scholarly journals Multi-scale relationship between land use/land cover types and water quality in different pollution source areas in Fuxian Lake Basin

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7283 ◽  
Author(s):  
Shihua Li ◽  
Shuangyun Peng ◽  
Baoxuan Jin ◽  
Junsong Zhou ◽  
YingXin Li
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Spatial Scale ◽  
Oxygen Demand ◽  
Buffer Zone ◽  
Pollution Source ◽  
Riparian Buffer ◽  
Lake Basin ◽  
Multi Scale ◽  
The Relationship

The spatial-temporal evolution of land use and land cover (LULC) and its multi-scale impact on the water environment is becoming highly significant in the LULC research field. The current research results show that the more significant scale impact on LULC and water quality in the whole basin and the riparian buffer scale is unclear. A consensus has not been reached about the optimal spatial scale problem in the relationship between the LULC and water quality. The typical lake basin of the Fuxian Lake watershed was used as the research area and the scale relationship between the LULC and water quality was taken as the research object. High resolution remote sensing images, archival resources of surveying, mapping and geographic information, and the monitoring data of water quality were utilized as the main data sources. Remote sensing and Geometric Information Technology were applied. A multi-scale object random forest algorithm (MSORF) was used to raise the classification accuracy of the high resolution remote sensing images from 2005 to 2017 in the basin and the multi-scale relationship between the two was discussed using the Pearson correlation analysis method. From 2005 to 2017, the water quality indicators (Chemical Oxygen Demand (COD), Total Phosphorous (TP), Total Nitrogen (TN)) of nine rivers in the lake’s basin and the Fuxian Lake center were used as response variables and the LULC type in the basin was interpreted as the explanation variable. The stepwise selection method was used to establish a relationship model for the water quality of the water entering the lake and the significance of the LULC type was established at p < 0.05.The results show that in the seven spatial scales, including the whole watershed, sub-basin, and the riparian buffer zone (100 m, 300 m, 500 m, 700 m, and 1,000 m): (1) whether it is in the whole basin or buffer zone of different pollution source areas, impervious surface area (ISA), or other land and is positively correlated with the water quality and promotes it; (2) forestry and grass cover is another important factor and is negatively correlated with water quality; (3) cropping land is not a major factor explaining the decline in water quality; (4) the 300 m buffer zone of the river is the strongest spatial scale for the LULC type to affect the Chemical Oxygen Demand (COD). Reasonable planning for the proportion of land types in the riparian zone and control over the development of urban land in the river basin is necessary for the improvement of the urban river water quality. Some studies have found that the relationship between LULC and water quality in the 100 m buffer zone is more significant than the whole basin scale. While our study is consistent with the results of research conducted by relevant scholars in Aibi Lake in Xinjiang, and Erhai and Fuxian Lakes in Yunnan. Thus, it may be inferred that for the plateau lake basin, the 300 m riparian buffer is the strongest spatial scale for the LULC type to affect COD.

scholarly journals The Estimation of Chemical Oxygen Demand of Erhai Lake Basin and Its Links with DOM Fluorescent Components Using Machine Learning

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3629
Author(s):  
Yuquan Zhao ◽  
Jian Shen ◽  
Jimeng Feng ◽  
Zhitong Sun ◽  
Tianyang Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chemical Oxygen Demand ◽  
Real Time ◽  
Oxygen Demand ◽  
Organic Pollution ◽  
Lake Basin ◽  
Erhai Lake ◽  
Real Time Monitoring ◽  
The Relationship

Water quality estimation tools based on real-time monitoring are essential for the effective management of organic pollution in watersheds. This study aims to monitor changes in the levels of chemical oxygen demand (COD, CODMn) and dissolved organic matter (DOM) in Erhai Lake Basin, exploring their relationships and the ability of DOM to estimate COD and CODMn. Excitation emission matrix–parallel factor analysis (EEM–PARAFAC) of DOM identified protein-like component (C1) and humic-like components (C2, C3, C4). Combined with random forest (RF), maximum fluorescence intensity (Fmax) values of components were selected as estimation parameters to establish models. Results proved that the COD of rivers was more sensitive to the reduction in C1 and C2, while CODMn was more sensitive to C4. The DOM of Erhai Lake thrived by internal sources, and the relationship between COD, CODMn, and DOM of Erhai Lake was more complicated than rivers (inflow rivers of Erhai Lake). Models for rivers achieved good estimations, and by adding dissolved oxygen and water temperature, the estimation ability of COD models for Erhai Lake was significantly improved. This study demonstrates that DOM-based machine learning can be used as an alternative tool for real-time monitoring of organic pollution and deepening the understanding of the relationship between COD, CODMn, and DOM, and provide a scientific basis for water quality management.

scholarly journals Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3250
Author(s):  
Fei Zhang ◽  
Ngai Weng Chan ◽  
Changjiang Liu ◽  
Xiaoping Wang ◽  
Jingchao Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Body ◽  
Water Quality Index ◽  
Quality Index ◽  
Lake Basin ◽  
Ebinur Lake ◽  
Remote Sensing Techniques ◽  
The Relationship ◽  
Ebinur Lake Basin

