Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China

Changqing Lin¹, Yushan Song^{1

2}, Peter K K Louie^{1

3}, Zibing Yuan⁴, Ying Li², Minghui Tao⁵, Chengcai Li⁶, Jimmy C H Fung^{1

7

8}, Zhi Ning^{1

8}, Alexis K H Lau^{1

8

9}, Xiang Qian Lao¹⁰

Affiliations

¹ Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China.
² Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
³ Environmental Protection Department, Hong Kong Government SAR, Hong Kong, China.
⁴ School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
⁵ Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.
⁶ Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China.
⁷ Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China.
⁸ Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, The Hong Kong University of Science and Technology, Hong Kong, China.
⁹ Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
¹⁰ Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.

PMID: 36092859
PMCID: PMC9446283
DOI: 10.1016/j.apr.2022.101549

Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China

Changqing Lin et al. Atmos Pollut Res. 2022 Oct.

. 2022 Oct;13(10):101549.

doi: 10.1016/j.apr.2022.101549. Epub 2022 Sep 6.

Authors

Affiliations

¹ Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China.
² Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
³ Environmental Protection Department, Hong Kong Government SAR, Hong Kong, China.
⁴ School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
⁵ Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.
⁶ Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China.
⁷ Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China.
⁸ Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, The Hong Kong University of Science and Technology, Hong Kong, China.
⁹ Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
¹⁰ Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.

PMID: 36092859
PMCID: PMC9446283
DOI: 10.1016/j.apr.2022.101549

Abstract

Photochemical regime for ozone (O₃) formation is complicated in the sense that reducing emission of nitrogen oxides (NO_x) may increase O₃ concentration. The lockdown due to COVID-19 pandemic affords a unique opportunity to use real observations to explore the O₃ formation regime and the effectiveness of NO_x emission control strategies. In this study, observations from ground networks during the lockdowns were used to assess spatial disparity of the Ratio of Ozone Formation (ROF) for nitrogen dioxide (NO₂) reduction in the Greater Bay Area (GBA) of China. The health risk model from Air Quality Health Index (AQHI) system in Hong Kong was adopted to evaluate the risk tradeoffs between NO₂ and O₃. Results show that the levels of O₃ increase and NO₂ reduction were comparable due to high ROF values in urban areas of central GBA. The ozone reactivity to NO₂ reduction gradually declined outwards from central GBA. Despite the O₃ increases, the NO_x emission controls reduced the Integrated Health Risk (IHR) of NO₂ and O₃ in most regions of the GBA. When risk coefficients from the AQHI in Canada or the global review were adopted in the risk analyses, the results are extremely encouraging because the controls of NO_x emission reduced the IHR of NO₂ and O₃ almost everywhere in the GBA. Our results underscore the importance of using a risk-based method to assess the effectiveness of emission control measures and the overall health benefit from NO_x emission controls in the GBA.

Keywords: Emission; Health risk; Lockdowns; Nitrogen dioxide; Ozone.

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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**
Cities in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China. Blue points mark the locations of air quality stations. Red squares mark the locations of meteorological stations.

**Fig. 2**
Spatial distribution of average NO₂ concentration in (a) 2019 and (b) 2020 over the GBA of China. Spatial distribution of average O₃ concentration in (c) 2019 and (d) 2020 over the GBA of China.

**Fig. 3**
Spatial distribution of the change in (a) NO₂ and (b) O₃ concentration from 2019 to 2020 over the GBA of China.

**Fig. 4**
Spatial distribution of the Ratio of Ozone Formation (ROF) for NO₂ reduction between 2019 and 2020 over the GBA of China.

**Fig. 5**
The change in city average of NO₂ (blue bars) and O₃ (green bars) concentration from 2019 to 2020 and the corresponding ROF for NO₂ reduction (red bars) over the GBA of China. The y axis on the left gauges the NO₂ and O₃ concentration changes, while the y axis on the right gauges the ROF values.

**Fig. 6**
Spatial distribution of the %AR of hospital admission for respiratory and cardiovascular diseases for (a) NO₂ in 2019, (b) NO₂ in 2020, (c) O₃ in 2019, and (d) O₃ in 2020 over the GBA of China. Panels (e) and (f) show the IHR (i.e., integrated %AR) of NO₂ and O₃ in 2019 and 2020, respectively.

**Fig. 7**
Spatial distribution of the change in %AR of hospital admission for respiratory and cardiovascular diseases from 2019 to 2020 for (a) NO₂ and (b) O₃ over the GBA of China. Panel (c) shows spatial distribution of the change in the IHR (i.e., integrated %AR) of NO₂ and O₃ from 2019 to 2020 over the GBA of China.

**Fig. 8**
City average of the change in %AR of hospital admission for respiratory and cardiovascular diseases from 2019 to 2020 for NO₂ (blue bars) and O₃ (green bars) over the GBA of China. Red bars represent the city average of the change in the IHR (i.e., integrated %AR) of NO₂ and O₃ from 2019 to 2020 over the GBA of China.

**Fig. 9**
Relationship between the change in the IHR of NO₂ and O₃ from 2019 to 2020 and the ROF for NO₂ reduction at all stations in the GBA of China.

**Fig. 10**
Spatial distribution of the change in the integrated %AR of mortality for NO₂ and O₃ from 2019 to 2020 over the GBA of China using the β coefficients from (a) the AQHI in Canada and (b) the global review.

See this image and copyright information in PMC

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Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China

Affiliations

Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China

Authors

Affiliations

Abstract

Conflict of interest statement

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