The coronavirus disease 2019 was declared a "global pandemic" by quality by reducing the concentration of PM 2.5 , PM 10 , CO, and NO 2 , but has also 125 decreased air quality by augmenting O 3 and SO 2 (Wang et al., 2020a) . During the 126 COVID-19 control period in northern China, the air quality improved due to reduced 127 emissions from the transportation and secondary industrial sectors. Specifically, the 128 transportation sector was linked to the NO 2 emission reductions, while lower emissions 129 from secondary industries were the major cause for the reductions of PM 2.5 and CO, and 130 SO 2 reduction was only linked to the industrial sector (Wang et al., 2020b) . In India, 131 around 43%, 31%, 10%, and 18% decreases in PM 2.5 , PM 10 , CO, and NO 2 were 132 observed during the lockdown period compared to previous years from 22 cities, and 133 the air quality index (AQI) reduced by 44%,33%,29%, 15% and 32% in north, south, 134 east, central and western India, respectively (Sharma et al., 2020) . 135 It was proposed that a polluted environment could enhance the transmission rate 136 of such deadly viruses under moderate-to-high humidity conditions. This is because the 137 aqueous atmospheric aerosols offer a conducive surface for adsorption/absorption of 138 organic molecules and viruses onto them, facilitating a pathway for higher rate of AQIi is the critical/dominant pollutant. The AQI is represented by a value on a scale of 198 0-500, which is divided into six categories, namely: "good" (0-50), "moderate" (51-199 100), "unhealthy for sensitive groups" (101-200), "unhealthy" (201-300), "very 200 unhealthy" (301-400), and "hazardous" (401-500). The levels, colors, and 201 corresponding raw concentration levels of the AQIi for the six pollutant species are 202 listed in Table 2 . The time period used in this study was from January 1 to May 17 for 203 the years 2018 to 2020; this time period was divided into three study periods for each compared. However, the overall AQI of all cities never reached the classification of 229 "very unhealthy" and or "hazardous" for any of the cities in this study.
In order to find the critical pollutant, the mean AQIi values for the six air 231 pollutant species (pre-lockdown, lockdown, and post-lockdown) were calculated and 232 analyzed.
Change ratio calculation: The change ratio (%) of the overall AQI and AQIi 234 were calculated for each city before and after lockdown for three years using Eq.
(2)-
(4), as shown below. The "change ratio_LD" is the change during the lockdown (LD) 236 period compared with the pre-lockdown period (BF); the "change ratio_AF" is the 
Spearman correlation between the AQI of air pollutants and meteorological 
The purpose of the AQI is to inform the public about air quality so that individuals 266 and communities can take adequate actions to protect their health. This is particularly 267 important during days with unhealthy air quality (AQI>100). Such days are 268 categorized as unhealthy for sensitive groups (orange), unhealthy (red), and very unhealthy (purple) using the USEPA standard (USEPA, 2018). The "sensitive groups" 270 affected when each pollutant has an AQI exceeding 100 have been shown in Table S1 271 in the Supplementary Information.
The results indicated that the dynamic degree of air pollution during lockdown Table S2 in the supplementary information.
Tokyo is a region with low levels of air pollution and there was no great change 280 in the percentage of unhealthy days for sensitive groups in the BF and LD periods 281 between 2018 and 2020. Typically, days in Tokyo were categorized as good or 282 moderate, and there were almost no days with unhealthy air quality during the three 283 years studied, with the exception of 3.1% of the BF period in 2018. The percentage of 284 good days increased from 74.5% to 82.5% during the LD period in 2020; however, 285 this change was less than that observed during the same period in 2019 (52.6% to 286 76.9%).
Daegu, which is situated in the Republic of Korea, represents a city with 288 mid-levels of air pollution and its unhealthy days (AQI>100) ranged between 9.1% 289 and 31.5%. Specifically, the percentage of moderate days (yellow) was more than 290 J o u r n a l P r e -p r o o f 50%, while the percentage of unhealthy days and unhealthy days for sensitive groups 291 were 1.9-7.4% and 25.9-27.8%, respectively, during the BF periods for three years 292 (Fig. 1) . The sum of the percentages of unhealthy days and unhealthy days for 293 sensitive groups reduced to 0% during the 2020 lockdown when compared to the 294 value of 17.2-18.1% during the same periods for the previous two years. The 295 percentage of unhealthy days for sensitive groups returned to 9.1% after lockdown 296 and then remained at the same level for the corresponding periods in 2018 and 2019.
Therefore, the number of unhealthy days (AQI>100) reduced during lockdown and 298 then returned to a typical level once lockdown was lifted.
For cities with high air pollution levels, there was a striking reduction in the 300 percentage of unhealthy days and unhealthy days for sensitive groups days during the 301 LD and AF periods. The percentage of unhealthy days (AQI>100) in Wuhan was 302 estimated to typically range between 51.3% and 86.3%. The BF period in 2018 was 303 excluded from this study because data for only one week was collected and the 304 corresponding percentage showed 100% unhealthy days. The results showed that the 305 percentage of days with AQI>100 first dropped to 46.0% and then to 25% during the 306 LD and AF periods in 2020. Specifically, the percentage of unhealthy days categories, as defined in Table 2 . If the AQI value of a specific pollutant remained 333 below 50 for an extended period, it could be excluded from the overall AQI 334 calculation. In addition, a specific pollutant was considered particularly important 335 when its AQI exceeded 100. Moreover, the maximum AQIi among the six pollutant 336 species determined the overall AQI, based on Eq. (1), and the corresponding pollutant 337 was considered as the "critical pollutant". species for the four cities; accordingly, the overall AQI was determined based on Eq.
