Can Changes in Urban Form Affect PM2.5 Concentration? A Comparative Analysis from 286 Prefecture-Level Cities in China

It is crucial to the sustainable development of cities that we understand how urban form affects the concentration of fine particulate matter (PM2.5) from a spatial–temporal perspective. This study explored the influence of urban form on PM2.5 concentration in 286 prefecture-level Chinese cities and compared them from national and regional perspectives. The analysis, which explored the influence of urban form on PM2.5 concentration, was based on two types of urban form indicators (socioeconomic urban index and urban landscape index). The results revealed that cities with high PM2.5 concentrations tended to be clustered. From the national perspective, urban built-up area (UA) and road density (RD) have a significant correlation with PM2.5 concentration for all cities. There was a significant negative correlation between the number of patches (NP) and the average concentration of PM2.5 in small and medium-sized cities. Moreover, urban fragmentation had a stronger impact on PM2.5 concentrations in small cities. From a sub-regional perspective, there was no significant correlation between urban form and PM2.5 concentration in the eastern and central regions. On the other hand, the influence of population density on PM2.5 concentration in northeastern China and northwestern China showed a significant positive correlation. In large- and medium-sized cities, the number of patches (NP), the largest patch index (LPI), and the contagion index (CONTAG) were also positively correlated with PM2.5 concentration, while the LPI in small cities was significantly negatively correlated with PM2.5 concentration. This shows that, for more developed areas, planning agencies should encourage moderately decentralized and polycentric urban development. For underdeveloped cities and shrinking cities, the development of a single center should be encouraged.


Introduction
In the 21st century, China has undergone rapid development in the terms of urbanization. However, at the same time there has been a sharp rise in PM 2.5 concentrations [1][2][3]. In particular, extensive economic growth has led to the aggravation of this situation, affecting the sustainable development of cities [4,5]. In the meantime, PM 2.5 pollution-induced issues wreak great damage to natural ecosystems and have a deleterious effect on the physical and mental health of people [6][7][8]. In addition, PM 2.5 pollution can cause other negative outcomes such as crises of government trust and social instability [9][10][11][12]. Therefore, it is of great significance to PM 2.5 pollution mitigation that we determine the distribution of PM 2.5 concentrations and distinguish the determinants of PM 2.5 pollution in China.
The relationship between urban form and air quality (especially PM 2.5 ) has been given more and more attention by urban planners and environmentalists [13]. Different social and economic conditions and different geographical characteristics affect the development and characteristics of cities in different regions. In some developed countries, some evidence suggests that cities with fairly low levels of urban fragmentation and spread have less PM 2.5 pollution than fragmented, dispersed, and complex cities [14,15]. The higher the degree of urban fragmentation, the denser the urban population, and the worse the urban air quality [16]. It seems that compact, low sprawling, and highly contiguous urban forms provide better air quality in developed countries. Some research results also point to the negative effects of a scattered population and inconvenient transportation on air quality [17,18].
China's environmental and socioeconomic conditions are different from those of developed countries. Different socioeconomic factors and geographical and climatic conditions cause great differences in PM 2.5 concentrations between China and developed countries [19]. Some researchers have studied the relationship between urban form and PM 2.5 concentration in China. For instance, based on 288 prefecture-level cities, Li [20] pointed out that small-scale, decentralized, and polycentric urban forms improve air quality in China. She et al. [21], through the study of the Yangtze River Delta, discovered that urban expansion accelerates energy consumption, resulting in a positive correlation with PM 2.5 concentration. Moreover, Zhang and Zhang [22] revealed that high population densities and numbers of cars might contribute to air pollution in urban agglomerations in China. Du et al. [12] came to a similar conclusion in the Pearl River Delta. However, most of these studies focus on the relationship between individual cities or urban agglomerations. In reality, each regional or spatial scale has a specific socioeconomic background and geographical and climatic conditions, which may lead to different research results. A discovery in one region cannot be used for another. The regional difference standpoint has been proven valid in several positive research studies [23][24][25]. In China, a few regions (such as the BTH region, Yangtze River Delta, and Pearl River Delta) have relatively concentrated populations and economies. Correspondingly, the PM 2.5 pollution level in these regions is higher than that in other regions. Consequently, it is necessary to study the influence of urban form on PM 2.5 concentration from both national and regional perspectives.
To correct the deviation in space, a spatial econometric model was used to analyze the impact of urban form on air quality in 286 cities in China. Moreover, spatial autocorrelation and spatial regression were conducted to distinguish the correlations between urban form and air quality in different regions of China. The index of urban form can be divided into two categories: a socioeconomic index and an urban landscape index. The urban landscape index was based on land-use data derived from satellites and calculated by FRAGSTATS software. According to their economic situations, the 286 prefecture level cities are divided into five regions, namely the eastern region, the central region, the northeastern region, the northwestern region, and the southwestern region. Then, the cities are divided into large cities, medium-sized cities, and small cities according to their populations. Considering China's national conditions and the distribution of data samples, cities with a population of 3 million or below are defined as small cities. Cities with a population of 3 million to 5 million are defined as medium-sized cities. Cities with a population of more than 5 million are defined as large cities.
This study differs from existing studies in the following aspects: (1) it conducted long-term spatiotemporal analyses of PM 2.5 concentrations annually in 286 prefecturelevel cities in China. (2) The urban landscape index and urban socioeconomic index were used to characterize urban form. (3) On the one hand, when the road density low, the development of road traffic is conducive to reducing PM 2.5 concentration. On the other hand, when road traffic develops to a very high level, increasingly crowded roads will increase PM 2.5 concentration. (4) Excluding the effects of meteorological and geographic conditions, most of the more-developed cities or areas, which have a higher degree of urban development (except for fragmentation, other urban-form indicators have less of an impact on PM 2.5 ), should exhibit moderately decentralized and polycentric urban development.
(5) For less-developed cities and shrinking cities, the single-center development model can better mitigate PM 2.5 pollution than the multicenter development model. The above points define the specificity of this study. The research results will further analyze the relationship between urban morphology and PM 2.5 concentration in combination with existing relevant research so as to provide a reliable reference for urban planning and urban air quality improvement. This paper is separated into five parts: the first part contains the introduction and research objectives; the second part contains the research methods and data sources; the third part contains the data sources and variable calculations; the fourth part contains the analysis and discussion; and the fifth part contains the conclusions and research prospects with a detailed discussion of the study's limitations. The flow chart of the article is shown in Figure 1. an impact on PM2.5), should exhibit moderately decentralized and polycentric urban development. (5) For less-developed cities and shrinking cities, the single-center development model can better mitigate PM2.5 pollution than the multicenter development model. The above points define the specificity of this study. The research results will further analyze the relationship between urban morphology and PM2.5 concentration in combination with existing relevant research so as to provide a reliable reference for urban planning and urban air quality improvement. This paper is separated into five parts: the first part contains the introduction and research objectives; the second part contains the research methods and data sources; the third part contains the data sources and variable calculations; the fourth part contains the analysis and discussion; and the fifth part contains the conclusions and research prospects with a detailed discussion of the study's limitations. The flow chart of the article is shown in Figure 1.

