Wetlands cover only around 6 % of the Earth's land surface, and are recognized as one of the three major ecosystems, alongside forests and oceans. The ecological structure and function of karst wetlands are unique due to the influence of geologic structure. At present, the unclear spectral morphology of surface water in karst wetlands poses a significant challenge in remote sensing estimation of non-optically active water quality parameters (NAWQPs). This study proposed a novel multi-scale spectral morphology feature extraction (MSFE) method to insight to spectral characteristics in surface water of karst wetlands, and further screen the sensitive features of NAWQPs. Then we constructed three remote sensing inversion strategies for NAWQPs (TN, TP, NH3_N, DO), including direct estimation, indirect estimation, and auxiliary estimation. Finally, we constructed a novel pH-based hierarchical analysis framework (pH_HA) to thoroughly explore the influence of alkalinity-biased characteristics of karst water on the spectral domain of NAWQPs and its estimation accuracy using in-situ hyperspectral data, respectively. We found that the spectral characteristics of karst waters at the first reflectance peak (580 nm) differed significantly from other water body types. The MSFE successfully captured the sensitive spectral domains for NAWQPs, and focused on between 500 and 600 nm and 900-960 nm. The sensitive features captured by MSFE improved estimation accuracy of NAWQPs (R2 >0.9). Direct estimation presented more stable performance compared to the auxiliary estimation (average RMSE of 0.366 mg/L), and the auxiliary estimation model further improved the retrieval accuracy of TN compared to direct estimation model (R2 increasing from 0.43 to 0.56). The novel hierarchical framework clearly revealed the notable changes in the sensitive spectral domains of NAWQPs under different pH values, and enabled more precise determination of spectral subdomains of NAWQPs, and identified the optimal spectral features. The pH_HA framework effectively improved the estimation accuracy of NAWQPs (R2 increased from 0.514 to over 0.9), and the estimation accuracies (R2) of four NAWQPs were all more than 0.9 when the pH value was over 8.5. Our works provide an effective approach for monitoring water quality in karst wetlands. 

A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation-reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms. 

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

Remote sensing technology plays a crucial role in the rapid and wide-scale monitoring of water quality, which is of great significance for water pollution prevention and control. In this study, the downstream and nearshore areas of the Huaihe River Basin were selected as the study area. By utilizing spectral information from standard solution measurements in the laboratory and in situ water quality data matched with satellite spatiotemporal data, inversion models for total phosphorus (TP) and ammonia nitrogen (NH3-N) water quality parameters were developed. The validation results using field measurements demonstrated that the inversion models performed well, with coefficients of determination (R2) of 0.7302 and 0.8024 and root mean square errors of 0.02614 mg/L and 0.0368 mg/L for total phosphorus and ammonia nitrogen, respectively. By applying the models to Sentinel-2 satellite images from 2022, the temporal and spatial distribution characteristics of total phosphorus and ammonia nitrogen concentrations in the study area were obtained. The ammonia nitrogen concentration ranged from 0.05 to 0.30 mg/L, while the total phosphorus concentration ranged from 0.10 to 0.40 mg/L. Overall, the distribution appeared to be stable. The southern region of the Guan River estuary showed slightly higher water quality parameter concentrations compared to the northern region, while the North Jiangsu Irrigation Main Canal estuary was affected by the dilution of river water, resulting in lower concentrations in the estuarine area.

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

Diclofenac (DCF) is often detected in diverse aquatic bodies, and ineffective management can lead to detrimental effects on human health and ecosystems. In this study, degradation of DCF by Fe(III) and Mn(II) activating bisulfite (BS) was investigated. In the Fe(III)/Mn(II)/BS system, 93.4% DCF was degraded at 200 μM BS within 120 s, and additional research on 1000 μM BS achieved 88.4% degradation efficacy. Moreover, kinetics fitting of DCF degradation with the different BS concentrations was studied to find the two highest reaction rates (200 and 1000 μM, kobs = 0.0297 and 0.0317 s−1, respectively). Whereafter, SO4•− and Mn(III) were identified as the main active species at these two concentrations, respectively. Density functional theory (DFT) calculations, molecular frontier orbital theory, and surface electrostatic potential (ESP) forecast electrophilic attack sites. DCF degradation pathways by radical and non-radical ways were proposed by attack site prediction and thirteen intermediates identified by UPLC-QTOF-MS. ECOSAR software 2.2 was used for toxicity assessment. This work studied DCF degradation by the Fe(III)/Mn(II)/BS process in the presence of different concentrations of BS, providing a new insight into water purification. 

