Exploring the altitude differentiation and influencing factors of PM2.5 and O3: a case study of the Fenwei Plain, China

Altitude differentiation has a substantial effect on the synergistic control of PM_2.5 and O₃ pollution. This study targets the Fenwei Plain, which is affected by mountain range blockage, divided into different altitude scales, and employs the methods of correlation analysis and geographical detector to explore the spatiotemporal heterogeneity of PM_2.5 and O₃ between different altitude zones and to identify the key controlling factors of pollutants between different altitude areas. The results showed that PM_2.5 showed a significant decreasing trend from 2014 to 2023, whereas O₃ exhibited an opposite trend. The concentrations of both pollutants decreased with increasing altitude, particularly for PM_2.5, which showed significant altitudinal differentiation under the influence of topography. PM_2.5 was negatively correlated with gross domestic product (GDP) and precipitation, and positively correlated with SO₂. In contrast, the correlation of O₃ with these factors was opposite to that of PM_2.5. For spatial differentiation, NO₂ and SO₂ were the main factors influencing the spatial differentiation of PM_2.5 and O₃ at different altitudes. The explanatory power of the spatial divergence of PM_2.5 and O₃ was greatly increased by the interactions between the two precursors and between the precursors and meteorological factors. Furthermore, the explanatory power of the PM_2.5 dominant factor increased with elevation, while the explanatory power of the O₃ dominant factor was relatively high across low, middle, and high altitudes. This study serves as a guide for reducing air pollution in the Fenwei Plain and offers a novel perspective for the study of PM_2.5 and O₃ influenced by terrain.

1 Introduction

Atmospheric pollution is currently the greatest barrier to the development of a global ecological civilization and a focal point for environmental studies (Zhang et al., 2021; Ioannis et al., 2020). In 2021, the Chinese government issued documents specifically to provide crucial directives for the management of air pollution. These directives mandate that amid the fight against O₃ pollution, achieve the synergistic control of fine particles and O₃ (Sun and Huang, 2021). In 2023, the Action Plan for Continuous Improvement of Air Quality similarly emphasizes the need to reduce PM_2.5 concentrations as the main line of action, with synergistic emission reductions of nitrogen oxides (NO_x) and volatile organic compounds (VOC_s), and sets the target of significantly reducing pollutant concentrations in key regions such as the Beijing-Tianjin-Hebei region, the Yangtze River Delta and the Fenwei Plain by 2025 (CPsGotPsRo, 2023). PM_2.5 and O₃, as typical air pollutants, have pollution areas that are both overlapped and differentiated pollution areas. At the same time that PM_2.5 continues to decline, it is essential to effectively halt the rise in O₃. The top priority for air pollution prevention is to achieve synergistic management of the two types of pollutants (Liu and Liao, 2021; Ji, 2021). Consequently, scientific understanding of the features of the spatiotemporal variation and influencing factors of PM_2.5 and O₃ has evolved into a crucial scientific foundation for the coordinated management of air pollution (Zhang et al., 2021; Wang et al., 2022a; Hui et al., 2021).

Scholars from both domestic and foreign countries have conducted multiple studies on PM_2.5 and O₃, focusing on the spatiotemporal characteristics of pollutants, the causes, health and ecological risks of environmental exposure, and influencing variables of air pollution (Yan et al., 2016; Bai et al., 2018; Chen et al., 2019; Huang C. et al., 2021; Lu et al., 2017; Xu et al., 2021a). For the spatiotemporal characteristics of air pollution, mathematical statistics, spatial autocorrelation analysis, spatial interpolation, hotspot analysis, and trend analysis were primarily employed to reflect the spatial-temporal features of pollutants (Bai et al., 2018; Huang C. et al., 2021; Lei et al., 2022; Wu et al., 2023; Ali-Taleshi et al., 2022). Research indicates that air pollution, including PM_2.5, O₃ and aerosol optical depth (AOD), is highly heterogeneous in space and time in different areas (Huang et al., 2024; Su et al., 2024; Dong et al., 2019). For the analysis of the causes of air pollution, backward trajectory analysis, potential source contribution function (PSCF) analysis, physical and chemical models, concentration weight matrices, and other techniques have been extensively used (Xiao et al., 2022; Ma et al., 2021; Fang et al., 2021; Masiol et al., 2017; Xu et al., 2021b). For the health and ecological risks of environmental exposure to pollutants, health risks and ecological risks due to pollutants are mainly assessed using methods such as environmental exposure risk modelling, ecological risk assessment and health exposure response functions (Xu et al., 2018; Zou et al., 2019; Zhao et al., 2022; Wang L. et al., 2022). For the influencing factors of air pollution, geographically weighted regression models, multiple linear regression models, geographical detectors, and other models and methods were primarily used to investigate the primary influencing factors of pollutants. The geographic detector, which is not constrained by conditions such as linearity and non-linearity and is extensively utilized to identify the spatial distinction between terrestrial objects and the underlying driving factors (Liu and Liao, 2021; Chen L. et al., 2020; He et al., 2022; Wei et al., 2019; Shen et al., 2022; Wang et al., 2022c). Additionally, several studies have shown that the primary contributors to PM_2.5 and O₃ pollution include precursor pollutants, societal and economic factors, and meteorological conditions. Meteorological factors impact PM_2.5 and O₃ through pollutant transport, diffusion, chemical transformations, and wet and dry deposition (Chen Z. et al., 2020). Temperature and precipitation play a significant role, but their influence on PM_2.5 and O₃ varies spatially (Xia et al., 2022). In addition, socio-economic factors such as economic development, urbanization, and population expansion impact PM_2.5 and O₃. Precursor pollutants like SO₂ and NO_x, primarily from burning fuels like industrial coal, are also essential for preventing and controlling air pollution as they directly contribute to the development of PM_2.5 and O₃ (Liu, 2021; Dai et al., 2021; Duan et al., 2021; Dan et al., 2019; Wu et al., 2021; Bo et al., 2020; Xiaoyuan et al., 2010).

