Water Quality Monitoring Stations Research Articles

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis and prediction is conducted for Johor River Basin incorporating the upstream to downstream water quality monitoring station data of the river. In this research, the numerical method is first used to calculate the WQI and identify the classes for validating the prediction results. Then, two ensemble and optimized machine learning models including gradient boosting regression (GB) and random forest regression (RF) are employed to predict the WQI. The study area selected is the Johor River basin located in Johor, Peninsular Malaysia. The initial phase of this study involves analyzing all available data on parameters concerning the river, aiming to gain a comprehensive understanding of the overall water quality within the river basin. Through temporal analysis, it was determined that Mg, E. coli, SS, and DS emerge as critical factors affecting water quality in this river basin. Then, in terms of WQI calculation, feature importance method is used to identify the most important parameters that can be used to predict the WQI. Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. Two ensemble ML approaches are chosen to predict the WQI in the study area and achieved a R2 of 0.86 for RF-based regression and 0.85 for GB-based ML technique. Finally, this research proves that using only the biochemical oxygen demand (BOD), the chemical oxygen demand (COD) and percentage of dissolved oxygen (DO%), the WQI can be predicted accurately and almost 96 times out of 100 sample, the water class can be predicted using GB ensembled ML algorithm. Moving forward, stakeholders may opt to integrate this research into their analyses, potentially yielding economic reliability and time savings.

Browning from headwaters to coastal areas in the boreal region: Trends and drivers

Browning of freshwaters, mainly caused by increased terrestrial organic carbon loading, has been widely studied during the last decades. However, there are still uncertainties regarding both the extent of browning in different aquatic ecosystems and the actual importance of different driving forces and mechanisms. To refine understanding of the extent and causes of browning and its temporal variation, we gathered a comprehensive dataset including 746 Finnish water quality monitoring stations representing various waterbody types: streams, rivers, lakes, and coastal waters. Monotonic trend analyses revealed that TOC concentrations increased in all waterbody types during the study period from 1990 to 2020, whereas non-linear trends indicated that upward trends in TOC concentrations have substantially decreased since the mid-2000s. However, despite the upward trends levelling off, non-linear analyses also indicated decreases in TOC concentrations at only a few stations. As a result, the TOC contents of the majority of Finnish waterbody types in 2020 were at a higher level than in 1990. To examine the driving forces of increasing TOC concentrations, we selected 100 riverine catchments and linked the detected trends to 24 different drivers, including both hydrometeorological and catchment characteristics. The increased TOC concentrations in surface waters could be connected to diverse human impacts: hydrometeorological variables impacted by climate change, decreased acidic deposition, and land use in terms of peatland drainage. The importance of increased temperatures was emphasized, and its role as a driver of increased leaching of organic carbon in the forthcoming years is expected to grow with climate change.

Effects of Land Use Types on Water Quality at Different Buffer Scales: Tianjin Section of the Haihe River Basin as an Example

It is important to explore the relationship between land use types and water quality to improve the surface water environment. Based on monthly water quality monitoring data from 16 nationally controlled surface water quality monitoring stations in Tianjin and land use data in 2021, GIS spatial analysis and mathematical and statistical methods were used to study the influence of land use types on surface water quality in buffer zones at different scales. The results showed that:① the land use types in the study area were mainly construction land, farmland, and water areas, which had significant effects on river water quality. Except for water temperature (WT) and pH, the farmland, construction land, and water areas were negatively correlated with each water quality indicator; forest land and grassland were positively correlated with dissolved oxygen (DO) and total nitrogen (TN) and negatively correlated with other water quality indicators. ② The water quality indicators showed obvious spatial differences in different seasons. The pH, DO and TN concentrations were higher in the dry season, whereas the permanganate index, ammonia nitrogen (NH4+-N), and total phosphorus (TP) concentrations were higher in the rainy season. ③ The results of the RDA analysis showed that the 800 m buffer zone land use had the greatest explanatory power for water quality changes in the dry season (50.4%), whereas the 3 000 m buffer zone land use could explain the water quality changes in the rainy season to the greatest extent (49.6%); from the average explanation rate of the dry and rainy seasons, the 3 000 m buffer zone was the best impact scale (50.0%) on water quality indicators in Tianjin. ④ The partial least squares regression (PLSR) analysis showed that the most important variables affecting surface water quality changes were construction land, farmland, and water areas. The predictive ability of the PLSR model of most water quality indicators was stronger in the dry season than that in the rainy season. In the dry season, all water quality indicators, except WT and pH, were most influenced by farmland. In the rainy season, construction land had the greatest influence on WT and NH4+-N concentrations, and the most important influencing factor for the remaining water quality indicators was still farmland. This study showed that the rational planning of land use types within 3 000 m of rivers or lakes was beneficial to improving the water quality of surface water.

