Regression models for the prediction of water quality in the stormwater of urban arid catchments

被引:2
|
作者
Nouh, M. [2 ]
Al-Noman, N. [1 ]
机构
[1] King Saud Univ, Riyadh, Saudi Arabia
[2] Univ Sharjah, Sharjah, U Arab Emirates
关键词
stormwater runoff; duststorm; water quality; arid catchments; hydrologic modeling; SAUDI-ARABIA; DUST; RUNOFF; RAINFALL; METALS; NORTH; FLOW; USA;
D O I
10.1139/S08-048
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Data from five residential urban arid catchments were used to develop regression equations for predicting mean concentrations of selected heavy metals in the stormwater runoff from duststorm and stormwater flow properties. The selected metals are copper (Cu), lead (Pb), nickel (Ni), zinc (Zn), and iron (Fe). The concentrations of the selected metals were predicted through two groups of equations. The first group of equations relates concentrations of suspended sediment with duststorm and stormwater parameters, whereas the second group relates the concentrations of the suspended sediment with those of heavy metals in the stormwater runoff. The results of the predictions encouraged recommendations on the use of the equations in the investigated catchments and identified the relative importance of the stormwater runoff and duststorms on the accumulation and transportation of heavy metals in the stormwater runoff. Based on the obtained results, recommendations concerning water quality control in arid areas are made.
引用
收藏
页码:331 / 344
页数:14
相关论文
共 50 条
  • [41] Water age in stormwater management ponds and stormwater management pond-treated catchments
    Morales, Kayla
    Oswald, Claire
    [J]. HYDROLOGICAL PROCESSES, 2020, 34 (08) : 1854 - 1867
  • [42] Stream water quality prediction using boosted regression tree and random forest models
    Alnahit, Ali O.
    Mishra, Ashok K.
    Khan, Abdul A.
    [J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2022, 36 (09) : 2661 - 2680
  • [43] Stream water quality prediction using boosted regression tree and random forest models
    Ali O. Alnahit
    Ashok K. Mishra
    Abdul A. Khan
    [J]. Stochastic Environmental Research and Risk Assessment, 2022, 36 : 2661 - 2680
  • [44] PREDICTION OF WATER QUALITY INDEX OF AN INDIAN RIVER USING ARITHMETIC INDEX AND REGRESSION MODELS
    Haridas, Divya Airattil
    Antony, Soloman Pooppana
    [J]. ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL, 2019, 18 (09): : 2035 - 2044
  • [45] Urban Air Quality Prediction Using Regression Analysis
    Mahanta, Soubhik
    Ramakrishnudu, T.
    Jha, Rajat Raj
    Tailor, Niraj
    [J]. PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY, 2019, : 1118 - 1123
  • [46] Simple Hydrograph Shapes for Urban Stormwater Water Quality Continuous Analyses
    Pitt, Robert
    Voorhees, John
    Burgess, Caroline
    [J]. JOURNAL OF WATER MANAGEMENT MODELING, 2012, : 279 - 302
  • [47] Stormwater Control Measures for Runoff and Water Quality Management in Urban Landscapes
    Sadeghi, K. Majid
    Loaiciga, Hugo A.
    Kharaghani, Shahram
    [J]. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 2018, 54 (01): : 124 - 133
  • [48] Relationship between selected pollution indicators of stormwater from urban catchments
    Gorska, Katarzyna
    Gorski, Jaroslaw
    Bak, Lukasz
    Salata, Aleksandra
    Muszynska, Joanna
    Gawdzik, Jaroslaw
    [J]. DESALINATION AND WATER TREATMENT, 2020, 199 : 473 - 485
  • [49] A hybrid regression model for water quality prediction
    Chakraborty, Tanujit
    Chakraborty, Ashis Kumar
    Mansoor, Zubia
    [J]. OPSEARCH, 2019, 56 (04) : 1167 - 1178
  • [50] A hybrid regression model for water quality prediction
    Tanujit Chakraborty
    Ashis Kumar Chakraborty
    Zubia Mansoor
    [J]. OPSEARCH, 2019, 56 : 1167 - 1178