
==== Front
Eco Environ Health
Eco Environ Health
Eco-Environment & Health
2772-9850
Elsevier

S2772-9850(24)00032-2
10.1016/j.eehl.2024.04.004
Original Research Article
Synergistic PM2.5 and O3 control to address the emerging global PM2.5-O3 compound pollution challenges
He Chao hechao@yangtzeu.edu.cn
ab⁎1
Liu Jianhua ab1
Zhou Yiqi c
Zhou Jingwei d
Zhang Lu e
Wang Yifei f
Liu Lu luliu@smail.nju.edu.cn
g⁎
Peng Sha pengsha@hbue.edu.cn
h⁎
a College of Resources and Environment, Yangtze University, Wuhan 430100, China
b Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
c School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
d Hydrology and Environmental Hydraulics Group, Wageningen University and Research, Wageningen 6700 HB, the Netherlands
e State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
f State Key Joint Laboratory for Environmental Simulation and Pollution Control, School of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
g State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
h Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry, Hubei University of Economics, Wuhan 430205, China
⁎ Corresponding authors. hechao@yangtzeu.edu.cnluliu@smail.nju.edu.cnpengsha@hbue.edu.cn
1 Co–first authors.

19 4 2024
9 2024
19 4 2024
3 3 325337
14 1 2024
5 3 2024
2 4 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
In recent years, the issue of PM2.5-O3 compound pollution has become a significant global environmental concern. This study examines the spatial and temporal patterns of global PM2.5-O3 compound pollution and exposure risks, firstly at the global and urban scale, using spatial statistical regression, exposure risk assessment, and trend analyses based on the datasets of daily PM2.5 and surface O3 concentrations monitored in 120 cities around the world from 2019 to 2022. Additionally, on the basis of the common emission sources, spatial heterogeneity, interacting chemical mechanisms, and synergistic exposure risk levels between PM2.5 and O3 pollution, we proposed a synergistic PM2.5-O3 control framework for the joint control of PM2.5 and O3. The results indicated that: (1) Nearly 50% of cities worldwide were affected by PM2.5-O3 compound pollution, with China, South Korea, Japan, and India being the global hotspots for PM2.5-O3 compound pollution; (2) Cities with PM2.5-O3 compound pollution have exposure risk levels dominated by ST + ST (Stabilization) and ST + HR (High Risk). Exposure risk levels of compound pollution in developing countries are significantly higher than those in developed countries, with unequal exposure characteristics; (3) The selected cities showed significant positive spatial correlations between PM2.5 and O3 concentrations, which were consistent with the spatial distribution of the precursors NOx and VOCs; (4) During the study period, 52.5% of cities worldwide achieved synergistic reductions in annual average PM2.5 and O3 concentrations. The average PM2.5 concentration in these cities decreased by 13.97%, while the average O3 concentration decreased by 19.18%. This new solution offers the opportunity to construct intelligent and healthy cities in the upcoming low–carbon transition.

Graphical abstract

Image 1

Highlights

• 47.5% of assessed cities worldwide are exposed to compound PM2.5-O3 pollution.

• Cities in developing countries experience higher exposure risks than developed countries.

• Significant positive spatial correlations between PM2.5 and O3 concentrations are observed.

• 52.5% of cities worldwide have the potential for synergistic PM2.5 and O3 reductions.

Keywords

PM2.5-O3 compound pollution
Population exposure risk
Spatial correlation
Synergistic treatment potential
==== Body
pmc1 Introduction

Elevated concentrations of fine particulate matter (PM2.5) and surface ozone (O3) are harmful to human health [1,2], ecosystems [3], and crop yields [4,5], and are a major contributor to climate change [6,7]. PM2.5 is composed of directly emitted primary PM2.5 and secondary PM2.5, which is formed from gaseous precursors, including SO2, nitrogen oxides (NOx), volatile organic compounds (VOCs), and NH3 [8]. O3 generation, beyond that originating from stratospheric transport, primarily occurs through complex photochemical reactions between NOx and VOCs under sunlight [9]. Recent collaborative efforts by the World Health Organization (WHO) and global governments have led to a notable reduction in PM2.5 concentrations worldwide, particularly in certain cities within affluent European and North American nations, where levels have approached or met the WHO's IT-1 target value of 35 μg/m3 [10]. However, according to data from the 2019 “Global Air Status Report” (https://www.stateofglobalair.org/), 54% of the global population lives in areas above the 35 μg/m3 threshold, resulting in approximately 2.9 million premature deaths from PM2.5 exposure. Concurrently, there is growing evidence that global O3 pollution is becoming more visible, with a wider range of impacts and longer pollution season [11]. According to the Global Burden of Disease (GBD), weighted O3 concentrations in 11 populous nations range from 45 to 68 ppb, approaching or exceeding the WHO guideline of 100 μg/m3. In 2019 alone, O3 exposure resulted in 365,000 premature deaths worldwide [12]. Amid this context, studies have shown that the health hazards of global air pollution will become more severe in the future, driven by climate change, and that the features of pollution have shifted from single soot-type pollution in the past to compound atmospheric pollution with multiple sources of emissions and multiple pollutants coexisting and interacting with each other [13]. Therefore, clarifying the issue of PM2.5 and O3 compound pollution has become an important atmospheric environmental issue for the next step of improving air quality and realizing environmental sustainability processes globally.

To effectively combat the global pollution caused by PM2.5 and O3 compounds, it is crucial to accurately identify the current challenges, gain knowledge from historical experiences of PM2.5 and O3 pollution management, and ultimately construct a synergistic control framework for PM2.5 and O3 pollution. Recognized as a global menace, scholars have rigorously examined PM2.5 and O3 pollution across diverse spatial scales, delving into their spatiotemporal distribution [14], regional transport mechanisms [15], chemical mechanisms [16,17], drivers [18,19], economic ramifications [20], and health implications [21]. For instance, Zhao et al. [22] examined the worldwide spatial and temporal trends and population exposure risk of PM2.5 concentrations from 2000 to 2016, clarifying the relationship between PM2.5 concentrations and population exposure risk. From a spatiotemporal lens, Lim et al. [23] identified principal socio-economic elements shaping the spatial alterations in global PM2.5 concentrations, subsequently proposing mitigation pathways tailored to nations' economic standings. Approaching from a sustainability perspective, Zhou et al. [24] explored the spatiotemporal trends and population exposure risk of global springtime O3 concentrations, pinpointing pivotal meteorological determinants influencing different regional O3 fluctuations and associated human risks. Further, studies by Zhang et al. [25] and Lyu et al. [26] provided comprehensive insights into the health hazards and climate impacts linked to global O3 pollution.

