Exploring the Dynamic Spatio-Temporal Correlations between PM2.5 Emissions from Different Sources and Urban Expansion in Beijing-Tianjin-Hebei Region

Abstract

Due to rapid urbanization globally more people live in urban areas and, simultaneously, more people are exposed to the threat of environmental pollution. Taking PM2.5 emission data as the intermediate link to explore the correlation between corresponding sectors behind various PM2.5 emission sources and urban expansion in the process of urbanization, and formulating effective policies, have become major issues. In this paper, based on long temporal coverage and high-quality nighttime light data seen from the top of the atmosphere and recently compiled PM2.5 emissions data from different sources (transportation, residential and commercial, industry, energy production, deforestation and wildfire, and agriculture), we built an advanced Bayesian spatio-temporal autoregressive model and a local regression model to quantitatively analyze the correlation between PM2.5 emissions from different sources and urban expansion in the Beijing-Tianjin-Hebei region. Our results suggest that the overall urban expansion in the study area maintained gradual growth from 1995 to 2014, with the fastest growth rate during 2005 to 2010; the urban expansion maintained a significant positive correlation with PM2.5 emissions from transportation, energy production, and industry; different anti-haze policies should be designated according to respective local conditions in Beijing, Tianjin, and Hebei provinces; and during the period of rapid urban expansion (2005–2010), the spatial correlations between PM2.5 emissions from different sources and urban expansion also changed, with the biggest change coming from the PM2.5 emissions from the transport sector.

Short-term association of PM2.5/PM10 on lung cancer mortality in Wuhai city, China (2015–2019): a time series analysis

“…Meanwhile, Wuhai's environmental governance and urban transformation management methods have been linked to a decline in particulate matter concentration in recent years. Further, it is compatible with the current state of pollution in the Beijing-Tianjin-Hebei region and nearby cities (Zhao and Xu, 2021). As noted previously, enhancing pollution management methods is critical for reducing public health damage.…”

Environmental Effects of City–County Mergers in China: Strengthening Governance or Aggravating Pollution?

“…In addition, urban management of subprovincial cities and municipalities directly under the central government has certain distinct features. Therefore, we deleted all the provincial capital cities, sub-provincial cities, and municipalities, and re-regressed the model (1). Column (2) of Table 4 reports these results.…”

“…(1) Urbanization has exacerbated environmental pollution. Zhao and Xu [1] believe that transportation, industry, and energy production may have negative effects on the environment in the process of urbanization. Wang et al [2] found that urbanization not only increases haze concentrations in the region, but it also has a spillover effect on neighbouring areas.…”

Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

“…Their research uses various climate indicators such as temperature (Xu et al 2021), soil conditions (Sachdeva et al 2018), precipitation (Brogan et al 2019), wind (Daşdemir et al 2021), El Nino-Southern Oscillation (Cardil et al 2021), and others. The methods used also vary, ranging from regression (Zhao and Xu 2021), classification (Sulova and Arsanjani 2021), clustering (Viana-Soto et al 2020), neural networks (Larsen et al 2021), to copula (Xi et al 2020).…”