Nonpoint source pollution

Lian, Qiyu; Yao, Lunguang; Ahmad, Zaki Uddin; Lei, Xiaobo; Islam, Faridul; Zappi, Mark E.; Gang, Daniel Dianchen

doi:10.1002/wer.1205

Cited by 19 publications

(9 citation statements)

References 128 publications

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“…During storm events, these pollutants accumulated on the surface are then washed away by rain and mixed with surface runoff. The pollutant load can be estimated using empirical equations, the statistical correlation method, and the output coefficient method. , However, these methods are extremely simple to accurately calculate pollutant loads.…”

Section: Overview Of the Watershed Modelmentioning

confidence: 99%

Watershed Models: Review of Approaches, Challenges, and Opportunities

Jiang,

Shen

2023

ACS EST Water

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Watershed modeling is a crucial tool needed for understanding, evaluating, and predicting the adverse impact of water pollution. The watershed model can be divided into an upland watershed model (UWSM) and a downstream waterbody model (DWBM). Neither a single UWSM nor DWBM are able to accurately simulate the pollutant transport along with the surface flow in complex upland watershed−waterbody systems. Therefore, coupled UWSM and DWBM are more desirable. This study categorized the coupled UWSM and DWBM into external and internal coupling models. The coupling mechanism, numerical accuracy, computational efficiency, and applications of these coupling models are discussed. A dynamic bidirectional coupled model for environment simulation (E-DBCM) is consistent with the natural flood and pollutant transport processes, which can improve computational efficiency while maintaining simulation accuracy. Compared with the original coupling models, it possesses three advantages: (1) reasonable coupling mechanisms, (2) complete function, and (3) high efficiency and simplicity. E-DBCM is a potential watershed water environment model, which can be widely used if improved further. The findings of this review will help accurately and efficiently predict and manage pollutants worldwide.

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Section: Overview Of the Watershed Modelmentioning

confidence: 99%

Watershed Models: Review of Approaches, Challenges, and Opportunities

Jiang,

Shen

2023

ACS EST Water

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“…The change rate 5% is typically used for sensitivity analysis [55]. Three rainfall events, i.e., [22][23] July 2018, 29 July 2018, and 21 August 2018, were selected for sensitivity analysis. The three events are contrasted with rainfall intensities, durations, and antecedent conditions.…”

Section: Model Parametersmentioning

confidence: 99%

“…Longer early drought days, which tend to accumulate more surface pollutants, lead to higher potentials of non-point source pollution in urban environments [22]. Previous studies investigated the properties of urban non-point source pollution based on in-situ monitoring and numerical simulations [23]. Among them, particular attention of in-situ monitoring has been given to the initial flushing effect of pollution [24,25].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulations of Non-Point Source Pollution in a Small Urban Catchment: Identification of Pollution Risk Areas and Effectiveness of Source-Control Measures

Pan

Guo

Yang

et al. 2021

Water

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Urban non-point source pollution is becoming a serious issue under the context of rapid urbanization and its impacts on surface hydrologic processes. The identification of non-point source risk areas and the effectiveness of source-control measures provides important first steps to improve the degrading aquatic environment but is challenged by the complex dynamics and variabilities of surface pollutants in urban environments. In this study, we investigate the spatial and temporal variabilities of non-point source pollution in a small urban catchment based on numerical simulations and in-situ samplings. Our results show that residential, industrial, and commercial land contribute to the most pollutant loadings and are the main constituents of the pollution risk area. Rainfall duration and intensity are the main factors in determining the temporal variations of urban non-point source pollution. There is no correlation between early drought days and pollution load. Numerical simulations show that it is more effective to increase urban vegetation coverage than to enhance road cleaning for effective non-surface pollution control. For enhanced road cleaning, it is more effective to improve the frequency of road cleaning than its efficiency. Our results provide important guidance for effective controls of non-point source pollution as well as the establishment of long-term surface pollutant monitoring network in complex urban environments.

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“…The degradation of these water bodies can be attributed to both point sources of runoff, pollution, and nonpoint sources (NPS) that are distributed throughout the landscape [2]. Point sources are readily identifiable (e.g., outlet pipes) and relatively straightforward to control, but the spatially distributed and intermittent NPS processes are much more challenging to handle [3][4][5][6][7]. As a result, these processes, in which runoff and pollutants that enter a water body cannot be tied to a specific spatial point, have been the leading pollution problem in the U.S. for more than 40 years [2,8].…”

Section: Introductionmentioning

confidence: 99%

Modeling Spatio-Temporal Dynamics of BMPs Adoption for Stormwater Management in Urban Areas

Zhang

Montas

Shirmohammadi

et al. 2023

Water

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Nonpoint source (NPS) pollution is a severe problem in the U.S. and worldwide. Best management practices (BMPs) have been widely used to control stormwater and reduce NPS pollution. Previous research has shown that socio-economic factors affect households’ adoption of BMPs, but few studies have quantitatively analyzed the spatio-temporal dynamics of household BMP adoption under different socio-economic conditions. In this paper, diverse regression approaches (linear, LASSO, support vector, random forest) were used on the ten-year data of household BMP adoption in socio-economically diverse areas of Washington, D.C., to model BMP adoption behaviors. The model with the best performance (random forest regression, R2 = 0.67, PBIAS = 7.2) was used to simulate spatio-temporal patterns of household BMP adoption in two nearby watersheds (Watts Branch watershed between Washington, D.C., and Maryland; Watershed 263 in Baltimore), each of which are characterized by different socio-economic (population density, median household income, renter rate, average area per household, etc.) and physical attributes (total area, percentage of canopy in residential area, average distance to nearest BMPs, etc.). The BMP adoption rate was considerably higher at the Watts Branch watershed (14 BMPs per 1000 housing units) than at Watershed 263 (4 BMPs per 1000 housing units) due to distinct differences in the watershed characteristics (lower renter rate and poverty rate; higher median household income, education level, and canopy rate in residential areas). This research shows that adoption behavior tends to cluster in urban areas across socio-economic boundaries and that targeted, community-specific social interventions are needed to reach the NPS control goal.

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Nonpoint source pollution

Cited by 19 publications

References 128 publications

Watershed Models: Review of Approaches, Challenges, and Opportunities

Watershed Models: Review of Approaches, Challenges, and Opportunities

Numerical Simulations of Non-Point Source Pollution in a Small Urban Catchment: Identification of Pollution Risk Areas and Effectiveness of Source-Control Measures

Modeling Spatio-Temporal Dynamics of BMPs Adoption for Stormwater Management in Urban Areas

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