Evaluating the Effectiveness of the “River Chief System”: An Empirical Study Based on the Water Quality Data of Coastal Rivers in Guangdong Province

The river chief system (RCS) is an innovative reform in China for strengthening the management of rivers and lakes. It is an important means of curbing the current severe water-environment situation. However, the policy impact of the RCS is still inconclusive in the existing literature. Using monthly data spanning from January 2015 to March 2022 from 25 water quality monitoring stations in rivers flowing into the sea across 13 prefecture-level cities in Guangdong Province, this study adopted regression discontinuity to evaluate the policy effects of the RCS on water quality. The results show that after the RCS’s full implementation in Guangdong Province, the concentrations of dissolved oxygen (DO) increased and water quality indicators, such as permanganate index (CODMn), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and total phosphorus (TP), decreased; NH3-N showed the largest decrease. These findings indicate that the RCS may contribute to a measurable improvement in reducing water pollution. However, no statistically significant changes in pH and total nitrogen (TN) were found, which indicates that the RCS fell short of achieving the policy effect of comprehensive water-pollution control. Therefore, in order to improve the RCS, it is necessary to refine the existing water-quality assessment indicators and to establish an evaluation system centered on the ecological health of rivers and lakes. Additionally, a paradigm shift from an administrative-boundary-based river management model to an overarching, holistic river-basin-based management approach is crucial for actualizing the holistic governance goals of the RCS.

Water quality emergency monitoring networks: A method for identifying non-critical variables based on Shannon's entropy

Abstract In the occurrence of environmental disasters involving water resources, deploying an emergency monitoring network for assessing water quality is within the first measures to be taken. Emergency networks usually cover a large set of water quality variables and monitoring stations along the watershed. Focusing on variables that represent greater risk to the environment and have less predictable spatial and temporal distribution is a strategy to optimize efforts on monitoring. The goal of this study is to assess the use of Shannon's entropy to identify non-critical water quality variables in an emergency monitoring network implemented in a watershed impacted by the collapse of a mining iron tailing dam, the Doce River watershed (Brazil). Monitoring stations were grouped into water quality subregions through cluster analysis and Shannon's entropy was used to estimate information redundancy of monitored variables. From information redundancy and after checking for compliance with environment normative, non-critical water quality variables were identified. Results indicated that non-critical variables represent 32–50% of the variables monitored. Emergency network managers find in this method a robust tool to improve the network performance. However, special attention should be paid to outliers' presence that can bias analyses based on Shannon's entropy.

Bottom water hypoxia suppresses fish chorusing in estuariesa).

Hypoxia in coastal ecosystems is increasing as a result of water quality declines from nutrient pollution. Hypoxia negatively affects fish populations and marine life, limiting their spawning habitats, population size, and growth. In this study, two approaches were used to understand the effect of hypoxia on the chorusing and reproductive behavior of fishes in estuaries. One approach used a water quality meter integrated with a prototype passive acoustic recorder, developed to monitor dissolved oxygen and fish chorusing simultaneously and continuously at sites with normoxic and hypoxic conditions. In a second approach, passive acoustic recorders were deployed near ambient water quality monitoring stations, monitored by the North Carolina agencies in estuaries where hypoxia occurs periodically. In both approaches, when hypoxia (dissolved oxygen < 4.0 mg/L) occurred, fish chorusing was diminished or ceased. A strong correlation was observed between bottom water dissolved oxygen and the power spectral density in a 100-200 Hz frequency band associated with red drum (Sciaenops ocellatus, Sciaenidae) calling. Passive acoustic monitoring stations and integrated passive acoustic and water quality meters should be used in estuarine hypoxia monitoring efforts to examine the expanding areas of hypoxia and its impact on fish critical spawning habitats.

