Scale relationship between landscape pattern and water quality in different pollution source areas: A case study of the Fuxian Lake watershed, China

Peng, Shuangyun; Li, Shihua

doi:10.1016/j.ecolind.2020.107136

Cited by 54 publications

(19 citation statements)

References 37 publications

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“…Different land-use types also differ in terms of their optimal range of influence on water quality in the basin [24]. In this study, the relationship between the water quality of the rivers and lakes and cropland and forest land was close at the 250 m and 500 m scales, whereas the relationship with urban construction land was weak, which is consistent with a previous study [51]. As reported previously, towns in the Baoan Lake basin are relatively concentrated in distribution, although the area accounts for a low proportion.…”

Section: Influence Of Different Offshore Scales On Water Qualitysupporting

confidence: 91%

Study on the Water Quality Characteristics of the Baoan Lake Basin in China under Different Land Use and Landscape Pattern Distributions

Ren

et al. 2022

IJERPH

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Land use and landscape pattern highly affect water quality. Their relationship can assist in land-use management and improve land-use efficiency. In this study, a water quality survey of rivers and lakes was performed in 2020 to analyze the effects of land use and the landscape pattern on the water quality of the rivers and lakes in the Baoan Lake basin and is expected to provide a reference for land use planning. The results demonstrated that the effects of land use on water quality were generally higher during the dry season than during the wet season; however, the opposite was demonstrated for the landscape pattern index. Cropland and urban land were closely correlated with deteriorating water quality, with contributions to total nitrogen, total phosphorous, and ammonia nitrogen in the basin. The impact of the landscape pattern of the basin on water quality was controlled by the original land-use type. In addition, the landscape configuration formed different land-use types to produce different effects on water quality. The basin scale better explained the changes in water quality, especially for construction land, followed by the 250 m and 500 m scales in the buffer zone.

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Section: Influence Of Different Offshore Scales On Water Qualitysupporting

confidence: 91%

Study on the Water Quality Characteristics of the Baoan Lake Basin in China under Different Land Use and Landscape Pattern Distributions

Ren

et al. 2022

IJERPH

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“…It plays a role in helping the decomposition process of organic material into small pieces, making it easier for microbes in the decomposition process (Mar'i et al, 2017). Sources of COD can be natural organic matter in water bodies and organic matter caused by domestic and industrial waste disposal activities and the entry of plant and animal decomposition runoff through the rain (Peng and Li, 2021). Based on the COD content, in class IV, the pollution level in 2020 decreased compared to 2016.…”

Section: Water Quality In the Klampok Sub-watershedmentioning

confidence: 99%

Water Quality Analysis using Pollution Index Method in Klampok Sub-watershed, Semarang Regency, Indonesia

Suwatanti

Maridi

Suntoro

2022

Caraka Tani J. Sustain. Agric.

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The Klampok Sub-watershed flows in the Semarang Regency, Central Java, Indonesia, located in the upstream part of the Jragung Watershed, used as a source of irrigation. Klampok Sub-watershed experiences environmental pressures in the form of decreasing water quality because of various human activities. For this reason, this study aims to determine the changes in the water quality and pollution index of the Klampok Sub-watershed in 2016 and 2020. The water quality observations were carried out in 2020 utilizing the Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Nitrate, Zinc (Zn) and Copper (Cu) parameters. The laboratory analysis results were compared with water quality standards based on Government Regulation Number 22 of 2021 and the status of water quality based on the pollution index method under the Decree of the Minister of the Environment Number 115 of 2003. The study results showed a change in the quality and index of water pollution in 2016 and 2020. The study results revealed a decrease in the value of water quality on the parameters BOD, COD, Zn and Cu, which indicated that the water quality was improving. Moreover, the Klampok Sub-watershed pollution index decreased in 2016 and 2020 so that the Klampok Sub-watershed had quality criteria for lightly polluted water to moderately polluted to lightly polluted at each sampling point. Therefore, several efforts to control pollution and management of the Klampok Sub-watershed, such as community outreach, water quality monitoring and industrial compliance, are expected to improve so that the Klampok Sub-watershed's sustainability can provide various benefits to the community.

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“…For the large cross-basin water transfer project like SNWTP, it would cost excessive time, labor and money to set up sampling points along the way to investigate water quality and analyze hydrodynamics and water quality variations, requiring the researchers to make a trade-off between model accuracy and research economy and efficiency. Compared with the mechanical model, the machine learning models have a simpler and faster modeling and simulation process, and the ability to consider more impact factors, with lower construction requirements and greater ability to analyze big data [13]. Comparing the machine learning models in our study with Zeng et al's mechanical model [49], the results showed that the MAE of our model was between 0.0006 to 0.0012 (Table 3), while the MAE of the mechanical model was equal to 0.2177, indicating that our models had better prediction ability than mechanical model for predicting Chl-a concentration variations.…”

Section: Performance Comparisons Of Machine Learning Models and Other Modelsmentioning

confidence: 99%

“…Owing to the simple and fast modeling process, the non-mechanistic model has been widely used in water quality prediction. Traditional non-mechanistic models (i.e., mathematical statistical models) for water quality prediction are based on simple mathematical and statistical data processing methods to analyze the relationship between water quality variations and their driving factors (e.g., hydrological factors, meteorological factors, landscape patterns) to predict and assess future water quality [11], such as regression analysis, cluster analysis, and discriminant analysis [12][13][14][15]. Although these methods are simple and fast, they require complete long-term data to build models, limiting their promotion in missing data areas.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

Liao

Zang

Wang

et al. 2021

Water

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Although water transfer projects can alleviate the water crisis, they may cause potential risks to water quality safety in receiving areas. The Miyun Reservoir in northern China, one of the receiving reservoirs of the world’s largest water transfer project (South-to-North Water Transfer Project, SNWTP), was selected as a case study. Considering its potential eutrophication trend, two machine learning models, i.e., the support vector machine (SVM) model and the random forest (RF) model, were built to investigate the trophic state by predicting the variations of chlorophyll-a (Chl-a) concentrations, the typical reflection of eutrophication, in the reservoir after the implementation of SNWTP. The results showed that compared with the SVM model, the RF model had higher prediction accuracy and more robust prediction ability with abnormal data, and was thus more suitable for predicting Chl-a concentration variations in the receiving reservoir. Additionally, short-term water transfer would not cause significant variations of Chl-a concentrations. After the project implementation, the impact of transferred water on the water quality of the receiving reservoir would have gradually increased. After a 10-year implementation, transferred water would cause a significant decline in the receiving reservoir’s water quality, and Chl-a concentrations would increase, especially from July to August. This led to a potential risk of trophic state change in the Miyun Reservoir and required further attention from managers. This study can provide prediction techniques and advice on water quality security management associated with eutrophication risks resulting from water transfer projects.

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Scale relationship between landscape pattern and water quality in different pollution source areas: A case study of the Fuxian Lake watershed, China

Cited by 54 publications

References 37 publications

Study on the Water Quality Characteristics of the Baoan Lake Basin in China under Different Land Use and Landscape Pattern Distributions

Study on the Water Quality Characteristics of the Baoan Lake Basin in China under Different Land Use and Landscape Pattern Distributions

Water Quality Analysis using Pollution Index Method in Klampok Sub-watershed, Semarang Regency, Indonesia

Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

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