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Heterogeneous influences of urban compactness on air pollution: Evidence from 271 prefecture-level cities in China



Fine particulate matter (PM2.5) pollution can harm the climate, environment, and human health. With sustainability receiving increasing attention, whether compact urban development can yield green environmental benefits has become an essential research proposition among urban planners. Based on compact city theory, the impact of compactness factors on PM2.5 pollution in 271 Chinese cities was empirically studied from a spatiotemporal perspective. The PM2.5 spatiotemporal nonstationarity distribution and influence mechanisms of compact urban development on PM2.5 pollution were systematically examined via exploratory spatial data analysis (ESDA) and geographically and temporally weighted regression (GTWR). The results indicated the following: (1) cities with high PM2.5 concentrations were mainly located in Beijing-Tianjin-Hebei, north of the Yangtze River Delta, and connected central regions. (2) There existed significant spatial and temporal heterogeneity in the urban compactness factor effects on air pollution. The long-term combined effects of the population and land compactness factors could significantly exacerbate urban air pollution. The most significant influence on land use balance occurred in the eastern and central regions east of the Hu Line and south of the Yangtze River. (3) Economic compactness enhancement could effectively mitigate air pollution. In southeastern and northwestern cities, enhanced road accessibility helped to reduce PM2.5 concentrations.
细颗粒物(PM 2.5 )污染会危害气候、环境和人类健康。随着可持续发展日益受到关注,紧凑的城市发展能否产生绿色环境效益已成为城市规划者重要的研究命题。基于紧凑城市理论,从时空​​角度实证研究了中国271个城市的紧凑因素对PM 2.5 污染的影响。通过探索性空间数据分析(ESDA)和地理时间加权回归(GTWR)系统研究了PM 2.5 时空非平稳分布以及城市紧凑发展对PM 2.5 污染的影响机制。结果表明:(1)PM 2.5 浓度较高的城市主要分布在京津冀、长三角以北及中部相连地区。 (2)城市紧凑因素对空气污染的影响存在显着的时空异质性。人口和土地紧凑因素的长期综合影响可能会显着加剧城市空气污染。对土地利用平衡影响最显着的地区是胡泾线以东、长江以南的东部和中部地区。 (3)经济紧凑性增强可以有效缓解大气污染。在东南部和西北城市,道路通达性的改善有助于降低PM2.5浓度。

Keywords: PM2.5 pollution; spatiotemporal heterogeneity; urban compactness; geographically and temporally weighted regression
关键词: PM 2.5 污染;时空异质性;城市紧凑度;地理和时间加权回归


1. Introduction

China's recent economic development has captivated the world. However, long-term economic development and urban expansion have caused significant environmental degradation, especially urban air pollution. Studies have determined that air pollution causes approximately 1.6 million deaths in China each year, accounting for 17% of the total deaths (Rohde and Muller, 2015). Fine particulate matter (PM2.5) pollution is one of the worst types of air pollution (Zhang et al., 2020). The PM2.5 pollution conditions in Chinese cities cannot be neglected. According to the 2019 China Ecological Environment Status Bulletin, air quality criteria were exceeded in 180 of China's 337 prefecture-level and above cities in 2019.
中国近年来的经济发展令世界瞩目。然而,长期的经济发展和城市扩张造成了严重的环境恶化,特别是城市空气污染。研究表明,中国每年约有 160 万人因空气污染死亡,占总死亡人数的 17%(Rohde 和 Muller,2015)。细颗粒物(PM 2.5 )污染是最严重的空气污染类型之一(Zhang et al., 2020)。中国城市PM2.5污染状况不容忽视。据《2019年中国生态环境状况公报》显示,2019年中国337个地级及以上城市中,有180个空气质量超标。

Given the above, over the past decade, the PM2.5 pollution distribution in China has been investigated in many studies. The PM2.5 concentration varies by year, season, and even moment of the day (Fontes et al., 2017). In Chinese cities, the PM2.5 concentration decreased from 2015 to 2019 (Wang et al., 2021), and is typically high in winter and low in summer (Wang et al., 2017; Zhao et al., 2019b; Wang et al., 2022). The daily average PM2.5 concentration fluctuates in a pulsed manner, and the hourly variance trend is bimodal (Wang et al., 2019). Considering the spatial distribution features, provinces, urban agglomerations, and cities have been studied.  At the regional and provincial levels, air pollution decreases from south to north and from coastal to inland regions and appears spatially aggregated (Wang et al., 2022). Lin et al. (2013) utilized a geographically weighted regression (GWR) model to evaluate PM2.5 spatial heterogeneity in China and observed high concentrations in northern and eastern China. Specifically, the PM2.5 concentration was high in the east and low in the west, with the Hu Line as the boundary (Wang et al., 2019). Yangtze River Delta and Pearl River Delta exhibited stable values (Hou et al., 2019). According to previous studies, PM2.5 reveals significant spatial heterogeneity and aggregation at the city level (Zhao et al., 2019b; Dai et al., 2021; Wei et al., 2021). Urban dispersion decreases from southeast to northwest (Liu et al., 2021), while Chinese coastline cities attain lower annual average PM2.5 concentrations than inland cities (Zhang et al., 2018a). Nevertheless, the long-term spatial and temporal changes in PM2.5 at the city level are remain poorly researched in terms of cooperative regional control.
鉴于此,近十年来,许多研究对中国PM 2.5 污染分布进行了调查。 PM 2.5 浓度因年份、季节甚至一天中的时刻而异(Fontes 等人,2017 年)。在中国城市,PM 2.5 浓度从2015年到2019年呈下降趋势(Wang et al., 2021),并且通常冬季高、夏季低(Wang et al., 2017;Zhao et al., 2021)。 ,2019b;王等人,2022)。 PM 2.5 日均浓度呈脉冲式波动,小时方差趋势呈双峰状(Wang et al., 2019)。考虑空间分布特征,对省份、城市群和城市进行了研究。  在区域和省份层面,空气污染从南到北、从沿海到内陆地区逐渐减少,并呈现出空间聚集性(Wang et al., 2022)。林等人。 (2013)利用地理加权回归(GWR)模型评估中国PM 2.5 空间异质性,并观察到中国北部和东部地区PM 2.5 浓度较高。具体来看,PM2.5浓度以胡志明线为界,东高西低(Wang et al., 2019)。长三角和珠三角表现出稳定的数值(Hou et al., 2019)。根据以往的研究,PM 2.5 在城市层面表现出显着的空间异质性和聚集性(Zhao et al., 2019b; Dai et al., 2021; Wei et al., 2021)。城市分散度从东南向西北递减(Liu等,2021),而中国沿海城市PM 2.5 年均浓度低于内陆城市(Zhang等,2018a)。 然而,对于城市层面 PM 2.5 的长期时空变化,在区域合作控制方面的研究仍然很少。

In contrast, studies have been performed to investigate the influencing mechanisms of the PM2.5 concentration, primarily from the viewpoints of natural geography, socioeconomics, and urban planning perspectives. The PM2.5 concentration is positively correlated with the ground pressure and negatively correlated with the relative humidity, air temperature, wind speed, and precipitation (Li et al., 2019). Topography and vegetation affect pollution levels (Zhang et al., 2018b). The normalized vegetation index was reported to explain 15.3% of the total variation in the PM2.5 concentrations using a multiscale geographically weighted regression (MGWR) model (Wei et al., 2021).
相比之下,人们主要从自然地理学、社会经济学和城市规划的角度来探讨PM2.5浓度的影响机制。 PM 2.5 浓度与地面压力呈正相关,与相对湿度、气温、风速、降水量呈负相关(Li等,2019)。地形和植被影响污染水平(Zhang et al., 2018b)。据报道,使用多尺度地理加权回归 (MGWR) 模型,归一化植被指数可以解释 PM 2.5 浓度总变化的 15.3%(Wei 等人,2021 年)。

Human activities are also associated with PM2.5. Air pollution is affected by population, urbanization, industrialization, foreign direct investment (FDI), and energy efficiency (Wang et al., 2017; Zou and Shi, 2020). The Population density (PD) and traffic density are anthropogenic factors influencing the PM2.5 distribution in urban agglomerations (Wu et al., 2021). PD and urban structure impose a joint moderating effect on air pollution (Han et al., 2020). Different effects are produced by the urban form and land use. In determining PM2.5 concentrations, green spaces, the number of vehicles, and the heat island effect can all play a role (Yuan et al., 2018). He et al. (2022) found that city polycentricity negatively influences PM2.5 based on the urban structure evolution. Haze pollution could be reduced by a fragmented, polycentric urban form (Zhou et al., 2018). The fractal size and compactness were associated with PM2.5 pollution (Ouyang et al., 2021). Land use also affects pollution. Built-up land can increase PM2.5 pollution (Zhou et al., 2021). In contrast, the fragmentation of arable land facilitates PM2.5 reduction, whereas increasing the artificial surface area generates the reverse effect (Xu et al., 2021). Built environment planning drives urban planning (Yuan et al., 2019). Duan et al. (2021) discovered that urban floor area share and road density may enhance PM2.5 levels in cities. Urban planners should condense high-population-density cities to minimize PM2.5 pollution (Lee, 2021). As a result, urban planners must determine whether urban compactness variables can improve the urban environment and increase the resource utilization efficiency.
人类活动也与 PM 2.5 相关。空气污染受到人口、城市化、工业化、外国直接投资(FDI)和能源效率的影响(Wang等,2017;Zou和Shi,2020)。人口密度(PD)和交通密度是影响城市群PM 2.5 分布的人为因素(Wu et al., 2021)。 PD和城市结构对空气污染具有共同调节作用(Han et al., 2020)。城市形态和土地利用产生不同的影响。在确定 PM 2.5 浓度时,绿地、车辆数量和热岛效应都会发挥作用(Yuan 等,2018)。他等人。 (2022)根据城市结构演化发现,城市多中心性对 PM 2.5 产生负面影响。碎片化、多中心的城市形态可以减少雾霾污染(Zhou et al., 2018)。分形大小和致密性与 PM 2.5 污染相关(Ouyang et al., 2021)。土地利用也会影响污染。建设用地会增加PM 2.5 污染 (Zhou et al., 2021)。相比之下,耕地的破碎化有利于PM 2.5 的减少,而增加人工表面积则会产生相反的效果(Xu et al., 2021)。建成环境规划驱动城市规划(Yuan et al., 2019)。段等人。 (2021) 发现城市建筑面积份额和道路密度可能会提高城市的 PM 2.5 水平。城市规划者应集中人口密度高的城市,以尽量减少 PM 2.5 污染(Lee,2021)。因此,城市规划者必须确定城市紧凑度变量是否能够改善城市环境并提高资源利用效率。