Distance makes a difference: The role of water pollution regulation on firms’ innovation in China

Throughout the imminent water crisis era and stringent water environment management, enterprises play a vital role in environmental governance. However, there is seldom analysis on how the water environment regulation influences an enterprise's innovation due to data deficiency. Using our database from 2000 to 2022, we combine geographic information from water quality monitoring stations with the data on listed companies in China to pinpoint what distance firms are exposed to the monitoring stations. Results from a spatial regression discontinuity design along the geographic distances demonstrate that water environmental regulation has a significant positive impact on the quantity and quality of enterprise innovation. This paper found that it is primarily driven by the decreased usage of intermediate goods, rising industry profits from the exit of low-productivity firms, and enhanced enterprise research and development (R&D) investment. Furthermore, the positive impact is more pronounced for privately-owned firms, firms closer to suppliers, polluting firms, and politically unaffiliated firms. Our study offers scientifically valuable insights for emerging market countries in formulating effective water environmental regulation policies to incentivize firms to innovate in environmental protection. Additionally, it provides novel ideas on synergizing water environmental regulation and firm innovation to foster a mutually beneficial relationship between environmental protection and sustainable development.

Non-traditional abiotic drivers explain variability of chlorophyll-a in a shallow estuarine embayment

Understanding the factors influencing eutrophication, as represented by concentrations of chlorophyll-a (Chl-a), is needed to inform effective management and conservation strategies promoting ecological resilience. The objective of this study was to evaluate a unique combination of abiotic explanatory factors to describe Chl-a concentrations within the study estuary (North Biscayne Bay, Florida, USA). Multiple linear regression determined the strength and direction of influence of factors using data from 10 water quality monitoring stations. The analysis also considered time scales for evaluating cumulative effects of freshwater inflow and wind. Results show that dominant drivers of Chl-a were temperature, freshwater volume (whose cumulative effects were evaluated up to a 60-day time scale), and turbidity, which were statistically significant at 60, 60, and 70 % of the investigated stations, respectively. All drivers collectively accounted for 22 to 63 % of the variability of Chl-a measurements. Of the nine variables evaluated, nutrient concentrations (orthophosphate and ammonia) were not among the top three overall drivers. Despite nutrients historically being cited in the literature as the most significant factor, this study asserts that non-nutrient factors often govern Chl-a levels, necessitating a paradigm shift in management strategies to bolster estuarine resilience against climate change.

Simulation and Application of Water Environment in Highly Urbanized Areas: A Case Study in Taihu Lake Basin

In the wake of frequent and intensive human activities, highly urbanized areas consistently grapple with severe water environmental challenges. It becomes imperative to establish corresponding water environment models for simulating and forecasting regional water quality, addressing the associated environmental risks. The distributed framework water environment modeling system (DF-WEMS) incorporates fundamental principles, including the distributed concept and node concentration mass conservation. It adeptly merges point source and non-point source pollution load models with zero-dimensional, one-dimensional, and two-dimensional water quality models. This integration is specifically tailored for various Hydrological Feature Units (HFUs), encompassing lakes, reservoirs, floodplains, paddy fields, plain rivers, and hydraulic engineering structures. This holistic model enables the simulation and prediction of the water environment conditions within the watershed. In the Taihu Lake basin of China, a highly urbanized region featuring numerous rivers, lakes and gates, the DF-WEMS is meticulously constructed, calibrated, and validated based on 26 key water quality monitoring stations. The results indicate a strong alignment between the simulation of water quality indicators (WQIs) and real-world conditions, demonstrating the model’s reliability. This model proves applicable to the simulation, prediction, planning, and management of the water environment within the highly urbanized watershed.

Contributions of the vegetation index (NDVI) in water quality prediction models in a semi-arid tropical watershed

In this work, a new approach to using NDVI as a predictor of water quality parameters in arid environments is proposed. Our focus was the Araçuaí river basin, which has a predominance of native cerrado vegetation and is subject to seasonal variations in rainfall and vegetation cover. MODIS images (MOD13Q1) from 2000 to 2018 were used to calculate the NDVI of the contributing areas of the water quality monitoring stations and to analyze its relationship with fourteen water quality parameters. The NDVI showed significant seasonality, with high values in the rainy season, and temporal trends of increase in stretches related to the main river. A strong and new relationship was observed between NDVI and six water quality parameters: color, total dissolved solids, total suspended solids, total solids, nitrate and turbidity, this being stronger at the local scale, with better performance at from models that use incremental NDVI, capturing local variations in vegetation cover, instead of regional NDVI, which reflects the general state of vegetation. The results demonstrate the potential of using these indices to develop and improve prediction models of water quality parameters in river basins and to expand the spatial and temporal coverage of water quality monitoring.

