Xueyan Liu1, Kangping Cui This email address is being protected from spambots. You need JavaScript enabled to view it.1, Yen-Kung Hsieh This email address is being protected from spambots. You need JavaScript enabled to view it.2, Ya-Fen Wang3, Ruoxin Wang1

1 School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
2 Marine Ecology and Conservation Research Center, National Academy of Marine Research, Kaohsiung 80661, Taiwan
3 Department of Environmental Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan


Received: April 2, 2022
Revised: June 16, 2022
Accepted: June 19, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.220160  

  • Download: PDF


Cite this article:

Liu, X., Cui, K., Hsieh, Y.K., Wang, Y.F., Wang, R. (2022). Study on Air Quality Index, Atmospheric Pollutants and Dry Deposition of PCDD/Fs in the Ambient Air near Southwest China. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.220160


HIGHLIGHTS

  • The air quality of two cities near southwest China was studied during 2020–2021.
  • AQI distribution of Chengdu and Chongqing in different seasons was analyzed.
  • The changes in PM2.5, NO2, and O3 concentrations were investigated.
  • The behavior of PCDD/F dry deposition in the ambient air was discussed.
 

ABSTRACT


This study investigated the AQI (air quality index), atmospheric pollutants (PM2.5, NO2, and O3), and dry deposition of PCDD/Fs in Chengdu and Chongqing, near southwest China from 2020 to 2021. The results showed that the implementation of strict epidemic prevention action led to a significant improvement in the air quality in 2020. However, the air quality index increased again in 2021 as the economic activity was recovered. In February 2020, at the height of the epidemic, the monthly average PM2.5 and NO2 concentrations were estimated as 50.9 µg m–3 and 21.2 ppb, respectively in in Chengdu, and 48.9 µg m–3 and 22.9 ppb, respectively in Chongqing. In February 2021, when the economy was returned to normal, the monthly average PM2.5 and NO2 concentrations were 60.3 µg m–3 and 36.2 ppb respectively in Chengdu and 52.4 µg m–3 and 35.1 ppb, respectively in Chongqing. In addition, since O3 concentrations were influenced by the VOCs–NOx ratio, the reduction of NOx emissions and the increase of VOC emissions during the epidemic control period caused a change in the VOCs–NOx ratio, and thus leading to an increase in O3 concentrations. The monthly average concentration of O3 from March 2021 to May 2021 was significantly lower than that of 2020. Furthermore, the dry deposition flux of PCDD/Fs also changed in the period before and after the epidemic. The dry deposition flux of PCDD/Fs in 2021 was significantly higher than that of 2020, which was associated with the fuel consumption of various industrial plants. This study indeed provided useful information for the contribution of scientific communities and important data bank for the future air quality control.


Keywords: AQI, NO2, O3, PM2.5, PCDD/F, Atmospheric dry deposition


1 INTRODUCTION


Several cases of novel coronavirus pneumonia were reported in December 2019 due to the new pandemic (Sun et al., 2020). In order to curb the spread and deterioration of the epidemic, all provinces and municipalities across the country have launched a Level-1 response to major public health emergencies, taking unprecedentedly strict control measures such as road traffic control, restricting the residents from going out, closing the scenic spots and commercial areas, stopping market gatherings, suspending work and classes, and shutting down non-essential factories. These measures significantly caused a huge impact on political, economic, and social life. But on the other hand, there was the stagnation of various industries and sectors that led to a significant improvement in ambient air quality. The Ministry of Ecology and Environment (https://www.mee.gov.cn/) of China's ambient air quality statistics report showed that the average percentage of Class-I days in 337 prefecture-level-and-above cities nationwide increased by 7.4% in January and by 12.7% in February 2020 compared to 2019. So far, two years have passed since the outbreak of the epidemic in China. Surprisingly, the changes in economy, politics, education, public health, fuel consumption, and ambient air quality of China in the past two years were extremely obvious. Hence, we assumed that this is a unique opportunity to study the changes in urban air quality in China and to provide new ideas and references for decision making in urban air pollution management.

Air pollution is the process by which large amounts of pollutants are released into the atmosphere and cause harm to humans, other organisms, and the natural environment (Kampa and Castanas, 2008). Some pollutants, such as PM2.5, sulfur dioxide, NOx, and ozone, would pose the higher risk of exposure to organisms even at lower concentrations. In addition, it was known that the particulate pollution (PM2.5 and PM10) has been strongly associated with respiratory, cardiopulmonary, and heart disease (Wong et al., 2008). Therefore, in order to assess the effects of air pollution on human health, a widely used quantitative method was needed to integrate the analysis of pollution from multiple pollutants and to communicate the ranking of health risks to the public in a simple form (Bruno and Cocchi, 2002). Air Quality Index (AQI) is usually intended to represent the individual pollutants on a common scale concentration where the health effects typically would occur at values common to all pollutants (Shooter and Brimblecombe, 2009). AQI quantitatively describes the air quality condition. Generally, AQI combines and calculates the values for six air pollutants: PM2.5, PM10, SO2, NO2, O3, and CO. It was suggested that the larger the value, the more serious would be the air pollution condition and the greater would be the risk to human health.

The full name of dioxin is polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) and thus it is abbreviated as PCDD/Fs. Actually, the PCDD/Fs are the class of tricyclic compounds containing 1–8 chlorines. The toxicity of PCDD/Fs would be related to the substitution position and the number of substitutions of chlorine atoms. Among all 210 congeners, only PCDD/Fs substituted by chlorine atoms at positions 2, 3, 7 and 8 at the same time were found to be more toxic, and there are totally 17 such species (Rordorf, 1989). The toxicity of 2,3,7,8-tetrachlorinated dibenzo-p-dioxin was found to be equivalent to 1000 times the toxicity of potassium cyanide (KCN), which was known as "the poison of the century" (Huwe, 2002). PCDD/Fs are extremely persistent and bio-accumulative (Schecter et al., 2006). The half-life of dioxins in humans is up to 7–11 years, and some PCDD/Fs are even up to 20 years (Pirkle et al., 1989; Flesch-Janys et al., 1995). PCDD/Fs have been shown to be a risk causative factor for cancer, immune deficiencies, reproductive and developmental abnormalities, autism spectrum disorder, and endocrine disruption (Guo et al., 2018; Schecter et al., 2006). It was shown that the distribution behavior of both gas and particle phases was the main determinant of the environmental trends of PCDD/Fs, while the PCDD/Fs entering the ecosystem mainly originated from the gravitational sedimentation of the particle phase (Tian et al., 2021a). Therefore, attention should be focused on the relationship between particulate matter and PCDD/Fs.

