Spatiotemporal Associations between PM_2.5 and SO₂ as well as NO₂ in China from 2015 to 2018

• Main Authors: Ke Li, Kaixu Bai
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: PM_2.5 pollution; SO₂; NO₂; spatiotemporal association; maximum covariance analysis
• Online Access: https://www.mdpi.com/1660-4601/16/13/2352

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 (SO₂) as well as nitrogen dioxide (NO₂) 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 SO₂ and NO₂ 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 SO₂ decreased significantly in northern China in terms of large values, indicating a fast reduction of high PM_2.5 and SO₂ loadings therein. The statistically significant coupled MCA mode between PM_2.5 and SO₂ 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 SO₂ rather than NO₂ therein, given the estimated relatively high importance of SO₂. In general, the evidence-based results in this study indicate a strong linkage between PM_2.5 and SO₂ in northern China in the past few years, which may help to better investigate the mechanisms behind severe haze pollution events in northern China.