In this study, fly ash, blast furnace slag, desulfurization gypsum, sludge, straw, and sawdust were utilized as raw materials. These materials were mixed at varying ratios, and Solanum nigrum was selected as the experimental plant for indoor pot trials. Farmland soil and contaminated soil from a metal mining wasteland were employed as controls. Variance analysis and Mantel tests were employed to analyze the effects of substrate ratios on Solanum nigrum growth characteristics, physicochemical properties of reconstructed soil, and relationships between substrate materials. A minimum dataset（MDS）for soil quality evaluation and entropy-weighted TOPSIS were applied to identify optimal substrate ratios. The results demonstrated that reconstructed soils formed by different solid waste ratios exhibited loose textures and enhanced water retention. Organic matter content was measured within a range of 39.01~70.03g/kg. All solid waste-based reconstructed soils were found to support Solanum nigrum growth, with biomass ranging from 0.11 to 3.18g/pot. A minimum dataset for soil quality assessment was established based on four critical indicators: chlorophyll content, plant height, pH, and water stability of 0.5~1mm aggregates. Subsequently, the entropy-weighted TOPSIS model was applied to systematically evaluate the comprehensive quality of the reconstructed soils. The optimal combination ratio, identified as fly ash: blast furnace slag : desulfurization gypsum : sludge : straw at a mass ratio of 4:2:1:1:2, demonstrated superior performance in both plant growth and soil functionality.

To comprehensively evaluate the reduction potential of diffuse nitrogen pollution under the field-ditch-pond system optimization, a watershed hydrological model was used to simulate the multi-scenario optimization of field ponding water level, ditch, and pond in a typical paddy field watershed. Results showed that different optimizations had different interception effects on diffuse nitrogen pollution, and that interception effects were different over distinct hydrological years. Under the optimization of field ponding water level, the total nitrogen loss from paddy fields after optimizing the drainage water level was reduced by 7.9% to 93.9% compared to conventional water level management, with the nitrogen interception effect being better in dry years than in wet years. Under ditch optimization, the reduction rate of nitrogen loss in the watershed increased from 0.8% to 26.7% after increasing the grass planting density of ditches, with the nitrogen interception effect being better in wet years than in dry years. Under pond optimization, the reduction rate of nitrogen loss in the watershed increased from 10.5% to 18.1% after increasing the catchment area of the pond, with the nitrogen interception effect being better in dry years than in wet years. Under the multi-optimization of the field-ditch-pond system, the interception effect of field ponding water level optimization on watershed nitrogen loss was better than that of pond optimization and ditch optimization. In summary, the multi-optimization of the field-ditch-pond system can effectively control the diffuse nitrogen pollution in paddy field watersheds and could promote the sustainable development of rice production.

The scouring of sediment deposits in sewer system constitutes a critical factor in overflow pollution. By integrating rainfall intensity variations, stratified anti-scouring characteristics, and dynamic pollutant transport, a dynamic transport model was developed to evaluate stratified sediment scouring under different rainfall intensities and its impact on water quality transformation. Experimental results demonstrate significant disparities in sediment scouring efficacy across varying rainfall intensities. Under light rainfall conditions, the scouring rate measured 6.04m³/h, primarily removing superficial sediment layers. Moderate and heavy rainfall events induced substantial enhancement of flow shear forces, with pipe discharge reaching 71.08m³/h during intense precipitation, capable of mobilising larger particles from underlying sediment strata. A pronounced "initial phase effect" was observed across all rainfall intensities, characterised by rapid pollutant concentration peaking during precipitation onset. Under heavy rainfall, sulphate concentrations surged to 17.89mg/L within 1min before stabilising at 8.95mg/L, while Total Chemical Oxygen Demand（TCOD）exhibited a swift ascent to 2106.3mg/L followed by stabilisation at 1056.6mg/L. In contrast, light rainfall conditions yielded markedly lower peak values of 10.29mg/L for SO₄^2- and 1100.60mg/L for TCOD, though similarly demonstrating rapid initial concentration escalation followed by gradual stabilisation.

