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Article

Analysis of the PM2.5–O3 Pollution Characteristics and Its Potential Sources in Major Cities in the Central Plains Urban Agglomeration from 2014 to 2020

by
Shu Quan
1,
Miaohan Liu
1,
Boxuan Chen
1,
Yuehua Huang
2,
Meijuan Wang
3,
Qingxia Ma
4 and
Yan Han
1,*
1
College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
2
School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
3
Kaifeng Weather Bureau, Kaifeng 475004, China
4
Henan University Integrated Prevention and Control of Air Pollution and Ecological Safety Key Lab of Henan, Kaifeng 475004, China
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(1), 92; https://doi.org/10.3390/atmos14010092
Submission received: 3 December 2022 / Revised: 24 December 2022 / Accepted: 26 December 2022 / Published: 31 December 2022
(This article belongs to the Special Issue Air Pollution in China (2nd Edition))
Browse Figures
Versions Notes

Abstract

:
To highlight the characteristics of PM2.5–O3 pollution in the Central Plains Urban Agglomeration, spatial and temporal characteristics, key meteorological factors, and source pollution data for the area were analyzed. These data from the period 2014–2020 were obtained from state-controlled environmental monitoring stations in seven major cities of the agglomeration. The results revealed the following: (1) Spatially, the PM2.5–O3 pollution days were aggregated in the central area of Xinxiang and decreased toward the north and south. Temporally, during the 2014–2020 period, 50 days of PM2.5–O3 pollution were observed in the major cities of the Central Plains Urban Agglomeration, with an overall decreasing trend. (2) A low-temperature, high-pressure environment appeared unfavorable for the occurrence of PM2.5–O3 pollution days. Wind speeds of 2.14–2.19 m/s and a southerly direction increased the incidence of PM2.5–O3 pollution days. (3) The external transport range in summer was smaller and mainly originated from within Henan Province. These results can provide important reference information for achieving a synergistic control of PM2.5–O3 pollution, determining the meteorological causes, as well as the potential sources, of PM2.5–O3 pollution in polluted areas and promoting air pollution control.

1. Introduction

Air pollution is a major environmental concern worldwide, which is being exacerbated by rapid urbanization and other anthropogenic activities [1,2,3]. In recent years, to control and minimize air pollution, varying measures have been implemented in different countries [4,5,6]. However, because of the diversity and complexity of pollution sources, composite air pollution has become the main mode of pollution in many countries [7,8,9], and thus, its management is gaining increasing attention. Among pollutants, PM2.5 and O3 share the same precursors and usually exhibit strong spatial and temporal correlations [10,11,12]. Considering that each of these pollutants can influence the transformation of the other, composite pollution involving PM2.5 and O3 occurs in many regions worldwide [13,14,15]. This implies that high levels of both PM2.5 and O3 have been reported concurrently in many areas [16,17,18,19]. Therefore, PM2.5 and O3 are pollutants that are currently deteriorating ambient air quality in different areas globally.
In recent years, studies on PM2.5 and O3 pollution have focused on spatial and temporal characteristics, meteorological causes, and sources. According to these studies, PM2.5 pollution is prevalent in winter, whereas O3 pollution is dominant in summer [20,21,22], and thus, PM2.5–O3 pollution exhibits apparent seasonal variations [23,24,25,26]. In areas such as the Yangtze River Delta, Pearl River Delta, Fenwei Plain, and Beijing–Tianjin–Hebei region, PM2.5–O3 pollution has been increasing in recent years [27,28,29]. PM2.5 and O3 share the same precursors—NOx and VOCs [30,31,32]—but interact in the atmosphere through diverse sources [33,34,35]. As the concentration of O3 increases, oxidation in the atmosphere also gradually increases [36,37,38,39], whereas photolysis of NO2 that generates OH radicals reduces the concentration of NO2 and inhibits the production of PM2.5 [31,37,40]. Conversely, an increase in the concentration of PM2.5 weakens solar radiation and reduces the occurrence of photoreactions, thereby reducing the concentration of O3 [41,42,43]. Meteorological factors also influence PM2.5–O3 pollution variably; high temperature and low humidity favor the generation of O3, but these can inhibit the production of PM2.5 [44,45,46]. According to back trajectory models, PM2.5–O3 pollution has been principally assigned to local and exogenous sources [17,47,48,49]. Existing studies on PM2.5–O3 pollution in areas such as the Yangtze River Delta, Pearl River Delta, Beijing–Tianjiny–Hebei region, and Fenwei Plain have focused on the short term, and thus, long-term studies for urban agglomerations such as the Central Plains are unavailable.
The Central Plains is a national strategic urban agglomeration in the middle reaches of the Yellow River Basin. It is an important hub through which the east and west and north and south are connected, and thus, its potential and advantages in relation to development are immense. Concerning the topography, the Central Plains Urban Agglomeration is dominantly flat, and its prominent inter-urban pollutant emission effects exacerbate the pollution levels in different areas [50,51,52]. The key cities in the Central Plains urban agglomeration (Anyang, Hebi, Puyang, Xinxiang, Kaifeng, Zhengzhou, and Jiaozuo), which are in the Beijing–Tianjin–Hebei air pollution transmission corridor, are increasingly experiencing problems regarding air pollution. Therefore, in the present study, PM2.5–O3 concentrations and related meteorological data were utilized to assess the spatial and temporal characteristics, the controlling meteorological factors, and the potential sources of this composite pollutant to improve our understanding of its magnitude in the Central Plains Urban Agglomeration. The findings of this study are expected to provide a theoretical basis for the management of PM2.5–O3 pollution in major cities in the Central Plains and for the integrated prevention and control of pollution in the region.