Concurrently, a plethora of studies have identified a regional synergy in the pollution patterns of PM2.5 and O3. This synergistic feature has been universally observed across cities globally [27]. For instance, Zhao et al. [28] examined the spatiotemporal association of PM2.5 and O3 pollution in 367 key cities in China from 2015 to 2019. Their findings highlighted that those regions with the most severe PM2.5 pollution concurrently suffered from intense O3 pollution. In a similar vein, Sicard et al. [29] scrutinized the interplay between PM2.5 and O3 during air pollution episodes in arid continental climates based on air quality data from 21 ground monitoring stations in the Middle East. They discerned that whenever PM2.5 concentrations surged, a concurrent oscillation in O3 concentrations was evident. Analogous phenomena have been documented in the US [30] and Europe [31,32] through multi-year air quality monitoring. Moreover, burgeoning evidence posits that PM2.5 and O3 share common precursors, with VOCs and NOx emerging as their most pivotal shared antecedents [33]. On one hand, NOx and VOCs influence PM2.5 concentrations by fostering the formation of nitrates and secondary organic aerosols, and simultaneously play a significant role in the chemistry of O3. On the other hand, the heterogeneous reactions on the surface of particulate matter can directly adsorb O3 or react with nitrogen oxides (NO2, NO3, N2O5), thereby affecting O3 concentration [34]. Specifically, from 2000 to 2019, there was a slight global decrease in PM2.5 exposure (on average, −0.2% per year). However, 65% of cities still showed an increasing trend in PM2.5 exposure levels. Additionally, the O3 exposure levels of the global urban population increased (on average, +0.8% per year) due to the reduced titration effect of NO on ozone [35]. Even at night, O3 levels continued to rise [36]. This shared origin trait of PM2.5 and O3 has been ubiquitously recognized globally. Therefore, coordinated control of PM2.5 and O3 compound pollution from the perspective of synergistic regional emissions and the same sources of PM2.5 and O3 has become the key to managing global compound pollution.

Facing the escalating global challenge of PM2.5 and O3 compound pollution, scholars have embarked on extensive research to elucidate the characteristics of pollution, driving factors, and underlying mechanisms, aiming to devise collaborative mitigation strategies. Such endeavors aspire to offer technical support for the continuous improvement of air quality and public health protection across diverse regions globally. For instance, Wang et al. [37] probed into the causality of PM2.5 and O3 compounded pollution from the perspective of active nitrogen transformation routes in atmospheric nitrogen cycling. Dai et al. [38], leveraging a refined emission inventory of the Yangtze River Delta in China and the WRF-CMAQ model, crafted synergistic control pathways for atmospheric PM2.5 and ozone pollution in the region. Ojha et al. [19] reviewed mechanisms and methods for the collaborative control of PM2.5 and O3, positioning it within the context of global warming. Meanwhile, Faridi et al. [39] furnished a comprehensive assessment of long-term trends and health implications of PM2.5 and O3 pollution in Tehran, grounded on real-time hourly concentration datasets from 21 air quality monitoring stations spanning 2006–2015. Such studies grant a pivotal theoretical foundation and empirical insight into the driving forces behind air pollution in various global regions. Nonetheless, there remain gaps in this arena. Historically, many studies gravitated towards analyzing a particular air pollutant, with scant research addressing the spatiotemporal correlation features of compounded pollutants, let alone delving into their intricate interrelations. Further, due to a dearth of pollutant concentration data, assessing the spatiotemporal evolution of pollutants on a global scale remains a challenge. Most critically, there's a conspicuous absence of research offering a holistic understanding of PM2.5 and O3 compounded pollution traits from a global viewpoint, especially within a sustainable development lens that evaluates exposure risks to populations. Concurrently, no framework has been discerned thus far that addresses the collaborative governance of global PM2.5 and O3 compounded pollution.

To address the identified knowledge gaps, this study utilizes PM2.5 and O3 concentration monitoring data from 120 cities globally spanning from 2019 to 2022. Leveraging methodologies such as spatial statistical analysis, time series analysis, exposure risk assessment, and spatial correlation analysis, this research represents the first comprehensive global-scale investigation into the spatiotemporal patterns, evolutionary characteristics, exposure risks, and spatial associations with precursor substances of combined PM2.5 and O3 pollution. This work deepens our understanding of the concurrent management of PM2.5 and O3 on a global scale, proposing an integrated framework for their co-management. The findings stand to foster collaboration between the air quality and climate communities, offering policymakers crucial insights to jointly address these persistently intertwined threats.

2 Materials and methods

2.1 Study area

In this study, we focus on 120 major cities worldwide. These cities are primarily located in Asia (57), Europe (28), North America (22), South America (8), Oceania (3), and Africa (2). The primary reasons for selecting these cities are as follows: Firstly, the chosen cities have high population densities, high anthropogenic emissions, high energy consumption, and elevated levels of air pollution [[40], [41], [42], [43]]. Secondly, data from these cities possess a complete time series, allowing for quantitative analyses over various temporal scales. Lastly, these cities have diverse geographical and climatic conditions. For instance, Beijing has a temperate monsoon climate, while Delhi has a semi-arid climate. These varying geographical and climatic conditions are crucial in enhancing our comprehension of the spatial heterogeneity of PM2.5 and O3 compound pollution. Given these facts, the chosen cities offer a suitable variety of diverse regions for our investigation. Moreover, to delve deeper into the compound pollution status of PM2.5 and O3 at the urban scale, we selected 10 cities out of the 120, namely Beijing (China), Tokyo (Japan), Seoul (South Korea), Delhi (India), Sydney (Australia), London (UK), Rome (Italy), Berlin (Germany), Los Angeles (US), and Mexico City (Mexico) for in-depth analysis. The spatial distribution of the 120 cities and the 10 focus cities is illustrated in Fig. 1.Fig. 1 Spatial distribution of the study areas. The red dots represent the selected 120 cities, while the green triangles indicate the 10 focal cities (a). The pie chart displays the number of countries from each continent (b), and the bar chart shows the number of cities included from each continent (c).

