Qingchun Guo  This email address is being protected from spambots. You need JavaScript enabled to view it.1,2,3,4, Zhenfang He1,2,5, Zhaosheng Wang6

1 School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
2 Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
3 Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing 100081, China
4 State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
5 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
6 National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China


Received: November 27, 2023
Revised: January 9, 2024
Accepted: February 28, 2024

 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.230274  


Cite this article:

Guo, Q., He, Z., Wang, Z. (2024). The Characteristics of Air Quality Changes in Hohhot City in China and their Relationship with Meteorological and Socio-economic Factors. Aerosol Air Qual. Res. 24, 230274. https://doi.org/10.4209/aaqr.230274


HIGHLIGHTS

  • The concentration of SO2, PM10, PM2.5, NO2, and CO declined from 2014 to 2022.
  • Reducing air pollutant emissions is beneficial for improving air quality.
  • NO2 plays a significant influence in the formation of O3 and PM2.5.
 

ABSTRACT


Air pollution affects sustainable development of the natural environment and social economy. In this article, the changes in air quality index (AQI) and six air pollutants in Hohhot during 2014–2022 are analyzed. The results imply that the annual average concentrations of five air pollutants (SO2, PM10, PM2.5, NO2, and CO) and AQI values declined year by year over 2014–2022. Compared with 2014, AQI in Hohhot fell by 22.5% in 2022. However, the annual average concentrations of O3 increased year by year. PM2.5 and PM10 were the major factors in influencing AQI. Among the five types of atmospheric pollutants, the relationship between NO2 and O3 is strongest, implying that NO2 plays a significant influence in the formation of O3 and PM2.5. Meteorological and socio-economic factors have a significant impact on air quality. The average wind speed (AWS), average pressure (AP), sulfur dioxide emissions (SOE), nitrogen oxide emissions (NOE), and particulate matter emissions (PME) have a positive effect on changes in air quality. The results provide the information of great importance for the management of air quality in Hohhot City.


Keywords: Air quality, Meteorological factor, Socio-economic factor, PM2.5, O3


1 INTRODUCTION


Air pollution affects ecosystem security, climate change, the COVID-19 pandemic, economic development and human health (Bashir et al., 2020; Guo and He, 2021; Ma et al., 2021; He et al., 2022; Xue et al., 2022; Alahmad et al., 2023; Guo et al., 2023b; Li et al., 2023). According to the World Health Organization (WHO) assessment report, 99% of the world's population lives in environments where air quality exceeds the recommended values of the WHO Air Quality Guidelines (AQG) (annual average PM2.5 concentration < 5 µg m–3). On a global scale, air pollution causes approximately 7 million premature deaths every year (Lei et al., 2023; Yu et al., 2023). Air pollution is a major risk factor for cardiovascular disease (Xue et al., 2023). Exposure to environmental air pollution may increase the risk of developing dementia (Wilker et al., 2023). There is also a good relationship between exposure to air pollution and an increased risk of cognitive impairment and severe depression (Li et al., 2021; Mo et al., 2023). Therefore, it is crucial to analyze the multiple temporal features and causes of air pollution in Hohhot City and develop air pollution mitigation strategies.

There are significant spatiotemporal differences in air pollution. On a global land scale, the annual average PM2.5 concentration over 2000–2019 is about 32.8 µg m–3, The region with the highest annual average PM2.5 concentration is in East Asia (50 µg m–3) (Yu et al., 2023). Due to the sharp decrease in anthropogenic emissions, the decline rate of NO2, SO2, and CO in China was more significant from 2013 to 2017 but slowed down slightly from 2018 to 2020 (Wei et al., 2023). From 2015 to 2020, the concentrations of NO2, SO2, CO, PM2.5, PM10, and O3 in China decreased by 27%, 63%, 37%, 39%, 40%, and 1.93%, respectively (Zhou et al., 2023).