2. Materials and Methods

2.1. Study Area and Meteorological Data

The PM2.5 and O3 data used in the present study were obtained from the Environmental Protection Bureau of cities in Henan Province. The meteorology data (temperature, relative humidity, wind direction, and wind speed) were retrieved from hourly recordings of meteorological stations that are available in the Henan Meteorological and Environmental Center, and the distribution of these stations are shown in Figure 1. Data covering the period from 1 January 2014 to 31 December 2020 for Puyang, Hebi, Anyang, Jiaozuo, Xinxiang, Kaifeng, and Zhengzhou in the Central Plains were utilized in the present study.
According to the Ambient Air Quality Standard (GB3095-2012) and secondary standards of the concentration limits of pollutants in the National Air Quality Standards, a daily average ρ(PM2.5) > 75 μg/m3 and a daily maximum 8 h sliding average ρ(O3) > 160 μg/m3 were used to define a PM2.5–O3 pollution day as one in which both pollutants exceeded the standard levels. Similarly, a day in which only PM2.5 was higher than the National Secondary Standard value was termed a PM2.5 pollution day, whereas that with just a higher O3 concentration was referred to as an O3 pollution day.
According to the meteorological industry standard of the China Meteorological Administration (QX/T152-2012), the months associated with different seasons are the following: March–May for spring, June–August for summer, September–November for autumn, and December–February for winter. The back trajectory analysis was conducted using the 2014–2020 Global Data Assimilation System (GDAS) data that were obtained from the U.S. National Centers for Environmental Prediction (NCEP).

2.2. Back Trajectory Analysis and Trajectory Clustering

In the present study, the posterior trajectory clustering involving a potential source contribution function (PSCF) model was utilized to determine transport pathways and potential source areas of pollutants in the study area through an integration of measured chemical concentrations and recorded meteorological data. The posterior trajectory clustering and PSCF analysis were conducted using the TrajStat plugin in the MeteoInfo 3.3.0 open source software developed by the Chinese Academy of Meteorological Sciences (Java Edition). Xinxiang (35.30° N and 113.92° E), which displayed the highest number of PM2.5–O3 pollution days (see the “Results and Discussion” section for further details), was considered a receiving point, whereas the starting height was set to 500 m (a height of 500 m can reflect the characteristics of the average atmospheric flow field), and these parameters were utilized to simulate 24 h back trajectories for different seasons from 1 January 2014 to 31 December 2020 (12:00 UTC), in association with GDAS flow field data. A systematic clustering method was used to group trajectories that reached the receiving point. All trajectories were regarded as different n classes, and the closest two were initially combined into a class, followed by others, until an optimum result was obtained.

