In this study, the earthworm Eisenia fetida was exposed to soils contaminated with polylactic acid microplastics (PLA MPs, 0.5% w/w) and arsenite (As(Ⅲ), 40 or 100mg/kg) under single and co-exposure scenarios for 28 days. The responses of six selected biomarkers and the bioaccumulation levels of the two pollutants in earthworms were analyzed to evaluate the joint toxic effects and underlying mechanisms of PLA MPs and As(Ⅲ). Bioaccumulation results indicated that the bioaccumulation of As(Ⅲ) in co-exposure groups decreased by an average of 34.2% compared to the single As(Ⅲ) exposure groups. Biomarker analysis revealed significant oxidative stress responses in earthworms, which intensified with increasing exposure concentration and duration. The Integrated Biomarker Response index (IBR) was used to quantify the integrated toxic effects of multiple biomarkers, showing a significant dose-effect relationship in both single and co-exposure groups. Notably, the IBR values in co-exposure groups were reduced by an average of 28.8% compared to those in single As(Ⅲ) exposure groups. Joint toxicity evaluation indicated antagonistic interactions between PLA MPs and As(Ⅲ) at both arsenic exposure levels. These findings systematically elucidate the toxicological effects and potential mechanisms of PLA MPs and As co-contamination in earthworms, providing scientific evidence for ecological risk assessment of such combined toxicity.

A field survey was conducted in the East China Sea in autumn 2023 to study the distributions of carbon monoxide (CO) concentration, estimate the sea-to-air fluxes, and measure the microbial consumption and dark production rates of CO in the surface seawater. The results showed that atmospheric CO mixing ratios of the East China Sea ranged from 61.15 to 234.26 ppbv, with an average of (98.47 ± 49.64) ppbv. A headspace analysis system was used to determine CO. The concentrations of CO in the surface seawater ranged from 0.20 to 1.63 nmol/L, with an average of (0.63 ± 0.71) nmol/L. The CO concentrations exhibited noticeable daily variations across different water layers, with a maximum at noon and a minimum around dawn. The results of vertical distributions showed that the CO concentrations decreased gradually with the increase of depth. The sea-to-air fluxes of CO ranged from 0.45 to 5.66μmol/(m²·d), with an average of (2.11 ± 2.89) μmol/(m²·d). The results of sea-to-air fluxes estimation showed that the East China Sea was a source of atmospheric CO. The microbial consumption rate constants of CO in the surface seawater ranged from 0.16 to 0.33/h, with an average of (0.22 ± 0.07)/h. The consumption process showed the characteristics of first-order reaction. Dark production rates varied from 0.023 to 0.20nmol/(L·h), with an average of (0.12 ± 0.14) nmol/(L·h). The concentrations of CO increased linearly with the increase of time. This survey complements the source-sink processes of CO in the East China Sea, and contributes to a further understanding of the CO biogeochemical processes and its climatic effect in the continental shelf sea area.

In this paper, an oil field in Northern Jiangsu Province is selected as the research object, and a multiphase flow reaction migration model CO₂-DSMRT for CO₂ displacement and storage is constructed to describe the transformation and fate of supercritical state/gas phase, dissolved water phase, dissolved oil phase and mineral phase CO₂ on a century scale. The sensitive parameters affecting the prediction results of the model are identified by Sobol method. Furthermore, PSO-LSSVM method was used to establish an alternative model to characterize the mapping relationship between sensitive parameter input and CO₂ multiphase output results, and Monte Carlo stochastic simulation was carried out to quantify the uncertainty of the model. The results show that after 100 years of CO₂ injection into the oil-water mixed layer, the gas phase CO₂ accounts for 61.2%, the oil-soluble phase CO₂ accounts for 17.8%, the water-soluble phase CO₂ accounts for 11.2%, and the mineral phase CO₂ accounts for 9.8%. The most sensitive parameter of co₂-dsmrt model to oil production is permeability, and the most sensitive parameter to reservoir CO₂ storage is porosity. The production investment ratio of CO₂ storage capacity and oil production under different confidence levels is significantly different. P10scenario is suitable for storage priority strategy, P50 scenario can achieve the balance between storage and economic benefits, and P90 scenario is more suitable for production optimization strategy.

The three-dimensional carbon footprint of resource-based cities in the Yellow River Basin was measured, and their configuration paths of carbon reduction and footprint minimization were clarified through the three-dimensional carbon footprint model, the TOE framework, and fuzzy-set qualitative comparative analysis. It was found that during the study period, the average carbon footprint of resource-based cities in the Yellow River Basin exhibited a fluctuating downward trend, characterized by "continuous decline-slight rebound-fluctuating decline". The number of high-carbon footprint cities significantly decreased, and there were significant differences in the carbon footprint of resource-based cities in the upper, middle, and lower reaches, which were arranged in the order of "upper reaches < lower reaches < middle reaches". The carbon reduction and footprint minimization paths of resource-based cities in the Yellow River Basin could be summarized into three types: the "technology-structure dominant" path covering 44% of the cities, the "organization dominant" path covering 50% of the cities, and the "environment dominant" path covering 37% of the cities. Based on the above conclusions, it was suggested that a global perspective should be maintained to continuously monitor the carbon footprint values of resource-based cities in the Yellow River Basin, and a combination of measures should be taken to reduce the carbon footprint of these cities based on the configuration effects.

