3.1. Spatial and Temporal Variations in Ground-Level PM10 Concentrations
We constructed an ET model for the retrieval of annual ground-level PM
10 concentrations in China from 2015 to 2021. Ultimately, the 10-fold cross-validation results showed that R
2, MAE, and RMSE were 0.878, 5.742 μg/m
3, and 8.826 μg/m
3, respectively, indicating that the model has a good fitting accuracy. Additionally, the small difference between R
2 values of the training set and the testing set suggests that the model did not experience overfitting during the training process.
Figure 3 displays the estimation accuracy at each ground monitoring station from 2015 to 2021. It can be observed that 47.01% of the stations have an R
2 exceeding 0.8, with the majority of these stations concentrated in the eastern region where there are more monitoring stations. The proportion of stations with an R
2 exceeding 0.6 reaches 74.79%. In regions with a dense station distribution, the model is able to capture features more comprehensively, resulting in a higher prediction accuracy. Conversely, in western regions where stations are sparse, the limited features available in the dataset during model training may lead to relatively larger errors. The proportion of stations with an MAE and RMSE within 5 μg/m
3 is 52.11% and 43.29%, respectively, and over 90% of the stations have an MAE within 10 μg/m
3. The proportion of stations with an MAE and RMSE exceeding 25 μg/m
3 is only 0.98% and 1.24%, respectively. Overall, the spatial distribution of stations accuracy is similar to that of R
2, with very few stations exhibiting large errors in regions such as Xinjiang, Tibet, and Qinghai. In previous studies, random forest (RF), geographically weighted regression (GWR), land-use regression (LUR), and space-time extremely randomized trees (STET) models have been applied to retrieve ground-level PM
10 concentrations, with R
2 values of 0.74, 0.81, 0.64, and 0.86, respectively [
44,
45,
46,
47]. The ET model demonstrates a better accuracy compared to previous research. This indicates that the constructed inversion model can effectively perform the retrieval of ground-level PM
10 concentrations in China.
Figure 4 illustrates the spatiotemporal distribution of annual average ground-level PM
10 concentrations in China from 2015 to 2021, with a spatial resolution of 1 km × 1 km, estimated based on the ET model. PM
10 exhibits significant spatial clustering features in
Figure 4, with the highest values occurring in the desert areas of Xinjiang, western Inner Mongolia, and the northwest of Qinghai and Gansu. Not only do these regions have annual average PM
10 concentrations exceeding 100 μg/m
3, but the spatial extent of high-concentration areas is also substantial. Additionally, relatively high PM
10 concentrations are observed in the Beijing–Tianjin–Hebei urban agglomeration and northern Henan Province, where the annual average PM
10 concentrations range from 100 μg/m
3 to 125 μg/m
3. In other regions with high PM
10 concentrations, they are mainly concentrated in economically relatively developed cities and their surrounding areas. Overall, the PM
10 pollution in the north is more severe than in the south, with annual average PM
10 concentrations generally below 75 μg/m
3 in the south. Northern Sichuan, Yunnan, Guizhou, and coastal cities in the south have lower annual average PM
10 concentrations, all below 50 μg/m
3. From the perspective of land-cover types, areas with more forest tend to have lower PM
10 concentrations, while areas covered by deserts tend to have higher PM
10 concentrations.
From 2015 to 2021, there was an overall downward trend in PM
10 concentration in China. We conducted linear fitting for each pixel of PM
10 over these seven years to clarify spatial change trends and performed significance tests on the trends (
Figure 5) to assess the statistical significance of the sample data, ensuring that the observed trend changes are significant. It can be seen that in most areas of central and eastern China, the
p-values are less than 0.05, indicating significance, while in western and northeastern China, the significance is less pronounced.
Figure 6 shows the results of the trend fitting, and to highlight areas with significant changes in China, we selected regions where the trend change was significant at a 95% confidence level (
p < 0.05) for plotting. In most areas passing the significance test, PM
10 concentration showed a significant downward trend, especially in northern Xinjiang, western and eastern Inner Mongolia, and major urban agglomerations in the Beijing–Tianjin–Hebei region and Shandong Province, where the decrease exceeded 3 μg/m
3/year. Areas with significant increases in PM
10 concentration were mainly in desert areas surrounding Xinjiang, as well as central Qinghai, the Qilian Mountains in Gansu, western Sichuan, and the Yunnan–Guizhou Plateau. However, the PM
10 concentrations in these areas still remain relatively low.
3.2. Land-Cover Change from 2015 to 2021
Figure 7 shows the overall spatial distribution of land-cover types in China in 2015 and 2021. Due to various factors such as geographical environment, climate conditions, and human activities, land cover exhibits diversity and complexity, with different types of land cover intermingling. Forests are predominantly concentrated in the eastern and southwestern regions, particularly in the northeast, North China, the Yangtze River Basin, southwestern, and northwestern parts. The forests in the northeast are mainly composed of coniferous trees, while those in the southern regions consist mainly of broad-leaved trees. Grasslands are mainly distributed in the northern and northwestern regions of China, such as Inner Mongolia, Xinjiang. Cropland is widely distributed in the eastern plain areas and along the valleys of rivers and lakes. Urban areas are scattered and mainly concentrated in the eastern coastal areas and around some important economic centers. Barren lands are primarily located in the northwestern region, such as the Taklamakan Desert and the Kumtage Desert. In recent decades, with the rapid economic development in China, significant changes have occurred in the spatial pattern of land cover, leading to a series of environmental issues [
48].