2.3. Analysis of Potential Sources

In the PSCF model, air mass trajectories passing through a region are considered influenced by local emissions, and thus, this contributes to concentrations of pollutants at an observation point associated with transport. The PSCF value was obtained from segmented endpoints of a trajectory under the spatial resolution of the grid using the following expression:
P S C F = m i , j n i , j
where n i , j is the number of endpoints of a trajectory of the i , j grid, and m i , j is the number of endpoints of the trajectory of the i , j grid that exceed the threshold. The thresholds were set according to the Ambient Air Quality Standard (GB3095-2012) and the concentration limits of pollutants in the National Air Quality Standards to a daily average of >75 μg/m3 for PM2.5 and a daily maximum 8 h sliding average (O3–8 h) of >160 μg/m3 for O3. When n i , j is small, the results of PSCF will be large and have a large uncertainty range. In order to reduce this uncertainty, a weight function W i j (Equation (2)) was introduced in the present study. When n i , j in a grid is less than three times the average number of trajectory endpoints per grid ( n a v e ) , W i j is needed to reduce the uncertainty of the PSCF results [53]. The Weighted PSCF model (WPSCF) derived from the addition of weights improves the location of potential source regions [54,55].
W i j = 1.00           3 n a v e < n i , j 0.70           1.5 n a v e < n i , j ≤ 3 n a v e 0.40           n a v e ≤ n i , j ≤ 1.5 n a v e 0.17           n i , j ≤ n a v e

3. Results and Discussion

3.1. Spatial and Temporal Characteristics of PM2.5–O3 Pollution

3.1.1. Spatial Characteristics of PM2.5–O3 Pollution

As shown in Figure 2, the PM2.5–O3 pollution days for cities in the Central Plains Urban Agglomeration were clustered around Xinxiang, and these days decreased toward the north and south. The PM2.5–O3 pollution days in spring exhibited the highest values throughout the study area and followed an expansive trend from the center to the northeast and southwest. Xinxiang showed the highest number of compound pollution days (31 days), whereas Hebi had the lowest number (8 days). Conversely, in the summer, PM2.5–O3 compounded pollution days were concentrated in the northwest of the study area, and the highest occurrences of 36 and 33 days were in Jiaozuo and Xinxiang, respectively. Hebi also showed the lowest number of compound pollution days (6 days) in the study area. In autumn, the PM2.5–O3 compound pollution days were highest in the northwest of the study area, and Zhengzhou and Xinxiang displayed the highest number of pollution days (12 and 11, respectively), whereas other regions had less than 10 days. Finally, in winter, no PM2.5–O3 pollution days occurred in the entire region.
As shown in Figure 2, the number of compound pollution days in Hebi was lower than in the surrounding areas in all seasons. This was likely due to the topography around Hebi, as it is located between Anyang, Puyang, and Hebi, and thus the pollutants are blocked by the mountains and cannot easily spread to Hebi.

3.1.2. Temporal Characteristics of PM2.5–O3 Pollution

As shown in Figure 3a, during the period from 2014 to 2020, 50 days of PM2.5–O3 pollution occurred in the cities of the Central Plains Urban Agglomeration that were studied. Overall, this pollution decreased from 15 days in 2014 to 1 day in 2020. The ρ(PM2.5) data exhibited an M-like shape, and the highest value of 108 μg/m3 occurred in 2019, whereas the lowest value of 70 μg/m3 emerged in 2017. The second highest value of 102 μg/m3 was found for 2014 and decreased to 70 μg/m3 in 2017. However, ρ(PM2.5) then increased to 78 and 108 μg/m3 in 2018 and 2019, respectively, whereas the ρ(O3) data exhibited a V-like shape, and the highest and lowest values of 210 and 152 μg/m3 were correspondingly associated with 2020 and 2018, respectively. As shown in Figure 3b, according to the monthly distribution, the PM2.5–O3 pollution days were concentrated in the spring and summer of each year (April–July). The month of June showed the maximum monthly occurrence of 13 days, followed by May with 11 days, whereas the late autumn and winter months (November–February) had no PM2.5–O3 pollution days. Variations in the meteorological conditions during different seasons may cause significant differences in the generation and propagation of PM2.5–O3 pollution [8,11,32], and this may account for the seasonal differences observed in the present study.