Fig. 1

2.2 Data sources and preprocessing

The daily records of PM2.5 and O3 concentrations across the 120 chosen cities were sourced from the World Air Quality Index (WAQI) portal (https://www.aqicn.org/). To analyze the co-sourced features of PM2.5 and O3 and their effect on the exposure risk of the population, we collected precursor emission inventories (VOCs, NOx) and population inventories from the European Commission (https://commission.europa.eu/) and The World Bank (https://www.worldbank.org/), respectively. Prior to conducting any analysis, based on the study by He et al. [27], we implemented data quality control measures on the daily PM2.5 and O3 concentrations obtained from 120 cities globally. We discarded anomalous data that did not meet the statistical criteria, such as daily PM2.5 and O3 concentrations that exceeded 999 μg/m3. In this study, the valid counting days for monthly and annual average concentrations of PM2.5 and O3 in cities are no less than 27 days and 360 days, respectively. Concurrently, this study evaluated the risk of exposure to PM2.5 and O3 pollution with reference to the new Air Quality Guidelines (AQG) issued by the World Health Organization in 2021 [44]. In the specific calculations, we utilized the rolling average of the maximum 8-h concentrations as the daily average concentration for O3.

2.3 Definition of PM2.5 and O3 compound pollution

Drawing from past epidemiological studies on the population exposure to PM2.5 and O3 [45,46] and the new AQG standards, we have chosen the daily average concentrations of PM2.5 and O3 to be 35 μg/m3 and 100 μg/m3, respectively, as the thresholds for categorizing the dominant pollution types of PM2.5 and O3. Based on this scheme, we classify the dominant pollution types of PM2.5 and O3 into the following four categories: Compound Pollution of PM2.5 and O3 (P–O), PM2.5 Dominant Pollution, O3 Dominant Pollution, and Clean. The detailed categorization criteria are illustrated in Table 1.Table 1 Compound Pollution Classification Standards.

Table 1PM2.5 (μg/m3)	O3 (μg/m3)	Pollution dominant type	
ρ(PM2.5) > 35	ρ(O3) > 100	P–O	
ρ(PM2.5) > 35	ρ(O3) < 100	PM2.5 dominated pollution	
ρ(PM2.5) < 35	ρ(O3) > 100	O3 dominated pollution	
ρ(PM2.5) < 35	ρ(O3) < 100	Clean	

2.4 Exposure risk assessment of compound pollution

This study discusses the risk of population exposure to long-term ambient PM2.5 and O3 based on the method by Lim et al. [23]. Initially, we employed the Mann-Kendall method [47,48] to analyze the changing trends of PM2.5 and O3 concentrations over the research period. The calculations for the Mann-Kendall method are as given in Equations (1), (2), (3), (4):(1) S=∑i=1n−1∑j=i+1nsgn(xj−xi)

where: n represents the total number of data points; Xi and Xj are data values in time series i and j. Xj is used as a reference point to compare with the remaining data points Xi. The sgn(xj-xi) is the sign function, with the specific formula as follows:(2) sgn(xj−xi)={+1,xj−xi>00,xj−xi=0−1,xj−xi<0

Additionally, the formula for calculating the variance is:(3) Var(S)=118[n(n−1)(2n+5)−∑k=1pqk(qk−1)(2qk+5)]

In the formula, n represents the total number of data points; p denotes the number of tied groups; qk indicates the number of data points contained in the k-th tied group. When dealing with large samples (n > 10), the standardized test statistic Z is used for calculations:(4) Z={S−1Var(S),S>00,S=0S+1Var(S),S<0

By evaluating the Z value, a statistically significant curve trend can be obtained. A positive Z indicates an increasing trend, while a negative Z indicates a decreasing trend. In a two-tailed trend test, for a given confidence level (significance level) α, if |Z| ≥ Z1-α/2, then the null hypothesis H0 is rejected. This means that, at the confidence level α, the time series data exhibits a significant increasing or decreasing trend. |Z| values greater than or equal to 1.645, 1.960, and 2.576 represent passing the significance test at confidence levels of 90%, 95%, and 99%, respectively.

Subsequently, combining the high or low levels and change trends of PM2.5 or O3 concentrations, we classified the exposure risk level of the population in different cities under the PM2.5 and O3 environments into six types: High Risk (HR), Stabilization (ST), Risk (R), Deep Stabilization (DST), Safety (S), and High Safety (HS). Among them, HR and ST both indicate extremely high pollutant concentrations, but HR denotes an increasing trend in pollutant concentration, while ST signifies a decreasing trend. R and DST mean high pollutant concentrations with respective increasing and decreasing trends. S and HS indicate low pollutant concentrations, with respective increasing and decreasing trends. Here, based on the epidemiological methods in Strak et al. [49], Guan et al. [45], and Guerreiro et al. [46], we define extremely high pollutant concentration criteria as ρ(PM2.5) > 35 μg/m3 or ρ(O3) > 120 μg/m3; High pollutant concentration criteria as 25 μg/m3 < ρ(PM2.5) < 35 μg/m3 or 100 μg/m3 < ρ(O3) <120 μg/m3; Low pollutant concentration criteria as ρ(PM2.5) < 25 μg/m3 or ρ(O3) < 100 μg/m3.

2.5 Spatial correlation analysis

In this study, Bivariate Moran's I (Bi-Moran's I), spatial statistical analysis, and spatial correlation analysis models were employed to investigate the spatial agglomeration characteristics, spatial correlations, and spatial associations with the main precursors (NOx and VOCs) of PM2.5 and O3 concentrations in the 120 global cities during the study period. The calculation for the Bi-Moran's I is as per Equation 5:(5) IiB=cxi∑jwijyj

In the formula, IiB represents the bivariate local Moran's index for region i; wij is an element of the spatial weight matrix; and c is a constant proportionality factor. This index is used to quantitatively describe the degree of association between variable x in region i and variable y in neighboring region j. Furthermore, the detailed calculation process of the spatial correlation analysis model can be found in the research by Lu et al. [50]. The spatial analyses and implementation involved in this study are primarily conducted using the GeoDa1.20 (http://geodacenter.github.io/), ArcGIS10.7 (https://www.esri.com/), and GWmodelS1.0.3 (https://github.com/GWmodel-Lab/GWmodelS/) software.

2.6 Analysis of synergistic changes in compound pollution

In this study, we measure the level of synergistic changes in PM2.5 and O3 concentrations based on the relative rate of change (ROC) of PM2.5 and O3 concentrations in 2019 and 2022, calculated as in Equation 6:(6) If={ROCi,PM2.5≥1andROCi,O3≥1,SynergisticIncreaseROCi,PM2.5<1andROCi,O3<1,SynergisticDecreaseROCi,PM2.5≥1andROCi,O3<1,PM2.5IncreaseandO3DecreaseROCi,PM2.5<1andROCi,O3≥1,PM2.5DecreaseandO3Increase

The ROCi in equation is calculated as follows:(7) ROCi=Ci,2022Ci,2019

where: ROC represents the relative change of PM2.5 and O3 in city i; Ci,2022 and Ci,2019 represent the concentrations of PM2.5 and O3 in city i in 2022 and 2019, respectively.

