“…In the initial phase, the aviation industry’s sluggish growth contributed to a reduction in PM2.5 emissions 55 . In the later period, as the economy gradually recovered, the aviation industry underwent substantial expansion, leading to heightened fuel combustion and consequently increased PM2.5 emissions 56 . Fig.…”
Since 2006, the rapid development of China’s aviation industry has been accompanied by a significant increase in one of its emissions, namely, PM2.5, which poses a substantial threat to human health. However, little data is describing the PM2.5 concentration caused by aircraft activities. This study addresses this gap by initially computing the monthly PM2.5 emissions of the landing-take-off (LTO) stage from Jan. 2006 to Dec. 2023 for 175 Chinese airports, employing the modified BFFM2-FOA-FPM method. Subsequently, the study uses the Gaussian diffusion model to measure the 24-hour average PM2.5 concentration resulting from flight activities at each airport. This study mainly draws the following conclusions: Between 2006 and 2023, the highest recorded PM2.5 concentration data at all airports was observed in 2018, reaching 5.7985 micrograms per cubic meter, while the lowest point was recorded in 2022, at 2.0574 micrograms per cubic meter. Moreover, airports with higher emissions are predominantly located in densely populated and economically vibrant regions such as Beijing, Shanghai, Guangzhou, Chengdu, and Shenzhen.
“…In the initial phase, the aviation industry’s sluggish growth contributed to a reduction in PM2.5 emissions 55 . In the later period, as the economy gradually recovered, the aviation industry underwent substantial expansion, leading to heightened fuel combustion and consequently increased PM2.5 emissions 56 . Fig.…”
Since 2006, the rapid development of China’s aviation industry has been accompanied by a significant increase in one of its emissions, namely, PM2.5, which poses a substantial threat to human health. However, little data is describing the PM2.5 concentration caused by aircraft activities. This study addresses this gap by initially computing the monthly PM2.5 emissions of the landing-take-off (LTO) stage from Jan. 2006 to Dec. 2023 for 175 Chinese airports, employing the modified BFFM2-FOA-FPM method. Subsequently, the study uses the Gaussian diffusion model to measure the 24-hour average PM2.5 concentration resulting from flight activities at each airport. This study mainly draws the following conclusions: Between 2006 and 2023, the highest recorded PM2.5 concentration data at all airports was observed in 2018, reaching 5.7985 micrograms per cubic meter, while the lowest point was recorded in 2022, at 2.0574 micrograms per cubic meter. Moreover, airports with higher emissions are predominantly located in densely populated and economically vibrant regions such as Beijing, Shanghai, Guangzhou, Chengdu, and Shenzhen.
“…The degradation of the ecological environment will have a negative impact on urban production and life. When the ecological environment deteriorates to the point where it seriously threatens human health, the government is required to allocate substantial funds and change development models to enhance the urban ecological environment [47]. Existing studies show that the interrelationship between urban development and the ecological environment is lagging, and optimizing urban development models can not immediately improve the ecological environment [48].…”
Section: Effect Of the Spatial Pattern Change In Steel Plants On Land...mentioning
It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting steel plants based on high-resolution remote sensing images, due to insufficient sample datasets and task complexity, we proposed a steel plant segmentation strategy that combines high-resolution remote sensing images, POI data, and OSM data. Additionally, we discussed the effect of POI data and OSM data on steel plant segmentation, analyzing the spatial pattern change in steel plants in Tangshan City during 2017–2022 and its relationship with land cover change. The results demonstrate that: (1) The proposed strategy can significantly improve the accuracy of steel plant segmentation. The introduction of POI data can significantly improve the precision of steel plant segmentation, however, it will to some extent reduce the recall of steel plant segmentation, and this phenomenon weakens as the distance threshold increases. The introduction of OSM data can effectively improve the effectiveness of steel plant segmentation, however, it has significant limitations. (2) During 2017–2022, the spatial distribution center of steel plants in Tangshan City moved obviously to the southeast, and the positive change in steel plants was mainly concentrated in the coastal regions of southern Tangshan City, while the negative change in steel plants was mainly concentrated in central Tangshan City. (3) There is a relatively strong spatial correlation between the positive change in steel plants and the transition from vegetation to built area, as well as the transition from cropland to built area.
“…Technological progress can lead to energy rebound effects, which can increase pollutant emissions. The dual nature of the impact of technological progress on air pollution (Sun et al, 2021;Dong et al, 2022;Yuan et al, 2022). Interestingly, the reality of production is that technological progress is biased (Hicks, 1963;Samuelson, 1965;Drandakis and Phelps, 1966).…”
Environmental policies and Environmentally biased technological advances have important theoretical value and practical significance for air pollution reduction and green economic development in China. The article is based on panel data for 270 cities in China from 2007 to 2020. Using a Spatial model, the direct, spillover and moderating effects of Environmentally biased technological progress on “local-neighbourhood” urban air pollution are examined empirically. On this basis, the PSTR model is used to further explore the non-linear effects of different types of environmental regulations. The study found that: 1) There is a significant positive spatial correlation between both air pollution and Environmentally biased technological progress in China. Environmentally biased technological progress significantly suppresses air pollution, with significant spatial spillover effects, and the conclusions remain robust after a series of robustness tests. 2) Different types of environmental regulations reinforce the dampening effect of Environmentally biased technological progress on air pollution. However, informal environmental regulation only reinforces the dampening effect of Environmentally biased technological progress on air pollution in the local cities. 3) There is a significant threshold effect of environmental regulation on the impact of Environmentally biased technological progress on air pollution. When the level of environmental regulation within a certain threshold interval, Environmentally biased technological progress has a dampening effect on air pollution. 4) The more eastern the city, the larger the city and the non-resource-based cities, the more significant the air pollution suppression effect of Environmentally biased technological advances. Based on this, this study points out that governments at all levels should pay attention to the spatial spillover effect of technological progress bias on air pollution, deepen the construction of joint prevention and control mechanisms for regional air pollution control; Establish multiple environmental regulation policies, formulate and implement environmental regulation policies according to local conditions and local technological progress bias; Formulate targeted environmental regulation policies and technological innovation policies according to the characteristics of different cities. It is an important theoretical reference to help China to fight the battle against air pollution in-depth and to promote further improvement of ecological and environmental quality.
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