Interpretable CEEMDAN-FE-LSTM-transformer hybrid model for predicting total phosphorus concentrations in surface water

The complexity of the biogeochemical cycle of phosphorus in lakes makes it challenging to produce efficient and accurate predictions of total phosphorus (TP) concentrations. In this study, a hybrid model is developed for TP predictions. This model combines the Complete ensemble empirical mode decomposition with adaptive noise, Fuzzy entropy, Long short-term memory, and Transformer (CF-LT). The introduction of data split-frequency reconstruction effectively solves the problems of over- and underfitting suffered by previous machine learning models in the face of high-dimensional data, while an attention mechanism overcomes the inability of these models to establish long-term dependencies between data when making long-term predictions. The CF-LT model is applied to predict TP concentrations from January 1, 2015, to December 31, 2020, at Yaoxiangqiao, Zhihugang, and Guanduqiao, three national water quality monitoring stations at the inlet of Taihu Lake, China. Moreover, the Shapley additive explanations are used to interpret the CF-LT model and identify the essential input features. The prediction results demonstrate that the CF-LT model achieves a coefficient of determination (R2) of 0.37–0.87 on the test dataset, representing an improvement of 0.05–0.17 (6%-85%) over the control models. In addition, the CF-LT model provides the best peak value predictions. The model interpretation results indicate that the turbidity and total nitrogen are the essential factors influencing TP predictions. This demonstrates that the TP concentrations at the inlet of Taihu Lake are closely related to the non-point pollution discharge and the status of aquatic plants. It's worth noting that these two indicators exert a more significant influence on the prediction of TP during wet season. This work provides a viable modeling strategy for predicting TP concentrations and guidance for early warning and treatment of surface water eutrophication in the Taihu Lake basin.

Mathematical forecasting simulation of salinity intrusion in Chao Phraya River

At present, at two water stations along Chao Phraya River, the main river of the central region of Thailand, the salinity in the river exceeds the raw water salinity standard for tap water production that must be lower than 0.25 g/L. During December to May, since 2013, the salinity value exceeded the raw water standard, strongly affecting the raw water management of Metropolitan Waterworks Authority. Surveillance and prediction of salinity level at the river mouth at Klong Lat Pho Water Quality Monitoring Station, downriver from Sam Lae station, in advance, will be beneficial to the Metropolitan Waterworks Authority’s raw water management. The purpose of this study was to forecast the hourly salinity levels at Khlong Lat Pho Water Quality Monitoring Station. The methods used were Exponential Smoothing and Winter’s Linear and Seasonal and Exponential Smoothing (EXPOW). Both forecasting methods showed a very low mean of square error (MSE): 0.1551 when the exponential smoothing method was used with alpha= 0.9995 and 0.4869 when the EXPOW was used with alpha = 0.0618, gamma = 0.9995, and beta = 0. 0001. Nevertheless, the exponential smooth method fell out of favor since, for long-term prediction, its predicted value converged to a constant value, 0.2405, useless for predicting seasonal variation. On the other hand, the EXPOW method, even though less effective in terms of MSE, produced more reliable short and long-term forecasts for salinity at Khlong Lad Pho Water Quality Monitoring Station.

New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting

Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds.

Impacts of droughts and heatwaves on river water quality worldwide

Droughts and heatwaves have a major impact on river water quality worldwide. However, previous studies of river water quality under these climate extremes are limited to a small number of river basins and regions, mainly located in North America, Europe or Australia. In this study, we estimate the large-scale effects of droughts, heatwaves and compound drought-heatwave events on river water quality for a total of 314,046 water quality monitoring stations worldwide over the period 1980–2021. We focus on 16 water quality constituents grouped into physical (e.g., temperature, salinity), chemical (e.g., pharmaceuticals, pesticides) and biological constituents (e.g., biochemical oxygen demand, faecal coliform). Further, we analyse the response of each constituent to droughts and heatwaves in relation to climate type, land use and level of wastewater treatment. We find a general deterioration in river water quality under droughts and heatwaves globally for most rivers and water quality constituents considered. For example, there is on average a 27 % increase in river water temperature, 17 % decrease in dissolved oxygen and 24 % increase in salinity under droughts and heatwaves. In addition, we find that climate type, land use and level of wastewater treatment have a significant effect on the magnitude of response in each water quality constituent during these extreme events. The median increase in river water temperature under compound drought-heatwaves is strongly driven by climate zone with higher warming rates at the polar climate zone (+4.5 °C) compared to the tropical zone (+2.1 °C). Increases in salinity under droughts are on average two times larger in irrigated regions compared to non-irrigated regions. The concentrations of nutrients (P and N) in rivers can either increase or decrease under droughts, depending on the nutrient form (dissolved vs. particulate) and land use (urban vs. rural). Higher levels of wastewater treatment contributed to a greater decrease in pathogenic concentrations under droughts and heatwaves (as indicated by faecal coliform). Pharmaceuticals show mixed responses mainly depending on the persistence of the constituent in surface waters, with for instance declines in diclofenac concentrations under droughts and heatwaves due to increased decay under higher water temperatures. The results of this study provide a broader understanding of how droughts and heatwaves affect river water quality compared to previous local and regional-scale analyses. In addition, this study could form the basis for large-scale modelling of river water quality under droughts and heatwaves.