3 Results

3.1 Temporal and spatial distribution of global PM2.5 and O3 concentrations

Fig. 2a and b depicts the spatial distribution and seasonal variations of the annual average PM2.5 concentrations in 120 global cities from 2019 to 2022. The annual average PM2.5 concentrations for these cities from 2019 to 2022 were 61.86, 56.92, 57.36, and 55.48 μg/m3, respectively, indicating a fluctuating downward trend. Among the selected cities, less than 1% were found to have low PM2.5 exposures (4-year average PM2.5 concentrations ≤ 25 μg/m3). These cities are primarily situated in Canada, Australia, and several countries in Europe, such as Vancouver (Canada, 18.46 μg/m3), Wollongong (Australia, 22.17 μg/m3), and Edinburgh (UK, 20.56 μg/m3). In contrast, high PM2.5 concentrations were found in 30% of the cities, where the average concentration of PM2.5 over four years exceeded 70 μg/m3. These cities are mainly found in eastern China, northern and southwestern India, and also include Santiago (Chile, 75.37 μg/m3) and Johannesburg (South Africa, 75.77 μg/m3). Notably, northern Indian cities such as Lucknow (146.5 μg/m3) and Delhi (161.9 μg/m3) registered 4-year average PM2.5 concentrations exceeding 140 μg/m3. Meanwhile, over 40% of cities had exposures to 4-year average PM2.5 concentrations ranging from 35 to 70 μg/m3, predominantly located in countries such as South Korea, Japan, France, the UK, and Germany. It was observed that the global PM2.5 concentrations were at their zenith during the winter and at a nadir during the summer. Compared to summer, the number of cities exposed to lower PM2.5 environments in winter decreased by 40%, while those exposed to higher PM2.5 levels more than doubled. Such shifts in exposure risk show spatial congruity, particularly in cities in India and China, where regions with milder PM2.5 concentrations in summer transition to regions with higher concentrations in winter.Fig. 2 The spatial distribution of annual average PM2.5 and O3 concentrations in 120 cities globally from 2019 to 2022 (a and c), and the spatiotemporal distribution of PM2.5 and O3 concentrations on a seasonal basis (b and d). The bar chart indicates the number of cities exposed to various PM2.5 and O3 concentration levels.

Fig. 2

In the 120 global cities examined, the annual average O3 concentrations displayed a decline similar to the trends observed for PM2.5 concentrations. The O3 concentrations were noted to decrease from 120.51 μg/m3 in 2019 to 116.16 μg/m3 in 2021, further diminishing to 114.57 μg/m3 in 2022. During the study period, it was found that 50.8% of the cities under consideration exhibited a 4-year average O3 concentration below 100 μg/m3. These cities are predominantly located in regions such as the US (67.6 μg/m3), Canada (59.4 μg/m3), Australia (63.6 μg/m3), and European countries (71.6 μg/m3). On the other hand, a smaller portion, around 13.3%, registered a 4-year average O3 concentration of less than 60 μg/m3. In stark contrast, nearly half of the cities globally presented a 4-year average O3 concentration surpassing 100 μg/m3 over the research span. Such cities were chiefly located in India (231.8 μg/m3), China (169.5 μg/m3), Japan (121.5 μg/m3), and South Korea (136.2 μg/m3). Notably, northern Indian cities like Chennai (217.3 μg/m3) and Kolkata (265 μg/m3), as well as Chengdu (173 μg/m3) in central China, recorded O3 concentrations far exceeding the O3 threshold set by the WHO's AQG in 2021. In terms of the seasonal trends, globally, the highest proportion of cities exposed to high O3 concentrations (>100 μg/m3) occurred in summer, representing 19.2% of the selected cities, followed by spring (14.2%), autumn (12.5%), and winter (8.3%). Cities persistently exposed to heightened O3 environments exhibited distinct spatial clustering, primarily in central China and northeastern India.

In the key cities of focus (Fig. S1), Delhi registered the pinnacle 4-year average PM2.5 concentration at 161.89 ± 60.56 μg/m3, while Sydney recorded the nadir at 25.03 ± 7.64 μg/m3. In terms of seasonal variations, PM2.5 concentrations in cities such as Berlin, London, Tokyo, Seoul, and Beijing predominantly exhibited a winter > spring > autumn > summer sequence. In contrast, other cities displayed varied seasonal changes: cities like Rome and Sydney peaked in the spring and bottomed out in the summer, while Los Angeles witnessed its minimum concentrations in spring. The highest and lowest O3 concentrations were identified in Delhi and Rome, respectively, with values of 140.2 ± 37.89 and 25.34 ± 5.74 μg/m3. Following closely are Mexico City, Seoul, and Beijing, all of which have O3 concentrations surpassing 60 μg/m3. In contrast, other cities exhibit O3 levels ranging between 30 and 50 μg/m3. Additionally, it was observed that the peak O3 concentrations for these focal cities occurred in summer, while the lowest levels were typically registered in winter (except for Delhi, where the minimum levels were observed in autumn), aligning with the global seasonal variations in O3 concentrations.

3.2 Global characteristics of PM2.5 and O3 compound pollution

While the PM2.5 concentrations in most global cities have yet to reach the thresholds set by AQG, urban O3 pollution is becoming increasingly severe. There's a noticeable trend of compound pollution involving both PM2.5 and O3 in various global regions. This subsection, based on the methodology provided in Section 2.3, offers a comprehensive analysis of the spatiotemporal variations in PM2.5 and O3 compound pollution across 120 global cities during the research period (Fig. 3). Spatial statistics reveal that only 25.8% of the studied cities enjoy a relatively unpolluted environment (Clean). A significant proportion of these cities reside in the US, representing approximately 82% of all US cities, with several others in Northern Europe. Conversely, almost half (47.5%) of the global cities evaluated were subjected to PM2.5-O3 compound pollution during the study timeframe. This form of pollution predominantly affected cities in countries such as Chile (4), China (22), South Korea (10), Japan (7), and India (9). Additionally, 25% of cities are exposed to a PM2.5 dominant polluted environment, predominantly found in Europe, accounting for roughly 67.9% of European cities (Fig. 3a). From a seasonal perspective, spring, summer, and autumn witness the peak periods for global PM2.5-O3 compound pollution. During these three seasons, an average of over 40% of cities experience PM2.5-O3 compound pollution. Notably, during the summer, almost 50% of the selected cities are exposed to PM2.5-O3 compound pollution. These cities are primarily clustered in South Korea, eastern and southern China, and northern India (Fig. 3b–e). In stark contrast, less than 25% of cities worldwide are exposed to PM2.5-O3 compound pollution in winter, such as Delhi and Mumbai in India, and Shijiazhuang and Chengdu in China. Conversely, during winter, 53.3% of global cities face PM2.5 dominant pollution. These cities were dispersed across various continents, with European nations and northern China marking significant regions for winter PM2.5 dominant pollution, for instance, cities like Rome (Italy), Paris (France), and Hamburg (Germany).Fig. 3 The spatial distribution (a) and seasonal variation (b–e) of PM2.5-O3 compound pollution conditions in 120 cities worldwide from 2019 to 2022.

