The human movement and economic activities have been drastically reduced due to the Coronavirus Disease 2019 (COVID‐19) outbreak, leading to the sharp decreases of pollutant emissions and remarkable air quality improvement. Nevertheless, however, the changes of gaseous pollutant concentrations and health effects across China during the COVID‐19 lockdown period remained poorly understood. Here, a random forest model was applied to assess the impact of COVID‐19 lockdown on pollutant concentrations and potential health effects. The results suggested that estimated NO₂, SO₂, and CO concentrations in China during January 23–March 31, 2020 decreased by 13.68%, 25.71%, and 7.42%, respectively compared with the same periods in 2018–2019. Nonetheless, the predicted 8‐h O₃ concentrations across China suffered from 1.29% increases during this period. The avoided premature all‐cause, cardiovascular disease (CVD), respiratory disease (RD), and chronic obstructive pulmonary disease (COPD) mortalities induced by NO₂ decrease during COVID‐19 lockdown period reached 3,954 (3,076–4,832), 635 (468–801), 612 (459–765), and 920 (653–1,186) cases. However, the increases of all‐cause, CVD, RD, and COPD mortalities due to O₃ increase during COVID‐19 lockdown period achieved 462 (250–674), 79 (29–129), 40 (−25–105), and 52 (−34–138) cases. The natural experiment demonstrated the drastic emission reduction measures could significantly decrease the NO₂, SO₂, and CO concentrations, while they significantly elevated the O₃ concentration. It is highly imperative to propose more coordinated air pollution control strategies to control O₃ pollution.

The pattern of urban land use and the level of urbanization in China’s pre-modernization period are of great significance for land use and land cover change (LUCC) research. The purpose of this study is to construct a 1910s spatial dataset of provincial land urbanization in pre-modern China. Using historical topographic maps, this study quantitatively reconstructs the built-up area of various cities in Zhejiang Province in the 1910s. The research indicates that: (1) During the early period of the Republic of China, there were a total of 252 cities and towns in Zhejiang Province, including 75 cities at or above the county level, 21 acropolis, and 156 towns. The total built-up area was 140.590 km². (2) The county-level urbanization level had significant agglomeration characteristics. The overall urbanization rate of land was 0.135%. (3) Hot spots analysis showed that the Hang-Jia-Hu-Shao plain is hot spot. (4) The correlation coefficient between the city wall perimeter data recorded in the local chronicles and the measured city wall perimeter was 0.908. The research showed that the military topographic maps possessed a good application prospect for the reconstruction of urbanization levels. The research results provide direct evidence for urbanization and urban land use in China’s pre-modernization period.

To curb the spread of the COVID-19 pandemic, many countries around the world imposed an unprecedented lockdown, producing reductions in pollutant emissions. Unfortunately, the lockdown-driven global ambient benzene changes still remain unknown. An ensemble machine-learning model coupled with chemical transport models (CTMs) was applied to estimate global high-resolution ambient benzene levels. Afterwards, the extreme gradient boosting (XGBoost) algorithm was employed to decouple the contributions of meteorology and emission reduction to ambient benzene. The change ratio (P_dew) of the deweathered benzene concentration from the pre-lockdown to lockdown period was in the order of India (−23.6 %) > Europe (−21.9 %) > the United States (−16.2 %) > China (−15.6 %). The detrended change (P^∗) of the deweathered benzene level (change ratio in 2020 − change ratio in 2019) followed the order of India ($P^{*}=-\mathrm{16.2}$ %) > Europe ($P^{\ast }=-\mathrm{13.9}$ %) > China ($P^{\ast }=-\mathrm{13.3}$ %) > the United States ($P^{\ast }=-\mathrm{6.00}$ %). Emission reductions derived from industrial activities and transportation were major drivers for the benzene decrease during the lockdown period. The highest decreasing ratio of ambient benzene in India might be associated with local serious benzene pollution during the business-as-usual period and restricted transportation after lockdown. Substantial decreases in atmospheric benzene levels had significant health benefits. The global average lifetime carcinogenic risk (LCR) and hazard index (HI) decreased from $\mathrm{4.89}\times {\mathrm{10}}^{-\mathrm{7}}$ and $\mathrm{5.90}\times {\mathrm{10}}^{-\mathrm{3}}$ to $\mathrm{4.51}\times {\mathrm{10}}^{-\mathrm{7}}$ and $\mathrm{5.40}\times {\mathrm{10}}^{-\mathrm{3}}$ , respectively. China and India showed higher health benefits due to benzene pollution mitigation compared with other countries, highlighting the importance of benzene emission reduction.