During the 14th Five-Year Plan period, China optimized and modified the key areas for air pollution avoidance and governance, focusing particularly on the three regions of the Fenwei Plain, Yangtze River Delta, and Beijing-Tanjin-Hebei (Liu and Liao, 2021; Ji, 2021; Qin et al., 2020; Dai et al., 2022). Numerous studies on air pollution have been conducted in the latter two economically developed regions, whereas research on the Fenwei Plain is relatively limited. Fenwei Plain is the most developed area of industrial and agricultural agriculture in Shaanxi Province, where transportation is primarily dependent on roads and the industrial structure is dominated by chemical industries. The region is primarily characterized by heavy industries, with coal accounting for more than 90% of energy consumption and coke production accounting for approximately 15% of the nation (Huang et al., 2019). The Fenhe Plain and the Weihe Plain, which are connected by the Fenhe River and the Weihe River, converge in the valley of the Yellow River, creating the distinctive basin terrain of the Fenwei Plain. This geographical feature hinders the spread of pollutants, making this area vital for air pollution control and management in China (Xu D. et al., 2021). In addition, prior research has demonstrated that topography resulted in notable geographic differences in PM_2.5 and O₃ pollution (Zhao et al., 2020; Huang X. et al., 2021). Various altitudinal regions may have distinct pollution characteristics and influencing factors. Existing research on PM_2.5 and O₃ pollution has mainly focused on a single scale or pollutant, neglecting spatial variations in elevation and the interactions between PM_2.5 and O₃. Additionally, these studies failed to adequately explore the connection between altitudes and pollutants under the special topography such as the Fenwei Plain.

In light of this, this study thoroughly examined the impact of topography on PM_2.5 and O₃ by focusing on the Fenwei Plain. The area was categorized into three altitude regions (low, middle, and high) based on digital elevation model (DEM) data. Firstly, we showed the spatial and temporal characteristics of PM_2.5 and O₃, along with elevation differences from 2014 to 2023. Secondly, the relationship between pollutants (PM_2.5 and O₃) and influencing factors was explored by combining meteorological (temperature and precipitation), socioeconomic (night light index, population, and GDP), and precursor factors (NO₂ and SO₂). Finally, the primary driving elements and interactions of PM_2.5 and O₃ at different altitudes were detected using the geographical detector. This study also compared the similarities and differences between the influencing variables of the two pollutants. The study’s findings are significant for the coordinated management of PM_2.5 and O₃ in the Fenwei Plain and offer a fresh outlook on regional PM_2.5 and O₃ pollution in comparable basin topographies. They also serve as a crucial foundation for creating tailored air pollution management strategies at varying altitudes.

2 Materials and methods

2.1 Data sources and processing

The following four segments make up the majority of the study’s data: (1): ChinaHighAirPollutants dataset (CHAP). The data is sourced from https://weijing-rs.github.io/product.html. It is a high-coverage, high-precision, and near-surface air pollutant dataset created by combining extensive data from ground-based observations, atmospheric reanalysis, emission inventories, and model simulations (Wei et al., 2022; Wei et al., 2023a). It considers the spatial and temporal heterogeneity of atmospheric pollution and was developed using artificial intelligence methods by Wei et al. (2020). The cross-validation coefficient of determination (CV-R²) is 0.92, and the root mean square error (RMSE) is 10.76 μg/m³, indicating great accuracy and suitability for this study (Wei et al., 2023b). This study obtained the annual average data for PM_2.5 and O₃ in the Fenwei Plain from the CHAP dataset for the period from 2014 to 2023, as well as the annual average raster data for NO₂ and SO₂ from 2014 to 2020. The spatial resolution was 1 km for the PM_2.5 dataset and 10 km for the O₃, NO₂, and SO₂ datasets. (2) Meteorological data. It primarily consists of temperature and precipitation raster data from the CRU TS website (https://crudata.uea.ac.uk/cru/data/hrg/) to provide monthly data covering the land surface with a spatial resolution of 0.5° for 2014–2020. The dataset was interpolated to a 1 km spatial resolution using the inverse distance weighted (IDW) method and processed as annual data in the study. (3) Socioeconomic data. It is primarily comprised of population, GDP, and night light data (NLI), which can reflect the economic situation. The Chinese GDP spatial distribution km grid dataset is maintained by the Chinese Academy of Sciences’ Institute of Geographic Sciences and Resources. Demographic data can represent the region’s population agglomeration degree. The University of Southampton initiated, developed, and generated the Open High Resolution Geospatial Dataset (https://hub.worldpop.org/) on population distribution, demographics, and dynamic data. The night light data, which can characterize the vitality of cities, was a raster dataset that calculated the average value of the grid using the NPP/VIIRS remote sensing light data set of 2014–2020. (4) DEM data. The information originates from the Resource Environmental Science and Data Centre (http://www.resdc.cn/). In order to find out the differences in spatiotemporal features and impact factors of PM_2.5 and O₃ between various altitudes, the Fenwei Plain was divided into low altitude area (110 km–815 km), middle altitude area (815 km–1,379 km), and high altitude area (1,379 km–3631 km) by using the natural breakpoint method in this study. The DEM map and division results are shown in Figure 1. All the above data were resampled to 1 km and cropped to the study area and different elevation scales for analysis. However, due to the limitations of the impact factor data, the multi-year average of the 2014–2020 data was used for the impact factor analyses in both the correlation analyses and geographical detector in this study.

