Industrial agglomeration and air pollution: A new perspective from enterprises in Atmospheric Pollution Transmission Channel Cities (APTCC) of Beijing-Tianjin-Hebei and its surrounding areas, China

Xiao, Cuicui; Zhou, Ji; Wang, Xin; Zhang, S.

doi:10.1371/journal.pone.0255036

Cited by 10 publications

(1 citation statement)

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“…Beijing-Tianjin-Hebei is an important industrial area and one of the largest urban agglomerations in northern China; it has a significant radiating effect on the surrounding economic development, and its strategic location is very important [14]. However, the past industrial activities of Beijing-Tianjin-Hebei have brought serious air pollution problems; in the 2019 national air quality assessment, Beijing-Tianjin-Hebei accounted for 16 of the 20 worst cities [15]. Therefore, PM 2.5 is widely regarded as the primary pollutant in the Beijing-Tianjin-Hebei region [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Spatiotemporal Variations and Drivers of PM2.5 in Beijing-Tianjin-Hebei from 2015 to 2020

2022

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Understanding the spatiotemporal heterogeneity and complex drivers of PM2.5 concentration variations has important scientific value for sustainable urban development. Taking Beijing-Tianjin-Hebei (BTH) as the research area, and using spatial analysis techniques and wavelet methods to explore the spatiotemporal heterogeneity of variations in PM2.5 concentrations, the research shows that in the past six years (2015–2020), the PM2.5 concentrations in the BTH area have a downward trend, and the mean is 59.41 μg/m3; however, the distribution pattern of PM2.5 pollution has changed very little, and the concentration in the south and southwest is still generally high. The continuous wavelet transform revealed that the PM2.5 concentrations in the study area have a short period of about a week to a half a month and a long period dominated by annual cycle. The effect of a single meteorological factor on PM2.5 concentrations is weak, but this effect has obvious spatial differentiation characteristics from coastal to inland and has a double-sided effect due to different geographical locations. The wavelet transform coherence revealed that dewpoint temperature at 2 m (TED), meridional wind at 10 m (WV) and air temperature at 2 m (TEM) are important single meteorological factors that affect the variation of PM2.5 concentrations. The multiple wavelet coherence reveals that in scenarios where two meteorological factors are combined, the combination of TED-mean wind speed (WS) is the best combination to explain the variation in PM2.5 concentrations (AWC = 0.77, PASC = 41%). In the combination of three meteorological factors, TEM-WV-WS explained the variations of PM2.5 concentrations in the BTH region to the greatest degree (AWC = 0.89, PASC = 45%). Finally, the research shows that the variations of PM2.5 concentrations in the BTH region can be better explained by a combination of 2–3 meteorological factors, among which temperature and wind are the key meteorological factors. This research will provide a new window for the multi-scale variation characteristics and multi-factor control relationship of PM2.5 concentrations in the BTH region and provide a new insight for the prevention and control of air pollution.

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Section: Introductionmentioning

confidence: 99%