High-concentration areas of soil heavy metals exist in ecological functional protection zones of Southwest China. To investigate their potential pollution risks and influencing factors, this study selected soils in the Xuanwei region of Yunnan Province as the research subject. Combining geographical, geological, and anthropogenic activity data, the content characteristics of eight heavy metal elements were analyzed in 1487 surface soil samples and 374 deep soil samples. The geo-accumulation index and potential ecological risk index were used to assess the potential pollution risks of surface soils, while principal component analysis（PCA）and geographical detector methods were employed to identify influencing factors. The results revealed that heavy metals in both surface and deep soils were enriched compared to the A-layer and C-layer background values of Chinese soils. Most heavy metals also exhibited enrichment relative to the A-layer and C-layer background values of Yunnan Province. Cd, Hg, and Pb posed relatively high potential pollution risks, whereas Cu, Cr, Ni, Zn, and As showed lower risks. The main influencing factors for heavy metal enrichment included geological background, clay minerals, organic matter, mining activities, and topography. Synergistic effects of multiple factors could exacerbate heavy metal enrichment, while pH, CaO, Light（light index）, and WIG exhibited minimal impacts.

A total of 78 water samples from mesotrophic and eutrophic urban lakes in Nanjing, Jiangsu Province, China were systematically analysed using three-dimensional excitation-emission matrix（EEM）fluorescence spectroscopy coupled with parallel factor analysis（PARAFAC）to characterize dissolved organic matter（DOM）composition and sources as well as their links with water physiochemistry parameters. This study was designed to unravel the effects of eutrophication on the fluorescence characteristics of DOM and its influence pathway. Three distinct fluorescent components were identified: anthropogenic-derived humic-like substances（C1）, terrestrial humic-like substances（C2）, and microbial-produced protein-like substances（C3）. Eutrophic urban lakes exhibited significantly higher fluorescence intensities in both C2 and C3 relative to mesotrophic urban lakes. Fluorescence indices confirmed dual DOM sources-terrestrial inputs and autochthonous production. Meanwhile, although the humification index of DOM in mesotrophic urban lakes exceeded that in eutrophic urban lakes, the humification index of DOM was below 1.0 for all samples. Moreover, the biological index of DOM exhibited an increasing trend with the elevation of trophic status of water column. Additionally, significantly elevated Chla concentration were observed in eutrophic lakes compared to mesotrophic lakes. Strong positive correlations were identified between Chla concentration and the fluorescence intensity of C2 and C3 components alongside the biological index of DOM, suggesting that eutrophication may induced autochthonous DOM by stimulating algae reproduction.

Based on the monitoring data of 47 typical fine chemical in-process sites in Shanghai, the pollution of soil and groundwater in this industry was analyzed, the health risk of pollutants was evaluated, and high-priority pollutants were screened. The findings revealed that the industries with the highest pollution risk were those involved in basic chemical raw material manufacturing, pesticide manufacturing, paint and similar product manufacturing, and specialty chemical product manufacturing, collectively representing 70% of the contaminated sites. In comparison, the chemical raw material manufacturing industry exhibited a pollution risk of 54.55%, while the daily chemical products manufacturing industry showed the lowest pollution risk（0%）. The most frequently detected contaminants in the soil and groundwater included arsenic, lead, mercury, nickel, zinc and total petroleum hydrocarbons（TPH）, which were present at relatively low concentrations. In contrast, benzene derivatives（BTEXs）, polycyclic aromatic hydrocarbons（PAHs）, and most chlorinated hydrocarbons（CAHs）were detected in small areas of certain sites, indicating more severe contamination levels. Furthermore, cyanides, antimony, manganese, and tetrachloroethylene were found in larger areas of certain sites, representing the highest contamination levels. The pollution profile of the basic chemical raw material manufacturing industry was the most complex, involving cyanide, antimony, arsenic, manganese, mercury, CAHs, PAHs, and TPH. On the other hand, pesticide manufacturing, paint and similar product manufacturing, and specialty chemical product manufacturing industries were primarily contaminated by CAHs and BTEXs. A notable observation was the evident soil-water compound pollution phenomenon involving chlorinated hydrocarbons and benzenes. Through comprehensive environmental exposure and human health risk assessments, the following were identified as high-priority soil pollutants: arsenic, chloroform, 1,2-dichloroethane, benzo[a]pyrene, antimony, vanadium, trichloroethylene, lead, carbon tetrachloride, and naphthalene. For groundwater, the high-priority pollutants included trichloroethane, tetrachloroethylene, cyanide,1,2-dichloroethane, benzene, methylene chloride, nickel, antimony, manganese and trichloroethylene.

