Based on the relevant studies conducted over the past few decades, in this article, the research status of atmospheric precipitation chemistry in China's offshore and coastal zones was systematically summarized in terms of precipitation pH and conductivity, the abundance and sources of major chemical constituents, the fluxes of nutrient wet deposition, and their ecological and environmental effects. The future research trends were also projected. The results showed that the pH and conductivity of precipitation in China's offshore and coastal zones were generally high in the north and low in the south due to the neutralization effect of alkaline ions from sand/construction dust in the northern region and the smaller precipitation amount there. The main chemical components in precipitation in China's offshore and coastal areas were dominated by 、、Ca²⁺、Cl^-、. The type of acid rain was primarily a mixture of sulfuric and nitric acids. The wet deposition characteristic of common ions in precipitation varied slightly across China's four major offshore sea areas. Except for the Bohai Sea, wet deposition was the main form in other sea areas, and it generally showed a pattern of wet season being greater than dry season. The Yellow Sea area had the highest ion deposition flux. Short-term heavy rainfall could affect the marine environment. The different components and proportions of input nutrients from wet deposition could influence the structure of marine plankton communities to some extent. Under the combined influences of sea, land, and human activities, the atmospheric precipitation in China's offshore and coastal waters exhibited chemical characteristics distinct from those in inland areas, which could have complex and far-reaching impacts on the offshore ecological environment. Future research should focus on land-sea coordination. It should delve deeper into the intrinsic influences and feedback mechanisms between terrestrial atmospheric pollutant emissions, precipitation chemical components, atmospheric deposition, and offshore ecosystem evolution. This approach may better explore the relationship between human activities and marine ecosystem evolution from the perspective of atmospheric deposition.

A total of 24 sediment samples were collected from the Yellow River Zhengzhou Section to explore the influence of confluence on the distribution and pollution of microplastics in the sediments of the middle and lower reaches of the Yellow River. To investigate the pollution distribution characteristics of microplastics, quantitative and qualitative analysis were conducted by using stereomicroscopy and fourier transform infrared spectroscopy method. Subsequently, cluster analysis was performed to examine the relationship of the microplastics pollution distribution between the main and tributary streams. The results showed that the mean values of microplastic abundance in the sediments were (1340.97 ± 314.84) n/kg and (2372.22 ± 604.17) n/kg during the wet season and dry season, respectively. Overall, the abundance of microplastics showed a trend of higher tributary than main stream, and higher downstream than upstream. Among them, fibrous microplastics accounted for the highest proportion, and dominated by blue, black, and transparent, with the majority of the particle size less than 2mm. The main polymer type was polyethylene terephthalate (PET), and the waste water discharges from washing and household waste disposal were identified as the major sources. Moreover, increased diversity in shapes and types of microplastic were observed after confluence, with a reduction in the fraction of the dominant colours (blue, black, clear) and an expansion in the proportion of small particle sizes (<2mm). This indicates that the tributaries, as the source, contributed to the composition and distribution of microplastics in the main stream sediments after confluence, even affecting their migration.

The study focused on the influence of calcium and magnesium ions (Ca²⁺ and Mg²⁺) in karst areas on adsorption behavior between algal organic matter (AOM) of Chlorella sp. and Pseudanabaena sp. and extracellular polymeric substances (EPS) extracted from bacterial cultures were conducted. The adsorption experiments, the fitting of kinetic and isotherm adsorption models, and the analysis of spectroscopic techniques revealed the influence of divalent cations on the biosorption between EPS and AOM. The results showed that in the presence of divalent ions (Ca²⁺=89.38mg/L, Mg²⁺=17.59mg/L), the biosorption efficiencies of the EPS were improved by bridging the negatively charged functional groups (which was consistent with adsorption under quasi-secondary kinetics). Among them, the adsorption efficiency of Chlorella AOM increased from 31.57% to 59.99% due to its richer functional group structure, which was significantly higher than that of Pseudanabaena AOM (from 59.59% to 62.77%). 3D-EEM analysis showed that aromatic protein (AP) and soluble microbial product (SMP) fractions dominated the adsorption system, and Ca²⁺ and Mg²⁺ altered the stability of fluorescent components during biosorption process. In combination with a specific UV wavelength SUVA₂₅₄, the adsorption induced an increase in the content of aromatic compounds. The characteristic peak shifts detected by FTIR in the bands of 3300~3600cm^-1 (O-H/N-H groups) and 1600~1650cm^-1 (C=O groups) revealed that divalent ions combined with related functional groups to form coordinate bonds and caused bridging effects. This study demonstrated that the divalent cations enhanced the adsorption strength of AOM and EPS through charge neutralization and bridging effects.