Spatial regression models
Extracting raster data

Study Area
Prefecture-level cities, with large urban area and population scale, have advantages in terms of their economic structure and geographical location. They are not only present in all parts of China but reflect the development trend of urbanization and regional economic characteristics [24]. In this study, 286 prefecture-level cities were selected as our sample. According to the degree of urban development in different regions and the different climatic and geographical conditions, Chinese cities are divided into the eastern region, central region, northeastern region, northwestern region, and southwestern region. Development of the above areas was uneven. The eastern regions have large cities, strong economic strength, and high population density [26]. Being the most developed area of China, the eastern region consumes large amounts of natural resources to maintain urban development and socioeconomic growth. The situation is similar in the eastern and central regions. Relative to the eastern and central regions, on the one hand development of the northeastern and northwestern regions is unbalanced, on the other hand problem of population loss is serious in those area. Most cities in northeastern China are resourcebased or resource-exhausted. Urban development has stagnated, and cities have contracted. The degree of urban development in northwestern China is low; most cities are in a stage of rapid or embryonic development, and the urban compactness and the stability of their spatial structure are low. It is worth thinking about how the PM2.5 concentration increased between 2000 and 2015 in two regions. To cut down PM2.5 concentration, it is essential to determine the relationship between urban form and PM2.5 concentration in