Based on the simple data of 261 cities from 2010 to 2020, the impact and mechanism of the policy of industrial transformation and upgrading demonstration zones on urban air quality were studied using the difference in differences method. It was found that the policy of industrial transformation and upgrading demonstration zones significantly improved the air quality of the demonstration zone cities: during the sample period, the concentration of PM_2.5 in the demonstration zone cities was reduced by an average of 3.238μg/m³, which accounted for approximately 7.711% of the average PM_2.5 concentration in the demonstration zone cities before the policy was implemented. This conclusion remained valid after robustness tests such as parallel trend testing, placebo testing, and the PSM-DID method. This conclusion remained valid after robustness tests such as parallel trend testing, placebo testing, and the PSM-DID method. Mechanism analysis revealed that the policy of demonstration zones mainly improved urban air quality by promoting the optimization of urban industrial structure and strengthening environmental regulations. Heterogeneity analysis found that, compared with resource-based cities, eastern and northern regions, the improvement effect of demonstration zone policies on urban air quality was more significant in old industrial cities, composite cities, central and western regions, and southern regions. The moderation effect analysis revealed that Chinese-style Decentralization had a moderating effect between the construction of demonstration zones and the improvement of air quality: compared with cluster-style demonstration zones and those that only received a qualified evaluation in the annual assessment, single-style demonstration zones and those that received excellent and good evaluations might have a more significant effect on air quality improvement.

This study employs a dual approach, utilizing both normalized carbon emission equivalents and eco-efficiency indicators based on data envelopment analysis (DEA), to evaluate633WWTPs within the Yangtze River Economic Belt. The mean eco-efficiency index was found to be 0.492. The eco-efficiency of WWTPs in the Yangtze River Economic Belt is significantly influenced by scale effects: mean index are 0.440 for plants with treatment scale ≤10,000m³/d, 0.507 for 10,000~100,000m³/d, and 0.729 for >100,000m³/d; notable differences in eco-efficiency exist across various regions and treatment processes: with sequencing batch reactors and continuously stirred tank reactors exhibit higher mean eco-efficiency index (0.569) than plug-flow processes (0.473). Additionally, as the BOD₅ concentration of influent in WWTPs increases, their eco-efficiency index rises but then declines. Moreover, advanced treatment processes exacerbate the environmental impact of wastewater treatment systems, WWTPs meeting Grade A discharge standards have a mean eco-efficiency index of 0.483, compared to 0.535 for Grade B-compliant plants.

This study takes the urban agglomeration on the northern slope of the Tianshan in Xinjiang as the research area. The ecosystem service supply and demand were analyzed through the InVEST model, ArcGIS software, and the Minimum Cumulative Resistance (MCR) model. The ecological security pattern were built according to the matching degree and coupling coordination of ecosystem service supply and demand. The results indicated that the ecosystem service supply and demand exhibited significant spatial heterogeneity. The mountainous and oases were the core output areas of ecosystem services, while the downtown areas with a highly concentrated population were the concentrated demand areas of ecosystem services. The information entropy and variation coefficient of ecosystem services were 1.48 and 0.41, respectively in 6,000m grid scale, and 1.44 and 0.43, respectively in town scale. 946 ecological sources with 10,003.70km² were confirmed by the comprehensive ecosystem service supply and demand ratio which were distributed in the central and western parts of urban agglomerations. 2,291 ecological corridors with a length of 22,191.89km were distributed as a net. 151 ecological nodes and 2,082km² obstacle points were distributed outside the urban center. 24 ecological coordination areas, 55 ecological conservation areas, 48 ecological improvement areas, 176 ecological control areas and 420 ecological restoration areas were classified through the ecosystem service supply and demand relationship in town scale. Ecological restoration projects, such as returning farmland to forests and restoring wetlands, should be prioritized in ecological restoration zones to alleviate ecological pressure.