Air pollution is influenced by meteorological conditions (Guo et al., 2020, 2023d, 2023c, 2023e). Meteorological conditions have a lagging and sustained impact on PM2.5 pollution. In the long term, air temperature and precipitation mainly have a negative impact on PM2.5 pollution, while relative humidity and sunshine duration exacerbate PM2.5 pollution in BTH (Deng et al., 2022). Among the five meteorological variables in China, wind speed, planetary boundary layer height and precipitation (PCP) show a single adverse effect, which means that the reduction of these three factors leads to the increase of PM2.5 concentration (Wang et al., 2023). The changes in meteorological conditions explain the about 86% increase in O3 concentration in Guangzhou, which is due to the increase in solar radiation and temperature, as well as the decrease in wind speed and humidity (Xu et al., 2023).

Meanwhile, air pollution is also influenced by socio-economic factors (Guo et al., 2021, 2023a). The growth of per capita GDP has exacerbated NO2 and PM2.5 pollution in the southeastern coastal areas in winter and spring. The monthly retail sales of social consumer goods have a greater impact on air pollution of the North China Plain. In the Fenwei Plain, the increase in per capita GDP electricity consumption contributes to the alleviation of O3 and PM10 in autumn and summer (Zhou et al., 2023). Urbanization usually suppresses air pollution in the Yangtze Delta. In the sub-dimensions of urbanization, population urbanization tends to exacerbate air pollution, while economic, land, and social urbanization have a remarkable mitigating effect on air pollution (Sun et al., 2023).

In this article, six air pollutants (PM10, NO2, PM2.5, CO, SO2, and O3) and AQIs of Hohhot City in China from 2014 to 2022 are analysed. The analysed results provide better understanding the relationship between pollutants and the impact of multiple factors on air quality. At the same time, the analysed results can help management departments to improve air quality.

 
2 MATERIAL AND METHODS


Hohhot, commonly known as Qingcheng in China, is the capital of Inner Mongolia (Fig. 1). Hohhot has a total area of 17200 square kilometres, a permanent population of 3.4956 million, an urban population of 2.7853 million, and an urbanization rate of 79.68% in 2021.

 Fig. 1. The location of the Hohhot City in Inner Mongolia.Fig. 1. The location of the Hohhot City in Inner Mongolia.

From 2014 to 2022, the air pollution in Hohhot City in China is investigated. The daily AQI values, and the daily concentrations of six air pollutants (PM10, NO2, PM2.5, CO, SO2, and O3) in Hohhot City are analysed (http://www.aqistudy.cn/) (accessed on 15 June 2023). Socio-economic data are from the Statistics Bureau of Hohhot City, including gross domestic product (GDP), production of the tertiary industry (PTI), per capita gross domestic product (PCGDP), permanent population (PP), urbanization rate (UR), sulfur dioxide emissions (SOE), nitrogen oxide emissions (NOE), particulate matter emissions (PME), and land green coverage (GC). The daily meteorological data are from the Meteorological Bureau of Hohhot City, including precipitation(P), average atmospheric temperature (AAT), average pressure (AP), average relative humidity (ARH), sunshine duration (SD), average wind speed (AWS). The AQI can quantitatively express air pollution levels. The six grades are unfolded in Table 1.

Table 1. AQI range and air quality levels.

AQI is from ‘Technical Regulation on Ambient Air Quality Index (on trial)’ (HJ 633–2012). Daily individual AQI (IAQI) is calculated from the concentrations of individual air pollutants, and the AQI value is determined to be the maximum IAQI of the six air pollutants. The corresponding air pollutant concentration limits are unfolded in Table 2.


Table 2. Individual air quality index (IAQI) and corresponding air pollutant concentration limit (µg m–3(CO (mg m–3))).

AQI and IAQI are calculated from the following equations:

 

where IAQI is individual AQI and n is pollutant; and

 

where IAQIn is individual AQI of air pollutant n, Cn is the concentration of air pollutant n, BPh is high-value pollutant concentration limit when close to Cn (in Table 2), BPj is low-value pollutant concentration limit when close to Cn (in Table 2), IAQIh is the individual AQI corresponding to BPh, and IAQIj is the individual AQI corresponding to BPj.

