Air quality changes and Grey relational analysis of pollutants exceeding standards during the COVID-19 pandemic in Wuhan

The COVID-19 has spread widely around the world, and the air quality during that period has changed signicantly. On the contrary, air quality also can affect the development of the pandemic. Therefore, it is pretty necessary to study air quality changes during the pandemic. This paper achieves this goal by applying the Over-standard multiples method and Grey relational analysis to study the individual and overall change trends of pollutants in Wuhan during the same period in the past seven years. The result shows that the concentrations of SO2 and O3 increased affected by the pandemic but still meet the standard. However, the pandemic promoted a decrease in PM2.5, PM10, and NO2 concentrations, but it had just reached the standard or even exceeded the standard. This article discussed the feasibility of using Grey relational analysis to analyze pollutants exceeding the standard from an overall perspective and provided new ideas for future research.


Introduction
In December 2019, unknown pneumonia patients appeared in Wuhan. The study found that it was caused by a new coronavirus. World Health Organization (WHO) named the new coronavirus-infected pneumonia as COVID-19. Nowadays, it has spread globally and presents a pandemic trend. (https://www.who.int/). Along with the COVID-19 pandemic, there are increasing medical wastes and medical wastewater. Due to their characteristics of space pollution and latent infection, inappropriate treatment can make them spread viruses and even cause environmental pollution. Besides, to control the pandemic, many countries carried out isolation blockade which reduces people's social activities and prompted companies to stop production. These can directly reduce air pollutants. On the other hand, there are also some studies showing that air pollutants can affect virus transmission even some health issues (Brandt et al. 2020;Ciencewicki and Jaspers 2007). Hence, it is pretty essential to study air quality changes during the COVID-19 pandemic.
Contemporarily, many scholars have carried out relevant researches in the world. (Dantas et al. 2020;Lal et al. 2020;Li et al. 2020;Nakada and Urban 2020) found that the lockdown reduces the emissions of SO 2 , NO x , PM 2.5 , CO, VOCs, and AQI, even low-to-moderate reduce Aerosol Optical Depth obviously in the corresponding research region. But at the same time, some of them found that ozone increased distinctly. Similarly, (Kerimray et al. 2020) indicated the concentrations of NO 2 , PM 2.5 , and CO reduced obviously.
But the concentrations of O 3 , benzene, and toluene were higher than those before lockdown in Almaty.
Analyzing the ndings, scholars have applied a variety of research methods, such as utilizing the cokriging method in ArcGIS, applying the WRF-CAMx modeling system, analyzing pollutants concentrations changes directly, and so on. However, few scholars have applied the Grey relational analysis (GRA) to analyze the changes in air quality during the pandemic. Taking Wuhan as an example, this paper rst analyzed the changes in the six monitoring pollutants respectively by introducing an Overstandard multiples method and found that the main pollutants are PM 2.5 and PM 10 (PM). Then, this paper utilized GRA to analyze the changes of PM, and analyzed the feasibility of this method. This paper not only analyzes the impact of the pandemic on air quality but also provides new ideas for analyzing the overall changes in the pollutants exceeding the standard.

Statistics date
The period analyzed in this article is the rst two months of the pandemic. However, starting at 10 am on January 23, Wuhan began to implement isolation blockade and it was during the Spring Festival (SF).
Under normal circumstances, People's trip frequency and the possibility of setting off recrackers will increase. Companies will stop production. These staged behaviors during SF will affect air quality to some extent. Aiming to avoid the impact on the analysis, this article took the Chinese New Year's Eve (NYE) as a breakpoint, and regarded the two months corresponding to the 15 days before and after the NYE's calendar date as the same period.

Statistical data and reference standard
This article discussed the average monthly concentration of PM 2.5 , PM 10 , SO 2 and NO 2 , the 95th percentile of the daily average of CO (CO), and the 90th percentile of the maximum daily average concentration of O 3 for 8 hours (O 3 ). The actual data is shown in Table 1.
This article took the Chinese GB3095-2012 Environmental Air Quality Standards as a reference standard( Environmental Air Quality Standards GB3095-2012 2012).

