Dust pollution data (particles<920μm) collected across China from 2000 to 2023 were integrated. The results showed that heavy metal contamination in road dust was generally higher than in soils,yet evaluation methods produced inconsistent conclusions. Therefore, a risk assessment system tailored to the unique characteristics of dust was considered essential.The spatial distribution of urban road dust pollution in China showed a steadily increasing severity from the northwest to the southeast, and pollution sources were found to be complex. Source control alone was found to be insufficient to ensure long-term effectiveness, necessitating an adaptive governance approach. Based on the resilience governance concept, anovel urban road dust pollution risk assessment system was constructedusing the Pressure-State-Response (PSR) model, Analytic Hierarchy Process (AHP), and entropy weight method, incorporating a socio-ecological perspective. An empirical analysis of 31 cities classified them into five clusters: high-pressure-risk, high-response-risk, combined high-state-response-risk, combined high-state-pressure-risk, and low-risk—yielding tiered and category-specific recommendations: priority population-exposure studies in dense traffic areas, establishment of dust-monitoring and feedback networks, targeted abatement of centralized emission sources, enhanced early-warning systems for extreme-weather dust events, and fixed-site surveillance in sensitive zones.
The research progress of high background of soil heavy metals related to carbonate rocks was systematically reviewed from the aspects of spatial distribution, element combination, spatial coverage, controlling factors, formation process, and risk characteristics. At present, it was generally believed that the main causation for the formation of this type of high background was the “secondary enrichment” of elements in the weathering process of parent rock, that is, the leaching of major elements, e.g. Ca, caused the relative enrichment of trace elements, e.g. Cd. In this process, iron and manganese minerals played an important role in the in-situ concentration of these elements. From the geographical perspective, the hot and humid climate was conducive to the formation of a higher background content. In general, Cd and other elements in soil developed from carbonate eluvium had the character of “high background with low activity”, while in the alluvial areas formed by carbonate weathered materials, high acid soluble state of Cd might have occurred with risks of activation under conditions such as soil acidification. With high spatial variation of soil heavy metals in such areas, especially in the medium and small spatial scales, problems such as differentiation between natural background and anthropogenic pollution, quantitative recognition of relative risks, and development of control measures for environmental management, were required to be solved in the future study.
Quantifying Hg flux in lake systems remains challenging due to the synergistic influences of multiple environmental drivers. To address this, we investigated Changshou Lake, an artificial reservoir situated in a karst region, through an integrated approach that included field sampling, path analysis, and machine learning, to elucidate the mechanisms of Hg flux exchange. The results indicated that Hg flux was jointly regulated by multiple factors via both direct and indirect pathways. Water Hg concentration (0.897), UV radiation (0.463), and solar radiation (0.446) were identified as exerting dominant direct effects on the production of dissolved gaseous mercury (DGM, Hg0). In contrast, the influence of wind speed on Hg flux was observed to be stronger through its indirect enhancement of solar radiation (0.252) than through direct diffusion (0.197). A hybrid machine-learning prediction mode [0.9 Gradient Boosting Regression (GBR) + 0.05 Random Forest (RF) + 0.05 Support Vector Regression (SVR)] was developed. This model was shown to improve generalization ability and stability while maintaining predictive accuracy, enabling dynamic prediction of Hg flux under the coupling of multiple lake parameters. The approach provides decision support for regional Hg risk assessment and management.
A carbonate-mineralizing bacterial strain C7-1was isolated from karst soil and identified as Serratia marcescens. Through single factor test combined with characterization technology, the cadmium (Cd) removal efficiency and underlyingmechanism of S. marcescens were analyzed, along with its ability toremove Cd, lead (Pb), zinc (Zn), and copper (Cu) from soil. The results indicated that the urea concentration was the main factor controlling Cd2+ removal.The maximum Cd2+ removal rate reached 74%, which was primarily via extracellular precipitation (42%) followed by surface adsorption (27%). Cd2+ may bind to CO32- to form CdCO3 based on scanning electron microscope-energy dispersive spectroscopy (SEM-EDS), fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The removal efficiencies of strain C7-1 for Cd2+, Pb2+, Zn2+ and Cu2+ achieved 22.9%~65.9% in soil filtrates. The heavy metal ions were found to form amorphous mineral precipitates through co-precipitation and were adsorbed onthe bacterial surfaces via functional groups, including hydroxyl, carboxyl, carbonate, phosphoric acid, and sulfuric acid groups.
To investigate the synoptic mechanisms underlying a rare, widespread floating dust weather process in Guangdong Province during 12~16 April 2025, analyses were conducted using multi-source integrated observational data and reanalysis data. The results reveal:The cold front guided by the East Asian trough generated a dust area in southwestern Mongolia and drove it southward into Guangdong Province.Under the influence of dust, PM10 concentrations exceeded standards across all 21 cities in Guangdong, resulting in a total of 54 polluted cities (Including 6heavy pollution). On April 13, provincial average PM10 concentration reached 305.2μg/m3, 2.4 times the magnitude of the second-highest concentration since 2014. Notably, Guangzhou registered the highest daily average PM10 concentration (400μg/m3), significantly exceeding levels observed during the dust intrusion event affecting Guangdong in 2009. Climate analysis of the dust source region revealed that influenced by northwesterly flow ahead of a ridge, the cumulative precipitation in most parts of western Mongolia from February to March was below 10mm, significantly lower than the historical average for the same period. This resulted in soil moisture dropping to the fourth lowest level in the past 30 years, creating conducive underlying surface conditions for dust emission. Analysis of the dust transport process identified that the cold front generated the strongest northerly winds in the Nanling Mountains region in the past three decades. Driven by extreme winds, dust particles were carried directly across the Nanling Mountains andtransported southwards into Guangdong Province. Backward trajectory analysis indicated that dust particles reaching the four major regions of Guangdong were initially transported southward from Inner Mongolia to Hubei Province. Subsequently, they followed three distinct pathways (northern, northeastern, and northwestern routes) southward, traversing mountainous terrain of varying elevations to enter Guangdong, ultimately causing the extensive floating dust event.
A two-month observation of atmospheric PM1 in suburban Guangzhou in autumn 2022 was conducted using a Soot-Particle High Resolution Time-of-Flight Aerosol Mass Spectrometer (SP-AMS). Sources apportionment of organic aerosol (OA) was performed using Positive Matrix Factorization (PMF). OA’s characteristic component particulate organic nitrates (pON) were quantified and explored. During the observation period, PM[ (15.90 ± 12.28) μg/m3] was dominated by organics (57.8%), with major components (excluding sulfate) exhibiting higher nocturnal concentrations than diurnal concentrations. OA was mainly contributedby local secondary formation (28.3%), long-range transport (23.9%), and primary emissions such as cooking, vehicle exhaust, and biomass burning (collectively 47.8%). pON contributed significantly to OA (24.7%), increasing markedly under low total nitrate concentrations. Its nocturnal increase may enhance next-day atmospheric oxidation capacity. pON mainly derived from local secondary formation (51.5%) and vehicle exhaust (39.8%). During high PM events, OA was dominated by local secondary formation and vehicle emissions; during high ozone conditions, organics and sulfate showed pronounced midday peaks (photochemical oxidation), secondary OA contribution rose to 59.8%, and pON was primarily from local secondary formation (60.8%).
To investigate changes in the emission characteristics of key pollutants following the transition from household coal-burning to biomass clean heating in the Fenwei Plain, field measurements were conducted in rural areas of Xianyang using a variety of residential biomass-burning devices. Results showed that, in automatically fed pellet stoves, particulate emissions were dominated by PM2.5, with emission factors for PM2.5 and PM10 of 1.14g/kg and 1.28g/kg, respectively, significantly lower than those of traditional stoves. Water-soluble ions in PM2.5 were dominated by K+ and Cl-, accounting for 81.5 %~94.3 % of total ionic mass, which was 25.9 %~69.6 % higher than that in traditional stoves. Due to the use of cleaner fuels and more efficient combustion, the total carbon (TC) emission factor in PM2.5 from pellet stoves ranged from 0.1to 0.4g/kg, in contrast to 0.6~3.6g/kg for traditional stoves. Levoglucosan (LG) in PM2.5 from pellet stoves showed the lowest emission factor, with a lower LG/OC ratio than that of traditional stoves, indicating a reduced contribution of advanced stoves to organic carbon aerosol emissions. By integrating the experimentally derived LG emission factors into an established empirical model, we estimated that the clean-heating transition had reduced the contribution of residential biomass burning to organic carbon aerosol by approximately 6.4 %.
