Research on the characteristics of the water quality of rainwater runoff from green roofs

2014 ◽  
Vol 70 (7) ◽  
pp. 1205-1210 ◽  
Author(s):  
Kena Gong ◽  
Qing Wu ◽  
Sen Peng ◽  
Xinhua Zhao ◽  
Xiaochen Wang
Keyword(s):  
Water Quality ◽  
Retention Rate ◽  
Oxygen Demand ◽  
Green Roofs ◽  
Water Runoff ◽  
Runoff Water ◽  
Retention Capacity ◽  
Storm Water Runoff ◽  
Quality Retention ◽  
Rainwater Runoff

This paper investigates the water quality characteristics of rainwater runoff from dual-substrate-layer green roofs in Tianjin, China. The data were collected from four different assemblies and three types of simulated rains. The storm-water runoff quality was monitored from early June through late October 2012 and from July through late November 2013. The results revealed that the runoff water quality would be improved to some extent with the ageing of green roofs and that the quality retention rate better reflected the pollutant retention capacity of the green roof than the pollutant concentration in the runoff water. The investigation clearly demonstrated that green roofs also effectively reduced the chemical oxygen demand and turbidity value and neutralised acid rain to stabilise the pH of the runoff.

Storm water pollution modelling: first-order atmospheric dustfall processes that affect runoff quality

1986 ◽  
Vol 13 (1) ◽  
pp. 95-105 ◽  
Author(s):  
Boregowda Shivalingaiah ◽  
William James
Keyword(s):  
Water Quality ◽  
Meteorological Conditions ◽  
Storm Water ◽  
Water Runoff ◽  
Linear Regression Models ◽  
Atmospheric Fallout ◽  
Runoff Water ◽  
Industrial Areas ◽  
Storm Water Management Model ◽  
Storm Water Runoff

The buildup of surface pollutants has been shown to be a controlling factor in the quality of storm water runoff. In industrial areas particularly, atmospheric fallout is an important component of surface pollutant loadings. Storm water runoff models presently in use do not consider the physics of atmospheric dustfall.Industries, vehicle exhausts, and blowing of wind over unprotected surfaces all introduce pollutants to the atmosphere. Redistribution of this material on the ground depends on local topography and prevailing meteorological conditions. The location of the industrial areas; the direction, velocity, and duration of wind; total precipitation; and source concentrations are important parameters in the prediction of atmospheric dustfall. The paper describes the physical processes of atmospheric fallout that are relevant to water quality modelling. A new model, called ATMDST, to predict dustfall on individual subcatchments in a metropolitan area using prevailing meteorological conditions is developed based on statistical methods. Results from average, one-variable and two-variable linear regression models were statistically compared with observed data. Finally, ATMDST is interfaced with the storm water management model version 3 (SWMM3) to compute runoff water quality. The model is applied to Hamilton, Ontario. Key words: atmospheric dustfall, air pollution, urban runoff, water quality, pollutant buildup, environmental modelling.

scholarly journals The influence of a green roof drainage layer on retention capacity and leakage quality

2018 ◽  
Vol 77 (12) ◽  
pp. 2886-2895 ◽  
Author(s):  
Anna Baryła ◽  
Agnieszka Karczmarczyk ◽  
Andrzej Brandyk ◽  
Agnieszka Bus
Keyword(s):  
Water Quality ◽  
Green Roof ◽  
Life Sciences ◽  
Meteorological Station ◽  
Runoff Water ◽  
Retention Capacity ◽  
Drainage Layer ◽  
Second Year ◽  
Substrate Moisture ◽  
Leachate Quality

Abstract The aim of the research was to determine the influence of the substrate and different drainage materials on retention capacity and runoff water quality from three green roof containers. Phosphates were chosen as the water quality indicator based on their potential adverse impact on water quality in urban rainwater collectors. The field experiment was conducted at the Warsaw University of Life Sciences Water Center meteorological station in years 2013–2015. In terms of precipitation, the monitoring period covered a wet (+147.1 mm), average (+42.7 mm) and dry (− 66.3 mm) year. Leakage from the containers was recorded when the substrate moisture exceeded 20% and precipitation exceeded 3.5 mm/d for washed gravel, or 5.0 mm/d for a polypropylene mat and expanded clay. Phosphates were observed in leachates from all containers, with higher values observed in the second year of monitoring. As the result of this study, it can be concluded that the polypropylene mat and aggregates create different conditions for the formation of the leachate, in both volumes and its chemistry. The drainage layer made from a polypropylene mat is the most effective in terms of rainwater retention capacity and the resulting leachate quality.