This study investigated the accumulation and translocation characteristics of emerging short-chain per- and polyfluoroalkyl substances (PFAS) in a typical submerged macrophyte Aquarius uruguayensis through controlled exposure experiments. The target pollutants included six short-chain PFAS (three perfluoroalkyl carboxylic acids (PFCAs) and three perfluoroether acids (PFEAs)). A. uruguayensis demonstrated bioaccumulation capability of all six short-chain PFAS from water, with whole-plant bioconcentration factors (BCF_total) ranging from 3.0 to 46.0L/kg, primarily detected in leaves with average shoot bioconcentration factors (BCF_shoot) of 2.5~62.2L/kg; In addition to roots and stems, A. uruguayensis leaves were also proven capable of accumulating PFASs independently, with stronger absorption capacity than roots and stems; A. uruguayensis showed selective accumulation of the six short-chain PFAS, preferentially concentrating hydrophilic small-molecule PFASs, with plant BCF showing significant negative correlations with logK_ow and molecular mass(ρ₁= -0.62, ρ₂= -0.81, P<0.05), and its BCF decreases with the increase of logK_ow and molecular mass; Short-chain PFAS underwent translocation within the plant, with a high contribution from roots to leaves, and a low contribution from leaves to roots.

Surface sediments of Dongting Lake were selected as the research object and the concentrations of non-extractable polycyclic aromatic hydrocarbons (PAHs) in the samples were analyzed using a combination of silanization and acidification methods. The study aims to investigate the impact of non-extractable residues on the pollution levels and source analysis of PAHs. The inclusion of non-extractable residues significantly altered the concentration and compositional characteristics of PAHs in the samples, raising the proportions of 3-ring PAHs from (19.78% ± 8.80%) in extractable fractions to (32.97% ± 11.16%) in total concentrations. The proportions of non-extractable PAHs did not exhibit a significant strong correlation with organic carbon or particle size, but were more affected by the total concentration of targets and pollution sources. Source apportionment results revealed that, after considering non-extractable residues, the main pollution sources shifted from traffic emissions (46.91%) to biomass and coal combustion sources (40.70%). The non-extractable residues play an important role in the source analysis of PAHs.

Dissolved organic matter (DOM) in surface water, which serves as a significant component within the carbon cycle, plays a vital role in the biogeochemical cycle of aquatic ecosystems. The triplet excited state of DOM (³DOM^*), singlet oxygen (¹O₂), along with hydroxyl radicals (·OH), which are the photochemical products generated from DOM, were taken as the research targets. Three regression algorithms were employed to constructed the machine learning (ML) models predictive models for evaluating importance of features including environmental factors, ultraviolet-visible light parameters and fluorescence parameters. The ML model based on the categorical boosting algorithm exhibited better performance for the prediction targets in the test set, with R² values of 0.9344, 0.8091, and 0.7775, respectively. Temperature, DOC, and pH were identified as the most important features for predicting ³DOM^*, ¹O₂ and ·OH, respectively. When applying the chained ML model incorporating with the categorical boosting algorithm, the prediction performance for ¹O₂ and ·OH was further optimized. For ¹O₂ the R² value in the test set increased from 0.8091 to 0.8233, and the RMSE decreased from 1.322 to 0.418. For ·OH the R² value in the test set rose from 0.7775 to 0.8094, and the RMSE dropped from 2.982 to 0.312. Additionally, the ML models perform better than the partial least squares regression model in predicting ³DOM^*, ¹O₂ and ·OH, indicating that the ML models possessed strong capability to handle non-linear relationships. The feature importance analysis based on ML successfully distinguished the key features in the DOM photochemical reaction. This finding can provide direct evidence for the intensive management of numerous influencing factors.

From October 2023 to September 2024, regular monitoring of water quality parameters and CO₂ concentrations in river water and groundwater was conducted in the upstream watershed of the Nantiao River in Lin'an, to quantitatively investigate their spatiotemporal variability. Results showed that both riverine and groundwater CO₂ concentrations exhibited pronounced temporal fluctuations. On a monthly scale, peak CO₂ concentrations in both river water and groundwater occurred in July, whereas the minima were temporally asynchronous, observed in December for rivers and January for groundwater, respectively. And seasonally, CO₂ concentrations in both river water and groundwater followed the order:summer > spring > autumn > winter. Overall, groundwater CO₂ concentrations averaged (152.04 ± 35.15) μmol/L, approximately 4.57 times higher than those in river water (33.24 ± 6.19μmol/L), indicating that groundwater may represent a significant potential source of aquatic CO₂ emissions in this region. CO₂ levels were markedly elevated in agricultural zones (river:68.47μmol/L; groundwater: 306.09μmol/L) compared to forested areas (river: 20.64μmol/L; groundwater: 73.93μmol/L). Significant positive correlations (P < 0.01) were observed between CO₂ concentrations and agricultural-related water quality indicators (e.g., TN, TP, DOC), whereas negative correlations were found with DO, pH, and water temperature. Multiple linear regression revealed that the optimal spatial scales of land-use type combination influence on CO₂ concentrations were 750m for river water (R² = 0.88) and 1250m for groundwater (R² = 0.58). Within these spatial buffers, riverine CO₂ concentrations were most strongly associated with residential land cover (r = 0.90, P < 0.01), while groundwater CO₂ was more influenced by cropland proportion (r = 0.83, P < 0.05). These findings suggest that intensive agricultural activities may significantly contribute to CO₂ enrichment in groundwater, which in turn serves as an important source of aquatic CO₂ emissions in the upstream watershed of the Nantiao River in Lin'an.