Pearson correlation coefficient (R) is used to investigate the influence of meteorological and socio-economic factors on air quality, and examine the relationship between air pollutants (Guo and He, 2021). The calculation formula is as follows:

 

Gl represents the air pollutants, Hl represents the influencing factors,  represents the average value of the air pollutants, and  represents the average value of the influencing factors.

 
3 RESULTS AND DISCUSSION


In the analysis and research, air quality changes on multiple time scales in Hohhot City from 2014 to 2022 are analyzed. After that, the relationship between air quality, meteorology, and socio-economic factors has been studied.

 
3.1 Annual Air Quality Changes in Hohhot City

As can be seen from Fig. 2(a), the air quality in Hohhot City is improving year by year. The values of AQI, PM2.5, PM10, SO2, NO2, CO, and O3 in Hohhot City in 2014 were, respectively, 86.6, 44.0 µg m–3, 115.8 µg m–3, 46.5 µg m–3, 44.7 µg m–3, 1.9 mg m–3, and 67.1 µg m–3. Nevertheless, the levels in 2022 are 67.1, 23.4 µg m–3, 51.3 µg m–3, 10.5 µg m–3, 28.6 µg m–3, 0.6 mg m–3, 94.3 µg m–3. Consequently, the values in 2022 were respectively 22.5%, 46.8%, 55.8%, 77.4%, 36.0%, 70.3%, –40.6%, lower than those in 2014. Therefore, air quality in 2022 was obviously better than 2014.

Fig. 2. Annual air quality changes in Hohhot City during 2014–2022.Fig. 2. Annual air quality changes in Hohhot City during 2014–2022.

Air quality levels in Hohhot City have changed from 2014 to 2022 (Fig. 2(b)). From 2014 to 2021, the total proportions of first and second levels increased from 71.0% to 90.1%, but the total proportions of levels III–VI decreased from 29.0% to 9.9%, indicating a significant improvement in air quality.

 
3.2 Seasonal Air Quality Changes

The air quality in Hohhot City also has obvious seasonal changes (Fig. 3). The seasonal average concentrations of CO, SO2, PM10, PM2.5, and NO2 are the lowest in summer and the highest in winter, but the trend of O3 concentration is significantly different from other pollutants, with the highest in summer and the lowest in winter. The seasonal air quality in 2022 is significantly better than in 2014. Due to winter heating, the emissions of atmospheric pollutants in winter are significantly higher than those in the other seasons, which is the main cause for the frequent occurrence of severe pollution in Hohhot City during winter. In summer, strong solar radiation and high photochemical reaction rate lead to high production rates of O3.

Fig. 3. Seasonal air quality changes in Hohhot City over 2014–2022.Fig. 3. Seasonal air quality changes in Hohhot City over 2014–2022.

In spring (Fig. 3(a)), the values of AQI, PM2.5, PM10, CO, NO2, and SO2 in Hohhot City in 2022 are respectively 27.8%, 50.0%, 65.1%, 77.6%, 44.6%, and 78.8%, lower than those in 2014. However, the O3 concentration in Hohhot City in 2022 is 73.3% higher than in 2014. Similarly, in summer, the values in Hohhot City in 2022 are respectively –1.8%, 48.2%, 58.8%, 52.0%, 40.5%, and 65.0%, lower than those in 2014. Nevertheless, the O3 concentration in 2022 is 17.3% higher than in 2014 (Fig. 3(b)). In autumn, the values in Hohhot City in 2022 are respectively 29.6%, 45.0%, 54.5%, 58.3%, 30.5%, and 73.0%, lower than those in 2014. However, the O3 concentration in 2022 is 35.3% higher than in 2014 (Fig. 3(c)). In winter, the values in Hohhot City in 2022 are respectively 30.2%, 45.3%, 43.6%, 75.9%, 30.5%, and 81.6%, lower than those in 2014. Nevertheless, the O3 concentration in 2022 is 67.6% higher than in 2014 (Fig. 3(d)). In summary, the concentrations of all five pollutants decrease from 2014 to 2022, but the O3 concentration increases. PM2.5 and PM10 show the greatest decline in spring. This may be due to a decrease in atmospheric pollutants (SO2, NOx, and PM) emissions and an increase in VOCs emissions.