Study Area
Prefecture-level cities, with large urban area and population scale, have advantages in terms of their economic structure and geographical location. They are not only present in all parts of China but reflect the development trend of urbanization and regional economic characteristics [24]. In this study, 286 prefecture-level cities were selected as our sample. According to the degree of urban development in different regions and the different climatic and geographical conditions, Chinese cities are divided into the eastern region, central region, northeastern region, northwestern region, and southwestern region. Development of the above areas was uneven. The eastern regions have large cities, strong economic strength, and high population density [26]. Being the most developed area of China, the eastern region consumes large amounts of natural resources to maintain urban development and socioeconomic growth. The situation is similar in the eastern and central regions. Relative to the eastern and central regions, on the one hand development of the northeastern and northwestern regions is unbalanced, on the other hand problem of population loss is serious in those area. Most cities in northeastern China are resource-based or resourceexhausted. Urban development has stagnated, and cities have contracted. The degree of urban development in northwestern China is low; most cities are in a stage of rapid or embryonic development, and the urban compactness and the stability of their spatial structure are low. It is worth thinking about how the PM 2.5 concentration increased between 2000 and 2015 in two regions. To cut down PM 2.5 concentration, it is essential to determine the relationship between urban form and PM 2.5 concentration in five different regions. Furthermore, through the comparison between countries and regions, we can better understand the impact of different urban forms on PM 2.5 concentration and provide better suggestions for decision makers to reduce PM 2.5 concentration. The classification of the study area is shown in Figure 2. five different regions. Furthermore, through the comparison between countries and regions, we can better understand the impact of different urban forms on PM2.5 concentration and provide better suggestions for decision makers to reduce PM2.5 concentration. The classification of the study area is shown in Figure 2. The PM2.5 concentrations in prefecture-level cities between 2000 and 2015 were taken from the dataset provided by Donkelaar's team [27]. This dataset combines the AOD inversion results from multiple satellite instruments. The geochemical transport model was used to correlate the total column measurements of aerosols with the near-surface PM2.5 concentration. The geographically weighted regression model (GWR) was combined with the global ground survey to adjust the residual PM2.5 bias [27]. The gridded data was set at 0.01 degrees. Compared with 210 ground-monitoring data from North America and Europe, this dataset showed a high degree of consistency (R2 = 0.81) [28]. The detection of PM2.5 concentration by the China Environmental Monitoring Station depends on the ground detection of air monitoring stations in each city. There are three methods for measuring PM2.5 concentration in China's air monitoring stations. The methods include the β Ray plus dynamic heating system method, the β Ray plus dynamic heating system combined with light scattering method, and the micro oscillating balance plus film dynamic measurement system method [29]. Compared with ground monitoring data (data from air monitoring stations in different cities), satellite measurement has both advantages and disadvantages. Since some of China's cities lacked air monitoring stations between 2000 and 2015, one of the advantages of this method is that it can measure PM2.5 concentrations in more cities. Its disadvantage is that it has low accuracy in areas with high reflectivity, such as snow-covered areas and is prone to extreme values. This will cause the PM2.5 concentrations in snow-covered areas (e.g., some cities in northern China, particularly in Xinjiang and Heilongjiang Provinces) to be lower than they should be.

PM 2.5 Concentration Data
The PM 2.5 concentrations in prefecture-level cities between 2000 and 2015 were taken from the dataset provided by Donkelaar's team [27]. This dataset combines the AOD inversion results from multiple satellite instruments. The geochemical transport model was used to correlate the total column measurements of aerosols with the near-surface PM 2.5 concentration. The geographically weighted regression model (GWR) was combined with the global ground survey to adjust the residual PM 2.5 bias [27]. The gridded data was set at 0.01 degrees. Compared with 210 ground-monitoring data from North America and Europe, this dataset showed a high degree of consistency (R2 = 0.81) [28]. The detection of PM 2.5 concentration by the China Environmental Monitoring Station depends on the ground detection of air monitoring stations in each city. There are three methods for measuring PM 2.5 concentration in China's air monitoring stations. The methods include the β Ray plus dynamic heating system method, the β Ray plus dynamic heating system combined with light scattering method, and the micro oscillating balance plus film dynamic measurement system method [29]. Compared with ground monitoring data (data from air monitoring stations in different cities), satellite measurement has both advantages and disadvantages. Since some of China's cities lacked air monitoring stations between 2000 and 2015, one of the advantages of this method is that it can measure PM 2.5 concentrations in more cities. Its disadvantage is that it has low accuracy in areas with high reflectivity, such as snow-covered areas and is prone to extreme values. This will cause the PM 2.5 concentrations in snow-covered areas (e.g., some cities in northern China, particularly in Xinjiang and Heilongjiang Provinces) to be lower than they should be.