Water Quality Prediction Based on Hybrid Deep Learning Algorithm

Pollution from many different sources severely affects the quality of our water supply. Over the past few years, a large number of online water quality monitoring stations have been used to gather time series data on water quality monitoring. These numbers are the foundation for deep learning techniques for forecasting water quality. In particular, typical deep learning approaches struggle to accurately estimate water quality in the presence of net promoter system (NPS) contamination. To overcome this shortcoming, a new deep learning model called long short-term memory (LSTM)–gray wolf optimization (GWO)–fish swarm optimization (FSO) was developed to enhance the precision of water quality prediction with NPS pollution. The well-established model may remedy the mechanism models’ inability to foretell changes in water quality on a minute-by-minute basis. Thamirabarani river watershed was used for the model’s application. Based on experimental data, the suggested model outperformed the mechanism model and the LSTM model in predicting extreme values. Maximum relative errors in anticipated against observed dissolved oxygen, chemical oxygen demand, and NH3─N values were 7.58%, 18.45%, and 22.25%, respectively. In comparison to the artificial neural network (ANN), back propagation neural network (BPNN), and recurrent neural network (RNN) models, the created LSTM–GWO–FSO model was shown to have greater computational performance (RNN). LSTM–GWO–FSO outperformed ANN, BPNN, and RNN regarding R2 of 3.1%–38.4% improvements. The suggested approach may provide a fresh perspective when predicting water quality in the presence of NPS contamination.

Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan

The escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climate fluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands of individuals succumb to waterborne diseases. Consequently, the water quality monitoring stations have been established worldwide. Regrettably, the real-time evaluation of Water Quality Index (WQI) is hindered by the intricate nature of off-site water quality parameters. Thus, there is a pressing need to create a precise and robust water quality prediction model. The dynamic and non-linear characteristics of water quality parameters pose significant challenges for conventional machine learning algorithms like multi-linear regression, as they struggle to capture these complexities. In this particular investigation, machine learning model called Feedforward Artificial Neural Networks (FANNs) was employed to develop WQI prediction model of Batu Pahat River, Malaysia exclusively utilizing on-site parameters. The proposed method involves a consideration of whether to include or exclude parameters such as BOD and COD, which are not measured in real time and can be costly to monitor as model inputs. Validation accuracy values of 99.53%, 97.99%, and 91.03% were achieved in three different scenarios: the first scenario utilized the full input, the second scenario excluded BOD, and the third scenario excluded both BOD and COD. It was suggested that the model has better predictive power between input variables and output variables. Factor contributed to river pollution has been identified and mitigation plan for Batu Pahat river pollution has been proposed. This could provide an effective alternative to compute the pollution, better manage water resources and mitigate negative impacts of climate change of river ecosystems.

Seasonal differences in water quality of highland lakes and its influencing factors: A case study of Dianchi Lake

Abstract Water quality is of great significance to human survival and development. It is of good theoretical value to study seasonal water quality variation and driving factors in plateau lakes. This paper studied the variation of water quality parameters in different seasons and their relationship with land use and climate factors in Dianchi Lake, Yunnan Province from 2001 to 2019. The results show that the factors restricting water environment in different seasons are different, but basically close. The water quality parameters of multiple water quality monitoring stations in different seasons showed a decreasing or insignificant trend. In the rainy season, the temperature and precipitation were positively correlated with most water quality parameters, while the driving effect of climate factors was not significant in the dry season. Different land types have different effects on water quality indexes in different seasons. The water quality score was higher in the rainy season than in the dry season. The scores of water quality stations in the south and middle of Dianchi Lake are higher than those in the north. In this study, the relationship between climate, land, and water quality is established, which provides a way for the research of water quality parameters.

