Abstract
Atmospheric visibility (AV), one of the most concerning environmental issues, has shown a continuous decline in China’s urban areas, especially in Southeastern China. Existing studies have shown that AV is affected by air pollutants and climate change, which are always caused by human activities that are linked to socioeconomic factors, such as urban size, residents’ activities, industrial activities, and urban greening. However, the contribution of socioeconomic factors to AV is still not well understood, especially from a long-term perspective, which sometimes leads to ineffective policies. In this study, we used the structural equation model (SEM) in order to quantify the contribution of socioeconomic factors on AV change in Xiamen City, China, between 1987–2016. The results showed that the annual average AV of Xiamen between 1987–2016 was 12.00 km, with a change rate of −0.315 km/year. Urban size, industrial activities, and residents’ activities were found to have a negative impact on AV, while the impact of urban greening on the AV was modest. Among all of the indicators, the number of resident’s vehicles, total retail sales of consumer goods, and household electricity consumption were found to have the highest negative direct impact on the AV. The resident population, urban built-up area, and secondary industry gross domestic product (GDP) were the most important indirect impact factors. Based on our results, we evaluated the existing environmental regulations and policies of Xiamen City.
Highlights
MethodsBuildings and mountain at Atmospheric visibility (AV) using identifiable objects (e.g., tall buildings and mountain ridges) at predetermined distances
According to the United States Environmental Protection Agency (USEPA), the atmospheric visibility (AV) of the non-polluted atmosphere varies from 145 km to 225 km in different areas [1].Atmospheric visibility (AV) is determined mostly by the light extinction of air pollutants, and the influence factors of AV areInt
We found that there was a negative correlation between the index of urban greening (UG) and AV change, which was contrary to previous studies [22,23,24,44]
Summary
Buildings and mountain at AV using identifiable objects (e.g., tall buildings and mountain ridges) at predetermined distances. The AV and meteorological hourly data were obtained through the official The
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