Urban Form Metrics
The urban form indicators were divided into two categories: urban index and landscape metric. The urban index is composed of urban built-up area (UA), population density (PD), and road density (RD). Urban built-up area (UA) reflects the degree of urban development. Population density (PD) represents the intensity of human social activities in a city. Road . Landscape metric is extremely useful for describing urban forms. It has several advantages for characterizing the heterogeneity of urban landscapes and the gap between urban land use patterns and governance processes, as well as analyzing urban development [31][32][33][34]. Three landscape metrics were ultimately selected to indicate urban forms in this study. These metrics were number of patches (NP), largest patch index (LPI), and contagion index (CONTAG). NP describes the heterogeneity of the whole urban landscape and represents the degree of fragmentation of urban patches. LPI represents the proportion of the largest urban patch to the whole urban landscape area. It reflects the direction and strength of human activities. CONTAG describes the agglomeration degree or extension trend of different urban patches in the landscape. The high value of CONTAG indicates that some urban patches in the landscape show good connectivity; otherwise, it indicates that the city has a dense pattern. Urban landscape data were derived from land-use datasets (30 m × 30 m) for China from 1998 to 2015 that were produced by the Institute of Remote Sensing and Digital Earth at the Chinese Academy of Sciences through the interpretation of Landsat TM or ETM images. The overall accuracy of classification for these datasets is more than 85% [35]. We used Fragstats 4.2 to calculate three urban landscape indicators for each city.

Spatial Autocorrelation Test
Moran's I statistic is one of the most commonly adopted measures for spatial autocorrelation. It has been used to test spatiotemporal characteristics by identifying spatial correlations and spatial heterogeneity [36]. When researching spatial correlations and spatial distribution patterns, respectively, we usually divide Moran's I into global Moran's I and local Moran's I [37]. The global Moran index describes the average correlation degree of all spatial units with the entire surrounding region, allowing us to explore whether there is spatial correlation at the regional level. The equation for calculating global Moran's I is as follows: where y i and y j represent the attribute values of the ith spatial element and the jth spatial element, respectively; y denotes the mean value of y; n is the total number of spatial elements; and W ij is the spatial weight value. The local Moran index can be used to observe the spatial aggregation in the local areas. The equation for calculating local Moran's I is as follows: The values of global Moran index and local Moran index both range from −1 to 1. For the global Moran index, when I G > 0, it means that the attribute values of all regions are positively correlated; otherwise, the attribute values of all regions are negatively correlated. For the local Moran index, an I L value above zero means that the indicators of a city are similar to those of surrounding cities. An I L value below zero means that a city is surrounded by cities with different indicators. To clarify the spatial aggregation of each urban form index, we generated a LISA cluster map in GeoDa on the basis of the Moran scatter diagram and the Moran index.