Water Resource Sustainability Management plays a vitally important role in ensuring sustainable development, especially in water-stressed arid regions throughout the world. In order to achieve sustainable development, it is necessary to study and monitor the water quality in the arid region of Central Asia, an area that is increasingly affected by climate change. In recent decades, the rapid deterioration of water quality in the Ebinur Lake basin in Xinjiang (China) has severely threatened sustainable economic development. This study selected the Ebinur Lake basin as the study target, with the purpose of revealing the response between the water quality index and water body reflectivity, and to describe the relationship between the water quality index and water reflectivity. The methodology employed remote sensing techniques that establish a water quality index monitoring model to monitor water quality. The results of our study include: (1) the Water Quality Index (WQI) that was used to evaluate the water environment in Ebinur Lake indicates a lower water quality of Ebinur Lake, with a WQI value as high as 4000; (2) an introduction of the spectral derivative method that realizes the extraction of spectral information from a water body to better mine the information of spectral data through remote sensing, and the results also prove that the spectral derivative method can improve the relationship between the water body spectral and WQI, whereby R2 is 0.6 at the most sensitive wavelengths; (3) the correlation between the spectral sensitivity index and WQI was greater than 0.6 at the significance level of 0.01 when multi-source spectral data were integrated with the spectral index (DI, RI and NDI) and fluorescence baseline; and (4) the distribution map of WQI in Ebinur Lake was obtained by the optimal model, which was constructed based on the third derivative data of Sentinel 2 data. We concluded that the water quality in the northwest of Ebinur Lake was the lowest in the region. In conclusion, we found that remote sensing techniques were highly effective and laid a foundation for water quality detection in arid areas.

Analysis of Water Quality Conditions of Lake Hachiroko Using Fuzzy C-Means

2019 ◽  
Vol 23 (3) ◽  
pp. 456-464
Author(s):  
Kai Matsui ◽  
◽  
Yoichi Kageyama ◽  
Hiroshi Yokoyama
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Oxygen Demand ◽  
Remote Sensing Data ◽  
Strong Relationship ◽  
Fuzzy Regression ◽  
Quality Estimation ◽  
Sensing Data ◽  
Using Data ◽  
The Relationship

Lake Hachiroko, Japan, has many water quality issues, evident from phenomena such as green algae blooms. Understanding the details of the surface water quality of the lake, and the effect of seasons on the quality, is important. In our previous studies, we conducted fuzzy regression analysis of remote sensing data and direct measurements of water quality. The results showed that estimation maps of water quality were well created, using only five data points of the water quality parameters. To obtain maps that are in good agreement with the experimental data, remote sensing data and water quality values should be acquired simultaneously. However, performing such simultaneous observations can affect the preparation of the water quality estimation maps. We overcame this obstacle by using fuzzy c-means clustering (FCM), and considered the effect of specific disturbances and uncertainties on the remote sensing data. Furthermore, FCM using only remote sensing data creates estimation maps in which relative water surface conditions are classified. Therefore, determining the relationship between FCM results and water quality facilitates the creation of low-cost, high-frequency water quality estimation maps. Our results indicated that FCM was particularly effective in determining the presence of suspended solids (SS) during water quality analysis. However, the relationship between FCM results and water quality has not been determined in detail. In this study, we analyzed the water quality conditions of Lake Hachiroko with FCM using the data collected by the Advanced Space-borne Thermal Emission and Reflection Radiometer on Terra and, the Operational Land Imager on Landsat-8. In addition, FCM results were compared with the maps created by fuzzy regression analysis and the actual conditions of water pollution. The results indicated that (i) the maps created using FCM are effective in determining the water surface conditions, (ii) the FCM maps using data obtained during August and September have a strong relationship with biochemical oxygen demand (BOD) and SS, and (iii) the FCM maps using data obtained during May and June have a strong relationship with chemical oxygen demand (COD), SS, and total nitrogen (T-N).

Riparian Buffer Zone and Whole Watershed Influences on River Water Quality: Implications for Ecosystem Services near Megacities

2016 ◽  
Vol 3 (2) ◽  
pp. 277-305 ◽  
Author(s):  
Mariana Z. Nava-López ◽  
Stewart A. W. Diemont ◽  
Myrna Hall ◽  
Víctor Ávila-Akerberg
Keyword(s):  
Water Quality ◽  
Ecosystem Services ◽  
River Water ◽  
Buffer Zone ◽  
River Water Quality ◽  
Riparian Buffer ◽  
Riparian Buffer Zone

Quantifying the impact of human activities on water quality based on spatialization of social data: a case study of the Pingzhai Reservoir Basin

2020 ◽  
Vol 20 (2) ◽  
pp. 688-699 ◽  
Author(s):  
Yongrong Zhang ◽  
Zhongfa Zhou ◽  
Haotian Zhang ◽  
Yusheng Dan
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Human Activities ◽  
Water Environment ◽  
Oxygen Demand ◽  
Pollution Source ◽  
Pollution Load ◽  
Social Data ◽  
Reservoir Basin ◽  
The Impact

Abstract In water pollution source research, it is difficult to quantify the impact of human activities on water quality. Based on pollution load theory and the concept of spatialization of social data, this study integrates land-use type, slope gradient, and spatial position, and uses the contribution of human activities to quantify the impact of farmland fertilizers, livestock and poultry wastes, and human domestic pollution on water quality in the study area. The results show that livestock manure is the largest source of total phosphorus (TP) and total nitrogen (TN) discharges in the research area, and domestic pollution is the largest source of chemical oxygen demand (COD) discharges. The total equal standard pollution load (as well as the load of each pollution source and its pollutant amount) is the highest in the Nayong River Basin and the lowest in the Baishui River Basin. The contributions of human activities to TP and TN have similar spatial distributions. The impact of human activities on COD discharge is minimal. The quantitative results of this model are basically consistent with the actual conditions in the Pingzhai Reservoir Basin, which suggests that the model reasonably reflects the impact of human activities on the water environment of the basin.

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