Studying the transition of land-cover types is conducive to better analyzing and understanding the spatial structure and quantity changes in land surfaces, providing critical information for land management, environmental protection, climate change, and sustainable development. The land-cover transition matrix effectively extracts the area conversion between different types of land cover. In this study, we computed the land-cover type transition matrices for 2015 and 2021 (
Table 3). From 2015 to 2021, significant conversions occurred among different land-cover types across China. Specifically, forests mainly received conversions from grasslands, with an area conversion reaching more than 170,000 km
2, possibly associated with afforestation projects and related activities. Grasslands had the largest areas converted both into and out of, as a substantial amount of grassland was converted into forests, while significant areas were also converted into croplands, closely related to China’s demand for food. Additionally, during the rainy season or snowmelt period, grasslands may be submerged by river or lake flooding, transforming them into wetlands and water. Conversely, through drainage and marshland drying, wetlands may revert back to grasslands [
49]. The conversion of urban areas over the seven years under study was the smallest among all land-cover type conversions, as the proportion of urban areas globally is extremely small. It is noteworthy that there were also considerable conversions between grasslands and barren lands, but overall the area converted from barren land to grassland was larger.
The results of the land-cover type transition matrix provide a quantitative description of land-cover changes from 2015 to 2021. To better understand the spatial dynamics of different land-cover types, spatial change maps of forests, grasslands, croplands, wetland and water, as well as urban areas across China from 2015 to 2021, were also plotted (
Figure 8). Both grasslands and forests witnessed large-scale conversions across various provinces in China. China has been focusing on afforestation and forest conservation efforts in regions surrounding the Beijing–Tianjin–Hebei urban agglomeration and the desert areas in the northwest, leading to the conversion of barren land into grasslands and forests. Areas converted into croplands are primarily located in the North China Plain and the Northeast China Plain, which are traditional agricultural areas critical for China’s food security. The Northeast China Plain was historically one of the main agricultural regions, but recent economic restructuring and urbanization have led to spatial shifts in cropland distribution. Land improvement and the rational use of cropland are issues of great concern for the Chinese government, resulting in significant changes in cropland spatial distribution over the seven years under study [
50]. Wetlands and water primarily originate from grassland conversions, appearing in river and lake basins. Urban expansion is driven by economic development, with urban areas continuously expanding outward from major cities [
51]. In many regions of northwest China and Inner Mongolia, extensive grassland degradation has occurred due to arid climates, fragile ecological environments, and unsustainable land management practices.
3.3. Nonlinear Relationship between Land-Cover Change and PM10 Concentrations Change
In order to investigate the relationship between land-cover types and PM
10 concentrations, we conducted statistical analysis on the average PM
10 concentrations for different land-cover types, as shown in
Table 4. From 2015 to 2021, the interannual variation range of PM
10 concentrations at various land-cover types was within 5 μg/m
3. It can be observed from the table that the atmospheric quality is the best for forest cover locations, with values within 40 μg/m
3 throughout the seven years, significantly lower than other land-cover types. Grassland areas exhibit the second lowest PM
10 concentration among the other land-cover types. Wetland and water, croplands, and urban areas have similar PM
10 concentrations, with wetland and water being slightly lower than croplands and urban areas. The PM
10 concentration in urban areas remains relatively stable, except for 2020, where it stayed between 61 and 60 μg/m
3, approximately 1 μg/m
3 lower compared to other years. This may be closely related to the sudden outbreak of the coronavirus disease 2019 (COVID-19) pandemic at the end of 2019. The lockdown policy implemented by China resulted in a reduction in traffic and industrial activities. Traffic restrictions reduced vehicle exhaust emissions, while industrial shutdowns reduced industrial emissions, leading to a decrease in PM
10 concentration in some urban areas. With the arrival of the post-pandemic era, economic and social activities gradually resumed, and industrial and transportation activities gradually returned to normal levels, leading to a resurgence of air pollutant emissions in some areas [
52,
53]. Barren land, with its exposed and loose surface, is highly susceptible to wind erosion, which easily generates large amounts of particulate matter into the atmosphere, resulting in air pollution. Moreover, desertification is predominant in barren lands, and sandstorms are one of the main sources of PM
10, making barren lands exhibit the highest PM
10 concentration among all land-cover types.
The differences in PM
10 concentration among different land-cover types are significant. To further explore the relationship between land-cover types and PM
10 concentration, we plotted the proportions of different graded PM
10 concentrations under different land-cover types.
