This study centers on environmental regulatory policies, employing a two-way fixed effect model to scrutinize their impact, underlying mechanisms, and theoretical implications on new quality productivity enhancement. A U-shaped correlation exists between environmental regulations and the enhancement of new quality productivity. Beyond a critical turning point, a 1% escalation in vertical environmental regulation intensity correlates with a 124.42% augmentation in high-quality economic development. Environmental regulations significantly bolster the advancement of new quality productivity levels in both eastern and western provinces of China. Environmental regulations serve as a catalyst in amplifying the mechanisms fostering new quality productivity, particularly by influencing the "new labor tools" and "new infrastructure" subsystems.

To investigate the effects of water flow disturbances on the growth and aggregation characteristics of Microcystis blooms, this study conducted controlled indoor experiments in a flume, with disturbance frequencies set at 30, 40, 50, and 60min^-1. The growth dynamics and size variation of Microcystis colonies were systematically analyzed under varying disturbance conditions. The results showed that low-intensity water flow disturbances (frequency<40min^-1 or velocity<0.026m/s) significantly promote the secretion of extracellular polymeric substances (EPS) in Microcystis, with a strong correlation observed between Chlorophyll-a and EPS concentrations (r²>0.85). Conversely, high-intensity disturbances (frequency>50min^-1 or velocity>0.034m/s) inhibited EPS secretion, leading to a weakened correlation between Chlorophyll-a and EPS concentrations (r²<0.8). Within the experimental ranges of flow velocity (0~0.08m/s) and turbulent kinetic energy (0~0.004m²/s²), the size of Microcystis colonies exhibited minimal variation (ranging from 0.4~0.6mm). Furthermore, low-intensity disturbances facilitated the formation of surface blooms with shorter durations, whereas higher-intensity disturbances suppressed bloom aggregation while extending algal survival periods.

The study used humic acid (HA) to drive the potassium permanganate/persulfate (PM/PMS+HA) system to investigate the removal of small molecule organic pollutants and the effectiveness of membrane fouling control. The experimental results showed that the PM/PMS+HA system exhibited excellent removal performance for different small molecule organic compounds, including Atrazine (ATZ), Phenol (Phenol), Diclofenac Sodium (DCF), Carbamazepine (CBZ), Ibuprofen (IBP) and Sulfamethoxazole (SMX). The first-order kinetic constants of the PM/PMS+HA system were all higher than 18×10^-2min^-1, far higher than the PM/PMS system, PM system, and ultrafiltration system alone. At the same time, the PM/PMS system has a good membrane fouling alleviation effect. When HA was used as the pollutant, the effluent specific flux of the PM/PMS system only decreased to 0.919 within 15 minutes, much higher than the 0.393 obtained by HA filtration alone. Meanwhile, when using the PM/PMS system for membrane cleaning, the membrane flux recovery rate reached 98.51%. The mechanism of the PM/PMS+HA system was explored through capture experiments and measurements using a UV spectrophotometer. The experimental results indicate that during the filtration process of PM/PMS+HA, it is mainly the rich electronic HA in the system that triggers the decomposition of the composite oxidant (PM-PMS). The decompositionof composite oxidants produces reactive oxygen species (^•OH、SO₄^•-、¹O₂) and reactive manganese(Mn(V)and Mn(VI)). The generated reactive oxygen species and reactive manganese oxidize pollutants, leading to the removal of new pollutants and a decrease in the molecular weight of membrane pollutants, thereby achieving the removal of new pollutants and the control of membrane pollution. The PM/PMS system driven by pollutants has achieved the coupling of ultrafiltration membranes with advanced oxidation technology, providing new ideas for the removal of small molecule organic compounds and membrane fouling control in ultrafiltration technology.

This study systematically investigated the synergistic interactions between biochar and the model electroactive microorganism Shewanella oneidensis MR-1 in electron transfer processes through comprehensive electrochemical analyses, kinetic modeling, and electron pathway characterization using chromium(VI)-contaminated soil as the experimental matrix. The biochar-based microbial agents demonstrated effective Cr(VI) bioremediation, with biological reduction mediated by MR-1identified as the predominant mechanism following dual-process kinetics. Optimal remediation performance (96.30% Cr(VI) reduction efficiency) was achieved under conditions of 25mg/kg Cr(VI) contamination, 5% (w/w) biochar-based microbial agents dosage, and 30% soil moisture content. Comparative analysis revealed distinct temporal remediation patterns: adsorption-based biochar-microbial composites exhibited rapid initial Cr(VI) sequestration but limited long-term stability, whereas encapsulation-based formulations showed gradual but sustained reduction capacity. Mechanistic studies demonstrated that biochar functioned as an effective microbial carrier, simultaneously enhancing MR-1proliferation and facilitating extracellular electron transfer from microbial cells to Cr(VI) contaminants through its conductive carbon matrix. Notably, the immobilized system maintained 60.44% reduction efficiency after three operational cycles, highlighting its potential for sustainable in situ remediation of chromium-contaminated soils.