3.2. Relationships between PM2.5–O3 Pollution and Meteorological Factors

Both PM2.5 and O3 are easily transported in the atmosphere, and, considering that these are products of complex photochemical reactions, their concentrations are strongly influenced by meteorological factors [29]. Therefore, the relationships between PM2.5–O3 pollution and meteorological factors were evaluated subsequently.
Figure 4a shows that the single pollutant and PM2.5–O3 pollution had a very complex relationship with air pressure and temperature. In fact, ρ(PM2.5) exhibited a significant negative correlation with temperature (−0.969), and ρ(O3) showed a strong negative correlation with air pressure (−0.988). However, ρ(PM2.5) in PM2.5–O3 pollution days showed a significant positive correlation with temperature (0.812), and ρ(O3) in PM2.5–O3 pollution days showed a significant positive correlation with air pressure (0.512). Figure 4a,b show that PM2.5–O3 pollution days occurred predominant in spring and summer, during which high temperatures and low pressures are common. The highest temperature in summer was 27.44 °C, whereas the lowest pressure was 993.56 hPa, and these conditions were associated with the lowest ρ(PM2.5) of 44 μg/m3 and the highest ρ(O3) of 96.92 μg/m3. In the winter, during which there were no days of PM2.5–O3 pollution, the lowest ρ(O3) of 31.05 μg/m3 and the highest ρ(PM2.5) of 119.05 μg/m3 were obtained. This season produced the minimum temperature of 2.55 °C and the maximum pressure 1015.30 hPa. These results indicate that the absence of PM2.5–O3 pollution days in winter was mainly because a low–temperature and high–pressure environment is unsuitable for the generation of O3, which reduced the occurrence of PM2.5–O3 pollution days.
The relative humidity values in summer (67.12%) and autumn (66.69%) were higher than those in spring (54.25%) and winter (54.67%). The proportion of ρ(PM2.5) and ρ(O3) was not strongly influenced by changes in humidity, and this was probably due to the superposition of various factors such as temperature and wind speed, as well as the atmospheric content of ·OH, i.e., this might be due the production and accumulation of PM2.5 and O3 being influenced by multiple factors [42]. Therefore, the relationship between PM2.5–O3 pollution and relative humidity is complex.
Winds can promote the formation and dispersion of atmospheric pollutants, and thus, the relationships between wind speed and frequency and PM2.5–O3 pollution were also examined. Figure 4b shows that the maximum wind speed of 2.64 m/s occurred in spring, followed by 2.19 m/s in summer, whereas the minimum wind speed of 1.97 m/s was recorded in autumn. Regarding the wind direction frequency, Figure 5 shows that southerly winds prevailed in the spring and summer, whereas northerly winds were dominant in autumn and winter in the cities in the Central Plains Urban Agglomeration. Considering the seasonal differences in PM2.5–O3 pollution days and the frequency of this pollution in spring and summer, it is evident that as part of the Beijing–Tianjin–Hebei pollution transmission channel cities, PM2.5–O3 pollution and O3 precursors were promoted in the cities in the Central Plains Urban Agglomeration in the presence of southerly winds with speeds of 2.14–2.19 m/s. The increase of the PM2.5–O3 pollutant and O3 precursors in the spring and summer was consistent with the predominantly southerly winds. The influence of wind direction and speed on pollutants in spring and summer involved two segments [4,24]. First, the PM2.5–O3 composite pollutant and O3 precursors were transported to the study area by wind, which favored their generation and accumulation and, thus, the increase in their concentrations. Second, the winds in the study area also transported the PM2.5–O3 pollutant and O3 precursors that were present in other areas, thereby causing daily variations in PM2.5–O3 pollution.