scholarly journals Novel advanced porous concrete in constructed wetlands: preparation, characterization and application in urban storm runoff treatment

2018 ◽  
Vol 78 (11) ◽  
pp. 2374-2382 ◽  
Author(s):  
Van Tai Tang ◽  
Kannan Pakshirajan
Keyword(s):  
Constructed Wetland ◽  
Oxygen Demand ◽  
Pollutant Removal ◽  
Storm Runoff ◽  
Storm Water ◽  
Water Runoff ◽  
Porous Concrete ◽  
Storm Water Runoff ◽  
Grass Growth ◽  
Urban Storm

Abstract Common porous concrete templates (CPCT) and advanced porous concrete templates (APCT) were employed in this study to construct wetlands for their applications in pollutant removal from storm runoff. The planting ability of the concrete was investigated by growing Festuca elata plants in them. Strength of the porous concrete (7.21 ± 0.19 Mpa) decreased by 1.8 and 4.9% over a period of six and 12 months, respectively, due to its immersion in lake water. The height and weight of Festuca elata grass growth on the porous concrete were observed to be 12.6–16.9 mm and 63.4–95.4 mg, respectively, after a duration of one month. Advanced porous concrete template based constructed wetland (APCT-CW) showed better removal of chemical oxygen demand (COD) (49.6%), total suspended solids (TSS) (58.9), NH3-N (52.4%), total nitrogen (TN) (47.7%) and total phosphorus (TP) (45.5%) in storm water, when compared with the common porous concrete template based constructed wetland (CPCT-CW) with 20.6, 29.8, 30.1, 35.4 and 26.9%, respectively. The removal of Pb, Ni, Zn by the CPCT-CW unit were 28.9, 33.3 and 42.3%, respectively, whereas these were 51.1, 62.5 and 53.8%, respectively, with the APCT-CW unit. These results demonstrate that the advanced porous concrete template in constructed wetland could be employed successfully for the removal of pollutants from urban storm water runoff.

scholarly journals Evaluating different machine learning methods to simulate runoff from extensive green roofs

2021 ◽  
Vol 25 (11) ◽  
pp. 5917-5935
Author(s):  
Elhadi Mohsen Hassan Abdalla ◽  
Vincent Pons ◽  
Virginia Stovin ◽  
Simon De-Ville ◽  
Elizabeth Fassman-Beck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Green Roof ◽  
Green Roofs ◽  
Storm Water ◽  
Water Runoff ◽  
Learning Methods ◽  
Retention Model ◽  
Machine Learning Methods ◽  
Storm Water Runoff ◽  
Extensive Green Roofs

Abstract. Green roofs are increasingly popular measures to permanently reduce or delay storm-water runoff. The main objective of the study was to examine the potential of using machine learning (ML) to simulate runoff from green roofs to estimate their hydrological performance. Four machine learning methods, artificial neural network (ANN), M5 model tree, long short-term memory (LSTM) and k nearest neighbour (kNN), were applied to simulate storm-water runoff from 16 extensive green roofs located in four Norwegian cities across different climatic zones. The potential of these ML methods for estimating green roof retention was assessed by comparing their simulations with a proven conceptual retention model. Furthermore, the transferability of ML models between the different green roofs in the study was tested to investigate the potential of using ML models as a tool for planning and design purposes. The ML models yielded low volumetric errors that were comparable with the conceptual retention models, which indicates good performance in estimating annual retention. The ML models yielded satisfactory modelling results (NSE >0.5) in most of the roofs, which indicates an ability to estimate green roof detention. The variations in ML models' performance between the cities was larger than between the different configurations, which was attributed to the different climatic characteristics between the four cities. Transferred ML models between cities with similar rainfall events characteristics (Bergen–Sandnes, Trondheim–Oslo) could yield satisfactory modelling performance (Nash–Sutcliffe efficiency NSE >0.5 and percentage bias |PBIAS| <25 %) in most cases. However, we recommend the use of the conceptual retention model over the transferred ML models, to estimate the retention of new green roofs, as it gives more accurate volume estimates. Follow-up studies are needed to explore the potential of ML models in estimating detention from higher temporal resolution datasets.

scholarly journals AN ANALYSIS ON THE EFFICIENCY OF GREEN ROOF IN MANAGING URBAN STORMWATER RUNOFF