Based on the civil aviation emission inventory for China in 2019, the impacts of aviation emissions on the atmospheric environment were quantified using the CAMx model. Furthermore, the contributions of various flight phases to ground-level pollutant concentrations and their vertical distribution characteristics were explored. The results showed that aviation emissions were found to contribute 0.045 and 0.26μg/m3 to the annual average ground-level PM2.5 and O3 concentrations in China in 2019, respectively, with significant regional and seasonal variations being exhibited. The impact of aviation emissions on PM2.5 was observed to be greater in winter than in summer, while the higher photochemical reaction efficiency in summer was found to facilitate the generation of O3. The influence of aviation emissions on ground-level PM2.5 concentrations was mainly concentrated during the landing and take-off cycles, whereas O3 formation was primarily attributed to emissions during the cruise phase. The vertical distribution of the contributions of aviation emissions to PM2.5 and O3 concentrations was observed to exhibit pronounced seasonal variations. In summer, the contribution ratios in East China, Central South, and Southwest regions were found to peak at the 5~9km altitude layer (4.7% for PM2.5 and 10.6% for O3), whereas in winter, they were concentrated in the lower 0~2km layer (6.9% for PM2.5 and 10.8% for O3). In contrast, in regions such as Northwest China and Xinjiang, the peak contribution of aviation emissions to PM2.5 concentrations during winter was primarily concentrated in the lower altitude layer below 1km (9.1% for PM2.5 and 5.9% for O3). However, in summer, influenced by dust storms, the peak contribution of PM2.5 concentration in the northwest region was observed at an altitude of approximately 4km (8.1%).
This study investigated cold and hot start processes of diesel vehicles under different load conditions in summer using a heavy-duty chassis dynamometer, with a focus on analyzing NOx and NH3 emission characteristics and influencing factors. The results showed that during cold starts, the exhaust gas temperature and coolant temperature rose slowly, and the NOx sensor was delayed in activation, with over 82% of NOx emissions occurring before the sensor became operational. During hot starts, the SCR system and sensor entered operation rapidly due to higher system temperatures, leading to significantly lower NOx emissions. NH3 emissions were significantly affected by the start condition and simulated load. In the initial phase of cold starts, the NH3 emission factor was higher than average because of low SCR temperatures. During hot starts, rapid urea hydrolysis without sufficient reaction with NOx resulted in increased NH3 emissions. Micro-scale operational modal analysis revealed that the difference in NOx emissions was mainly determined by engine and SCR temperatures, whereas the difference in NH3 emissions was associated with SCR temperature and urea injection quantity. The study emphasizes the need to address pollutant emissions during summer cold-start processes and proposes synergistic optimization of temperature control and SCR urea injection strategies to reduce the environmental impact.
Based on the International Civil Aviation Organization (ICAO) standard emission model, combined with statistical data from the Civil Aviation Administration of China (CAAC), an updated 2023~2024 air pollutant and carbon fusion emission inventory for the landing and take-off (LTO) cycles of civil aviation airports in mainland China was constructed, and the accuracy of the emission inventory was verified using the CHAP dataset. The results showed that in 2023, NOx, CO, HC, SO2, PM, and CO2 emissions from LTO cycles at Chinese civil aviation airports were 1.277×105, 1.010×105, 0.084×105, 0.037×105, 0.010×105, and 176.739×105t, respectively. In 2024, the corresponding emissions were 1.353×105, 1.070×105, 0.089×105, 0.039×105, 0.010×105, and 187.189×105t, with a total LTO phase emission increase of 5.91% compared to 2023. The spatial and temporal characteristics of emissions in 2023 and 2024 indicated that economically developed eastern regions (such as Beijing, Shanghai, and Guangzhou) had CO2 emissions accounting for more than 26% of the national total due to dense flight operations, whereas emissions were lower in western regions due to the limited number of regional airports. Empirical models showed a strong correlation between NOx emissions and CHAP concentration data (r=0.50), validating the reliability of the inventory.
Taking the typical urban tunnel (Jinhua Tunnel) in Xi'an as the research object, the light-absorption characteristics, molecular composition, and impact on visibility of brown carbon (BrC)from vehicle emissionswere investigated by employing aerosol size distribution measurements, PM2.5 chemical speciation, combined withUV-vis spectrophotometry, and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The results showed that aerosols within the tunnel were predominantly ultrafine particles (0.3~0.6μm), with the highest number concentration (2.38×105#/cm3) observed in the internal section, consistent with traffic flow patterns.OC (31.11μg/m3) and EC (7.97μg/m3)concentrations in PM2.5 were significantly higher than those in the open atmosphere.Elevated levels of SO42-and Ca2+ indicated the synergistic effect of primary vehicle emissions and secondary aerosol formation. Optical analysis revealed a high BrC absorption coefficient of 329.2 Mm-1 in the UV region (200nm) during weekends. At 550nm, the absorption contribution to total extinction was 0.32%. However, this contribution might be underestimated because the full visible spectrum and particle size effects were not considered. At the molecular level, 12548 molecular formulas were identified, primarily categorized as CHO (57.7%), CHOS (19.6%), and CHON (17.3%) compounds. CHON compounds exhibited strong light absorption (34.5% with DBE/C>0.5), while CHONS compounds, characterized by extremely low volatility (91.3% identified as ELVOCs), were more likely to accumulate in the particle phase. This study elucidated the relationships between molecular conjugation, heteroatom interactions, and the light-absorbing capacity of BrC within the tunnel. The visibility effects were quantified, providing a data basis for the refined management of traffic-related BrC, and for improving tunnel air quality and optimizing ventilation and lighting conditions.
Ammonia (NH3), a cornerstone chemical for sustaining modern society, is traditionally produced via the energy-intensive and carbon-emissive Haber-Bosch process. In light of growing sustainability demands, the electrocatalytic nitrate reduction reaction (NO3RR), powered by intermittent renewable energy, has recently emerged as a multidisciplinary research frontier. This reaction not only enables ammonia synthesis under ambient conditions but also offers the dual benefit of wastewater remediation and value-added chemical production. This Review highlights recent advances in single-atom site catalysts for NO3RR. We first discuss the reaction mechanisms underlying nitrate-to-ammonia conversion, with an emphasis on in situ characterization techniques essential for mechanistic insights. We then provide a comprehensive overview of atomically dispersed catalysts in NO3RR, focusing on the structure–activity relationships that inform the rational design of efficient systems. Finally, we summarize key findings and offer perspectives on current challenges and future directions. This Review aims to inform the development of environmentally benign ammonia synthesis strategies and nitrate valorization technologies, contributing to the advancement of green catalysis in the context of carbon neutrality.
Addressing the challenge of nitrogen-containing wastewater treatment, the photocatalytic-microbial coupled denitrification technology has emerged as a new research hotspot due to its advantages of high efficiency, cost-effectiveness, and environmental friendliness. After a comparative analysis of the advantages and disadvantages of different photocatalytic denitrification technologies, a detailed introduction to the current research status of domestic and international photocatalytic-microbial coupled denitrification processes is provided. The review emphasizes three major forms of the coupled system, including indirect sequential coupling of photocatalytic and microbial, direct coupling, as well as bioelectrochemical-photocatalytic coupling systems. The intrinsic interaction mechanisms within the coupled system are deeply explored, such as the physiological and ecological responses of microorganisms to photocatalysts, the optimization of microbial communities by the photocatalytic process, the migration and transformation mechanisms of photocatalysts within biofilms, and the electron transfer processes between photocatalysts and microorganisms. Finally, the article points out the existing bottlenecks in this technology, such as the development of visible-light materials, the safety of nanomaterials, the long-term stability of the system, and the scaling up for engineering applications. Suggestions are made for deepening the mechanism research, developing new coupled processes, optimizing materials, and promoting engineering applications, with the aim of providing a reference for the efficient and green treatment of nitrogen-containing wastewater.
Outbreak of blue-green algae posesa threat todrinking water safety. Coagulation is a key technology for algae removal in waterworks, andiron salt is one of the most commonly used coagulants. To further explore the mechanism of iron salt for removing algae, Microcystis aeruginosa and algal organic matters were selected as the target pollutants. The effect of crystal transformation of the main hydrolysis products on removal efficiency and coagulation process was investigated. The results indicated thatthe transformationof crystalline state, crystalline phase, and crystal form of hydrolysis products were mainly affected by the coagulant dosage and pH value. With the increase of dosage and pH, the main hydrolysis product FeO(OH) was replaced by amorphous Fe(OH)3(PDF#22-0346), and then converted to Fe3O4. Amorphous Fe(OH)3 finally transformed into quasicrystalFe (OH)3(PDF#38-0032). The surface characteristics of algal flocs were affected by the crystalline state and crystal form change of hydrolysis products. The maximum SBET and Vt values of algal flocs were observed at a dosage of 0.7mmolFe/L, which werebeneficial to adsorb algal organic matters andresulted in the optimal treatment efficiency. Within the range of 0.1~0.5mmolFe/L, the crystal transformation of the main hydrolysis products had a more significant effect on algae removal efficiency, particle size and growth rate of algal flocs.
