Abstract
Urbanization processes at both global and regional scales are taking place at an unprecedent pace, leading to more than half of the global population living in urbanized areas. This process could exert grand challenges on the human living environment. With the proliferation of remote sensing and satellite data being used in social and environmental studies, fine spatial- and temporal-resolution measures of urban expansion and environmental quality are increasingly available. This, in turn, offers great opportunities to uncover the potential environmental impacts of fast urban expansion. This paper investigated the relationship between urban expansion and pollutant emissions in the Fujian province of China by building a Bayesian spatio-temporal autoregressive model. It drew upon recently compiled pollutant emission data with fine spatio-temporal resolution, long temporal coverage, and multiple sources of remote sensing data. Our results suggest that there was a significant relationship between urban expansion and pollution emission intensity—urban expansion significantly elevated the PM2.5 and NOx emissions intensity in Fujian province during 1995–2015. This finding was robust to different measures of urban expansion and retained after controlling for potential confounding effects. The temporal evolution of pollutant emissions, net of covariate effects, presented a fluctuation pattern rather than a consistent trend of increasing or decreasing. Spatial variability of the pollutant emissions intensity among counties was, however, decreasing steadily with time.
Highlights
In 2008, more than half of the world’s population lived in urbanized areas, overtaking the rural population for the first time, and the urbanization rate was projected to reach 66% or so by2050 [1,2]
Urban expansion was statistically significantly associated with higher PM2.5 and NOx emission intensity
This study proposed a Bayesian dynamic spatio-temporal statistical model to analyze the relationship between urban expansion and pollutant emissions while explicitly capturing the spatial autocorrelation, heterogeneity, and temporal dynamic effects
Summary
In 2008, more than half of the world’s population lived in urbanized areas, overtaking the rural population for the first time, and the urbanization rate was projected to reach 66% or so by2050 [1,2]. In 2008, more than half of the world’s population lived in urbanized areas, overtaking the rural population for the first time, and the urbanization rate was projected to reach 66% or so by. Decades of fast and stable economic growth were an important driving force of the large-scale rural-to-urban migration process [4]. Explosive population growth combined with limited living space in urban areas caused a series of issues to be solved urgently, among which tackling air pollution and the associated health problems was a priority [4]. Deterioration of air quality in urban areas was mainly caused by emissions of pollutants such as PM2.5 (particulate matters with an aerodynamic diameter ≤ 2.5 μm), NOx and SO2 [5,6]. A large number of studies had proven the negative effects of exposures to PM2.5 , NOx and SO2 on human health, such as premature mortality [7], lung cancer [8], and cerebrovascular diseases [9]
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