The compact city concept was conceived in 1973 and has since been frequently reiterated by scholars (Dajani, 1974). It remains one of the hottest issues in modern urban research due to its numerous connections with the concept of sustainable development (Handayanto et al., 2017). According to Gordon and Richardson (1997), the compact city should be monocentric and of a high-density. Others have proposed a compact cities should be high-density, mixed-use areas (Breheny, 1997; Ewing, 1997; Burton, 2002). Overall, researchers agree that a compact city is one that delivers sustained welfare to its citizens while conserving sufficient land, with a moderately high density (including population and structures), mixed land use functions, and efficient transportation.
紧凑城市的概念于1973年提出,此后一直被学者们频繁重申(Dajani,1974)。由于它与可持续发展概念的众多联系,它仍然是现代城市研究中最热门的问题之一(Handayanto 等,2017)。根据 Gordon 和 Richardson(1997)的观点,紧凑城市应该是单中心的、高密度的。其他人提出紧凑型城市应该是高密度、混合用途的区域(Breheny,1997;Ewing,1997;Burton,2002)。总体而言,研究人员一致认为,紧凑型城市是一种为公民提供持续福利、同时保留足够土地、适度高密度(包括人口和建筑)、混合土地利用功能和高效交通的城市。

Currently, there exists no agreement on an acceptable criterion for the measurement of urban compactness, which is still influenced by a variety of circumstances (Zhao et al., 2020). The theoretical evolution of compact cities underpins the majority of current urban compactness measurement research. Urban spatial compactness evaluation has evolved concurrently with the evolution of the concept of compact cities. Urban compactness is commonly measured via systemic and statistical examination of multiple city dimensions (morphological, functional, demographic, economic, and other dimensions). Indicators of the morphological dimension include the city size, density, urban landscape pattern index, and built-up land concentration (Burton, 2002; Ewing et al., 2003; Angel et al., 2010). The functional dimension includes nonmorphological indicators such as the land use mix, accessibility, and work-population balance (Burton, 2002; Ewing et al., 2003; Jia et al., 2019). The demographic dimension includes nonformal indicators such as PD (Schwarz, 2010). There have been numerous studies on the spatial compactness of cities, but few have focused on the functional compactness of cities dominated by human activities (Lan et al., 2021).
目前,对于城市紧凑度的衡量标准尚未达成一致,仍然受到各种情况的影响(Zhao等,2020)。紧凑城市的理论演变支撑着当前大多数城市紧凑度测量研究。城市空间紧凑度评价是与紧凑城市概念的演变同时发展的。城市紧凑度通常通过对多个城市维度(形态、功能、人口、经济和其他维度)的系统和统计检查来衡量。形态维度的指标包括城市规模、密度、城市景观格局指数、建设用地集中度等(Burton,2002;Ewing等,2003;Angel等,2010)。功能维度包括土地利用组合、可达性、工作人口平衡等非形态指标(Burton, 2002; Ewing et al., 2003; Jia et al., 2019)。人口维度包括非正式指标,例如PD(Schwarz,2010)。关于城市空间紧凑性的研究有很多,但很少关注以人类活动为主的城市功能紧凑性(Lan et al., 2021)。

Changes in environmental effect indicators correspond to varying compactness degrees. Environmental effects of urban compactness factors are mainly reflected in two aspects: energy consumption and environmental pollution. Some researchers argue that the more compact a city's spatial use and population distribution, the more efficient its resource utilization will be (Newman and Kenworthy, 1989; Jia et al., 2019). Compact cities with mixed functional development have better air quality (Borrego et al., 2006). Liu et al. argued compact urban construction forms, weaker urban land divisions, and looser road networks reduce PM2.5 pollution (2021). The compact city form is correlated with reduced PM2.5 pollution emissions (Fan et al., 2018). In the US metropolitan areas, single compactness index and street connectivity all have a negetive impact on PM2.5 concentrations (Lee, 2019). Under the current development of Chinese cities, compact urban forms and a more concentrated monocentric development mode can effectively reduce particulate pollution (Zhao et al., 2019a).
环境影响指标的变化对应着不同的致密程度。城市紧凑度因素的环境效应主要体现在能源消耗和环境污染两个方面。一些研究人员认为,城市的空间利用和人口分布越紧凑,其资源利用效率就越高(Newman and Kenworthy,1989;Jia et al.,2019)。具有混合功能开发的紧凑城市具有更好的空气质量(Borrego 等,2006)。刘等人。认为紧凑的城市建设形式、较弱的城市土地划分和较宽松的道路网络可减少 PM 2.5 污染(2021 年)。紧凑的城市形态与 PM 2.5 污染排放量的减少相关(Fan et al., 2018)。在美国大都市区,单一紧凑度指数和街道连通性都会对 PM 2.5 浓度产生负面影响(Lee,2019)。在当前中国城市的发展下,紧凑的城市形态和更加集中的单中心发展模式可以有效减少颗粒物污染(Zhao等,2019a)。

Moreover, studies have considered urban compactness factors in environmental performance assessment. Gordon and Richardson (Gordon and Richardson, 1997) argued that congested cities cause excessive traffic and extended travel times, compromising the air quality. In terms of unscaled cities in the Yangtze River Delta, compact towns exhibited higher PM2.5 concentrations than disordered towns (Tao et al., 2020). Huang Q et al. (2021) constructed a spatial panel model and found that urban compactness attained a positive correlation with PM2.5 pollution.
此外,研究在环境绩效评估中考虑了城市紧凑度因素。戈登和理查森(Gordon and Richardson,1997)认为,拥挤的城市会导致交通过多和旅行时间延长,从而损害空气质量。就长三角地区的无规模城市而言,紧凑的城镇PM 2.5 浓度高于无序的城镇(Tao et al., 2020)。黄Q等人。 (2021)构建了空间面板模型,发现城市紧凑度与PM 2.5 污染呈正相关。

Despite the fact that scholars have investigated the relationship between urban compactness and air pollution, there remain certain flaws. First, the indicators chosen are solely based on spatial morphological compactness, but functional compactness is insufficiently considered. In addition, most researchers have not explored the spatial heterogeneity effect of urban compactness factors on air pollution. Furthermore, the existing multi-indicator calculation method cannot depict the relationship between each factor and PM2.5, which should be investigated from a multidimensional perspective.
尽管学者们研究了城市紧凑度与空气污染之间的关系,但仍然存在一定的缺陷。首先,指标的选择仅仅基于空间形态的紧凑性,而对功能的紧凑性考虑不足。此外,大多数研究人员尚未探讨城市紧凑因素对空气污染的空间异质性影响。此外,现有的多指标计算方法无法刻画各个因素与PM 2.5 之间的关系,需要从多维度的角度进行考察。

This work primarily provides two contributions. First, while PM2.5 management in China has achieved substantial progress over the last decade, the changes in PM2.5 driving mechanisms under policy control have not been extensively investigated. The spatial and temporal dynamics of PM2.5 over the past ten years were investigated through empirical analysis of 271 Chinese cities from 2010 to 2019, and additional feedback on the results of environmental pollution management in China is offered. This contributes to a more comprehensive approach to PM2.5 environmental management that better agrees with China's development goals and long-term sustainability objectives. Second, from a spatiotemporal viewpoint, the impact of urban compactness variables on PM2.5 was investigated in four dimensions in this study: population compactness, economic compactness, land compactness, and road compactness. Regarding the limitations of preexisting research, the geographically and temporally weighted regression (GTWR) method was used to study spatiotemporal variability on multiple spatial scales. This could help elucidate the influencing mechanism of urban compactness variables on air pollution and serve as a guide for decision-making in China to accurately regulate urban air pollution and establish urban development policies.
这项工作主要提供了两个贡献。首先,尽管中国的PM 2.5 管理在过去十年中取得了长足的进步,但政策控制下PM 2.5 驱动机制的变化尚未得到广泛研究。通过对2010年至2019年中国271个城市的实证分析,调查了过去十年PM 2.5 的时空动态,并对中国环境污染治理的结果提供了补充反馈。这有助于采取更全面的 PM 2.5 环境管理方法,更好地符合中国的发展目标和长期可持续发展目标。其次,从时空角度,本研究从人口紧凑度、经济紧凑度、土地紧凑度和道路紧凑度四个维度考察了城市紧凑度变量对PM 2.5 的影响。鉴于现有研究的局限性,采用地理和时间加权回归(GTWR)方法来研究多个空间尺度上的时空变异性。这有助于阐明城市紧凑度变量对空气污染的影响机制,为我国精准调控城市空气污染、制定城市发展政策提供决策指导。

The remainder of the paper is structured as follows: Section 2 provides a detailed account of the data collection and analysis methods employed in this study. Section 3 presents the results and engages in a discussion. Section 4 extends the discussion to present the conclusions and offer policy recommendations.
本文其余部分的结构如下:第 2 部分详细介绍了本研究中采用的数据收集和分析方法。第 3 节介绍结果并进行讨论。第四节扩展讨论以提出结论并提出政策建议。

2. Theoretical Analytical Framework
2 理论分析框架

2.1 Grounded in the Perspective of Population Density
2.1 立足人口密度视角

Population compactness manifests spatially through the concentration of population. This results in an elevation in population density. Such spatial distribution of the population often yields reduced levels of carbon emissions (Glaeser and Kahn, 2010). Given that pollutants stemming from the combustion of fossil fuels used in residents' daily lives constitute a primary source of haze pollution (Mukherjee and Agrawal, 2017), the development of urban spaces characterized by population compactness facilitates centralized energy supply. This, in turn, enables economies of scale in residents' energy consumption and consequently mitigates environmental pollution. Vande also discovered that high-density development promotes a more compacted residential and occupational lifestyle, resulting in adverse effects on urban energy consumption and carbon emissions (Vande et al. 2007).
人口密集度在空间上表现为人口的集中程度。这导致人口密度上升。人口的这种空间分布通常会降低碳排放水平(Glaeser 和 Kahn,2010)。鉴于居民日常生活中化石燃料燃烧产生的污染物是雾霾污染的主要来源(Mukherjee和Agrawal,2017),人口密集的城市空间发展有利于集中能源供应。这反过来又可以实现居民能源消费的规模经济,从而减轻环境污染。 Vande还发现,高密度开发促进了更加紧凑的居住和职业生活方式,从而对城市能源消耗和碳排放产生不利影响(Vande等,2007)。

Furthermore, compact urban spatial development patterns aid in reducing haze pollution concentration (Liang Chang et al., 2021). However, the efficacy of such patterns varies significantly depending on the scale of the city. In general, larger cities, as opposed to smaller ones, are more prone to experiencing congestion effects brought about by excessive agglomeration. This dilutes the scale effects of environmental pollution control and thereby exacerbates environmental issues.
此外,紧凑的城市空间发展模式有助于降低雾霾污染浓度(Liang Chang等,2021)。然而,这种模式的功效根据城市规模的不同而有很大差异。一般来说,大城市比小城市更容易受到过度集聚带来的拥堵效应。这削弱了环境污染治理的规模效应,从而加剧了环境问题。

Hypothesis 1: Higher population compactness contributes to improved environmental quality.

