Xiaodong Wu1, Guangming Shi This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Xing Xiang2, Fumo Yang1,2 

1 Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
2 National Engineering Research Center on Flue Gas Desulfurization, Chengdu, Sichuan 610065, China


Received: June 1, 2021
Revised: September 8, 2021
Accepted: September 8, 2021

 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.210126  


Cite this article:

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


HIGHLIGHTS

  • 1342 pollution episodes in Sichuan Basin were identified and analyzed.
  • Pollution events occurred more frequently in the southern and western basin area.
  • PM2.5 concentrations were related to pollution durations non-monotonically.
 

ABSTRACT


Many studies have been conducted to explore the characteristics of PM2.5 pollution events in Sichuan Basin, China. However, they focused on either specific regional pollution events from different aspects or the megacities, such as Chengdu and Chongqing. To provide a panorama gram of PM2.5 pollution episodes in the whole basin area, we identified all the PM2.5 pollution events in 17 cities during 2016-2019 and analyzed the characteristics of these events. In total, 1342 episodes were identified and the characteristics of episode numbers, durations and PM2.5 concentrations were analyzed in each city. We found that the characteristics of the temporal and spatial distribution of the episode numbers and durations were similar to the annual average of PM2.5 concentrations, which were higher in the Southern Sichuan and Western Sichuan Plain spatially and occurred most frequently in winter, followed by spring, autumn and summer. Non-monotonical relationships were obtained between the PM2.5 concentrations and pollution durations and there was a duration threshold in each city. For episodes with durations shorter than the threshold, their PM2.5 concentrations increased with duration. The duration thresholds were 6–8 days and 5–7 days in Southern Sichuan and Western Sichuan Plain, respectively. We also found that the air quality deteriorated in 2019 in most cities. Synthetically considering the numbers, durations and concentrations of pollution episodes, more concerns should be taken for the prevention of PM2.5 pollution in Yibin in the Southern Sichuan, Chengdu and Leshan in the Western Sichuan Plain, Neijiang in the Central Hills, and Bazhong, Dazhou, Nanchong in the Northeastern Sichuan. These results could help understanding the characteristics of PM2.5 episodes in Sichuan Basin and providing implications for pollution control strategies in future.


Keywords: Sichuan Basin, PM2.5 concentration, Pollution episode, Episode duration


1 INTRODUCTION


PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) is one of the most concerned air pollutants in recent years. PM2.5 would reduce the atmospheric visibility and furtherly increase the traffic risks. As suspended particles in the atmosphere, PM2.5 would play an important role in climate change through both directly scattering or absorbing incident solar energy and indirectly interacting with clouds. Above all, PM2.5 was deemed to be harmful to human health. According to the World Health Organization, the deaths caused by air pollution were approximately 7 million and the most mortality was attributed to the exposure to excessive concentration of PM2.5. The particles, as carriers of harmful substances, such as heavy metals, viruses and bacteria, can penetrate deeply into the lungs and cause diseases such as lung cancer, cardiopulmonary mortality, etc. (Fan et al., 2020; Pope et al., 2002). For example, Chen et al. (2020) found that coronavirus disease 2019 (COVID-19) saved lives by preventing exposure to air pollutants during the outbreak.

Along with the rapid industrialization and economic development, severe air pollution and negative public health effects became important environmental problems in China (Fu and Chen, 2017). Serious haze events with high PM2.5 concentrations, wide spatial coverages and long temporal durations have occurred persistently in China (An et al., 2019; Zhang et al., 2019). As an example, the nationwide haze event occurred in January 2013 affected about 800 million people and lasted close to 1 month (Zhang et al., 2014). To improve air quality and protect public health, the government of China has promulgated a series of plans, such as Air Pollution Prevention and Control Action Plan in 2013 and Three-year Action Plan to Fight Air Pollution in 2018. Number of air pollution prevention measures were implemented and the air quality was significantly improved (Wang et al., 2017; Xiao et al., 2020). Nevertheless, haze pollution may still occur more or less in different regions and needs to be addressed persistently in China.

Sichuan Basin is one of the four traditional hazy regions in China due to frequent stagnation and high relative humidity (RH) (Shi et al., 2019; Zhao et al., 2018a). The basin, located in southwestern China, is surrounded by the Tibetan Plateau to the west, the Wuling mountains to the east, the Daba Mountains to the north and the Yunnan-Guizhou Plateau to the south. Exceeding 83 million people reside in the basin and the Gross Regional Product was more than 40678 trillion-yuan RMB in 2018 (Sichuan Provincial Bureau of Statistics, 2019). The intensive air pollutant emissions accompanied with adverse meteorological conditions and complex topography led to frequently occurred severe haze events characterized by high PM2.5 concentrations. Many studies have analyzed typical haze events in Sichuan Basin. Such studies focused on the variations of concentration and composition of PM2.5 either during specific events (Qiao et al., 2019a; Zhou et al., 2019a) or on a longer time scale (Huang et al., 2018; Wu et al., 2019; Zeng and Zheng, 2019). However, most of these studies were conducted in Chengdu and Chongqing, the two megacities in Sichuan Basin (Cai et al., 2018; Chen et al., 2015; Li et al., 2017; Li et al., 2016; Liao et al., 2017; Tian et al., 2019; Zhang et al., 2017; Zhou et al., 2017), and the concerns about the characteristics of other cities were relatively scarce (Zhao et al., 2018a; Zhao et al., 2018b).

In this study, we identified all the PM2.5 pollution episodes during 2016-2019 in 17 cities within Sichuan Basin. The occurrence frequency, pollution degrees and time durations of these episodes were analyzed on an inter- and intra-annual scale, and were compared between different cities. This comprehensive investigation could provide a panorama gram of PM2.5 pollution episodes in Sichuan Basin, which might be helpful for assessing the past emission control measures and implicating the future strategies.