There have been significant changes in the proportions of air quality levels in the seasons over 2014–2022 (Table 3). In spring, the proportion of Level I increases by 27.0 percentage points between 2014 and 2022. Similarly, in summer, the proportion of level I increases by corresponding 0.7%. In autumn, the proportion of Level I increases by corresponding 12.0%. In winter, the proportion of Level I increases by corresponding 25.2 percentage points. The proportion of Level I increases the most in winter, and the proportion of Level II increases the most in autumn. The maximum decline of the proportions of Level III occurs in autumn. The maximum decline of the proportions of Level IV occurs in Winter. In 4 seasons, the air quality levels in 2022 are better than those in 2014.

Table 3. The proportions of AQI levels over 2014–2022 in Hohhot City (%).

 
3.3 Monthly Air Quality Changes during 2014–2022

The air quality in Hohhot City also has obvious monthly changes (Fig. 4). The maximum values of AQI, PM2.5, PM10, CO, NO2, and SO2 in Hohhot City is in January, and the minimum value is in June, August, or September. The changes of the five pollutants show a U-shaped pattern. However, the concentrations of O3 are different from other pollutants, with the maximum value in July and the minimum value in January. The changes of the O3 concentrations show an inverted V-shaped pattern. The reason for the persistent heavy pollution in January is not only due to the still large emissions of major atmospheric pollutants, but also related to the large-scale sustained and stable meteorological conditions. The long-term atmospheric stability, coupled with unfavorable factors such as high humidity and inversion, has led to the continuous accumulation and long-term retention of PM2.5 pollution, resulting in heavy pollution in January. The cause of heavy O3 pollution in July is strong ground radiation and high afternoon temperatures, which may result in O3 pollution.

Fig. 4. Monthly air quality changes in Hohhot City during 2014–2022.Fig. 4. Monthly air quality changes in Hohhot City during 2014–2022.

 
3.4 The Relationship between the Air pollutants in Hohhot City

The Pearson’s correlation coefficients (R) between six air pollutants and AQI values are completed and shown in Table S1 and Fig. 5. On the annual time scale, the R between AQI and PM2.5 is the best (R = 0. 854) (p < 0.01), followed by PM10 (R = 0. 750) (p < 0.05). These results suggest that PM2.5 and PM10 are the major factors in affecting AQI levels. The R between PM10 and PM2.5 was the best (R = 0. 916) (p < 0.01), indicating that PM2.5 is a large proportion of PM10. The strong relationship between PM2.5 and NO2 (R = 0.771) (p < 0.05), which indicated that NO2 played a significant effect in the formation of PM2.5. Nevertheless, the correlations between O3 and AQI, PM10, PM2.5, NO2, SO2, and CO, are negative. The good relationship is between O3 and CO, NO2 (R = –0.708, –0.30568, respectively) (p < 0.05), which suggested that CO and NO2 were two important factors in the formation of O3. Therefore, NO2 played a very important effect in the formation of PM2.5 and O3. On monthly and daily time scales, the correlations of pollutants is similar with the annual time scale. Moreover, the correlation between AQI and PM2.5 in 2015 was 0.901 as high as in 2017 (p < 0.01).

Fig. 5. Monthly correlation coefficients (R) between the air pollutants in Hohhot City from 2014 to 2022.Fig. 5. Monthly correlation coefficients (R) between the air pollutants in Hohhot City from 2014 to 2022.

Under high PM2.5 conditions, O3 concentration is strongly inhibited. The mechanism is that PM2.5 inhibits the chemical generation of O3 through the heterogeneous absorption of HO2 radicals and NOx. The suppression of O3 by PM2.5 will also make O3 generation more affected by VOCs emissions, which reduces the sensitivity of O3 to NOx reduction. Only when both NOx and VOCs are reduced can the trend of O3 rise be effectively curbed. Especially when the concentration of PM2.5 is high, the increase in O3 caused by the decrease in PM2.5 will be more significant, and reducing VOCs will be more effective at this time. Simultaneously reducing NOx and VOCs can more effectively achieve a win-win situation for haze and ozone control (Li et al., 2019a).