Fig. 3

As global air pollution concerns intensify, countries worldwide have issued stringent air pollution control strategies based on their specific conditions, leading to a shift in the dominant forms of air pollution. For the first time, Fig. S2 reveals the spatial characteristics of changes in dominant air pollution types across 120 global cities from 2019 to 2022. Overall, there was a positive shift towards cleaner urban environments: cities classified under the “Clean” category rose from 24 in 2019 to 26 in 2021 and further rose to 31 in 2022. Meanwhile, cities dominated by PM2.5 pollution increased slightly from 37 in 2019 to 38 in 2021 but then saw a reduction to 33 by 2022. Furthermore, cities under the bracket of O3 dominant pollution never exceeded 5% of the analyzed cities throughout the study period. Spatially, our analysis found that 24 cities, such as Berlin and London in Europe, consistently showed PM2.5 as the dominant pollutant during the entire study timeframe. In stark contrast, 46 cities persistently witnessed PM2.5-O3 compound pollution. Key cities in this category include Shijiazhuang and Shenyang in China, as well as Delhi and Mumbai in India. Seven cities, primarily situated in parts of India and Europe, transitioned from being PM2.5-dominant to experiencing PM2.5-O3 compound pollution. Meanwhile, 11 cities, predominantly located in Japan, some European regions, and Chile (including Yokohama, Japan; Lyon, France; and Rancagua, Chile), shifted from the PM2.5-O3 compound pollution to either PM2.5 dominant pollution or O3 dominant pollution. Furthermore, eight cities, dispersed across regions like the US, Australia, and Germany (for example, Chicago, US; Sydney, Australia; and Wiesbaden, Germany), transitioned from either O3 or PM2.5 dominant pollution to a “Clean” classification within the research period.

3.3 Exposure risk assessment of compound pollution

The intensification of PM2.5 and O3 pollutants in the atmosphere presents diverse environmental exposure risks in cities globally. Fig. 4 illustrates the spatial distribution of air pollution exposure risks in 120 global cities during the study period. Our analysis identifies the primary types of compound pollution exposure risks in global cities as ST + ST, ST + HS, DST + HS, ST + DST, ST + HR, ST + DST, ST + HR, HS + HS, ST + HS, and DST + HS. Notably, ST + ST and ST + HS emerge as the most critical exposure risk types related to PM2.5-O3 compound pollution. Among the selected cities, 29 exhibit the ST + ST exposure risk type, predominantly located in China, South Korea, Japan, India, and Chile. Characterized by PM2.5 and O3 concentrations exceeding 35 μg/m3 and 100 μg/m3 respectively, these cities, though witnessing a declining trend, will subject their populations to significant compound pollution risks in the future. In contrast, 23 cities worldwide manifest the ST + HS compound pollution exposure risk type, mainly situated in Thailand, the UK, France, and Germany. Such cities, while presenting PM2.5 concentrations above 35 μg/m3 and O3 concentrations below 100 μg/m3, display a substantial decline in pollutant concentrations. Consequently, while they currently experience significant compound pollution risks, the consistent decline in O3 levels suggests a hopeful trajectory towards reduced risks. Additionally, 12, 10, and 10 cities globally show compound pollution exposure risks of DST + HS, ST + DST, and ST + HR, respectively. This includes Chicago, Boston, and Miami in the US, Shijiazhuang and Qingdao in China, and Delhi and Lucknow in India. In these cities, at least one of the PM2.5 or O3 concentrations falls below the AQG threshold and exhibits a continued declining trend, which results in a gradual reduction in compound pollution risks. From a demographic perspective, in densely populated Asian regions (over 200 million), the compound pollution risks are largely categorized into three types: ST + DST (10), ST + HR (10), and ST + ST (25). In Europe, a cumulative population exceeding 20 million is exposed to environments with compound pollution risk levels of HS + HS (5) and ST + HS (15). In North America, the predominant exposure risk is DST + HS (7).Fig. 4 Exposure risk assessment of compound pollution across 120 cities from 2019 to 2022 (a); the line chart indicates the population count; the heatmap shows the number of city sites under different compound pollution exposure risks. PM2.5 and O3 pollution exposure risk assessment for 120 cities from 2019 to 2022 (b–c). Exposure risk assessment for select cities from 2019 to 2022 (d); blue borders represent cities in developed countries, and green borders indicate cities in developing countries. Smaller square or oval borders suggest a city population of less than 1 million (106), while larger ones indicate the opposite.