Degradation of Diclofenac by Bisulfite Coupled with Iron and Manganous Ions: Dual Mechanism, DFT-Assisted Pathway Studies, and Toxicity Assessment

Comparative analysis of RNN, LSTM and CNN algorithms for marine data prediction

Reservoirs play important roles in the drinking water supply for urban residents, agricultural water provision, and the maintenance of ecosystem health. Satellite optical remote sensing of water quality variables in medium and micro-sized inland waters under oligotrophic and mesotrophic status is challenging in terms of the spatio-temporal resolution, weather conditions and frequent nutrient status changes in reservoirs, etc., especially when quantifying non-optically active components (non-OACs). This study was based on the surface reflectance products of unmanned aerial vehicle (UAV) multispectral images, Sentinel-2B Multispectral instrument (MSI) images and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) by utilizing fuzzy C-means (FCM) clustering algorithm was combined with band combination (BC) model to construct the FCM-BC empirical model, and used mixed density network (MDN), extreme gradient boosting (XGBoost), deep neural network (DNN) and support vector regression (SVR) machine learning (ML) models to invert 12 kinds of optically active components (OACs) and non-OACs. Compared with the unclustered BC (UC) model, the mean coefficient of determination (MR) of the FCM-BC models was improved by at least 46.9%. MDN model showed best accuracy (R2 in the range of 0.60–0.98) and stability (R2 decreased by up to 13.2%). The accuracy of UAV was relatively higher in both empirical methods and machine learning methods. Additionally, the spatio-temporal distribution maps of four water quality variables were mapped based on the MDN model and UAV images, all platforms showed good consistency. An inversion strategy of water quality variables in various monitoring frequencies and weather conditions were proposed finally. The purpose of introducing the UAV platform was to cooperate with the satellite to improve the monitoring response ability of OACs and non-OACs in small and micro-sized oligotrophic and mesotrophic water bodies. 

Estimation of water quality variables based on machine learning model and cluster analysis-based empirical model using multi-source remote sensing data in inland reservoirs, South China

Aquatic ecological problems caused by pharmaceutical and personal care products (PPCPs) are increasingly becoming an issue of concern. In this study, the seasonal and spatial occurrence and environmental risk of 20 PPCPs were studied at 19 sampling points in the surface waters of the Lianjiang River basin (southern of China); its watershed is about 10,100 km2. Sample preparation was performed using solid-phase extraction, and determination was performed by using a high-performance liquid-phase tandem triple quaternary mass spectrometer. Nine PPCPs were detected with total concentrations of 19.5–940.53 and 6.07–186.04 ng L−1 during the wet (August 2021) and dry (April 2022) seasons, respectively. Four kinds of compounds—sulfamethoxazole (SMX), sulfamonomethoxine (SMM), caffeine (CAF), and florfenicol (FFC)—had a detection rate of more than 50% in both seasons. CAF, carbamazepine (CBZ), and FFC were higher in the wet season than in the dry season in the Lianjiang River possibly due to the higher usage of PPCPs and increased tourism during summer. SMX and SMM showed higher average concentrations in the dry season possibly due to lower biodegradation in the dry season and a slight dilution effect from rainfall. The concentrations of SMX, SMZ, SMM, and FFC were strongly correlated with NO3−, according to redundancy analysis and Spearman’s correlation analysis. The results of the risk quotient revealed that the ecological effect of CAF concentrations on green algae had low risk at all sampling points except R16.

/pdf/occurrences-seasonal-variations-and-potential-risks-of-6rdo74xq.pdf

Occurrences, Seasonal Variations, and Potential Risks of Pharmaceutical and Personal Care Products in Lianjiang River, South of China

A framework for tracing the sources of nitrate in surface water through remote sensing data coupled with machine learning

Sources and concentration of DOC, DIC, and POC in the Lianjiang River: A representative karst tributary of the Beijiang River

Evolution of and mechanisms controlling water chemistry in the Beijiang River Basin over the past four decades

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