Figure 1

Figure 1. DEM and altitude classification map of the Fenwei Plain.

2.2 Methods

2.2.1 Theil-Sen Median and Mann-Kendall

Theil-Sen media trend analysis and Mann-Kendall significance test are two non-parametric tests. The method does not require the data to satisfy normal distribution, and is also capable of eliminating the interference of outliers and reflecting the overall trend of the time series (Li et al., 2023). In this study, Theil-Sen median trend analysis and Mann-Kendall test were used to analyze the trends of PM_2.5 and O₃. The formula is shown in Equation 1 (Chen and Zhang, 2024):

$\beta =Median\frac{X_i-X_j}{i-j},\forall i>j(1)$

where $i$ and $j$ represent time series; Median represents the median value; $\mathrm{\beta }$ denotes the trend of the pollutant, and the positive or negative of $\mathrm{\beta }$ denotes the direction of the trend. When $\mathrm{\beta }$ > 0, it is an upward trend; the opposite is a downward trend.

The Mann-Kendall (MK) significance test used to detect the significance of data under long time series. The formulas for its calculation are shown as Equations 2–5:

$Z=\left\{\begin{array}{l}\frac{S-1}{\sqrt{Var\left(S\right)}},S>0\\ 0,S=0\\ \frac{S+1}{\sqrt{Var\left(S\right)}},S<0\end{array}\right.(2)$

$S={\displaystyle \sum _{i=1}^{n-1}}{\displaystyle \sum _{j=i+1}^n}sgn\left(X_i-X_j\right)(3)$

$sgn\left(X_i-X_j\right)=\left\{\begin{array}{c}1,X_i-X_j>0\\ 0,X_i-X_j=0\\ -1,X_i-X_j<0\end{array}\right.(4)$

$Var\left(S\right)=\frac{n\left(n-1\right)\left(2n+5\right)-{\displaystyle \sum _{i=1}^m}{t}_i\left(t_i-1\right)\left(t_i+5\right)}{18}(5)$

where $n$ is the number of time series data; m is the number of knots (recurring data sets) in the sequence; $t_i$ is the width of the knot (number of data identities). The Z-test value was obtained using the sign function (sgn) and the variance of the series (Var(S)). The calculated Z-value was used to determine whether the time series data was significant or not. If |Z| $\le Z_{1-\mathrm{\alpha }}$, the trend is insignificant; if |Z| $>Z_{1-\mathrm{\alpha }}$, the trend is significant.

2.2.2 Spatial autocorrelation analysis

Spatial autocorrelation is a method for determining whether spatially constant variables are dependent on one another within the same distribution. This study used the global Moran’s I to calculate whether pollutants have noticeable spatial agglomeration features. The formula is shown in Equation 6 (Li et al., 2022):

$Mora{n}^{\prime }sI=\frac{n{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}{\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}}{W}_{ij}\left(X_i-\overline{X}\right)\left(X_j-\overline{X}\right)}{\left({\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}{\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{n}}}{W}_{ij}\right){\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}{\left(X_i-\overline{X}\right)}^2}(6)$

where n represents the total amount of grids; $X_i$ and $X_j$ represents the pollutant concentrations of grids i and j, where i is not equal to j; $\overline{X}$ represents the average pollution concentration; $W_{ij}$ represents the spatial weight matrix. The Moran’s I index will be a value between −1 and 1. The global Moran’s I > 0 represents a positive correlation, indicating that PM_2.5 or O₃ similar grids tend to have a spatially clustered distribution. The global Moran’s I < 0 denotes negative correlation, which shows that PM_2.5 or O₃ similar grids tend to have a spatially discrete distribution. The global Moran’s I = 0 represents no correlation, indicating that PM_2.5 or O₃ grids tend to have a random distribution.

Additionally, this study employed the local Moran’s I to assess the clustering of pollutants’ local regions, thereby revealing the similarity or difference between spatial objects and their surrounding space. The formula is shown in Equation 7 (Yuan et al., 2022):

$LocalMora{n}^{\prime }sI=\frac{n\left(X_i-\overline{X}\right){\displaystyle \sum _{j=1}^n}{W}_{ij}\left(X_j-\overline{X}\right)}{{\displaystyle \sum _{i=1}^n}{\left(X_i-\overline{X}\right)}^2}(7)$

where $X_i$ is the value of the corresponding attribute of grid i, and $X_j$ is the value of the corresponding attribute of grid j; $W_{ij}$ is the spatial weight matrix between grid i and j. Four categories can be derived from the local Moran’s I findings: ‘High-High’ type represents that pollutant as being relatively high compared with the value of the adjacent area. ‘High-Low’ type represents that pollutants are enclosed by low-value areas. ‘Low-High’ type represents that pollutants are enclosed by high-value areas. ‘Low-Low’ type represents that pollutant concentration and the value of the neighboring unit as being relatively low.

