To systematically assess the environmental risks of natural steroidal estrogens in vegetables, there is an urgent need to establish an efficient, reliable, universal, and convenient extraction and detection system for these estrogens. This study demonstrated that the three natural steroidal estrogens showed a strong linear relationship, with a correlation coefficient greater than 0.9995. The detection limits for estradiol, 17β-estradiol, and estrone were found to be 0.36~3.23μg/kg, 0.76~3.67μg/kg, and 13.97~20.12μg/kg, respectively. The average recoveries for these substances ranged from 104.9% to 130.5%, while the relative standard deviations were between 4.9% and 18.7%. In root samples, the average recoveries were between 80.5% and 129.9%, with relative standard deviations ranging from 4.2% to 38.8%. This method detected natural estrogens in vegetable samples randomly collected in Nanjing city. The results indicated that natural estriol was detected in all vegetable samples, with a 100% detection rate in both the ground and root parts. The detection rate for 17β-estradiol was 75% in the ground part and 100% in the root. In contrast, the detection rate for estrone was 43.8% in the ground part and 18.8% in the root. Further studies are needed to assess the potential risks associated with estrone. Overall, The method exhibits high accuracy and precision, fulfilling the requirements for analysis and determination. Consequently, it provides scientific evidences for effectively assessing and control the environmental risks associated with natural steroidal estrogens in vegetables.

This review systematically explores the application of machine learning technology in the field of microplastics, covering classification and identification, quantitative analysis, and prediction of adsorption properties. By combing through recent literature, it has been found that technologies such as convolutional neural networks (CNN) and support vector machines (SVM) are of great significance for improving the accuracy and efficiency of microplastic detection. In classification and identification, CNN models can accurately distinguish the types and shapes of microplastics; during quantitative analysis, machine learning can quickly determine the concentration of microplastics with the help of image and spectral data. In terms of predicting adsorption properties, models based on quantitative structure-property relationships (QSPR) have shown higher accuracy and robustness than traditional models. However, there are currently challenges such as poor data quality, difficulties in collection and annotation, and a lack of model interpretability. Future research should focus on diversifying datasets and enhancing model interpretability to promote the further application of machine learning technology in microplastic research.

The scientific prevention and effective control of environmental health risks are essential to achieving the vision of a‘Beautiful China’. However, China currently lacks comprehensive guidelines for medium and long-term environmental health risk management. This study discusses the ideal levels of environmental health risk management required to meet key milestones of a‘Beautiful China. Through the review of previous management efforts, we identify several critical challenges: insufficient risk prevention and control system, inadequate regulatory standards for conventional pollutants, and limited research base and understanding of emerging pollutants. In the end, this study concludes by proposing a future management system that prioritizes public health protection through systematic prevention and control of both conventional pollutants and emerging pollutants.

Human lung cancer cell A549were used as the test cell. Three kinds of organophosphorus flame retardants, i.e., tris(2-butoxyethyl) phosphate (TBOEP), tris(1-chloro-2-propyl)phosphate (TCIPP), and tri(4-isopropylphenyl)phosphate (IPPP), were selected as representative compounds, which are frequently detected in the environment. The toxic effects of the three compounds on A549cell were studied through multiple toxicity test endpoints. Results showed that the three OPFRs could inhibit cell viability, stimulate the production of excessive reactive oxygen species and reduce the mitochondrial membrane potential in cell, induce cell inflammation and cause DNA damage. All the cytotoxicity indicators were dose-dependent. OPFRs with low concentrations had less effect, and the toxic effect enhanced with the increase of OPFRs concentrations. Compared with the three OPFRs, their toxic effects on A549cells ranked as TBOEP > TCIPP ≈ IPPP. Especially, when the cells were exposed to TBOEP with a high concentration of 500 μmol/L, the cell viability was less than 5%, the amount of reactive oxygen species in cell increased by three times, the mitochondrial membrane potential decreased by 46.5%, the secretion of inflammatory factors IL-6 and TNF-α increased by 124.4% and 262.7%, and the content of DNA damage markers increased significantly.

To investigate the environmental behavior of organophosphate esters (OPEs) in the surroundings of the electronics industry, an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to determine the concentration levels of 18 OPEs in soil, atmospheric, and wastewater samples, which were collected from surrounding areas of typical electronic industry enterprises in Shenzhen. Correlation analysis and principal component analysis (PCA) were applied to identify pollution sources, and the health risk levels of people of different age groups were evaluated. The results indicated that the detection rates of 18 OPEs in various environmental media were found to range between 40.9% and 100.0%. In 34 soil samples, 21 atmospheric samples, and 30 wastewater samples, the mean concentrations of ∑18 OPEs were determined to be 283ng/g (12.2~857ng/g), 4.48×10⁵ pg/m³(3.12×10³~2.95×10⁶ pg/m³), and 1.11×10⁶ ng/L (5.39×10⁵~2.73×10⁶ ng/L), respectively. Tris(2,4-di-tert-butylphenyl)phosphate (AO168=O), tris(4-tert-butylphenyl) phosphate (T4tBPP), tris(2-chloropropyl) phosphate (TCPP), and bisphenol A bis(diphenyl phosphate) (BPADP) were identified as the predominant OPEs across all media. The correlation and principal component analysis (PCA) results demonstrated that OPE contamination in the vicinity of electronic industry enterprises was predominantly influenced by industrial production emissions and traffic-related discharges. Non-carcinogenic and carcinogenic health risks associated with multi-pathway exposure to OPEs in soil and atmospheric media across different age groups were found to remain within acceptable thresholds; however, the health risks posed by long-term cumulative OPE exposure were identified as requiring sustained scientific attention.