2.2 Grounded in the Perspective of Economic Density
2.2 立足经济密度视角

Economic density primarily manifests as the spatial agglomeration of economic activities. Industrial agglomeration, a form of spatial organization, demonstrates positive effects on environmental governance through three key externalities, as highlighted by Glaser: the common labor market, intermediate inputs, and knowledge spillovers (Glaser, 2011).
经济密度主要表现为经济活动的空间集聚程度。正如 Glaser 所强调的那样,产业集聚是一种空间组织形式,它通过三个关键外部性对环境治理产生积极影响:共同劳动力市场、中间投入和知识溢出(Glaser,2011)。

In the context of the common labor market formed by industrial agglomeration, a diverse workforce with various skills emerges. Firms can easily match suitable labor to their changing product market demands. This, in terms of environmental pollution, allows firms to access labor with enhanced pollution treatment capabilities, thus strengthening their pollution control efforts.

When examining intermediate inputs, firms located in agglomeration areas often produce similar or identical pollutants during their production activities. Industrial agglomeration fosters economies of scale in pollution control, leading to reduced pollution control costs. Within agglomeration areas, numerous forward and backward-linked enterprises participate in constructing circular economy models, which promote the reuse of pollutants to achieve energy conservation and emission reduction goals.

From the perspective of knowledge spillovers, industrial agglomeration provides a platform and conduit for different firms to engage in technology exchange and information sharing. It encourages the sharing and exchange of environmental knowledge and pollution control technologies among firms, accelerating technological innovation in pollution control and enhancing environmental pollution control capabilities. Levinson's research also found that 60%-95% of the reduction in pollution emissions in the U.S. manufacturing sector from 1987 to 2001 can be attributed to technological advancements (Levinson, 2009).
从知识溢出的角度来看,产业集聚为不同企业进行技术交流和信息共享提供了平台和渠道。鼓励企业间环境知识和污染治理技术的共享和交流,加快污染治理技术创新,提升环境污染治理能力。 Levinson的研究还发现,1987年至2001年美国制造业污染排放量的减少有60%-95%可归因于技术进步(Levinson,2009)。

Hypothesis 2: Economic density is positively correlated with environmental quality.

2.3 Grounded in the Perspective of Compact Land Use
2.3 立足紧凑用地视角

As rapid urbanization progresses, urban spaces expand into the suburbs, extending beyond city boundaries. However, this expansion often leads to urban sprawl, which diminishes land use efficiency. Urban sprawl results in the spatial dispersion of the population and the separation of residential and workplace areas. Consequently, it increases commuting distances and times, leading to a heavier reliance on motor vehicles and a significant elevation in energy consumption and carbon emissions in the region (Fujiwara et al., 2009).

Moreover, urban sprawl alters land use patterns and has a profound impact on environmental pollution. During the process of urban sprawl, large swaths of agricultural land are converted into urban development areas, leading to a continuous decline in the proportion of agricultural land. This reduction in urban green spaces weakens the environment's purification capacity, exacerbating haze pollution.

Additionally, urban sprawl affects environmental pollution by altering the industrial structure. It reduces the share of agriculture in the economic structure while increasing the proportions of the secondary and tertiary sectors. Emissions from the secondary and tertiary sectors are significantly higher than those from agriculture, resulting in a decline in environmental quality. Furthermore, urban sprawl reduces population density and economic density, increasing the demand for buildings (Banzhaf and Lavery, 2010). This, in turn, leads to an increase in the emissions of particulate matter during the construction of buildings, exacerbating haze pollution. Compact land development patterns, on the other hand, can effectively alleviate the environmental impacts of urban sprawl, including emissions from automobiles, building pollution, and damage to green areas.
此外,城市扩张通过改变产业结构而影响环境污染。降低农业在经济结构中的比重,提高第二、三产业的比重。第二、三产业排放明显高于农业,导致环境质量下降。此外,城市扩张降低了人口密度和经济密度,增加了对建筑物的需求(Banzhaf 和 Lavery,2010)。这反过来又导致建筑施工过程中颗粒物排放增加,加剧雾霾污染。另一方面,紧凑的土地开发模式可以有效缓解城市扩张对环境的影响,包括汽车排放、建筑污染、绿地破坏等。

Hypothesis 3: Increasing land use compactness contributes to improved environmental quality.

2.4 Grounded in the Perspective of Compact Transportation
2.4 立足于紧凑运输的视角

Compact transportation is characterized by diverse transportation infrastructure and strong accessibility, which has a significant impact on the environment in three main aspects.Firstly, transportation infrastructure plays a pivotal role in promoting local economic development. This, in turn, increases the demand for environmental quality among the populace, compelling governments to strengthen environmental regulations. Consequently, there is a 'cleansing effect' (Chen et al., 2016) observed. Notably, the upgrading of transportation infrastructure reduces the cost of factor mobility and significantly restricts the entry of polluting enterprises into developed regions (Cai Hongbo et al., 2021).
紧凑型交通的特点是交通基础设施多样、可达性强,对环境的影响主要体现在三个方面。一是交通基础设施对促进地方经济发展发挥着举足轻重的作用。这反过来又增加了民众对环境质量的要求,迫使政府加强环境监管。因此,观察到了“清洁效应”(Chen et al., 2016)。值得注意的是,交通基础设施的升级降低了要素流动成本,极大地限制了污染企业进入发达地区(蔡洪波等,2021)。

Secondly, compact transportation mitigates environmental pollution through transportation shift effects. Research indicates that traffic congestion increases the operating time of motor vehicles, leading to higher fuel consumption and worsening urban haze pollution. Improvements in public transportation density, such as rail transit, encourage travelers to shift away from private cars and non-public transportation modes, resulting in a 'transportation shift effect' or 'Mohring effect.' This shift leads to reduced tailpipe emissions from road traffic vehicles due to decreased usage, ultimately lowering urban air pollution emissions. Chen and Whalley also found that the introduction of rail transit significantly reduces CO emissions directly associated with automobile tailpipes(Chen and Whalley, 2012).
其次,紧凑型交通通过交通转移效应减轻环境污染。研究表明,交通拥堵会增加机动车的运行时间,导致燃油消耗增加,城市雾霾污染加剧。轨道交通等公共交通密度的提高,鼓励出行者放弃私家车和非公共交通方式,从而产生“交通转移效应”或“莫林效应”。这种转变导致道路交通车辆由于使用量减少而减少尾气排放,最终降低城市空气污染排放。 Chen 和 Whalley 还发现,轨道交通的引入显着减少了与汽车尾气直接相关的二氧化碳排放(Chen 和 Whalley,2012)。

Lastly, compact transportation reduces pollution emissions through economies of scale, particularly in the case of public transportation. While road traffic often experiences congestion due to excessive demand, leading to air pollution, public transportation benefits from economies of scale in passenger capacity. This reduction in traffic congestion offers distinct advantages in improving urban air quality (Mohring, 1972).

Hypothesis 4: Higher levels of transportation compactness contribute to improved environmental quality.

3. Materials and Methods
3. 材料与方法

3.1. Emission data

The analysis in this paper is based on panel data for 271 Chinese cities at the prefecture level from 2010 to 2019. Due to missing data and an irregular statistical data quality, the study area excludes Taiwan, Hong Kong, Macau, Xinjiang, and Tibet. PM2.5 concentration data were obtained from the Global PM2.5 Concentration dataset of the Atmospheric Composition Analysis Group at Dalhousie University, which is estimated using the aerosol thicknesses acquired from remote sensing and atmospheric transport models. The data have been validated for global use due to their comprehensive coverage, extensive duration, and great accuracy (Lee et al., 2012).
本文的分析基于2010年至2019年中国271个地级城市的面板数据。由于数据缺失和统计数据质量不规范,研究区域不包括台湾、香港、澳门、新疆和西藏。 PM 2.5 浓度数据取自达尔豪斯大学大气成分分析小组的全球 PM 2.5 浓度数据集,该数据集使用从遥感和大气传输模型获得的气溶胶厚度进行估计。由于覆盖面广、持续时间长、准确性高,这些数据已被验证可在全球使用(Lee et al., 2012)。

3.2 Urban compactness indicators
3.2 城市紧凑度指标

The compact city theory underpins urban compactness measurement. Breheny (1997) defines a compact city as one that prioritizes urban density, land use mix, public transportation, and resident quality of life. Based on the preexisting research, this study selects urban compactness indicators from four dimensions: population, economic, land use, and traffic and road compactness. Specifically, indicators of urban compactness factors specifically cover population density, GDP per capita, land area share of built-up areas, land balance, road area per capita, and road network density.
紧凑城市理论是城市紧凑度测量的基础。 Breheny (1997) 将紧凑型城市定义为优先考虑城市密度、土地利用组合、公共交通和居民生活质量的城市。在已有研究的基础上,本研究从人口、经济、土地利用、交通和道路紧凑度四个维度选取城市紧凑度指标。具体来说,城市紧凑度指标具体包括人口密度、人均GDP、建成区用地面积比重、土地平衡、人均道路面积、路网密度等。