This study investigated the impacts of pipeline environmental factors (hydraulic shear force, temperature, darkness/light conditions, pipe materials) and continuous flow conditions on odor production characteristics of three typical odor-producing algae: Microcystis aeruginosa, Pseudanabaena galeata, and Oscillatoria. Key findings revealed that hydraulic shear force significantly promoted 2-MIB release in chain-forming Pseudanabaena galeata and filamentous Oscillatoria, with rupture rates exceeding 90% under high shear conditions. In contrast, spherical Microcystis aeruginosa exhibited structural tolerance with rupture rates below 30%. Temperature markedly influenced β-cyclocitral production in Microcystis aeruginosa, reaching 40ng/L at 30℃. Although low temperature induced cell rupture, it suppressed metabolic activity, resulting in lower odorant concentrations. Darkness and pipe materials showed limited effects, only slightly increasing rupture rates (<5%) and odorant concentrations (<8ng/L). Continuous flow experiments demonstrated accelerated 2-MIB release in Pseudanabaena galeata and Oscillatoria under high flow velocity, achieving peak concentrations (up to 85ng/L) within 0.5hours-significantly faster than batch experiments. The study identifies hydraulic shear force and temperature as primary control factors for odor production, providing scientific guidance for odor management in water distribution systems.
The release of iron in drinking water distribution systems (DWDSs) poses a severe threat to water supply safety. By establishing a static simulation experimental system to systematically investigate the coupled effects of fulvic acid (FA), sulfate ions (SO42-), and alkalinity (Alk) on iron release in DWDSs and the changes of pipe scale micromorphology, crystalline composition, and elemental speciation. Based on principal component analysis (PCA), a prediction model for iron release was developed, quantitatively characterizing the relative importance of various influencing factors and their interaction intensities. Results showed that under conditions of FA=4mg/L, SO42-=240mg/L, and Alk =100mg/L CaCO3, iron release reached the maximum of 4.82 and 3.60mg/L for steel and cast iron pipes, respectively. SO42- and FA were positively correlated with iron release, while alkalinity showed negative correlations. The relative influences of these factors followed the order: alkalinity > SO42- > FA.
Graphene oxide-chitosan (GO-CS) composite materials were prepared using the hydrothermal method, and a single-stage autotrophic nitrogen removal reactor was constructed for continuous experiment. Batch experiments were conducted to evaluate its concentration-response characteristics, and molecular biology method was applied to elucidate the succession patterns of microbial communities. The effect of GO-CS addition on the performance of single-stage autotrophic nitrogen removal was systematically investigated. The results showed that 20mg/L GO-CS promoted anaerobic ammonium oxidation by facilitating electron transfer; at a concentration of 50mg/L, the activity of AnAOB was inhibited which might be there were other reactions which competing for the use of GO-CS as an electron transfer; and after long-term exposure to 100mg/L GO-CS, AnAOB achieved adaptive recovery by increasing the secretion of extracellular polymeric substances (EPS) and regulating functional genes.
In order to improve the water pollution control level in the basin and solve the shortcomings of existing optimization emission reduction technologies, a data driven theory-based optimization emission reduction model for river pollution was developed with the combination of Bidirectional Long and Short Memory Network (Bi-LSTM) algorithm and Bayesian Optimization (BO) algorithm considering the receiving water body as the research object. The developed optimization emission reduction model was then applied to Songhua River basin from Baidu Town and Sandao to Hongkeli section. The results show that the Bi-LSTM algorithm can effectively train the spatial neural network architecture, and improve the study accuracy of water quality spatial relationship. The reduction rates of TN in Baidu Town and Sandao are [14.12%, 38.84%] and [15.01%, 38.98%], respectively, and TP hardly needs to be reduced under the water quality standard III. While the reduction rates of TN and TP in Baidu Town are [19.08%, 39.72%] and [0.00%, 41.93%], and the ones in Sandao are [18.43%, 40.09%] and [0.00%, 36.24%], respectively under the water quality standard II. The optimal reduction strategies can be obtained by the developed optimization emission reduction model under different reduction scenarios to provide decision support for accurate reduction and intelligent control of river pollution.
The accuracy of particulate organic matter (POM) detection in sewage was established as the fundamental basis for the systematic characterization of water pollution features. A multi-dimensional synergistic pretreatment system was developed, through which efficient fragmentation and solubilization of POM were achieved by coupled energy field interactions—alkaline hydrolysis (chemical energy), thermal treatment (thermal energy), and ultrasonic cavitation (mechanical energy). This integrated approach was found to enhance sample homogeneity, thereby optimizing spectroscopic detection outcomes. Based on single-factor experimental results, Box-Behnken response surface methodology (RSM) was employed to conduct multi-parameter collaborative optimization of critical control parameters (pH, temperature, and ultrasonic energy) in the composite pretreatment process. The results indicated that: significant differential impacts of the treatments on POM disruption efficiency were observed. Ultrasonic treatment contributed more to TCOD detection accuracy (ACOD was improved from 60.8%±14.9% to 93.3%±3.7%) and total carbohydrate (TCHO) detection accuracy (ACHO was increased from 59.1%±9.6% to 97.9%±2.8%), while alkaline treatment showed superior optimization effects on total protein (TPN) detection accuracy (APN was enhanced from 32.7%±9.7% to 82.7%±3.9%); Optimal process parameters were obtained through RSM model optimization: pH=13.3, T=66.5°C, t=5min, under which enhanced detection accuracies of 96.1% for TCOD, 98.7% for TPN, and 97.5% for TCHO were achieved. The synergistic pretreatment method was demonstrated to have broad applicability across various wastewater scenarios, including primary pipeline sewage, influent of wastewater treatment plants, and effluent from biochemical reactors, effectively reducing detection duration while maintaining robust universality.
A one-year study was carried out to investigate the changes in water quality parameters such as water temperature, residual chlorine, turbidity, pH, UV254, dissolved organic carbon (DOC) and trihalomethanes(THMs) along the main pipe of a regional water supply system in Suzhou (2km sampling interval). The results of spatial scale analysis showed that the concentration of THMs at the end of the water supply pipe network at 8~10km was at a relatively high level; the results of time scale analysis showed that the concentration of THMs at the end of the water supply pipe network reaches the highest peak in June, with the concentration of 39.06 μg/L. Four typical machine learning algorithms were selected to construct the prediction model of THMs in the pipe network, and the interpretable gradient boosting tree (GBDT) algorithm was able to effectively avoid model overfitting, and the R2 of the four THMs prediction accuracies reached 0.839, 0.906, 0.836, and 0.935, respectively. The further application of Shapley's additional explanation (SHAP) to the global analysis of the GBDT model revealed that the water temperature, UV254, DOC, and the distance of the water supply were positively correlated with the generation of THMs, while the residual chlorine showed a significant negative correlation with the THMs. The local analysis of SHAP showed that residual chlorine concentration in the water supply network lower than 0.45, 0.55 and 0.55mg/L can significantly reduce the influence weights of residual chlorine on TCM, BDCM and DBCM. Based on the data-driven model constructed in this study, the generation concentration of THMs can be efficiently predicted by environmental factors, and the residual chlorine dosage can be controlled in real time to prevent the over-generation of THMs in summer or at the end of the pipe network.
This paper reviewed current pollution situation of used and waste textile. the global trade of used and waste textiles was analyzed based on the United Nations Comtrade Database. The problems faced in China in this field were analyzed by tracking the management of used and waste textiles in the international community and typical countries, and in combination with China's used and waste textile policies and recycling practices. China didn’t effectively distinguish between "waste" and "used" textiles, lacked authoritative data on the generation, encountered numerous obstacles in industrial development, and there were disputes over the export of second-hand clothing. It was suggested that China's used and waste textile management should distinguish between "waste" and "used" textiles from the source, improve the data statistics system to separately count their relevant data, cultivate enterprises with long industrial chains to unblock industrial channels, track international trends and implement export restrictions in a timely manner.
This paper focuses on the problems of landfill gas leakage and the difficulty in controlling malodorous gases during the landfilled waste excavation. A field trial was conducted on a 30m×30m site in a Hainan landfill, equipped with 13injection, 12 extraction, and 16 monitoring wells. An intensive aeration pretreatment experiment was conducted with a gas injection flow rate of 340m3/h and a gas extraction flow rate of 360m3/h to explore the control effects of aerobic intensive aeration on methane and malodorous gases. The results indicate that within 2h of the first intensive aeration, the ambient landfill gas was rapidly displaced, with the oxygen content in the test area exceeding to above 17%, the average methane content decreasing to 1.9%, and the average hydrogen sulfide content decreasing from 21.3mg/m3 to 5.7mg/m3. In contrast, ammonia levels rose sharply to 286.7mg/m3, which is attributed to the stripping of pore leachate by aeration-induced disturbance. Prolonging the aeration duration was found to be crucial for suppressing the rebound of methane and hydrogen sulfide after aeration ceased. After the first 9-day aeration test, methane concentrations at depths of 4.5m and 8.5m were reduced to 0.33% and 0.61%, with hydrogen sulfide inhibition rates of 81.0% and 83.9%, respectively. However, within 8hours of stopping aeration, methane levels rebounded to above 4% and 6%, and inhibition rates for hydrogen sulfide dropped to 65.6% and 74.1%. In contrast, after the second 30-day aeration test, methane concentrations remained below 3%, and hydrogen sulfide inhibition rates were maintained at 81% and 91%, even 48 hours post-aeration. It was estimated that the two trials cumulatively removed approximately 5779.6 mol of ammonia, accounting for 45% of the total ammonia nitrogen in the pore leachate of the test area. The study demonstrates that appropriate short-term, high-intensity aeration pretreatment can effectively inhibit odorous gas emissions during the subsequent excavation of landfill mass.