3.3. Analysis of the PM2.5–O3 Pollution Sources

According to the back trajectory analysis of seasonal data, the air mass transport paths varied significantly, and the external transport ranges of the pollutants were high. Based on the inflection point of the total spatial variance of the trajectory clustering, the inputs of ρ(PM2.5) and ρ(O3) could be grouped into six major airflow trajectories for all seasons, and these are presented in Table 1.
In the study area, the air currents that were responsible for the input of pollutants in spring were mainly No. 1 from Puyang and No. 4 from the south of Henan. These currents, which accounted for 50% of the frequency, contributed 120.2 and 55.6 μg/m3 of ρ(PM2.5) and ρ(O3), respectively. In addition to the data shown in Figure 6a, the sources of PM2.5 pollution in spring were mainly in the central part of Henan (south of Kaifeng, west of Zhoukou, and northeast of Zhumadian), and thus, pollution was minimally influenced by external factors. The WPSCF analysis for O3 produced no evident range, but high values were found for Zhoukou and Fuyang.
Conversely, in summer, the dominant air currents were No. 2 from the northwest of Jiangsu Province and southwest of Lu and No. 5 from the central part of Yu, and these were associated with frequencies of 28.26% and 26.09%, respectively. For the pollutant input from the external air currents, the concentration of O3 in summer was 2–3 times higher than in autumn and winter, while the input PM2.5 was the lowest of the year. Owing to the favorable temperature and humidity, ρ(O3) in summer was high, and its transmission intensity was enhanced. The results and data shown in Figure 6b revealed that potential sources of high PM2.5 pollution were the northeastern part of Nanyang and the central part of Heze. Pollutants generated in the north of the Beijing–Tianjin–Hebei region are easily transported to the study area because of the prevailing wind direction, and this explained the prominent pollution trajectories. In contrast, the potential source areas of O3 appeared predominantly in the central part of Henan Province (Xinxiang, Zhengzhou, Kaifeng, Xuchang, Pingdingshan, and Shangqiu) and in the west of Shandong Province (Heze). High values were also obtained from the WPSCF analysis, which demonstrated that most of O3 pollution during the period that was studied originated within the study area.
Relatedly, in autumn, the principal air currents were No. 4 from the east of Yu and No. 2 from the south of Lu, and these represented frequencies of 35.16% and 20.88%, respectively, and the provincial sources exceeded 30%. The maximum inputs of 64.3 and 97.6 μg/m3 corresponding to ρ(PM2.5) and ρ(O3), respectively, were both associated with the No. 2 air current from the south of Shanxi Province. Figure 6c shows that the potential sources of PM2.5 were mainly in Henan Province (Hebi, central part of Anyang, southwest of Kaifeng, and Luoyang). Lower but significant values were also associated with areas outside the province including Handan and Liaocheng. The diffused spatial distribution of high pollution in the study area was attributed to the prevailing northerly winds in autumn. Potential source areas were in the south of the study area (Zhengzhou and Kaifeng) and southwest of Heze.
Lastly, in winter, the prevalent air currents were No. 5 from the center of Henan Province, No. 3 from the south of Shanxi Province, and No. 1 from different places in the northwest of the study area. The occurrence frequency of these air currents accounted for more than 20%. Hence, the the winter airflow mainly originated from the interior and northwest of Henan Province. The No. 1 current contributed the highest O3 concentration of 40.1 μg/m3, whereas No. 3 was responsible for the maximum ρ(PM2.5) input of 113.7 μg/m3. According to these results and data shown in Figure 6d, high ρ(PM2.5) values during winter were recorded in the east of Anyang, Hebi, Xinxiang, and Luoyang. The wide range of airflow sources available in winter indicated that under low–temperature conditions, contaminating precursors travel farther and have greater impacts. Potential sources of PM2.5 were dominantly found in the province, and these were related to the burning of fuels for heating during winter. Even though a significant potential source area of O3 was not evident, Jincheng is an area from which high values were obtained.
In summary, in addition to the local emissions, PM2.5–O3 pollution appeared influenced by the transport of pollutants between neighboring cities. Changes in the seasons altered the transport pathways of the pollutants in and outside the study area. Potential source areas of the pollutants were not evident from winter to spring, but in summer and autumn, Heze, which is adjacent to Henan Province, appeared as a main potential source.
This paper has certain limitations. The effects of PM2.5–O3 pollution involve complex processes, which are also related to the production and transport of its precursors and the interactions of chemical substances. Therefore, further studies on PM2.5–O3 pollution need to be carried out by researchers.

