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Observation-Based Diagnostics of Reactive Nitrogen Recycling through HONO Heterogenous Production: Divergent Implications for Ozone Production and Emission Control.

Kezhen ChongYuhang WangMingming ZhengHang QuRuixiong ZhangYoung Ro LeeYi JiLewis Gregory HueyHua FangWei SongZheng FangCheng LiuYang GaoJianhui TangXin-Ming Wang
Published in: Environmental science & technology (2024)
Understanding of nitrous acid (HONO) production is crucial to photochemical studies, especially in polluted environments like eastern China. In-situ measurements of gaseous and particulate compositions were conducted at a rural coastal site during the 2018 spring Ozone Photochemistry and Export from China Experiment (OPECE). This data set was applied to investigate the recycling of reactive nitrogen through daytime heterogeneous HONO production. Although HONO levels increase during agricultural burning, analysis of the observation data does not indicate more efficient HONO production by agricultural burning aerosols than other anthropogenic aerosols. Box and 1-D modeling analyses reveal the intrinsic relationships between nitrogen dioxide (NO 2 ), particulate nitrate (pNO 3 ), and nitric acid (HNO 3 ), resulting in comparable agreement between observed and simulated HONO concentrations with any one of the three heterogeneous HONO production mechanisms, photosensitized NO 2 conversion on aerosols, photolysis of pNO 3 , and conversion from HNO 3 . This finding underscores the uncertainties in the mechanistic understanding and quantitative parametrizations of daytime heterogeneous HONO production pathways. Furthermore, the implications for reactive nitrogen recycling, ozone (O 3 ) production, and O 3 control strategies vary greatly depending on the HONO production mechanism. On a regional scale, the conversion of HONO from pNO 3 can drastically enhance O 3 production, while the conversion from NO 2 can reduce O 3 sensitivity to NOx changes in polluted eastern China.
Keyphrases
  • heavy metals
  • south africa
  • nitric oxide
  • machine learning
  • dna methylation
  • genome wide
  • deep learning
  • data analysis
  • human health
  • drinking water