A derived probability distribution approach to stormwater quality modeling

Chen, Jieyun; Adams, Barry J.

doi:10.1016/j.advwatres.2006.02.006

Cited by 33 publications

(19 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the experimental study by Vaze and Chiew (2002) showed that a significant rainfall event of 39.4 mm can remove only 35 % of pollutant loads. Therefore, in practice, the amount of accumulated pollutants on catchment surfaces has two parts; pollutants build-up during the t d and residual pollutants not washed off by the previous storm events (Chen and Adams 2007). Hence, Charbeneau and Barrett (1998) proposed the following build-up model which accounts for the mass not washed off during the previous rainfall event.…”

Section: Pollutant Build-upmentioning

confidence: 99%

Advances in Landscape Runoff Water Quality Modelling: A Review

Hossain

Imteaz

2015

Springer Geography

View full text Add to dashboard Cite

As the recognition of the concept 'Sustainable Development' is increasing throughout the world, understanding the adverse impact of water quality parameters is highly important for the protection and improvement of aquatic environments from the impact of pollution. Therefore, the measurement of water quality parameters is required to protect and improve aquatic environments from the impact of pollution. The estimation of water quality parameters from direct field measurement is costly, time-consuming and sometimes impossible. Therefore, mathematical approaches of water quality models have become prevalent in recent years for the purpose of watershed management strategies. However, water quality model parameters vary not only spatially (i.e. catchment to catchment), but also temporally (i.e. differ among different rainfall events). Because depending upon the catchment characteristics such as soil permeability and initial pollutant loads, the impact of the actual land-use and management changes. There exists a wide range of water quality models to be used in managing water quantity and quality with respect to a variety of environmental impacts. This chapter presents a rigorous literature review regarding available catchment water quality modelling techniques developed in recent years. The goal is to identify the most effective water quality modelling technique which will help in the analysis, improvement and update of best management practices (BMPs). The selected modelling technique will help watershed management authorities to enable them implement economically viable and effective management and mitigation strategies to protect aquatic environments from the impact of pollution.

show abstract

Section: Pollutant Build-upmentioning

confidence: 99%

Advances in Landscape Runoff Water Quality Modelling: A Review

Hossain

Imteaz

2015

Springer Geography

View full text Add to dashboard Cite

show abstract

“…Alley [1981] pointed out that the residual pollutant remaining after the previous storm runoff should be considered in determining pollutant accumulation rates. This assumption was adopted by a number of other studies [Zhang and Yamada, 1996;Chen and Adams, 2007]. In this study, the formulation of the pollutant buildup model is based on the assumption that there is always a residual or initial amount of pollutant available after the previous runoff event.…”

Section: Pollutant Buildup Processmentioning

confidence: 99%

“…Nonlinear pollutant buildup was first established from field data collected by Sartor and Boyd [1972], and they suggested an exponential relationship between the amount of solids available on the surface and the duration of the antecedent dry period. This concept has been employed in several subsequent studies [e.g., Behera et al, 2006;Deletic et al, 1997;Chen and Adams, 2007]. The exponential formulation is adopted in this study.…”

Section: Pollutant Buildup Processmentioning

confidence: 99%

“…Li and Adams [2000] developed analytical probabilistic models with derived probability distribution theory to analyze the runoff quantity/quality control performance of various combinations of storage/treatment systems. Chen and Adams [2007] developed closed-form analytical runoff water quality models for simulating urban catchment pollutant (suspended solids, total phosphorus, COD, BOD 5 and Fe) buildup and washoff processes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analytical modeling of polycyclic aromatic hydrocarbon loading and transport via road runoff in an urban region of Beijing, China

Zhang

Keller

Wang

2009

Water Resources Research

View full text Add to dashboard Cite

[1] An analytical runoff water quality model is developed for simulating polycyclic aromatic hydrocarbon (PAH) loading and transport via road runoff in an urban area. The model considers buildup and washoff processes of suspended solids in storm runoff and considers dissolved and particle-bound PAHs. Pollutant deposition and removal processes, which have typically been treated with a constant rate in previous models, are depicted as functions of relative humidity and wind speed to reflect the influence of meteorological conditions and are integrated into the pollutant buildup function. Pollutant input via rainwater and canopy throughfall from roadside trees is considered in the washoff model. The model is calibrated and verified with field data from an urban catchment in Beijing, China. The verification indicates that the runoff model is capable of providing results in good agreement with field measurements. A Monte Carlo framework is used to evaluate the uncertainty of PAH loading under various scenarios of atmospheric conditions.

show abstract

“…For the runoff pollutant parameters, uncertainty concentration distributions were characterized. Several types of probability distributions are shown in Table 3 and Figure 5 as both probability density functions (pdf's) and cumulative distribution functions (cdf's) in equation 1 to 3 [21].…”

Section: Probability Uncertainty Distributions From Driveway Runoff Qmentioning

confidence: 99%