2.2.3 Pearson correlation analysis

Pearson correlation analysis reflects the correlation between the two variables. The correlations between pollutants and influencing factors were depicted in this study using Pearson correlation analysis (Yang et al., 2019). The formula is shown in Equation 8:

$r=\frac{{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}\left(X_i-\overline{X}\right)\left(Y_i-\overline{Y}\right)}{\sqrt{{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}{\left(X_i-\overline{X}\right)}^2}\sqrt{{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}{\left(Y_i-\overline{Y}\right)}^2}}(8)$

where $X_i$ and $Y_i$ are the data of two variables respectively; $\overline{X}$and $\overline{Y}$ are the average of two variables, respectively; $r$ is the correlation coefficient, and $r$ range is between −1 and 1. The closer $\left|r\right|$ is to 1, and the stronger the correlation is, the closer $\left|r\right|$ is to 0, demonstrating that the two variables seldom have any link.

2.2.4 Geographical detector

Geographical detector can explain the spatial differentiation of geographical phenomena and elucidate their underlying causes. The fundamental idea is that the spatial distributions of X and Y should tend to be similar if X, the independent variable, significantly influences Y, the dependent variable (Cao et al., 2013). Utilizing the factor detector and interaction detector in the geographical detector, the primary influencing variables and interactions of the spatial distribution of pollutants were examined in this paper.

Factor detector involves the detection of the space difference of Y, the dependent variable, and exploring the extent to which a factor X explains the spatial differentiation of Y, measured by the value of $q$ (Wang and Xu, 2017). The specific formula is shown in Equation 9:

$q=1-\frac{1}{N{\sigma }^2}{\displaystyle \sum _{i=1}^L}{N}_i{{\sigma }_i}^2(9)$

where $q$ is the explanatory power of the factor to the variable Y; i = 1, 2, 3; $L$ is the stratification of factor X or variable Y; and $N$ are the number of units in layer $i$ and the whole region, respectively. ${{\sigma }_i}^2$ and ${\sigma }^2$ are the variances of layer $i$ and region Y, respectively. The explanation power of factors X to Y increases with $q$ value, which has a range of values from 0 to 1.

Interaction detector is the process of finding interactions between various risk variables, i.e., determining whether X_i and X_j together will increase or decrease the explaining ability of the dependent variable Y or whether X is not dependent on Y (Lin et al., 2021). The interaction results are presented in Table 1.

Table 1

Table 1. Geographic detector interaction results.

3 Results

3.1 The temporal variation characteristics of PM_2.5 and O₃

Figure 2 depicts the annual variation of PM_2.5 and O₃ concentrations in Fenwei Plain from 2014 to 2023. The mean PM_2.5 in Fenwei Plain was 41.66 μg/m³, which exceeded the national secondary concentration limit of 35 μg/m³. The mean O₃ was 98.67 μg/m³, which was lower than the national primary concentration limit of 100 μg/m³. Figure 2A shows the overall downward trend of PM_2.5 in Fenwei Plains (decline coefficient: 1.4347 μg/m³/a), but it begins to show a slight increasing trend in 2021. In contrast to the tendency for PM_2.5 to change over time, O₃ showed an upward trend (upper coefficient: 3.5459 μg/m³/a), peaking in 2023 after a brief decrease in 2021. Figure 2B demonstrates that the concentration change trend of the two pollutants at different altitudes was essentially consistent with the annual change of the entire region, with an upward trend for O₃ and a downward trend for PM_2.5. The concentration level decreased from high to low as follows: low altitude area > middle altitude area > high altitude area. The PM_2.5 in low altitude areas was decreasing the most, with a concentration difference of 10–20 μg/m³. Compared to PM_2.5, the difference in O₃ between different altitudes is smaller. However, after 2016, the differences began to gradually increase, especially at middle and high altitudes.

Figure 2

Figure 2. Annual change of PM_2.5 and O₃ in Fenwei Plain from 2014 to 2023 [The red area in (A) is the PM_2.5 error band; the blue area is the O₃ error band]. (A) The entire area, (B) The different altitudes.

3.2 The spatial variation characteristics of PM_2.5 and O₃

3.2.1 Spatial variation characteristic of PM_2.5

Figure 3 depicts the spatial variation of PM_2.5 in Fenwei Plain from 2014 to 2023. From 2014 to 2017, concentrations were greater than 75 μg/m³ at most low to middle altitudes areas. Over time, the extent of the high pollution zone is gradually reduced, with concentrations below 75 μg/m³ throughout the Fenwei Plain by 2020. It is evident that the Fen Wei Plain’s PM_2.5 pollution is steadily getting better. Notably, PM_2.5 exhibited a spatial pattern of high concentration in the middle areas and low concentration in the surrounding areas. The high pollution areas are mainly found in the low altitude areas such as Yuncheng, Xianyang, Weinan, and Linfen, while the PM_2.5 concentration in the high altitude and other marginal areas is low. This phenomenon may be attributed to topographical factors; specifically, the terrain of the Fenwei Plain is relatively low in its center while rising on all sides, which hampers pollutant dispersion and contributes to significant PM_2.5 pollution within the Fenwei Plain Basin.

Figure 3

Figure 3. Annual change of PM_2.5 spatial variation in the Fenwei Plain from 2014 to 2023 (A–J).

To further reflect the trend of PM_2.5, this study used Theil-Sen trend analysis and Mann-Kendall significance to explore the trend of PM_2.5 from 2014 to 2023. The results are displayed in Figure 4. In the majority of areas, the PM_2.5 slope was smaller than 0, suggesting a general downward trend in PM_2.5. The type of declining trend is dominated by significant declines, with a few areas of slight declines concentrated in high altitude areas of the Fenwei Plain. Moreover, in contrast to the spatial distribution pattern of PM_2.5, the slope of PM_2.5 overall decreased as elevation decreased, and the more significant the declining tendency, the lower the height. Even if the PM_2.5 concentrations were higher at lower elevations, it was evident that these areas have been remarkably treated, particularly in Xianyang, Yuncheng, and Weinan.