Methodology
Method of over-standard multiples To analyze the speci c situation of each pollutants, this article introduced the method of over-standard multiples and utilized the over-standard multiples (OSM) to describe the degree of air pollution. The calculation formula is as follows: (see Equation 1 in the Supplementary Files)

Grey relational analysis
Grey relational analysis is part of the grey system theory (Morán et al. 2006) and can be used to explore the degree of correlation between systems. When analyzing, the known data are usually constructed into several sequences according to the evaluation purpose. Secondly, select a sequence that can be regarded as a reference, with others to be evaluated are called comparison sequences.

(See Equations and Calculations in the Supplementary Files)
In this article, regarding the concentration limits as a reference sequence and the pollutant concentrations on the statistics date as comparative sequences, the GRD of them directly re ects the air quality. The greater the GRD, the better the air quality.

Changes in the concentration of each pollutant
As shown in Figure 1, the GRD of most of the pollutants have decreased compared with 2014, that is, the concentration of pollutants has dropped. However, in the second month, the concentration of O 3 has increased. Furthermore, the concentrations of O 3 in these two months, and the concentrations of SO 2, O 3 in the second month is higher than last year. Besides, the GRD of SO 2, O 3 , and CO is negative which means the concentrations of these pollutants are in line with the standard. The concentrations of NO 2 have also reached the standard in the past two years. However, the OSM of PM 2.5 and PM 10 has always been pretty large which means PM 2.5 and PM 10 are the main pollutants exceeding the standard.

Changes in pollutants exceeding the standard
To analyze the changes of main pollutants PM exceeding the standard from an overall perspective, this paper introduced GRA. The result is as follows: (See Figure 2) As shown in Figure 2, according to the change trends of GRD, it is found that the concentration of PM has improved signi cantly compared with 2014. Comparing with last year, the situation is the same.

Discussion And Conclusions
In terms of methods, the OSM can not only show the trends of pollutant concentration changes but also intuitively show the degree of pollutants exceeding the standard. Comparing the two analysis results in section 4, it is found that when GRA is used to analyze the over-standard of pollutants from an overall perspective, the analysis result is good for a single whole. But when the comparative analysis between groups is performed, the result is not ideal. According to formulas 2 and 3, although the correlation degree of pollutants depends on the actual concentration of it, it is also affected by the minimum and maximum concentrations in the same group. Therefore, it is not feasible to compare the concentration according to the correlation between different groups.
From the perspective of pollutant concentrations, during the COVID-19 pandemic, the concentrations of O 3 and SO 2 mainly showed an upward trend, especially in the second month, that is, after the lockdown.
However, it still did not exceed the concentration limits. The concentrations of NO 2 had dropped, but it had only been reached the standard in the past two years. The increase in O 3 concentration could be due to the decrease in PM and NOx (Sharma et al. 2020). Although some enterprises stopped production in the second month, considering the increase in SO 2 concentration mainly related to industrial emissions in China (Wang. and Huang. 2015), it may be that the sharp increase in shipments of medical products and medical wastes led to an increase in SO 2 concentration.
According to the result of GRA, the situation of pollutants exceeding the standard had improved. However, it should be noted that although the concentrations of PM 2.5 and PM 10 had decreased, it is still close to or even exceeded the limits. Furthermore, during the lockdown, their concentrations were lower than before, the rst month. The main reasons for the decrease in PM 2.5 , PM 10 , and NO 2 concentrations are the shutdown of the enterprise and the reduction of vehicle exhaust emissions (Liu. 2017;Zheng. et al. 2014) that are mainly caused by quarantine blockade during the pandemic.
Furthermore, some scholars have pointed out that when the society fully resumes and the economy recovers, the improvement in air quality will not last forever, or even worse than before the pandemic (Quirin 2020;Wang and Su 2020). Hence, scholars should learn from the experiences during the pandemic and formulate environmental protection measures suitable for normal implementation.

Declarations Funding
This study was nancially supported by the Department of Education of Liaoning Province Fund (NO.19-1162). Figure 1 a Changes of six pollutants' over-standard multiples in Wuhan during the same period in 2014-2020. b Changes of six pollutants' over-standard multiples in Wuhan during the same period in 2014-2020 Page 9/9

Supplementary Files
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