Population compactness is dominated by population density (PD). According to the theory of compact cities, increasing population density in city centers creates agglomeration economy effects, reduces land waste, and improves urban space usage, but it also raises rigid demand for urban housing, infrastructure, and transportation, which may increase urban haze. GDP per capita (PGDP) measures economic compactness. High-income cities will attract a large labor force and firms, affecting energy consumption and industrial structure. It's subject to regional development level, industry dispersion, and spatial disparities.
人口紧凑度主要由人口密度(PD)决定。根据紧凑城市理论,城市中心人口密度的增加会产生集聚经济效应,减少土地浪费,提高城市空间利用率,但也会增加对城市住房、基础设施和交通的刚性需求,从而可能增加城市雾霾。人均国内生产总值 (PGDP) 衡量经济紧凑度。高收入城市将吸引大量劳动力和企业,影响能源消耗和产业结构。受地区发展水平、产业分散、空间差异等因素影响。

Land use diversity is another hallmark of compact cities. The ratio of land area to built-up area (CI) is utilized to measure land development intensity in this study. When building land is scheduled to rise significantly, it will increase ecological protection pressure, reduce the amount of land inhabited by green spaces and other regions, and promote environmental degradation. The land balancing degree (J) reflects land function blending, which is influenced by three factors: more construction land, less forest and arable land, and a higher land usage rate. The equilibrium of the urban land use can be easily defined based on the information entropy formula

J= HHm=-iPilogPilogN,#(1)

where J denotes the degree of land use balance, H denotes the information entropy of the land use, Hm denotes the maximum entropy value, Pi corresponds to the probability of the event and N denotes the number of urban land use types.
其中 J 表示土地利用平衡程度, H 表示土地利用信息熵, Hm 表示最大熵值, Pi 表示城市土地利用类型的数量。

Road area per capita (PR) and road network density (RN) measure road compactness. Per capita road area represents the city's transportation intensity. High road density causes traffic congestion, lowers urban transportation efficiency, and may harm the environment. Road network density indicates city accessibility. Higher road network density shortens driving distances and reduces exhaust emissions.
人均道路面积 (PR) 和道路网络密度 (RN) 衡量道路的紧凑度。人均道路面积代表了城市的交通强度。高道路密度会导致交通拥堵,降低城市交通效率,并可能危害环境。道路网络密度表明城市的可达性。更高的路网密度可以缩短行驶距离并减少废气排放。

3.3 Control variables
3.3 控制变量

In addition, various studies have shown that urban air pollution is closely related to social factors such as urban greening level, energy consumption, industrial structure, and openness (Wang et al., 2017; Author et al., 2020). Therefore, this paper introduces the following four socioeconomic indicators as control variables: urban greening area coverage (GC), electricity consumption (EC), the industrial share of secondary industry (SGDP), and foreign direct investment as a proportion of GDP (FGDP).
此外,多项研究表明,城市空气污染与城市绿化水平、能源消耗、产业结构、开放程度等社会因素密切相关(Wang等,2017;Author等,2020)。因此,本文引入以下四个社会经济指标作为控制变量:城市绿化面积覆盖率(GC)、用电量(EC)、第二产业比重(SGDP)、外商直接投资占GDP比重(FGDP) 。

The data on urban compact factors and control variables comes mostly from the China Urban Statistics Yearbook, China Urban Construction Statistical Yearbook, and the wind database. Table 1 and Table 2 presents the definition of all variables and statistical description of all variables, respectively. (see Supplementary Table S1 and Table S2 online).
城市紧凑因子和控制变量的数据主要来自《中国城市统计年鉴》、《中国城市建设统计年鉴》和wind数据库。表1和表2分别给出了所有变量的定义和所有变量的统计描述。 (参见在线补充表 S1 和表 S2)。


3.4 Spatial correlation test
3.4 空间相关性检验

The spatial autocorrelation measures the degree of correlation between physically adjacent, reflecting the spatial distribution of research items in the study area. The global Moran's I is used to depict the spatial dependence of variables within the study area as a whole, which is calculated as follows:
空间自相关衡量物理相邻之间的相关程度,反映了研究区域内研究项目的空间分布情况。全局Moran's I用于刻画整个研究区域内变量的空间依赖性,计算公式如下:

Moran's I= ni=1nj=1nwij(xi-)(xj-)i=1nj=1nwij(xi-)2,∀j≠i,#(2)

where xi present the PM2.5 concentrations in spatial regions i, and is the average PM2.5 concentration in spatial regions. wij is the spatial weight matrix, and n is the sample size. The significance level of Moran's I is judged by the z-value, which is calculated as follows:
其中 xi 表示空间区域i中的PM 2.5 浓度, 是空间区域中的平均PM 2.5 浓度。 wij 为空间权重矩阵,n为样本量。 Moran's I 的显着性水平通过 z 值来判断,计算公式如下:

Z= 1-E(I)Var(I).#(3)

As Table 1 shows, all of the ten years passed the 1% significance test, showing the geographical distribution of PM2.5 in prefecture-level cities in China has a significant positive association and spatial aggregation.
如表1所示,十年均通过了1%显着性检验,表明我国地级城市PM 2.5 的地理分布存在显着的正相关性和空间聚集性。

Table 1 Global Moran's I Statistics of PM2.5 in China from 2010 to 2019
表1 2010-2019年中国PM 2.5 的全球Moran's I统计


Moran’s I












































































Local Moran's I is the specific analysis method for local spatial autocorrelation. The specific formula regarding the local Moran's I is:
局部Moran's I是局部空间自相关的具体分析方法。局部Moran's I的具体公式为:

Local Moran's I= n(xi-)j=1mwij(xj-)i=1n(xi-)2, ∀j≠i,#(4)

where m represents the number of cities adjacent to city i. The local indicators of spatial association (LISA) cluster maps depict the spatial agglomeration of Local Moran's I.
其中 m 表示与城市 i 相邻的城市数量。局部空间关联指标 (LISA) 聚类图描绘了局部 Moran's I 的空间集聚。

Figure 1 shows four clustering states for PM2.5 pollution in China’s cities: high-high(H-H), low-low(L-L), high-low(H-L), and low-high(L-H). From 2010 to 2019, PM2.5 high-value aggregation zones were in Beijing-Tianjin-Hebei, Shandong, and the Yangtze River Delta. The L-L clustering shows cities spatially clustered at low PM2.5 values, which are mainly in Inner Mongolia, northeastern Heilongjiang, southwestern cities west of the Hu Line and southeast coastal cities. The findings revealed that PM2.5 had a considerable spatial autocorrelation in 271 studied cities. For analysis, an acceptable spatial econometric model should be used.
图1显示了中国城市PM 2.5 污染的四种聚类状态:高-高(H-H)、低-低(L-L)、高-低(H-L)和低-高(L-H)。 2010年至2019年,PM 2.5 高值聚集区位于京津冀、山东、长三角地区。 L-L聚类显示PM 2.5 值较低的城市空间聚集,主要分布在内蒙古、黑龙江东北部、胡线以西的西南城市和东南沿海城市。研究结果表明,PM 2.5 在 271 个研究城市中具有相当大的空间自相关性。为了进行分析,应使用可接受的空间计量经济模型。

3.5 Geographically and temporally weighted regression model
3.5 地理和时间加权回归模型

Fixed spatial variable interactions are commonly assumed in spatial econometrics, which violates geospatial heterogeneity and non-stationarity. The geographically weighted regression model (GWR) is a crucial tool for analyzing spatial data's spatial variability (Stewart Fotheringham et al., 1996). GWR, on the other hand, examines cross-sectional data without a time dimension, resulting in a bias towards panel data analysis. The geographically and temporally weighted regression model (GTWR) incorporates the time dimension and may explain non-stationary spatiotemporal fluctuation of regression coefficients, thus overcoming the GWR model's shortcomings. In this study, we first constructed a fundamental model:
空间计量经济学通常假设固定的空间变量相互作用,这违反了地理空间异质性和非平稳性。地理加权回归模型(GWR)是分析空间数据空间变异性的重要工具(Stewart Fotheringham 等,1996)。另一方面,GWR 检查的是横截面数据,没有时间维度,导致面板数据分析存在偏差。地理和时间加权回归模型(GTWR)结合了时间维度,可以解释回归系数的非平稳时空波动,从而克服了GWR模型的缺点。在本研究中,我们首先构建了一个基本模型:

lnPMit&=β0+β1(lnPDi)+β2(lnPGDPi)+β3(lnJi)+β4(CIi)+β5(lnPRi)&+β6(lnRNi)+β7(GCi)+β8(lnECi)+β9(SGDPi)+β10(FGDPi)+ εit,#(5)

where PMi refers to the annual average PM2.5 concentration of the ith city in year t ; β0 represents the coefficient of intercept term; β1~β10 are the coefficients of each independent variable; εit is the independent random error term. The ten independent variables are PD, PGDP, J, CI, PR, RN, GC, EC, SGDP and FGDP. To remove magnitudes from the data and reduce geographical heteroskedasticity, the numerical data were logarithmically processed while the percentage data were untreated. The superiority of GTWR lies in the introduction of temporal and spatial variables in the model. The mathematical expression is as follows:
其中 PMii th 城市 t 年平均PM 2.5 浓度; β0 表示截距项的系数; β1~β10 为各自变量的系数; εit 是独立随机误差项。十个自变量是 PD、PGDP、J、CI、PR、RN、GC、EC、SGDP 和 FGDP。为了从数据中消除幅度并减少地理异方差,对数值数据进行对数处理,而百分比数据未经处理。 GTWR的优越性在于模型中引入了时间和空间变量。数学表达式如下:

yi=β0(ui,vi,ti)+k=1pβk(ui,vi,ti)xik+εi, i=1,2,…,n.#(6)

where (ui,vi,ti) denotes the spatiotemporal coordinates at sample point i. βk(ui,vi,ti) is the kth regression coefficient at the ith sample point, εi is the random error, and εi~N(0, σ2). The regression coefficients can be estimated using the local weighted least squares method, calculated as:
其中 (ui,vi,ti) 表示样本点i处的时空坐标。 βk(ui,vi,ti) 为第i th 个样本点的k th 个回归系数, εi 为随机误差, εi~N(0, σ2)


where Wi(ui,vi,ti) is the spatiotemporal kernel function. The expression for the elements of row i and column of the spatiotemporal kernel function matrix is:
其中 Wi(ui,vi,ti) 是时空核函数。时空核函数矩阵第i行第i列元素的表达式为:


where dijST is the spatiotemporal distance calculated based on the spatiotemporal ellipsoidal coordinate system, and hST is the spatiotemporal window width parameter. This study applied the Akaike information criterion to calculate the optimal adaptive bandwidth.
其中, dijST 为基于时空椭球坐标系计算的时空距离, hST 为时空窗宽参数。本研究应用Akaike信息准则来计算最优自适应带宽。