Figure 4

Figure 4. Annual change trends (A) and significance test (B) of PM_2.5 from 2014 to 2023.

3.2.2 Spatial variation characteristic of O₃

Figure 5 depicts the O₃ spatial variation from 2014 to 2023. From 2014 to 2016, O₃ in the majority of the Fenwei Plain was below the national concentration limit of 100 μg/m³. In 2017, O₃ in the Fenwei Plain increased significantly, with the most notable increases in Yuncheng and Linfen. Between 2017 and 2023, O₃ pollution progressively spread from the central to the eastern regions, leading to an overall distribution of O₃ that was east-high and west-low. By 2023, O₃ level in the Fenwei Plain exceeded 110 μg/m³ in the east and 100 μg/m³ in the west. By 2023, the concentration of O₃ in the eastern region of the Fenwei Plain was higher than 110 μg/m³, and the concentration in the western region was also more than 100 μg/m³.

Figure 5

Figure 5. Annual change of O₃ spatial variation in the Fenwei Plain from 2014 to 2023 (A–J).

The O₃ trend analysis and significance test findings from 2014 to 2023 are shown in Figure 6. Over the whole region, the O₃ slope were more than 0. The O₃ concentration exhibited a noteworthy increase in the majority of the regions, particularly in the central regions of Jinzhong, Linfen, and Taiyuan, where the upward trend was particularly considerable and the slopes were greater. Moreover, although it was less noticeable than that of PM_2.5, the trend of O₃ also displayed an altitude differentiation feature.

Figure 6

Figure 6. Annual change trends (A) and significance test (B) of O₃ from 2014 to 2023.

3.2.3 Spatial distribution characteristics of PM_2.5 and O₃ at different altitudes

Figure 7 depicts the multi-year average values of PM_2.5 and O₃ at different altitudes. The PM_2.5 concentrations varied significantly at low, middle, and high altitudes, followed by: low altitude area 50.97 μg/m³ > middle altitude area 38.28 μg/m³ > high altitude area 31.22 μg/m³. The concentration difference of O₃ is not obvious, followed by: low altitude area 100.07 μg/m³ > middle altitude area 99.11 μg/m³ > high altitude area 95.92 μg/m³. In low altitude area, PM_2.5 was greater than 45 μg/m³, whereas the concentration in the majority of regions was between 45–60 μg/m³. The highest concentration of O₃ was in the central region, while the concentration was substantially higher in the northern region than the southern region. In the middle altitude area, the range of PM_2.5 in most areas was 20–60 μg/m³, and the range of O₃ was 90–100 μg/m³. In high altitude area, the PM_2.5 range was 0–35 μg/m³, and the O₃ range was 80–95 μg/m³. Combining the spatiotemporal distribution of PM_2.5 and O₃ at different altitudes revealed that the two pollutants had similar changes as a result of the terrain, as concentrations for both were as follows: low altitude area > middle altitude area > high altitude area. This phenomenon may be attributable to the influence of social and economic factors such as vehicles, factories, and other pollutants in low-altitude regions, which result in high pollutant concentrations, while less artificial pollution in high-altitude regions results in low concentrations. In addition, compared to the change in O₃, the elevation gradient of PM_2.5 was significantly greater, indicating that terrain significantly affected PM_2.5 but had a smaller impact on O₃.

Figure 7

Figure 7. Spatial characteristics of PM_2.5 and O₃ in Fenwei Plain at different altitudes (A–F).

3.2.4 Spatial clustering characteristics of PM_2.5 and O₃

The analysis of the global spatial autocorrelation of PM_2.5 and O₃ in Fenwei Plain revealed that the global Moran’s I of PM_2.5 and O₃ was greater than 0.9, indicating significant spatial autocorrelation and spatial agglomeration. Figure 8A shows that the ‘High-High’ clustering of PM_2.5 was primarily found in low altitude areas of Linfen, Yuncheng, Xianyang, Luoyang, Weinan, and Xi’an, as well as at the junction of Lvliang, Taiyuan, and Jinzhong. These areas and their surroundings exhibited high levels of PM_2.5 pollution, making them the regions with significant PM_2.5 pollution in Fenwei Plain. The “low-low” clustering of PM_2.5 was primarily found in high altitude areas like the west side of Taiyuan, the central part of Lvliang, the southeastern part of Jinzhong, and the southern edge of the Fenwei Plain. This suggests that these areas and their surroundings have generally low levels of PM_2.5 and are less polluted. As shown in Figure 8B, the clustering of high levels of O₃ was predominantly found in middle and low altitude areas, such as Yuncheng, Weinan, the northern part of Luoyang, the estern part of Linfen, northern part of Jinzhong, and the northwestern fringe region. This suggests that O₃ levels are generally elevated in these areas and their vicinity. The “low-low” clustering of O₃ was seen in the middle and high altitude areas of Xi’an, and Baoji, suggesting that O₃ is low in these areas and their surroundings. PM_2.5 and O₃ exhibited spatial concentration overlap. Overall, the overlapping areas of PM_2.5 and O₃ high pollution centers were mostly found in the low altitude areas of Yuncheng, Weinan, Linfen, and Luoyang. The low pollution overlap areas were mainly found in the middle and high altitude areas, such as Baoji, and Xi’an. PM_2.5 and O₃ exhibited local spatial synergies, indicating a necessity for coordinated treatment of both pollutants.

