Sensitivity to experimental data of pollutant site mean concentration in stormwater runoff

Mourad, M.; Bertrand-Krajewski, Jean-Luc; Chebbo, Ghassan

doi:10.2166/wst.2005.0043

Cited by 33 publications

(18 citation statements)

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“…Even the simplest regression models have an additional calibration coefficient to SMC estimates, implying they will not perform well at sites with limited sampling, potentially overfitting on smaller datasets. This is supported by Mourad et al (2005aMourad et al ( , 2005bMourad et al ( , 2005c and Obropta and Kardos (2007) who suggested that SMCs required fewer data than regression or build-up washoff models. Furthermore, the study by May and Sivakumar (2013) found that the flow-weighted mean began performing less accurately on smaller datasets, potentially due to outlying storm event(s), which dominated the calibration of SMC.…”

Section: Authors' Replymentioning

confidence: 62%

“…Regression and/or process-based models are not solely limited by the absence of strong correlations between concentration and explanatory variables such as discharge, but also by a sparsity of available stormwater quality data, due to economic factors preventing the sampling of multiple storm events at a single catchment (Brezonik and Stadelmann, 2002;Driver and Tasker, 1990;Mourad et al, 2005aMourad et al, , 2006Sliva and Williams, 2001). This sparsity of data makes it difficult to isolate typical trends accurately, supporting the use of simpler models such as SMC estimates.…”

Section: Authors' Replymentioning

confidence: 99%

“…Even the flow-weighted mean allows the calculation of an associated standard deviation, although the chosen method may not be universal (Mourad et al, 2005a). The associated confidence interval subsequently allows the modeller to determine the reliability of the estimate of annual load from the catchment of interest, and the likelihood of exceeding a specified load input into a receiving water.…”

Section: Authors' Replymentioning

confidence: 99%

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Discussion: Prediction of long-term urban stormwater loads at single sites

May

Sivakumar

Sarmadi

et al. 2014

Proceedings of the Institution of Civil Engineers - Water Manag

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May and Sivakumar (2013) have predicted the long-term urban stormwater loads at single sites, using five different measures of central tendency. They have stated that their motivation in using measures of central tendency is one in which, in contrast to regression and process-based methods, there is no need for correlating urban stormwater loads with other variables (e.g. discharge), as well as less complexity. Overall, the flow-weighted mean concentration has produced the most accurate predictions of long-term loads. However, the following points are worth discussing:The definite superiority of the flow-weighted mean method obviously shows that there is sufficient correlation between loads and runoff, and therefore it suggests that regression methods could derive more accurate results. Interestingly, the authors claim in a different study (May and Sivakumar, 2009a) that the comparison between different statistical models shows that regression models are more applicable than simple estimates of mean concentration in urban areas. Therefore, here, May and Sivakumar should compare different mean functions at least with a simple regression model to support their new claim.From an environmental point of view, evaluating the trends, extreme events, dispersion tendency characteristics and bias are usually more effective than using a single value (e.g. long-term mean) for analysing/mitigating natural or human-induced side effects. For instance, Colin (1995) reports that urban stormwater loads have bias, which means the mode and arithmetic mean of samples are not related to the same load value; hence, pollution management for an area can be adjusted depending on the side of bias. In a different example, Vogel et al. (2005) state that the natural characteristics of long-term stormwater loads, at least in their case studies, are more sophisticated than using simple statistical methods. Therefore, Vogel et al. (2005) introduce a multivariate probability distribution function to model loadconcentration-discharge relationships in order to predict short and long-term stormwater loads. Furthermore, they derive benefit from using L-moment statistical analysis to discover the complexities of this phenomenon.Overall, the current study presents valuable points; however, the title and concepts should clarify that the aim of the research is simply to compare the performance of different measures of central tendency in deriving site mean concentration, and not to predict long-term urban stormwater loads. Authors' replyThe goal of the study by May and Sivakumar (2013) was to derive estimates of long-term load at a given catchment using a variety of estimates of site mean concentration (SMC) based on different measures of central tendency. The methodology was chosen for its inherent simplicity, making it readily applicable at sites with limited sampling.Regression and/or process-based models are not solely limited by the absence of strong correlations between concentration and explanatory variables such as discharge, but also by a spars...

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Section: Authors' Replymentioning

confidence: 62%

Section: Authors' Replymentioning

confidence: 99%

Section: Authors' Replymentioning

confidence: 99%

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Discussion: Prediction of long-term urban stormwater loads at single sites

May

Sivakumar

Sarmadi

et al. 2014

Proceedings of the Institution of Civil Engineers - Water Manag

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“…The most commonly used method to estimate the central EMC distribution value is the site mean concentration (SMC) computed as the arithmetic mean of the EMCs. However, the minimum number of EMCs and their statistical distribution are required to find a reliable SMC as a representative mean value (Mourad et al 2005).…”

Section: Data Analysesmentioning

confidence: 99%

“…REMC is a volume-weighted EMC mean for one specific site for different storm runoff volumes. This is a valid approach where a significant correlation exists between the EMC and runoff volume (Mourad et al 2005) and produces the most accurate long-term load estimates (May and Sivakumar 2012).…”

Section: Data Analysesmentioning

confidence: 99%

Relationship Between Non-Point Source Pollution and Korean Green Factor

Lee¹,

Park²,

Kim³

et al. 2015

Terr. Atmos. Ocean. Sci.

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In determining the relationship between the rational event mean concentration (REMC) which is a volume-weighted mean of event mean concentrations (EMCs) as a non-point source (NPS) pollution indicator and the green factor (GF) as a low impact development (LID) land use planning indicator, we constructed at runoff database containing 1483 rainfall events collected from 107 different experimental catchments from 19 references in Korea. The collected data showed that EMCs were not correlated with storm factors whereas they showed significant differences according to the land use types. The calculated REMCs for BOD, COD, TSS, TN, and TP showed negative correlations with the GFs. However, even though the GFs of the agricultural area were concentrated in values of 80 like the green areas, the REMCs for TSS, TN, and TP were especially high. There were few differences in REMC runoff characteristics according to the GFs such as recreational facilities areas in suburbs and highways and trunk roads that connect to major roads between major cities. Except for those areas, the REMCs for BOD and COD were significantly related to the GFs. The REMCs for BOD and COD decreased when the rate of natural green area increased. On the other hand, some of the REMCs for TSS, TN, and TP were still high where the catchments encountered mixed land use patterns, especially public facility areas with bare ground and artificial grassland areas. The GF could therefore be used as a major planning indicator when establishing land use planning aimed at sustainable development with NPS management in urban areas if the weighted GF values will be improved.

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