PM_2.5 control in the Guanzhong region has achieved significant progress, but the situation regarding ozone pollution remains severe. To continue advancing the fine management of VOCs (volatile organic compounds) in the Guanzhong region, with Xi'an and Xianyang as representatives, six industries—packaging printing, electronic product manufacturing, industrial coatings, paint and ink manufacturing, furniture manufacturing, and rubber products — were selected for sample collection. This study examines the VOC emission profiles, ozone formation potential (OFP), and secondary aerosol formation potential (SOAFP) of typical industries in the Guanzhong region. The results show that in the packaging printing and industrial coating industries, the primary VOCs emitted are oxygenated volatile organic compounds (OVOCs) (53%~73%) and alkanes (17%~34%); in the electronic product manufacturing industry, they are OVOCs (68%) and alkenes (29%); in the paint and ink manufacturing industry, the main VOCs are OVOCs (61%) and aromatic hydrocarbons (22%); in the furniture manufacturing industry, the primary VOCs are aromatic hydrocarbons (78%) and OVOCs (19%); and in the rubber products industry, alkanes (78%) dominate. Key characteristic species emitted by typical industries in the Guanzhong region include ethanol, acetone, isopropanol, formaldehyde, ethyl acetate, and m-/p-xylene. Based on the Maximum Incremental Reactivity (MIR) method and the Aerosol Formation Coefficient (FAC) method, it was found that OVOCs, aromatic hydrocarbons, and alkenes are the main contributors to OFP, while aromatic hydrocarbons are the primary source of SOAFP. The major emission sources are the paint and ink manufacturing, industrial coatings, and furniture manufacturing industries, which should be prioritized for control.

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.

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.

More than 100 volatile organic compounds (including 36 carcinogens) were identified in the clothing-mediated thirdhand smoke (THS) by using a proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). Significant differences were found in the component and amount for the THS released from different clothing material. Specifically, the amount of THS released from polyester was significantly lower than that released from cotton. Furthermore, the amount of THS released from clothing exponentially decreased as increasing time, and the decrease ratio was more than 80% after 30 minutes, suggesting that the clothing-mediated THS pollution could be reduced if the smokers stay outdoors for more than 30 minutes after smoking cigarettes. This study provided a useful method for the investigations of clothing-mediated THS pollution, deepening the understanding on the release characteristics of THS pollution from clothing, which should have great significance in reducing the THS exposure for non-smokers.

To promote the reduction and recycling of construction and demolition (C&D) waste in housing, a dynamic material flow model was established to simulate the evolving characteristics of housing flow-stock in both urban and rural areas of Beijing from 1949 to 2100. The amount of urban and rural housing C&D waste generated was predicted. The results showed that from 1949 to 2100, cyclical fluctuations were observed in the volume of new construction and demolition of housing in Beijing’s urban and rural areas, with the housing stock following an S-shaped curve. The area of new housing construction in urban and rural regions peaked at 31.456million m² in 2012 and 7.887 million m² in 2015, while the demolition area reached its maximum of 15.008 million m² in 2094 and 4.535 million m² in 2016. The saturation values of housing stock in urban and rural areas were 800 and 1.247 million m², respectively. By the mid-to-late 21 st century, Beijing was anticipated to experience a surge in C&D waste generation, which will reach its peak and then persist at elevated levels with periodic fluctuations. The apex of C&D waste generation was projected to occur in 2094, with an estimated total output of 23.964 million tons. Cement, brick, sand, and gravel were the predominant components of C&D waste by weight, accounting for 90.2%~95.5% of the total weight of housing C&D waste in urban areas and 92.2%~94.1% of that in rural areas, while the weight proportion of iron and steel ranged from 0.1% to 4.5% and 0.1% to 3.0%, respectively. The long-lifetime scenario could defer the peak of C&D waste, with a maximum reduction potential of 72.0%. Additionally, recycling C&D waste as urban minerals could lead to a dramatic decrease in future demand for primary steel, with a reduction of up to 98.5%.

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.

