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Unsupervised PM_2.5 anomalies in China induced by the COVID-19 epidemic.

ABSTRACT: To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM_2.5) levels, we developed a "conditional variational autoencoder" (CVAE) algorithm based on the deep learning to discern unsupervised PM_2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM_2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM_2.5 anomaly as a time node, we examine changes in PM_2.5 before and after the time node to assess the response of PM_2.5 to the COVID-19 lockdown. The rate of decrease of PM_2.5 around the time node in northern China is 3.5 times faster than southern China, and decreasing PM_2.5 levels in southern China is 3.5 times of that in northern China. Results were also compared with anomalous PM_2.5 occurring in Chinese's Spring Festival from 2017 to 2019, PM_2.5 anomalies during around Chinese New Year in 2020 differ significantly from 2017 to 2019. We demonstrate that this method could be used to detect the response of air quality to sudden changes in social activities.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC8247192 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The COVID-19 epidemic broke out in Wuhan, Hubei in December 2019 and in January 2020 and was later transmitted to the entire country. Quarantine measures during Chinese New Year effectively alleviated the spread of the epidemic, but they simultaneously resulted in a decline in anthropogenic emissions from industry, transportation, and import and export of goods. Herein, we present the major chemical composition of non-refractory PM2.5 (NR-PM2.5) and the concentrations of gaseous pollutants in an urban site in Shanghai before and during the quarantine period of the COVID-19 epidemic, which was Jan. 8-23 and Jan. 24-Feb. 8, respectively. The observed results show that the reduction in PM2.5 can be mainly attributed to decreasing concentrations of nitrate and primary aerosols. Nitrate accounted for 37% of NR-PM2.5 before the quarantine period when there was no emission reduction. During the quarantine period, the nitrate concentration decreased by approximately 60%, which is attributed to a reduction in the NOx concentration. Ammonium, as the main balancing cation, showed an approximately 45% simultaneous decrease in concentration. The concentrations of chloride and hydrocarbon-like organic aerosols from primary emissions also declined due to limited human activities. By contrast, sulphate and oxygenated organic aerosols showed a slight decrease in concentration, with their contributions increasing to 27% and 18%, respectively, during the quarantine period, which resulted in two pollution episodes with PM2.5 exceeding 100 μg/m3. This study provides a better understanding of the impact of quarantine measures on variations of the PM2.5 concentration and chemical compositions. Atmospheric oxidation capacities based on the oxidant (Ox = O3 + NO2) and oxidation ratios have been discussed for elucidating the source and formation of haze in an environment with lower anthropogenic emissions. With increasing contribution of secondary aerosols, lower NOx and nitrate concentrations did not completely avoid haze in Shanghai during the epidemic.

Project description:This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = -0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = -0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = -0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.

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Unsupervised PM_2.5 anomalies in China induced by the COVID-19 epidemic.

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Dataset Information

Unsupervised PM2.5 anomalies in China induced by the COVID-19 epidemic.

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OmicsDI is part of the ELIXIR infrastructure

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Unsupervised PM_2.5 anomalies in China induced by the COVID-19 epidemic.