Fig. 4

The analysis of the individual trends in PM2.5 and O3 concentrations shows that approximately 63.3% (or 76) of cities worldwide are exposed to an environment with a PM2.5 risk type of ST. These cities are primarily located in Germany, France, China, India, South Korea, Japan, Thailand, and Chile, with examples including Tokyo (Japan), Busan (South Korea), Chongqing (China), Lucknow (India), Rome (Italy), Paris (France), and Santiago (Chile). Furthermore, cities with a PM2.5 exposure risk type of DST account for about 14.2% globally. They are predominantly found in the eastern and southern parts of the US, as well as in southeastern Canada, like Toronto (Canada) and Chicago (US). Moreover, it was noted that approximately 16.7% of the cities globally present PM2.5 exposure risks classified as R and HR. These cities, primarily located in the western US (including Phoenix and Philadelphia), display PM2.5 concentrations oscillating between 25 and 35 μg/m3 and exceeding 35 μg/m3, respectively, both indicating an increasing pattern. Additionally, a combined 5.8% of the cities, exemplified by Vancouver, exhibited PM2.5 exposure risks defined as HS and S, characterized by concentrations under 25 μg/m3. It's noteworthy that those within the ‘S’ classification reveal a rising PM2.5 trend (Fig. 4b). Regarding O3, over 50% of cities worldwide have O3 exposure risk types of HS and S. These cities are largely spread across the eastern US and most European regions, such as Berlin (Germany), London (UK), and Miami (US). Additionally, cities with O3 exposure risk types of ST or HR are primarily located in northern India, eastern China, South Korea, and Japan. Among these, cities with an exposure risk type of HR represent 12.5% and are chiefly centered in southeastern China, including cities like Shanghai and Jinan (Fig. 4c). From a combined perspective of population and economic levels, cities exposed to PM2.5 (or O3) concentrations below 35 μg/m3 (or 120 μg/m3) are largely found in developed countries. Examples are Chicago, San Antonio, Helsinki, and Sydney. About 125 million people in these areas can enjoy the reduced exposure risks brought by good air quality (low pollutant concentrations). In stark contrast, cities in developing nations like Beijing, Mumbai, and Delhi continue grappling with exacerbated pollutant concentrations. An estimated populace of 218 million endures heightened pollution environments, consequently intensifying their vulnerability to associated exposure risks.

3.4 Spatial association between PM2.5-O3 compound pollution and precursors

The environmental exposure risks caused by PM2.5-O3 compound pollution are increasingly severe. A quantitative elucidation of the spatial correlation between PM2.5 and O3 concentrations, along with their spatial association with precursors, holds paramount importance for devising coordinated emission reduction strategies for PM2.5 and O3 concentrations under forthcoming sustainable development paradigms. In Fig. 5a and b, the scatter plot of PM2.5-O3 bivariate Moran's I and the spatial clustering distribution for 120 global city stations are depicted. It can be observed that the bivariate Moran's I for PM2.5 and O3 is 0.435 (Moran's I > 0 indicates clustering), and it has passed the significance test (P < 0.05). Such results underscore a significant positive spatial correlation between PM2.5 and O3 concentrations. Specifically, 43 cities worldwide have their bivariate Moran's I for PM2.5 and O3 concentrations in the first quadrant, indicating a High–High spatial clustering pattern. Predominantly, these cities are located in regions such as China, India, South Korea, and Thailand, marked by high levels of both PM2.5 and O3 concentrations. Meanwhile, bivariate Moran's I for PM2.5 and O3 concentrations in 48 cities, mainly in the US, Canada, the UK, and France, were observed in the third quadrant, indicating a low–low spatial clustering pattern with low PM2.5 and O3 concentrations. In addition, certain cities in Japan and Mexico were ascertained to manifest either a low-high or high-low spatial clustering paradigm.Fig. 5 Distribution of PM2.5-O3 Bi-Moran's I and spatial distribution characteristics for 120 city sites from 2019 to 2022 (a–b); spatial distribution characteristics and trend features of the spatial correlation coefficient of PM2.5-O3 for 120 city sites from 2019 to 2022 (c–d); global spatial distribution of NOx and VOCs (e–f).

Fig. 5

To further study the spatial association features between PM2.5 and O3 concentrations, we employed spatial correlation analysis methods to quantitatively reveal the correlation between PM2.5 and O3 concentrations (Fig. 5c). The results indicate that the correlation coefficient (Correlation) of PM2.5-O3 for the selected cities during the study period is all greater than zero, indicative of a positive correlation between PM2.5 and O3 concentrations. Specifically, in 58 cities located in eastern China, Japan, South Korea, the western US, and central Chile, the correlation coefficient of PM2.5 and O3 concentrations exceeded 0.6. In 41 cities in India, the UK, and the eastern US, this coefficient ranged between 0.4 and 0.6, such as in Delhi (0.584), Miami (0.443), and London (0.441). Additionally, fewer than 25 cities, predominantly in central and southern Europe, exhibited a Correlation below 0.4, with cities like Madrid and Zürich registering 0.3 and 0.202, respectively. Upon conducting multivariate regression analyses on PM2.5 and the correlation coefficient (R2 = 0.13128) (Fig. 5d), it was discerned that as PM2.5 concentration remained below 110 μg/m3 (STD: 1.717), the Correlation increased concomitant with the elevation of PM2.5 concentration. However, upon reaching a peak value of 0.623, the Correlation began to wane with increasing PM2.5 concentrations. These observations suggest that over 60% of cities worldwide exhibit a marked synergistic fluctuation between PM2.5 and O3 concentrations, underscoring the potential for coordinated management approaches in subsequent years. Significantly, through the analysis of the potential spatial associations between PM2.5 and O3 concentrations and their predominant precursors (NOx and VOCs) (Fig. 5e and f), it was ascertained that regions in China and India, characterized by elevated PM2.5 and O3 concentrations, also reported the highest emissions of NOx and VOCs, each exceeding an annual emission threshold of 1 million tons. Trailing them was the west coast of the US, with annual emissions of NOx and VOCs surpassing 500,000 tons. Such findings underscore the pivotal role that the cumulative emission effects of NOx and VOCs assume in shaping regional atmospheric pollution.

3.5 Potential for global coordinated management of PM2.5-O3 compound pollution

Based on the preceding sections, it can be conclusively deduced that PM2.5-O3 compound pollution manifests characteristics of overlapping pollution types, intertwined processes, and interactions across multiple scales. These distinct features serve as a robust scientific underpinning for the evaluation of potential coordinated management of PM2.5-O3 compound pollution, as depicted in Fig. 6a. In this segment, the potential was analyzed by examining the ratio of annual average concentration changes of PM2.5 and O3 between 2019 and 2022 across 120 global cities (Fig. 6b and c). Statistical results indicate that between 2019 and 2022, 63 cities achieved a coordinated decline in the annual average concentrations of PM2.5 and O3, representing 52.5% of the total cities studied. These cities registered an average decrease of 13.97% in PM2.5 and 19.18% in O3 concentrations. Geographically, a majority of these cities are situated in China (16), South Korea (8), and Japan (7). In contrast, 14 cities, representing 11.67% of the total, experienced a concurrent augmentation in the annual average concentrations of PM2.5 and O3 during the assessment period. Their average concentrations surged by 6.17% and 23.99%, respectively. Predominantly, these cities are located in the US (6) and India (2). Furthermore, a seesaw effect—characterized by a decrease in PM2.5 concentration concurrent with an increase in O3 concentration, or vice versa—was observed in the annual average concentrations of PM2.5 and O3 in 43 cities throughout the study's duration. These cities spanned diverse global locations, with the Asian region (20) exhibiting the most marked seesaw effect.Fig. 6 Mechanism features of PM2.5-O3 compound pollution (a), quadrant distribution of regional synergistic management potential (b), and spatial distribution (c). Specifically, (b) categorizes the variations in PM2.5 and O3 concentrations into the following four types based on their ratio: Synchronized increase of PM2.5 and O3 concentrations (First quadrant, both PM2.5 and O3 concentration ratios >1); increase in PM2.5 concentration with a decrease in O3 concentration (Second quadrant, PM2.5 concentration ratio >1 and O3 concentration ratio <1); synchronized decrease of PM2.5 and O3 concentrations (Third quadrant, both PM2.5 and O3 concentration ratios <1); decrease in PM2.5 concentration with an increase in O3 concentration (Fourth quadrant, PM2.5 concentration ratio <1 and O3 concentration ratio >1). The bar chart in (c) indicates the number of cities for each synergistic change type.