Spatial Regression Models
Regression analysis has been used in most studies to explore the relationship between urban form and PM 2.5 concentration [27,35,38,39]. Some regression analyses ignore the influence of spatial heterogeneity, such as least-squares regression analysis and ridge regression analysis [24]. A multitude of studies has proven that the spatial regression Sustainability 2022, 14, 2187 6 of 20 model can effectively solve the spatial dependence issue [40]. There are two commonly used spatial regression models: the spatial lag model (SLM) and the spatial error model (SEM). The calculation formula is as follows: where y represents the PM 2.5 concentration of each prefecture-level city; ρ is the spatial lag coefficient of urban forms; W represents the spatial weight; X represents the urban form index; β represents the regression coefficient vector; ε denotes the random error vector; γ is the residual correlation parameter, and δ represents a vector of the error terms. To determine which model to use, mainly relied on the Lagrange multiplier (LM) test and the robust LM test. It is worth noting that the mean is always greater than the median. This indicates that there are some cities with more serious PM 2.5 pollution, making the mean value larger. In addition, PM 2.5 concentrations increased sharply in 2000-2005. The annual growth rate of the average concentration of PM 2.5 was more than three times that of the entire 15 years. The change in PM 2.5 concentration standard-reaching rate between cities also illustrates this point. In China's ambient air quality standards, the concentration limit of fine particulate matter (PM 2.5 ) is divided into level I and level II, of which the level I standard is 15 µg/m 3 . This standard applies to areas such as nature reserves. The secondary standard is 35 µg/m 3 . This standard applies to residential areas, commercial areas, and other areas. In 2000, only 97 of 286 prefecture-level cities had PM 2.5 concentrations exceeding 35 µg/m 3 , with a standard-reaching rate of 66%. However, 225 cities had PM 2.5 concentrations above 35 µg/m 3 in 2005, and the standard-reaching rate was only 21.1%. Among the 286 prefecture-level cities, the number of cities that met the Grade II Standards was 189, 61, 69, and 78 in 2000, 2005, 2010, and 2015, respectively. For detailed data on PM2.5 concentration, see Table S1 in the Supplementary Materials. As shown in Figure 4, the distribution of PM2.5 gradually concentrated in (the northeastern region, the central region, and the eastern region). The nor gion exhibited a high concentration of PM2.5 in 2015. This scenario could be the coal consumption and winter heating in that region. A similar problem is in developed European countries. In Poland, for example, solid fuel heatin As shown in Figure 4, the distribution of PM 2.5 gradually concentrated in some areas (the northeastern region, the central region, and the eastern region). The northeastern region exhibited a high concentration of PM 2.5 in 2015. This scenario could be attributed to the coal consumption and winter heating in that region. A similar problem is also present in developed European countries. In Poland, for example, solid fuel heating in Krakow causes air pollution in surrounding cities [41]. PM 2.5 levels were also extraordinarily high in some cities in Central China, especially in the BTH region. This may be due to most cities in the central region being dependent upon secondary industries for their economic activities [42]. Additionally, heavy vehicle emissions also lead to an increase in PM 2.5 concentrations [43]. Apart from the above factors, the PM 2.5 pollution level in cities can be significantly affected by the pollution levels of their neighboring areas, especially in cities with high concentrations [44,45]. As shown in Figure 4, the distribution of PM2.5 gradually concentrated in some area (the northeastern region, the central region, and the eastern region). The northeastern r gion exhibited a high concentration of PM2.5 in 2015. This scenario could be attributed t the coal consumption and winter heating in that region. A similar problem is also presen in developed European countries. In Poland, for example, solid fuel heating in Krakow causes air pollution in surrounding cities [41]. PM2.5 levels were also extraordinarily hig in some cities in Central China, especially in the BTH region. This may be due to mo cities in the central region being dependent upon secondary industries for their econom activities [42]. Additionally, heavy vehicle emissions also lead to an increase in PM2.5 con centrations [43]. Apart from the above factors, the PM2.5 pollution level in cities can b significantly affected by the pollution levels of their neighboring areas, especially in citie with high concentrations [44,45].

. Spatiotemporal Variation of Urban Form
As is shown in Figure 5, the population density of all cities increased slightly from 423.4 in 2000 to 433.9 in 2015, indicating that the urban population has become more con centrated over the past two decades. Urban built-up areas also became larger during th period, as is indicated by the urban area index, which increased from 61.  The trend of the change in spatial autocorrelation was the same as that of PM 2.5 concentration. As shown in Table 1.

Spatiotemporal Variation of Urban Form
As is shown in Figure 5, The LISA cluster map of urban form in Chinese cities from 2000 to 2015 is shown in Figure 6. As can be seen, the spatial distribution of population density had hardly changed. From the graph, high-high clusters of population density mainly exist in the eastern region, while low-low clusters of population density exist in the northeast, the northwest, and Guangxi. The main changes were concentrated in Shaanxi Province and Henan Province. From 2000 to 2015, the spatial aggregation of population density in Shaanxi Province became stronger, while that of Henan province became weaker. On the whole, urban built-up areas can be divided into four categories of spatial pattern. The LISA cluster map of urban form in Chinese cities from 2000 to 2015 is shown in Figure 6. As can be seen, the spatial distribution of population density had hardly changed. From the graph, high-high clusters of population density mainly exist in the eastern region, while low-low clusters of population density exist in the northeast, the northwest, and Guangxi. The main changes were concentrated in Shaanxi Province and Henan Province. From 2000 to 2015, the spatial aggregation of population density in Shaanxi Province became stronger, while that of Henan province became weaker. On the whole, urban built-up areas can be divided into four categories of spatial pattern.
High   Specifically, HH indicates a city with a high population density (urban area, road density) surrounded by cities with high population density (urban area, road density); LL indicates a city with a low population density (urban area, road density) surrounded by cities with low population density (urban area, road density); HL indicates a city with a high population density (urban area, road density) surrounded by cities with low population density (urban area, road density); and LH indicates a city with a low population density (urban area, road density) surrounded by cities with high population density (urban area, road density).