4. Conclusions

Based on the pollutant data of PM2.5 and O3, as well as on meteorological data, this paper investigated the spatial and temporal characteristics of PM2.5–O3 pollution in key cities of the Central Plains Urban Agglomeration from 2014 to 2020, analyzed the effects of corresponding meteorological conditions on PM2.5–O3 pollution in each season, and explored the potential sources of the pollutants using backward trajectory and potential source analysis methods. The principal findings can be summarized as follows:
(a)
Spatially, the PM2.5–O3 pollution days in the Central Plains Urban Agglomeration were clustered around the center of Xinxiang and then decreased toward the north and south. The highest concentration of PM2.5–O3 during the pollution days occurred in the spring for the entire study area, and the trend expanded from the center to the northeast and southwest. Temporally, during the period from 2014 to 2020, 50 days of PM2.5–O3 pollution occurred in major cities in the Central Plains Urban Agglomeration, with a maximum of 15 composite pollution days in 2015 and a minimum of 1 day in 2020, and the overall trend of the interannual variation of composite pollution in the major cities of the Central Plains urban agglomeration decreased in combination. According to the monthly distribution, the PM2.5–O3 pollution days were concentrated in the spring and summer of each year. The month of June was associated with the highest number of pollution days (13), followed by May (11), whereas no PM2.5–O3 pollution day was observed in the late autumn and winter (November–February).
(b)
Regarding the influence of meteorological factors, composite, ρ(PM2.5), and ρ(O3) pollution exhibited strong correlations with the air pressure and temperature. The parameter ρ(PM2.5) was significantly negatively correlated with the air temperature (−0.969), whereas ρ(O3) was significantly negatively correlated with the air pressure (−0.988). The results associated with a temperature of 2.55 °C and an air pressure of 1015.30 hPa revealed that a low–temperature and high–pressure environment was not conducive to the generation of a PM2.5–O3 pollution day. As part of the Beijing–Tianjin–Hebei pollution transmission corridor, the major cities in the Central Plains Urban Agglomeration appeared as favorable areas for the accumulation of the PM2.5–O3 pollutant and O3 precursors when the wind speed varied from 2.14 to 2.19 m/s, and the winds were southerly. These conditions promoted the occurrence of PM2.5–O3 pollution days.
(c)
The transport paths of air masses varied significantly among the seasons, and the external contributions of pollutants in spring, autumn, and winter were higher than in summer. The inflow of PM2.5–O3 pollution in spring originated mainly from provinces around the study area. The input of ρ(O3) was highest from the central part of Henan Province in summer, whereas that of ρ(PM2.5) was predominantly from the southwest of Hebei Province. In autumn, inputs of both ρ(PM2.5) and ρ(O3) originated mainly from the south of Shanxi Province, whereas in winter, ρ(O3) was transported from many areas in the northwest of the study area, but the highest input of ρ(PM2.5) was from the south of Shanxi Province. The potential sources of PM2.5 were shifted and diffused by the prevailing winds from the central part of Henan Province in spring to the south (northeast of Nanyang and center of Heze) in summer. In autumn and winter, these sources were mainly in the study area, including the central part of Anyang, Hebi, Xinxiang, Zhengzhou, and Kaifeng. Potential source areas for O3 were not evident in spring and winter, whereas in summer and autumn, O3 pollution originated mainly from areas in the province and from Heze city in Shandong province, which is adjacent to Henan Province.
This study revealed the spatial and temporal variation of PM2.5–O3 pollution in key cities of the Central Plains Urban Agglomeration in recent years and the seasonal meteorological characteristics affecting its concentration. Additionally, it showed the influence of seasonal regional transport. Due to the limited data on pollutants and the scope of the analysis methods, further research is needed to combine long–term fine meteorological and environmental observations with the analysis of chemical reactions between pollutants. This will improve our understanding of PM2.5–O3 pollution.

