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

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.

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Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.142876 ◽

2020 ◽

pp. 142876 ◽

Cited By ~ 1

Author(s):

Daniel Saboe ◽

Hamidreza Ghasemi ◽

Ming Ming Gao ◽

Mirjana Samardzic ◽

Kiril D. Hristovski ◽

...

Keyword(s):

Machine Learning ◽

Water Quality ◽

Real Time ◽

Quality Parameters ◽

Potentiometric Sensor ◽

Water Quality Parameters ◽

Learning Tools ◽

Real Time Monitoring ◽

Sensor Signals

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Estimation of Chemical Oxygen Demand by Ultraviolet Spectroscopic Profiling and Physical Parameters Using IPW-PLS Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.316-317.606 ◽

2013 ◽

Vol 316-317 ◽

pp. 606-609 ◽

Cited By ~ 1

Author(s):

Jia Chen Li ◽

Ping Jie Huang ◽

Di Bo Hou ◽

Guang Xin Zhang

Keyword(s):

Water Quality ◽

Chemical Oxygen Demand ◽

Organic Pollutant ◽

Oxygen Demand ◽

Organic Pollution ◽

Quality Analysis ◽

Ultraviolet Absorption Spectrum ◽

Physical Parameters ◽

Wave Band ◽

Full Spectrum

In recent years, water pollution is increasing, especially organic pollution. The chemical oxygen demand (COD) is one of the most important evaluation index. Compared with the traditional chemical analysis method of COD, the method of spectrum analysis of organic pollutant concentration in water quality detection is fast, no chemical reagent and simple operation etc, and it is a kind of green testing technology. The current spectrum method of water quality analysis is usually based on single wavelength or the feature band extracted. It is lack of methods with the full spectrum scan. Based on water quality research,we choose the spectral analysis of COD as the objective. Combining the ultraviolet absorption spectrum with the conventional five physical parameters as the absorbance in the last wave band, we use the iterative predictor partial least squares algorithm to realize the rapid detection of water quality COD.

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An inquiry into the relationship between water quality parameters: Biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) in Iraqi Southern region

10.1063/5.0069000 ◽

2021 ◽

Author(s):

H. Alewi ◽

W. Obeed ◽

M. Abdulridha ◽

G. Ali

Keyword(s):

Water Quality ◽

Chemical Oxygen Demand ◽

Biochemical Oxygen Demand ◽

Oxygen Demand ◽

Quality Parameters ◽

Southern Region ◽

Water Quality Parameters ◽

The Relationship

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

PeerJ ◽

10.7717/peerj.7283 ◽

2019 ◽

Vol 7 ◽

pp. e7283 ◽

Cited By ~ 2

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.

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Spatio-temporal Variations in Water Quality Parameter Trends in River Waters

Revista de Chimie ◽

10.37358/rc.18.10.6659 ◽

2018 ◽

Vol 69 (10) ◽

pp. 2940-2952 ◽

Cited By ~ 1

Author(s):

Martina Zelenakova ◽

Pavol Purcz ◽

Radu Daniel Pintilii ◽

Peter Blistan ◽

Petr Hlustik ◽

...

Keyword(s):

Water Quality ◽

Chemical Oxygen Demand ◽

Quality Indicators ◽

Water Resource Management ◽

Oxygen Demand ◽

Temporal Variations ◽

Water Quality Parameter ◽

Agricultural Activity ◽

Water Quality Indicators ◽

Nitrite Nitrogen

Evaluating trends in water quality indicators is a crucial issue in integrated water resource management in any country. In this study eight chemical and physical water quality indicators were analysed in seven river profiles in the River Laborec in eastern Slovakia. The analysed water quality parameters were biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), pH, temperature (t), ammonium nitrogen (NH4+-N), nitrite nitrogen (NO2--N), nitrate nitrogen (NO3--N), and total phosphorus (TP). Data from the monitored indicators were provided by the Ko�ice branch of the Slovakian Water Management Company, over a period of 15 years from 1999 to 2013. Mann�Kendall non-parametric statistical test was used for the trend analysis. Biochemical and chemical oxygen demand, ammonium and nitrite nitrogen content exhibit decreasing trends in the River Laborec. Decreasing agricultural activity in the area has had a significant impact on the trends in these parameters. However, NO2--N was the significant parameter of water quality because it mostly exceeds the limit value set in Slovak legislation, Regulation No. 269/2010 Coll. In addition, water temperature revealed an increasing trend which could be caused by global increase in air temperature. These results indicate that human activity significantly impacts the water quality.

