Effects of urbanisation on PM2.5 concentrations: A systematic review and meta-analysis

Offsite Ecological Impacts in the Anthropocene: Definition, Mechanisms, and Challenges

Human activities increasingly disturb biodiversity and ecosystems far beyond their immediate areas. As human activities intensify on Earth's surface, these offsite disturbances threaten biodiversity at regional and global scales. Despite their significance, offsite ecological impacts remain poorly understood, often confused with related phenomena (e.g., edge effects) and excluded from evaluation frameworks. This study clarified the definition of offsite ecological impacts, examined their mechanisms (sources, paths, and drivers), and discussed their intensification under global change. We (1) clarify the offsite ecological impacts from other offsite phenomena, such as secondary, indirect, and competition impacts; (2) identify key drivers, including mining, urbanization, road networks, agriculture, and emerging technologies (e.g., renewable energy infrastructure), and explain how they contribute to offsite ecological impacts; (3) analyze the mechanisms by which disturbances spread, such as pollutants (e.g., heavy metals and microplastics) transported via air, soil, water, and biological or anthropogenic vectors; and (4) highlight challenges in identifying and mitigating offsite impacts, emphasizing how global environmental changes complicate predictions and hinder effective solutions. Addressing these challenges requires improved spatial monitoring, predictive modeling, and innovative conservation strategies. This framework advances the understanding of offsite ecological impacts in the Anthropocene, helping to balance human development with biodiversity conservation and supporting the UN Biodiversity Goals.

Time series analysis of PM2.5 pollution risk based on the supply and demand of PM2.5 removal service: a case study of the urban areas of Beijing

Demonstrating the temporal changes in PM2.5 pollution risk in regions facing serious PM2.5 pollution problems can provide scientific evidence for the air pollution control of the region. However, research on the variation of PM2.5 pollution risk on a fine temporal scale is very limited. Therefore, we developed a method for quantitative characterizing PM2.5 pollution risk based on the supply and demand of PM2.5 removal services, analyzed the time series characteristics of PM2.5 pollution risk, and explored the reasons for the temporal changes using the urban areas of Beijing as the case study area. The results show that the PM2.5 pollution risk in the urban areas of Beijing was close between 2008 and 2012, decreased by approximately 16.3% in 2016 compared to 2012, and further decreased by approximately 13.2% in 2021 compared to 2016. The temporal variation pattern of the PM2.5 pollution risk in 2016 and 2021 showed significant differences, including an increase in the number of risk-free days, a decrease in the number of heavily polluted days, and an increase in the stability of the risk day sequence. The significant reduction in risk level was mainly attributed to Beijing's air pollution control measures, supplemented by the impact of COVID-19 control measures in 2021. The results of PM2.5 pollution risk decomposition indicate that compared to the previous 2 years, the stability and predictability of the risk variation in 2016 increased, but the overall characteristics of high risk from November to February and low risk from April to September did not change. The high risk from November to February was mainly due to the demand for coal heating during this period, a decrease in PM2.5 removal service supply caused by plant leaf fall, and the common occurrence of temperature inversions in winter, which hinders the diffusion of air pollutants. This study provides a method for the analysis of PM2.5 pollution risk on fine temporal scales and may provide a reference for the PM2.5 pollution control in the urban areas of Beijing.

Evaluating forest depletion and structural change effects on environmental sustainability in Pakistan: Through the lens of the load capacity factor

The pace of species extinction and deforestation has increased dramatically due to the substantial increase in global environmental degradation. This trend is approaching the crucial temperature threshold of 2 °C and calls for more attention. Although previous research has observed the individual impacts of forest depletion, structural change, economic growth, and urbanization on various sustainability outcomes, there has been no previous research into their interrelationships with an emphasis on the load capacity factor (LCF). Furthermore, no previous study has examined the environmental impacts of the abovementioned variables by contrasting the results of LCF and CO2 emissions in Pakistan. Therefore, this research suggests a theoretical framework that integrates these concepts, provides a roadmap for an effective and sustainable mitigation strategy for Pakistan and compares LCF results with CO2 emissions. Using the time-series data from 1970 to 2021, a unique and sophisticated dynamic Autoregressive Distributed Lag (DARDL) technique, the authors found that (i) a 1 % rise in forest depletion leads to a decline in load capacity factor by 0.026 %. (ii) A one per cent upsurge in structural change fosters environmental sustainability by raising the load capacity factor by 0.084 %. (iii) An increase of 1 % in economic growth dwindles the load capacity factor by 0.027 %. (iv) A one per cent surge in urbanization enhances the load capacity factor by 0.029 %. The findings suggest that Pakistan's Government should promote afforestation by emphasizing the constructive role of structural change in achieving environmental sustainability.