Oxidative potential (OP) is a crucial indicator for evaluating the capacity of PM_2.5 to trigger oxidative stress. Therefore, this study employed dithiothreitol (DTT) method to measure the OP of PM_2.5. During the observation period, the results indicated that the daily average concentration of atmospheric PM_2.5 in Taiyuan severely exceeded the standard, with a maximum concentration reaching 150.91µg/m³, signifying severe air pollution. The daily average values for volume-normalized (DTT_v) and mass-normalized (DTT_m) DTT activity were (2.90±1.07)nmol/(min·m³) and (38.34±18.91)pmol/(min·µg), respectively. Meanwhile, a significant positive correlation was observed between PM_2.5 mass concentration and DTT_v (r=0.916, P<0.01), while a negative correlation was found with DTT_m. Furthermore, DTT_v exhibited significant correlations (P<0.05) with organic carbon (OC), elemental carbon (EC), metallic elements (Fe, Mn, Zn, Pb), and ionic components (K⁺, Cl^-, etc.) within PM_2.5. These phenomena suggested that DTT activity primarily depends on specific components of PM_2.5. The study further integrated the positive matrix factorization (PMF) model with the multiple linear regression algorithm. Quantitative analysis revealed that solid fuel combustion sources, such as coal combustion, were the most important sources of OP in Taiyuan, contributing 54.7%, followed by motor vehicle sources (23.3%) and dust sources (22.0%).

Based on the LMDI decomposition model, the contribution of driving factors of transportation carbon emissions is quantified. The Tapio decoupling model is used to analyze the relationship between carbon emissions and economic growth, and the efforts made by each factor to achieve decoupling are quantitatively analyzed. The results show that: Economic output is the decisive factors leading to an increase on transportation carbon emissions, with a contribution rate of 115.93% to carbon emissions; Industrial structure has the most significant inhibiting effect on carbon emissions. The decoupling index of transportation carbon emissions in the national, eastern, central, and western regions is all in a downward trend, experiencing the decoupling trend of expansive negative decoupling → weak decoupling → strong decoupling. The driving factors of transportation carbon emissions in the four regions generally show the phased characteristic of "no decoupling effort → weak decoupling effort → strong decoupling effort". Industrial structure has made varying degrees of decoupling effort in 30provinces, with transportation intensity and population size becoming key factors hindering carbon decoupling in the vast majority of provinces.

In this paper, we reviewed the measured data of heavy metals in farmland soils in Guangdong Province since 2010. We applied a meta-analysis approach to combine the data based on “average concentration” “number of sampling points” and “standard deviation”. The results showed that the concentration of lead（Pb, 48.83mg/kg）and mercury（Hg, 0.18mg/kg）in Guangdong Province are among the highest in China, likely influenced by industrial and mining activities. In addition, subgroup analyses explored the effects of spatial distribution and cropping systems across different cities. The results revealed that heavy metal concentrations were higher in the economically developed Pearl River Delta and mineral-rich regions. Notably, some of the most polluted cities include Foshan city（As:18.32mg/kg, Cr:73.5mg/kg, Ni: 35.4mg/kg）, Shaoguan city（Pb: 86.61mg/kg）, Shenzhen city（Cd: 0.51mg/kg）, Meizhou city（Cu: 61.1mg/kg）, etc. Among them, farmland soil Cd concentration was significantly correlated with regional industrial output value（r=0.77, P<0.01）, with additional impacts from factors such as the local population and energy consumption. The analyses of different farming systems showed that Cd, Pb, Zn, Cr, and Hg concentrations in soils were higher in land and paddy fields; while Cu, Zn, As, and Ni were enriched in orchards and facility-based agriculture, which may be related to the soil physicochemical properties, fertiliser and the geological backgrounds in different farming systems. This study provides province-wide information on the spatial distribution of heavy metals in agricultural soils and cropping systems, which is of great significance to the management and control strategies of heavy metal pollution in agricultural soils in Guangdong Province.

Based on the methodology of life cycle assessment（LCA）, the carbon footprint of the typical wind power system with generation and electricity storage（WPSGES）in China was calculated, so as to identify the reduction potential of carbon emission from life cycle stages. The results showed that the carbon footprint of WPSGES was 8.44gCO₂/（kW·h）, which mainly came from the manufacturing process by 6.25gCO₂/（kW·h）（74.05%）. Such processes as construction, operation, and end of life only contributed 1.04, 1.91 and -0.74gCO₂/（kW·h）, respectively. It was also confirmed that expanding the system boundary, including power generation and storage, could reduce gross carbon footprint of WPSGES.