Figure 8

Figure 8. Spatial autocorrelation of PM_2.5 (A) and O₃ (B) in the Fenwei Plain from 2014 to 2023.

3.3 Analysis of influencing factors on PM_2.5 and O₃

3.3.1 Correlation analysis

This study uses Pearson correlation analysis to study the linear relationship between PM_2.5, O₃, and three types of influential factors. Table 2 shows the results of the correlation analysis of PM_2.5 and O₃ with the three categories of influencing factors at various altitude scales. For the entire Fenwei Plain, the correlated coefficients between the main influential factors and PM_2.5 were as follows: SO₂ (0.8638) > GDP (−0.824) > precipitation (−0.5779), and for O₃, GDP (0.8896) > SO₂ (−0.8555) > precipitation (0.6385). The two pollutants had comparable primary influence factors, but their coefficients were opposite. This could be a significant inverse correlation between PM_2.5 and O₃. For the low, middle, and high altitude areas, the relevant PM_2.5 and O₃ coefficients were extremely similar throughout the entire region. The correlated coefficients between PM_2.5 and the three main influence factors (precipitation, GDP, and SO₂) decreased progressively with increasing altitude, whereas the correlation coefficient between O₃ and the three most influential factors was not significantly correlated with altitude.

Table 2

Table 2. PM_2.5 and O₃ correlation coefficients in Fenwei Plain from 2014 to 2020.

3.3.2 Analysis of the drivers of PM_2.5 and O₃ spatial divergence

Correlation analysis does not imply causation. PM_2.5 and O₃ spatial pollution characteristics are the result of numerous complex factors, such as meteorology, social economy, and precursor pollutants, and are not only a single linear relationship or influenced by a single pollutant. Therefore, this paper further identified the driving factors of PM_2.5 and O₃ spatial differentiation at low, middle, and high altitudes in the Fenwei Plain by using the detector and interaction detector of the geographic detector.

3.3.2.1 Analysis of driving factors in low altitude area

Tables 3, 4 demonstrate the results of factor detection and interaction detection of PM_2.5 and O₃ in low altitude area of the Fenwei Plain. Table 3 shows that the sig for all the influences was less than 0.05, indicating that all passed the test of significance. NO₂ (0.2930) and SO₂ (0.5413) were the main drivers of PM_2.5 and O₃, respectively. Table 4 shows that the results of the interaction detection of the two pollutants are double factor enhancement and non-linearly enhanced. NO₂ and precipitation (0.3849) and NO₂ and temperature (0.387) were the predominant interaction factors of PM_2.5. Nevertheless, the explanatory power of the interaction of the remaining influencing factors on the spatial differentiation of PM_2.5 differed less from that of the dominant factors. This may be due to the fact that the mechanisms affecting PM_2.5 at low altitudes are more complex and susceptible to a combination of meteorological, precursor and socio-economic factors. The q values between SO₂ and other influencing factors were all greater than 0.5793, which was the most important interaction center. It can be seen in Table 4 that the interaction increased the two pollutants’ spatial differentiation’s explanatory power. NO₂ and precipitation (0.3849) and NO₂ and temperature (0.387) were the predominant interaction factors of PM_2.5, but each factor’s ability to explain the spatial difference of PM_2.5 was still modest. The dominant interaction factors of O₃ were SO₂ and precipitation (0.8383), SO₂ and NO₂ (0.7233), and precipitation and temperature (0.7043), all of which have high explanatory power. It indicates that O₃ in low altitude area was primarily influenced by the interaction of precursor contaminants and meteorological factors. And the spatial differentiation of PM_2.5 and O₃ in low altitude area was formed by a variety of influencing factors. Furthermore, precipitation and temperature (0.7043), and NO₂ and temperature (0.5778) also had high explanatory power for O₃.

Table 3

Table 3. Results of PM_2.5 and O₃ factor detectors at low altitude in Fenwei Plain.

Table 4

Table 4. Results of PM_2.5 and O₃ interaction detectors at low altitude in Fenwei Plain.

3.3.2.2 Analysis of driving factors in middle altitude area

Tables 5, 6 demonstrate the results of factor detection and interaction detection of PM_2.5 and O₃ in the middle altitude area of the Fenwei Plain. According to Table 5, NO₂ (0.3149) and SO₂ (0.3661) were, respectively, the primary drivers of PM_2.5 and O₃ levels. As can be seen from Table 5, the results of the two pollutant interaction detectors at mid-altitude remain of two types: two-factor enhancement and non-linear enhancement. Table 6 demonstrated that there are still two types of outcomes from the two pollutant interaction detectors at mid-altitude: non-linear enhancement and two-factor enhancement. NO₂ and temperature (0.4486), NO₂ and precipitation (0.4051), SO₂ and temperature (0.03717), NO₂ and people (0.3571) were the primary interaction factors for PM_2.5. For O₃, SO₂ remained the most significant interactive center, with q values between SO₂ and other influencing factors all having exceeded 0.4184. Notably, the q values for the interactions between SO₂ and precipitation, as well as between SO₂ and temperature, were both greater than 0.6. In addition, NO₂ and precipitation (0.5216) also had high explanatory power for O₃. The dominant interaction factors had a high degree of consistency in low and middle altitude areas. Among these, the interpretation of the main driving factors and interactor factors for PM_2.5 had improved, whereas the interpreting power for O₃ had been slightly diminished.

Table 5

Table 5. Results of PM_2.5 and O₃ factor detectors at middle altitude in Fenwei Plain.

Table 6

Table 6. Results of PM_2.5 and O₃ interaction detectors at middle altitude in Fenwei Plain.