This study analyzed and discussed the distribution and changes of AQI in two important cities in southwest China, namely Chengdu and Chongqing, during 2020 and 2021. In addition, the monthly average concentration of three major air pollutants (nitrogen dioxide, ozone, and PM2.5) over a two-year period was also analyzed. Finally, the changes in atmospheric dry deposition flux of total-PCDD/Fs-WHO2005-TEQ in 2020–2021 in the two cities were also further investigated and discussed.

 
2 METHODS


The air quality was analyzed in two cities in southwest China: Chengdu (31°67′N, 104°06′E) and Chongqing (29°59′N, 106°54′E) from 2020 to 2021 as shown in Fig. 1. PM2.5, PM10, SO2, CO, NO2, and O3 in these cities were investigated and integrated as AQI (http://www.tianqihoubao.com/lishi/).

Fig. 1. Location of Chengdu and Chongqing in southwest China.
Fig. 1. Location of Chengdu and Chongqing in southwest China.

Chengdu, the capital of Sichuan Province, is an important central city in the western region with a resident population of 20,937,800. Chengdu is located in southwest China, the western part of the Sichuan Basin and the hinterland of the Chengdu Plain. It has a humid subtropical monsoon climate with abundant precipitation and 12 main streams such as the Min River and the Tuo River, as well as dozens of tributaries.

Chongqing, a municipality directly comes under the Central Government of China, is one of the important cities in China and the economic center of the upper reaches of the Yangtze River, with a resident population of 32,054,200. The topography of Chongqing decreases from north to south towards the Yangtze River valley, with hills and low mountains in the northwest and central part of the city, and it has two large mountain ranges, Daba Mountain and Wuling Mountain, in the southeast, with more sloping land. Chongqing has a humid subtropical monsoon climate, and the main rivers located here are Yangtze, Jialing, Wu, and Ful rivers.

 
2.1 Air Quality Index (AQI)

The calculation of AQI can be referred to the Technical Regulation on Ambient Air Quality Index (on trial) issued by the Ministry of Ecology and Environment, the People's Republic of China (Shen et al., 2017).

The daily AQI value was calculated from the average concentration of PM2.5, PM10, SO2, NO2, and CO for 24 h and the maximum concentration of O3 for 8 h per day. The range of AQI values related to air quality could be divided into six categories (Hu et al., 2015) as follows:

Class I: 0–50, Good, Green;

Class II: 51–100, Moderate, Yellow;

Class III: 101–150, Unhealthy for sensitive Groups, Orange;

Class IV: 151–200, Unhealthy, Red;

Class V: 201–300, Very unhealthy, Purple;

Class VI: 300–500, Hazardous, Maroon.

 
2.2 PCDD/F Concentration and Dry Deposition Flux of PCDD/Fs

The monthly PCDD/Fs concentration in Chengdu and Chongqing cities could be simulated by regression analysis (Lee et al., 2016; Huang et al., 2011). In addition, to reduce the systematic error, two regression equations were used and the results were averaged from both. The two regression analysis equations are:

  

Y1, Y2: Total concentration of PCDD/Fs (pg m–3);

X: PM10 concentration in the urban ambient atmosphere (µg m–3).

The calculation method reported by Tian et al. (2021b) was used for the calculation of dry deposition flux of PCDD/Fs in this work, where the relevant parameters were obtained by using the methods reported in previous studies (Sheu et al., 1996; Shih et al., 2006).

 
3 RESULTS AND DISCUSSION


 
3.1 AQI Distribution

The proportions of the six AQI classes in Chengdu and Chongqing in different seasons for 2020–2021 are shown in Figs. 2(a–d).

The AQI distribution for the four seasons in Chengdu in 2020 is shown in Fig. 2(a). It was seen that in spring, summer, fall and winter the daily AQI ranged between 14 and 124, 16 and 91, 17 and 135, and 23 and 238, and seasonally the averages were 64.7, 38.9, 54.7, and 83.5 respectively. Furthermore, in spring, the proportions of AQI for classes I, II, III, IV, V, and VI were 31.5%, 60.9%, 7.6%, 0%, 0%, and 0%, respectively. In summer, the proportions of AQI for classes I, II, III, IV, V, and VI were 66.3%, 33.7%, 0%, 0%, 0%, and 0%, respectively. In autumn, the proportions of AQI for classes I, II, III, IV, V, and VI were 56.0%, 33.0%, 11.0%, 0%, 0%, and 0%, respectively. In winter, the proportions were 15.4%, 54.9%, 25.3%, 2.2%, 2.2%, and 0%, respectively.

Fig. 2(a). The proportions of the six AQI classes for Chengdu in spring, summer, autumn, and winter in 2020.
Fig. 2(a). The proportions of the six AQI classes for Chengdu in spring, summer, autumn, and winter in 2020.

The data of spring, summer, autumn, and winter (Fig. 2(b)) for 2021 in Chengdu showed that the daily AQI ranged between 21 and 136, 18 and 92, 13 and 181, and 16 and 203, and averaged 60.9, 43.4, 48.3, and 90.7, respectively. In spring, the proportions of AQI for classes I, II, III, IV, V, and VI were 38.0%, 55.4%, 6.6%, 0%, 0%, and 0% respectively. In summer, the proportions of AQI for classes I, II, III, IV, V, and VI were 68.5%, 31.5%, 0%, 0%, 0%, and 0%, respectively. In autumn, the proportions of AQI for classes I, II, III, IV, V, and VI were 63.0%, 31.5%, 1.2%, 3.3%, 0%, and 0%, respectively. In winter, the proportions were 13.3%, 53.3%, 23.3%, 8.9%, 1.1%, and 0%, respectively.

Fig. 2(b). The proportions of the six AQI classes for Chengdu in spring, summer, autumn, and winter in 2021.Fig. 2(b). The proportions of the six AQI classes for Chengdu in spring, summer, autumn, and winter in 2021.

By comparing the AQI distribution in Chengdu for 2021 and 2020, it was found that the AQI distribution for both spring and summer in 2021 was basically the same as the distribution in 2020. However, the AQI distribution for the autumn and winter in 2021 was somewhat different from those in 2020. In the autumn of 2021, there were three consecutive days of severe pollution with AQI for class IV, but no such cases happened in the autumn of 2020. In addition, the proportion of AQI > 150 in winter 2021 (10.0%) is significantly higher than that in winter 2020 (4.4%).

The AQI distribution for 2020 and 2021 in another important city in southwest China, Chongqing, is shown in Figs. 2(c) and 2(d).

Fig. 2(c). The proportions of the six AQI classes for Chongqing in spring, summer, autumn, and winter in 2020.Fig. 2(c). The proportions of the six AQI classes for Chongqing in spring, summer, autumn, and winter in 2020.