4. Results and Discussion
4 结果与讨论

4.1 Spatial and temporal variation of PM2.5 concentrations
4.1 PM 2.5 浓度时空变化

4.1.1 Temporal variation of PM2.5
4.1.1 PM 2.5 的时间变化

The annual average PM2.5 monitoring statistics for 271 Chinese cities from 2010 to 2019 can be found as Supplementary Figure S1 online. PM2.5 has an inverted U-shaped change curve that rises and then lowers nationally. In 2013, the higher and lower quartiles of PM2.5 concentrations had the most dispersion, indicating that the national mean PM2.5 concentration variation was likewise the largest.
2010年至2019年中国271个城市的年平均PM 2.5 监测统计数据可在网上找到,如补充图S1。 PM 2.5 的变化曲线呈倒 U 形,在全国范围内先上升后下降。 2013年,PM 2.5 浓度高、低四分位数的离散程度最大,表明全国PM 2.5 平均浓度变化同样最大。

Since national PM2.5 concentration data are fragmented, this study splits national interannual PM2.5 values into six intervals according to Chinese ambient air quality standards (GB3095-2012). Besides, PM2.5 by classification for 271 Chinese cities from 2010 to 2019 can be found as Supplementary Fig. S2 online.
由于全国PM 2.5 浓度数据较为分散,本研究根据中国环境空气质量标准(GB3095-2012)将全国PM 2.5 值分为六个区间。此外,2010年至2019年中国271个城市的PM 2.5 分类可以在网上找到,作为补充图S2。

After 2013, the percentage of cities with an excellent or good air quality rose to 63.8% in 2019. The percentage of cities experiencing slight haze pollution dropped to 29.15% in 2019. After 2014, the number of cities with moderate pollution decreased to 7.01% in 2019. Since 2016, the number of heavily polluted cities has remained at zero. Cities with severe pollution only first emerged in 2011, 2013, and 2014, with the greatest share in 2014, at 1.48%
2013年后,空气质量优良的城市比例上升至2019年的63.8%。轻度雾霾污染的城市比例下降至2019年的29.15%。2014年后,中度污染的城市比例下降至7.01% 2019年,2016年以来,重污染城市数量保持为零。 2011年、2013年、2014年才首次出现严重污染城市,其中2014年占比最大,为1.48%

Since the implementation of the Air Pollution Prevention and Control Action Plan by the Chinese central government in 2013, national and regional air quality improvement trends have become obvious. However, 36.16% of cities remains polluted in a slight or moderate way. Haze pollution levels remain far from the WHO threshold of 10 μg/m3, indicating that China should implement more air pollution management efforts.
2013年中央实施《大气污染防治行动计划》以来,全国和各地区空气质量改善趋势明显。但仍有36.16%的城市仍处于轻度或中度污染状态。雾霾污染水平与世界卫生组织10微克/立方米的阈值仍相距甚远 3 ,这表明中国应加大空气污染治理力度。

4.1.2 Spatial variation in PM2.5 pollution
4.1.2 PM 2.5 污染的空间变化

Accurate identification of the spatial pattern evolution features and the distribution pattern of PM2.5 pollution is critical for the development of cross-regional linkage pollution management strategies in China. Figure 2 shows PM2.5 spatial dispersion in China from 2010 to 2019.
准确识别PM 2.5 污染的空间格局演变特征和分布格局对于制定我国跨区域联动污染治理策略至关重要。图2显示了2010-2019年中国PM 2.5 空间分布情况。

The area with annual mean PM2.5 concentrations below 35 μg/m3 mostly occurred west of the Hu Line until 2016, when it expanded to three northeastern provinces and two southeastern regions of Guangdong, Jiangxi, and Zhejiang. The number of lightly polluted cities in the region declined from 62 to 45 before 2013 and increased to 63.85% in 2019. In 2010 and 2019, Hohhot exhibited an excellent air quality. Lightly contaminated areas were located in Liaoning and northern Jilin, Ningxia, Gansu, the Sichuan basin in the west, and Guangdong and Jiangxi in 2010. Polluted cities accounted for 47.6% in 2010 to 50.18% in 2013, thereafter shrinking toward the center of the heavily polluted range. Shandong, Henan, and Hebei provinces east of the Hu Line exhibited moderate to heavy pollution. From 2010 to 2013, pollution in China spread southward. From 2013 to 2019, pollution decreased but still accumulated in Shandong, Henan, Hebei, Beijing-Tianjin-Hebei, and the Yangtze River Delta economic zone. Shijiazhuang, Handan, Xingtai, and Hengshui were the only cities with severe pollution in 2013.
PM 2.5 年均浓度低于35微克/立方米 3 的区域主要集中在胡志明线以西,直到2016年,范围扩大到东北三省和广东东南两个地区,江西、浙江。该地区轻度污染城市数量从2013年之前的62个下降到45个,2019年上升到63.85%。2010年和2019年,呼和浩特市空气质量优良。 2010年,轻污染地区分布在辽宁及吉林北部、宁夏、甘肃、西部四川盆地以及广东、江西等地。污染城市占比由2010年的47.6%下降到2013年的50.18%,此后向中部地区收缩。重度污染范围。胡锦涛线以东的山东、河南、河北省呈现中度至重度污染。 2010年至2013年,中国污染向南扩散。 2013年至2019年,山东、河南、河北、京津冀、长三角经济区污染有所减少,但污染仍在累积。石家庄、邯郸、邢台、衡水是2013年仅有的污染严重的城市。

Overall, cities with excellent and good air quality levels were located predominantly west of the Hu Line before 2016 and dramatically spread to the southern coastal districts in 2019. Light pollution was mostly observed east of the Hu Line, with Shandong, Henan, and Hebei as the center until 2013 and then decreasing in these regions. Although air management policies have been effective in northeastern China, Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Chengdu-Chongqing urban agglomeration, some of these regions remain polluted at mild or moderate levels. The higher PM2.5 levels in the central and eastern regions are related to economic development and substantial coal use for domestic heating. In addition to a lower economic development level, the lower PM2.5 levels in Southwest China can be attributed to favorable climatic circumstances (e.g., wind speed, precipitation, and boundary layer height) in regard to pollution dispersion.
总体来看,2016年之前,空气质量优良的城市主要分布在胡安线以西,2019年则大幅向南部沿海地区扩散。光污染主要集中在胡安线以东,其中山东、河南、河北为重点。直到 2013 年,这些地区的人口数量逐渐减少。尽管东北、京津冀、长三角、成渝城市群等空气治理政策取得成效,但部分地区仍处于轻度或中度污染状态。中东部地区PM 2.5 水平较高与经济发展和生活取暖大量使用煤炭有关。除了经济发展水平较低之外,西南地区PM 2.5 水平较低还可以归因于有利于污染扩散的气候条件(例如风速、降水和边界层高度)。

The above research reveals that the number of cities with low concentrations (35 μg/m3) subsequently increased as the region expanded toward the east and south geographically. The number of cities with PM2.5 concentrations over 35 μg/m3 decreased, and these cities gradually contracted east of the Hu Line.
上述研究表明,随着区域向东、向南扩展,低浓度(35 μg/m 3 )城市数量随之增加。 PM 2 .5 浓度超过35微克/立方米 3 的城市数量减少,且这些城市逐渐向胡志明线以东收缩。

4.2 Global regression results
4.2 全局回归结果

It is significant to explore the spatiotemporal heterogeneity of the effects of compactness factors on PM2.5 in each city. Before employing the GTWR model, global ordinary least squares (OLS) was utilized to verify that the selected variables globally affect the dependent variable.
探讨各城市致密性因素对PM 2.5 影响的时空异质性具有重要意义。在采用 GTWR 模型之前,使用全局普通最小二乘法 (OLS) 来验证所选变量是否全局影响因变量。

According to Table 2, five explanatory variables passed the 1% significance level test except for PR with a significance level of 10%. The estimated coefficients of the effect of PGDP on the PM2.5 concentration were negative, and the coefficients of the effects of PD, land use balance degree (J), construction land development intensity (CI), road area per capita (PR), and road network density (RN) were all positive. The variance inflation factor (VIF) values of all independent variables were less than 3, indicating no multicollinearity among the variables. Among the explanatory variables with positive effects on the PM2.5 concentration, J exhibited the highest intensity, followed by PD, RN, PR and CI. The summary of GTWR regression coefficients exhibits a clear change in direction from negative to positive. This suggests that the effect of each variable on PM2.5 is spatially heterogeneous, requiring further geographic analysis.
根据表2,除PR的显着性水平为10%外,5个解释变量通过了1%的显着性水平检验。 PGD​​P对PM 2.5 浓度的影响估计系数为负,PD、土地利用平衡度(J)、建设用地开发强度(CI)、人均道路面积的影响系数为负。人均(PR)和道路网络密度(RN)均为正值。所有自变量的方差膨胀因子(VIF)值均小于3,表明变量之间不存在多重共线性。在对 PM 2.5 浓度有正向影响的解释变量中,J 的强度最高,其次是 PD、RN、PR 和 CI。 GTWR回归系数的总结显示出从负到正的方向的明显变化。这表明每个变量对 PM 2.5 的影响在空间上是异质的,需要进一步的地理分析。

Table 2 Coefficients results of the global OLS and GTWR
表2 全局OLS和GTWR的系数结果











































































Note: *, **, *** represent 10%, 5%, 1% significant levels, respectively.