3.3.2.3 Analysis of driving factors in high altitude area

Tables 7, 8 demonstrate the results of factor detection and interaction detection of PM_2.5 and O₃ in high altitude area of the Fenwei Plain. According to Table 7, the factors with the highest explanatory power for PM_2.5 in this region were SO₂ (0.6151) and NO₂ (0.5571), whereas the factors with the highest explanatory power for O₃ were SO₂ (0.6616) and precipitation (0.6081). Table 8 shows that for PM2.5, the q values of the interactions between NO₂ and SO₂ and the other influences were all higher than 0.5833, particularly for SO₂ and temperature (0.7423), SO₂ and precipitation (0.7107), and NO₂ and temperature (0.7035). These interactions had a very significant explanatory power for the spatial differentiation of PM_2.5. For O₃, SO₂ and NO₂ were also at the center of the interaction, with SO₂ and precipitation (0.8717) and NO₂ and precipitation (0.831) having the highest explanatory power for O₃. However, the explanatory power of NO₂ interacting with the rest of the influences on the spatial differentiation of O₃ decreased compared to the q value of PM_2.5. Compared to low and middle altitudes, the explanatory power of the dominant interaction factors for both pollutants was significantly higher, particularly for the socio-economic factors.

Table 7

Table 7. Results of PM_2.5 and O₃ factor detectors at high altitude in Fenwei Plain.

Table 8

Table 8. Results of PM_2.5 and O₃ interaction detectors at high altitude in Fenwei Plain.

4 Discussion

4.1 Spatial heterogeneity of PM_2.5 and O₃ and their causes

This study revealed the spatial heterogeneity and influencing factors of PM_2.5 and O₃ in the Fenwei Plain from the perspective of the impact of altitude on air pollutants. From 2014 to 2023, an overall decrease in PM_2.5 was observed in the Fenwei Plain. This reduction is primarily attributed to the Chinese government’s strategic air pollution prevention initiatives, including the promotion of clean heating solutions and stringent regulation of coal consumption. Notably, the enforcement of the ‘Three-Year Action Plan to Win the Blue Sky Defense War’ in 2018, along with the ‘Autumn and Winter Action Plan for Comprehensive Air Pollution Control in the Fenwei Plain for 2018–2019,’ led to a marked decrease in PM_2.5. Concurrently, these measures also effectively mitigated the escalating trend in O₃ (Zhou et al., 2023). The distribution of PM_2.5 and O₃ in the area was greatly influenced by topography, resulting in the formation of pollution concentration areas in topography characterized by a “trumpet mouth” and basin, featuring low elevation surrounded by mountains, as well as in some plain areas. This pattern aligns well with research on comparable topographical features (Shu et al., 2023). This phenomenon occurred because the topography in the low-altitude areas of the Fenwei Plain results in low average yearly wind speeds, leading to stagnant airflow zones that hinder the spread of pollutants (Shu et al., 2022). However, the terrain had a varying effect on PM_2.5 and O₃. PM_2.5 pollution was more influenced by terrain compared to O₃. It exhibited a notable elevation pattern, particularly in the northwest to southeast high pollution area, which aligns with the terrain. This is mainly due to the fact that O₃ was mostly influenced by regional air transport, had a wide pollution diffusion range, was less affected by terrain, and showed slight variations in concentration at different elevations (Shu et al., 2024). Therefore, PM_2.5 and O₃ showed a notable difference in distribution between middle and high altitude areas in the Fenwei Plain due to its unique topography. However, their pollution levels exhibited homology in low-altitude regions, forming a coordinated control zone that included Yuncheng, Linfen, Weinan, Luoyang, and other low-altitude and border areas.

PM_2.5 and O₃ are formed in complicated atmospheric processes that are impacted by a range of causes, both anthropogenic and natural, under the influence of topography (Zhang et al., 2019; Gong et al., 2022). This study also revealed the correlation between PM_2.5 and O₃ and their influencing factors. We found that SO₂, a precursor of PM_2.5, undergoes gas-phase reactions in the atmosphere with a significant positive correlation with PM_2.5 (Xue et al., 2023). In addition, GDP and PM_2.5 had a negative relationship due to the fact that, with air pollution abatement policies, which realized a parallel between GDP growth and PM_2.5 abatement, making it possible to control PM_2.5 pollution at the same time as economic growth, and economic development is no longer dependent on air pollution. Precipitation can efficiently eliminate PM_2.5 particles by scouring them, effectively reducing PM_2.5 pollution. This results in a negative relationship between rainfall and PM_2.5 (Chen Z. et al., 2020). In addition, precipitation, SO₂, and GDP also had significant effects on O₃, but in the opposite direction to PM_2.5. This could be because PM_2.5 emission reduction primarily depends on sulfur and one-time PM_2.5 but NO_x and VOC emissions are still very high. When NO_x and VOC_s are in a certain ratio, O₃ is produced through a chemical reaction, which causes O₃ pollution to continue to intensify while PM_2.5 pollution is under control (Committee CSfESOPC, 2020). Ultimately, this leads to a negative correlation between the two pollutants and opposite correlation coefficients for the influencing factors. Correlations can only reflect linear relationships between pollutants and influencing factors, and geographic detector results provide a good probe for the reasons for the spatial divergence of PM_2.5 and O₃. The geographical detector results showed that PM_2.5 and O₃ at various altitudes were primarily affected by the combined impact of meteorological and precursor factors, with minimal influence from socio-economic factors. The correlation between PM_2.5 and O₃ was due to their partial homology and interconnectedness, resulting in their influencing factors being in high agreement at various altitude scales (Sun et al., 2023). We also discovered that differences in the explanatory power of primary effects within the same altitude range were minimal, but significant disparities existed between altitudes, further confirming the significant impact of elevation. The correlation between PM_2.5 and O₃ was due to their partial homology and interconnectedness, resulting in their influencing factors being in high agreement at various altitude scales. This may be due to complex humanitarian factors such as population, industry, and altitude in the low and middle altitude regions, whereas the influence of the high altitude area was more singular, resulting in a greater overall interpretation of the driving factors in this area.