Fig. 2(d). The proportions of the six AQI classes for Chongqing in spring, summer, autumn, and winter in 2021.Fig. 2(d). The proportions of the six AQI classes for Chongqing in spring, summer, autumn, and winter in 2021.

The AQI distribution for the four seasons in Chongqing in 2020 is shown in Fig. 2(c). In spring, summer, fall and winter the daily AQI ranged between 21 and 89, 15 and 78, 14 and 104, and 31 and 190, and seasonally averaged 52.7, 38.9, 43.1, and 71.6, respectively. Furthermore, in spring, the proportions of AQI for classes I, II, III, IV, V, and VI were 38%, 62%, 0%, 0%, 0%, and 0%, respectively. In summer, the proportions of AQI for classes I, II, III, IV, V, and VI were 81.5%, 18.5%, 0%, 0%, 0%, and 0%, respectively. In autumn, the proportions of AQI for classes I, II, III, IV, V, and VI were 67.0%, 31.9%, 1.1%, 0%, 0%, and 0%, respectively. In winter, the proportions were 22.0%, 64.8%, 12.1%, 1.1%, 0%, and 0%, respectively.

In order to compare the changes of AQI in 2020 and 2021, the distribution of AQI in Chongqing for the four seasons in 2021 is shown in the Fig. 2(d). The data in spring, summer, fall, and winter showed that the daily AQI ranged between 21 and 75, 17 and 72, 17 and 112, and 23 and 151, and seasonally averaged 46.1, 36.7, 47.0, and 80.6, respectively. Further examination of the AQI distribution over the four seasons revealed that the proportions of AQI for classes I, II, III, IV, V, and VI were 63%, 37%, 0%, 0%, 0%, and 0% respectively in spring. In summer, the proportions of AQI for classes I, II, III, IV, V, and VI were 84.8%, 15.2%, 0%, 0%, 0%, and 0%, respectively. In autumn, the proportions of AQI for classes I, II, III, IV, V, and VI were 63.0%, 32.6%, 3.3%, 0%, 0%, and 0%, respectively. In winter, the proportions were 15.6%, 55.6%, 26.7%, 2.1%, 0%, and 0%, respectively.

The analysis of AQI in Chongqing for 2020 and 2021 showed that the AQI was significantly better in spring and summer than that of in autumn and winter. Furthermore, by comparing the AQI for both years, it was found that the AQI for spring and summer of 2021 (seasonal averaged values were 46.1 and 36.7) was better than that of spring and summer of 2020 (seasonally averaged values were 52.7 and 38.9). Conversely, the AQI in the autumn and winter of 2021 were higher than that of the autumn and winter of 2020. It was studied that the proportion of AQI > 100 was significantly higher in autumn and winter of 2021 (3.3% and 28.8%) than that of autumn and winter of 2020 (1.1% and 12.2%).

By comparing the AQI distribution of Chengdu and Chongqing in 2020 and 2021, it was found that the air quality in Chongqing was better than that of in Chengdu. The seasonal averages of AQI in Chongqing in 2020 and 2021 were better than that of in Chengdu. Furthermore, Chongqing had AQI values below 100 in the spring of both 2021 and 2020, while Chengdu had AQI > 100 in the spring of both 2020 and 2021. In the winter of both 2020 and 2021, Chengdu had an AQI > 200 in some days, indicating a severe air pollution. However, Chongqing did not experience these phenomena in the winter. In addition, it was observed that the seasonal AQI levels in both cities were arranged in the order as follows: winter > spring > autumn > summer, suggesting that the air quality levels were significantly better in summer than that of in winter. The reason for this phenomenon could be attributed to the fact that Chengdu is located in the Sichuan basin, which is surrounded by mountains and has a high frequency of static winds throughout the year, and thus predisposing the city to severe air pollution events on a regional scale (Ning et al., 2018; Cao et al., 2020). Previous studies have also shown that the Sichuan basin could experience several severe haze events in winter, which were inextricably linked to the high intensity of local pollutant emissions, complex topography, and high population density (Wu et al., 2021).

Moreover, the comparison of data showed that both Chengdu and Chongqing had better AQI in the winter of 2020 than that of in the winter of 2021, due to the influence of the strict epidemic policy, which was consistent with the data published by the Chinese Ministry of Ecology and Environment. However, we would further explore the seasonal concentration changes of some common pollutants from 2020 to 2021, as well as the temporal concentration changes caused by the epidemic, and analyze the degree of contribution of each pollutant to the AQI.

 
3.2 PM2.5 Concentration

Ambient fine particulate matter air pollution (PM2.5) is generally a major risk factor for illness and death (Apte et al., 2015). The World Health Organization (WHO) has classified PM2.5 as a Group 1 carcinogen in 2013 (WHO, 2017). Commonly, PM2.5 comes from various industrial processes such as power generation, metallurgy, petroleum, chemistry, textile printing and dyeing, as well as smoke and dust emission during fuel combustion in the heating and cooking processes. In addition, exhaust emissions into the atmosphere from various types of transportation (using fuel) in the process of operation are also an important source of PM2.5.

As shown in Fig. 3(a), the daily ranges of PM2.5 concentration in Chengdu from January to December 2020 were between 41 and 118, 14 and 107, 9 and 75, 7 and 93, 12 and 73, 10 and 53, 4 and 40, 4 and 53, 13 and 52, 6 and 94, 14 and 103, and 19 and 188 µg m–3, and the averages were 68.3, 50.9, 39.3, 38.5, 40.5, 28.0, 21.0, 23.0, 23.8, 29.7, 55.7, and 62.3 µg m–3, respectively.

Fig. 3. The monthly average PM2.5 concentration (a) in Chengdu in 2020; (b) in Chengdu in 2021; (c) in Chongqing in 2020; (d) in Chongqing in 2021.Fig. 3. The monthly average PM2.5 concentration (a) in Chengdu in 2020; (b) in Chengdu in 2021; (c) in Chongqing in 2020; (d) in Chongqing in 2021.

As shown in Fig. 3(b), the daily range of PM2.5 concentrations in Chengdu from January to December 2021 were between 27 and 119, 8 and 150, 17 and 76, 6 and 66, 12 and 57, 9 and 68, 7 and 51, 10 and 37, 6 and 34, 4 and 51, 18 and 137, and 28 and 155 µg m–3, and the averages were 73.7, 60.3, 48.8, 28.2, 37.2, 31.5, 21.4, 17.6, 18.8, 23.5, 50.0, and 64.4 µg m–3, respectively.