 
2 DATA AND METHODS


 
2.1 Study Region

The study region covers the basin bottom of Sichuan Basin, including 17 cities of Sichuan Province (Fig. 1), China. Considering the topography showing in Fig. 1, these cities were grouped into 4 city clusters according to the locations and topographical characteristics of cities. The first cluster, named Western Sichuan Plain, represents the plain area located to the west of Longquan Mountain, including Chengdu (CD), Deyang (DY), Mianyang (MY), Meishan (MS), Leshan (LS) and Yaan (YA). The second one covers the mountainous area in Northeastern Sichuan, including Guangyuan (GY), Bazhong (BZ), Dazhou (DZ), Nanchong (NC) and Guangan (GA). The third cluster consists of hills in the center of the basin, namely Central Hills, including Suining (SN), Ziyang (ZY) and Neijiang (NJ). The last cluster lies in Southern Sichuan and consists of Zigong (ZG), Yibin (YB) and Luzhou (LZ).

Fig. 1. The topography of the study area. The 17 cities are marked with their short names.Fig. 1. The topography of the study area. The 17 cities are marked with their short names.

 
2.2 PM2.5 Observations

The real-time hourly concentrations of PM2.5 in these 17 cities were retrieved from the publishing website of China National Environmental Monitoring Center (http://106.37.208.233:20035/). The PM2.5 concentration data from January 2016 to December 2019 were analyzed in this study. All the measurements were conducted at the national air quality monitoring sites located in each city. The data at the sites in the same city, excluding the background sites, were averaged to obtain the hourly citywide average PM2.5 concentration for each city.

Because of the amendments to the monitoring protocol, the reported PM2.5 concentrations were expressed at actual environmental conditions rather than standard conditions (0°C and 101.325 kPa) since September 2018. The average PM2.5 concentrations before September 2018 were transferred to actual conditions by implementing Eq. (1) (Jin et al., 2020).

 

where Ts and Ps are the temperature and pressure under standard conditions (Ts = 273 K and Ps = 101.325 kPa), and Cs is the reported PM2.5 concentration before transforming. Pa, Ta and Ca are the ambient air pressure, ambient temperature, and the transformed concentration under actual environmental condition, respectively. The hourly ambient air temperature and pressure data were obtained from China Meteorological Administration (http://data.cma.cn/) and the information of the meteorological stations used in this study were listed in Table S1 in Supplementary Data.

 
2.3 Identification of Pollution Episodes

Adapting from Zheng et al. (2016), the original hourly PM2.5 concentrations were smoothed by conducting 24-hour moving average to avoid frequent fluctuations. According to the National Ambient Air Quality Standards of China (NAAQSC), a daily PM2.5 concentration of more than 75 µg m3 was considered to exceed the Grade II national standards of air quality. Hence, the timepoint with moving concentration larger than 75 µg m3 was noted as polluted and the continuous polluted periods were identified as pollution episodes. The starting time, ending time and duration of each episode were recorded for further analyses. An example of identified episodes in January 2017 in Chengdu was presented in Fig. S1.

 
3 RESULTS


 
3.1 Overview of PM2.5 Concentrations

The monthly average PM2.5 concentrations for each city during 2016–2019 were shown in Fig. 2 to present the spatial-temporal characteristics of PM2.5 in the study area. Spatially, higher concentrations of PM2.5 were generally observed in city clusters of Southern Sichuan and Western Sichuan Plain. This distribution feature was consistent with the other studies (Liu et al., 2016; Zhao et al., 2018a) and mainly caused by both relatively more local emissions and external transportation in these regions (Qiao et al., 2019b; Zhao et al., 2019). The most polluted cities were CD, DZ, NJ and ZG in Western Sichuan Plain, Northeastern Sichuan, Central Hills and Southern Sichuan, respectively. Obviously, YB became the most polluted city in Southern Sichuan since 2019. Temporally, January and December held the highest PM2.5 concentrations in all the studied cities, followed by February and November. The PM2.5 concentrations kept decreasing since March and reached the lowest values in July or August. It is noted that the high PM2.5 concentrations persisted in several cities until May, such as CD, MS, ZY, LZ and ZG.

Fig. 2. The spatial-temporal distribution of monthly average PM2.5 concentrations in the 17 cities classified with their locations during 2016–2019.Fig. 2The spatial-temporal distribution of monthly average PM2.5 concentrations in the 17 cities classified with their locations during 2016–2019.

Varying inter-annual trends of PM2.5 concentrations were observed at different locations in the most polluted months during 2016-2019. In January, most cities in the study area except the five cities in Northeastern Sichuan showed an increasing trend of PM2.5 concentrations from 2016 to 2017. Since 2017, PM2.5 concentrations in most cities kept decreasing whereas the concentrations in several cities increased in 2019, including LS in the Western Sichuan Plain, YB and LZ in the Southern Sichuan, and eight cities in the Northeastern Sichuan and Central Hills. In February, the increasing trend from 2016 to 2017 kept true only in the Western Sichuan Plain and the rebound of PM2.5 concentrations in 2019 disappeared in all cities. In December, most cities in the Northeastern Sichuan, Central Hills and Southern Sichuan, except GY and BZ, presented both the increasing trend from 2016 to 2017 and rebound in 2019. While PM2.5 concentrations of the cities in the Western Sichuan Plain only showed the rebound trend in 2019 and kept decreasing from 2016 to 2018. As to the PM2.5 concentrations in November, simple decreasing trends were observed in almost every city in the study area.

The interannual variations of the annually and seasonally average PM2.5 concentrations during 2016–2019 were listed in Table 1. On an annual basis, the air quality in the study area was obviously improved from 2016 to 2019 in perspective of PM2.5 pollution. The PM2.5 concentrations exceeded the Grade II standard of NAAQSC in 16 out of the 17 cities in the study area except GY with an annual average of 24.9 µg m3 in 2016. But the number of cities which met this standard increased from 1 in 2016 to 5 in 2019, including YA, GY, BZ, GA and SN. Among the cities that exceeded the standard, the highest PM2.5 concentrations were 65.4 µg m3 (ZG) in 2016 and 47.2 µg m3 (YB) in 2019.