Evaluating the Influence of Land Use and Landscape Pattern on the Spatial Pattern of Water Quality in the Pearl River Basin

Differences in land use and landscape patterns have become crucial factors affecting regional water quality. In order to investigate the effects of different land use and landscape patterns on water quality, this study used dissolved oxygen (DO), ammonia nitrogen (NH4+-N), and potassium permanganate index (CODMn) from 147 conventional water quality monitoring stations in the Pearl River basin of China from January to December 2021 as representative water quality parameters. The quantitative relationship between land use, landscape pattern, and water quality in the Pearl River basin was investigated using geographic information system technology (GIS) and partial least squares (PLS). The results showed that the overall water quality of the Pearl River basin was relatively positive and mainly threatened by organic pollution. The water quality of the Pearl River basin was affected by the spatial characteristics of land use and landscape pattern, showing a poorer spatial pattern on the eastern and western ends and a better one in the central part of the basin. The developed PLS regression model could better explain the quantitative relationship between water quality, land use, and landscape pattern, concluding that unused urban land has the greatest impact on water quality, with an impact coefficient of more than 0.10. The interspersion juxtaposition index (IJI) for representing landscape patterns had the greatest impact on water quality indicators, with an impact coefficient of −0.15 on DO, 0.13 on NH4+-N, and 0.15 on CODMn, respectively. Meanwhile, land use types such as unused land and water and landscape patterns indicated by the Shannon diversity index (SHDI) and the contagion index (CONTAG) had significant effects on watershed water quality. The results of the study provide a reference value for the optimal adjustment of land use structure and water quality improvement in the basin.

Spatio-temporal analysis of colored dissolved organic matter over Ebinur Lake in Xinjiang, China

A lack of water quality monitoring stations in arid regions makes it difficult to estimate the exact level of water pollution. Satellite technology is crucial for monitoring the water quality of salt lakes, but due to the diversity of terrestrial water bodies, it is challenging to establish relationships between lake water color parameters and remote sensing reflectance. This study aims to evaluate the reliability of the colored Quasi-Analytical Algorithm Dissolved Organic Matter (QAA-CDOM) model over Ebinur Lake. The absorption coefficient measured on May 19, 2021 was used to analyze the optical properties of Ebinur Lake. The findings show that the QAA-CDOM model performed well in estimating the absorption coefficient of CDOM at 443 nm, with the r2 and RMSE values of 0.69 and 0.12 m−1, respectively. The Landsat 8 OLI data and QAA-CDOM model were then used to derive the spatial-temporal characteristics of the CDOM concentration over Ebinur Lake from 2014 to 2021. The concentration of CDOM changes significantly across time and space, ranging from 0 to 2.5 m−1, with the lowest concentration in May and the highest in August. The central region of the lake has a lower concentration of CDOM than the other regions due to the reflectance of the shallow parts of the lake. This study clarified the inherent optical characteristics of saline lakes and proved the applicability of using the QAA-CDOM model to monitor surface water quality in arid regions of China. This research may be useful for future analyses of the carbon cycle calculations in salt lakes in arid regions.

Feature multi-level attention spatio-temporal graph residual network: A novel approach to ammonia nitrogen concentration prediction in water bodies by integrating external influences and spatio-temporal correlations

Accurate prediction of ammonia nitrogen concentration in water is of great significance for urban water quality management and pollution early warning. In order to improve the prediction accuracy of ammonia nitrogen concentration in water, this study developed a novel model based on graph neural networks called Feature Multi-level Attention Spatio-Temporal Graph Residual Network (FMA-STGRN). The FMA-STGRN model utilizes external influencing factors such as meteorological factors and point of interest data, as well as the spatio-temporal correlation information of ammonia nitrogen concentration between water quality monitoring stations, to accurately predict the concentration of ammonia nitrogen in water. The model consists of four main components: feature multi-level attention module, spatial graph convolution module, temporal-domain residual decomposition module, and feature fusion and output module. Through the organic combination of these four modules, FMA-STGRN can more effectively explore the complex spatio-temporal correlation relationships between water quality monitoring stations and more accurately integrate and utilize external influencing factors, thereby improving the prediction accuracy of ammonia nitrogen concentration in water. Experimental results show that the FMA-STGRN model outperforms other benchmark models such as RF, MART, MLP, LSTM, GRU, ST-GCN, and ST-GAT in various aspects. In addition, a series of feature ablation experiments were conducted to further reveal the key contributions of meteorological factors and point of interest data to the model performance. Overall, our research provides a powerful and practical tool for water quality monitoring and urban water management, with broad application prospects.