For comparison, in this study, TWR, GWR, and GTWR models were also calculated, and the findings can be found as Supplementary Table S3 online. R2 increased from 28% for the global OLS model to 47% for the TWR model, 78% for the GWR model, and 80.4% for the GTWR model, while AICc decreased from 984.29 to 285.38 for the TWR model, -1915.74 for the GWR model, and -2115.98 for the GTWR model. The model with a lower AICc and higher R2 values achieves more robust applicability. The GTWR model could match the data well since this model can manage both geographical and temporal variations. The GWR model achieved a better fit than the TWR model, revealing that the temporal nonstationarity is lower than the spatial nonstationarity
为了进行比较,在本研究中,还计算了 TWR、GWR 和 GTWR 模型,结果可以在网上找到补充表 S3。 R 2 从全局OLS模型的28%增加到TWR模型的47%,GWR模型的78%,GTWR模型的80.4%,而AICc从TWR的984.29下降到285.38型号,GWR 型号为 -1915.74,GTWR 型号为 -2115.98。具有较低 AICc 和较高 R 2 值的模型具有更稳健的适用性。 GTWR 模型可以很好地匹配数据,因为该模型可以管理地理和时间变化。 GWR模型比TWR模型具有更好的拟合效果,表明时间非平稳性低于空间非平稳性

4.3 GTWR regression results
4.3 GTWR回归结果

4.3.1 Temporal variation analysis
4.3.1 时间变化分析

Based on the GTWR regression results, the characteristics of each core explanatory variable were aggregated and presented at the 95% significance level. Figure 3 displays the temporal change trend of the coefficients of each compactness factor. The effect of each compactness factor on PM2.5 is temporally nonstationary. PD, J, CI, and RN all contribute to PM2.5 pollution.
基于GTWR回归结果,对每个核心解释变量的特征进行汇总并以95%的显着性水平呈现。图3显示了各紧凑度因子系数随时间的变化趋势。每个紧凑度因子对 PM 2.5 的影响在时间上是不稳定的。 PD、J、CI 和 RN 都会造成 PM 2.5 污染。

PD increased air pollution slowly but significantly over ten years. From 2013 to 2016, the pace of increase among them tended to slow down, and the effect difference was also relatively small during this period. Urbanization and construction in China have accelerated in the recent decade. The demand for housing, infrastructure, and transportation of the urban population has increased, which has impacted the haze concentration. The trend of the influence of land use balance on air pollution has remained relatively stable, and the intercity variation over ten years has only slightly increased. This indicates that the positive effect of land development patterns on air pollution in cities remained relatively constant from 2010 to 2019. There occurred a slowly decreasing trend in the effect of CI on air pollution from 2010 to 2014 and a gradually increasing trend after 2014. This may be related to the increase in the construction land supply growth rate after 2014. The impact of RN on air pollution slowly increased before 2015 and then declined more significantly afterward, with small overall fluctuations. The effect of PR on PM2.5 pollution, however, substantially declined from 2010 to 2015 and then exhibited an upward trend.
十年来,PD 缓慢但显着地增加了空气污染。 2013年至2016年,其增长速度趋于放缓,且该时期效应差异也较小。近十年来,中国的城镇化和建设不断加速。城市人口对住房、基础设施和交通的需求增加,对雾霾浓度产生影响。土地利用平衡对空气污染的影响趋势保持相对稳定,十年间城际变化仅略有增加。这表明,2010-2019年,土地开发模式对城市空气污染的正向作用保持相对稳定。2010-2014年,CI对空气污染的正向作用呈现缓慢下降的趋势,2014年后逐渐上升的趋势。可能与2014年以后建设用地供应增速提高有关。RN对大气污染的影响在2015年之前缓慢上升,之后下降更为明显,总体波动较小。然而,公共关系对PM 2.5 污染的影响从2010年到2015年大幅下降,然后呈现上升趋势。

The GDP per capita imposed a significant negative effect on PM2.5 pollution and exhibited considerable spatial variation. Until 2013, most of China's urban economies adopted a high-pollution and emission expansion approach that exacerbated the deterioration in their ecological environment. The relationship between PGDP and PM2.5 pollution (see Supplementary Fig. S3 online), which conforms to an inverted U-shaped environmental Kuznets curve (EKC). Most cities will still occur on the left side of the inflection point in 10 years, but there is a tendency to gravitate toward the right side of the inflection point. Regional development shifted to target green GDP growth after environmental regulation in 2013. With increasing income and the need for a green environment, urban economic development in China has become intensive. This has achieved simultaneous economic growth and environmental improvement.
人均GDP对PM 2.5 污染有显​​着的负效应,且表现出较大的空间差异。直到2013年,中国大部分城市经济采取了高污染、高排放的扩张方式,加剧了生态环境的恶化。 PGD​​P与PM 2.5 污染之间的关系(参见网上补充图S3),符合倒U型环境库兹涅茨曲线(EKC)。 10年后大多数城市仍将出现在拐点左侧,但有向拐点右侧倾斜的趋势。 2013年环境整治后,区域发展转向绿色GDP增长目标。随着收入的增加和对绿色环境的需求,中国城市经济发展变得集约化。实现了经济增长和环境改善同步。

4.3.2 Geographical variation analysis
4.3.2 地理差异分析

The regression coefficients that passed the 95% significance level among the 271 cities were mapped to assess the direction and amount of the influence of urban compactness factor characteristics on PM2.5 concentrations. Considering nonsmoothness over time, the regression findings for four years, 2010, 2013, 2016, and 2019, were chosen as the key analytic results.
绘制271个城市中通过95%显着性水平的回归系数,评估城市紧凑因子特征对PM 2.5 浓度影响的方向和大小。考虑到随时间的不平滑性,选择2010年、2013年、2016年和2019年四年的回归结果作为关键分析结果。

(1) Population compactness

According to the GTWR model estimation results, the percentage of cities with a positive correlation between PD and PM2.5 pollution was 61.99%, 68.27%, 88.93%, and 79.33% from 2010-2019, and the number of cities increased. As shown in Fig. 4, the direction of the correlation between PD and PM2.5 pollution was generally positive in terms of the spatial distribution of the regression coefficients.
根据GTWR模型估算结果,2010-2019年PD与PM 2.5 污染呈正相关的城市比例分别为61.99%、68.27%、88.93%和79.33%,城市增加。如图4所示,从回归系数的空间分布来看,PD与PM 2.5 污染的相关方向总体呈正相关。

The cities with the most notable positive effect of PD on air pollution were mainly located in the northeastern and northwestern regions, including Heilongjiang, Jilin, Liaoning, Inner Mongolia, Beijing-Tianjin-Hebei region, and Shanxi. In the above cities, the increase in PD led to an increased demand for transportation agglomeration, urban housing, and energy consumption, which promoted the emission of air pollutants, resulting in a more damaging effect of compactness on the environment. Over time, the cities with the most notable positive effects gradually shifted from Northeast China to North China, West China, and Central China. The number of cities where PD helps mitigate PM2.5 pollution decreased year by year. As of 2019, only a small number of cities were located in Guangdong and Hunan. In these cities, the compact population clustering effect improved the efficiency of urban traffic operation and resource utilization, which was more conducive to improving the air quality in the cities.
PD对空气污染正向效应最显着的城市主要位于东北和西北地区,包括黑龙江、吉林、辽宁、内蒙古、京津冀地区、山西等。在上述城市中,PD的增加导致交通集聚、城市住房和能源消耗的需求增加,促进了空气污染物的排放,导致紧凑性对环境的破坏作用更大。随着时间的推移,积极效应最显着的城市逐渐从东北地区转移到华北、西部和中部地区。 PD帮助减轻PM 2.5 污染的城市数量逐年减少。截至2019年,仅有广东、湖南等少数城市。在这些城市中,紧密的人口集聚效应提高了城市交通运行效率和资源利用效率,更有利于改善城市空气质量。

(2) Economic compactness

The indicator of the economic compactness is the GDP per capita (PGDP). Figure 5 shows that the proportion of cities with a negative impact of the GDP per capita on PM2.5 pollution decreased from 83.03% in 2010 to 62.36% in 2016, increasing to 73.06% in 2019.
经济紧凑度的指标是人均GDP(PGDP)。图5显示,人均GDP对PM 2.5 污染产生负面影响的城市比例从2010年的83.03%下降到2016年的62.36%,2019年增加到73.06%。

Figure 6 shows that the cities with a more significant negative impact of economic compact development on the PM2.5 concentration were mainly concentrated in the economically developed eastern provinces, such as Beijing, Tianjin, Hebei, Jiangsu, Guangdong, and Fujian, and in the central and western regions with a better air quality, including Guangxi, Sichuan, Chongqing, Yunnan, and Guizhou. Compact economic development of the above cities could effectively alleviate environmental pollution.
从图6可以看出,经济紧凑发展对PM 2.5 浓度负向影响较为显着的城市主要集中在东部经济发达省份,如北京、天津、河北、江苏、广东等。福建、广西、四川、重庆、云南、贵州等空气质量较好的中西部地区。上述城市经济的紧凑发展可以有效缓解环境污染。

Cities with compact economic growth in the eastern regions exhibit better economic foundations, which could promote improved production efficiency with the help of industrial structure optimization and technological progress.

Despite the small scale of economic development, cities in Southwest China learned from the economic growth process in middle and eastern Chinese regions and focused on a green economic development model instead of absorbing pollution-intensive industries from the eastern region, thereby avoiding the conventional approach of pollution first, treatment later. Cities with a positive impact of economic compactness on air pollution were mainly concentrated in the northeast, west, and downstream of the Yangtze River Economic Belt, including Liaoning, Jilin, Heilongjiang, Inner Mongolia, Gansu, Shanxi, and cities in Zhejiang. These cities are dominated by resource- and energy-intensive industries, which directly generate higher energy consumption. Short-term crude and compact economic development could aggravate local air pollution.