Table 1. The variation of seasonal PM2.5 concentrations during 2016–2019 (unit: µg m–3).

Table 1. (continued).

Along with the overall improvement of air quality, the trend and degree of improvement varied in different cities. The PM2.5 concentrations increased from 2016 to 2017 in most cities in the Western Sichuan Plain except CD and MS. And it is worth noting that the rebound of PM2.5 concentrations occurred in 2019 in as many as 10 cities, including DY, MS, LS, BZ, DZ, NC, ZY, NJ, YB and LZ. Furthermore, a unique phenomenon was that the PM2.5 concentrations kept increasing since 2017 in GY. As a result, the PM2.5 concentration in GY increased by 13.3% from 2016 to 2009. In the study area, there were 3 cities (CD, SN and ZG) remaining downtrend of PM2.5 concentrations all the years from 2016 to 2019. The largest decline rate of PM2.5 concentrations from 2016 to 2019 was 31.5% and occurred in MS (from 53.7 µg m3 to 36.8 µg m3), followed by 30.7% in ZG, 28.0% in LZ, 25.9% in NJ and 22.4% in CD. In BZ, the PM2.5 concentration was 35.4 µg m3 in 2016 and gradually decreased to 30.0 µg m3 in 2018. However, the tremendous rebound of PM2.5 concentration in 2019 resulted in its turn back to the original level in 2016. As a result, the decline rate of PM2.5 concentrations in BZ was only 1.4%. Another notable fact was that the PM2.5 concentrations in YB varied slightly compared with other cities in Southern Sichuan, decreased from 50.1 µg m3 in 2016 to 47.2 µg m3 in 2019. This made YB become the most polluted city in the study area in 2019 rather than ZG in previous years.

The seasonal characteristics of PM2.5 concentrations were similar between cities in 2019 but diverged in 2016. For each year, winter represented January, February and December, and autumn, summer, and spring represented March to May, June to August, and September to November, respectively. In 2019, the concentrations decreased in the order of winter, spring, autumn and summer in all cities. In 2016, this descending order was disrupted in DY, MY, LS, YA, SN, BZ, NJ and ZG. In these cities, although the highest and lowest concentrations also occurred in winter and summer, respectively, the concentrations in autumn were higher than those in spring. Because of the extremely higher concentrations, the interannual variations of annually average PM2.5 concentrations were dominated by concentrations in winter.

 
3.2 Numbers and Durations of Pollution Episodes

The total numbers of PM2.5 pollution episodes in each city during 2016–2019 were shown in Fig. 3(a). The differences of episode numbers between city clusters were roughly consistent with the distribution of PM2.5 concentrations, namely more episodes occurred in the Southern Sichuan and Western Sichuan Plain. Nonetheless, within the city clusters the episode numbers did not completely correspond to their PM2.5 concentrations, such as in MS and LZ with lower PM2.5 concentrations and the largest episode numbers in 2016, respectively. In all the cities, the number of pollution episodes in 2016 was much larger than those in the subsequent years. The largest episode number exceeded 40 days, which occurred in LZ in 2016. While in 2019, the largest number was about 28 in YB, as the PM2.5 concentrations in YB became the highest. Seasonally, most of the episodes appeared in winter, followed by spring and autumn. And no PM2.5 pollution events happened in summer since 2017.

Fig. 3. The seasonal variations of the numbers and durations of episodes during 2016–2019.Fig. 3The seasonal variations of the numbers and durations of episodes during 2016–2019.

As to the interannual variations, the total numbers of pollution episodes presented a decreasing trend in general during 2016–2019 but fluctuated with different patterns between cities. The episode numbers in MS, YA and NJ descended straightly from 2016 to 2019. In CD, the episode numbers decreased obviously from 2016 to 2017 and then kept increasing. In the remaining cities, the episode numbers decreased before 2018 and rebounded in 2019 except NC and GA, in which the numbers rebounded in 2018. Seasonally, the numbers of pollution episodes occurred in spring, summer and autumn decreased significantly in most cities from 2016 to 2019. But there were exceptions to the trends in these three seasons in the study area. For example, no significant differences in the episode numbers were identified in YB in spring and ZG in spring and autumn. And the numbers in spring increased since 2017 in CD. In winter, the reduction of episode numbers was not obvious in most cities because of the increasing trends from 2018 to 2019.

Along with the total numbers, the total durations of PM2.5 pollution episodes in each city were presented in Fig. 3(b). The order of cities according to episode durations almost exactly matched the order according to annual PM2.5 concentrations. The cities with highest PM2.5 concentrations showed the longest pollution durations, such as ZG, LZ and CD in 2016 and YB, DZ and ZG in 2019. Similar to the variation trends of episode numbers and PM2.5 concentrations, the durations of pollution episodes were reduced significantly in the study region. More than 2000 hours were in the pollution status in the 3 most polluted cities in 2016, accounting for 23% of the total hours in this year. Since 2017, the polluted hours seldom exceeded 1500, except ZG and YB in 2018. Also, the episode durations of several cities rebounded in 2019. Among the cities with episode numbers rebounded in 2019, most presented growth in durations, but to a lesser extent. And some of these cities showed an opposite decreasing trend of episode durations, such as CD, MY and GA. This indicated that PM2.5 pollution events with long durations were effectively eliminated in the study area.

