The relationship between ethylene oxide (EO) and cardiovascular disease risk was investigated in this article. A total of 5041 adults were enrolled, and ethylene oxide levels were measured in serum using hemoglobin ethylene oxide conjugate (HbEO) as a biomarker. Data were obtained from the National Health and and Nutrition Examination Survey (NHANES) database in the United States between 2013 and 2018. Weighted logistic regression models and restricted cubic spline plots were used to examine the dose-response relationship between HbEO and cardiovascular disease risk. Subgroup analyses were conducted to evaluate heterogeneity in the study. Through multiple regression analysis, it was found that serum HbEO levels were positively associated with cardiovascular disease risk, and statistical significance was maintained after adjustment for multiple confounders (OR: 1.15, [95% CI: 1.02~1.30], P=0.021). For most cardiovascular diseases, increased exposure to ethylene oxide was associated with elevated OR values, although this increase was not always statistically significant. Additionally, HbEO was positively correlated with the incidence of angina pectoris and heart attack, with the highest OR values observed after full adjustment (OR=1.36 [95% CI: 1.11~1.68] and OR=1.35 [95% CI: 1.14~1.60], respectively). Angina pectoris was the most affected outcome.A significant non-linear relationship was identified between HbEO and overall cardiovascular disease risk (P-overall=0.002, P-non-linear=0.006). Dose-response analysis indicated that elevated serum HbEO levels were associated with an increased risk of angina, with evidence supporting a linear relationship (p-overall=0.012, p-non-linear=0.748). A significant non-linear association was also observed between HbEO and heart attack risk (p-overall<0.001, p-non-linear=0.011).In summary, ethylene oxide metabolites in the blood were associated with an increased incidence of cardiovascular diseases, particularly angina pectoris and heart attack. Further research is needed to confirm these findings.

In the context of global climate change, the health impacts of heatwave events exhibit marked regional heterogeneity, posing a severe threat to public health. This study analyzed daily respiratory disease outpatient data, meteorological monitoring data, and air pollution data from Handan City (2016~2021) using a Generalized Additive Model (GAM) to evaluate the risk of respiratory disease visits under 12 heatwave definitions (based on P96-P99 percentile thresholds lasting 2~4 days). Results demonstrate significant definition-dependent variability in heatwave frequency and duration: extreme heatwaves (P99 threshold, 4days) occurred only twice (9 cumulative days) over six years, while moderate heatwaves (P96 threshold, 2days) reached 20 occurrences (66 cumulative days). During heatwave periods, temperatures were significantly higher with reduced variability compared to non-heatwave periods, accompanied by statistically significant differences in daily outpatient volumes.Forheatwaves lasting 2~3 days, health risks initially increased and then decreased with rising intensity, whereas for events lasting 4 days, risks progressively declined with increasing intensity. Peak risks for P96-P98-defined heatwaves occurred at lag 3~5 days, while P99-defined extreme heatwaves showed immediate peak risks at lag1day. Long-duration heatwaves ((4days) posed significantly higher and more prolonged risks compared to short-duration events ((2days). All heatwave types exhibited a "bimodal lag pattern" with acute (lag 0~3) and delayed (lag 5~7) effect phases. The risk profile for low-intensity, short-duration heatwaves was relatively stable, whereas high-intensity, long-duration events demonstrated greater risk fluctuations. These findings provide regionally tailored evidence to inform the development of dynamic heatwave health early warning systems.

The benefits of the ultra-low emission retrofit (ULER) in Suzhou’s iron and steel industry in 2023 were evaluated in this study. Following the retrofits, annual emissions of SO₂, NO_x, and PM declined by 66.67%, 65.64%, and 53.48%, respectively, with substantial reductions in key processes such as sintering, coking, and blast furnace ironmaking. Evaluated by the mesoscale atmospheric chemical model WRF-Chem, the average concentrations of SO₂, NO₂, PM_2.5 and PM₁₀ in Suzhou and its surrounding urban agglomerations decreased by 5.02%, 1.83%, 5.16% and 5.06% respectively, while O₃ levels increased slightly by 0.93%. Suzhou exhibited the largest reductions in SO₂ and NO₂ concentrations, whereas the most significant decreases in PM_2.5 and PM₁₀occurred in Taizhou. Given that PM_2.5 pollution in China is the most severe in winter, the exposure-response relationship function was used to quantify the health benefits. The total number of premature deaths due to PM_2.5 pollution from all causes, cardiovascular diseases and respiratory diseases was reduced by 105 (95%CI: 88~122), 31 (27~36) and 12 (10~14) respectively. Geographically, the reductions in premature mortality showed a pattern of "higher in the north and lower in the south"; Nantong achieved the greatest health gain, with premature deaths from all causes reduced by 24 (15~32).