A combined conditioning system using NaClO as the oxidizing agent was employed to condition sludge. The variations in sludge dewatering performance before and after conditioning were investigated to validate the enhancing effect of ultraviolet irradiation on the advanced oxidation process. Meanwhile, the reasons for the deterioration in dewatering performance were discussed when NaClO was added alone. The effects of dosage ratios (NaClO/FeSO4), Fe2+ dosage, and pH value on sludge dewatering performance were investigated using the capillary suction time (CST) and specific resistance to filtration (SRF) as key evaluation indicators. The results demonstrated that the ultraviolet irradiation activated a higher concentration of strong oxidative free radicals based on the activation of Fe2+ towards NaClO, thereby promoting the decomposition of EPS. With the addition of UV irradiation, the CST reduction rate increased from 61.5% to 65.4%, while the SRF reduction rate increased from 71.4% to 79.8%. During the experiment, the TB-EPS of the sludge were converted into the LB-EPS of the sludge. As a result of this conversion, the decomposition and oxidation of protein played a crucial role in enhancing sludge dewaterability. Analysis revealed that the deterioration in dewatering when NaClO was added alone was attributable to the insufficient oxidation capacity within the system and the excessive formation of bubbles during the conditioning process.
To investigate the dynamic variation patterns of leachate levels within the landfill mound, source leakage, and groundwater contamination plumes, a one-hydrologic-year monitoring was conducted at a landfill site in southern China. The results indicated a significant positive correlation between leachate levels and rainfall. Levels at monitoring points SW1-1, SW1-2, ZS1-2, and ZS1-3 ranged from 4.4m to 5.8m, 6.7m to 11.0m, 4.3m to 6.3m, and 4.0m to 6.8m, respectively, exhibiting annual fluctuations of 1.4m to 4.3m. The estimated source leakage rate of leachate ranged from 9.49m3/d to 10.90m3/d. The areas close to the landfill dam were significantly impacted by leakage. NH4+-N concentrations at the landfill dam shoulder monitoring well QC1-19 increased with rising leachate levels but attenuated rapidly downstream. The spatial extent of the contamination plume exhibited seasonal variability. During wet seasons, plume migration was dominated by source leakage, extending the plume boundary beyond 120m from the landfill dam. Conversely, during dry seasons, natural attenuation mechanisms, such as biodegradation, became dominant, confining the plume boundary near the leakage source. Mn concentrations were generally elevated and demonstrated an inverse relationship with ORP. Conversely, Fe concentrations exhibited no clear discernible trend. NH4+-N concentrations increased with decreasing pH. The relationships between CODCr and pH, as well as the dynamic interactions among CODCr, NH4+-N, NO3-, and ORP, were observed to be complex.
To address the shortcoming of lacking optimization and adjustment methods for long-term monitoring indicators of the groundwater risk control project at contaminated sites, a typical large-scale contaminated site impacted by hazardous waste landfill was focused in the present study. By collecting the existing groundwater monitoring data during the construction, effect evaluation and long-term monitoring periods, a feature dataset regarding exceedances of monitoring wells was constructed for decision-making optimization exploration. Further, the three decision tree models, including Chi-squared Automatic Interaction Detector (CHAID), Exhaustive CHAID(E-CHAID), and Classification and Regression Trees (CART), were employed to identify key monitoring indicators influencing groundwater monitoring well exceedances. The results indicated that it was feasible to use decision tree models to predict groundwater monitoring well exceedances. Based on the all evaluated metrics, including accuracy, precision, recall, and the F1-score (harmonic mean of precision and recall), the CART model significantly outperformed both the CHAID and E-CHAID models, suggesting that the overall optimization algorithm of the CART model was much suitable for predicting groundwater pollution exceedance events. 1,2,4-Trichlorobenzene and Nickel were identified as the most significant impact factors on the CART model's predictions. Moreover, the Fluoride, Petroleum Hydrocarbons, Trichloromethane, Dichloromethane, Cadmium, and cis-1,2-Dichloroethylene also demonstrated obvious influences. Consequently, it was recommended that the particular attention should be paid to the pollution dynamics of these 8key monitoring indicators during subsequent long-term monitoring of site groundwater.
This study evaluated six inorganic commercial amendments-Ningliang (NL), Tianxiang (TX), Longchang (LC), Wonong (WN), Gefeng (GF), and Baijinhui (BJH) using high-throughput sequencing of 16S rRNA genes to perform rhizosphere metagenomic analysis. We investigated the effects of applying soil amendments on cadmium (Cd) and mercury (Hg) accumulation, translocation characteristics in wheat (Triticum aestivum L.) and the structure of rhizobacterial community in Cd-Hg co-contaminated soil. The field experiment demonstrated that soil amendments significantly reduced the grain Cd enrichment (BCFCd) by 20.93%~64.87% and grain Hg enrichment (BCFHg) by 13.44%~34.66%, and the proportion of available Cd in soil decreased by 25.06%~39.58%. Notably, the treatments of LC and WN exhibited the optimal performance in immobilizing Cd and inhibiting Cd translocation in wheat, with WN concurrently suppressing Hg translocation. Microbial analysis indicated that amendments changed the rhizobacterial communities by enriching Cd-tolerant bacteria (e.g., Proteobacteria, Gemmatimonadota), thereby reducing the bioavailability of heavy metals (HMs). PICRUSt2 functional prediction indicated that rhizobacteria synergistically mitigated the toxicity of HMs through the enrichment of amino acid metabolism and carbohydrate metabolism. In summary, amendments could block Cd and Hg enrichment in wheat by direct reduction of HMs bioavailability in soil, and indirect enrichment of rhizosphere metal-tolerant bacterial microbiomes.
The influence of Fe-Ca layered double hydroxide-modified corn straw biochar (Fe-Ca LDH-CSB) on nutrient cycling processes in the rhizosphere soil of Bidens pilosa L. was investigated through controlled pot experiments. The application of Fe-Ca LDH-CSB was found to significantly increase the soil C/N ratio and organic matter content by 31.1~45.6% and 3.6~10.3%, respectively (P<0.05). Furthermore, the addition of biochar was observed to markedly upregulate functional genes involved in carbon (C), nitrogen (N), and phosphorus (P) cycling. At a 2% (mass ratio) amendment rate, the relative abundances of genes associated with C fixation (e.g., fbaA, korA, sucC, accD), N cycling (e.g., GDH2, nosZ, nifH, hao), and P cycling (e.g., ppk1, ppa, TC.PIT, pstB) were increased by 1.1~1.4-fold, 1.7~23.5-fold, and 1.1~1.2-fold, respectively, compared with the control. A significant positive correlation between available phosphorus and the abundance of C-, N-, and P-cycling genes (P<0.01) was revealed by Mantel test analysis. Sucrase activity was found to be positively correlated with C- and N-cycling genes (P<0.01) but negatively correlated with P-cycling genes (P<0.05). Partial least squares path modeling indicated that nutrient cycling was indirectly enhanced by Fe-Ca LDH-CSB through alterations in soil physicochemical properties and the reshaping of functional microbial taxa, thereby promoting the expression of nutrient-cycling genes. Taxonomic annotation revealed that Proteobacteria, Actinobacteria, and Chloroflexi were the dominant microbial hosts harboring key C-, N-, and P-cycling genes. Collectively, these findings demonstrated that the application of Fe-Ca LDH-CSB improved rhizosphere soil conditions and microbial community structure, thereby strengthening the biogeochemical cycling of C, N, and P in the rhizosphere of Bidens pilosa L.
sing 200soil As samples collected from suburban Changchun, this study combined the extreme gradient boosting tree (XGBoost) and the GeoShapley algorithm to explore the heterogeneity and nonlinear relationship between As and environmental factors, and to identify the local influencing factors and sources of As. The main conclusions are as follows: ① The XGBoost model had a fitted R2 of 0.958, which was better than the geographically weighted regression and multiscale geographically weighted regression models, with fitted and predicted R2 values of 0.958 and 0.729, respectively. ② The results of GeoShapley showed that the average contribution rate of manganese content ranked first and was the most significant characteristic variable affecting soil As, while the spatial location characteristic ranked second in importance, confirming the necessity of considering spatial coordinates. ③ The spatial distribution of GeoShapley values of influencing factors revealed that manganese content, alkali-hydrolyzable nitrogen, and sulfur content were the primary influencing factors for local As content at most sampling points, accounting for 99% of the samples. This indicated that parent material and agricultural production are the main sources of As. ④ The nonlinear relationship plots showed that there was a threshold effect between As content and various environmental variables, especially when the manganese content was higher than 2g/kg, the alkali-hydrolyzable nitrogen was higher than 175mg/kg, and the sulfur content was lower than 20mg/kg, As reached the peak, which should be paid special attention to in pollution prevention and control. This paper confirms the superiority of GeoShapley algorithm and the necessity of considering the spatial location, and provides data support for the governmental departments to formulate the policy of localized prevention and control of soil As pollution.