In Fig. 4, the distribution of episode numbers against the durations was presented. Generally, the pollution durations were concentrated at about 1 day in the study area. Although the occurrence frequencies decreased obviously along with the increasing durations in most cities, the numbers of pollution events lasting up to 3 days were noticeable. In several cities with severe pollution, such as CD, DY, ZG, LS and YB, the frequencies of pollution events lasting up to 5 days were also relatively high. Particularly, the longest durations were 17 days in ZG and 15 days in CD, respectively. The episode numbers were more evenly distributed for durations of 1–3 days in 2016 compared to those in following years. Specifically, the longest durations in most cities occurred in 2017 except BZ, ZY, YB and LS, in which the longest durations occurred in 2019. The frequencies of long-lasting pollution events were much higher in 2017 than those in other years, which might cause the reduction of episode numbers in 2017 as shown in Fig. 3(a). In 2018, long-lasting pollution events were effectively eliminated compared with other years. Pollution events lasting longer than 6 days occurred only in 3 cities, YB, ZG and DZ. Accompanied with episode numbers and durations, the frequencies of long-lasting processes increased significantly in 2019 in most cities. It is worth noting that this phenomenon was not observed in ZG, in which 2019 is the year with the lowest frequency of long-lasting pollution events.

Fig. 4. The numbers of pollution episodes with different durations during 2016–2019.Fig. 4The numbers of pollution episodes with different durations during 2016–2019.

 
3.3 PM2.5 Concentrations During Pollution Episodes

The PM2.5 concentrations during the pollution episodes were presented in Fig. 5. As shown in the upper panel, compared with the overall average PM2.5 concentrations, the concentrations in all pollution episodes varied to a lesser extent between cities, ranging between 75.0–158.3 µg m3 in 2016 and 75.0–143.9 µg m3 in 2019. The general variation trends of average PM2.5 concentrations in all pollution episodes were descending from 2016 to 2019 except the steady trends in DZ and NJ and the ascending trend in NC. And the variations trends fluctuated between years and differed between cities. From 2016 to 2017, the average PM2.5 concentrations in all pollution episodes increased in most cities except DY, MY, LS, ZG, GA and DZ. These increase trends were kept only in 4 cities, DY, MY, DZ and SN, from 2017 to 2018. Particularly, the maximum average concentration occurred in DZ in 2018 and reached 191.2 µg m3. Unlike the distinct rebound of episode numbers and durations in 2019, only 3 cities, LS, NC and NJ, showed an increased trend of concentrations in all pollution episodes from 2018 to 2019.

Fig. 5. The average PM2.5 concentrations in all pollution episodes (upper panel) and in each episode (lower panel) during 2016–2019. The pollution processes were sorted in ascending order according to durations and stacked in the vertical direction. The heights of the stacks represented the durations and the colors represented concentrations.Fig. 5The average PM2.5 concentrations in all pollution episodes (upper panel) and in each episode (lower panel) during 2016–2019. The pollution processes were sorted in ascending order according to durations and stacked in the vertical direction. The heights of the stacks represented the durations and the colors represented concentrations.

The average PM2.5 concentrations in each pollution episode were presented in the lower panel of Fig. 5. The pollution duration was not consistently related to the average concentration during the same episode. The concentrations kept relatively low values (~80 µg m3) during the episodes with short durations and varied slightly along with durations. High average concentrations usually occurred during relatively long-lasting episodes but the concentrations did not increase monotonously along with the durations. However, the highest concentrations were observed during episodes with the longest durations in most cities in 2017. These persistent pollution events with high PM2.5 concentrations might be the causes of the higher PM2.5 concentrations during episodes in 2017 compared with 2016 as shown in the upper panel of Fig. 5.


3.4 Relationship between PM2.5 Concentrations and Durations

The relationships between the hourly PM2.5 concentrations and episode durations during identified pollution events were shown in Fig. 6. The statistical metrics, including the 75th, 50th, 25th percentiles and the averages, varied in similar trends along with increasing durations in each city. Generally, the relationships between PM2.5 concentrations and episode durations were not monotonous and could be divided into 2 categories according to the length of episode durations. A threshold length of episode duration could be obtained to identify these categories. For the first category with durations shorter than the thresholds, the PM2.5 concentrations showed an increasing trend along with episode durations in most cities.

Fig. 6. The box-whisker plot of 24-hour moving average PM2.5 concentrations during pollution episodes with different durations in days. The upper, middle and lower bars of the box represent the 75th, 50th and 25th percentiles, respectively. The diamonds mark the average concentrations and upper and lower whiskers show the maximum and minimum concentrations, respectively.Fig. 6The box-whisker plot of 24-hour moving average PM2.5 concentrations during pollution episodes with different durations in days. The upper, middle and lower bars of the box represent the 75th, 50th and 25th percentiles, respectively. The diamonds mark the average concentrations and upper and lower whiskers show the maximum and minimum concentrations, respectively.

While for the second category with durations longer than the thresholds, the PM2.5 concentrations varied in diverse trends in different cities. Taking CD as an example, the PM2.5 concentrations kept increasing with episode durations until pollution events lasted 7 days and decreased dramatically when durations furtherly increased. For the pollution events with durations longer than 7 days, the increasing trends of PM2.5 concentrations were observed again. But in ZG the PM2.5 concentrations varied to a slighter extent within the second category. Resulting from the non-monotonical variation trend, the maximum concentrations always appeared during the pollution events with durations around the thresholds in most cities except LZ and SN, in which the maximum concentrations occurred during the longest pollution events. Furthermore, the duration thresholds presented distinct characteristics in two most polluted regions of Sichuan Basin. In Southern Sichuan, the thresholds were between 6-8 days and a little longer than those in the Western Sichuan Plain, about 5–7 days.

 
4 DISCUSSION


Generally, the air quality in Sichuan Basin was significantly improved during 2016–2019 in perspective of PM2.5 pollution. But some features needed to be concerned for continuous improvement of air quality. Firstly, the PM2.5 pollution deteriorated in many cities in 2019. The annually average PM2.5 concentrations in 10 out of 17 cities were higher in 2019 than those in 2018. This might be caused by the combined effects of pollution numbers and durations. The numbers of pollution events rebounded in 2019 in 15 cities except NC and GA. And in most cities, the frequencies of long-lasting pollution events were higher in 2019. Secondly, the improvement of air quality was not synchronized between cities in the study area. In the severely polluted cities, such as ZG, CD, LZ, the annually average PM2.5 concentrations decreased by more than 20% during 2016–2019. But the annually average PM2.5 concentrations varied in a small range in some cities, such as YB, DZ, BZ, and even increased by 13.3% in GY. Similar characteristics of the numbers and durations of pollution episodes in these cities were observed and the causes need to be analyzed more thoroughly. The variation trend of PM2.5 pollution was complicated in Sichuan Basin, which reflected the complexity and difficulty of pollution prevention in this region. This made it essential to analyze the causes and influencing factors of PM2.5 pollution in order to effectively control pollution.