Influence of Urban Form on PM2.5 Concentrations at the National Scale
It can be seen from Table 2 that the test results of the correlation coefficient (R2), bass information content criterion (SC), log likelihood (log likelihood), and Akaike info criterion (AIC) are significant, which proves that SEM has relatively high goodness of fit and can accurately evaluate the influence of urban form on the long-term variations in PM2.5 Specifically, HH indicates a city with a high population density (urban area, road density) surrounded by cities with high population density (urban area, road density); LL indicates a city with a low population density (urban area, road density) surrounded by cities with low population density (urban area, road density); HL indicates a city with a high population density (urban area, road density) surrounded by cities with low population density (urban area, road density); and LH indicates a city with a low population density (urban area, road density) surrounded by cities with high population density (urban area, road density).

Influence of Urban Form on PM 2.5 Concentrations at the National Scale
It can be seen from Table 2 that the test results of the correlation coefficient (R2), bass information content criterion (SC), log likelihood (log likelihood), and Akaike info criterion (AIC) are significant, which proves that SEM has relatively high goodness of fit and can accurately evaluate the influence of urban form on the long-term variations in PM 2.5 concentration. From the perspective of all cities in China, not all urban form indicators are significantly correlated with PM 2.5 concentration. Specifically, UA (0.015 in 2000 and 0.009 in 2015) was significantly positively correlated with PM 2.5 concentrations. RD showed a negative correlation in 2000 (−7.516) and a significant positive correlation in 2015 (22.432) with PM 2.5 concentrations. This phenomenon shows that when the urban road density is at a low level, increasing road construction will reduce road congestion and lead to a decrease in PM 2.5 concentration, while when the urban road density is at a higher level, increasing road construction will increase the concentration of PM 2.5 . In general, urban expansion and urban road network density have become the major factors impacting PM 2.5 concentrations in all cities in China.

Influence of Urban Form on PM 2.5 Concentrations at the Region Scale
The spatial regression results of five regions by city size are shown in Tables Table 3). The reasons for this are manifold. On the one hand, the increase in road density is closely related to economic development. On the other hand, as roads become more developed, the compactness of cities will also increase correspondingly, further affecting PM 2.5 concentration.
In the northeastern region, only PD had a significant correlation with PM 2.5 concentration between 2000 and 2015 (0.022 in 2000 and 0.065 in 2015). However, when the analysis was refined according to the size of the cities, the results were different. For large and small cities in northeastern China, in addition to PD, the impact of NP, LPI, and CONTAG on PM 2.5 was also significant. It is worth noting that NP and LPI were negatively correlated with PM 2.5 concentration in large cities, while CONTAG was positively correlated with PM 2.5 . The opposite was true of small cities (Table 5). Cities in the northwest faced the same situation as those in the northeast. PD was still the most important factor affecting PM 2.5 concentration in northwestern China. For small-and medium-sized cities in northwestern China, UA also played a positive role in PM 2.5 concentration, especially in 2015 (medium 0.02 and small 0.064). UA was negatively correlated with PM 2.5 concentration in large cities. Otherwise, LPI (0.048) had the greatest positive impact on PM 2.5 in 2015. This state of affairs also confirms that large cities, which have become diverse, continuous, and uncompact, have been able to reduce PM 2.5 pollution. Notes: *** represents a significance level of 1%, ** represents a significance level of 5%, and * represents a significance level of 10%. The urban population of large cities is more than 5 million, the urban population of medium cities is more than 3 million and less than 5 million, and the urban population of small cities is less than 3 million.  Notes: *** represents a significance level of 1%, ** represents a significance level of 5%, and * represents a significance level of 10%. The urban population of large cities is more than 5 million, the urban population of medium cities is more than 3 million and less than 5 million, and the urban population of small cities is less than 3 million. Notes: *** represents a significance level of 1%, ** represents a significance level of 5%, and * represents a significance level of 10%. The urban population of large cities is more than 5 million, the urban population of medium cities is more than 3 million and less than 5 million, and the urban population of small cities is less than 3 million.  Notes: *** represents a significance level of 1%, ** represents a significance level of 5%, and * represents a significance level of 10%. The urban population of large cities is more than 5 million, the urban population of medium cities is more than 3 million and less than 5 million, and the urban population of small cities is less than 3 million.  Notes: *** represents a significance level of 1%, ** represents a significance level of 5%, and * represents a significance level of 10%. The urban population of large cities is more than 5 million, the urban population of medium cities is more than 3 million and less than 5 million, and the urban population of small cities is less than 3 million. Notes: *** represents a significance level of 1%, ** represents a significance level of 5%, and * represents a significance level of 10%. The urban population of large cities is more than 5 million, the urban population of medium cities is more than 3 million and less than 5 million, and the urban population of small cities is less than 3 million.