The correlations (R) between six air pollutants and AQI under different AQI scopes are achieved (Table S2). When the AQI is good or excellent, the correlations (R) between PM10 and AQI are the best (R = 0.658 and 0.606, respectively) (p < 0.01), followed closely by PM2.5 (R = 0.561 and 0.406, respectively) (p < 0.01). Additionally, when the AQI is slight, moderate, heavy pollution, the correlations (R) between PM2.5 and AQI are the best (R = 0.403, 0.385, and 0.371, respectively) (p < 0.01), followed closely by PM10. These results further reveal that PM2.5 and PM10 are the primary factors affecting air quality. Meanwhile, at different AQI scopes, the correlations (R) between O3 and NO2, PM10, SO2, PM2.5, and CO are mainly negative. Among the five types of atmospheric pollutants, the relationship (R) between NO2 and O3 is strongest (−0.772 ≤ R ≤ −0.461) (p < 0.01), implying that NO2 is the most important factor in the formation of O3 among the five air pollutants, which allows us to further deduce that NO2 plays a significant influence in the formation of O3 and PM2.5.

 
3.5 The Relationship between the Air Quality and Meteorological Factors

The precipitation(P), average pressure (AP), average atmospheric temperature (AAT), average relative humidity (ARH), sunshine duration (SD), and average wind speed (AWS) are the most important meteorological factors affecting air quality. The P, AP, AAT, ARH, SD, and AWS in in Hohhot City in 2014 are 394.8 mm, 886.5 hPa, 7.7°C, 46%, 2517.2 h, and 3.4 m s–1, respectively. These values in 2022 are 254.4 mm, 886.3 hPa, 7.6°C, 46.5%, 2919.4 h, 2.9 m s–1, respectively.

As shown in Table S3 and Fig. 6, on the time scales of year, month, and day, the related coefficients (R) between air quality and meteorological factors were relatively good. On an annual time scale, air quality (AQI, PM10, PM2.5, SO2, CO, and NO2) was positively correlated with AWS, and Air quality (PM2.5, PM10, SO2, CO, and NO2) was negatively correlated with SD and ARH. However, except of average pressure (AP) and SD, the correlations between O3 and meteorological factors were negative. Meteorological factors have a significant impact on the changes of air quality. The sunshine duration (SD) is the main meteorological factor responsible for annual and monthly changes of air quality. On the monthly and daily time scales, air quality (AQI, PM10, PM2.5, SO2, CO, and NO2) was negatively correlated with P, AAT, and SD, and positively correlated with AWS, AP. The precipitation(P), average pressure (AP), AAT, and SD is the main meteorological factors responsible for monthly and daily changes in air quality.

Fig. 6. Monthly correlation coefficients (R) between air quality and meteorological factors in Hohhot City from 2014 to 2022. Fig. 6. Monthly correlation coefficients (R) between air quality and meteorological factors in Hohhot City from 2014 to 2022.

The correlations (R) between the air quality and meteorological factors under different AQI scopes are achieved (Table S4). When the AQI is good, air quality (AQI, PM10, PM2.5, SO2, CO, and NO2) was negatively correlated with P, AAT, and SD, and positively correlated with AP and AWS. However, the correlations between O3 and P, AAT, SD were positive. When the AQI is slight pollution, air quality (AQI, PM10, PM2.5, SO2, CO, and NO2) was negatively correlated with P, AAT, and positively correlated with AP. However, the correlations between O3 and P, AAT, AWS, and SD were positive. When the AQI is moderate pollution, air quality (PM2.5, SO2, CO, and NO2) was negatively correlated with AAT, SD, AWS, and positively correlated with AP, ARH. However, the correlations between O3 and P, AAT, SD, and AWS were positive. When the AQI is heavy pollution, air quality (AQI, PM2.5, and NO2) was negatively correlated with AAT, SD, AWS, and positively correlated with AP, ARH. However, the correlations between O3 and P, AAT, SD, and AWS were positive. When the AQI is severe pollution, air quality (PM2.5, SO2, CO, and NO2) was negatively correlated with P, AAT, and positively correlated with AP, ARH. However, the correlations between O3 and P, AAT, and SD were positive.

