An Investigation of the Relationships between Rainfall Conditions and Pollutant Wash-Off from the Paved Road

Yuan, Quanzi; Guerra, Heidi B.; Kim, Young‐Chul

doi:10.3390/w9040232

Cited by 26 publications

(15 citation statements)

References 19 publications

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“…Additionally, pollutant build-up and wash-off processes were found to be significantly influenced by a range of catchment characteristics like land use, impervious area fraction, and urban characteristics [6]. Yuan et al [7], exploiting a data set of 30 rainfall events, found that the effects of rainfall conditions on pollutant concentration (N tot , P tot , TSS, chemical oxygen demand (COD), ammonium nitrogen (NH 4 -N), nitrate-nitrogen (NO 3 -N), phosphate (PO 4 -P), and heavy metals) depend on the nature of the pollutant itself. In particular, the antecedent dry period tended to affect the particle wash-off from impervious areas due to the predominance of particles with size <30 µm.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds

2019

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Protection of surface water quality plays a crucial role for sustainable urban watershed management since the wash-off from impervious contaminated surfaces generates transport phenomena from a range of pollutants (like nutrients, such as total nitrogen (Ntot) and total phosphorus (Ptot)). This leads to the consequent reduction of water quality, and to phenomena, such as eutrophication and the presence of algae blooms. For this reason, a comprehensive understanding of nutrient build-up and wash-off is essential for efficient stormwater treatment design. However, data scarcity could represent one of the main limitations in this context. This manuscript presents a methodological framework able to tackle such limitations by an in-depth investigation of the main factors that influence the build-up and wash-off from impervious surfaces, including rainfall, watershed, and drainage-network characteristics. The outcomes highlight the key role played by the antecedent dry period, among the rainfall characteristics, and the width of the overland flow path, among the catchment/drainage characteristics. It is also confirmed as appropriate to use suspended solids as a surrogate for the investigation of the behavior of other pollutant species. Additionally, the capability of this approach in assessing modeling performance was successfully tested. The results of the present study are expected to contribute valuable knowledge for defining effective management strategies to minimize stream pollution and protect the health of aquatic ecosystems in urban watersheds characterized by data scarcity.

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Section: Introductionmentioning

confidence: 99%

A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds

2019

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“…The AdB outcomes were sensitive to both rainfall depth and runoff volume, with a sensitivity higher to runoff volume than rainfall depth. Runoff volume was more relevant to TSS concentration than rainfall depth, likely because runoff is directly linked to detachment and transport of sediment [89]. Thus, when available, runoff volume data could be a better predictor of TSS concentration that rainfall data.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Moeini

Shojaeizadeh

Geza

2021

Water

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Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable.

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“…That mainly results from the complex processes involved in the formation and transport of sediments and pollutants within the catchment. In a vast majority of papers, the factors that influence the quality of sediments are defined in general terms [22][23][24][25][26][27]. The studies state that the quality of sediments depends on atmospheric conditions, catchment characteristics, or stormwater runoff dynamics.…”

Section: Independent Variables and Their Selectionmentioning

confidence: 99%

Modeling of Heavy Metal (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and PAH Content in Stormwater Sediments Based on Weather and Physico-Geographical Characteristics of the Catchment-Data-Mining Approach

Bąk

Szeląg

Sałata

et al. 2019

Water

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The processes that affect sediment quality in drainage systems show high dynamics and complexity. However, relatively little information is available on the influence of both catchment characteristics and meteorological conditions on sediment chemical properties, as those issues have not been widely explored in research studies. This paper reports the results of investigations into the content of selected heavy metals (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and polycyclic aromatic hydrocarbons (PAHs) in sediments from the stormwater drainage systems of four catchments located in the city of Kielce, Poland. The influence of selected physico-geographical catchment characteristics and atmospheric conditions on pollutant concentrations in the sediments was also analyzed. Based on the results obtained, statistical models for forecasting the quality of stormwater sediments were developed using artificial neural networks (multilayer perceptron neural networks). The analyses showed varied impacts of catchment characteristics and atmospheric conditions on the chemical composition of sediments. The concentration of heavy metals in sediments was far more affected by catchment characteristics (land use, length of the drainage system) than atmospheric conditions. Conversely, the content of PAHs in sediments was predominantly affected by atmospheric conditions prevailing in the catchment. The multilayer perceptron models developed for this study had satisfactory predictive abilities; the mean absolute error of the forecast (Ni, Mn, Zn, Cu, and Pb) did not exceed 21%. Hence, the models show great potential, as they could be applied to, for example, spatial planning for which environmental aspects (i.e., sediment quality in the stormwater drainage systems) are accounted.

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An Investigation of the Relationships between Rainfall Conditions and Pollutant Wash-Off from the Paved Road

Cited by 26 publications

References 19 publications

A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds

A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Modeling of Heavy Metal (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and PAH Content in Stormwater Sediments Based on Weather and Physico-Geographical Characteristics of the Catchment-Data-Mining Approach

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