To achieve carbon peaking and carbon neutrality in China, photovoltaic (PV) power generation has become increasingly important for promoting a low-carbon transition. The central and western desert areas of China have been identified as major areas for the construction of large PV bases. Remote sensing technology has been used to map the spatial distribution and development status of PV power stations quickly and accurately in ecologically fragile areas, as well as assess the ecological and environmental impact of their construction. However, current remote sensing monitoring of PV power stations focuses mainly on mapping and time series analysis to measure their development process and assess the environmental conditions on a large scale over a long period of time. Therefore, we constructed a random forest model based on image spectral and texture features and mapped 2022 PV power stations in the junction area of Hobq Desert, Ulan Buh Desert, Tengger Desert, and Mu Us Sands in China. Following that, we identified the construction time of the PV power stations by identifying the turning points of the normalized construction land index (NDBI) time series from 1990–2022 using the LandTrendr method. To assess the ecological impact of PV power stations, we used the NDVI to measure the change in vegetation condition before and after the construction of PV power stations and constructed NDVI changes for PV power stations constructed in different years. The results showed that this mapping method achieved an overall classification accuracy of 96.65% and a Kappa coefficient of 0.92. The root mean square error (RMSE) for construction year identification was less than 0.5, and the number of new PV power stations increased significantly after 2010, reaching a total area of 14.52 km² by 2016, which is consistent with the trend driven by national and regional development plans. Furthermore, the study found that the vegetation cover level could be restored to the average level before construction within 5–6 years and continued to increase after that. These findings may help government policymakers and practitioners make decisions on PV power station planning and ecological environment protection, thus contributing promptly to the achievement of China’s dual carbon goals.

The precise identification of pollution sources constitutes a cornerstone for effective water environment management in mountainous watersheds. This study employed principal component analysis–absolute principal component scores–multiple linear regression (PCA-APCS-MLR) receptor modeling to analyze monthly water quality indicators across the Longxi River Basin. Results revealed comparable water quality between the main stream and its tributaries, with no statistically significant differences identified. Water quality exhibited a distinct spatial pattern, with superior conditions in the upstream and downstream segments compared to the middle reaches. Water quality parameters exhibited significant seasonal variations. During the wet period, the degradation of water quality was primarily driven by diffuse agricultural sources, contributing 42.9%, followed by watershed background levels and surface runoff. In the dry season, rural domestic wastewater (39.3%) was the leading pollution source. For Permanganate index (COD_Mn) exceedance, basin background and agricultural non-point sources in the wet season were the main contributors (46.8% and 44.7%, respectively). For ammonium nitrogen (NH₃-N), wet season agricultural non-point sources (44.4%) and dry season rural domestic pollution (71.8%) were key contributors. Agricultural non-point sources were the dominant pollution source for total nitrogen (TN) in the wet season (84.2%). Effective water quality improvement in the Longxi River Basin hinges on targeted strategies—to mitigate diffuse agricultural sources through optimized fertilization, and to enhance the collection and treatment of rural domestic sewage. This study not only enhances the understanding of pollution source distribution and quantification in mountainous watersheds, but also serves as a vital reference for formulating targeted water environment management strategies.

Emerging organic pollutants (EOPs) in aquatic environments have attracted increasing attention because many occur at trace levels, undergo transformation during environmental transport, and contribute to poorly resolved mixture risks. Traditional targeted analysis is inherently restricted to predefined compounds, whereas high-resolution mass spectrometry (HRMS)-based full-scan workflows provide broader opportunities for discovering known unknowns and previously unrecognized contaminants. This review critically synthesizes an omics-based analytical framework for aquatic environments, covering sample digitalization, instrumental analysis and acquisition modes, chemical fingerprint/non-target screening, suspect screening, effect-directed analysis, and confidence-based structural identification. Particular emphasis is placed on practical decision points and trade-offs, including dissolved versus particulate-associated analytes, LC-HRMS versus GC-HRMS coverage, hard versus soft ionization, DDA- versus DIA-type acquisition, database dependence, and the persistent difficulty of linking analytical features to toxicological relevance. The review also discusses emerging directions involving artificial intelligence, chemometrics, organometallic contaminants, and microplastic-associated chemicals. By clarifying conceptual boundaries and highlighting current limitations, this article aims to support the development of more critical, transparent, and risk-oriented workflows for the discovery and prioritization of emerging pollutants in aquatic environments.

Identifying pollution sources and quantifying their contributions are essential for water environment management in mountainous watershed. This study used PCA-APCS-MLR receptor modeling to analyze monthly water quality indicators in the Longxi River basin. Results showed no significant difference between the water quality of the main stream and tributaries. The water quality of the upstream and downstream was better than the middle reaches. Significant differences were found in wet and dry seasons. In the wet season, agricultural non-point source pollution was the main driver of water quality, contributing 42.9%, followed by watershed background levels and surface runoff. In the dry season, rural domestic wastewater (39.3%) was the leading pollution source. For CODMn exceedance, basin background and agricultural non-point sources in the wet season were the main contributors (46.8% and 44.7%, respectively). For NH3-N, agricultural non-point sources in the wet season and rural domestic pollution in the dry season were the key contributors (44.4% and 71.8%, respectively). Agricultural non-point sources were the dominant pollution source for TN in the wet season (84.2%). Effective measures for improving water quality in the Longxi River Basin may include optimizing fertilization strategies and enhancing the collection and treatment of domestic sewage to control the agricultural non-point sources and rural domestic sewage. This study deepens the distribution and quantitative analysis of water pollution sources in mountainous watersheds, and also provides an important reference for water environment management.

Backward Lagrangian stochastic modeling is widely used to estimate emission rates from land surfaces to the atmosphere. It is also applied to calculate concentrations of pollutants due to known emission sources. A key component of this modeling technique is the concentration estimator, which relies on tracer particle trajectories to establish the relationship between concentration, emission rate, and meteorological condition. A commonly used concentration estimator is closely examined and shown to have potential inaccuracies. An alternative estimator is derived and compared with the existing one. The new estimator is tested using backward Lagrangian stochastic modeling in both Gaussian and non-Gaussian turbulence. The results demonstrate that, in many cases, the two estimators are equivalent, which explains the general success of the popular estimator. However, if the vertical velocities of some tracer particles are extremely slow when hitting the source, a significantly higher ratio of concentration to emission rate will be obtained. This spuriously high ratio will result in overestimation of the concentration if the purpose is to calculate concentrations from a known emission rate and underestimation of the emission rate if the model is used to calculate the emission rate from measured concentrations. The new estimator can avoid this unjustifiable behavior and therefore exhibits superior performance.