Volatile organic compounds（VOCs）were identified as chemicals that caused odor pollution in cars. Nanostructured water ion（EWNS）technology was demonstrated to be capable of producing large amounts of hydroxyl radicals to degrade VOCs, though systematic research on odor VOC removal had not been conducted. In this study, VOCs components from 9cars were first analyzed offline using TD-GC-MS, and the main odor VOCs in actual car interiors were determined through the comprehensive scoring method. A mixture gas containing the average concentration ratio of detected odor VOCs was then introduced into the experimental vehicle, where degradation efficiency was evaluated using EWNS technology. Human health risk assessment was also performed. The results revealed that aliphatic compounds were detected most frequently（7species）in car interior VOCs, while aromatic compounds were found to have the highest detection rate and concentration. Through odor identification scoring, xylene, toluene, ethyl acetate, o-xylene, n-butanol, and hexanal were selected as representative odor VOCs. After EWNS treatment, significant removal effects were observed for all representative odor VOCs, with toluene removal rate being recorded at 92.8%. However, EWNS degradation efficiency was found to vary with placement locations in the vehicle, where better removal effects were achieved in rear positions compared to front positions, potentially associated with hydroxyl radical diffusion efficiency. Meanwhile, the carcinogenic risks of benzene and ethylbenzene were significantly reduced, transitioning from carcinogenic risk to risk-free status.

The combination of ground remote sensing and UAV remote sensing was used to estimate the nitrogen content of typical grassland vegetation in Inner Mongolia. This experiment was carried out in the grassland ecology research base of Inner Mongolia University from August to September 2023, and the ground ASD spectral data and UAV Resonon data were collected. Based on ASD data, four spectral parameters of vegetation index, hyperspectral characteristic variable, continuum removal variable and wavelet coefficient were constructed, and LASSO was used to screen sensitive parameters. Five models of multiple linear, XGBoost, SVM, ANN and KNN were constructed to estimate the nitrogen content of vegetation. The results showed that the SVM method based on wavelet coefficients was the optimal model（validation set R²=0.72, RMSE and MAE were 0.26 and 0.18, respectively）. Finally, the model was used to estimate and map the UAV Resonon data（validation set R²=0.41, RMSE and MAE were 0.42 and 0.32, respectively）. The research showed that the combination of ASD and UAV images with machine learning algorithms could be used to realize the estimation of grassland vegetation nitrogen content, and was provided basic data and technical support for optimizing fertilization and improving forage quality.

This research conducted an experiment on DNAPL contamination in a saturated porous medium within a three-dimensional sandbox and performed synchronized dynamic monitoring using electrical resistivity tomography（ERT）. The resistivity images obtained from ERT were used to determine the spatial distribution of DNAPL contaminants, which were then compared with the numerical simulation model established in the sandbox experiment. The absolute value of the relative error in the diameter of the DNAPL distribution area obtained from the numerical simulation and the DNAPL distribution area determined by the ERT monitoring ranged from 2.00% to 27.50% across different spatial locations. The absolute value of the relative error in the diameter of the DNAPL distribution area obtained from the numerical simulation and the DNAPL distribution area determined by the ERT monitoring ranged from 2.7% to 40.58% at different time points. The results demonstrate the feasibility of using the numerical simulation software Petrasim to predict the distribution range of DNAPL contamination in saturated sandy soil.

The rapid detection of biological toxicity of Marine pollution is of great significance to the emergency monitoring of sudden Marine water pollution accidents, the rapid investigation and evaluation of Marine environment ecological risks, and the protection of Marine environment. However, existing biological toxicity detection methods have a long toxic response time, and it is difficult to realize the rapid response of Marine pollutants. In view of this, this study has taken the active marine microalgae, Platymonas subcordiformis, as the test organism to study its mobility response to typical heavy metal pollutants Mercury（Hg）and Chromium（Cr）in seawater, in order to provide a basis for establishing a rapid detection method for marine biological toxicity. The results ha showed that within 2h, the motion parameters of the Platymonas subcordiformis, including movement mode, movement ability, and swimming velocity, exhibited significant response characteristics to 0.075~2.5mg/L Hg and 0.1~3mg/L Cr, and there was a good dose-response relationship between the motion parameters and the concentrations of the two independent heavy metals；The 2h-EC₅₀ values of Hg and Cr obtained based on different motion parameters were 0.63~2.38mg/L and 0.60~2.49mg/L, respectively；Overall, among different motion parameters, the curve velocity（VSL）has the most sensitive response to the toxicity of heavy metals Hg and Cr. The 2h-EC₅₀ values of Hg and Cr obtained using VSL as the response index are 0.63 and 0.60mg/L respectively. The above results are comparable to traditional toxicity tests（including microalgae 72-hour growth inhibition test, 24~48 hours photosynthesis inhibition test, and 96-hour fish death test）, and the toxicity response time is significantly reduced, indicating that microalgae motility as a new biological testing indicator can quickly and effectively evaluate the toxicity of marine heavy metal pollutants.