(3) Land use compactness

The land compactness indicators include J and CI. According to Fig. 3(c) in 4.3.1, the effect of J on PM2.5 pollution remained stable over time and was generally favorable. From the distribution of the regression coefficients given in Fig. 7, the proportion of cities with a positive impact of land use balance on PM2.5 pollution decreased year by year, from 73.8% to 68.63% in 2010.
土地紧实度指标包括J和CI。根据4.3.1中图3(c),J对PM 2.5 污染的影响随时间推移保持稳定,总体有利。从图7回归系数的分布来看,土地利用平衡对PM 2.5 污染产生正向影响的城市比例逐年下降,由2010年的73.8%下降到68.63%。

Cities with high positive effects exhibited obvious spatial clustering characteristics. They were mainly located in the eastern and central regions east of the Hu Line and south of the Yangtze River, including Jiangsu, Zhejiang and Shanghai, Guangdong, Guangxi, Fujian, Henan, Anhui, Hunan, Hubei, and Jiangxi. The high urbanization level and increase in the land use balance in the above regions led to a decrease in arable land, forestland, and unused land and expansion of residential land and industrial, mining, and transportation land, thus further weakening the urban land use orderliness. Therefore, the positive impact of the enhanced land use compactness on air pollution in these areas was increased. Cities with balanced land use contributing to PM2.5 pollution reduction were mainly concentrated in areas west of the Hu Line and north of the Yangtze River. Cities with more substantial negative impacts were primarily located in Heilongjiang, Inner Mongolia, and Beijing-Tianjin-Hebei region. These cities exhibited more efficient land use and compact development patterns that could mitigate air pollution.
正向效应高的城市表现出明显的空间集聚特征。主要分布在江、浙、沪、广东、广西、福建、河南、安徽、湖南、湖北、江西等东部和中部地区,以沪线以东、长江以南地区。上述地区城市化水平较高,土地利用均衡性增强,导致耕地、林地、未利用地减少,居民用地和工矿交通用地扩张,城市用地秩序进一步弱化。因此,土地利用紧凑度的提高对这些地区空气污染的积极影响加大。土地利用平衡对PM 2.5 污染减排贡献的城市主要集中在胡锦涛线以西和长江以北地区。负面影响较严重的城市主要分布在黑龙江、内蒙古、京津冀地区。这些城市表现出更有效的土地利用和紧凑的发展模式,可以减轻空气污染。

Figure 8 shows the distribution of the urban construction land share regression coefficients. The proportion of cities where the construction land share positively affects air pollution first decreased and then increased, from 85.24% in 2010 to 29.15% in 2016, and increased again to 60.14% in 2019. This indicates that the impact of urban construction land expansion on air pollution fluctuated in this decade.

Cities with high positive effects were mainly located in the western, central, and southeastern regions, including Sichuan, Shaanxi, Henan, Heilongjiang, Anhui, Hunan, Yunnan, and Zhejiang. Construction land area expansion in these cities negatively impacted the air quality. Among the above cities, the developed regions in the south were less affected by the possible implications of increased urban construction levels. These regions applied scientific dust reduction measures and green materials, which helped reduce the emission of air pollutants such as construction dust. Cities in part of the central and western regions were more likely to be affected by construction land expansion.

(4) Traffic road compactness

The traffic road compactness indicators include PR and RN, which reflect the surface and line densities of the traffic intensity, respectively. The effect of PR on PM2.5 pollution tended to gradually shift from a positive effect to a negative effect over ten years. According to Fig. 9, the proportion of cities with a positive regression coefficient for this variable decreased from 54.46% in 2010 to 25.09% in 2019. Moreover, the proportion of cities with negative regression coefficients increased from 23.99% in 2010 to 47.23% in 2019.
交通道路紧凑度指标包括PR和RN,分别反映交通强度的面密度和线密度。公关对PM 2.5 污染的影响在十年间呈现出逐渐由正作用转为负作用的趋势。从图 9 可以看出,该变量回归系数为正的城市比例从 2010 年的 54.46%下降到 2019 年的 25.09%,回归系数为负的城市比例从 2010 年的 23.99%上升到 2019 年的 47.23%。 2019.

This change indicates that the overall urban transportation infrastructure planning in China has become more rational and environmentally friendly. In 2019, cities with the most substantial positive impact of PR on air pollution were concentrated in Henan, Shandong, Zhejiang, Anhui, and Fujian. The increase in the road traffic surface density in the above cities exacerbated the PM2.5 pollution intensity. This may be due to the higher intensity of road transportation in these areas, thus increasing the number of motor vehicles per unit area and resulting in excessive tailpipe emissions, which are harmful to air quality. The distribution of cities with adverse impacts of PR on PM2.5 continuously spread from northwest to southeast. Cities with a more significant impact were mainly concentrated in Inner Mongolia, Heilongjiang, Gansu, and the middle and lower reaches of the Yangtze River. Among the above cities, those in the inland northwestern region are not well developed in terms of the traffic level and exhibit low motor vehicle ownership. Nevertheless, the increase in the traffic intensity could promote interregional economic exchange, accelerate local resource integration and utilization, and reduce air pollution. In contrast, the middle and lower reaches of the Yangtze River contain better-developed economies and more vital transportation infrastructure. Therefore, the increase in PR could reduce the occurrence of traffic congestion events and improve the intracity transportation efficiency, which in turn could alleviate PM2.5 pollution.
这一变化标志着我国城市交通基础设施总体规划更加合理、更加环保。 2019年,公关对空气污染产生最实质性积极影响的城市集中在河南、山东、浙江、安徽和福建。上述城市道路交通面密度的增加加剧了PM 2.5 污染强度。这可能是由于这些地区道路交通强度较高,从而增加了单位面积机动车数量,导致尾气排放过多,对空气质量有害。公关对PM 2.5 不利影响的城市分布由西北向东南不断扩展。影响较显着的城市主要集中在内蒙古、黑龙江、甘肃、长江中下游地区。上述城市中,西北内陆地区交通水平不发达,机动车保有量较低。尽管如此,交通强度的增加可以促进区域间经济交流,加速当地资源整合和利用,减少空气污染。相比之下,长江中下游地区经济更加发达,交通基础设施更加重要。因此,PR的增加可以减少交通拥堵事件的发生,提高城市内交通效率,从而减轻PM 2.5 污染。

As shown in Fig. 10, the proportion of cities with a positive impact of RN on air pollution was the highest in 2013, at 63.47%, which then continued to decrease to 34.31% in 2019. Correspondingly, the proportion of cities with a negative impact of RN on air pollution increased from 16.61% in 2013 to 35.06% in 2019. With the implementation of environmental management policies starting in 2013, urban planning has become more scientific and sustainable. Within this context, the accessibility and rationality of transportation have become the primary goal of urban road construction.

In 2019, cities with the most notable pollution mitigation effects were mainly located along the southeast coast and in the northwest, including Guangdong, Fujian, Jiangxi, Guangxi, and Gansu. The enhanced RN level in these cities could improve the transportation efficiency, thus contributing to air treatment. Cities with the most substantial promoting effect of RN on air pollution were concentrated in parts of the Yangtze River Delta, including Jiangsu, Shanghai, and Anhui. RN in these cities may be too high, resulting in intensive motor vehicle exhaust emissions and thus a positive effect on air pollution. It could be found that a higher road compactness and favorable RN are beneficial to PM2.5 concentration reduction. However, urban planning should also improve the urban transportation network by providing various forms of public transportation to avoid overly dense traffic patterns causing an unbearable environmental pressure.
2019年,污染减排效果最显着的城市主要分布在东南沿海和西北地区,包括广东、福建、江西、广西、甘肃等。这些城市RN水平的提高可以提高交通效率,从而有助于空气治理。 RN对空气污染促进作用最显着的城市集中在长三角部分地区,包括江苏、上海、安徽。这些城市的RN可能过高,导致机动车尾气排放密集,从而对空气污染产生积极影响。可以发现,较高的道路密实度和良好的RN有利于PM 2.5 浓度的降低。但城市规划还应完善城市交通网络,提供多种形式的公共交通,避免交通过于密集造成难以承受的环境压力。


5. Conclusions and Policy Recommendations

5.1 Conclusion
5.1 结论

Based on the ESDA method, in this study, the spatial and temporal characteristics of PM2.5 pollution in China from 2010 to 2019 were investigated. The GTWR model was applied to investigate the influence mechanism of various urban compactness factors on PM2.5 pollution. The main findings are as follows:
本研究基于ESDA方法,对2010—2019年中国PM 2.5 污染时空特征进行了调查。应用GTWR模型研究了城市各种紧凑度因素对PM 2.5 污染的影响机制。主要发现如下:

(1) From 2010 to 2019, the PM2.5 concentrations in 271 cities followed an inverted U-shaped trend, i.e., first rising and then falling. The PM2.5 concentration decreased by 12.91%, and the overall decrease reached 27.60%. The PM2.5 concentrations in the eastern, central, and western regions decreased by 24.67%, 25.60%, and 34.33%, respectively, over 10 years. Cities with an excellent air quality (<35 μg/m3) spread from west of the Hu Line to the southern coastal areas. Areas with light pollution and above (>35 μg/m3) were mainly distributed east of the Hu Line, and these areas were concentrated around Shandong, Henan, and Hebei. Since the Chinese government implemented the Action Plan for the Prevention and Control of Air Pollution in 2013, haze management in China has significantly improved. Satisfactory results have been achieved in integrated cross-regional air management.
(1)2010年至2019年,271个城市PM 2.5 浓度呈现先上升后下降的倒U型趋势。 PM 2.5 浓度下降12.91%,总体下降27.60%。 10年来,东、中、西部地区PM 2.5 浓度分别下降了24.67%、25.60%和34.33%。空气质量优良(<35微克/立方米 3 )的城市从胡线以西向南部沿海地区扩展。轻度污染及以上(>35微克/立方米 3 )区域主要分布在胡志明线以东,且集中在山东、河南、河北周边。自2013年中国政府实施《大气污染防治行动计划》以来,中国雾霾治理取得显着改善。跨区域大气一体化管理取得良好成效。