Using the data from Food and Agriculture Organization of the United Nations (FAO) and the China Statistical Yearbook spanning 1961 to 2021, this paper systematically analyzes the current status and evolution patterns of methane (CH₄) emissions from rice, nitrous oxide (N₂O) emissions from wheat, and N₂O emissions from maize in the world's five major grain-producing countries. It further explores the long-term trends and driving factors of CH₄ and N₂O emissions from China’s three staple grain crops. The results show that in recent years, China’s rice CH₄ emissions and wheat N₂O emissions consistently ranked highest among the five countries while maize N₂O emissions and the corresponding emissions per unit grain mass both ranked second. However, the carbon emissions per unit grain mass for both crops was in the middle-to-lower range among the five nations. Between 1961 and 2021, China’s rice CH₄ emissions, wheat N₂O emissions, and maize N₂O emissions increased by 13.9%, 474.0%, and 831.5%, respectively. Conversely, their non-CO₂ carbon emissions per unit grain mass significantly decreased by 69.9%, 40.5%, and 38.3%, respectively. The heavy application of chemical nitrogen fertilizer was identified as the key driver that triggers the rapid increase in wheat and maize N₂O emissions. This study reveals that China has made significant progress in reducing carbon emissions from its three major grain crops. Nevertheless, the rapid rise in N₂O emissions from wheat and maize has become a major constraint hindering further progress in decarbonizing China’s grain production.

The composite quasi-natural experiment empowered by the "dual-carbon linkage" was reconstructed based on carbon emission trading and low-carbon city construction. Using panel data from 280 prefecture-level cities in China from 2011 to 2022, a double machine learning model was employed to examine the impact of the "dual-carbon linkage" on urban green transformation. It was found that the "dual-carbon linkage" had a significant positive effect on both urban economic transformation performance and green innovation performance, with coefficients of 0.027 and 0.083, respectively, significant at the 1% statistical level. This indicated that the "dual-carbon linkage" contributed to comprehensively accelerating urban green transformation and demonstrated a more pronounced empowering effect compared to single institutional innovations. Mechanism analysis based on the "government-market-society" three-dimensional framework revealed that the "dual-carbon linkage" indirectly facilitated urban green transformation through government environmental improvement effects by reducing environmental pollution and enhancing environmental enforcement (with coefficients of -0.727 and 0.721, respectively), market resource allocation effects by promoting industrial upgrading and accelerating economic agglomeration (with coefficients of 0.134 and 0.332, respectively), and social participation and governance effects by raising public environmental awareness and optimizing public service environments (with coefficients of 0.487 and 0.052, respectively). Heterogeneity analysis showed that the incentivizing effects of the "dual-carbon linkage" on green transformation were more pronounced in cities with lower bureaucratic redundancy (coefficients of 0.110 and 0.147, respectively), greater financial technology support (coefficients of 0.039 and 0.067, respectively), higher market integration (coefficients of 0.057 and 0.102, respectively), better sci-tech business environments (coefficients of 0.121 and 0.138, respectively), as well as in cities located in the Yangtze River Economic Belt (coefficients of 0.041 and 0.069, respectively) and eastern regions (coefficients of 0.063 and 0.046, respectively). This study provided empirical evidence and decision-making insights for local governments to coordinate and unleash the synergistic force of "dual carbon" institutional innovations and accelerate the comprehensive green transformation of the economy and society during the "15th Five-Year Plan" period.

