臭氧
环境科学
亚硝酸
大气科学
污染
氮氧化物
气候学
化学输运模型
气象学
化学
地理
有机化学
无机化学
地质学
燃烧
生物
生态学
作者
Baoye Hu,Gaojie Chen,Jinsheng Chen,Lingling Xu,Xiaolong Fan,Youwei Hong,Mengren Li,Ziyi Lin,Mingqiang Huang,Fuwang Zhang,Hong Wang
标识
DOI:10.1016/j.scitotenv.2023.164477
摘要
Two ozone (O3) processes, summer episode dominated by local production and autumn episode dominated by regional transport, were chosen to investigate the role of HONO in different pollution processes. Meteorological conditions, diurnal variation of O3, potential source contribution factor (PSCF) analysis, concentration weighted trajectory (CWT) models, and the distribution of the eight-hour maximum values of O3 on mainland China all prove that summer O3 was mainly locally generated while autumn O3 episode was more susceptible to regional transport. The gaps between observations and simulation results with the default HONO chemistry in Master Chemical Mechanism (MCM) of Observation Based Model (OBM) were higher in summer episode (0.58 ppb) than autumn episode (0.37 ppb). Although we implemented nine additional sources in the model to revise the HONO chemistry, the simulated values were still lower than the observed values. HONO promoted O3 production by accelerating the reaction of HO2 + NO and RO2 + NO, and promoted loss of O3 by the reaction of OH + NO2 and RO2 + NO2. The net production rate of O3 with HONO constraint increased by 28.50 % in summer and 22.43 % in autumn, which also indicated that HONO played more important role in the O3 production in summer. The difference of NOx of daily RIR between the cases with and without HONO constraint was higher in summer O3 episode (0.15 %/%) than that in autumn O3 episode (0.09 %/%), the same as to VOCs with −0.20 %/% in summer O3 episode and − 0.14 %/% in autumn O3 episode, which indicated that the presence or absence of the HONO constraint has a greater impact on the RIR value in the case of dominant local generation. In brief, the O3 sensitivity would be more favorable for VOCs without HONO constrained in the model, which would inevitably mislead policy makers to develop efficient policies to control O3 pollution.
科研通智能强力驱动
Strongly Powered by AbleSci AI