Fig. 6

4 Discussion

4.1 PM2.5 and O3 compound pollution and synergistic control of spatial heterogeneity

The cities with frequent PM2.5 and O3 compound pollution are mainly in the Asian region, especially in China and India, where the number of compound pollution episodes is higher than in other regions, and the exposure risk of PM2.5 and O3 compound pollution was at the ST + ST level during the study period. One of the most important reasons for this is the high-speed economic development that has led to significant anthropogenic emissions, particularly of VOCs and NOx, which are precursors that promote O3 production [[51], [52], [53]]. For instance, Beijing and the Pearl River Delta in China effectively controlled particulate matter pollution, represented by PM2.5, after the implementation of the Action Plan for the Prevention and Control of Air Pollution. However, compound pollution with high concentrations of PM2.5 and O3 has become the main problem nowadays. There are multiple reasons contributing to this change, but the fundamental reason is the higher emission intensity in these regions, while the meteorological conditions have been more favorable for O3 generation in recent years [[54], [55], [56], [57], [58]]. Furthermore, at the O3 chemistry level, this phenomenon occurs because the effects of precursors NOx and VOCs are not linear, and O3 concentrations may rebound as NOx emissions are reduced [[59], [60], [61]]. Similarly, the main reason for the sharp increase in O3 concentrations in India in recent years is closely related to the emission of O3 precursors. According to Chen et al. [62], reducing NOx emissions by 50% in India in 2018 resulted in a 10%–50% increase in O3. Conversely, a 50% reduction in VOC emissions can lead to a 60% reduction in O3. In 2019, India's annual average PM2.5 concentration was 91.7 μg/m3, which is still higher than the WHO IT-1 (35 μg/m3) [63]. This means that while India has not yet met the PM2.5 standard, O3 pollution has increased, resulting in more compound pollution events.

Compared to Asian cities, European and North American cities have relatively low levels of PM2.5 and O3 compound pollution. This pollution is mainly dominated by either PM2.5 or O3, and most of the population exposure risk status is DST + HS and ST + HS. The industrial structure of most cities in Europe and North America is dominated by tertiary and emerging industries, which are most notably characterized by low emissions and high returns. Compared to most Asian cities that are still reliant on secondary industries, Europe and North America have lower levels of industrial emissions, which means that PM2.5 concentrations are also significantly lower than in Asian cities [8,[64], [65], [66]]. Some North American cities have PM2.5 concentrations that reach the AQG levels set by the WHO. Unfortunately, increased emissions of ozone precursors and unfavorable meteorological factors have led to O3 pollution becoming a new challenge to atmospheric pollution in some North American and European cities [67,68]. Equally important, air pollution in the eastern US and southern Europe has been worsened by wildfires and cross-border pollutant transport, which has serious implications for the health of regional populations [69,70].

Analysis of the characterization of synergistic emissions of PM2.5 and O3 reveals that there is a significant positive spatial correlation between global PM2.5 and O3 concentrations. This relationship is mainly determined by the homology of PM2.5 and O3 concentrations. Previous studies have shown that PM2.5 precursors include SO2, NOx, NH3, VOCs, and primary PM2.5. Among these, NOx and VOCs are the most significant precursors in O3 chemistry [71,72]. At the same time, we find significant spatial consistency between the spatial and temporal patterns of global NOx and VOC emissions and the associated strengths of PM2.5 and O3 concentrations. In other words, regions with stronger spatial correlation between PM2.5 and O3 concentrations have higher emissions of NOx and VOCs, further suggesting that synergistic emission reduction of NOx and VOCs is key to achieving synergistic control of PM2.5 and O3, for example, pollutants such as VOCs, NOx, etc. can be reacted into other compounds by electrocatalysis and thermal catalysis [73,74]. From the characteristics of synergistic changes in PM2.5 and O3 concentrations, 52.5% of the selected cities showed synergistic decreases. These cities are mainly located in East and South Asia. Appropriate adjustment of industrial layout in the future will greatly reduce the trend of PM2.5 and O3 compound pollution in these cities and realize sustainable development.

4.2 PM2.5-O3 correlation analysis of key cities

Through correlation analysis, we found high correlation areas and seasonal characteristics of PM2.5 and O3 concentrations. In Asian cities, particularly in East and South Asia, the interactions are greater because the static weather conditions in winter caused by Siberian high pressure often reduce vertical mixing in the atmosphere, leading to a build-up of pollutants close to the ground [75,76]. In addition, high summer temperatures and intense solar radiation provide favorable conditions for O3 formation in the tropical and subtropical regions of Asia. However, in monsoon climates, increased rainfall may wash out atmospheric pollutants, including precursors of O3. Previous studies have identified wind speed and shortwave radiation as the primary factors contributing to the fluctuations in PM2.5 concentrations in the Beijing area [77,78]. For O3, temperature is the most important correlation factor that affects its change [79]. Furthermore, based on research into air pollution mechanisms, it has been discovered that there is a strong positive correlation between PM2.5 concentration and extinction coefficient. Additionally, carbon-containing aerosols, which are one of the main components of aerosols, can also absorb light [80,81]. Therefore, areas with high concentrations of PM2.5, meaning high levels of atmospheric aerosols, will have a greater impact on local light intensity and, consequently, on the local production of O3 [9,82]. Previous studies have indicated that PM2.5 and O3 concentrations in Delhi exhibit distinct seasonal trends, with differences between summer and winter. Therefore, it is recommended to analyze them separately on a seasonal basis. During winter, high concentrations of PM2.5 have a significant impact on incident solar radiation, which affects O3 concentrations. In summer, PM2.5 is diluted due to ventilation effects, but O3 concentrations increase due to atmospheric oxidation [[83], [84], [85]]. In contrast, while Tokyo and Seoul have significantly better environmental levels than most Chinese and Indian cities, they still fall short of meeting WHO standards. The chemical industry and combustion source sectors in Japan have a significant impact on local VOC emissions, which indirectly contribute to local PM2.5 and O3 pollution [86]. The establishment of a “Road Transport” department in Seoul has led to an increase in the number of registered vehicles and kilometers driven, resulting in increased local PM2.5 and O3 pollution [87].