The analysis of the monthly average PM2.5 concentrations in Chengdu in 2020 and 2021 showed that the PM2.5 concentrations in winter of 2021 (73.7, 60.3, and 64.4 µg m–3 in January, February and December, respectively) were significantly higher than that of the PM2.5 concentrations in winter of 2020 (68.3, 50.9, and 62.3 µg m–3, respectively). In other seasons, there was no much difference in the monthly average PM2.5 concentrations in Chengdu both in 2020 and 2021, which indicated that PM2.5 was an important influencing factor for the change of air quality in winter. In addition, during the calculation of AQI, it was found that PM2.5 was the dominant pollutant which could lead to the higher AQI in winter, and its higher concentration was the main reason for the high incidence of respiratory diseases in winter (Sancini et al., 2014).

In addition, the monthly average distribution of PM2.5 concentrations in Chongqing in 2020 is shown in the Fig. 3(c). The daily ranges PM2.5 concentrations in Chongqing from January to December were 21 and 90, 19 and 85, 14 and 66, 14 and 61, 14 and 45, 13 and 39, 8 and 24, 8 and 30, 9 and 35, 8 and 65, 15 and 68, and 22 and 143 µg m–3, and the averages were 49.5, 48.9, 36.4, 32.4, 27.8, 22.0, 15.9, 17.5, 18.1, 25.8, 36.1, and 56.0 µg m–3, respectively.

For the purpose of comparison, the monthly average distribution of PM2.5 concentrations in Chongqing in 2021 is shown in the Fig. 3(d). The daily ranges of PM2.5 concentrations in Chongqing from January to December were 38 and 115, 15 and 92, 17 and 54, 12 and 35, 14 and 50, 13 and 48, 12 and 26, 9 and 30, 12 and 34, 9 and 39, 18 and 84, and 25 and 115 µg m–3, and the averages were 64.8, 52.4, 31.3, 24.5, 29.2, 25.0, 16.8, 17.2, 19.1, 23.5, 47.1, and 59.4 µg m–3, respectively.

It was observed that the monthly average of PM2.5 concentrations in both cities were lower in January and February 2020 than in 2021. This was mainly because of the reason that the winter of 2020 was the worst period of the epidemic, when a large number of factories were closed for holidays and residents largely did not leave their homes under a strict epidemic prevention policy. As a result, PM2.5 pollution from factory emissions, from gasoline and from diesel transportation was greatly reduced (Rodríguez-Urrego et al., 2020; Chauhan and Singh, 2020). Due to the effective control of the epidemic by the government, the epidemic control policy was much more relaxed in 2021 when compared to 2020, so there was a significant increase in PM2.5 monthly concentration in the winter of 2021.

 
3.3 NO2 Concentration

NO2 in the air is closely related to man-made pollution through the daily life activities such as factory production and road traffic, which mainly comes from industrial gas emissions, chemical fuel combustion, and vehicle exhaust from transportation (Li et al., 2015). It is a known fact that NO2 pollution plays an important role in air pollution and NO2 is not only the main primary pollutant, but also can be transformed into many secondary pollutants under photochemical reaction conditions (Liu et al., 2016). NO2 has a strong irritating effect on the human respiratory mucosa, and its harmful effects on the lungs are significantly higher than that of those of SO2 and NO, which would lead to emphysema and even death in serious cases (Lamsal et al., 2013; Ogen, 2020).

As shown in Fig. 4(a), the daily ranges of NO2 concentrations in Chengdu from January to December 2020 were 13 and 85, 7 and 42, 12 and 63, 19 and 92, 20 and 65, 17 and 58, 10 and 54, 14 and 47, 18 and 52, 13 and 63, 21 and 72, and 23 and 69 ppb, and the averages were 41.1, 21.2, 38.9, 42.9, 39.5, 31.2, 28.0, 27.7, 32.6, 34.4, 49.9, and 45.1 ppb, respectively.

Fig. 4. The monthly averaged NO2 concentration (a) in Chengdu in 2020; (b) in Chengdu in 2021; (c) in Chongqing in 2020; (d) in Chongqing in 2021.
Fig. 4. The monthly averaged NOconcentration (a) in Chengdu in 2020; (b) in Chengdu in 2021; (c) in Chongqing in 2020; (d) in Chongqing in 2021.

As shown in Fig. 4(b), the daily ranges of NO2 concentrations in Chengdu from January to December 2021 were 27 and 74, 18 and 66, 21 and 67, 15 and 51, 15 and 46, 17 and 60, 16 and 37, 14 and 36, 12 and 32, 13 and 42, 21 and 57, and 21 and 72 ppb, and the averages were 50.5, 36.2, 43.9, 30.6, 32.7, 36.0, 25.4, 22.9, 22.8, 29.4, 38.2, and 46.9 ppb, respectively.

In addition, the monthly average distribution of NO2 concentrations in Chongqing in 2020 is shown in the Fig. 4(c). The daily ranges of NO2 concentrations in Chongqing from January to December were 16 and 60, 9 and 31, 22 and 58, 27 and 74, 24 and 59, 25 and 62, 22 and 47, 17 and 44, 25 and 50, 24 and 74, 28 and 76, and 32 and 65 ppb, and the averages were 37.9, 22.9, 35.8, 47.7, 40.7, 37.2, 31.5, 29.9, 37.0, 39.6, 49.7, and 45.2 ppb, respectively.

As shown in Fig. 4(d), the daily ranges of NO2 concentrations in Chongqing from January to December 2021 were 29 and 72, 18 and 52, 23 and 57, 21 and 40, 18 and 46, 17 and 48, 18 and 31, 15 and 31, 17 and 36, 18 and 44, 15 and 48, and 21 and 57 ppb, and the averages were 46.6, 35.1, 36.6, 30.5, 31.6, 29.0, 23.3, 21.8, 24.7, 25.8, 35.8, and 39.9 ppb, respectively.

From Fig. 4, it was obvious that both Chengdu and Chongqing showed a significant decrease in the monthly average of NO2 concentration in February 2020. This was because of the reason that February 2020 was the most serious period of the epidemic, coinciding with the Chinese New Year holiday, when a large number of enterprises shut down their production, people across the country were isolated at home, and all kinds of transportation vehicles were restricted from traveling, which largely curbed the NO2 emissions. During the epidemic, similar conditions were observed in several places around the world (Oo et al., 2021; Santoso et al., 2021). Then most plants started to resume work and production on a large scale in March and April 2020 when the worst of the epidemic was passed and so the monthly average concentration of NO2 increased again.

In February 2021, the epidemic prevention measures were relatively relaxed, and hence the monthly average NO2 concentration was increased significantly when compared with February 2020, which showed that the impact of the epidemic on the production and life of Chinese society was huge and was closely related to NO2 emissions. In addition, the variation of NO2 concentration was more influenced by the COVID-19 lockdown policy when compared to PM2.5. This result suggested that the sources of NO2 emissions were mainly from various types of factories and motor vehicle exhaust. In contrast, PM2.5 was still the main cause of winter air pollution in February 2020, although its concentration was decreased when compared to February 2021.

