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Motovalibashi Naeini A, Tabesh M, Soltaninia S. Modeling the effect of land use change to design a suitable low impact development (LID) system to control surface water pollutants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 932:172756. [PMID: 38670368 DOI: 10.1016/j.scitotenv.2024.172756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/01/2024] [Accepted: 04/23/2024] [Indexed: 04/28/2024]
Abstract
Growth in urbanization has led to increased impervious surfaces, exacerbating flood risks and water quality degradation. This study investigated the impact of land use change and Low-Impact Development (LID) systems on urban runoff quality and quantity in the second region of Tehran. Pioneering an innovative approach, the integration of the Land Change Modeler (LCM) with the Stormwater Management Model (SWMM) signifies a paradigm shift in urban water management. Combined with other hydrological models, this new approach provides a comprehensive method for assessing the future effectiveness of LID practices. The Event Mean Concentration Method (EMC) was used in this study to measure Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Phosphorus (TP), and Zinc (Zn) in urban runoff from five land uses. Results pinpointed transportation land uses as the primary source of pollutants. Using LCM, the study forecasted a surge in urban runoff pollutants by 2030, particularly in the Northwest area of the region due to anticipated land use shifts towards commercial and residential land uses. Model results showed an 11 % increase in TSS over a decade, highlighting the importance of land use change in runoff quality. The study used three types of LIDs to reduce contaminants in dense urban areas. Assessing the impact of LID scenarios on runoff pollutants using SWMM revealed that the bio-retention cell had the best performance, reducing TSS by 20.92 %, and the vegetative swale had the worst performance, reducing TSS by 8.43 %. The study also concluded that combining LIDs would be more effective than using them separately. The results of this study suggest that LID systems can be an effective way to reduce urban runoff pollutants and improve water quality in the second region of Tehran. However, more research is needed to optimize the design and placement of LID systems in different urban areas.
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Affiliation(s)
| | - Massoud Tabesh
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Shahrokh Soltaninia
- Department of Civil Engineering, Islamic Azad University, Khomeinishahr Branch, Isfahan, Iran
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Xiong Q, Song Y, Shen J, Liu C, Chai Y, Wang S, Wu X, Cheng C, Wu J. Fluorescence fingerprint as an indicator to identify urban non-point sources in urban river during rainfall period. ENVIRONMENTAL RESEARCH 2024; 245:118009. [PMID: 38141914 DOI: 10.1016/j.envres.2023.118009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 12/25/2023]
Abstract
Nowadays, the urban non-point source (NPS) pollution gradually evolved as the main contributor to urban water contamination since the point source pollution was effectively controlled. It was imperative to perform urban NPS identification in urban river to meet the requirements of precise source governance. In this study, the real-time detection about water quality parameters and fluorescence fingerprints (FFs) was performed for BX River and its outlets during rainfall period. EEM-PARAFAC and component similarity analyses discovered that the pollution encountered by BX River mainly came from road runoff and untreated municipal wastewater (UMWW) overflow. The C1 (tryptophan-like) and C3 (terrestrial humic-like) components located at Ex/Em = ∼230(280)/340 and ∼275/430 nm were both detected in these two kinds of urban NPS. The C2 components of road runoff and UMWW overflow displayed remarkable differences, which located at Ex/Em = 250/385 and 245/365 nm, respectively, thus could be served as indicators for distinguishing them. During rainfall period, the outflow from rainwater outlets (RWOs) constantly showed similar FF features to road runoff, while the FFs of outflow from combined sewer outlets (CSOs) alternated between those of road runoff and UMWW overflow. The FF features of sections in BX River changed in response to the dynamic variations in FFs of the outlets, which revealed real-time pollution causes of BX River. This work not only realized the identification and differentiation of urban NPS, but also elucidated the dynamic variations of pollution characteristics throughout the entire process of "urban NPS-outlets-urban river", and demonstrated the feasibility of FF technique in quickly diagnosing the pollution causes of urban river during rainfall period, which provided important guidance for urban NPS governance.
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Affiliation(s)
- Qiuran Xiong
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yiming Song
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jian Shen
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Chuanyang Liu
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yidi Chai
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Siting Wang
- Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China
| | - Xiaojin Wu
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Cheng Cheng
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
| | - Jing Wu
- Research Center of Environmental Technology in Water Pollution Source Identification and Precise Supervision, School of Environment, Tsinghua University, Beijing, 100084, China; Research and Development Center of Advanced Environmental Supervision Technology and Instrument, Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou, 215163, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
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