4.5 Article

Storm runoff quality and pollutant loading from commercial, residential, and industrial catchments in the tropic

Journal

ENVIRONMENTAL MONITORING AND ASSESSMENT
Volume 185, Issue 10, Pages 8321-8331

Publisher

SPRINGER
DOI: 10.1007/s10661-013-3175-6

Keywords

Event mean concentration; Multiple linear regression; Stormwater; Tropical environment; Urban runoff

Funding

  1. Japanese Society for the Promotion of Science (JSPS)
  2. Ministry of Higher Education (MOHE) in Malaysia

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Information on the pollution level and the influence of hydrologic regime on the stormwater pollutant loading in tropical urban areas are still scarce. More local data are still required because rainfall and runoff generation processes in tropical environment are very different from the temperate regions. This study investigated the extent of urban runoff pollution in residential, commercial, and industrial catchments in the south of Peninsular Malaysia. Stormwater samples and flow rate data were collected from 51 storm events. Samples were analyzed for total suspended solids, 5-day biochemical oxygen demand, chemical oxygen demand, oil and grease (O&G), nitrate nitrogen (NO3-N), nitrite nitrogen, ammonia nitrogen, soluble reactive phosphorus, total phosphorus (TP), and zinc (Zn). It was found that the event mean concentrations (EMCs) of pollutants varied greatly between storm characteristics and land uses. The results revealed that site EMCs for residential catchment were lower than the published data but higher for the commercial and industrial catchments. All rainfall variables were negatively correlated with EMCs of most pollutants except for antecedent dry days (ADD). This study reinforced the earlier findings on the importance of ADD for causing greater EMC values with exceptions for O&G, NO3-N, TP, and Zn. In contrast, the pollutant loadings are influenced primarily by rainfall depth, mean intensity, and max 5-min intensity in all the three catchments. Overall, ADD is an important variable in multiple linear regression models for predicting the EMC values in the tropical urban catchments.

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