River flow reconstruction under the background of long-term climate change is of great significance for understanding the regional response to future drought and flood disasters, and the sustainable development of water resources. Investigating the basic characteristics and changing trends of the streamflow of the Ganjiang River is scientifically important to mitigate drought and flood disasters in the future. This study reconstructed drought and flood grade series of five regional stations of the Ganjiang River based on spatially explicit and well-dated local chronicle materials and used a linear regression model of modern drought/flood grades and precipitation to reconstruct historical precipitation for the past 515 years. The relationships between the modern precipitation of five regional stations and streamflow of Waizhou Station, which is the last hydrological station of the Ganjiang River were analyzed through principal component regression. The adjusted R² is 0.909, with a low relative bias of −1.82%. The variation of streamflow from AD 1500 to AD 2014 was reconstructed using the proposed model. Result shows that high flows occur in nine periods and low flows occur in 11 periods. Extremely low stream flow in 515 years appears during the middle and late 17th century. Cumulative anomaly and Mann-Kendall mutation test results reveal that a transition point from predominantly low to high flows occur in AD 1720. Redfit power spectrum analysis result shows that the variation periods of streamflow are 2–5, 7–8 years, and approximately 32 years, where the most significant period is 2–3 years. Continuous wavelet transform indicates that the corresponding relation occurs between streamflow and El Niño/Southern Oscillation for eight years. Streamflow is affected by temperature and East Asian monsoon that is controlled by solar activities. The flood may be related to strong solar activity, monsoon failure, and vice versa. Hydrological frequency curve analysis shows that the streamflow of the Ganjiang River once in a hundred years may reach up to 1031 × 10⁸ m³ for flood or 485 × 10⁸ m³ for drought and the standard of once in a millennium runoff may reach up to 1188 × 10⁸ m³ for flood or 450 × 10⁸ m³ for drought. These results may provide basic hydrological data for the sustainable development of society and serve as a reference for mitigating the impact of drought and flood disasters in the future.

Air pollution, particularly fine particulate matter, poses significant health and environmental risks, with exposure levels exhibiting considerable spatial inequality. However, few studies have comprehensively examined how urban form and environmental factors influence air pollution exposure and its spatial inequality. This study investigates how urban and environmental factors affect particulate matter pollution (PM₁, PM_2.5, and PM₁₀) and its spatial inequality across 85 counties in Fujian, China. Twelve indicators across urban form, socio‐economic, and environmental domains were analyzed using principal component analysis and partial correlation networks. Our results show that while overall air pollution levels exhibit substantial variability, spatial inequality in exposure does not always correlate directly with these levels. Notably, while urbanized counties display higher pollution exposure, significant disparities in pollution distribution are observed within regions of similar pollution levels. Urban and socio‐economic features such as population density and road density are strongly correlated with higher pollution exposure, especially in more urbanized areas. In contrast, environmental factors, such as vegetation coverage and precipitation, significantly mitigate pollution levels. Principal component analysis reveals that development density and environmental changes primarily drive overall pollution levels, while economic activity and segregation contribute to the spatial inequality of exposure. Network analysis further corroborates that high‐density urban development exacerbates pollution exposure, while socio‐economic segregation contributes to uneven distribution across the population. Our findings underscore the need for integrated urban planning strategies that address these urban and environmental factors to reduce air pollution inequality and promote more equitable urban environments.