 
3.6 The Relationship between the Air Quality and Socio-economic Factors in Hohhot City

The Hohhot Government has formulated and implemented a series of air pollution prevention and control policies since 2014. Hohhot Government has implemented the "Action Plan for Air Pollution Prevention and Control", the "Three Year Action Plan for Blue Sky Defense", the "Deepening the Battle of Pollution Prevention and Control", and the comprehensive control plan for autumn and winter air pollution in key areas for many consecutive years. The total emissions of sulfur dioxide, nitrogen oxides, and particulate matter have been decreasing year by year. As shown in Table 4, Air quality (PM10, SO2, and CO) was positively correlated with PME, SOE, NOE (p < 0.01), and negatively correlated with GDP, PTI, PCGDP, PP, UR, and GC (p < 0.01 or p < 0.05). Nevertheless, O3 was positively correlated with PTI (p < 0.05) and negatively correlated with NOE, SOE (p < 0.01 or p < 0.05).

Table 4. Correlation coefficients (R) between air quality and socio-economic factors.

The reduction of NOx over 2013–2017 helps to control O3 (Liu and Wang, 2020). Cleaner production and energy consumption control over 2014–2020 contributed the largest reduction of PM2.5 in China (Wang et al., 2022). Reducing fossil fuel combustion will result in a significant decrease in emissions of precursor substances such as SO2. Even in countries with the most severe air pollution, PM2.5 concentrations and associated mortality risks will continue to decrease (Huang et al., 2023). In the North China Plain, anthropogenic emissions are the main reason for the increase of near surface O3 concentration. During 2008–2018, the impact of anthropogenic emissions increased linearly, while the impact of meteorological conditions on O3 was relatively weak (Ma et al., 2023).

 
3.7 Comparison with Other Literatures

The change characteristics of air quality in different regions are different. The PM2.5 concentrations from 2013 to 2017 in the Yangtze River Delta region, the Beijing Tianjin Hebei region, and the Pearl River Delta region reduced from 67, 105, and 47 µg m–3, to 44, 64, 34 µg m–3, respectively. However, the O3 concentration in these areas has sharply increased. In order to achieve the goals of reducing O3 and PM2.5 by 5% and 25% by 2025, respectively, regional SO2, NOx, NH3, VOCs, and primary PM2.5 emissions should be reduced by 23%, 18%, 14%, 17%, and 33% compared to 2017, respectively (Dong et al., 2023). Based on the harmonic model, decreases in PM10, PM2.5, SO2, NO2, and CO levels were found in 91.9%, 90.7%, 94.3%, 75.2%, and 88.7% of Chinese cities over 2016–2020, respectively, while an increase in O3 was found in 87.2% of Chinese cities (Gao et al., 2023). The trough O3 increases by 4.5 µg m–3 per year in Beijing from 2014 to 2019. The interaction between O3 and PM2.5 in summer can be divided into three typical phases. The first phase is the synchronous and rapid growth of O3 and PM2.5. The second phase is a rapid increase in PM2.5 at high O3 concentrations. The third phase is the high PM2.5 pollution, which inhibits the production of O3 (Wu et al., 2022). The PM2.5 concentration in Beijing decreases by 57% from 2000 to 2021 (Guo et al., 2023f). The concentrations of NO2, SO2, and CO in Anhui Province over 2015–2021 show an overall downward trend. However, the concentrations of PM10 and PM2.5 slowly increased before 2017 and then decreased, while the concentration of O3 significantly increased before 2018 and then slowly decreased. On a monthly scale, O3 shows an M-shaped variation, but the remaining five pollutants show a U-shaped variation (Jia et al., 2023). The concentration of air pollutants has been decreasing year by year in Chengdu-Chongqing urban agglomeration (CCUA) during 2015–2021. Except for O3, the air pollutants in autumn and winter are higher than that in summer (Tan et al., 2023). Compared to 2018, winter PM2.5 over the North China Plain reduced by 15% and 29% in 2019 and 2020, respectively (Du et al., 2022). Except for O3, all air pollutants show a downward trend in 2003–2019 in Cyprus (Vrekoussis et al., 2022). The PM2.5 concentrations in 2017 in our study is respectively 2, 24 µg m–3, lower than those in the Yangtze River Delta region, the Beijing Tianjin Hebei region. The annual trough growth of O3 in our study is 1.5 µg m–3 lower than that in Beijing. Although the magnitude of changes in air quality in these regions is different from our research, the trend of change is the same. That is to say, except for ozone, the concentrations of other pollutants have decreased.