This paper used the multi-region input-output model to calculate the embodied carbon and its industrial structure of China's inter-city industrial trade based on the perspective of value-added trade, depict the structural characteristics of the embodied carbon transfer network, and the mechanism of the embodied carbon transfer network is revealed through the exponential random graph model (ERGM). The study found that: The inter-city industrial trade embodied carbon transfer network is dense but without scale, with small world structure and miscompatibility. Resource-intensive and capital-intensive industries contribute more than 90% of the embodied carbon transfer. Cities with high embodied carbon net outflow are mainly resource-based and industrial cities in the Yellow River Basin and the Bohai Rim region, while cities with high embodied carbon net inflow are mainly the national and regional central cities east of Hu Huanyong Line. Large-scale embodied carbon transfer mainly occurs among the cities within the provinces, and the inter-city embodied carbon transfer network has a certain provincial boundary effect. The embodied carbon transfer of provincial cities presents the "core-edge" structure around the provincial central cities. The embodied carbon transfer of inter-provincial cities shows the radial structure from the resource-based cities and industrial cities in the Yellow River basin and the Bohai Rim region to Beijing, Shanghai, Hangzhou, Ningbo, Suzhou, Chongqing, Guangzhou, Guangzhou and Shenzhen and other central cities. In the mechanism of inter-city embodied carbon transfer network, mutualism and preference dependence effect are important endogenous mechanisms. Cities with developed economy, dense population, high per capita consumption level and advanced industry are more inclined to flow into the embodied carbon. Cities with higher comparative advantages and low energy efficiency in resource-based industries are more inclined to outflow embodied carbon. Policy proximity and geographic proximity have a positive impact on the inter-city embodied carbon transfer network, and technical proximity has a negative impact.

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.

This article constructed a multidimensional urban sprawl measurement index system from the structural dimension, morphological dimension, density dimension, and efficiency dimension. Based on map visualization, standard deviation ellipse, and cold and hot spot analysis, it explored the spatiotemporal characteristics and migration evolution patterns of China's urban comprehensive sprawl from 2005 to 2020. The spatiotemporal geographically weighted model (GTWR) was used to empirically examine the spatiotemporal heterogeneity of the impact of multidimensional urban sprawl on carbon emission intensity. Research shows that: (1) The comprehensive urban sprawl in China exhibited a spatial difference of "high in the east and low in the west", but the urban sprawl in the eastern coastal and northeastern regions has declined in the later stage of the sample. The standard deviation ellipse shows a trend of centripetal clustering, and the center of gravity of the distribution shifts towards the southwest as a whole. The analysis of hot and cold spots presents regional differences of "hot in the east and cold in the west". (2) The overall urban sprawl has a significant impact on carbon emissions, and over time, it plays a positive promoting role in an increasing number of cities. The positive promotion area is mainly concentrated in the central and western regions and coastal areas, while the negative inhibition area is mainly the North China Plain and the Pearl River Delta. (3) There is significant spatiotemporal heterogeneity in the influencing factors of each dimension. In terms of temporal trends, the structural dimension promotes carbon emissions in most cities and its influence increases year by year; The form dimension has shifted from a promoting effect to a inhibiting effect on carbon emissions in most cities; The density dimension and efficiency dimension suppress carbon emissions in most cities, but the density dimension shows a polarization trend year by year, while the influence of the efficiency dimension weakens overall. In terms of spatial distribution, the influence of structural dimension and density dimension is stronger in the southeastern, western, and northeastern regions, while the significant effect of morphological dimension is in the northeastern border and central western regions, and the significant effect of efficiency dimension is in the central and western regions.

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.

This study establishes a carbon emission reduction measurement model for the secondary ash recycled ceramsite project from the perspective of carbon footprint, combined with the National Certified Voluntary Emission Reduction (CCER) methodology. Taking the 40000 tons/year secondary ash recycled ceramsite project as an example, empirical analysis is conducted to evaluate the project's carbon emission reduction. Based on the analysis of key carbon emission factors, the carbon emission reduction potential of the secondary ash recycled ceramsite project is optimized and evaluated. The results show that the total CO₂e emission reduction of the 40000 tons/year secondary ash slag regenerated ceramsite project in 2023 is 32600 tons, of which the ceramsite production stage contributes to 95% of the emission reduction. From the perspective of carbon footprint analysis, the total annual CO₂e emissions of the project are about 64900 tons, and the processing, production, and raw material acquisition stages are key links in the carbon emissions of the ceramsite project. From the analysis of CO₂ emission source categories, the substitution of solid waste materials such as secondary ash and sludge is the key to carbon reduction in the ceramsite industry. In addition, the priority order of adding solid waste materials is secondary ash, sludge, and waste soil. Regarding the optimization of carbon emission reduction potential, under four low-carbon scenarios of green raw materials, clean power grid, low-carbon transportation, and recycling, the secondary ash regenerated ceramsite project achieved CO₂e emission reductions of 69300, 34200, 35600 and 32800 tons, respectively. Under the green raw material scenario, the ceramsite industry has a carbon emission reduction potential of 9million tons.