4.2 Recommendations to control PM_2.5 and O₃ pollution

Based on these findings, this study proposes the following recommendations for PM_2.5 and O₃ pollution in the Fenwei Plain and similar places with basin topography and “trumpet mouth” topography: (Zhang et al., 2021): The topography laid down the basic spatial pattern of PM_2.5 and O₃ pollution, making them homogenous, consistent, and related. In this context, synergistic PM_2.5 and O₃ management zones should be delineated by taking topographical factors into account, so as to manage PM_2.5 and O₃ from their sources. In particular, it is crucial to enhance cooperation between regional administrations and encourage synergistic management in extremely polluted low-altitude areas near the border of the two counties. In addition, it is essential to deal with the issue of spatial aggregation of PM_2.5 and O₃ resulting from an irrational industrial structure and energy layout. This can be achieved by accelerating industrial upgrading, transformation, and restructuring the industrial sector. (Ioannis et al., 2020). Meteorological and predecessor variables influenced the regional heterogeneity of PM_2.5 and O₃ distributions. On the basis of regional coordination, non-topographic factors should be comprehensively considered to ensure accurate governance in these regions. Meteorological influences are unpredictable and challenging to control accurately, but focusing on the underlying socioeconomic causes of precursors is crucial. Thus, enhancing regulation of industrial pollutant discharges, advancing high-quality projects for ultra-low carbon emissions in the steel, cement, and coking sectors, focusing on industrial transformation in the Fenwei Plain and innovative city development, implement clean heating, and address air pollution from PM_2.5 and O₃ at its core.

4.3 Research limitations and future prospects

This study offers insights for implementing targeted strategies to minimize air pollution. However, it does have certain constraints. The study picked a limited range of data on pollutants, socio-economic characteristics, and meteorological factors, despite the numerous influencing factors of PM_2.5 and O₃ and the interconnections between pollutants. This study did not consider the intricate relationship between air pollutant transport and topography, namely, the transport characteristics of air pollutant movement. In future studies, we will utilize more accurate and relevant data and methodologies to enhance the analysis of factors influencing PM_2.5 and O₃ pollution. This will involve integrating distinct viewpoints and environmental elements and thoroughly examining the interaction between topography and air pollutant dispersion to more precisely identify the transfer and synergistic mechanisms of PM_2.5 and O₃ under the influence of complex topography.

5 Conclusion

(1) Based on the characteristics of temporal change, PM_2.5 in the Fenwei Plain decreased from 2014 to 2023, with a decrease coefficient of −2.9318 μg/m³/a; O₃ increased, with an increase coefficient of 5.2922 μg/m³/a. PM_2.5 and O₃ at all altitudes aligned with the general trend. From 2014 to 2023, PM_2.5 and O₃ decreased as altitude increased, with low altitude having the highest levels and high altitude having the lowest levels. The difference of PM_2.5 between various altitude areas ranged from 10 to 20 μg/m³, but the variance of O₃ at different elevations was minimal.

(2) For the characteristics of spatial change, the range of high PM_2.5 pollution gradually decreased from 2014 to 2023, mainly concentrated in low altitude areas. However, O₃ pollution gradually spreaded from the central region to the east in the Fenwei Plain.

(3) According to the results of the correlation analysis, PM_2.5 and O₃ showed strong correlations with GDP, SO₂, and precipitation across the whole Fenwei Plain, as well as at different altitudes. Specifically, PM_2.5 was negatively associated with GDP and precipitation, and positively related to SO₂; O₃ was positively related to GDP, and precipitation was negatively related to SO₂. Owing to the opposite trends of PM_2.5 and O₃, the correlations between these two pollutants and their main influencing factors were contradictory.

(4) The geographical detector findings reveal that NO₂ and SO₂ were the primary influencers of PM_2.5 and O₃ across various altitudes. The spatial variations of PM_2.5 and O₃ within the Fenwei Plain stemmed from a complex interplay of factors, with precursor pollutants at the epicenter of these interactions. The hierarchy of significance was as follows: interactions among precursor pollutants, interactions between precursor pollutants and meteorological factors, and interactions between precursor pollutants and socio-economic factors. The explanatory power of the interaction factors for O₃ was notably high across low, middle, and high altitude regions. Furthermore, the explanatory power of these interactions for spatial differentiation of PM_2.5 increased significantly with rising altitude.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

ZY: Conceptualization, Data curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing–original draft, Writing–review and editing. LY: Conceptualization, Funding acquisition, Resources, Supervision, Writing–review and editing. YY: Data curation, Supervision, Writing–review and editing. XW: Funding acquisition, Project administration, Writing–review and editing. ZC: Formal Analysis, Visualization, Writing–review and editing. HL: Formal Analysis, Visualization, Writing–review and editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Science Research Foundation of Yunnan Education Bureau 2020 (2020J0098); and the Yunnan Normal University Graduate Research Innovation Fund (YJSJJ23-B100).

Conflict of interest

Author XW was employed by Yunnan Surverying and Mapping Institute Co. Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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