There are significant correlations between different pollutants in Chinese cities. O3 is negatively correlated with the remaining five air pollutants and the other five pollutants is positively correlated (Zhou et al., 2023). Our research findings are the same as the literature. In high pollution areas, there is an urgent need for coordinated control measures for multiple pollutants. While the air quality was excellent or severe pollution, the correlations (R) between PM10 and AQI were 0.85 and 0.48, respectively. The formation rate of O3 mainly depends on the photochemical reaction of NOx and VOCs. O3 was also affected by the Radiant intensity of the sun (Guo et al., 2019). When the air quality was excellent or severe pollution in our research, the correlations (R) between PM10 and AQI were 0.606 and 0.021, respectively. The correlations in our research are lower than the literature. Except for O3, five pollutants are positively correlated. But 5 pollutants are negatively correlated with O3 (Jia et al., 2023). AQI is positively correlated with PM2.5 and PM10 on multiple time scales, and positively correlated with SO2, CO, NO2, and O3 on short-term scales in CCUA (Tan et al., 2023). However, AQI is positively correlated with PM2.5, PM10, SO2, CO, and NO2 on multiple time scales, and negatively correlated with O3 on multiple time scales in our research. These results are similar to our research, with the exception of O3, all other five pollutants showing a positive correlation. AQI is positively correlated with five pollutants at multiple time scales.

The meteorological conditions in different regions are also different. Cold air activities in Northern China decrease, and air humidity increases, but precipitation is minimal, making it more prone to severe haze pollution weather in calm and stable weather. In summer, high temperature is conducive to accelerating the reaction rate of mechanistic organic photochemistry with O3 and improving the output of O3 (Sun et al., 2019). Air pollution index (API) in Lanzhou and Xi’an was related to minimum temperature, average temperature, and maximum temperature (Guo et al., 2020). PM10, SO2, PM2.5, and CO are chiefly influenced by air pressure and dew point temperature, but O3 is chiefly influenced by air temperature (Huang et al., 2021). O3 is mainly generated by air pollutants such as NOx, CO, and VOCs through photochemical reactions in the atmosphere. Elevated temperatures may increase the emissions of VOCs, the main precursor of O3, thereby increasing the concentration of ozone in the atmosphere. In the Yangtze River Delta and Pearl River Delta regions, the impact of meteorological conditions is more significant, leading to a decrease of 3.25–4.42 ppb in O3 between 2008 and 2018 (Ma et al., 2023). Atmosphere temperature and wind speed are major factors for air pollutants in China during 2015–2016 (Li et al., 2019b). Except for O3, temperature has the most prominent negative correlation effect on the five pollutants. The effect of sunlight duration on O3 is most significant in Anhui Province (Jia et al., 2023). In addition, in the short term, AQI is positively correlated with temperature and sunshine duration, and negatively correlated with humidity and rainfall in CCUA (Tan et al., 2023).The growth trend of O3 in most cities in China is mainly determined by changes in meteorological factors such as temperature and solar radiation, while the downward trend of PM2.5 is mainly determined by the emission reduction measures of PM2.5 and its precursors. A single meteorological factor could explain approximately 57%–80% of O3 changes and 20%–33% of PM2.5 changes (Pan et al., 2023). In 2020, meteorological conditions in the BTH region were relatively favorable. Meteorological factors and emission reduction caused a decrease of 18.6% and 10.5% in PM2.5 concentration compared to 2018, respectively (Du et al., 2022). These findings are consistent with our findings that ozone is positively correlated with temperature and solar radiation. What's different is that, The difference is that precipitation and atmospheric pressure are the main influencing factors for the other five atmospheric pollutants in our research.