The Relationship between PM 2.5 and Urban Areafrom a National Perspective
According to the estimated results in Table 2, several important conclusions can be drawn at the national scale. First, the correlation coefficients of urban areas (built-up areas) were significantly positive in 286 prefecture-level cities, implying that the expansion of urban areas aggravates the pollution of PM 2.5 at the national scale and especially in large cities. Liu [46] and She [21] also found a positive relationship between the urban area and urban air pollution. The emergence of this situation may be due to the expansion of the urban area, which leads to population growth and increased road traffic. These factors aggravate energy consumption and increase PM 2.5 pollution.
Second, the correlation coefficients of population density were significantly positive in 2000, 2005, and 2010 but not significant in 2015, implying that increased population density leads to more PM 2.5 pollution. An increase in population density not only increases the demand for consumption and work resources but also aggravates housing congestion and traffic jams [15].
Third, the correlation coefficients of road density were significantly negative in 2000 but positive in 2015. It is possible that the road density has a positive correlation with PM 2.5 when the road density reaches a certain limit, and the correlation increases gradually. It was found that some emerging cities that are developing show opposite results in terms of road density compared to some other large cities. For example, in Yangquan, a city located in the northwestern region of China, the road density increased from 0.095 to 0.1429 between 2005 and 2015, but the annual average concentration of PM 2.5 decreased from 63.6 µg/m 3 to 57.2 µg/m 3 . However, further research is needed by the authors to ascertain exactly what this limit is and whether it is more relevant to industrial development or to urban planning.
Fourth, the correlation coefficient of NP in small-and medium-sized cities is significantly and negative, but not for large cities. This shows that the impact of urban fragmentation on PM 2.5 concentration is only reflected in cities on a general scale. The correlation coefficients of the large patches index were significant and positive in 2005 and 2010. This indicates that small, dispersed, and polycentric cities exhibited less PM 2.5 pollution than compact and larger cities. A similar finding was observed by Wu et al. [32] and She et al. [21], who both concluded that a more uniform distribution of urban patches might be better for mitigating particulate matter in large urban agglomerations (for example, the YRD region). The more complex the urban form is, the greater the average distance between urban patches and the smaller the concentration of PM 2.5 . This shows that the multicenter urban form can improve air quality.
Lastly, unlike previous studies, we found that urban compactness (CONTAG) does not promote PM 2.5 concentrations at the national scale. Urban connectivity, or connectivity between centers, had little effect on PM 2.5 concentrations. With an increase in the population and a change in policy, cities, especially larger cities, begin to change from a single center to a double-or multicenter model.