This study analyzed the carbon reduction effect of national green data centers on cities and its mechanism. Then, based on the pilot and construction work of national green data centers, a quasi-natural experiment was constructed. Using the difference-in-differences method and panel data of 283 cities from 2011 to 2022, the carbon reduction effect of national green data centers was empirically analyzed, and its mechanism and heterogeneity were explored. The pilot of national green data centers has a significant carbon reduction effect, with a coefficient of -0.013, which is significant at the 5% statistical level. The pilot of national green data centers significantly reduces the carbon emission intensity of cities. This result remains valid after multiple robustness tests, including parallel trend tests, placebo tests, exclusion of selection bias, exclusion of the impact of other policies, and exclusion of the impact of the epidemic. The pilot of national green data centers can reduce the carbon emission intensity of cities by promoting the development level of the digital industry and the green technological innovation level of the region. The impact of the pilot of national green data centers on the development level of the digital industry and the green technological innovation level is significantly positive at the 1% statistical level, with coefficients of 0.039 and 0.061, respectively. The carbon reduction effect of national green data centers is more significant in non-energy-rich cities, cities with high environmental protection levels, and cities with high information levels. The impact of the pilot of national green data centers on these three types of cities is significantly at least at the 10% statistical level, with coefficients of -0.016, -0.017, and -0.016, respectively. Therefore, efforts should be made to promote the green transformation of data centers and expand the scope of the pilot of national green data centers.

Packed column experiments and numerical simulations were conducted to investigate the co-transport behavior of nanoscale iron supported on biochar (nFe/BC) pyrolyzed at 500℃ and 800℃, respectively, with arsenic (As) in contaminated soil. The results showed that the mobility of nFe/BC (nFe/BC500 and nFe/BC800) in As-contaminated soil was obviously lower than that of pristine biochars (BC500 and BC800), decreasing by about 57.8% and 45.5% in As-contaminated soil, respectively. This is likely because zeta potentials of nFe/BC became less negative due to the adherence of positively charged Fe onto the BC. Therefore, electrostatic repulsion between nFe/BC and soil grain was weakened, resulting in a lower mobility of nFe/BC. Also the mobility of nFe/BC was reduced with an increase in pyrolysis temperature. This is likely because that the surface charge of nFe/BC produced at high temperature was less negative, due to the lower density of O-containing functional groups. Therefore, the total repulsive interaction energies between nFe/BC and soil grain were reduced. A two-site kinetic retention model was successfully employed to simulate the transport of nFe/BC in soils, further illustrating the co-transport characteristics of nFe/BC. Additionally, pristine BCs facilitated the transport of As due to the competition between BCs and As for the available sorption sites on the soil surface. However, nFe/BC first inhibited the transport of As, and then promoted it. The main reason could be because the iron substance or Fe₃O₄ on the surface of nFe/BC reacted with As, and then fixed it in soil. Once the reaction between nFe/BC and As was completed, nFe/BC lost its original inhibitory effect, and instead acted as a carrier to promote As transport in soil. This could cause potential risks of As to the groundwater environment.

To address the co-contamination of phthalic acid esters (PAEs) and cadmium (Cd) in agricultural soils of Guangxi province, a novel approach using immobilized functional microbial agent has been proposed. A composite microbial consortium, composed of three functional bacterial strains including Gordonia sp., Rhodococcus sp., and Bacillus sp., was developed with the ability to tolerate Cd and degrade PAEs. The microbial agent was immobilized on a thiol-modified montmorillonite-biochar composite carrier with optimized preparation conditions to enhance their remediation capabilities. The synergistic remediation efficacy of the agent on PAEs-Cd co-contaminated soils and the underlying mechanisms were elucidated. The results demonstrated that the composite microbial consortium achieved a degradation rate of 92.7% for total PAEs within 5days, while the carrier material exhibited a Cd saturation adsorption capacity of 15.2mg/kg. The optimal immobilization conditions were determined to be 30℃, with a bacteria-to-carrier ratio of 1:20 (V/M) for 1day. Under these conditions, the immobilized microbial agent achieved a degradation rate of 95.4% for ΣPAEs within 5days. When applied at a dosage of 1% to PAEs-Cd co-contaminated soils, the immobilized microbial agent resulted in 54.14% PAEs elimination and 37.06% decrease of exchangeable Cd after 50days. The immobilized microbial agent exhibited favorable synergistic remediation efficacy for PAEs-Cd co-contamination. The research findings provided a theoretical basis for the remediation of PAEs-Cd co-contamination in farmland soil of Guangxi and filled the theoretical gap in the control and remediation of PAEs-Cd co-contamination.