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Shazmin Shareena Ab. Azis ◽  
Muhammad Najib Mohamed Razali ◽  
Nurul Hana Adi Maimun ◽  
Nurul Syakima Mohd Yusoff ◽  
Mohd Shahril Abdul Rahman ◽  
...  
Keyword(s):  
Flash Flood ◽  
Meta Analysis ◽  
Green Roof ◽  
Drainage System ◽  
Green Roofs ◽  
Storm Water ◽  
Water Runoff ◽  
Extensive Green Roof ◽  
Storm Water Runoff ◽  
Urban Storm

Modernization has created new impervious urban landscape contributed to major catastrophe. Urban drainage system incapable to convey the excess rainwater resulting in flash flood due to heavy rainfall. The combination of green roof on building have tremendously proved to control stormwater efficiently. This study is conducted to review the efficiency of intensive and extensive green roof in reducing urban storm water runoff. This study identifies characteristic of green roof that contributes to lessening urban storm water runoff. Data was collected based on rigorous literature reviews and analyzed using meta-analysis. Overall, findings revealed intensive green roof performed better in reducing storm water runoff compared to extensive green roof. Green roof performance increases as the depth of substrate increased. Origanum and Sedum plants are both highly effective for intensive and extensive green roofs. The performance of green roof reduces as degree of roof slope increased.

scholarly journals Experimental results of the hydrological performance of a permeable pavement laboratory rig

2020 ◽  
Vol 69 (3) ◽  
pp. 210-223 ◽  
Author(s):  
Vasiliki G. Ioannidou ◽  
Scott Arthur
Keyword(s):  
Water Quality ◽  
Green Infrastructure ◽  
Suspended Solids ◽  
Concrete Block ◽  
Experimental Results ◽  
Storm Water ◽  
Water Runoff ◽  
Retention Capacity ◽  
Quality Aspects ◽  
The Impact

Abstract There is an increasing number of everyday flood incidents around the world, the impact of which poses a challenge to society, the economy and the environment. Under the Water Framework Directive (2000/60/EC), green infrastructure through the use of sustainable drainage systems (SuDS) is the recommended policy to manage and treat storm water runoff. Given the limited published experimental information on permeable interlocking concrete block pavements (PICPs), this paper presents novel results from an experimental laboratory study on a permeable interlocking concrete block pavement rig, investigating the short-term hydrology of the pavement, and water quality aspects related to the retention capacity of suspended solids (SS) through the pavement structure. Results of the volume analysis demonstrate high capability of the permeable structure to reduce the concentration time and attenuate the storm. Water quality testing was employed mainly as an indicator of the tendency of the suspended solids retention by the structure, indicating increasing tendency in the sediment mass retention progressively after each rainfall event. Experimental results obtained in the present study have direct application on the implementation of PICPs in car parking lots, urbanised pavement structures and pedestrianised walkways.

Does upward seepage of river water and storm water runoff determine water quality of urban drainage systems in lowland areas? A case study for the Rhine-Meuse delta

2009 ◽  
Vol 23 (21) ◽  
pp. 3110-3120 ◽  
Author(s):  
Kim Vermonden ◽  
Marion A. A. Hermus ◽  
Marije van Weperen ◽  
Rob S. E. W. Leuven ◽  
Gerard van der Velde ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
Storm Water ◽  
Water Runoff ◽  
Urban Drainage ◽  
Drainage Systems ◽  
Storm Water Runoff ◽  
Urban Drainage Systems

scholarly journals Selection of monitoring locations for storm water quality assessment

2014 ◽  
Vol 69 (12) ◽  
pp. 2397-2406
Author(s):  
J. G. Langeveld ◽  
F. Boogaard ◽  
H. J. Liefting ◽  
R. P. S. Schilperoort ◽  
A. Hof ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Assessment ◽  
Water Quality Monitoring ◽  
Storm Water ◽  
Water Runoff ◽  
Monitoring Campaign ◽  
Water Characteristics ◽  
Storm Water Runoff ◽  
Receiving Waters ◽  
Selection Of

Storm water runoff is a major contributor to the pollution of receiving waters. Storm water characteristics may vary significantly between locations and events. Hence, for each given location, this necessitates a well-designed monitoring campaign prior to selection of an appropriate storm water management strategy. The challenge for the design of a monitoring campaign with a given budget is to balance detailed monitoring at a limited number of locations versus less detailed monitoring at a large number of locations. This paper proposes a methodology for the selection of monitoring locations for storm water quality monitoring, based on (pre-)screening, a quick scan monitoring campaign, and final selection of locations and design of the monitoring setup. The main advantage of the method is the ability to prevent the selection of monitoring locations that turn out to be inappropriate. In addition, in this study, the quick scan resulted in a first useful dataset on storm water quality and a strong indication of illicit connections at one of the monitoring locations.

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