High anthropogenic emissions combined with unfavorable meteorological conditions were the main reasons for the PM2.5 pollution (Miao et al., 2018; Zhou et al., 2019b). The influences of human activities had decreased significantly in recent years in the study area, and meteorological conditions played more and more important roles in PM2.5 pollution processes (Hu et al., 2019). Several specific meteorological factors, such as high relative humidity, temperature inversion and low planetary boundary layer height, would be conducive to the growth and accumulation of particles (Feng et al., 2020; Ma et al., 2019). For example, the pollution episodes in winter and spring were mainly due to the strong secondary conversion of NOx under appropriate weather conditions (Huang et al., 2018; Tian et al., 2019). The highest occurrence frequencies of air stagnation in the southwest and western margins of the Sichuan Basin might be the primary cause for frequent and persistent heavy pollution events (Liao et al., 2018). Furthermore, biomass burning was another important contributor to PM2.5 pollution in the Sichuan Basin from February to October (Chen and Xie, 2014; Wu et al., 2019). Understanding the effects of these factors on the numbers, durations and concentrations of PM2.5 pollution episodes could provide appropriate pathways to continuously improve the air quality in Sichuan Basin.

Because of different emission patterns and meteorological conditions, different characteristics of pollution episodes were observed in other regions of China, such as the Yangtze River Delta (YRD), the Beijing-Tianjin-Hebei region (BTH), and the Guanzhong Basin (GZB). Generally, the durations of pollution processes in SCB were close to those in GBZ (Li et al., 2021) and longer than those in BTH and YRD (Zheng et al., 2016). But wintertime heavy pollution events occurred more frequently with higher PM2.5 concentrations in BTH, YRD and GZB (Shen et al., 2020; Wei et al., 2020; Zhu et al., 2020). For example, the maximum PM2.5 concentration during pollution process could reach 471 µg m3 in YRD (Yu et al., 2020) and even 499 µg m3 in GZB (Yu et al., 2020). In perspective of the inter-annual variation trend, it was found that the number of pollution episodes decreased sharply in 2017 and began to rebound after this year in BTH (Liu et al., 2019; Wang et al., 2021), while the rebound occurred after 2018 in SCB. And the number and concentration of heavy pollution events in GZB have declined since 2017 (Watson et al., 2021). Moreover, PM2.5 pollution was severely influenced by dust processes in GZB and BTH (Li et al., 2018; Shen et al., 2020; Xuan, 2005) and cargo ships along rivers and near coastal areas in YRD (Wan et al., 2020; Wang et al., 2019). These differences indicated that localized measures need to be raised in SCB besides those referenced from other regions.

 
5 CONCLUSIONS


In this study, we identified all PM2.5 pollution events (a total of 1342 events) during 2016–2019 which occurred in 17 cities in Sichuan Basin, southwestern China. And the characteristics of the numbers, durations and PM2.5 concentrations of all the episodes were analyzed. It was found that distribution characteristics of the episode numbers and durations were similar to those of the annually average PM2.5 concentrations, which were higher in the Southern Sichuan and Western Sichuan Plain than those in the Northeastern Sichuan and Central Hills. In contrast, during pollution episodes the PM2.5 concentrations were more evenly distributed in the area, indicating the regional characteristics of pollution in the basin.

In all the 17 cities, pollution episodes occurred most frequently in winter, followed by spring, autumn and summer. It is noted that there has been no pollution event appearing in the study area in summer since 2017. Although the air quality has improved significantly during 2016–2019 in perspective of PM2.5, the patterns varied intricately between cities and during different periods. The most noticeable feature was that the concentrations, episode numbers and durations all rebounded in 2019 in most cities.

The relationships between PM2.5 concentrations could be divided into 2 categories according to the length of episode durations. PM2.5 concentrations increased along with durations during episodes shorter than the threshold length of duration in most cities. During episodes longer than the threshold, the relationships between concentrations and durations varied between cities. The threshold durations were 6–8 days and 5–7 days in Southern Sichuan and Western Sichuan Plain, respectively. Considering the numbers, durations and concentrations of pollution episodes, more attention should be taken to the prevention of PM2.5 pollution in YB in the Southern Sichuan, CD and LS in the Western Sichuan Plain, NJ in the Central Hills, and BZ, DA, NC in the Northeastern Sichuan.

In the future, further analyses should be not only focused on the pollution episodes but also extended to the accumulation and dissipation processes. Furthermore, the influences of meteorological conditions on the characteristics of pollution episodes and the causes of the inter-annual and seasonal variations trends will be thoroughly studied.

 
ACKNOWLEDGMENTS


This research was supported by the National Key R&D Program of China (2018YFC0214002 and 2018YFC0214001), the Key S&T Program of Sichuan Province (2018SZDZX0023 and 2019YFS0495), the National Natural Science Foundation of China (41875162 and 22076129), the Fundamental Research Funds for the Central Universities (YJ201871 and YJ201891), and the Young Talent Team Science and Technology Innovation Project of Sichuan Province (2020JDTD0005).


