Spatiotemporal Associations between PM 2.5 and SO 2 as well as NO 2 in China from 2015 to 2018

Ke Li and Kaixu Bai
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Ke Li: School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Kaixu Bai: School of Geographic Sciences, East China Normal University, Shanghai 200241, China

IJERPH, 2019, vol. 16, issue 13, 1-12

Abstract: Given the critical roles of nitrates and sulfates in fine particulate matter (PM 2.5 ) formation, we examined spatiotemporal associations between PM 2.5 and sulfur dioxide (SO 2 ) as well as nitrogen dioxide (NO 2 ) in China by taking advantage of the in situ observations of these three pollutants measured from the China national air quality monitoring network for the period from 2015 to 2018. Maximum covariance analysis (MCA) was applied to explore their possible coupled modes in space and time. The relative contribution of SO 2 and NO 2 to PM 2.5 was then quantified via a statistical modeling scheme. The linear trends derived from the stratified data show that both PM 2.5 and SO 2 decreased significantly in northern China in terms of large values, indicating a fast reduction of high PM 2.5 and SO 2 loadings therein. The statistically significant coupled MCA mode between PM 2.5 and SO 2 indicated a possible spatiotemporal linkage between them in northern China, especially over the Beijing–Tianjin–Hebei region. Further statistical modeling practices revealed that the observed PM 2.5 variations in northern China could be explained largely by SO 2 rather than NO 2 therein, given the estimated relatively high importance of SO 2 . In general, the evidence-based results in this study indicate a strong linkage between PM 2.5 and SO 2 in northern China in the past few years, which may help to better investigate the mechanisms behind severe haze pollution events in northern China.

Keywords: PM 2.5 pollution; SO 2; NO 2; spatiotemporal association; maximum covariance analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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