This study measured the levels of Digital-Intelligence Integration (DII) and ecological resilience in cities within the Yellow River Basin from 2011 to 2022 using the entropy weight TOPSIS method and a coupling coordination model. The spatio-temporal evolution of DII was further analyzed. Building on these measurements, seemingly unrelated regression (SUR) and moderated mediation models were employed to investigate the effects, underlying mechanisms, and moderating factors influencing the relationship between DII and urban ecological resilience in the region. The results showed that the overall level of DII in the Yellow River Basin increased from 0.144 to 0.353 during the study period. By 2022, most cities had transitioned out of states of extreme and severe imbalance. A distinct spatial clustering pattern was observed for DII, with cluster centers predominantly located in the Shandong Peninsula and provincial capitals. The gap between these cluster centers and non-central cities was found to be widening. DII exerted a significantly positive overall effect on urban ecological resilience (regression coefficient = 0.197, p<0.01). However, its effect on the resistance dimension of ecological resilience was significantly negative (coefficient = -0.011, p<0.01), suggesting a potential weakening effect. Green innovation and public environmental concern acted as significant mediators. The regression coefficients of DII on green innovation and public environmental concern were 0.744 (p<0.01) and 0.345 (p<0.05), respectively. Financial development positively moderated these mediating roles, as the interaction terms between financial development and DII yielded significant coefficients of 0.122 and 0.146 (both p<0.01) for green innovation and public environmental concern, respectively. Heterogeneity analysis indicated that DII significantly enhanced urban ecological resilience only in downstream regions (coefficient=0.302, p<0.01), with no significant effects observed in upstream or midstream regions. Moreover, non-resource-based cities and those designated as big data comprehensive pilot zones exhibited stronger effects (coefficients = 0.235 and 0.285, respectively, both p<0.01) compared to other cities. These findings provide scientific support and policy references for optimizing urban ecological resilience in the Yellow River Basin in the era of digital intelligence.

The study focused on the spatiotemporal distribution characteristics of retired power batteries and critical metal resources in China. It used the LSTM neural network model to predict the sales volume of electric vehicles. It also estimated the stock of retired power batteries and critical metal resources by applying the Weibull distribution model, and further analyzed their spatial distribution characteristics. The research found that from 2012 to 2035, there were significant regional differences and spatial agglomeration in the cumulative retirement volume of power batteries and the cumulative stock of critical metal resources in China. Currently, Guangdong, Zhejiang, Shandong, Beijing, Shanghai, and Henan had the highest cumulative retirement volume of power batteries and the highest cumulative stock of critical metal resources, while Tibet, Qinghai, Ningxia, Heilongjiang, Xinjiang, and Inner Mongolia had the lowest. By 2035, Jiangsu was projected to replace Beijing as one of the six provinces with the highest cumulative retirement volume, with a cumulative retirement volume of 2.2546 million tons. Gansu was projected to replace Inner Mongolia as one of the provinces with the lowest cumulative retirement volume, with a cumulative retirement volume of 153,600 tons. In terms of spatial agglomeration, the high-high agglomeration area was found to be Jiangsu, while the low-low agglomeration areas included Xinjiang, Qinghai, Sichuan, Gansu, Inner Mongolia, and Jilin. It was projected that the scope of high-high agglomeration would expand further in the future (with Anhui and Shanghai being added), while the scope of low-low agglomeration would contract (with Sichuan no longer being part of this category). Regarding the distribution of processing capacities of the white-listed enterprises for the comprehensive utilization of retired power batteries, it was found that although China had an overall surplus in processing capacity, there was a spatial misalignment. It was anticipated that in the future, the recycling and regeneration capacities in Shanghai, Jiangsu, Hebei, Guangxi, Tianjin, Guangdong, Zhejiang, Yunnan, and Henan would not be sufficient to meet their own demands, and it was necessary to further optimize the layout of recycling and processing capacities.

Taking Xingtai, a typical industrial city, as the study object, the pollution characteristics of O₃ were analyzed based on air quality monitoring and meteorological data from 2018 to 2022. The geographical detector method was employed to investigate the individual and combined impacts of meteorological factors and precursors on O₃ pollution. An air quality model was used to assess the sensitivity of O₃ generation, and EKMA curves were adopted to determine optimal emission reduction ratios in different control areas. The research revealed that the monthly average O₃ concentrations in Xingtai exhibited an inverted V-shaped pattern, with peak values observed in June (199.28μg/m³) and trough values recorded in January and December (37.16μg/m³). The hourly O₃ concentrations and their variation ranges across seasons followed the order: summer > spring > autumn > winter. Significant seasonal differences were identified in the driving factors affecting surface O₃ concentrations. On an annual scale, meteorological factors were found to exert greater influence on O₃ pollution than precursor pollutants. Temperature and solar radiation were identified as the main positive driving factors for O₃ concentration increases, while humidity and precipitation served as primary negative drivers. CO and NO₂ were positive drivers in spring and summer but showed opposite effects in autumn and winter. The central urban area with surrounding counties, along with Qinghe and Linxi counties, were classified as VOCs control areas. The northwestern and central-eastern counties were designated as synergistic control areas for both VOCs and NO_x. The optimal emission reduction ratios for between VOCs and NO_x were determined to be 1.5:1 and 1:1, respectively, in the different control areas.