This study focused on Handan, a representative steel industry city, where the characteristics of ozone pollution were systematically analyzed based on atmospheric pollutant and meteorological data from 2014 to 2023. The Random Forest model was utilized to investigate the correlation between key meteorological factors and O₃ concentrations, as well as their relative contributions. Furthermore, the WRF-CAMx model, combined with the EKMA curve method, was employed to examine O₃ formation sensitivity and corresponding precursor emission reduction strategies in different districts. The results indicated that the monthly average O₃-8h concentration exhibited distinct seasonal variations (summer > spring > autumn > winter), with the highest levels of exceedance occurring between June and July. Hourly O₃ concentrations followed a unimodal trend, which peaked between 15:00 and 16:00. Correlation analysis revealed that relative humidity (RH), solar radiation (SR), and temperature (T) were significantly correlated with O₃ concentrations, with correlation coefficients of -0.69, 0.68, and 0.58, respectively. Their relative contributions were determined to be (24.90±2.41)%, (18.66±2.30)%, and (25.56±2.02)%. It was found that O₃ pollution was more likely to occur when T > 30℃ and 36% < RH < 56%. The O₃ control zone analysis showed that Handan’s central and western areas were VOCs-controlled, with an optimal VOCs-to- NO_x emission reduction ratio of 1.5:1. The eastern districts were identified as NO_x-controlled, while other areas required coordinated control of both VOCs and NO_x, with an optimal reduction ratio of 1:1.5. These findings provided a scientific basis and policy recommendations for the emission reduction and control of O₃ pollution in industrial cities.

This study constructs an indicator system for China's "zero-waste cities" construction. It uses the entropy weight method to measure the system's level across 30 provinces (autonomous regions and municipalities) from 2011 to 2022. The coupling coordination model is employed to analyze the development coordination between subsystems, while the gravity model and social network analysis explore the spatial network characteristics of the system. Lastly, the quadratic assignment procedure (QAP) is used to identify the driving factors behind the system. The results show that: the coupling correlation and coordination between subsystems have improved over time, with regional coordination levels in the order of: Class Ⅰ > Class Ⅱ > Class Ⅲ > Class Ⅳ > Class Ⅴ. The spatial network connectivity of each region in China's "zero waste city" construction system shows an upward trend, with increasing network density and more strong connection networks. However, the overall network remains at a low level and is unevenly developed. The network connection level of Class Ⅰ regions is higher than others, with network efficiency tending towards stability. Economic development, urbanization, innovation capacity, and industrial structure coordination significantly influence the spatial network. Economic development has a greater impact on Class Ⅰ and Class Ⅴ regions, whereas urbanization affects Class Ⅱ and Class Ⅲ regions more significantly.

This study, based on the 2017 multi-regional input-output table of China, constructed an Environmentally Extended Multi-Regional Input-Output (EE-MRIO) model to track the virtual cropland flow paths. It then quantified the value of land ecological products using the equivalent factor method and designed a “Beneficiary Pays” compensation standard. The research findings include: The Huang-Huai-Hai Plain (Henan, Shandong) and the Northeast Plain (Heilongjiang), leveraging their resource endowment advantages and policy support effects, have formed core virtual cropland supply regions; meanwhile, the Huang- Huai-Hai Plain (Henan, Shandong) and the Yangtze River Delta (Jiangsu) have emerged as centers of land resource use due to population concentration and industrial transformation needs. The national net virtual cropland flow shows a “gradient transfer” characteristic, moving from north to south and from west to east, basically adhering to the “proximity effect”. Major grain-producing regions such as Heilongjiang, Jilin, and Inner Mongolia are the main net outflow sources of virtual cropland, while economically developed regions like Guangdong and Zhejiang are the core net inflow destinations. Key path analysis reveals significant transfers of land ecological products along cross-provincial flow chains such as Heilongjiang-Jilin, Inner Mongolia-Shanxi, and Heilongjiang-Zhejiang, highlighting the dependence of developed regions on the land ecological products of major grain-producing areas. By constructing a modified matrix incorporating compensation for land ecological products in each region, the study not only clarifies the accountability for land ecological protection, but also provides a scientifically sound basis for compensation. This opens up a new path for the sustainable utilization of land resources and the harmonious coexistence of economic development.