In addition, the analysis of the NO2 concentrations distribution in Chengdu and Chongqing throughout the year revealed that the NO2 concentration in summer was significantly lower than that of in winter. In winter, the operation of heating equipment caused more energy being consumed by combustion, and thus the thermal power generation as well as industrial fuel combustion have contributed more to NO2 emissions than that of vehicle exhaust. Hence, this was the main reason for elevated NO2 in winter.

 
3.4 O3 Concentration

O3, a pale blue gas with a distinctive odor, is one of the major pollutants that produce photochemical smog and it is an important greenhouse gas (Bojkov, 1986; Worden et al., 2008). It was studied that it could have serious effects on climate, ecosystems, and the health of living organisms when the O3 concentrations near the ground are too high (Nuvolone et al., 2018). In fact, O3 exposure can cause damage to the respiratory and cardiovascular systems of sensitive populations (elderly or children). High levels of O3 can even trigger the symptoms such as coughing and breathing difficulties and lead to an increased incidence of respiratory diseases such as asthma and chronic obstructive pulmonary disease (Yang et al., 2012; Bell et al., 2006).

The distribution of O3 concentrations in Chengdu and Chongqing in 2020 and 2021 is shown in Fig. 5. As shown in Fig. 5(a), the daily ranges of O3 concentrations in Chengdu from January to December 2020 were 7 and 69, 28 and 69, 22 and 80, 29 and 117, 35 and 138, 42 and 134, 20 and 113, 34 and 113, 27 and 77, 12 and 53, 6 and 39, and 6 and 35 ppb, and the averages were 21.7, 48.8, 50.3, 63.6, 92.2, 86.2, 66.5, 70.4, 48.7, 28.7, 24.1, and 15.2 ppb, respectively.

Fig. 5. The monthly average O3 concentration (a) in Chengdu in 2020; (b) in Chengdu in 2021; (c) in Chongqing in 2020; (d) in Chongqing in 2021.Fig. 5. The monthly average Oconcentration (a) in Chengdu in 2020; (b) in Chengdu in 2021; (c) in Chongqing in 2020; (d) in Chongqing in 2021.

As shown in Fig. 5(b), the daily ranges of O3 concentrations in Chengdu from January to December 2021 were 9 and 45, 12 and 63, 13 and 83, 24 and 102, 38 and 120, 29 and 120, 46 and 121, 33 and 138, 35 and 87, 13 and 69, 5 and 45, and 7 and 40 ppb, and the averages were 26.1,50.5, 43.1, 52.0, 72.5, 71.2, 81.8, 73.3, 59.7, 29.3, 22.5, and 19.7 ppb, respectively.

In addition, the monthly average distribution of O3 concentrations in Chongqing in 2020 is shown in the Fig. 5(c). The daily ranges of O3 concentrations in Chongqing from January to December were 6 and 40, 9 and 63, 12 and 84, 21 and 85, 30 and 114, 25 and 118, 16 and 93, 51and 116, 10 and 70, 5 and 32, 5 and 42, and 5 and 21 ppb, and the averages were 18.6, 32.2, 44.5, 46.3, 70.9, 56.1, 48.2, 78.9, 40.5, 16.3, 22.7, and 11.0 ppb, respectively.

As shown in Fig. 5(d), the daily ranges of O3 concentrations in Chongqing from January to December 2021 were 7 and 37, 10 and 66, 13 and 59, 12 and 63, 16 and 100, 33 and 96, 27 and 115, 30 and 125, 33 and 79, 21 and 90, 9 and 42, and 8 and 37 ppb, and the averages were 18.8, 35.4, 32.2, 35.5, 48.3, 56.6, 64.4, 61.5, 56.6, 35.9, 19.8, and 19.7 ppb, respectively.

he distribution of O3 concentrations in Chengdu and Chongqing showed that the highest O3 concentrations occurred from May to August, with a significant decrease in January, October, November and December. This was due to the reason that in the troposphere, the formation of O3 was mainly due to the photochemical reactions of NOx. It was hypothesized that in the atmosphere, NO2 would generate O3 by photolysis reaction (λ ≤ 424 nm), and O3 would further react with NO to form NO2 and O2, and thus it would not lead to the accumulation of O3 near the ground (Fishman and Seiler, 1983). However, in summer, however, normally the photochemical reactions would be dominated by intense UV light and high temperature, and thus facilitating the accumulation of O3 near the ground (Xu et al., 2020). In addition, in the summer (sunny weather), in the strongest ultraviolet time mainly between 12:00 to 16:00, due to high temperature, low relative humidity, and weak wind transport capacity the ozone pollution would more likely to occur (Sartor et al., 1995). However, the photochemical reactions do not play a dominant role while the solar radiation ability is weak in winter, and hence the higher NOx concentration would promote the consumption of O3. So, the O3 concentration would be significantly lower in winter than that of in summer.

Furthermore, it was known that the volatile organic compounds (VOCs) are also an important factor affecting the O3 production. The VOCs will be oxidized to peroxy radicals (HO2 and RO2) when VOCs are present in the atmosphere, and these radicals will react competitively with O3, and thus disrupting the reaction balance between NOx and O3, which in turn will leads to the accumulation of O3 and pollution (Wang et al., 2017). In previous studies it was also reported that the formation of O3 depended on the ratio of VOCs–NOx (Pusede and Cohen, 2012). As it can be seen from the Fig. 5, the monthly average O3 concentrations in Chengdu and Chongqing during the 2020 epidemic (March–June) were observed to be higher than that of those in 2021, and O3 pollution was more severe particularly in Chengdu. From Fig. 4 it was observed that there was a significant reduction in the monthly average concentrations of NO2 due to the epidemic lockdown policy, but however, this did not imply a reduction in O3 generation. At the same time, due to strict quarantine measures of the epidemic, the long-term isolation of urban residents cooking at home could enhance the emissions of VOCs and CO, which caused the VOCs–NOx emissions ratio out of balance, and thus aggravating the O3 pollution (Sicard et al., 2020). It was identified that the concentrations of O3 showed a complex non-linear relationship with NOx and VOCs emissions and it was mainly dependent on the VOCs–NOx ratio. Hence, it was suggested that the multiple influencing factors must be considered for the treatment of O3 pollution (Liu et al., 2021).

