Elucidating ozone and PM_2.5 pollution in Fenwei Plain reveals the co-benefits of controlling precursor gas emissions in winter haze

Abstract

Abstract. Fenwei Plain, home to 50 million people in central China, is one of the most polluted regions in China. In 2018, Fenwei Plain is designated as one of the three key regions for the “Blue Sky Protection Campaign”, along with the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions. However, compared to BTH and YRD, our understanding of the current status of air pollution in the Fenwei Plain is limited partly due to a lack of detailed analysis of the transformation from precursor gases to secondary products including secondary organic aerosol (SOA) and ozone. Through the analysis of 7 years (2015–2021) of surface monitoring of the air pollutants in Xi’an, the largest city in the Fenwei Plain, we show that roughly 2/3 of the days exceeded either the PM2.5 or the O3 level-1 air quality standard, highlighting the severity of air pollution. Moreover, an increase in O3 pollution in the winter haze was also revealed, due to the constantly elevated reactive oxygenated volatile organic compounds (OVOCs), and in particular formaldehyde with ozone formation potential of over 50 μg m−3 in combination with the reduced NO2. The abrupt decrease of NO2, as observed during the lockdown in 2020, provided real-world evidence of the control measures, targeting only NOx (70 % decrease on average), were insufficient to reduce ozone pollution because reactive OVOCs remained constantly high in a VOC-limited regime. Model simulation results showed that with NO2 reduction from 20–70 %, the self-reaction rate between peroxy radicals, a pathway for SOA formation, was intensified by up to 75 %, while the self-reaction rate was only reduced with a further reduction of VOCs of > 50 %. Therefore, a synergic reduction in PM2.5 and O3 pollution can only be achieved through a more aggressive reduction of their precursor gases. This study elucidates the status of ozone and PM2.5 pollution in one of the most polluted regions in China, revealing a general trend of increasing secondary pollution i.e., ozone and SOA in winter haze. Controlling precursor gas emissions is anticipated to curb both ozone and SOA formation which will benefit not just the Fenwei Plain but also other regions in China.

Diagnosing ozone–NOx–VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations

“…The elevated NO 2 levels in autumn led to a negative contribution to O 3 , whereas the facilitating effect of PM 2.5 was enhanced. This stems from the relatively moderate PM 2.5 con-centrations slightly affecting photochemical reaction rates, while the increased NO 2 amplified the reactive uptake of NO 2 by PM 2.5 , generating more OH radicals that promote O 3 formation (Lin et al, 2023;Tan et al, 2022). In winter, PM 2.5 pollution exceeding 75 µg m −3 suppressed O 3 formation through scattering and absorbing solar radiation that activates atmospheric chemical processes, which counteracted the promoting effect of high PM 2.5 through the conversion of NO 2 to HONO.…”

Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning

“…As a response to the Covid-19 outbreak, strict lockdown measures were initiated in major cities across China in 2020, including the megacity of Shanghai in the Yangtze River Delta (YRD) region (He et al, 2020;Wu et al, 2021). The lockdown measures started in late January and lasted roughly 1 month, during which normal human activities were constrained substantially Lin et al, 2023a). The lockdown measures, such as shutting down cross-city travel and requiring people to stay at home, were strictly implemented to minimize human activities (Liu et al, 2020;Zhao et al, 2020).…”

“…The impacts of vehicular emissions of NO 2 on public health are significant both through direct harm via inhalation and as a precursor to secondary pollutants such as ozone and particulate matter (PM) (Li et al, 2019;Lu et al, 2019;Lin et al, 2023a). Although NO 2 concentration is regulated by air quality standards, NO x (NO + NO 2 ) is only now being limited through new emission standards for new vehicles (Grange et al, 2017).…”

Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5-year monitoring-based machine learning

“…The impacts of vehicular emissions of NO2 on public health are significant both through direct harm on inhalation and as a precursor to secondary pollutants such as ozone and particulate matter (PM) (Lin et al, 2022b;Lyu et al, 2022;Li et al, 2019;Lu et al, 2019). Although NO2 concentrations are regulated by air quality standards, limitations of NOx (NO+NO2) emissions are becoming new emission standards for new vehicles (Grange et al, 2017).…”

“…The decline in NO2 attributable to the lockdowns was also shown to be not as large as expected in 11 cities globally after a de-weathering process (Shi et al, 2021). However, most of these tree-based machine learning studies did not quantify the importance of the input variables, making these the machine learning process non-explainable or like a "black box" (Lin et al, 2022b;Wang et al, 2022a) An explainable machine learning algorithm such as the SHapley Additive exPlanation (SHAP) can quantify the impact of meteorological variables (Lundberg et al, 2020;Qin et al, 2022;Wang et al, 2022a). However, few studies have applied the explainable machine learning algorithm to study the trend in vehicular emissions.…”