Based on water quality indicators, climate indicators, and wetland operation parameters, data from previous studies were collected to predict the effluent concentrations of ammonia nitrogen (NH₄⁺-N), COD, sulfamethoxazole (SMX), and some heavy metals in constructed wetlands using three machine learning models. The results showed that the Random Forest model slightly outperformed XGBoost and LightGBM in overall performance, demonstrating more stable R² and RMSE values. In particular, it achieved higher accuracy in predicting NH₄⁺-N and SMX concentrations, with R² values of 0.93, 0.89, and 0.87, respectively, for NH₄⁺-N. In contrast, the models performed relatively weaker in COD predictions, with R² values of 0.71, 0.61, and 0.64, respectively. By incorporating the SMOTE data augmentation technique, the prediction performance and accuracy of the models were significantly enhanced, especially for COD, where improvements ranged from 7.04% to 26.23%. This study combines scientific data analysis with machine learning algorithms, providing a feasible approach for practical engineering applications.

Based on the super-efficiency SBM model to measure the carbon emission efficiency (CEE) of China's chemical industry across 30 provinces from 2007 to 2021, this study employs spatial analysis methods and kernel density estimation to characterize the spatiotemporal evolution patterns of CEE at both the national and regional levels. Furthermore, a Tobit regression model is applied to identify its influencing factors. Although the CEE of China's chemical industry exhibited a fluctuating upward trend during the study period, the overall level remained relatively low, with a mean value of 0.629. Moreover, a persistent regional disparity was observed, following the order of eastern China (0.750) > western China (0.584) > central China (0.530). The spatial distribution of CEE ultimately displayed a "southwest-northeast" orientation, with significant shifts in spatial patterns gradually forming a "tripartite balance" structure, though most regions remained at low efficiency levels. Additionally, the mean Gini coefficient was 0.322, indicating substantial spatial heterogeneity overall. The CEE in eastern China surpassed that of the central and western regions, with hypervariable density identified as the primary source of regional disparities. The overall evolutionary trend of CEE in the chemical industry was positive, with interprovincial gaps gradually narrowing. While the trends in eastern, central, and western China were generally favorable, attention should be paid to the increasing divergence in the western region. Industrial agglomeration, energy structure, and economic development level significantly promoted CEE, with the energy structure of the chemical industry having the strongest impact which coefficient is 0.9942. Therefore, the government should prioritize optimizing the energy structure of the chemical industry while fully leveraging the positive effects of industrial agglomeration and regional economic development. Additionally, enhancing interregional collaboration and formulating region-specific policies are crucial for further improving the CEE of the chemical industry.

Taking the Pengxi River, a typical tributary bay of the Three Gorges Reservoir, as an example, continuous monitoring of the water flow, water quality, and algal bloom in the bay during the drawdown period in 2023 was carried out. The hydrodynamics, thermal stratification, and water quality evolution patterns of the tributary bay were analyzed, and the occurrence, disappearance, and influencing factors of algal blooms were revealed. The results show that during the observation period, the chlorophyll a of phytoplankton in the Pengxi River bay was positively correlated with water temperature (r=0.43, P<0.05) and euphotic layer depth (r=0.38, P<0.05), and negatively correlated with upstream inflow (r=-0.53), flow velocity (r=-0.54), and mixed layer depth Z_mix (r =-0.37). However, nutrients were not the limiting factors for the occurrence and disappearance of algal blooms. When the water temperature was suitable and the thermal stratification was stable, algal blooms began to occur. A gradual water level drawdown (<0.2m/day) fails to notably enhance flow velocity or break thermal stratification in the bay, resulting in minimal suppression of algal blooms in tributary bays. During the drawdown period, rainfall and upstream inflow could significantly affect the hydrodynamic processes and nutrient levels in the bay, which were the key factors determining the occurrence and disappearance of algal blooms in Gaoyang Lake. Increasing the discharge flow from Hanfeng Lake (>80m³/s)could effectively control algal blooms in the Pengxi River bay.