 
3.5 Dry Deposition Flux of PCDD/Fs

As shown in Fig. 6(a), the monthly dry deposition flux of Total-PCDD/Fs-WHO2005-TEQ in Chengdu from January to December 2020 was12.5, 9.3, 10.6, 9.4, 10.7, 7.0, 5.4, 5.5, 5.7, 7.2, 12.3, and 13.1 ng m2 month1. As shown in Fig. 6(b), the monthly dry deposition flux of Total-PCDD/Fs-WHO2005-TEQ in Chengdu from January to December 2021 was 15.6, 10.9, 13.6, 6.8, 8.7, 7.3, 5.2, 4.5, 4.4, 5.5, 10.8, and 17.5 ng m2 month1.

Fig. 6. The monthly dry deposition flux of total-PCDD/Fs-WHO2005-TEQ (a) in Chengdu in 2020; (b) in Chengdu in 2021; (c) in Chongqing in 2020; (d) in Chongqing in 2021.Fig. 6. The monthly dry deposition flux of total-PCDD/Fs-WHO2005-TEQ (a) in Chengdu in 2020; (b) in Chengdu in 2021; (c) in Chongqing in 2020; (d) in Chongqing in 2021.

Furthermore, as shown in Fig. 6(c), the monthly dry deposition flux of Total-PCDD/Fs-WHO2005-TEQ in Chongqing from January to December 2020 was 9.7, 8.5, 8.6, 7.8, 7.5, 5.6, 4.6, 5.3, 4.6, 6.6, 9.1, and 12.0 ng m2 month1. As shown in Fig. 6(d), the monthly dry deposition flux of Total-PCDD/Fs-WHO2005-TEQ in Chongqing from January to December 2021 was 413.4, 9.4, 8.2, 5.9, 7.0, 5.9, 4.5, 4.5, 4.9, 5.6, 10.1, and 12.1 ng m2 month1.

Analysis of the dry deposition distribution of PCDD/Fs in Chengdu and Chongqing revealed hat the dry deposition flux of PCDD/Fs generally showed a trend of the highest in winter and the lowest in summer, which is consistent with the monthly average concentration distribution of PM2.5 and NO2. Similarly, the elevated dry deposition flux of PCDD/Fs in winter was caused by the cold weather leading to the opening of a large number of heating facilities and therefore there would be more fossil fuel combustion, leading to the increased particulate emissions (Cheruiyot et al., 2016). The dry deposition flux of PCDD/Fs was primarily from the particulate phase, and so the increase of particulate emissions inevitably led to an increase in dry deposition flux of PCDD/Fs (Yu et al., 2021). In addition, another trend was also observed: the dry deposition flux of PCDD/Fs in 2020 was significantly lower than that of in 2021 due to the strict epidemic control. This was because of the reason that a large number of industrial production activities, such as pharmaceutical industry, metal smelting, and waste-to-energy incineration, were forced to be affected during the outbreak closure, and thus reduced the pollutant emissions (Tian et al., 2021b; Liu et al., 2013). In addition, the quarantine of residents further reduced PCDD/F originating from exhaust emissions from vehicles. In 2021, the epidemic lockdown policy was lifted, a large number of factories resumed work and production, and urban life returned to normal, and so the dry deposition flux of PCDD/Fs was again increased in 2021 when compared to 2020.

 
4 CONCLUSION


  1. Both Chongqing and Chengdu regions showed the better air quality in spring and summer than that of in autumn and winter both in 2020 and 2021. In addition, Chongqing had better air quality than Chengdu, which has repeatedly experienced heavy pollution with AQI > 200, due to its geographical location. The impact of the epidemic blockade led to the significant improvements in air quality in Chengdu and Chongqing in 2020. In the epidemic era, as the economy recovered and industries began to resume work and production, the air quality again declined, indicating the negative correlation of dependence between economic development and air quality.

  2. Attributed to the epidemic quarantine measures, PM2.5 pollution from factory emissions, gasoline and diesel transportation was greatly reduced in January to March 2020, resulting into a significant reduction in PM2.5 concentrations. As the economy recovered, the PM2.5 concentrations was again rebounded in the post-epidemic era.

  3. The steep drop in monthly average NO2 concentrations in February 2020 significantly reflected the impact of the lockdown on air quality. The quarantine measures limited the use of the most fuel-fired transportation, and thus leading to a decrease in NO2 concentrations. The rebounded NO2 monthly average concentrations in 2021 clearly demonstrated that the social activities have returned to normal in the post-epidemic era and the economy was restarted and thus the air pollution was increased.

  4. During the outbreak period of epidemic in 2020, O3 concentrations increased rather than decreasing, and thus O3 pollution was increased significantly. This anomaly was due to the complex non-linear relationship between O3 concentration and VOCs–NOx emissions, and it was mainly influenced by the VOCs–NOx ratio. Although the NOx emissions were reduced, the large number of cooking activities during the isolation period increased the VOC emissions, and thus breaking the VOCs–NOx ratio, which in turn increased the O3 production.

  5. The PCDD/Fs dry deposition flux was also associated with control measures. This was due to the fact that a large number of industrial production activities such as pharmaceuticals, metal smelting and waste incineration were forced to be affected during the epidemic closure, and thus reduced the pollutant emissions. In 2021, the economic recovery will inevitably require the expansion of emissions from the above plants, leading to the increased dry deposition flux of PCDD/Fs again.

  6. PM2.5, NO2 and PCDD/Fs dry deposition flux showed a trend of the highest in winter and the lowest in summer, while ozone showed a trend of the highest in summer and the lowest in winter. In winter PM2.5 and NO2 were the main influencing factors for elevated AQI, while in summer the dominant factor for AQI was O3. Therefore, in order to improve the air quality, it should be focused on the seasonal characteristic pollutants for different seasons, and all kinds of complex human and meteorological factors should also be taken into the account.

  7. The urban lockdown caused by the epidemic surprisingly resulted into a improvement in air quality, but however at the cost of a heavy blow to the economy only. Thus, it was suggested that in the post-epidemic era, the epidemic period could be considered or studied as a lesson to understand the relationship between the economic development and the air pollution and also to find a balance between the both.