To evaluate the effects of organic fertilizer substitution on rice yield and soil quality under the conditions of equal nutrient substitution, a continuous six-year (2018~2023) field experiment was conducted. Using a no-fertilizer treatment as control (CK), this study examined the effects of chemical fertilizer (CF), 25% organic fertilizer substitution (COF25%), and 50% organic fertilizer substitution (COF50%) under low nitrogen application rates (135kg/hm2 in early rice season, 150kg/hm2 in late rice season) on rice biomass, yield, and soil properties. Results showed that compared with CK, fertilization promoted rice plant growth, enhanced nitrogen uptake, and increased yield. However, no significant differences (P>0.05) were observed in rice yield, nitrogen use efficiency, or partial factor productivity of nitrogen between CF and COF. Organic fertilizer substitution significantly increased soil organic carbon (SOC) by 8.40%~13.04% compared to CK. Moreover, the extent of SOC improvement increased with higher organic fertilizer substitution rates. All fertilization treatments significantly increased soil total phosphorus (24.30%~33.33%) and available potassium (25.60%~35.20%) contents. COF50% treatment resulted in a 18.18% significant increase in total nitrogen (TN) compared to CK. Analysis using the soil quality index area method revealed that soil quality improved with increasing organic fertilizer substitution rates. The soil quality index area values for COF25% and COF50% treatments were 88.81% and 222.40% higher, respectively, than that of the CF treatment. In conclusion, 25%~50% organic fertilizer substitution ensured normal rice growth and stable yield while enhancing soil quality through increased SOC content.
Lake Daihai remains ice-covered for approximately one-third of the year, forming a multi-media system comprising ice, water, and sediment. To clarify the migration patterns of nutrients at the ice-water-sediment interfaces, a systematic study was conducted on the spatiotemporal distribution, source-sink characteristics, and driving factors of nitrogen nutrients in the three phases (ice, water, sediment) before the freezing period (October) and during the freezing period (January, February). The results revealed significant spatiotemporal heterogeneity of nitrogen nutrients across the media. Total nitrogen (TN) and ammonium nitrogen (NH4+-N) concentrations increased slightly in the ice layer. The TN concentration in the lower ice layer was higher in February than in January, with the lower ice layer exhibiting higher concentrations compared to other layers. From pre-freezing to late freezing periods, TN and nitrate nitrogen (NO3--N) concentrations continuously increased in the water and sediment. The NO3--N concentration in the water increased by 62.5% between January and February. Spatially, TN and NH4+-N concentrations in the sediment were generally higher in the central area and lower in the eastern part, and overall exceeded those in the under-ice water. During the freezing period, the fractionation coefficients followed the order: NO3--N< TN <NH4+-N. At the ice-water interface, NO3--N dominated TN migration, with a higher flux observed in January than in February. Sediment acted as a "source" for TN and NH4+-N, but as a "sink" for NO3--N. The flux of nitrogen nutrients at the ice-water interface had a greater impact on their concentrations than the sediment-water interface flux. The dynamic changes of nutrients in the water were jointly influenced by the freezing-induced salt rejection effect and endogenous sediment release. Key environmental drivers for the distribution and migration of nitrogen nutrients before and during the freezing period included ice growth rate, dissolved oxygen, salinity, and water temperature.
To investigate the influence of changes in water salinity (composition and concentration) on key nitrate reduction processes in lake wetlands of arid regions, field surveys and salinity-controlled experiments were conducted in Bosten Lake and Xiangsi Lake wetlands located in the arid zone of northwestern China. Field surveys revealed that denitrification rates in Bosten Lake and the surrounding Xiangsi Lake wetland ranged from 7.2 to 131.4 μmol/(kg·h), and salinity was identified as a key environmental factor affecting denitrification. The rates of dissimilatory nitrate reduction to ammonium (DNRA) varied from 2.6 to 19.9 μmol/(kg·h), which were primarily influenced by the ratio of dissolved organic carbon to nitrate in the water. In the salinity-controlled experiments, increased salinity (0.2‰~6.6‰) significantly inhibited denitrification rates: with increasing sodium sulfate concentration, denitrification rates declined by 25.7% (Bosten Lake) and 21.0% (Xiangsi Lake), respectively; while increasing sodium chloride concentration led to respective decreases of 36.7% and 31.1% in the two wetlands. Changes in salinity also significantly impacted DNRA rates: DNRA rates first increased and then decreased with rising sodium sulfate concentration in both wetlands, whereas they decreased by 26.1% (Bosten Lake) and 27.8% (Xiangsi Lake) with increasing sodium chloride concentration. Increased salinity exerted a stronger inhibitory effect on denitrificationcompared to DNRA, resulting in an elevated proportion of DNRA in total dissimilatory nitrate reduction. Overall, increased salinity will elevate lake nitrogen concentrations by inhibiting denitrification, while enhancing nitrogen retention potential by increasing the proportion of DNRA, thereby exacerbating the risk of nitrogen pollution in these lakes.
Based on land use, night-time light, POI data, etc., we used the coupling coordination degree and XGBoost-SHAP model, the response characteristics of ecosystem service value (ESV) and the spatial differentiation and driving factors of coupling coordination degree (CCD) under the multi-dimensional urban-rural gradient of Chengdu-Chongqing Economic Circle from 2012 to 2022 were analyzed from the grid scale. It was found that the ESV of the Chengdu-Chongqing Economic Circle showed an increasing trend during the study period, with a total increase of 12.98 billion CNY. In terms of spatial differentiation along the urban-rural gradient, the proportion of suburban space was observed to rise from 0.55% to 0.89%, representing a growth rate of 61.8%, making it the fastest-growing spatial category and indicating a notable trend of suburbanization. The proportion of urban space increased from 0.15% to 0.21%, while that of rural space decreased from 96.67% to 94.25%. A negative correlation between CCD and ESV was identified in the urbanization process of the Chengdu-Chongqing Economic Circle. The CCD was found to be at the preliminary coordination stage and exhibited a slight declining trend. Along the transition from rural to urban areas, ESV was shown to decrease, while CCD demonstrated an increasing trend. Key factors influencing the urban–rural gradient and the CCD of ecosystem service value were identified as population density, normalized difference vegetation index (NDVI), PM2.5 concentration, and land use change, all of which were revealed to exhibit nonlinear effects.
In order to clarify the transport dynamics and ecological risks of pesticides in ditches and ponds, this paper presenteda pesticide transport model based on a monitoring study on drainage process and pesticide concentrations in field ditches (FD) in a small agricultural watershed in Yangzhou of Jiangsu Province, China. With the proposed model, we analyzed the spatial and temporal variations of chlorpyrifos (CPF) from FD to main ditches (MD) and ponds (PD) under different drainage flow and concentrationconditions, and evaluated the ecological risks of the CPF to aquatic animals. The monitoring results showed that peak drainage flow did not synchronize with peak pesticide concentrations in FD, the pesticide loading process was either with low flow and high concentration or high flow and low concentration. The model predictions of CPF transport process showed that ditches and ponds had a significant buffering effect on pesticide loading pulses, thus reducing peak concentrations of pesticide in drainage discharge. In the low-flow and high-concentration event, the observed peak concentration of CPF was as high as 330μg/L in FD, but reduced to 0.05μg/Lin MD and 0.00002μg/L in PD, resulting in reductions of 99.98% and 100% in MD and PD, respectively. In the high-flow and low-concentration event, the peak concentration of CPF was 2.5μg/L in FD, 0.02μg/L in MD, and 0.0006μg/L in PD, resulting in reductions of 99.2% and 99.98% in MD and PD, respectively. These results showed supportive effect of high-flow process on the output process. Under both types of drainage flow events, CPF concentrations were at low levels in MD (<0.1μg/L) and PD (<0.001μg/L),posing relatively low ecological risks to 16species of aquatic animal species that might be present in the ditches and ponds, except one species with a medium to high risk. Under a hypothetical unfavorable combination of synchronized high flow and high concentration event, the calculated peak concentrations of CPF was 2.55μg/Lin MD and 0.039μg/L in PD, making the number of aquatic animals with high ecological risk rise to 6species, regardless of the great reductions of 99.2% and 99.99% in MD and PD, respectively. Findings from this research may provide a scientific basis for pesticide pollution control and aquatic environmental protection in similar regions.