The Relationship between PM 2.5 and Urban Form from the Sub-Regional Perspective
From the overall situation of each region, the impact of the urban area on PM 2.5 concentration varies with city size. Compared with small-and medium-sized cities, the change in PM 2.5 concentration in big cities is more easily affected by the urban area. An increased urban built-up area corresponds to greater traffic demand and energy consumption, causing comparatively worse air quality [14,47]. The impact of the urban built-up area on PM 2.5 concentration in northwestern China is more significant. The reason for this situation may be that the cities in northwestern China are in the initial stage of development, and the rapid increase in the urban area makes PM 2.5 pollution more serious.
Additionally, the population density of the cities in the northeast and northwest has a great influence on the concentration of PM 2.5 , but the population density in the east has little influence. This kind of regional difference may be caused by differences in the speed of land urbanization and population urbanization. On the one hand, the reason is that the population density of the cities in the northeast and northwest is smaller than that in the east; on the other hand, in recent years, some cities in northeastern China have been facing resource depletion contraction, which makes population density more sensitive to PM 2.5 concentration. The differences between northwestern China and northeastern China are as follows: first of all, most cities in northwestern China are in a period of rapid development; the built-up area is increasing and population growth is stagnant. The speed of land urbanization and population urbanization in northwestern cities was in a serious decoupling state: the urban land expansion speed was faster than that of the population expansion [48]. Secondly, the urban contraction in northeastern China is more serious than that in northwestern China, which has resulted in northeastern China becoming a single-core area, increasing the influence of population and road density on PM 2.5 . Lastly, the northwestern region is at a high altitude and has more mountains than the northeastern region; the less compact urban form helps disperse pollutants over the mountainous terrain, which results in less urban air pollution [21,49].
The influence of patch number and maximum patch index on PM 2.5 concentration was significant in northeastern China. It is worth noting that the results of large cities are the opposite of those of small cities. The results of large cities are similar to those of other developed regions. When cities tend to be polycentric, PM 2.5 pollution is reduced. However, the results obtained by small cities are just the opposite. When cities develop intensively and reduce the degree of urban fragmentation, it is easier to reduce the concentration of PM 2.5 . This result is not consistent with those obtained by other researchers. For instance, Namdeo et al. [50] found that a more compact urban layout helps to reduce urban traffic and improve industrial efficiency, thereby improving air quality. Lu and Liu indicated a negative correlation between compact urban form and air pollution in most cities of China. Bechle et al. [47] demonstrated that urban compactness was not a significant predictor of air pollution in 83 global cities. Fan et al. [39] found that a more compact urban form leads to less PM 2.5 pollution in China, especially in the northern region.
In southwestern China, the influence of urban form on PM 2.5 concentration was not significant. This may be caused by topography, weather, or industrial conditions. Most of the cities in southwestern China are concentrated in intermountain basins, river valleys, and alluvial fans, and the annual rainfall is substantial [26]. These conditions result in lower levels of air pollution in these places than in other places [42]. In addition, in southwestern China, the lack of coal industry was also an important reason for this result.

Limitations and Future Directions
There are three limitations to this study. The first is that the concentration of PM 2.5 is affected by many factors, and while it is certain that urban form is one of the key factors, other factors may play a more important role in influencing PM 2.5 concentration in some areas. For example, many of the southwestern cities are located in the mountainous valley zones, and their terrains are narrow. At the same time, rainfall is more concentrated in these areas. The terrain and meteorological factors are two of the main reasons for the changes in PM 2.5 concentration. Not all PM 2.5 pollution can be attributed to urban form. The second point is that the mechanism of influence of urban form on PM 2.5 concentration is not completely distinct, and it needs to be further studied in the future. The main reason for this is that the resolution of land-use data is low, and it is difficult to accurately determine the spatial distribution of roads, commercial areas, and residential areas through land-use data with a 1 km resolution. The calculation results of urban forms, such as the road density and patch number, may be biased. The last point is that based on data availability, our study focuses on the average annual variation in PM 2.5 concentration from 2000 to 2015. Future studies should analyze the relationship between urban forms, PM 2.5 concentrations, and seasonal variations on a spatiotemporal scale.

Conclusions
This study, using 286 prefecture-level Chinese cities as its sample data, examined the spatial patterns and temporal trends of PM 2.5 concentration from 2000 to 2015 and further explored the influence of urban form on PM 2.5 concentration. The results show that the PM 2.5 concentration significantly increased during the period from 2000 to 2005. The cities with heavy PM 2.5 pollution were mainly concentrated in the eastern and central regions of China, especially in the large cities and their surrounding areas. The cities with large changes in PM 2.5 concentration were mainly distributed in northeastern China. Specifically, many cities in the BTH and Yangtze River Delta regions, as well as central Liaoning and Shandong provinces, had more serious PM 2.5 pollution. Moreover, cities with high PM 2.5 concentrations be located close to one another, which indicates that PM 2.5 concentration is regional. From the national point of view, urban area and road density are related to higher PM 2.5 concentrations. On the other hand, there is little correlation between urban fragmentation and PM 2.5 concentration.
In the Northeast and Northwest China, the urban form and population density have more influence on PM 2.5 concentration. The reason for this is that the speed of land urbanization and population urbanization in the northeastern and northwestern regions of China was in a seriously decoupled state, and there was a serious phenomenon of urban contraction. Therefore, moderately compact and single-center urban development is conducive to the air quality of small-and medium-sized cities in northeastern and northwestern China. For the cities in the eastern and central regions, and most large-scale cities in China, it is more important to control unplanned urban and road expansion to encourage cities to develop in a decentralized and multicenter manner.
Author Contributions: Conceptualization, C.S. and X.C.; methodology, C.S. and X.C.; formal analysis, C.S. and T.L.; data curation, C.S., S.Z. and T.L.; writing-original draft preparation, C.S.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.