REFERENCES


  1. Apte, J.S., Marshall, J.D., Cohen, A.J., Brauer, M. (2015). Addressing global mortality from ambient PM2.5. Environ. Sci. Technol. 49, 8057–8066. https://doi.org/10.1021/acs.est.5b01236

  2. Bell, M.L., Peng, R.D., Dominici, F. (2006). The exposure-response curve for ozone and risk of mortality and the adequacy of current ozone regulations. Environ. Health Perspect. 114, 532–536. https://doi.org/10.1289/ehp.8816

  3. Bojkov, R.D. (1986). Surface ozone during the second half of the nineteenth century. J. Appl. Meteorol. Climatol. 25, 343–352. https://doi.org/10.1175/1520-0450(1986)025<0343:SODTS​H>2.0.CO;2

  4. Bruno, F., Cocchi, D. (2002). A unified strategy for building simple air quality indices. Environmetrics 13, 243–261. https://doi.org/10.1002/env.512

  5. Cao, B., Wang, X., Ning, G., Yuan, L., Jiang, M., Zhang, X., Wang, S. (2020). Factors influencing the boundary layer height and their relationship with air quality in the Sichuan Basin, China. Sci. Total Environ. 727, 138584. https://doi.org/10.1016/j.scitotenv.2020.138584

  6. Chauhan, A., Singh, R.P. (2020). Decline in PM2.5 concentrations over major cities around the world associated with COVID-19. Environ. Res. 187, 109634. https://doi.org/10.1016/j.envres.​2020.109634

  7. Cheruiyot, N.K., Lee, W.J., Yan, P., Mwangi, J.K., Wang, L.C., Gao, X., Lin, N.H., Chang-Chien, G.P. (2016). An overview of PCDD/F inventories and emission factors from stationary and mobile sources: What we know and what is missing. Aerosol Air Qual. Res. 16, 2965–2988. https://doi.org/10.4209/aaqr.2016.10.0447

  8. Fishman, J., Seiler, W. (1983). Correlative nature of ozone and carbon monoxide in the troposphere: Implications for the tropospheric ozone budget. J. Geophys. Res. Oceans. 88, 3662–3670. https://doi.org/10.1029/JC088iC06p03662

  9. Flesch-Janys, D., Berger, J., Gum, P., Manz, A., Nagel, S., Waltsgott, H., Dwyer, J.H. (1995). Exposure to polychlorinated dioxins and furans (PCDD/F) and mortality in a cohort of workers from a herbicide-producing plant in Hamburg, Federal Republic of Germany. Am. J. Epidemiol. 142, 1165–1175. https://doi.org/10.1093/oxfordjournals.aje.a117575

  10. Guo, Z., Xie, H.Q., Zhang, P., Luo, Y., Xu, T., Liu, Y., Zhao, B. (2018). Dioxins as potential risk factors for autism spectrum disorder. Environ. Int. 121, 906–915. https://doi.org/10.1016/j.envint.​2018.10.028

  11. Hu, J., Ying, Q., Wang, Y., Zhang, H. (2015). Characterizing multi-pollutant air pollution in China: Comparison of three air quality indices. Environ. Int. 84, 17–25. https://doi.org/10.1016/j.​envint.2015.06.014

  12. Huang, C.J., Chen, K.S., Lai, Y.C., Wang, L.C., Chang-Chien, G.P. (2011). Characterization of atmospheric dry deposition of polychlorinated dibenzo-p-dioxins/dibenzofuran in a rural area of Taiwan. Aerosol Air Qual. Res. 11, 448–459. https://doi.org/10.4209/aaqr.2011.03.0033

  13. Huwe, J.K. (2002). Dioxins in food: A modern agricultural perspective. J. Agric. Food Chem. 50, 1739–1750. https://doi.org/10.1021/jf011265f

  14. Kampa, M., Castanas, E. (2008). Human health effects of air pollution. Environ. Pollut. 151, 362–367. https://doi.org/10.1016/j.envpol.2007.06.012

  15. Lamsal, L.N., Martin, R.V., Parrish, D.D., Krotkov, N.A. (2013). Scaling relationship for NO2 pollution and urban population size: A satellite perspective. Environ. Sci. Technol. 47, 7855–7861. https://doi.org/10.1021/es400744g

  16. Lee, K.L., Lee, W.J., Mwangi, J.K., Wang, L.C., Gao, X., Chang-Chien, G.P. (2016). Atmospheric PM2.5 and depositions of polychlorinated dibenzo-p-dioxins and dibenzofurans in Kaohsiung area, southern Taiwan. Aerosol Air Qual. Res. 16, 1775–1791. https://doi.org/10.4209/aaqr.2016.​04.0168

  17. Li, A., Zhang, J., Xie, P., Hu, Z., Xu, J., Mou, F., Liu, W. (2015). Variation of temporal and spatial patterns of NO2 in Beijing using OMI and mobile DOAS. Sci. China Chem. 58, 1367–1376. https://doi.org/10.1007/s11426-015-5459-x

  18. Liu, C., Henderson, B. H., Wang, D., Yang, X., Peng, Z.R. (2016). A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China. Sci. Total Environ. 565, 607–615. https://doi.org/10.1016/j.scitotenv.2016.03.189

  19. Liu, G., Zheng, M., Jiang, G., Cai, Z., Wu, Y. (2013). Dioxin analysis in China. Trends Analyt. Chem. 46, 178–188. https://doi.org/10.1016/j.trac.2012.05.012

  20. Liu, Y., Wang, T., Stavrakou, T., Elguindi, N., Doumbia, T., Granier, C., Brasseur, G.P. (2021). Diverse response of surface ozone to COVID-19 lockdown in China. Sci. Total Environ. 789, 147739. https://doi.org/10.1016/j.scitotenv.2021.147739

  21. Ning, G., Wang, S., Ma, M., Ni, C., Shang, Z., Wang, J., Li, J. (2018). Characteristics of air pollution in different zones of Sichuan Basin, China. Sci. Total Environ. 612, 975–984. https://doi.org/​10.1016/j.scitotenv.2017.08.205

  22. Nuvolone, D., Petri, D., Voller, F. (2018). The effects of ozone on human health. Environ. Sci. Pollut. Res. 25, 8074–8088. https://doi.org/10.1007/s11356-017-9239-3

  23. Ogen, Y. (2020). Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality. Sci. Total Environ. 726, 138605. https://doi.org/10.1016/j.scitotenv.2020.​138605

  24. Oo, T.K., Arunrat, N., Kongsurakan, P., Sereenonchai, S., Wang, C. (2021). Nitrogen Dioxide (NO2) level changes during the control of COVID-19 pandemic in Thailand. Aerosol Air Qual. Res. 21, 200440. https://doi.org/10.4209/aaqr.200440

  25. Pirkle, J.L., Wolfe, W.H., Patterson, D.G., Needham, L.L., Michalek, J.E., Miner, J.C., Phillips, D.L. (1989). Estimates of the half-life of 2,3,7,8-tetrachlorodibenzo-p-dioxin in Vietnam veterans of Operation Ranch Hand. J. Toxicol. Environ. Health Part A 27, 165–171. https://doi.org/10.1080/​15287398909531288

  26. Pusede, S.E., Cohen, R.C. (2012). On the observed response of ozone to NOx and VOC reactivity reductions in San Joaquin Valley California 1995-present. Atmos. Chem. Phys. 12, 8323–8339. https://doi.org/10.5194/acp-12-8323-2012