Microbial community structures, functional characteristics, and their relationships with hydrochemical environmental factors in arid areas of oasis area of the Hotan River Basin with fluoride-containing groundwater were investigated. Three groups of groundwater were analyzed: the LF group (ρ(F-) ≤ 1.0mg/L), MF group (1.0mg/L<ρ (F-) ≤ 2.0mg/L), and HF group (ρ (F-)>2.0mg/L). The diversity, structural composition, and functional characteristics of the bacterial communities in the groundwater of the three groups were investigated using high-throughput sequencing. The results showed that ① While the diversity of the bacterial communities in the three groups was less affected by F contents, significant differences in community compositions were observed. ②Proteobacteria represented the dominant phylum in all three groups, with relative abundance of 67.61% (LF group), 52.02% (MF group), and 41.43% (HF group). The dominant bacterial genus in the HF group was Sphingobium, which had relatively low abundance in the LF and MF groups. The relative abundance of Gallionella, Hydrogenophaga, Nitrospira, and Ferritrophicum was markedly higher in the LF or MF groups compared with the HF group. ③ F-, HCO3-, Ca2+, NO3-, and Cl- were key indicators that affected bacterial community structures in F-containing groundwater. ④ PICRUSt2 functional prediction indicated that metabolic functions dominated in all three groups, while bacteria in the HF group showed reduced activities in pathways involved in co-factor abundance and vitamin metabolism compared with the LF group. ⑤ Gallionella, Hydrogenophaga, Nitrospira, and other chemoautotrophic carbon-fixing microorganisms, which had relatively high abundance in the HF group were significantly negatively correlated with the F- and HCO3- contents, and significantly positively correlated with the Ca2+content (P<0.01). At the same time, Nitrospira was also positively correlated with NO3- and negatively correlated with NH4+(P<0.01). The structural and functional differences in microbial communities in the groundwater of the oasis area of the Hotan River Basin, as well as their correlations with environmental factors, reflect the bidirectional relationship between chemoautotrophic carbon-fixing microorganisms and groundwater F- contents in arid areas.
Based on the Global dataset of solar-induced chlorophyll fluorescence (GOSIF) and the Optimal Parameters-based Geographical Detector (OPGD) model, systematically analyzed the spatio-temporal patterns and driving mechanisms of vegetation photosynthesis on the Loess Plateau (LP) and its six ecological subregions from 2002 to 2022. The results indicated that: (1) On the temporal scale, the annual mean SIF across the LP region demonstrated significant growth with spatial heterogeneity. The hilly-gully region (Subregion B2) showed the highest increase rate[0.0024W/(m2·μm·sr·a)], while sandy and irrigated agricultural areas displayed the lowest [0.0006W/(m2·μm·sr·a)]. (2) On the spatial scale, the spatial distribution of annual SIF on the LP was significantly different, Notable degradation was observed in localized areas, particularly within the high-plain-gully Subregion A1 (central Qinghai Province) and the Guanzhong Plain. 11.36% of the region (southern rocky mountain and valley plain areas) exhibited SIF changes that deviated from the regional trend. (3) Anthropogenic factors dominated human activities and climate change exerted dual influences on vegetation photosynthetic evolution, with the former acting as the dominant driver and the latter governing spatial divergence. Precipitation primarily regulated SIF variations in the high-plain-gully Subregion A1, while solar radiation dominated the evolution in Subregion A2, hilly-gully regions, and rocky mountain-valley plain areas. Factor interactions synergistically amplified SIF spatial heterogeneity.
Analyzed the changes in vegetation cover (FVC) in the arid and semi-arid regions of northern China from 2000 to 2023, the impacts of FVC changes on ecosystem services were evaluated, and the impact thresholds were identified. The results showed that the FVC in these regions was characterized by a spatial pattern of being low in the west and high in the east, and low in the north and high in the south. Areas with increasing FVC accounted for 40.01% of the total study area. Overall ecosystem services were observed to decrease gradually from south to north, with high-value areas mainly distributed in southern Gansu Province and northwestern Xinjiang, while low-value areas were concentrated in central and western Inner Mongolia and northern Ningxia. The overall trend of ecosystem services was found to be increasing, with 85.01% of the area showing an upward trend. The FVC level was positively correlated with carbon fixation, soil conservation, and water production services, with significantly positive correlation areas accounting for 54.56%, 7.9%, and 11.43%, respectively. The increase in FVC was found to promote the comprehensive ecosystem services index (CIES) from 0.09 to 0.23. The FVC impact threshold in forest and farmland areas was not significant, and the positive effect of increasing FVC tended to weaken when grassland vegetation coverage reached 0.87.
The zero-valent iron-activated persulfate (ZVI-PS) method was used for sludge dewatering. The dewatered sludge was then mixed with humic carbon (HC) and carbonized to successfully prepare a hybrid biochar catalyst. When applied to levofloxacin (LVFO)-containing wastewater treatment in a photofenton system, the catalyst exhibited excellent performance under circumneutral conditions. It effectively addressed the traditional Fenton system’s dependence on acidic environments, thereby significantly broadening the applicable pH range. This study systematically investigated the effects of catalyst dosage, H2O2 concentration, xenon lamp power, and pH on LVFO degradation efficiency. The results showed that under optimal conditions (catalyst dosage: 1g/L, H2O2 dosage: 0.6mL/L, xenon lamp power: 300W, pH 7), the LVFO degradation rate by the hybrid biochar reached 92.58%—far higher than the 55.86% achieved by humic carbon. This superior performance was primarily attributed to the abundant iron species on the hybrid biochar surface, which facilitate iron cycling and thus enhance catalytic activity. Active species quenching experiments revealed that the system degrades LVFO mainly through the non-radical pathway of singlet oxygen (1O2), which explained its excellent degradation capacity under circumneutral conditions. After five cycles of reuse, the catalyst’s iron leaching rate was below 0.56%, and it retained high catalytic efficiency. A potential LVFO degradation pathway was proposed based on three-dimensional fluorescence spectroscopy and intermediate product detection.
Microplastics (MPs) are emerging contaminants of global concern, posing a potential and serious threat to ecosystems and human health due to their small size, ease of migration, and slow degradation. MPs pollution control is urgently needed. Artificial intelligence (AI) is becoming increasingly integral to MPs pollution management, leveraging its superior data processing, pattern recognition, and predictive power. This paper presents a comprehensive review of recent advances in AI applications across the MPs management lifecycle, including collection and detection, source apportionment, impact assessment, and strategic intervention. The AI models utilized in each phase are comparatively evaluated, with a focus on their accuracy and limitations. This paper critically examines the extant challenges and outlines prospective development pathways, ultimately aiming to facilitate more informed and effective strategies for the scientific and intelligent management of MPs pollution.
To quantitatively assess fiber microplastic emissions and their spatial distribution resulting from household laundering, a release and transport flux model was developed. This model, which integrates global textile consumption data with microplastic emission factors from laundering, was used to evaluate how variations in washing behaviors and wastewater treatment capacities across different countries and regions influence fiber microplastic release patterns. The simulation results indicated that the global emission of fiber microplastics from household washing reached approximately 2298.8kt in 2022. India, China, and the United States were identified as the top three contributors, with estimated emissions of 624.5kt, 471.4kt, and 332.8kt, respectively. Following wastewater treatment, significant regional differences in microplastic removal efficiency were observed. North America exhibited the highest removal rate at 78%, while removal efficiencies in Asia and Africa below the global average. Ultimately, fiber microplastics not retained during wastewater treatment entered the marine environment via river systems. The Ganges River (16.5kt), the Yangtze River (12.4kt), and the Indus River (5.1kt) were identified as the rivers with the highest annual fluxes of fiber microplastics into the ocean.
Toxicity thresholds (ECx) forCd, and in combination with tetracycline and imidacloprid, were determined based onthe soils collected from six typical greenhouse vegetable farms in China. Three toxicity endpoints includingthe growth of Brassica napus, the growth of the red earthworm Aedes aegypti and the soil microbial substrate-induced respiration (SIR) were applied, and the quantitative relationships and prediction models were established between the toxicity of the pollutants and key soil properties. The results showed that the toxicity dose-effect relationships of the pollutantsacross different soils and test endpoints typically followed a distinct S-shaped curve. For example, the toxicity thresholds forCd alone on B. napuswere 0.6~8.5mg/kg (EC10) and 4.3~14.7mg/kg (EC50), respectively. For the combined pollution of Cd and tetracycline, the corresponding values were 0.1~2.2mg/kg (EC10) and 1.2~6.9mg/kg(EC50). For the combination of Cd and imidacloprid, the values were 0.1~3.7mg/kg (EC10) and 1.4~10.0mg/kg(EC50). The toxicity thresholds of pollutants varied significantlydepending on the test endpoints, e.g., the sensitivity order for Cd and tetracycline was Chinese cabbage > earthworm > microorganisms, the toxicity thresholds for the combined pollutants were consistently lower than those for the single pollutants, indicating a synergistic effect. Additionally, a low-dose stimulation effect (hormesis) on the growth of B. napus was observed, with a maximum stimulation rate of 103% to 110%. Soil pH was identified as the primary factor controlling and predicting the toxicity thresholds. A comprehensive prediction model based on soil pH, cation exchange capacity (CEC), and organic carbon (OC) content was developed, which can effectively predict the toxicity thresholds and assess the ecological risks associated with the combined pollution of Cd and emerging contaminants in greenhouse vegetable soils.