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Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽

10.3390/w13111547 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1547

Author(s):

Jian Sha ◽

Xue Li ◽

Man Zhang ◽

Zhong-Liang Wang

Keyword(s):

Neural Network ◽

Water Quality ◽

Deep Learning ◽

Real Time ◽

Best Management Practices ◽

Input Data ◽

Management Practices ◽

Quality Parameters ◽

Water Quality Parameters ◽

Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

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Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers

The Science of The Total Environment ◽

10.1016/j.scitotenv.2018.10.069 ◽

2019 ◽

Vol 651 ◽

pp. 2323-2333 ◽

Cited By ~ 26

Author(s):

Angelika M. Meyer ◽

Christina Klein ◽

Elisabeth Fünfrocken ◽

Ralf Kautenburger ◽

Horst P. Beck

Keyword(s):

Water Quality ◽

Real Time ◽

Real Time Monitoring

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Assessment of the Water Quality Characteristics of Kaduna River Receiving Wastewater Discharges

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2019.01.0103 ◽

2019 ◽

Vol 3 (1) ◽

pp. 56-61

Author(s):

H. Garba ◽

C. A. Elanu

Keyword(s):

Water Quality ◽

Dissolved Oxygen ◽

Chemical Oxygen Demand ◽

Biochemical Oxygen Demand ◽

Oxygen Demand ◽

Domestic Wastewater ◽

Quality Characteristics ◽

Chemical Characteristics ◽

Mean Concentration ◽

Concentration Levels

An assessment of the chemical characteristics of industrial and domestic wastewater discharges on seven parameters into Kaduna River on a bimonthly basis was carried out. PH, dissolved oxygen (DO), chloride, nitrite, chemical oxygen demand (COD), biochemical oxygen demand (BOD) and iron were analyzed to determine their concentration levels. From the analysis, the highest mean concentration of the parameters were 8.24 of pH, 7.7 mg/l of DO, 233.4 mg/l of chloride, 55.68 mg/l of COD, 27.95 mg/l of nitrite, 122.22 mg/l of BOD, and 17.05 mg/l of iron. After comparing with prescribed standards, it can be concluded that there is evidence of organic and inorganic accumulation of contaminants into River Kaduna.

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Evaluation of Shatt Al-Arab River Using Organic Pollution Index

Basrah Journal of Agricultural Sciences ◽

10.37077/25200860.2019.150 ◽

2019 ◽

Vol 32 ◽

pp. 153-162

Author(s):

Nada M. Al-Baghdadi ◽

Abdulaziz M. Abdullah ◽

Entisar N. Sultan

Keyword(s):

Chemical Oxygen Demand ◽

Water Samples ◽

Oxygen Demand ◽

Pollution Index ◽

Environmental Parameters ◽

Organic Pollution ◽

Biological Oxygen Demand ◽

Annual Rate ◽

Ammonium Ion ◽

The Third

The current study has evaluated Shatt Al-Arab water using organic pollution index. The study included three stations, the first Al-Sharash, the second Al-Salhiya and the third area Al-Dweab. Water samples were monthly collected from December 2017 to November 2018. Five environmental parameters were used to calculate the organic pollution index (OPI) including Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Active nitrate (NO3), Ammonium ion (NH4) and Total Phosphate (TP). The highest biological oxygen demand was 9 mg.l-1 at Al-Sharash station in July and the lowest values 1mg.l-1 in the Al- Dweab station during December, January, February and April. The highest chemical oxygen demand (250 mg.l-1 was recorded at Al-Sharash station in September and the lowest value was 6.2 mg.l-1 in Al-Dweab station during December. Nitrates was recorded the highest value 41.51 mg nitrogen-nitrate.l-1 at Al-Salhiya station in May and lowest value 1.49 mg nitrogen- nitrate.l-1 at Al-Sharash station during December. The ammonium ion was recorded in highest value 7.7 mg.l-1 at Al- Salhiya station in December and the lowest value 0.5 mg. l-1 at Al-Sharash station during June, while phosphate value was 0.23 and 1.99 mg.l-1 were recorded at Al-Sharash station in August and July respectively. The highest value of organic pollution index was 2.56 at Al-Salihiya station in August and lowest value 0.69 in the Al-Dweab station in April, while the annual rate of organic pollution index for the first, second and third stations were 1.55, 1.81 and 1.47 respectively, and 1.61 for Shatt Al-Arab water.

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