Based on the indoor two-dimensional sandbox DNAPL remediation experiment, the changes of DNAPL were monitored by the imaging method and the resistivity imaging method to evaluate the effectiveness and feasibility of the resistivity imaging method for monitoring. The results showed that: In the DNAPL infiltration experiment, both the resistivity imaging method and the imaging method could reflect that the DNAPL infiltration was mainly vertical and was mainly affected by gravity. Through the spatial moment analysis of the saturations obtained by the two methods, it was found that the trends of the horizontal centroids of the two methods were quite different, while the trends of the changes in the vertical centroid positions were basically the same. In the DNAPL remediation experiment, with the injection of the SDS solution, the relative resistivity value at the center of the DNAPL pollutant decreased from 25 Ω·m to - 10 Ω·m, and the saturation decreased from 0.42 to 0.15. When the correlation analysis was carried out at specific positions in the sandbox, the correlations between resistivity and saturation were 0.72, 0.84, and 0.92 respectively, and the correlation coefficients were all greater than 0.6, indicating that the two monitoring methods had consistency in interpreting the remediation process. By comparing the monitoring effects of the two methods on the DNAPL infiltration and remediation processes, it was found that during the infiltration process, the imaging method had a better monitoring effect. The imaging method could provide high - precision infiltration images and had a meticulous ability to capture the dynamic changes of DNAPL. During the remediation process, the resistivity imaging method had a better effect. The resistivity imaging could penetrate the medium to monitor the diffusion of the surfactant in different layers and spaces and obtain the macroscopic distribution trend.

In recent years, frequent coastal water environmental issues have been caused by economic and population development. In this study, water quality sampling was conducted in the Guan River, Huai River, and their adjacent coastal areas from October 30 to November 1, 2022, and from June 12 to June 14, 2023. The eutrophication status of the water bodies in the region was evaluated using the organic pollution index, nitrogen-to-phosphorus ratio, and eutrophication index method. Based on the Delft3D model, a three-dimensional hydrodynamic water quality model was established for the coastal areas of the Guan River and Huai River in northern Jiangsu Province. The transport and transformation processes of chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), chlorophyll-a (Chl-a), total nitrogen (TN), and total phosphorus (TP) were simulated. Six different discharge scenarios were designed, including varying the pollutant discharge volume from the rivers (reduced by 20%, reduced by 50%, and increased by 30%) and different discharge timings (normal discharge and discharge during ebb tide when estuary gates were opened). The results showed that the water quality concentration distribution in the coastal areas of the Guan River and Huai River exhibited significant temporal and spatial characteristics. The average concentrations of COD, NH₃-N, Chl-a, and TN were higher in summer than in winter. Spatially, the average COD concentration in the Guan River channel was lower than that in the estuarine coastal area, while the average concentrations of NH₃-N, Chl-a, TN, and TP gradually decreased along the Huai River channel toward the estuary. The overall water quality concentration in the study area displayed a fan-shaped diffusion trend from the river channels to the estuarine coastal zone. Future scenario simulations indicated that reducing the discharge volume by 50% and discharging pollutants during ebb tide when the gates were opened could most effectively reduce pollutant concentrations in the study area.

This study constructed a data-driven RF-LSTM model to predict Total Nitrogen (TN) concentrations in the Haihe River, based on monitoring data from 69 water quality stations collected between 2021 and 2024. By coupling the Random Forest (RF) algorithm with a Long Short-Term Memory (LSTM) neural network, the model also explored the spatiotemporal variation and key influencing factors of TN. Results showed that TN concentrations were relatively high in 2021 and lower in 2022, with annual averages ranging from 4.97 to 5.22mg/L. Across the five watersheds, TN levels displayed a U-shaped seasonal pattern, peaking in January and February and reaching the lowest levels in August. Temperature and dissolved oxygen were identified as the main influencing factors. TN concentration showed a highly significant negative correlation with temperature and a highly significant positive correlation with dissolved oxygen. The RF-LSTM model outperformed the traditional standalone LSTM model in prediction accuracy (R²:0.830~0.939, R²:0.818~0.931) and demonstrated stronger generalization performance.