To compare their capture efficiency, four types of iron (hydro)oxide-loaded biochar functionalized with ferrihydrite, goethite, hematite, or magnetite were synthesized via co-precipitation method at a consistent iron-to-biochar mass ratio. The modification significantly improved electrostatic attraction between the biochar and aged microplastics, thereby increasing retention capacity. Among these materials, magnetite-loaded biochar demonstrated the highest removal efficiency exceeding 90% and maximum adsorption capacity of 25.02mg/g, representing a 5.53-fold improvement over unmodified biochar. This superior performance is likely attributed to its highest iron oxide loading (4.15wt%). Adsorption data followed both the Langmuir monolayer model and pseudo-first order kinetics. Notably, magnetite-loaded biochar maintained over 95% retention under varying ionic strengths, cation valences, and pH conditions. It also preserved greater than 90% of microplastics in multi-bed volume tests and real-water samples. Moreover, after multiple alkaline washing regeneration cycles, the material remained stable adsorption performance, demonstrating its excellent reusability potential.
The sedimentation behavior of microplastics (MPs) in water bodies is synergistically regulated by the intrinsic properties of the particles, the photoaging degree, and the chemical conditions of the solution. Existing studies have overlooked the sedimentation behavior for different types of aged MPs in karst small water bodies. Through simulation experiments, the sedimentation behavior of three types of MPs (polystyrene (PS), polyamide 6 (PA6), and polyethylene terephthalate (PET)) with different photoaging degrees wasinvestigated in weakly dynamic water bodies and environments with different concentrations of CaCl2. The results showed that the sedimentation ratios of the three types of MPs exhibited a decreasing trend with the deepening of photoaging under pure water conditions,while the opposite result was observed in the CaCl2 environment.Compared with pristine MPs, the sedimentation ratios of MPs aged for 1000h decreased by 38.8% to 56.0%in pure water. In contrast, the sedimentation ratios increased by 23.8% to 93.3% and 66.1% to 357.1% in 10mmol/L and 100mmol/L CaCl2 solution, respectively. Ca2+ can compress the electrical double layer of MPs, reducing the electrostatic repulsion between particles. Simultaneously, It can also bridge with the oxygen-containing functional groups on the surface of aged MPs. These effects facilitate the homogeneous aggregation of particles, therebypromoting their sedimentation. The results of Pearson correlation analysis and DLVO theory calculations indicated that bridging of Ca2+ was an important external factor regulating the aggregation and sedimentation of MPs before and after aging. Although the photoaging degree and salt ions influenced the sedimentation process of MPs, the fact that Peclet values of the three polymer MPs were below 1demonstratedthat material densitywas the dominantfactor governing their sedimentation behavior. the sedimentation of MPs is primarily governed by polymer density. Photoaging regulates interparticle interactions by changing the oxygen-containing functional groups and size distribution of MPs. While the compression of the electrical double layer and bridging effects induced by Ca2+ further influence their aggregation and sedimentation behavior of MPs.
This study constructs a comprehensive framework for assessing regional carbon inequality by integrating and extending the logarithmic mean Divisia index (LMDI) with production-theoretical decomposition analysis (PDA). Based on panel data from 273 Chinese cities (2006~2019), we examine the dynamic evolution and drivers of per capita carbon emission inequality from structural, technological, and efficiency perspectives. Results indicate significant carbon inequality in China, with the inequality index first decreasing from 15.18 (2006) to 14.51 (2008) before fluctuating upward to 22.59 (2019). Rising potential carbon intensity and shifts in regional economic structure were the main drivers of increased inequality, while regional economic growth, production efficiency improvements, and narrowing inter-city technology gaps helped mitigate it—though carbon reduction technologies showed no significant equalizing effect. Dynamic analysis reveals that potential carbon intensity fluctuations had the strongest impact on inequality changes, while technological progress and efficiency gains effectively curbed its rise. Regionally, the southern coast exhibited the highest carbon inequality, while the northwest had the lowest, with drivers varying in direction and intensity across regions.
A carbon intensity accounting method was developed to account for the distinct data characteristics of inland river ship based on carrying capacity and transport distance. The correlation between ship carbon intensity indicator (CII) and factors including vessel age, ship type, port of registry and average speed was analyzed based on operational data collected from ships of 5,000GT and above registered in Chongqing after static parameter filling and dynamic trajectory repair. The results indicated that the carbon intensity values of the study sample in October 2021 were all on the order of 10-6, ranging between 2.47×10-6 and 10.53×10-6t/(t·km). As vessel age increases, the CII demonstrates an upward trend, whereas carbon emissions exhibit a negative correlation with carrying capacity. The overall energy efficiency levels of different ship types were quantified and ranked based on the median and mean values of their CIIs. The resulting order from highest to lowest energy efficiency was Ro-Ro vessels, general cargo ships, container ships, dry bulk carriers, and passenger ships. Vessels registered in main urban area of Chongqing generally demonstrate superior energy efficiency compared to those from its outer districts and counties. Thus establishing a standardized carbon emission monitoring system was crucial to enhance the overall energy efficiency of the ship fleet registered in Chongqing. The CII demonstrated a strong correlation with ship average speed. Furthermore, different ship types exhibit distinct variation characteristics, each reaching its minimum carbon intensity at different specific speed points.
Based on China’s construction industry low-carbon technology patents and carbon emission data from 2003 to 2022, the subject collaboration networks and knowledge fusion networks were constructed using social network analysis, and a coupling coordination degree model was designed based on their topological structures. Further, an autoregressive distributed lag (ARDL) model and generalized impulse response function (GIRF) were applied to empirically examine the impact mechanism of the coordinated development of dual-network coupling on the construction industry’s carbon emission intensity. The results indicated that: ① The subject collaboration networks and knowledge fusion networks continued to optimize, with the coupling coordination level increasing from 0.4043 to 0.9591, showing an evolution from disorder to high coordination; ② During the sample period, the coupling coordination development index lagged by one period was found to exert a significant negative impact on carbon emission intensity, reflecting its delayed promoting effect on emission reduction; ③ The coupling coordination development index was identified as a one-way Granger cause of carbon emission intensity. Its negative shock peaked at -1.865 in the second lag and then gradually stabilized, demonstrating a significant stage-based adjustment effect. Relying on a framework integrating structural measurement and dynamic analysis, the results provided quantitative tools and empirical support for systematic low-carbon innovation policies.
Direct air carbon capture (DAC) technology reached the stage of commercial deployment, yet its large-scale application still faced challenges of high energy consumption and resource-environmental impacts. An exergy-based life cycle assessment (ExLCA) framework for DAC systems was established, with their performance being evaluated across energy, resource, and environmental dimensions. It was shown that under grid-electricity and natural gas energy supply scenarios, the system’s life cycle energy consumption was 10.22GJ/tCO2, with thermal energy demand for capture, CO2 compression, and oxygen production through air separation being identifiedas the dominant energy-intensive processes, which accounted for 62.72%, 13.41%, and 8.98%, respectively. The cumulative exergy consumption over the life cycle was 9.19GJ/t CO2, with electricity and thermal energy contributing 39.98% and 58.93% to total exergy consumption, while material-related exergy was accounted for only 1.09%, which was negligible. In terms of environmental benefits, a life cycle carbon removal efficiency of 47.50% was achieved by the system, with thermal energy and electricity consumption contributing 53.18% and 33.43% to total carbon emissions. Carbon removal efficiency could be increased to 88.60% by transitioning to biomass and wind power as energy sources. Furthermore, 80kg of water and 1465(m2×a) per ton of CO2 removed were required by the system, highlighting the need for careful resource management in large-scale deployment.
HEK293-α-syn cells and primary cortical neurons of wild-type mice were used as experimental models to deeply explore the environmental neurotoxicity and mechanism of Maneb exposure at different concentrations (0, 0.1, 1.0, 5.0mg/L). The results showed that Maneb exposure inhibited the activity of HEK293-α-syn cells in a dose-dependent manner and induced primary neuronal damage. The higher the Maneb exposure concentration, the more significant its toxic effect. In the 0.1mg/L experimental group, the apoptosis rate of neurons was close to 60%. In addition, Maneb exposure induced the aggregation of α-syn in HEK293-α-syn cells and primary neurons, stimulated its abnormal phosphorylation at the S129 site, and upregulated the level of insoluble phosphorylated α-syn, further forming Lewy body (LB)-like aggregates, indicating that Maneb induces Parkinson's (PD)-like neurotoxicity.