The impact of socio-economic factors varies in different regions (Xu et al., 2019). Economic growth and innovation have positive influences on SO2 from 2005 to 2016 in China. Urbanization, industry, and transportation are the three factors that cause SO2 pollution. Foreign direct investment has a negative influence on SO2 (Jiang et al., 2020). Urbanization rate, population, value of the secondary economy, days of heating and cooling have a positive impact on air pollutant emissions, while per capita GDP, population density, precipitation and relative humidity (RH) have a negative impact on air pollutant emissions (Ren and Matsumoto, 2020). The difference from our research is that Urbanization rate, population, and value of the secondary economy have a negative impact on air pollutant emissions. Terrain meteorology, economic development, pollution sources, and urbanization are closely related to air pollution (Bai et al., 2019). The economic growth and environmental protection policies have a positive impact on environmental governance in Anhui Province (Jia et al., 2023). Economic growth has the most important influence on PM2.5 concentration in China. The socioeconomic factors urban green area (UGS), power consumption (PC) are positively impacting the emissions of PM2.5 in Yellow river basin (YRB) (Bhatti et al., 2023). In addition, power consumption and its structure, urban land use structure, industrial soot emissions, and transportation have been identified as key factors affecting PM2.5 levels in China (Li et al., 2022). Dense artificial buildings and urban landforms further weaken the wind speed, making it easier for atmospheric pollutants to accumulate and more difficult to diffuse. These findings are consistent with our research in that urbanization rate, population, and Production of the Territory Industry (PTI) have a positive impact on air pollution. The difference is that economic growth and environmental protection policies have had a negative impact on air pollution in our research.

 
4 CONCLUSIONS


This study analyzes the multiple time scale changes and their causes of air quality in Hohhot City in China. The annual values of five air pollutants and AQI from 2014 to 2022 show a decreasing trend, but the O3 concentration increases from 2014 to 2022. From 2014 to 2022, PM2.5 and PM10 show the greatest decline in spring. The monthly changes of the five pollutants show a U-shaped pattern. However, the concentrations of O3 is inverted V-shaped.

This research focuses on the correlation between air quality, meteorological factors and socio-economic factors on the annual, monthly, and daily scales. The sunshine duration (SD) is the main meteorological factor for annual and monthly changes in air quality. The precipitation(P), average pressure (AP), average atmospheric temperature (AAT), and sunshine duration (SD) is the main meteorological factors for monthly and daily changes in air quality. The sulfur dioxide emissions (SOE), nitrogen oxide emissions (NOE), and particulate matter emissions (PME) have a positive effect on annual changes in air quality. The reduction of atmospheric pollutants emissions has played a key role in improving air quality. PM2.5 and PM10 are the main factors influencing AQI. Among the five types of atmospheric pollutants, the relationship between NO2 and O3 is strongest, implying that NO2 plays a significant influence in the formation of O3 and PM2.5.

In future, the research can be further extended to hourly air pollution and other factors. In addition, other influence factors, such as social consumption, planetary boundary layer and water vapor, can be further taken into consideration.

 
ACKNOWLEDGMENTS


This research was supported by Shandong Provincial Natural Science Foundation (Grant No. ZR2023MD075), LAC/CMA (Grant No. 2023B02), State Key Laboratory of Loess and Quaternary Geology Foundation (Grant No. SKLLQG2211), Shandong Province Higher Educational Humanities and Social Science Program (Grant No. J18RA196), the National Natural Science Foundation of China (Grant No. 41572150), and the Junior Faculty Support Program for Scientific and Technological Innovations in Shandong Provincial Higher Education Institutions (Grant No. 2021KJ085).


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