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Impacts of TROPOMI-Derived NO X Emissions on NO 2 and O 3 Simulations in the NCP during COVID-19.

Yizhi ZhuCheng LiuQihou HuJiahua TengDaian YouChengxin ZhangJinping OuTing LiuJinan LinTianyi XuXinhua Hong
Published in: ACS environmental Au (2022)
NO 2 and O 3 simulations have great uncertainties during the COVID-19 epidemic, but their biases and spatial distributions can be improved with NO 2 assimilations. This study adopted two top-down NO X inversions and estimated their impacts on NO 2 and O 3 simulation for three periods: the normal operation period (P1), the epidemic lockdown period following the Spring Festival (P2), and back to work period (P3) in the North China Plain (NCP). Two TROPOspheric Monitoring Instrument (TROPOMI) NO 2 retrievals came from the Royal Netherlands Meteorological Institute (KNMI) and the University of Science and Technology of China (USTC), respectively. Compared to the prior NO X emissions, the two TROPOMI posteriors greatly reduced the biases between simulations with in situ measurements (NO 2 MREs: prior 85%, KNMI -27%, USTC -15%; O 3 MREs: Prior -39%, KNMI 18%, USTC 11%). The NO X budgets from the USTC posterior were 17-31% higher than those from the KNMI one. Consequently, surface NO 2 levels constrained by USTC-TROPOMI were 9-20% higher than those by the KNMI one, and O 3 is 6-12% lower. Moreover, USTC posterior simulations showed more significant changes in adjacent periods (surface NO 2 : P2 vs P1, -46%, P3 vs P2, +25%; surface O 3 : P2 vs P1, +75%, P3 vs P2, +18%) than the KNMI one. For the transport flux in Beijing (BJ), the O 3 flux differed by 5-6% between the two posteriori simulations, but the difference of NO 2 flux between P2 and P3 was significant, where the USTC posterior NO 2 flux was 1.5-2 times higher than the KNMI one. Overall, our results highlight the discrepancies in NO 2 and O 3 simulations constrained by two TROPOMI products and demonstrate that the USTC posterior has lower bias in the NCP during COVD-19.
Keyphrases
  • monte carlo
  • molecular dynamics
  • sars cov
  • air pollution
  • public health
  • risk assessment
  • respiratory syndrome coronavirus