Figure 9 illustrates significant differences in air quality among different land-cover types. The PM
10 concentrations in forested areas are all within 75 μg/m
3, with over 90% of forested areas having PM
10 concentrations below 50 μg/m
3. More than half of grassland areas have PM
10 concentrations below 50 μg/m
3. However, wetland and water, croplands, and urban areas have PM
10 concentrations exceeding 50 μg/m
3, with over half of these areas surpassing this threshold. Currently, China’s primary standard for PM
10 concentration is 40 μg/m
3, indicating that there is still a PM
10 exposure risk in many areas with frequent human activities, such as croplands and urban areas. Additionally, barren lands are consistently affected by sandstorms, with 47.8% of the regions experiencing PM
10 concentrations exceeding 100 μg/m
3.
Table 5 shows the concentration of variation in PM
10 when the land-cover transition occurred. Where there was no change in land-cover type, PM
10 concentrations showed a decreasing trend, except for barren land. It can be seen that among all types of transitions, only three transitions show an increase trend in PM
10 concentration. Two of these transitions result in an increase in PM
10 concentration due to conversion to barren land, while the other land-cover type transitions show a decreasing trend. There is a significant decrease in PM
10 concentration when various land-cover types are converted to forest, especially when barren land is converted to forest, with a decrease of −17.34 μg/m
3; compared to the PM
10 concentration baseline of barren land, the PM
10 concentration decreased by 20.73 μg/m
3. Additionally, when forest is converted to other land-cover types, the change in PM
10 is minimal. But compared to the baseline of forest, the decrease in the PM
10 concentration is much smaller, indicating that the land-cover type switch decreased the suppression of PM
10. When various land-cover types are converted to urban and wetland and water bodies, there is also a significant decrease trend in PM
10 concentration, ranging from −5.11 μg/m
3 to −11.48 μg/m
3 and −2.51 μg/m
3 to −14.23 μg/m
3, respectively. Conversely, there is relatively little change in the PM
10 concentration when converted to cropland.
It is difficult to quantitatively assess the complex nonlinear response mechanism of PM
10 concentration to land-cover types by simple statistical analysis. In this study, GAM models were constructed to calculate the nonlinear response characteristics of the PM
10 concentration to the proportion of land-cover types in 2015 and 2021. The overall interpretability of the GAM models was 75.6% in 2015 and 76.7% in 2021. Among all land-cover types, only wetland and water did not pass the significance test, while the other five types of land cover passed the significance test at
p < 0.01 (
Table 6). This might be due to the unique spatial distribution of wetland and water, often found within other land-cover types, resulting in relatively small sample sizes and areas. The results in the table indicate that forests were the dominant factor in the variation in PM
10 concentration in 2015, while in 2021, the dominant factor shifted to barren land, which may be closely related to the occurrence of a severe dust storm in China in March 2021 [
54]. In nonlinear fitting, the F-statistic is an indicator that assesses the overall significance of the GAM model and can help to determine whether the model is appropriate and whether the explanatory variables have a significant effect on the response variable. This also resulted in a significant difference in the F-statistic for barren land between these two years. Overall, forests and barren land remain the primary factors driving the changes in PM
10 concentration.
The trend of PM
10 concentration changes with variations in the proportion of different land-cover types remains unclear. We plotted the fitted smoothed curves for 2015 and 2021 using the R language with the average PM
10 concentration and the proportion of each land-cover type area as the dependent variable and independent variable, respectively (
Figure 10 and
Figure 11). The proportion of the land-cover type area at all sample points is indicated at the x-axis. It can be observed that in both years, the smooth curve trends between various land-cover types and PM
10 were generally similar. This may be due to the limited time span covered by the dataset and the fact that land-cover types in many areas show a relatively stable state during this time, which leads to the similarity of the nonlinear patterns. Forests exhibited a significant inhibitory effect on PM
10 concentration changes. Before the proportion of forest area exceeds 60%, the PM
10 concentration decreases rapidly as the proportion increases. When it exceeds 60%, it still maintains a certain inhibitory effect, but the effect on the change in the PM
10 concentration is not as significant. The inhibitory effect of grasslands is relatively moderate. Changes in wetland and water are also relatively gentle. An increase in cropland land leads to an increase in PM
10 concentration. When the proportion of cropland reaches around 40%, the rate of increase in the PM
10 concentration is the fastest. As the proportion of cropland continues to increase, the effect on PM
10 concentrations levels off. The relationship between urban and PM
10 concentration shows an initial increase followed by a decrease as the urban area proportion rises, with the PM
10 concentration reaching its peak when the urban area proportion reaches about 10%. The curve also reveals that during urban expansion, the rate of increase in the PM
10 concentration exceeds the rate of decrease. The pattern is similar to the environmental Kuznets curve. The environmental Kuznets curve describes the relationship between economic growth and environmental pollution. The curve has an inverted U-shape, representing the rise in environmental pollution with GDP growth in the early stages of economic development and the improvement in environmental quality in the later stages as people become more aware of environmental protection and government environmental policies are implemented. In both years, the degrees of freedom of the fitted curve for barren land were less than two, indicating that the relationship between barren land and PM
10 concentration is very close to linear. In desert areas, which are the most important natural sources of PM
10 within barren land types, there is a significant trend of increasing PM
10 concentration.