REFERENCES


  1. An, Z.S., Huang, R.J., Zhang, R.Y., Tie, X.X., Li, G.H., Cao, J.J., Zhou, W.J. Shi, Z.G., Han, Y.M., Gu, Z.L., Ji, Y.M. (2019). Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes. Proc. Natl. Acad. Sci. U.S.A. 116, 8657–8666. https://doi.org/10.1073/pnas.1900125116

  2. Cai, H.K., Gui, K., Chen, Q.L. (2018). Changes in haze trends in the Sichuan-Chongqing region, China, 1980 to 2016. Atmosphere 9, 277. https://doi.org/10.3390/atmos9070277

  3. Chen, K., Wang, M., Huang, C.H., Kinney, P.L., Anastas, P.T. (2020). Air pollution reduction and mortality benefit during the COVID-19 outbreak in China. Lancet Planet. Health 4, 210–212. https://doi.org/10.1016/s2542-5196(20)30107-8

  4. Chen, Y., Xie, S.D. (2014). Characteristics and formation mechanism of a heavy air pollution episode caused by biomass burning in Chengdu, Southwest China. Sci. Total Environ. 473–474, 507–517. https://doi.org/10.1016/j.scitotenv.2013.12.069

  5. Chen, Y., Luo, B., Xie, S.D. (2015). Characteristics of the long-range transport dust events in Chengdu, Southwest China. Atmos. Environ. 122, 713–722. https://doi.org/10.1016/j.atmosenv.2015.10.045

  6. Fan, J., Shang, Y.N., Zhang, X.J., Wu, X.N., Zhang, M., Cao, J.Y., Luo, B., Zhang, X.L., Wang, S.G., Li, S.Z., Liu, H.Q., Wu, P.L. (2020). Joint pollution and source apportionment of PM2.5 among three different urban environments in Sichuan Basin, China. Sci. Total Environ. 714, 136305. https://doi.org/10.1016/j.scitotenv.2019.136305

  7. Feng, X.Y., Wei, S.M., Wang, S.G. (2020). Temperature inversions in the atmospheric boundary layer and lower troposphere over the Sichuan Basin, China: Climatology and impacts on air pollution. Sci. Total Environ. 726, 138579. https://doi.org/10.1016/j.scitotenv.2020.138579

  8. Fu, H.B., Chen, J.M. (2017). Formation, features and controlling strategies of severe haze-fog pollutions in China. Sci. Total Environ. 578, 121–138. https://doi.org/10.1016/j.scitotenv.2016.10.201

  9. Hu, Y.L., Wang, S.G., Yang, X., Kang, Y.Z., Ning, G.C., Du, H. (2019). Impact of winter droughts on air pollution over Southwest China. Sci. Total Environ. 664, 724–736. https://doi.org/10.1016/j.scitotenv.2019.01.335

  10. Huang, X.J., Zhang, J.K., Luo, B., Wang, L.L., Tang, G.Q., Liu, Z.R., Song, H.Y., Zhang, W., Yuan, L., Wang, Y.S. (2018). Water-soluble ions in PM2.5 during spring haze and dust periods in Chengdu, China: Variations, nitrate formation and potential source areas. Environ. Pollut. 243, 1740–1749. https://doi.org/10.1016/j.envpol.2018.09.126

  11. Jin, L., Wang, B., Shi, G.M., Seyler, B.C., Qiao, X., Deng, X.F., Jiang, X., Yang, F.M., Zhan, Y. (2020). Impact of China’s recent amendments to air quality monitoring protocol on reported trends. Atmosphere 11, 1199. https://doi.org/10.3390/atmos11111199

  12. Li, L.L., Tan, Q.W., Zhang, Y.H., Feng, M., Qu, Y., An, J.L., Liu, X.G. (2017). Characteristics and source apportionment of PM2.5 during persistent extreme haze events in Chengdu, southwest China. Environ. Pollut. 230, 718–729. https://doi.org/10.1016/j.envpol.2017.07.029

  13. Li, M.S., Jia, L., Zhang, F.Y., Hu, M.G., Shi, Y., Chen, X. (2016). Characteristics of haze weather in Chongqing, China and its determinants analysis based on automatic monitoring stations. Atmos. Pollut. Res. 7, 638–646. https://doi.org/10.1016/j.apr.2016.02.012

  14. Li, X., Bei, N., Tie, X.X., Wu, J.R., Liu, S.X., Wang, Q.Y., Liu, L., Wang, R.N., Li, G.H. (2021). Local and transboundary transport contributions to the wintertime particulate pollution in the Guanzhong Basin (GZB), China: A case study. Sci. Total Environ. 797, 148876. https://doi.org/10.1016/j.scitotenv.2021.148876

  15. Li, X.Y., Liu, X.D., Yin, Z.Y. (2018), The impacts of taklimakan dust events on chinese urban air quality in 2015. Atmosphere 9, 281. https://doi.org/10.3390/atmos9070281

  16. Liao, T.T., Wang, S., Ai, J., Gui, K., Duan, B.L., Zhao, Q., Zhang, X., Jiang, W.T., Sun, Y. (2017). Heavy pollution episodes, transport pathways and potential sources of PM2.5 during the winter of 2013 in Chengdu (China). Sci. Total Environ. 584–585, 1056–1065. https://doi.org/10.1016/j.scitotenv.2017.01.160

  17. Liao, T.T., Gui, K., Jiang, W.T., Wang, S.G., Wang, B.H., Zeng, Z.L., Che, H.Z., Wang, Y.Q., Sun, Y. (2018). Air stagnation and its impact on air quality during winter in Sichuan and Chongqing, southwestern China. Sci. Total Environ. 635, 576–585. https://doi.org/10.1016/j.scitotenv.2018.04.122

  18. Liu, X.Y., Chen, Q.L., Che, H.Z., Zhang, R.J., Gui, K., Zhang, H., Zhao, T.L. (2016). Spatial distribution and temporal variation of aerosol optical depth in the Sichuan basin, China, the recent ten years. Atmos. Environ. 147, 434–445. https://doi.org/10.1016/j.atmosenv.2016.10.008

