer configurations were fabricated. Then, the cumulative leaching quantity (CLQ) and total leaching rate (TLR) of NH4+, TN and TP in the outflow of nine different depth simulated rainfall events under local rainfall characteristics were evaluated and recorded. Furthermore, the impact of different substrate configurations on the pollution interception effects of EGRs for rainfall runoff was studied. Results show that a mixed adsorption substrate in the EGR substrate layer has a more significant rainfall runoff pollution interception capacity than a single adsorption substrate. PVL and PVGL, as EGRs with layered configuration substrate layers, exhibited good NH4+-N interception capacity. The CLQ and TLR of NH4+-N for PVL and PVGL were -114.613 mg and -63.43%, -121.364 mg and -67.16%, respectively. Further, the addition of biochar as a modifier significantly slowed down the substrate layer TP leaching effect and improved the interception effect of NH4+-N and TN. Moreover, although polyacrylamide addition in the substrate layer aggravated the nitrogen leaching phenomenon in the EGRs outflow, but the granular structure substrate layer constructed by it exhibited a significantly inhibited TP leaching effect.


2013 ◽  
Vol 130 ◽  
pp. 297-305 ◽  
Author(s):  
J. Scott MacIvor ◽  
Liat Margolis ◽  
Curtis L. Puncher ◽  
Benjamin J. Carver Matthews

2015 ◽  
pp. 959-966 ◽  
Author(s):  
G. Varras ◽  
K.-TH. Vozikis ◽  
C. Myriounis ◽  
I.L. Tsirogiannis ◽  
E. Kitta

2013 ◽  
Vol 69 (4) ◽  
pp. 727-738 ◽  
Author(s):  
Yanling Li ◽  
Roger W. Babcock

Green roofs reduce runoff from impervious surfaces in urban development. This paper reviews the technical literature on green roof hydrology. Laboratory experiments and field measurements have shown that green roofs can reduce stormwater runoff volume by 30 to 86%, reduce peak flow rate by 22 to 93% and delay the peak flow by 0 to 30 min and thereby decrease pollution, flooding and erosion during precipitation events. However, the effectiveness can vary substantially due to design characteristics making performance predictions difficult. Evaluation of the most recently published study findings indicates that the major factors affecting green roof hydrology are precipitation volume, precipitation dynamics, antecedent conditions, growth medium, plant species, and roof slope. This paper also evaluates the computer models commonly used to simulate hydrologic processes for green roofs, including stormwater management model, soil water atmosphere and plant, SWMS-2D, HYDRUS, and other models that are shown to be effective for predicting precipitation response and economic benefits. The review findings indicate that green roofs are effective for reduction of runoff volume and peak flow, and delay of peak flow, however, no tool or model is available to predict expected performance for any given anticipated system based on design parameters that directly affect green roof hydrology.


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