Urbanisation has played a crucial role in improving the Chinese economy and the quality of life for its people since the government started implementing the policy in the early 1980s. It has led to a linear and exponential economic growth, accompanied by some of the largest human migrations in history (Han et al., 2014, Fang et al., 2015, Han et al., 2018). Nearly half of the Chinese population is speculated to migrate and stay in urban cities, leading to more cities constantly undergoing rapid development (Zund and Bettencourt, 2019). The urban population in China has increased by 300 million (1/5th of the Chinese population) from 2005 to 2018, indicating an exponential desire among Chinese residents to live in urban cities (Jizhe, 2020). Rapid urbanisation has led to a high standard of living in urban areas while, on the other way, led to high levels of PM2.5 in the atmosphere, which is found at unhealthy levels in major Chinese cities (Han et al., 2014, Wang et al., 2016, Han et al., 2018, Luo et al., 2018). Even though the government has implemented measures and policies that help mitigate the PM2.5 levels, multiple provincial and city-based studies have shown that the values are not constantly at healthy levels (Yang et al., 2018, Song et al., 2019, Yue et al., 2020).
There are significant differences among Chinese provinces and even great differences between the capital city and other cities in a developing country. Multiple studies have observed uneven economic development among regions, and the gap between rich and poor provinces is vast (Xu et al., 2019, Wang et al., 2021). The developed provinces are practically located in the southeastern coastal areas of China, such as Beijing, Shanghai, Jiangsu, and Zhejiang. In contrast, the poorer provinces in China are concentrated in the northwest and southwest regions, such as Guangxi, Yunnan, and Guizhou province, Qinghai Province, Gansu Province, to name a few.
Since gross domestic product (GDP) is a strong indicator of urbanisation, it indicates a massive migration from rural to urban areas. Even though there are multiple time-series studies on specific Chinese cities, time-series studies among major urbanised Chinese cities after the proposed guidelines coupled with GDP and population are also not well established (Luo et al., 2018, Wang et al., 2016, Zhou et al., 2018a, Liu et al., 2020a). It is commonly believed in the scientific community that different urban PM2.5 and PM10 concentration levels among cities are highly attributable to the imbalance in the urbanisation progress (Han et al., 2014). One of the significant factors that influence urbanisation is the population of people living in urban cities and the transportation that these people use.
Anthropogenic activities are often the major contributor to a series of pollution episodes that causes heavy surface level pollution. Data from model simulation and satellite imagery suggest that PM2.5 concentrations are higher in many regions of China than in other countries, mainly in urban areas (Jahn et al., 2013, Wang et al., 2013, Ming et al., 2017, Zhang et al., 2019, Sun et al., 2019). In 2013, the Chinese government established the five-year air combat plan named “Air pollution prevention and control action plan”, which proposed to reduce PM2.5 in all the urban cities to a substantial level. This policy aims to achieve the National Ambient Air Quality Standard (NAAQS) (35 µg/m3) and World Health Organization (WHO) (15 µg/m3) guideline values. The issue remains that when compared with the 24hour- PM2.5 values proposed by WHO, most urbanised Chinese cities still fail to reach those proposed targets (Cai et al., 2017, Li et al., 2022). The Chinese government has also implemented increasing the proportion of green spaces in most urbanised cities to mitigate the PM2.5 pollution (Chen et al., 2016, Wang et al., 2019, Wang et al., 2020). Seasonal weather patterns and wind direction seem to significantly affect the fluctuation of values, resulting in most urban residents inhaling high levels of PM2.5 (Ming et al., 2017, Mao et al., 2018). In terms of seasonality, previous studies showed that a higher concentration of PM2.5 over cities was recorded during winter followed by spring, with the lowest recorded during the summer season (Wang et al., 2016, Zhan et al., 2018, He et al., 2021). Air pollution research in China about PM2.5 has received extensive attention, including the analyses of spatiotemporal variation (Chen et al., 2016, Xu et al., 2017, Guan et al., 2017, Yang et al., 2018, Song et al., 2019, Sun et al., 2019, Wang et al., 2014), source apportionment (Zheng et al., 2005, Wang et al., 2013, Zhang et al., 2015, Li et al., 2018), chemical composition (Wen et al., 2016, Ming et al., 2017, Li et al., 2018) and health effects (Liu et al., 2017, Cao et al., 2018, Qi et al., 2018, Weber et al., 2016).
Similarly, multiple PM2.5 studies have analysed social and economic factors to investigate the impact of urbanisation and anthropogenic activities (Luo et al., 2018, Zhou et al., 2018b, Zhou et al., 2018a, Wang et al., 2021). A common denominator that stands out from all these studies is that the cities with less total area tend to have lower PM2.5 concentrations (Xu et al., 2019, Liu et al., 2020b). However, urban population size and GDP might have a positive linear relationship based on the geographical location (Luo et al., 2018) or fit the Environmental Kuznets Curve (EKC) (Zhao et al., 2019). The EKC is a hypothesis that could be applied to understand the relationship between economic development and environmental pollutants with an inverted U-shaped curve (Grossman and Krueger, 1995). The environmental Kuznets curve hypothesises that a country or city’s economic development initially increases environmental pollutants, but after achieving a certain level of economic growth, the country or city begins to improve its relationship with the environment, which will be evident through the decreasing levels of environmental pollutants. We found that only few studies have specifically focused on long term relationships between socioeconomic variables and PM2.5 pollution on a city level in China.
Our study addresses the lack of knowledge by accessing ground-based monitoring datasets from 2013 to 2019. In China, we studied ten megacities (Beijing, Tianjin, Chengdu, Chongqing, Hangzhou, Guangzhou, Shanghai, Shenzhen, Suzhou and Wuhan) based on their population, population density, GDP, Industrial emissions, Green area and Secondary industry share. We want to point out that the Chinese government started recording and publishing ground-based PM2.5 concentrations only in 2013, challenging long-term monitoring. We implemented the rate of change from 2013 to 2017 to understand the impact of the “China air pollution combat plan” and from 2013 to 2019 to observe whether the changes were retained after 2017. We performed regression models to identify the socioeconomic factors influencing PM2.5 from 2013 to 2019. The findings from our study have the potential to understand the impact of the socioeconomic factors driving PM2.5 in these Chinese megacities and attempt to produce a quantitative explanation of their relationship.