Anthropogenic pollution plays an important part in deteriorating the water quality of rivers all over the world, especially in urban areas of Africa where water quality monitoring is still seriously constrained by the limited test facility and capability. In this study, for evaluating the impact of urbanization on the river water quality, we investigated four typical urban rivers of Tanzania through the upper-urban-down gradient assessment approach and analyzed by water quality index (WQI) and statistical methods. The physicochemical indices monitored in these rivers revealed that the contents of those indicators of TN, TP, PO₄³⁻, NH₄⁺, COD_Mn, and NO₃⁻ were accumulated significantly in the lower reaches of the cities, which indicated the life-type pollution characteristics in such urban rivers of Africa. The following main conclusions are achieved from this study. The water quality of 30% of the investigated river sections is in the medium to good status based on the subjective WQI with sensory factors included. Moreover, the sections with obvious water quality decline are mainly limited to the river segments within the urban central area, and severe pollution of water bodies is closely related to large cities, indicating an increasing pollution tendency with the quickly growing population. Therefore, to help formulate water pollution control policies in response to the rapid urban expansion in African countries, it is necessary to adopt an economical and feasible method to carry out early monitoring of surface water quality timely.

Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R² = 0.812, RMSE = 0.414 mg L⁻¹, MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R² = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management.

The quantitative evaluation of the coupling coordination degree (CCD) between the regional economy and eco-environment systems is of great importance for the realization of sustainable development goals, which could identify economic or eco-environmental cold areas. To date, traditional evaluation frameworks mainly include the indicator system construction based on statistical data, which seldom utilize the geo-spatiotemporal datasets. Hence, this study aimed to evaluate the CCD change trend of the Yangtze River Delta (YRD) and explore the relationship between the CCD, economy, and eco-environment on the county scale. In this study, YRD was selected as the study area to evaluate its level of CCD at different periods, and then the nighttime difference index (NTDI) and eco-environmental comprehensive evaluation index (ECEI) were calculated to represent the difference in the development of the regional economy and the eco-environmental quality (EEQ). The CCD between the two systems was then calculated and analyzed using global, local, and Geary’s C spatial autocorrelation indicators, in addition to change trend methods. The main findings showed that: (1) During the period 2000–2020, the economic system in YRD showed a continuously upward trend (0.0487 a⁻¹), with average NTDI values of 0.2308, 0.2964, 0.3223, 0.3971, and 0.4239, respectively. In spatial terms, the economy system showed a distribution of “high in the east and low in the west”. (2) YRD’s EEQ indicated a gradual upward trend (from 0.3590 in 2000 to 0.3970 in 2020), with a change trend value of 0.0020 a⁻¹. Spatially, the regions with high ECEI were mainly located in southwestern counties. (3) In the past 20 years, the CCD between economic and eco-environment systems showed an increased change trend, with a change trend value of 0.0302 a⁻¹. The average CCD values for the five periods were 0.3992, 0.4745, 0.4633, 0.5012, and 0.5369. The overall level of CCD improved from “moderate incoordination” to “low coordination”. (4) Both NTDI and ECEI indexes have a positive effect on the improvement of regional CCD. However, the contribution of NTDI is a little higher than that of ECEI.

Lake Taihu is a vital source of surface water for the Yangtze River Delta region, so effective monitoring of its water quality is essential for protecting the water source. However, most existing studies on unmanned aerial vehicle (UAV)-based water quality remote sensing have focused on single large rivers or lakes, primarily employing validation methods involving randomly selected samples. This makes it difficult to assess the generalisability of the models to unfamiliar watercourses. This study focuses on 13 inflow rivers on Xishan Island, a central island in Lake Taihu, which are characterized by short flow paths, independent catchment areas, and varying land use influences. Using a UAV multispectral remote sensing platform, we have designed a water quality monitoring and assessment framework tailored to multi-river systems with small sample sizes. First, various water body indices were developed and analysed for correlation with measured water quality parameters. Then, machine learning algorithms such as Backpropagation (BP) neural networks, Random Forest, XGBoost, Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) were selected to construct retrieval models. For accuracy evaluation, a spatial independent validation strategy was employed whereby one sample was forcibly set aside from each river to constitute the validation set. Using this method, we generated spatial distribution maps of water quality parameters for the inflow rivers and evaluated the influencing factors of spatial variation in water quality by area, taking into account water body functional types and ecological characteristics. The experimental results indicate that under the conditions of spatial independent validation strategy, the SVM model achieved the highest retrieval accuracy for dissolved oxygen (R² = 0.892, RMSE = 0.414 mg/L and MRE = 0.057), whereas the XGBoost model achieved the highest retrieval accuracy for turbidity (R² = 0.853, RMSE = 0.632 NTU and MRE = 0.065). The spatial pattern of water quality exhibited a pronounced gradient: dissolved oxygen (DO) concentrations followed the order of aquaculture area rivers > agricultural area rivers > urban area rivers, while turbidity displayed the opposite trend, reflecting that surrounding land use types, phytoplankton density, and human activity intensity are the dominant factors driving the spatial differentiation of river water quality on Xishan Island in spring. The full-chain technical framework of “multi-river synchronous retrieval—spatially independent validation strategy—area mechanistic assessment” proposed in this study provides a replicable evaluation paradigm for rapid water quality monitoring of Lake Taihu islands and similar watersheds, and holds significant implications for the construction of the Lake Taihu Eco-Island and the protection of the water environment.