  27. Rodríguez-Urrego, D., Rodríguez-Urrego, L. (2020). Air quality during the COVID-19: PM2.5 analysis in the 50 most polluted capital cities in the world. Environ. Pollut. 266, 115042. https://doi.org/​10.1016/j.envpol.2020.115042

  28. Rordorf, B.F. (1989). Prediction of vapor pressures, boiling points and enthalpies of fusion for twenty-nine halogenated dibenzo-p-dioxins and fifty-five dibenzofurans by a vapor pressure correlation method. Chemosphere 18, 783–788. https://doi.org/10.1016/0045-6535(89)90196-3

  29. Sancini, G., Farina, F., Battaglia, C., Cifola, I., Mangano, E., Mantecca, P., Palestini, P. (2014). Health risk assessment for air pollutants: Alterations in lung and cardiac gene expression in mice exposed to Milano winter fine particulate matter (PM2.5). PLoS One 9, e109685. https://doi.org/​10.1371/journal.pone.0109685

  30. Santoso, M., Hopke, P.K., Permadi, D.A., Damastuti, E., Lestiani, D.D., Kurniawati, S., Khoerotunnisya, D., Sukir, S.K. (2021). Multiple air quality monitoring evidence of the impacts of large-scale social restrictions during the COVID-19 pandemic in Jakarta, Indonesia. Aerosol Air Qual. Res. 21, 200645. https://doi.org/10.4209/aaqr.200645

  31. Sartor, F., Snacken, R., Demuth, C., Walckiers, D. (1995). Temperature, ambient ozone levels, and mortality during summer, 1994, in Belgium. Environ. Res. 70, 105–113. https://doi.org/10.1006/​enrs.1995.1054

  32. Schecter, A., Birnbaum, L., Ryan, J.J., Constable, J.D. (2006). Dioxins: An overview. Environ. Res. 101, 419–428. https://doi.org/10.1016/j.envres.2005.12.003

  33. Shen, F., Ge, X., Hu, J., Nie, D., Tian, L., Chen, M. (2017). Air pollution characteristics and health risks in Henan Province, China. Environ. Res. 156, 625–634. https://doi.org/10.1016/j.envres.​2017.04.026

  34. Sheu, H.L., Lee, W.J., Su, C.C., Chao, H.R., Fan, Y.C. (1996). Dry deposition of polycyclic aromatic hydrocarbons in ambient air. J. Environ. Eng. 122, 1101–1109. https://doi.org/10.1061/​(ASCE)0733-9372(1996)122:12(1101)

  35. Shih, M., Lee, W.S., Chang-Chien, G.P., Wang, L.C., Hung, C.Y., Lin, K.C. (2006). Dry deposition of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in ambient air. Chemosphere 62, 411–416. https://doi.org/10.1016/j.chemosphere.2005.04.064

  36. Shooter, D., Brimblecombe, P. (2009). Air quality indexing. Int. J. Environ. Pollut. 36, 305–323. https://doi.org/10.1504/IJEP.2009.021834

  37. Sicard, P., De Marco, A., Agathokleous, E., Feng, Z., Xu, X., Paoletti, E., Calatayud, V. (2020). Amplified ozone pollution in cities during the COVID-19 lockdown. Sci. Total Environ. 735, 139542. https://doi.org/10.1016/j.scitotenv.2020.139542

  38. Sun, Y., Lei, L., Zhou, W., Chen, C., He, Y., Sun, J., Worsnop, D.R. (2020). A chemical cocktail during the COVID-19 outbreak in Beijing, China: Insights from six-year aerosol particle composition measurements during the Chinese New Year holiday. Sci. Total Environ. 742, 140739. https://doi.org/10.1016/j.scitotenv.2020.140739

  39. Tian, X., Cui, K., Sheu, H.L., Hsieh, Y.K., Yu, F. (2021a). Atmospheric wet deposition of PCDD/Fs in the ambient air. Aerosol Air Qual. Res. 21, 210096. https://doi.org/10.4209/aaqr.210096

  40. Tian, X., Cui, K., Sheu, H.L., Hsieh, Y.K., Yu, F. (2021b). Effects of rain and snow on the air quality index, PM2.5 levels, and dry deposition flux of PCDD/Fs. Aerosol Air Qual. Res. 21, 210158. https://doi.org/10.4209/aaqr.2019.03.0112

  41. Wang, T., Xue, L., Brimblecombe, P., Lam, Y.F., Li, L., Zhang, L. (2017). Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects. Sci. Total Environ. 575, 1582–1596. https://doi.org/10.1016/j.scitotenv.2016.10.081

  42. Wong, C.M., Vichit-Vadakan, N., Kan, H., Qian, Z. (2008). Public Health and Air Pollution in Asia (PAPA): A multicity study of short-term effects of air pollution on mortality. Environ. Health Perspect. 116, 1195–1202. https://doi.org/10.1289/ehp.11257

  43. Worden, H.M., Bowman, K.W., Worden, J.R., Eldering, A., Beer, R. (2008). Satellite measurements of the clear-sky greenhouse effect from tropospheric ozone. Nat. Geosci. 1, 305–308. https://doi.org/10.1038/ngeo182

  44. World Health Organization (WHO) (2017). Evolution of WHO air quality guidelines: past, present and future. WHO Regional Office for Europe, Copenhagen. 

  45. Wu, X., Shi, G., Xiang, X., Yang, F. (2021). The characteristics of PM2.5 pollution episodes during 2016–2019 in Sichuan Basin, China. Aerosol Air Qual. Res. 21, 210126. https://doi.org/​10.4209/aaqr.210126

  46. Xu, K., Cui, K., Young, L.H., Wang, Y.F., Hsieh, Y.K., Wan, S., Zhang, J. (2020). Air quality index, indicatory air pollutants and impact of COVID-19 event on the air quality near central China. Aerosol Air Qual. Res. 20, 1204–1221. https://doi.org/10.4209/aaqr.2020.04.0139

  47. Yang, C., Yang, H., Guo, S., Wang, Z., Xu, X., Duan, X., Kan, H. (2012). Alternative ozone metrics and daily mortality in Suzhou: The China Air Pollution and Health Effects Study (CAPES). Sci. Total Environ. 426, 83–89. https://doi.org/10.1016/j.scitotenv.2012.03.036

  48. Yu, F., Cui, K., Sheu, H.L., Hsieh, Y.K., Tian, X. (2021). Atmospheric concentration, particle-bound content, and dry deposition of PCDD/Fs. Aerosol Air Qual. Res. 21, 210059. https://doi.org/​10.4209/aaqr.210059


Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

7.3
2022CiteScore
 
 
77st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

Call for Papers for the special issue on: "Carbonaceous Aerosols in the Atmosphere"

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.