Water temperature is an important indicator of water quality for surface water resources because it impacts solubility of dissolved gases in water, affects metabolic rates of aquatic inhabitants, such as fish and harmful algal blooms (HABs), and determines the fate of water resident biogeochemical nutrients. Furthermore, global warming is causing a widespread rise in temperature levels in water sources on a global scale, threatening clean drinking water supplies. Therefore, it is key to increase the frequency of spatio-monitoring for surface water temperature (SWT). However, there is a lack of comprehensive SWT monitoring datasets because current methods for monitoring SWT are costly, time consuming, and not standardized. The research objective of this study was to estimate SWT using data from the Landsat-8 (L8) and Sentinel-3 (S3) satellites. To do this, we used machine learning techniques, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), simple neural network (ANN), and deep learning techniques (Long Short Term Memory, LSTM, and Convolutional Long Short Term Memory, 1D ConvLSTM). Using deep and machine learning techniques to regress satellite data to estimate SWT presents a number of challenges, including prediction uncertainty, over- or under-estimation of measured values, and significant variation in the final estimated data. The performance of the L8 ConvLSTM model was superior to all other methods (R2 of 0.93 RMSE of 0.16 °C, and bias of 0.01 °C). The factors that had a significant effect on the model’s accuracy performance were identified and quantified using a two-factor analysis of variance (ANOVA) analysis. The results demonstrate that the main effects and interaction of the type of machine/deep learning (ML/DL) model and the type of satellite have statistically significant effects on the performances of the different models. The test statistics are as follows: (satellite type main effect p *** ≤ 0.05, Ftest = 15.4478), (type of ML/DL main effect p *** ≤ 0.05, Ftest = 17.4607) and (interaction, satellite type × type of ML/DL p ** ≤ 0.05, Ftest = 3.5325), respectively. The models were successfully deployed to enable satellite remote sensing monitoring of SWT for the reservoir, which will help to resolve the limitations of the conventional sampling and laboratory techniques.

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A global freshwater pollution catastrophe is looming due to pollutants of emerging concern (PECs). Conventional water treatment methods are limited in removing PECs such as pharmaceuticals and dye house effluent from aquatic systems. This study provides an effective potential solution by developing an innovative wastewater treatment method based on solar-light-responsive semiconductor-based photocatalysts. A sol-gel synthesis technique was used to produce Fluorine-Sm3+ co-doped TiO2 (0.6% Sm3+) (FST3) photocatalysts. This was followed by loading multi-walled carbon nanotubes (MWCNTs) in the range of 0.25 to 1 wt% into the FST3 matrix. Solid state UV-visible spectroscopy measurements showed a bathochromic shift into the visible light region after the co-doping of TiO2, whereas XRD analysis confirmed the presence of predominantly anatase polymorphs of TiO2. The FT-IR and EDX results confirmed the presence of the F and Sm3+ dopants in the synthesised photocatalysts. XRD and TEM measurements confirmed that the crystallite sizes of all synthesised photocatalysts ranged from 12–19 nm. The resultant photocatalysts were evaluated for photocatalytic degradation of Brilliant Black BN bis-azo dye in aqueous solution under simulated solar irradiation. FST3 completely degraded the dye after 3 h, with a high apparent rate constant (Ka) value (2.73 × 10−2 min−1). The degree of mineralisation was evaluated using the total organic carbon (TOC) technique, which revealed high TOC removal (82%) after 3 h and complete TOC removal after 4 h. The incorporation of F improved the optical properties and the surface chemistry of TiO2, whereas Sm3+ improved the quantum efficiency and the optical properties. These synergistic effects led to significantly improved photocatalytic efficiency. Furthermore, incorporating MWCNTs into the F and Sm3+ co-doped TiO2 (0.6% Sm3+) improved the reaction kinetics of the FST3, effectively reducing the reaction time by over 30%. Recyclability studies showed that after 5 cycles of use, the FST3/C1 degradation efficiency dropped by 7.1%, whereas TiO2 degradation efficiency dropped by 33.4% after the same number of cycles. Overall, this work demonstrates a sustainable and efficient dye-removal technique.

Plastic pollution in aquatic ecosystems has been identified as a growing global water pollution threat that is negatively impacting water quality and, as a result, affecting the health of humans, aquatic animals, and wildlife. Therefore, it presents a global environmental catastrophe that requires immediate attention. Plastics in water (in their different forms, macro-, meso-, micro-, and nanoplastics) are contaminants of emerging concerns that have since evolved to be a global environmental threat. Despite increasing levels of pollution in aquatic ecosystems, there are insufficient monitoring data to evaluate the extent of the catastrophe. Traditional methods of monitoring plastics in water are constrained by high sampling costs, intensive labor, and limited temporal and spatial coverage, which results in limited monitoring data. Thus, insufficient monitoring data limit our understanding of the true quantities and persistence of plastic particles in aquatic ecosystems, as well as the extent to which they impact the aquatic environment. There is increasing availability of free big geospatial data (amounting to petabytes/day) from satellite sensors for potentially monitoring plastics. This provides a possible solution to these challenges by minimizing the fieldwork required and therefore reducing the costs and sampling time. The study purpose of this review is to analyze advances in emerging technology such as the use of satellite sensors to monitor the occurrence of macro- and microplastics in freshwater, ultimately aimed at creating new operational monitoring solutions. This review: (1) examines the literature to identify trends, accomplishments, and limitations of using satellite data to monitor plastics in water; (2) identifies and compares traditional, and machine and deep learning satellite image classification methods for monitoring plastics in water; and (3) identifies research gaps and summarizes future perspectives and recommendations to improve monitoring methods.