Comparative in vitro toxicological effects of water-soluble and insoluble components of atmospheric PM2.5 on human lung cells.

Fine particulates in city air significantly impact human health, but the hazardous compositional mechanisms are still unclear. Besides the toxicity of environmental PM2.5 to in vitro human lung epithelial cells (A549), the independent cytotoxicity of PM2.5-bound water-soluble (WS-PM2.5) and water-insoluble (WIS-PM2.5) fractions were also compared by cell viability, oxidative stress (reactive oxygen species, ROS), and inflammatory injury (IL-6 and TNF-α). The cytotoxicity of PM2.5 varied significantly by sampling season and place, with degrees greater in winter and spring than in summer and autumn, related to corresponding trend of air PM2.5 level, and also higher in industrial than urban site, although their PM2.5 pollution levels were comparable. The PM2.5 bound metals (Ni, Cr, Fe, and Mn) may contribute to cellular injury. Both WS-PM2.5 and WIS-PM2.5 posed significant cytotoxicity, that WS-PM2.5 was more harmful than WIS-PM2.5 in terms of decreasing cell viability and increasing inflammatory cytokines production. In particular, industrial samples were usually more toxic than urban samples, and those from summer were generally less toxic than other seasons. Hence, in order to mitigate the health risks of PM2.5 pollution, the crucial targets might be components of heavy metals and soluble fractions, and sources in industrial areas, especially during the cold seasons.

Analysis of the spatiotemporal evolution and influencing factors of green development level in the manufacturing industry.

The manufacturing sector is the main battlefield of energy saving and carbon reduction in China, and vigorously promoting energy saving and carbon reduction in manufacturing and enhancing the green development level are the key links to support China's realization of the dual-carbon goal. The article adopts the SBM-GML model to measure the level of green development of the manufacturing industry in China. Based on this, it analyzes the spatio-temporal characteristics and the evolution law of the level of green development of the manufacturing industry by using the Dagum Gini Coefficient and Kernel Density Estimation. Using a spatial econometric model to explore the influencing factors of the level of green development of the manufacturing industry. The study finds that the green development level of the manufacturing industry has achieved remarkable results in recent years, but there are differences in the development level of each region. The regional differences in the level of green development of the manufacturing industry are significant. The optimization of manufacturing structure is a key factor influencing the level of green development of the manufacturing industry, and there is a positive spatial spillover effect of manufacturing structure optimization. However, The green development of the manufacturing industry shows a negative spatial spillover effect. The article proposes optimization paths based on the requirements of dual-carbon targets and regional characteristics, which is an important inspiration and reference for the green development level of the manufacturing industry in the world.

Socioeconomic Factors Influence the Spatial and Temporal Distribution of Blue-Green Infrastructure Demand: A Case of Nanjing City.

Blue-green infrastructure provides a variety of ecosystem services and is becoming an increasingly vital part of urban ecosystem protection. It is an ecological facility for ecological conservation and environmental protection, and a foundation for realizing people's needs for a better life. This study selects indicators from four dimensions: social, economic, environmental, and ecological, and the demand for blue-green infrastructure is assessed comprehensively. The results show that: (1) the demand for blue-green infrastructure varies spatially with the development of the city; (2) the total demand for blue-green infrastructure in Nanjing from 2000 to 2020 shows a pattern of "high in the center and low in the periphery"; (3) the level of economic development, urban spatial pattern, and decision management orientation have different degrees of influence on the demand for blue-green infrastructure, with the urban spatial pattern having the greatest impact. Therefore, in the future, blue-green infrastructure should be optimized by taking into account the spatial characteristics of demand in Nanjing.

Characteristics and Spatial-Temporal Differences of Urban "Production, Living and Ecological" Environmental Quality in China.

The article analyses the spatial and temporal differences in the environmental quality of production, living and ecology of 285 cities in China from 2010 to 2020 by using the entropy method, the Theil index and correlation analysis. The study concludes the following: (1) in terms of overall differences, the overall differences in the "production, living and ecological" environmental quality indices of 285 cities during the study period undergo a process of "narrowing-widening-narrowing". The differences within the four major zones of the country are higher than those between the four major zones, and the differences within the zones show an increasing trend year by year. (2) In terms of temporal differences, the combined scores of "production, living and ecological" environmental quality of the 285 cities in the study period show a decreasing trend, and the contribution of the PLE subsystem scores are, in descending order, production environmental quality > living environmental quality > ecological environmental quality. (3) In terms of overall ranking, the head effect of the combined production, living and ecological environmental quality (PLE) scores of cities in the study period is significant, and the top 10 cities in terms of combined scores are all small and medium-sized cities with significant regionalization characteristics. (4) In terms of spatial pattern, there is a significant spatial gradient in the east, central and western regions, with the overall PLE scores of the four major regions in descending order: eastern region > central region > western region > northeastern region. The regions with high scores in the "production, living and ecological" environmental quality of cities can be divided into three types: multi-core, dual-core and single-core. (5) In terms of influencing factors, there is a logarithmic curve relationship between the combined production, living and ecological environmental quality (PLE) score and the built-up area (BUA) of cities. The study proposes to optimize the layout of urban production, strengthen the industrial links of urban clusters, improve the level of public services, ensure the equalization of urban public services, strengthen the management of ecological environment and improve the quality of ecological environment in order to optimize the quality of urban "production, living and ecological" environment.