Author Contributions

Conceptualization, S.Q. and M.L.; Formal analysis, M.L. and B.C.; Funding acquisition, Y.H. (Yan Han); Methodology, S.Q.; Project administration, Y.H. (Yan Han); Resources, Y.H. (Yuehua Huang), M.W. and Q.M.; Supervision, Y.H. (Yan Han); Visualization, S.Q. and M.L.; Writing—original draft, B.C.; Writing—review & editing, S.Q. and Y.H. (Yan Han). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Innovative Research Group Project of the National Natural Science Foundation of China (grant number 42105071), the Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security (grant number No. PAP202101), and the Applied Technology Research Fund Project of Key Laboratory of Meteorological Disaster Prevention and Mitigation in Kaifeng (grant number No. BQK202103). The funding sources had no involvement in the study design; collection, analysis, or interpretation of the data; writing of the report; or the decision to submit this article for publication.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Special thanks to Y.H. (Yan Han) for her patient instruction and meticulous review.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Overview of the study area. The stations in the figure (red stars) indicate conventional ground-based meteorological stations in major cities in the Central Plains Urban Agglomeration.
Figure 1. Overview of the study area. The stations in the figure (red stars) indicate conventional ground-based meteorological stations in major cities in the Central Plains Urban Agglomeration.
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Figure 2. Plots showing the seasonal variations of PM2.5–O3 pollution days in the key cities of the Central Plains Urban Agglomeration from 2014 to 2020.
Figure 2. Plots showing the seasonal variations of PM2.5–O3 pollution days in the key cities of the Central Plains Urban Agglomeration from 2014 to 2020.
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Figure 3. (a) Chart showing the Interannual distribution of PM2.5, O3 and PM2.5–O3 composite pollution days and (b) the intermonthly distribution of PM2.5–O3 composite pollution days.
Figure 3. (a) Chart showing the Interannual distribution of PM2.5, O3 and PM2.5–O3 composite pollution days and (b) the intermonthly distribution of PM2.5–O3 composite pollution days.
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Figure 4. (a) Chart showing the correlations between PM2.5–O3 pollution and meteorological factors and (b) the seasonal variation of PM2.5, O3, and PM2.5–O3 pollution versus meteorological factors in the Central Plains Urban Agglomeration from 2014 to 2020. Note: the colors in (a) represent the degree of correlation, with red representing a positive correlation, yellow representing no correlation, and green representing a negative correlation.
Figure 4. (a) Chart showing the correlations between PM2.5–O3 pollution and meteorological factors and (b) the seasonal variation of PM2.5, O3, and PM2.5–O3 pollution versus meteorological factors in the Central Plains Urban Agglomeration from 2014 to 2020. Note: the colors in (a) represent the degree of correlation, with red representing a positive correlation, yellow representing no correlation, and green representing a negative correlation.
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Figure 5. Plot showing the seasonal frequency distribution of wind speeds and directions associated with PM2.5–O3 pollution in the Central Plains Urban Agglomeration from 2014 to 2020. Note: the values inside the circles represent the cumulative frequency of wind speed in that direction (%). (a–d) represent spring, summer, autumn, winter.