(1). As Table 4 shows, PM 2.5 was responsible for the overall AQI for more than 90.4% significantly; therefore, the changes observed were remarkable, especially for cities 352 that generally experience heavy pollution due to transportation and industrial emissions.
Ozone was the second most important species for overall AQI in Tokyo, which ranged 354 from 13.9-28.3% days; however, it was always categorized as good. What's more, the 355 ambient ozone was associated with the reduction of COVID-19 transmission, which is 356 probably due to its virucidal activity and possible impact on host defense. Specifically, 357 exposure to ambient ozone may trigger slight allergic reactions to human, which could 358 enhance pulmonary innate immunity (Ran et al., 2020). Table 3 and Fig. 3 . For the majority of time, air quality was within the good 369 category, corresponding to 0-12 μg/m3 based on The reduction in AQI for the unhealthy days for sensitive groups and unhealthy 375 categories was also associated with a change in the dominant pollutant in many cities.
For example, PM 2.5 exhibited a similar trend to overall AQI with the exception of the 377 part "MB LD2019" (Fig. 4) Table S3 , PM 10 pollution was generally less in 2020 394 than that in the previous two years. The percentage of days with AQI>100 (unhealthy and unhealthy for sensitive groups) in the BF period in 2020 were 0% and 23.8%, 396 respectively, and were lower than those of the corresponding percentages in 2018 397 (8.7-12.5%) and 2019 (44.6-48.2%) for the same periods. days for sensitive groups decreased to 0%, while the historical level for the same time 405 period was 3.7-3.8% (Fig. 5 and Table S3 ). The percentage of good days during the 406 lockdowns in Mumbai and Wuhan increased markedly from 1.2% to 29.6% and 30.4% 407 to 55.3%, respectively, while the percentage increased from 0% to 1.9% and 13.0% to 408 21.6%, respectively, in 2019. The percentage of good days was 51.3% for the AF 409 period, which was much lower than the BF period and slightly higher than the LD 410 period. Thus, PM 10 pollution in Mumbai and Wuhan significantly reduced during and 411 after their lockdowns. This trend was also observed in the change in the mean AQI of 412 PM 10 , as shown in Fig. 6 . For Daegu, the percentage of "good" days increased from 413 83.3% to 94.5% during lockdown, the historical data was 68.6-70.8%. However, there 414 was no remarkable change after lockdown comparing with previous years. The "good" 415 days always were 100% for Tokyo during three years. The mean AQIi of NO 2 in the four cities in the three study years was 2.8-25.2, 430 which was below 50 and therefore classified as good ( Table 3) . The mean AQI for 431 NO 2 for the BF, LD, and AF periods for the three years showed a decreasing trend. The Change ratio_TL for 2020 was slightly lower than the annual variance for the 465 cities that had already ended their lockdowns.
The mean AQIi for CO in the study years was 3.2-19.1 and therefore below 50 467 ( Table 3) . The mean AQI for CO gradually decreased in the BF, LD, and AF periods Table 5 . In general, SO 2 emissions from natural sources 489 are usually much lower than anthropogenic emissions. In addition, SO 2 in urban and 490 industrialized areas is largely from combustion without emission controls of 491 sulfur-containing fuels and from uncontrolled metal processing facilities that roast 492 sulfide ores to make metal oxides. In some regions where coal is burned for residential Spearman's correlation showed that precipitation and wind speed were significantly 501 negatively correlated with the most frequent critical pollutant, PM 2.5 (r = -0.4, p < 502 0.05). Wind gust was negatively correlated with PM 10 (r = -0.3, p < 0.05). Therefore, 503 wind speed and precipitation could affect the overall AQI. In comparation with 2019, 504 the mean wind speed in Wuhan city before and during lockdown was reduced from 505 1.6-2.3 to 0.8-1.7 m/s, respectively, and mean precipitation was reduced from 38.6 to 506 36.4 mm in 2020. The decrease in these two meteorological variables could have 507 enhanced PM 2.5 pollution. However, the mean AQI for PM 2.5 was observed to 508 decrease from 129.0 to 99.9 during lockdown, indicating that this change might not be 509 due to the change in weather conditions. In Tokyo, the mean AQI for PM 2.5 during the Table 6 : Spearman correlation between the AQI of air pollutants and meteorological 520 variables (temperature, humidity, wind speed, precipitation and air pressure), and the overall 521 523 Moreover, there was a positive correlation between precipitation with CO, NO 2 , 524 SO 2 , and O 3 (r = 0.5-0.6, p < 0.05). In addition, there was a negative correlation 525 between humidity and SO 2 and O 3 (r = -0.5, p < 0.05), while pressure was also 526 negatively correlated with SO 2 , (r = -0.3, p < 0.05).
The meteorological changes contributed substantially to the observed changes in "Good": air quality is satisfactory and air pollution posed or little or no risk; "Moderate": air quality is acceptable, however, for some pollutants there may be a 679 moderate health concern for a very small number of people who are unusually sensitive to air pollution; "Unhealthy for Sensitive Groups": Members of sensitive groups 680 may experience health effects, the general public is not likely to be affected, the sensitive group definition see Table S1 in Supplement Information; "Unhealthy": 681 everyone may begin to experience health effects; "Very Unhealthy": health warnings of emergency conditions; "Hazardous": health alert. and post-lockdown (AF) periods in the three study years (2018) (2019) (2020) . The boxplots represent the 712 distance between the first and third quartiles, while whiskers are set at the most extreme (lower 713 and upper) data point not exceeding 1.5 times the quartile range from the median. Different letters 714 indicate significant differences. "a", "b" and "c" are used for showing the significant differences 715 between the "BF", "LD" and "AF" periods for the same year. "A", "B" and "C" are used for 716
showing the significant differences between the same periods of 2018, 2019 and 2020, p<0.05. 717 718 J o u r n a l P r e -p r o o f 