Using meteorological station observations, ERA5-L and reanalysis data, and Landsat remote sensing imagery from 2010 to 2023, we analysed the spatiotemporal characteristics of dust storms in the Ebinur Lake Basin of Xinjiang Uygur Autonomeus Region, northwest China. The generalized grey correlation model was applied to quantitatively assess the influence of surface wind speed, soil moisture, and vegetation coverage on the frequency of dust storm occurrences. The results showed an overall upward trend in dust storm days during 2010~2023 (+0.08d/a). In particular, there was a significant increase from 2010 to 2014 (+0.60d/a), followed by a short decline from 2014 to 2017 (-0.70d/a), and a rise after 2018(+0.31d/a). Spring was identified as the peak season for dust storm occurrences, with a lower frequency observed in summer and autumn, and no recorded events in winter. Spatially, the Jinghe meteorological station emerged as the high-frequency centre over the past 14 years. Compared with the late 20th century, the standard deviation of annual dust storm days across the five meteorological stations decreased by 88.42%, indicating a substantial reduction in regional disparities. It revealed that surface wind speed was the most critical driver of dust storms. Soil moisture was the second most influential factor, showing a distinct seasonal lag effect, while vegetation coverage exerted a comparatively minor influence on dust storm occurrence.

To investigate the variation rules, pollution characteristics, and their regional transport of fine particulate matter (PM_2.5) components in different seasons from Hohhot and Wuhai cities. In this study, the concentration of PM_2.5 and its chemical components, such as water-soluble ions, organic carbon (OC), elemental carbon (EC), and metallic elements, and meteorological data, were obtained from the atmospheric super station in June-August and December of 2022, and January-February of 2023 from Hohhot and Wuhai. The sources and transport paths were also analyzed according to the Positive Definite Matrix Factor Decomposition (PMF) model and the Backward Trajectory (HYSPLIT) model. PM_2.5 mass concentration in winter was higher than that in summer in both cities. PM_2.5 mass concentration in Wuhai was higher than that in Hohhot in the same season. PM_2.5 in both cities is alkaline in winter and summer. The alkalinity of PM_2.5 in Hohhot was stronger than that in Wuhai during summer, and weaker than that in Wuhai during winter. The content of K, Mn, Cu, Ba, Ti, K⁺, and Cl^- in PM_2.5 in winter was higher than that in summer in the two cities. The two cities were more affected by biomass combustion, coal-fired emissions, and motor vehicle emissions in winter. PM_2.5 in Hohhot during summer mainly came from combustion sources, secondary inorganic sources, industrial sources, soil dust sources, and mixed sources motor of vehicle and road dust, while in winter it mainly came from coal combustion sources, biomass combustion sources, secondary inorganic sources, soil dust sources, mixed sources of motor vehicle and road dust, and mechanical wear sources. PM_2.5 in Wuhai during summer mainly came from combustion sources, secondary inorganic sources, industrial sources, soil dust sources, motor vehicle sources, road dust sources, while in winter it mainly came from combustion sources, secondary inorganic sources, industrial sources, soil dust sources, mixed sources of motor vehicle and road dust, and mechanical wear sources. Hohhot city was dominated by secondary sources in both summer and winter. Wuhai city was dominated by the mixture of motor vehicle and road dust in summer, while by secondary inorganic sources in winter. Hohhot City was mainly affected by air currents from southern Shanxi Province, northern Shaanxi Province, and southern Mongolia in summer, with potential source areas being southern Shanxi Province, southern Hebei Province, and northern Henan Province. Wuhai was mainly affected by air currents from eastern Xinjiang, Alxa League, southern Shaanxi Province, and Bayannur City in summer. Hohhot and Wuhai were affected by air currents from Alxa League and southern Mongolia in winter, with the difference that PM_2.5 transport in Wuhai in winter was also affected by air currents from northern Xinjiang. The potential source areas for winter in Hohhot are local, Baotou., Bayannur, Ordos, northeastern Alashan League, and the southern region of Mongolia, while that in Wuhai City during summer and winter were the eastern part of Xinjiang, Hexi Corridor region, Alashan League, Ordos City, Ulanqab City, and Wuhai City. The research results provide data support for the prevention and control of air pollution in typical cities along the Yellow River Basin.