Figure 5. Plot showing the seasonal frequency distribution of wind speeds and directions associated with PM2.5–O3 pollution in the Central Plains Urban Agglomeration from 2014 to 2020. Note: the values inside the circles represent the cumulative frequency of wind speed in that direction (%). (a–d) represent spring, summer, autumn, winter.
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Figure 6. Characteristics of the WPSCF potential source contribution function distribution values of PM2.5–O3 compound pollution in the four seasons in key cities in the Central Plains Urban Agglomeration. Note: (a–d) represent spring, summer, autumn, winter.
Figure 6. Characteristics of the WPSCF potential source contribution function distribution values of PM2.5–O3 compound pollution in the four seasons in key cities in the Central Plains Urban Agglomeration. Note: (a–d) represent spring, summer, autumn, winter.
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Table
Table 1. Clustering data of the posterior term trajectories in different seasons.
Table 1. Clustering data of the posterior term trajectories in different seasons.
SeasonNo.Frequency of Occurrence(%)Route Areaρ(O3)
(μg/m3)		ρ(PM2.5) (μg/m3)
Spring126.09Puyang95.155.6
28.70Northeastern Inner Mongolia, Hebei Province, Beijing101.335.3
315.22Southwestern Inner Mongolia, Northern Shaanxi Province, Southern Shaanxi Province99.039.6
423.91Southern Henan Province120.251.1
516.30Southern Shanxi Province113.053.4
69.78Bohai71.339.2
Summer114.13Southern Henan Province130.821.6
228.26Northwestern Jiangsu Province, southwestern Shandong Province118.430.6
39.78Northern Shandong Province, Southern Hebei Province102.222.7
416.30Southwestern Hebei Province119.131.8
526.09Central Henan Province174.928.9
65.43Southern Inner Mongolia, western Hebei Province, south126.722.8
Autumn116.48Southern Hebei Province48.257.2
220.88Southern Shanxi Province64.397.6
33.30Western Inner Mongolia, Southern, Northern Shaanxi Province, Southern Shaanxi Province54.655.5
435.16Eastern Henan Province38.491.2
58.79Northern Shaanxi Province, Southern Shaanxi Province63.024.8
615.38Eastern Hebei Province, northwestern Bohai Sea, northwestern Shandong Province, southern Hebei Province50.690.1
Winter120.51Southwestern Inner Mongolia, Northern Shaanxi Province, Southern Shaanxi Province40.179.0
22.56Eastern Henan Province30.890.8
321.79Southern Shanxi Province30.3113.7
414.10Northern Shanxi Province, Southern Hebei Province32.663.6
523.08Central and Northern Henan Province22.4105.4
617.95Southern Hebei Province28.697.4
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Quan, S.; Liu, M.; Chen, B.; Huang, Y.; Wang, M.; Ma, Q.; Han, Y. Analysis of the PM2.5–O3 Pollution Characteristics and Its Potential Sources in Major Cities in the Central Plains Urban Agglomeration from 2014 to 2020. Atmosphere 2023, 14, 92. https://doi.org/10.3390/atmos14010092

AMA Style

Quan S, Liu M, Chen B, Huang Y, Wang M, Ma Q, Han Y. Analysis of the PM2.5–O3 Pollution Characteristics and Its Potential Sources in Major Cities in the Central Plains Urban Agglomeration from 2014 to 2020. Atmosphere. 2023; 14(1):92. https://doi.org/10.3390/atmos14010092

Chicago/Turabian Style

Quan, Shu, Miaohan Liu, Boxuan Chen, Yuehua Huang, Meijuan Wang, Qingxia Ma, and Yan Han. 2023. "Analysis of the PM2.5–O3 Pollution Characteristics and Its Potential Sources in Major Cities in the Central Plains Urban Agglomeration from 2014 to 2020" Atmosphere 14, no. 1: 92. https://doi.org/10.3390/atmos14010092

APA Style

Quan, S., Liu, M., Chen, B., Huang, Y., Wang, M., Ma, Q., & Han, Y. (2023). Analysis of the PM2.5–O3 Pollution Characteristics and Its Potential Sources in Major Cities in the Central Plains Urban Agglomeration from 2014 to 2020. Atmosphere, 14(1), 92. https://doi.org/10.3390/atmos14010092

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