Meteorology is a known driver of pollen concentrations, using Yulin City in Shaanxi Province—a hotspot for pollen allergies in China—as a case study, this research integrates 2019~2021 pollen concentration data and meteorological observations. Through statistical analysis and synoptic diagnosis, we investigate the meteorological drivers of an explosive pollen surge from August 4~7, 2021. Key findings reveal:Pollen concentrations in Yulin show a clear bimodal seasonality with spring and autumn peaks. Anther dehiscence in Artemisia ordosica is triggered by rising temperature and low huidity, with winds of 1.5~3.0m/s being optimal for dispersal. Enhanced near-surface winds during the event facilitated local dispersion, but shifting wind directions impeded stable transport pathways, promoting localized accumulation. Synoptic diagnosis shows that a transition from upper-level through to a ridge, combined with mid-low-level anticyclonic activity, was critical. This pattern promoted subsidence, which suppresses atmospheric diffusion by reducing boundary-layer height and forming temperature inversions. Total cloud cover modulates surface radiation budgets and vertical motions, thereby influencing boundary-layer thermal structure. The key driver of the diurnal peaks was the synergy between periods of low cloud cover (<2oktas) and boundary-layer compression.
The growth and death processes of microorganisms result in the release of pathogen-associated molecular patterns (PAMPs). Chronic exposure to PAMPs through the gastrointestinal, respiratory, and dermal routes poses a risk of inflammatory effects. The presence of large numbers of microorganisms in the air, water, and soil provides extremely favorable conditions for the release of PAMPs. This study summarizes the characteristics of endotoxins, peptidoglycan, teichoic acids, lipoproteins, and CpG DNA, which are pivotal substances in PAMPs. The occurrence and health risks of PAMPs in different environmental media, such as air, water, and soil, are introduced. The recognition mechanism of PAMPs and their ability to trigger disease are also discussed. The synergistic enhancement of inflammatory responses induced by combined exposure to PAMPs and environmental pollutants is analyzed. Based on current research of PAMPs in the environmental field, this study highlights the urgent need to accelerate research into standardized detection systems, exposure risk thresholds, and epidemiological studies of PAMPs, given the existing limitations. The aim of these efforts is to advance research progress on PAMPs in environmental science and to provide a reference for a comprehensive understanding and assessment of the health risks posed by PAMPs.
Water, energy, and carbon are critical resource and environmental elements that underpin socio-economic development. Exploring the coupling coordination among water-energy-carbon (WEC) and promoting their synergistic development are essential for advancing regional green and high-quality growth. Taking 95 cities in the Yangtze River Economic Belt (YREB) as the study area, WEC evaluation indicators were constructed from the dual-control perspective of total amount and intensity. The coupling coordination degree (CCD) model was applied to measure the CCD of WEC from 2006 to 2019, and the spatial durbin model was employed to identify the key influencing factors. The results showed that the CCD of WEC, water-energy, water-carbon, and energy-carbon in the YREB ranged from 0.6724 to 0.9987, and were classified into four levels: primary, intermediate, good, and quality coordination, with the median and mean showing an overall upward trend and notable improvement. Since 2014, more than 75% of the cities had reached the quality coordination level each year. All four categories of CCD exhibited positive spatial clustering, with cities achieving quality coordination in the other three categories evolving from scattered to contiguous distribution patterns, while energy-carbon remained widely distributed. Significant positive spatial spillover effects were detected for all four categories, indicating that improvements in local coupling coordination positively impacted that of spatially connected cities. Economic development level, environmental regulation, technological innovation, and NDVI significantly promoted local CCD, whereas fixed asset investment exerted a suppressing effect. Furthermore, economic development level and industrial structure upgrading showed positive and negative spatial spillover effects on spatially connected cities, respectively, while the negative spillovers of environmental regulation and technological innovation were more pronounced among geographically adjacent cities compared to economically linked cities.
The National Key Ecological Function Zone Policy is an important part of China's ecological governance system, but whether it promoted county-level green economic growth remained unclear.Using county panel data for 2005~2020, the policy’s impact on green economic growth was analyzed with a difference-in-differences design and machine-learning methods. The results show that the policysignificantly increased county green economic growth by 2.77%. Mechanism analyses indicated that the policy raised the value of ecosystem services by 6.59%, increased transfer payments by 11.71%, enhanced green technological innovation by 1.63%, and reduced the number of moderately polluting firms by 11.72%. The economic structure was further modernized. The heterogeneity analysis showed that the overall policy effect was stronger in western regions. Machine learning results show that the promotion effect was weaker where the welfare base was stronger, the fiscal burden was heavier, and the functional zone covered a larger area. These findings help us better understand how the policy promotes green economic growth and its shortcomings. The results provide useful reference for improving the policy according to local conditions and further promoting regional green economic growth.
The establishment of National Agricultural Green Development Pilot Zones (NAGDPZ) was regarded as a major policy initiative aimed at facilitating the green transformation of agriculture and was expected to significantly influence agricultural carbon emissions. However, limited research had been conducted in this area. Based on panel data from 284 cities spanning the period from 2013 to 2023, a quasi-natural experiment was constructed using the implementation of the NAGDPZ. The difference-in-differences (DID) method was employed to systematically evaluate the effect of the NAGDPZ policy on agricultural carbon emissions.The following findings were obtained:(1) The NAGDPZ policy was found to significantly reduce agricultural carbon emissions. A statistically significant negative effect at the 10% level was observed, with an estimated coefficient of -0.008. The robustness of this result was confirmed through parallel trend tests, placebo tests, propensity score matching DID (PSM-DID), exclusion of potential confounding policies, and controls for endogeneity.(2) Mechanism analysis revealed that the emission reduction effect was primarily driven by improvements in agricultural production efficiency, the development of digital inclusive finance, and the promotion of rural innovation and entrepreneurship. Specifically, the NAGDPZ policy had a significantly positive impact on agricultural total factor productivity, the digital inclusive finance index, and the rural innovation and entrepreneurship index, with coefficients of 0.019, 0.008, and 0.018, respectively.(3) Heterogeneity analysis indicated that the carbon reduction effect varied across regions. Significant negative impacts were observed in southern China, non-major grain-producing areas, and regions with higher levels of agricultural financial support, with coefficients of -0.008, -0.016, and -0.015, respectively. In light of these findings, it is recommended that the establishment of NAGDPZ be expanded to further enhance the policy's effectiveness in reducing agricultural carbon emissions.
Based on the 2020 emission inventory of 68 integrated cement plants in the Beijing-Tianjin-Hebei (BTH) region, three emission reduction scenarios were developed using 37 technologies and the CALPUFF model, two exposure-response functions (IER and GEMM), and three economic valuation methods (Value of statistical life(VSL), Age-adjusted value of statistical life(A_VSL), Amended human capital(AHC))were employed to assess the PM2.5 reduction potential, concentration changes, and health impacts. The result demonstrated that under the technology-based reduction scenarios S1 and S2, the primary PM2.5 emissions from reduction technologies and the total PM2.5 concentration declined by 2.17‰~5.50‰ and 3.20%~12.10%, respectively. Under each scenario in three provinces, the avoidable deaths estimated by IER were about 60% of the result estimated by GEMM. Moreover, the avoided deaths under S1 and S2 in each city were 4%~5% and 9%~10% of the data under S0. The main beneficiary group of life extension years was concentrated in the 65+ and 70+ age groups, and the life extension years in Shijiazhuang, Baoding, and Tangshan contributed 57% of the total years in Hebei. Avoidable economic losses under S1were about 50% of those under S2 and only 5% of those under S0 (ideal scenario) which meant “zero emission”was realized in the cement industry. The value evaluated by VSL was highest, followed by the value evaluated by A_VSL and AHC. Taking the estimation of GEMM as an example, the avoidable economic loss in Beijing was the highest among the three provinces under three scenarios, whereas the avoidable economic loss evaluated by VSL and AHC in Hebei was higher than that in Beijing under S0 (4.489 billion yuan vs. 3.873 billion yuan; 0.36 billion yuan vs. 0.34 billion yuan).
Dust pollution data (particles<920μm) collected across China from 2000 to 2023 were integrated. The results showedthat heavy metal contamination in road dust was generally higher than in soils,yet evaluation methods produced inconsistent conclusions. Therefore, a risk assessment system tailored to the unique characteristics of dust was considered essential.The spatial distribution of urban road dust pollution in China showed a steadily increasing severity from the northwest to the southeast, and pollution sources were found to be complex. Source control alone was found to be insufficient to ensure long-term effectiveness, necessitating an adaptive governance approach. Based on the resilience governance concept, anovel urban road dust pollution risk assessment system was constructedusing the Pressure-State-Response (PSR) model, Analytic Hierarchy Process (AHP), and entropy weight method, incorporating a socio-ecological perspective. An empirical analysis of 31 cities classified them into five clusters: high-pressure-risk, high-response-risk, combined high-state-response-risk, combined high-state-pressure-risk, and low-risk—yielding tiered and category-specific recommendations: priority population-exposure studies in dense traffic areas, establishment of dust-monitoring and feedback networks, targeted abatement of centralized emission sources, enhanced early-warning systems for extreme-weather dust events, and fixed-site surveillance in sensitive zones.
Monthly, Started in 1981 Supervisor: China Association for Science and Technology Sponsor: Chinese Society For Environmental Sciences Editor-in-Chief: ZHANG Yuan-hang CN 11-2201/X ISNN 1000-6923
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