  19. Liu, Z.R., Hu, B., Ji, D.S., Cheng, M.T., Gao, W.K., Shi, S.Z., Xie, Y.Z., Yang, S.H., Gao, M., Fu, H.B., Chen, J.M., Wang, Y.S. (2019). Characteristics of fine particle explosive growth events in Beijing, China: Seasonal variation, chemical evolution pattern and formation mechanism. Sci. Total Environ. 687, 1073–1086. https://doi.org/10.1016/j.scitotenv.2019.06.068

  20. Ma, X.Y., Jia, H.L., Sha, T., An, J.L., Tian, R. (2019). Spatial and seasonal characteristics of particulate matter and gaseous pollution in China: Implications for control policy. Environ. Pollut. 248, 421–428. https://doi.org/10.1016/j.envpol.2019.02.038

  21. Miao, Y.C., Liu, S.H. Guo, J.P., Huang, S.X., Yan, Y., Lou, M.Y. (2018). Unraveling the relationships between boundary layer height and PM2.5 pollution in China based on four-year radiosonde measurements. Environ. Pollut. 243, 1186–1195. https://doi.org/10.1016/j.envpol.2018.09.070

  22. Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K., Thurston, G.D. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287, 1132–1141. https://doi.org/10.1001/jama.287.9.1132

  23. Qiao, B.Q., Chen, Y., Tian, M., Wang, H.B., Yang, F.M., Shi, G.M., Zhang, L.M., Peng, C., Luo, Q., Ding, S.M. (2019a). Characterization of water soluble inorganic ions and their evolution processes during PM2.5 pollution episodes in a small city in southwest China. Sci. Total Environ. 650, 2605–2613. https://doi.org/10.1016/j.scitotenv.2018.09.376

  24. Qiao, X., Guo, H., Tang, Y., Wang, P.F., Deng, W.Y., Zhao, X., Hu, J.L., Ying, Q., Zhang, H.L. (2019b). Local and regional contributions to fine particulate matter in the 18 cities of Sichuan Basin, southwestern China. Atmos. Chem. Phys. 19, 5791–5803. https://doi.org/10.5194/acp-19-5791-2019

  25. Shen, F.Z., Zhang, L., Jiang, L., Tang, M.Q., Gai, X.Y., Chen, M.D., Ge, X.L. (2020). Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China. Environ. Int. 137, 105556. https://doi.org/10.1016/j.envint.2020.105556

  26. Shi, G.M., Yang, F.M., Zhang, L.M., Zhao, T.L., Hu, J. (2019). Impact of atmospheric circulation and meteorological parameters on wintertime atmospheric extinction in Chengdu and Chongqing of Southwest China during 2001-2016. Aerosol Air Qual. Res. 19, 1538–1554. https://doi.org/10.4209/aaqr.2018.09.0336

  27. Sichuan Provincial Bureau of Statistics (2019). Sichuan Statistical Yearbook 2019. http://tjj.sc.gov.cn/tjnj/cs/2019/indexch.htm

  28. Tian, M., Liu, Y., Yang, F.M., Zhang, L.M., Peng, C., Chen, Y., Shi, G.M., Wang, H.B., Luo, B., Jiang, C.T., Li, B., Takeda, N., Koizumi, K. (2019). Increasing importance of nitrate formation for heavy aerosol pollution in two megacities in Sichuan Basin. southwest China. Environ. Pollut. 250, 898–905. https://doi.org/10.1016/j.envpol.2019.04.098

  29. Wan, Z., Ji, S.J., Liu, Y.T., Zhang, Q., Chen, J.H., Wang, Q. (2020). Shipping emission inventories in China's Bohai Bay, Yangtze River Delta, and Pearl River Delta in 2018. Mar. Pollut. Bull. 151. https://doi.org/10.1016/j.marpolbul.2019.110882

  30. Wang, G., Zhu, Z.Y., Zhao, N., Wei, P., Li, G.H., Zhang, H.Y. (2021). Variations in characteristics and transport pathways of PM2.5 during heavy pollution episodes in 2013-2019 in Jinan, a central city in the north China Plain. Environ. Pollut. 284, 117450. https://doi.org/10.1016/j.envpol.2021.117450

  31. Wang, J.D., Zhao, B., Wang, S.X., Yang, F.M., Xing, J., Morawska, L., Ding, A.J., Kulmala, M., Kerminen, V.M., Kujansuu, J., Wang, Z.F., Ding, D., Zhang, X.Y., Wang, H.B., Tian, M., Petaja, T., Jiang, J.K., Hao, J.M. (2017). Particulate matter pollution over China and the effects of control policies. Sci. Total Environ. 584, 426–447. https://doi.org/10.1016/j.scitotenv.2017.01.027

  32. Wang, Z., Qin, C.H., Zhang, W., Liu, Y. (2019). Impact of vessel activities in the surrounding waters of China on mainland air quality. IOP Conf. Ser.: Mater. Sci. Eng. 631, 032006. https://doi.org/10.1088/1757-899x/631/3/032006

  33. Watson, J.G., Cao, J.J., Wang, X.L., Chow J.C. (2021). PM2.5 pollution in China's Guanzhong Basin and the USA's San Joaquin Valley mega-regions. Faraday Discuss. 226, 255–289. https://doi.org/10.1039/d0fd00094a

  34. Wei, N., Wang, N.L., Huang, X., Liu, P.P., Chen, L. (2020). The effects of terrain and atmospheric dynamics on cold season heavy haze in the Guanzhong Basin of China. Atmos. Pollut. Res. 11, 1805–1819. https://doi.org/10.1016/j.apr.2020.07.007

  35. Wu, P., Huang, X.J., Zhang, J.K., Luo, B., Luo, J.Q., Song, H.Y., Zhang, W., Rao, Z.H., Feng, Y.P., Zhang, J.Q. (2019). Characteristics and formation mechanisms of autumn haze pollution in Chengdu based on high time-resolved water-soluble ion analysis. Environ. Sci. Pollut. Res. Int. 26, 2649–2661. https://doi.org/10.1007/s11356-018-3630-6