(2) There existed a significant positive correlation in the spatial distribution of PM2.5 in Chinese cities, and the spatial correlation first decreased and then exhibited fluctuating growth. In terms of the local spatial agglomeration distribution, PM2.5 pollution in most cities mainly revealed high-high agglomeration and low-low agglomeration. Cities with high-high aggregation accounted for 18.45% of the total number of cities studied in 2019, primarily located in Beijing-Tianjin-Hebei and the Yangtze River Delta, north of the Yangtze River, as the central region connected to these two economies. The number of cities with low-low pollution agglomeration accounted for 20.30% of the total number of cities in 2019, mainly in the western region west of the Hu Line and in the southeastern coastal cities. Very few cities exhibited high-low and low-high correlation patterns of their PM2.5 distribution.
(2)中国城市PM 2.5 的空间分布存在显着的正相关性,且空间相关性先下降后呈现波动增长。从局部空间集聚分布来看,大部分城市PM 2.5 污染主要呈现高-高集聚和低-低集聚的特征。高高聚集城市占2019年研究城市总数的18.45%,主要分布在京津冀和长三角、长江以北,作为连接这两个经济体的中部地区。 2019年低低污染集聚城市数量占城市总数的20.30%,主要集中在胡锦涛线以西的西部地区和东南沿海城市。很少有城市表现出 PM 2.5 分布的高-低和低-高相关模式。

(3) The urban compactness characteristics of China imposed heterogeneous effects on PM2.5 pollution. The long-term combined effect of population and land compactness could increase urban PM2.5 pollution. Compact economic development represents an efficient approach to reduce air pollution. The positive impact of traffic compactness factors on air pollution diminished over time, while the negative impact increased.
(3)中国城市的紧凑特征对PM 2.5 污染产生了异质性影响。人口和土地紧凑度的长期综合影响可能会增加城市 PM 2.5 污染。紧凑的经济发展是减少空气污染的有效途径。随着时间的推移,交通拥挤因素对空气污染的积极影响逐渐减弱,而消极影响则不断增加。

Few cities could reduce air pollution in terms of PD, primarily Guangdong and Hunan. The cities in the northeastern and northwestern regions exhibited the most significant positive effects of PD on air pollution. Compact economic development and air pollution in China adhered to an inverted U-shaped EKC, and most cities still occurred on the left side of the inflection point. Cities where air pollution is restrained by compact economic development were mostly located in the economically developed eastern provinces and central and western regions with a superior air quality. Cities with increasing pollution were mostly found in the northeastern, western, and downstream regions of the Yangtze River Economic Belt. The impact of land use balance on air pollution was spatially clustered. Cities with stronger negative effects were concentrated west of the Hu Line and north of the Yangtze River. In contrast, cities with the most positive influence occurred east of the Hu Line and south of the Yangtze River. The positive impact of CI on air pollution tended to first decrease and then increase. Cities with a positive impact were located in the western, central, and southeastern regions, while developed areas in the south were less affected. PM2.5 concentration reduction was aided by increased road accessibility. Year after year, the number of cities with a negative impact of PR on air pollution increased, expanding from northwest to southeast. Cities with notable positive effects were concentrated in Henan, Shandong, Zhejiang, Anhui, and Fujian. Cities where RN reduced pollution the most occurred in the southeast and northwest. Certain cities in the Yangtze River Delta suffered the most severe air pollution problems.
很少有城市能够减少空气污染的PD,主要是广东和湖南。东北和西北地区城市PD对空气污染的积极影响最为显着。我国经济发展与空气污染呈紧凑型倒U型EKC,大部分城市仍出现在拐点左侧。经济紧凑发展抑制空气污染的城市大多位于经济发达的东部省份和空气质量较好的中西部地区。污染加剧的城市主要分布在长江经济带东北部、西部和下游地区。土地利用平衡对空气污染的影响在空间上呈聚集性。负面影响较强的城市集中在胡锦涛线以西、长江以北地区。相比之下,积极影响最大的城市出现在胡锦涛线以东和长江以南地区。 CI对空气污染的正向影响呈先减小后增大的趋势。受到正面影响的城市主要分布在西部、中部和东南部地区,而南方发达地区受到的影响较小。 PM 2.5 浓度的降低得益于道路通达性的提高。公关对空气污染产生负面影响的城市数量逐年增加,从西北向东南扩展。积极效应显着的城市集中在河南、山东、浙江、安徽、福建。 RN 减少污染最多的城市出现在东南部和西北部。长三角部分城市空气污染问题最为严重。

5.2 Policy implications
5.2 政策影响

Based on the experimental results and findings, the following recommendations were proposed in this study.

(1) Regional integration should be promoted as an entry point to generate inhibitory effects of urban compactness factors on air pollution. Air pollution exhibits typical externality, namely, cross-border contamination has remained one of the essential features of haze pollution in recent years. The regional ecological environment is an organic whole. Hence, comprehensive management of haze pollution should not be separately implemented in the control of local pollutant emission sources and should simultaneously overcome administrative boundaries, thus achieving collaborative prevention and management of regional haze pollution. From the spatial and temporal evolution patterns of PM2.5 pollution in China, the hardest-hit areas in terms of PM2.5 pollution were mainly concentrated east of the Hu Line, north of the Yangtze River in Shandong, Henan, and the Beijing-Tianjin-Hebei urban agglomeration, and these areas exhibited spatial differentiation characteristics involving the high-pollution area as the center with the pollution level decreasing toward the periphery according to a gradient. Therefore, haze prevention and control in China should first focus on this part of the high-pollution cycle, strengthen the accountability of emission regulation, and enhance environmental planning and environmental legislation in regional planning.
(一)以推进区域一体化为切入点,发挥城市紧凑因素对大气污染的抑制作用。空气污染呈现出典型的外部性,即跨境污染仍然是近年来雾霾污染的本质特征之一。区域生态环境是一个有机的整体。因此,雾霾污染综合治理不应在局部污染物排放源控制中单独实施,而应同步突破行政界限,实现区域雾霾污染的协同防治。从我国PM 2.5 污染时空演变格局来看,PM 2.5 污染重灾区主要集中在胡安线以东、长江以北地区山东、河南、京津冀城市群河流呈现出以高污染区为中心、向外围污染水平呈梯度递减的空间分异特征。因此,我国雾霾防治首先应重点关注这部分高污染循环,强化排放监管问责,加强区域规划中的环境规划和环境立法。

(2) The population structure should be optimized, and mixed urban land development patterns should be reasonably planned. In this paper, it was argued that the scale effect of PD far exceeds the agglomeration effect, thus exerting a significant positive impact on air pollution. Therefore, the urban system structure should be reasonably planned in compact construction to relieve the population pressure. In specific cities in Northeast and Northwest China, attention should be given to improving the energy consumption efficiency, optimizing the housing and transportation infrastructure configuration, promoting the reasonable concentration of population, and reducing the negative impact of population concentration on haze control. In the urban planning process, decisions should be made based on city conditions, and by guiding and establishing the optimal urban PD or urban population scale, the positive economic impact of agglomeration should be maximized to promote the rapid development of the city and regional economy

In addition, attention should be given to the rational development of the land structure and construction land expansion in urbanization construction. The main body of urban space development will change from incremental expansion to stock optimization development. During the stock optimization period, the focus should be on adjusting the urban spatial structure to improve intensive land use and mixed functional layout with a high efficiency and quality. In cities containing more developed secondary industries, the principle of rational and efficient land use should be scientifically applied in unified planning to eliminate the problem of a disorganized layout and inefficient use of industrial land. Cities in the central and western regions at the early stage of urbanization should reasonably increase the urban construction land area, improve the construction level and reduce urban construction land scattering.

(3) The road network system should be improved, and the accessibility to urban transportation should be enhanced. In this study, it was found that road infrastructure construction in cities could significantly impact haze pollution. Road structure adjustment and optimization could effectively reduce vehicle transportation emissions and alleviate traffic congestion. Road structure optimization should start from two aspects: reasonable control of PR and efficient RN planning. In cities with a high intensity of urban road transportation, such as Henan, Shandong, Zhejiang, Anhui, and Fujian, disorderly road expansion should be reduced. High-density network traffic patterns within urban areas should be accelerated to reduce the road surface density, thus improving the efficiency of transportation facilities and reducing haze pollution. Road infrastructure should be strengthened in the inland areas in the northwest to enhance the three-dimensional network of land transportation and fully manifest the superiority of road compactness in regard to haze control.

In contrast, it was found that RN improvement could effectively alleviate urban traffic congestion and suppress air pollution. Therefore, urban road networks should be rationally planned to enhance urban traffic accessibility, traffic efficiency, and compact development. In cities in the Yangtze River Delta and Pearl River Delta urban agglomerations, RN overconcentration could cause intensive motor vehicle exhaust emissions. These cities should simultaneously optimize their public transportation networks and adopt more public transportation modes to connect the various city centers. The use and promotion of new energy vehicles (NEVs) should also be promoted to reduce particulate pollutants resulting from traffic congestion and motor vehicle emissions.


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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of

the authors

Informed consent

This article does not contain any studies with human participants performed by any of

the authors

Additional information

Correspondence and requests for materials should be addressed to the corresponding author.

Author contributions

XXX (first author): Conceptualization and WritingOriginal draft preparation. XXX: Software, Visualization and Writing. XXX (corresponding author): Funding acquisition and Theoretical analysis. XXX: Methodology and Software. XXX: Data collection.
XXX(第一作者):概念化与写作——初稿准备。 XXX:软件、可视化和写作。 XXX(通讯作者):资金获取与理论分析。 XXX:方法和软件。 XXX:数据收集。

Figure legends

Figure 1: LISA cluster maps of PM2.5 in 2010, 2013, 2016 and 2019
图1:2010年、2013年、2016年和2019年PM 2.5 的LISA簇图

Figure 2: Spatial distributions of PM2.5 in 2010, 2013, 2016 and 2019
图2:2010年、2013年、2016年和2019年PM 2.5 的空间分布

Figure 3: Temporal effects of the six compactness factors on PM2.5 pollution
图3:六个紧密度因子对PM 2.5 污染的时间影响

Figure 4: Coefficient map of PD in 2010, 2013, 2016 and 2019

Figure 5: The coefficient map of PGDP in 2010, 2013, 2016 and 2019

Figure 6: Distribution and coefficient maps of PGDP in 2019

Figure 7: Coefficient map of J in 2010, 2013, 2016 and 2019

Figure 8: The coefficient map of CI in 2010, 2013, 2016 and 2019

Figure 9: Coefficient map of PR in 2010, 2013, 2016 and 2019

Figure 10: Coefficient map of RN in 2010, 2013, 2016 and 2019