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. 2023 Sep 1:308:119864.
doi: 10.1016/j.atmosenv.2023.119864. Epub 2023 May 23.

Unraveling the O3-NOX-VOCs relationships induced by anomalous ozone in industrial regions during COVID-19 in Shanghai

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Unraveling the O3-NOX-VOCs relationships induced by anomalous ozone in industrial regions during COVID-19 in Shanghai

Bingqing Lu et al. Atmos Environ (1994). .

Abstract

The COVID-19 pandemic promoted strict restrictions to human activities in China, which led to an unexpected increase in ozone (O3) regarding to nitrogen oxides (NOx) and volatile organic compounds (VOCs) co-abatement in urban China. However, providing a quantitative assessment of the photochemistry that leads to O3 increase is still challenging. Here, we evaluated changes in O3 arising from photochemical production with precursors (NOX and VOCS) in industrial regions in Shanghai during the COVID-19 lockdowns by using machine learning models and box models. The changes of air pollutants (O3, NOX, VOCs) during the COVID-19 lockdowns were analyzed by deweathering and detrending machine learning models with regard to meteorological and emission effects. After accounting for effects of meteorological variability, we find increase in O3 concentration (49.5%). Except for meteorological effects, model results of detrending the business-as-usual changes indicate much smaller reduction (-0.6%), highlighting the O3 increase attributable to complex photochemistry mechanism and the upward trends of O3 due to clear air policy in Shanghai. We then used box models to assess the photochemistry mechanism and identify key factors that control O3 production during lockdowns. It was found that empirical evidence for a link between efficient radical propagation and the optimized O3 production efficiency of NOX under the VOC-limited conditions. Simulations with box models also indicate that priority should be given to controlling industrial emissions and vehicle exhaust while the VOCs and NOX should be managed at a proper ratio in order to control O3 in winter. While lockdown is not a condition that could ever be continued indefinitely, findings of this study offer theoretical support for formulating refined O3 management in industrial regions in Shanghai, especially in winter.

Keywords: COVID-19 confinement; Machine learning; Ozone; Photochemical box model; Volatile organic compounds.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Sampling locations of the ground sites of Shanghai city (30.706°N, 121.278 °E). The base map was from © Google Maps.
Fig. 2
Fig. 2
Concept of deweathering and detrending air pollutant level. Cobs and Cdew are the observed the deweathered (by RF1) average concentrations of air pollutant in the second and third weeks before the lockdown start date; Ci,obs and Ci,dew are the daily observed and deweathered average concentrations of an air pollutant in the ith day starting in the second week after the lockdown start date. Pobs and Pdew are the observed and deweathered percentage changes in air pollutant levels after versus before the lockdown began. Ci,BAU (by RF2) is the hypothetical concentration for the ith day starting in the second week after the lockdown date under “business-as-usual” (i.e., no lockdown) conditions. The percentage change Delta (i.e., the change in air pollutant concentration arising from lockdown effect alone) is given by Pdew-PBAU.
Fig. 3
Fig. 3
Comparison of the observed and predicted daily average air pollutants concentrations by RF1 and RF2 model. (a) NOX. (b) VOCs. (c) O3. The center line and the box edges represent the median and quartiles for the observed air pollutants concentrations, respectively. Dashed lines show the mean diel variations for the lockdown episodes after weather normalization (RF1). Diel variations of the predicted concentration of a pollutant under the w/o lockdown scenario (RF2, right axis) and the percent change between two models are also shown (color bar).
Fig. 4
Fig. 4
Model simulation. (a) Simulated primary daytime sources and sink of ROX. Shades of blue represent production reactions, and shades of red represent loss rates. (b) Summary of daytime average budgets of ROX radicals (in pptv h−1). The blue, red and black lines indicate the primary production, destruction and recycling pathways, respectively. (c) Model-simulated average chemical budgets of O3. (d) Model-calculated daytime average RIRs for the major O3 precursor groups during lockdown.
Fig. 5
Fig. 5
Isopleth diagram for O3 production during the COVID-19 lockdown. (a) The O3 isopleth diagram versus NOX and VOCs. The red symbol represents the base case. (b) Response of O3 to NOX (top) and VOCs (bottom). These plots are slices through the surface at the red horizontal and vertical lines in the isopleth diagram, with the base case shown as the red symbol.
Fig. 6
Fig. 6
Relationship of P (O3)net increment percentage (R–P(O3)net) with NOX reduction percentages for individual sources of VOCs when VOCs is reduced by 20%. The highlighted area represents the abatement ratio of individual sources of VOCs/NOX.

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