Development of Multiple Linear Regression Models for Predicting the Stormwater Quality of Urban Sub-Watersheds

Arora, Amarpreet Singh; Reddy, A. S.

doi:10.1007/s00128-013-1160-y

Cited by 22 publications

(6 citation statements)

References 13 publications

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“…Both natural and human activities cause air pollution in rural areas (Dey et al 2004 ). Natural sources of air pollution occur when pollutants are generated from various natural sources (i.e., animals, plants, land resources, forest fires, coal fires, dust storms, volcanic eruptions, and sand storms) and are dispersed in the atmosphere naturally (Arora and Reddy 2013 , 2014 , 2015 ; Li et al 2010 ). Solid fuel combustion (cooking and heating) as well as agriculture activities, mining (opencast and underground) activities, industrial activities (cement production and coal processing), all contribute significant air pollutants into the ambient atmosphere (Xia et al 2005 ).…”

Section: Resultsmentioning

confidence: 99%

Impact Assessment of Aerosol Optical Depth on Rainfall in Indian Rural Areas

et al. 2022

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Aerosol significantly influences the life cycle of clouds and their formation. Many studies reported worldwide on anthropogenic aerosols and their impact on clouds and their optical properties. Atmospheric remote sensing provides the best way to estimate indirectly air quality surveillance and management in megacities of developing countries like India where many cities have elevated concentration profiles of air pollutants with inadequate coverage of spatial and temporal monitoring. The results of the study highlighted the impact on rainfall patterns due to aerosol optical depth (AOD) and fine particulate matter (PM2.5) for a total of 7 years (2015–2021) over five different Indian rural sites by using MODerate Resolution Imaging Spectroradiometer (MODIS). The AOD (550 nm) and PM2.5 were retrieved from the MODIS sensor Terra satellites and the MEERA 2 model, respectively. Also, we have analyzed in this study the relationship of AOD (550 nm) with PM2.5 and meteorological variables (temperature relative humidity and precipitation) over Indian rural sites during 2015–2021. The maximum concentration of AOD (550 nm) has been measured for Gandhi college (2.94 ± 0.44) and minimum for ARM college (0.01 ± 0.28), while the maximum concentration of PM2.5 has been measured for ARM College 296.37 (µg m −3 ) and minimum for Karunya University 0.02 (µg m −3 ). Also, the relation between AOD (550 nm) with total precipitation is measured positively for all locations except Gandhi college whereby PM2.5 associated with total precipitation is measured negatively for all locations except ARM college. Finally, the relationship between PM2.5 and AOD (550 nm) is measured positively in all selected locations except Singhad Institute. The maximum rainfall has been observed for monsoon months (June–August) and post-monsoon months (October) for all locations during the study period. The maximum total precipitation has been measured for Singhad 11,674.7 (mm) and the minimum for Karunya University 4563.41 (mm). However, the results of the study indicated that there was no direct trend observed in AOD in five different selected rural Indian sites.

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Section: Resultsmentioning

confidence: 99%

Impact Assessment of Aerosol Optical Depth on Rainfall in Indian Rural Areas

et al. 2022

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“…There is thus a strong demand for predictive models that can be applied across a range of locations and conditions, to predict the general characteristics of stormwater and performance of a range of stormwater treatment measures (Phillips and Thompson 2002). It is practically impossible to analyze each and every storm event, but one can predict the pollutant concentration using the regression models (Arora and Reddy 2014). This prediction can serve as an effective tool in deciding for the scheme of treatment for particular design storm with the known concentrations of pollutants that are expected to be taken care off.…”

Section: Stormwater Quality Assessment Through Modelingmentioning

confidence: 99%

Conceptualizing a decentralized stormwater treatment system for an urbanized city with improper stormwater drainage facilities

Arora

Reddy

2014

Int. J. Environ. Sci. Technol.

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Urbanization involving replacement of previous areas with impervious surfaces and drainage channel modifications results in increased runoff volume leading to flooding and also in deteriorating the quality of the stormwater runoff. An effective and efficient stormwater management is needed in the urban areas for mitigating the impacts and overcoming the problems associated with the urban stormwater. An attempt has been made in the present study to investigate the relationships between stormwater quality and five different land uses for the Patiala city of Punjab State. Stormwater samples collected from these locations, between April 2010 and March 2011, were analyzed for various organic and inorganic water quality parameters and heavy metals. Inter-parameter relationships studied provided vital information in deciding upon the treatment units most likely to be suitable for that particular sub-watershed. Multiple regression modeling helped in assessment of water quality parameters and predicting the quality well before actually analyzing the parameters and thus aided in developing treatment schemes suitable for taking care of the stormwater quality even under worst set of conditions. The outcomes of the research study effectively contributed toward conceptualizing a decentralized stormwater treatment system that would be capable of coping with the stormwater runoff-associated problems faced by the city and can serve as a model for other underdeveloped and developing cities having improper stormwater management facilities.

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“…Stormwater runoff from highways has been found to be toxic to many aquatic species, particularly during critical stages of development (Chen et al, 2016;Gazendam et al, 2011;Mayer et al, 2011;Wu et al, 2014). Common pollutants include sediments, nutrients (TP, TN, TKN), polycyclic aromatic hydrocarbons (PAHs), and various species of metals including Co, Cr, Cu, Ni, Pb, Zn and others (Van Bohemen and Van De Laak, 2003;Lau et al, 2009;MacKay et al, 2011;Chow et al, 2013;Arora and Reddy, 2014;Gill et al, 2014).…”

Section: Introductionmentioning

confidence: 99%