  36. Xiao, Q.Y., Geng, G.N., Liang, F.C., Wang, X., Lv, Z., Lei, Y., Huang, X.M., Zhang, Q., Liu, Y., He, K.B. (2020). Changes in spatial patterns of PM2.5 pollution in China 2000–2018: Impact of clean air policies. Environ. Int. 141, 105776. https://doi.org/10.1016/j.envint.2020.105776

  37. Xuan, J. (2005). Emission inventory of eight elements, Fe, Al, K, Mg, Mn, Na, Ca and Ti, in dust source region of East Asia. Atmoss Environ. 39, 813–821. https://doi.org/10.1016/j.atmosenv.2004.10.029

  38. Yang, X.C., Xiao, H., Wu, Q.Z., Wang, L.N., Guo, Q.Y., Cheng, H.Q., Wang, R.R., Tang, Z.Y. (2020). Numerical study of air pollution over a typical basin topography: Source appointment of fine particulate matter during one severe haze in the megacity Xi'an, Sci. Total Environ. 708, 135213. https://doi.org/10.1016/j.scitotenv.2019.135213

  39. Yu, C., Zhao, T.L., Bai, Y.Q., Zhang, L., Kong, S.F., Yu, X.N., He, J.H., Cui, C.G., Yang, J., You, Y.C., Ma, G.X., Wu, M., Chang, J.C. (2020). Heavy air pollution with a unique "non-stagnant" atmospheric boundary layer in the Yangtze River middle basin aggravated by regional transport of PM2.5 over China. Atmos. Chem. Phys. 20, 7217–7230. https://doi.org/10.5194/acp-20-7217-2020

  40. Zeng, S.L., Zheng, Y.Y. (2019). Analysis of a Severe Pollution Episode in December 2017 in Sichuan Province. Atmosphere. 10, 156. https://doi.org/10.3390/atmos10030156

  41. Zhang, J.K., Luo, B., Zhang, J.Q., Ouyang, F., Song, H.Y., Liu, P.C., Cao, P., Schafer, K., Wang, S.G., Huang, X.J., Lin, Y.F. (2017). Analysis of the characteristics of single atmospheric particles in Chengdu using single particle mass spectrometry. Atmos. Environ. 157, 91–100. https://doi.org/10.1016/j.atmosenv.2017.03.012

  42. Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., Xu, X., Wang, J., He, H., Liu, W., Ding, Y., Lei, Y., Li, J., Wang, Z., Zhang, X., Wang, Y., Cheng, J., Liu, Y., Shi, Q., Yan, L., et al. (2019). Drivers of improved PM2.5 air quality in China from 2013 to 2017. PNAS 116, 24463–24469. https://doi.org/10.1073/pnas.1907956116

  43. Zhang, R.H., Li, Q., Zhang, R.N. (2014). Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013. Sci. China Earth Sci. 57, 26–35. https://doi.org/10.1007/s11430-013-4774-3

  44. Zhao, S.P., Yu, Y. Yin, D.Y., Qin, D.H., He, J.J., Dong L.X. (2018a). Spatial patterns and temporal variations of six criteria air pollutants during 2015 to 2017 in the city clusters of Sichuan Basin, China. Sci. Total Environ. 624, 540–557. https://doi.org/10.1016/j.scitotenv.2017.12.172

  45. Zhao, S.P., Yu, Y., Yin, D.Y., Qin, D.H., He, J.J., Li, J.L., Dong, L.X. (2018b). Two winter PM2.5 pollution types and the causes in the city clusters of Sichuan Basin, Western China. Sci. Total Environ. 636, 1228–1240. https://doi.org/10.1016/j.scitotenv.2018.04.393

  46. Zhao, S.P., Yu, Y., Qin, D.H., Yin, D.Y., Dong, L.X., He, J.J. (2019). Analyses of regional pollution and transportation of PM2.5 and ozone in the city clusters of Sichuan Basin, China. Atmos. Pollut. Res. 10, 374–385. https://doi.org/10.1016/j.apr.2018.08.014

  47. Zheng, G.J., Duan, F.K., Ma, Y.L., Zhang, Q., Huang, T., Kimoto, T., Cheng, Y.F., Su, H., He, K.B. (2016). Episode-based evolution pattern analysis of haze pollution: Method development and results from Beijing, China. Environ. Sci. Technol. 50, 4632–4641. https://doi.org/10.1021/acs.est.5b05593

  48. Zhou, X.N., Geng, Q., Li, X.Y., Chen, D. (2017). Research on Haze Characteristics in Chongqing Jiangbei Airport from 2001 to 2016. Francis Acad Press, London, pp. 7–13.

  49. Zhou, Y., Luo, B., Li, J., Hao, Y.F., Yang, W.W., Shi, F.T., Chen, Y.J., Simayi, M., Xie, S.D. (2019a). Characteristics of six criteria air pollutants before, during, and after a severe air pollution episode caused by biomass burning in the southern Sichuan Basin, China. Atmos. Environ. 215, 116840. https://doi.org/10.1016/j.atmosenv.2019.116840

  50. Zhou, Z.H., Tan, Q.W., Deng, Y., Wu, K.Y., Yang, X.Y., Zhou, X.L. (2019b). Emission inventory of anthropogenic air pollutant sources and characteristics of VOCs species in Sichuan Province, China. J. Atmos. Chem. 76, 21–58. https://doi.org/10.1007/s10874-019-9386-7

  51. Zhu, Y.Y., Gao, Y.X., Wang, W., Lu, N., Xu, R., Liu, B., Li, J.J. (2020). Assessment of emergency emission reduction effect during the heavy air pollution episodes in Beijing, Tianjin, Hebei, and Its Surrounding Area ("2+26" Cities) from October to December 2019. Environ. Sci. 41, 4402–4412. https://doi.org/10.13227/j.hjkx.202003198 (in Chinese).


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.

5.9
2020CiteScore
 
 
81st percentile
Powered by
Scopus






2020 Impact Factor: 3.063
5-Year Impact Factor: 2.857

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.