Air pollution is generally less problematic in Europe than in Asia due to milder climatic conditions and better atmospheric dispersion. However, seasonal peaks in PM2.5 occur during the winter months due to increased heating demand. Moderate high temperatures in Europe promote the formation of O3. However, extensive environmental policies and emission controls have reduced O3 precursor emissions, aiding in the regulation of O3 levels. In general, the more moderate changes in PM2.5 and O3 concentrations in the European region and the observed positive correlation between PM2.5 and O3 concentrations may be due to the decisive role of secondary photochemical processes in the formation of secondary particulate matter, especially in the absence of anthropogenic sources [88]. Previous studies have shown that the most significant sources of O3 and PM2.5 in London, Berlin, and Rome are boundary conditions, transport, biological emissions, and heating systems in winter [89,90]. For London, the most significant non-road transport emissions are likely from shipping activities in the English Channel [91].

In North America, industrial activities and automobile use are significant sources of PM2.5, particularly in urban and industrially dense areas. However, environmental regulations and policies, such as the Clean Air Act, help to control PM2.5 emissions. In addition, the transportation of pollutants across borders and high local ambient temperatures may exacerbate environmental pollution [92]. Environmental studies have reported that the composition of PM2.5 varies in areas of different dimensions due to factors such as geographical and climatic conditions, socio-economic status, and local industrial emissions [93,94]. These differences in PM2.5 composition may affect the interaction between PM2.5 and O3 [95]. Although Los Angeles is considered to be one of the most polluted areas in the US, its pollution levels are still lower than those of many cities in Asia [96]. Stricter emission standards have effectively controlled VOCs and NOx emissions in Los Angeles by reducing motor vehicle emissions, including petrol evaporation [[97], [98], [99]]. Mexico City has successfully reduced primary pollutant emissions over the past few decades. However, it still faces challenges in reducing secondary pollutant emissions, such as PM2.5 [100]. Previous studies have shown that the main reason for high local levels of O3 and PM2.5 during the outbreak closure was air quality exchange through valley passages. Domestic heating is a major contributor to local PM2.5 pollution, and increased solar radiation and household activities also contribute to O3 pollution [101].

Previous studies have shown that reducing emissions from wood heaters and power stations in the Sydney area can extend the life expectancy of the local population and have a positive impact on the local economy [102]. Sydney has experienced mild temperatures and meteorological conditions throughout the year. However, due to the intensification of the heat island cycle and the enhancement of urban roughness, there has been a heightened correlation between temperature and wind speed on local O3 concentrations. Additionally, there has been a high frequency of O3 and PM2.5 pollution extremes that are strongly correlated with the worsening of local hill fire events [103].

4.3 Policy and recommendations

In this study, we reveal the dynamic change characteristics of global PM2.5 and O3 compound pollution, exposure risk level, spatial clustering characteristics, and synergistic change rules, and propose the following policies and recommendations for global PM2.5 and O3 pollution treatment.

(1) As implications for future air pollution mitigation strategies, developed cities are advised to prioritize preventive pollution measures, ensuring the curtailment of high pollution incidents potentially triggered by unfavorable meteorological conditions or human-induced emissions. On the contrary, for cities in developing countries, like Delhi in India and the Beijing-Tianjin-Hebei region in China, it's imperative to draft strict air pollution control policies while placing emphasis on regional economic growth. Simultaneously, there should be proactive promotion of the green transformation of traditional industries, aiming to minimize industrial emissions, residential emissions, and transport-related emissions resulting from the growth of conventional industries.

(2) To meet the stipulated benchmarks for PM2.5 and O3, regions severely affected by compound pollution (such as China and India) should focus on strengthening end-point control measures in the industrial and transportation sectors, emphasize adjusting the industrial structure and substituting sources for processes like petrochemicals, industrial painting, and wood furniture, and optimize the energy structure of motor vehicles.

(3) Broadly speaking, in order to address the inequalities in air pollution exposure and associated risks, governmental departments across countries should actively explore spatial variations of air pollution exposure inequalities and their potential determinants under the 2030 United Nations Sustainable Development Goals. Economic development, income levels, industrial adjustments, education standards, and racial considerations should be incorporated into regional and national environmental health plans. It is vital to synchronize regional air pollution interventions with enhancements in healthcare. Addressing the challenges of unequal air pollution exposure is integral to forging a sustainable society.

(4) For regions achieving a coordinated decrease in PM2.5 and O3, local governmental departments should further refine the implementation plans for synergistic management of air pollutants and establish robust mechanisms to prevent a resurgence of PM2.5-O3 compound pollution events. For areas witnessing synchronized increases in PM2.5 and O3 concentrations, we recommend initially constructing high temporal and spatial resolution regional emission inventories, understanding the pollution mechanisms and potential sources of PM2.5 and O3 from atmospheric chemistry and regional transmission perspectives, and formulating targeted pollution reduction policies based on these findings.

Overall, such initiatives are crucial for promoting both high-quality ecological protection and high-quality economic development collaboratively.

4.4 Research limitations and prospects

This study has some limitations. It focuses on a short-term period from 2019 to 2022 to analyze the trends of PM2.5 and O3 concentrations. Typically, a 10-year time series is considered sufficient to assess short-term changes in air pollution levels, attributing observed fluctuations predominantly to changes in emissions rather than meteorological variations. The decision to focus on a shorter timeframe in this study is primarily driven by the emergent nature of PM2.5-O3 compound pollution challenges and the urgency in addressing them. However, this approach does bear limitations. The relatively brief period may not fully encapsulate the broader impacts of long-term meteorological patterns and emission change trends on air quality. As such, the findings presented herein should be interpreted with caution, acknowledging the potential for meteorological variations to influence the observed pollution levels over this period. To mitigate these limitations, this study incorporates a review of existing literature and attempts to contextualize the findings within the broader scope of ongoing research in the field of air quality and pollution control. By highlighting these limitations, the study aims to provide a transparent and critical assessment of its findings, contributing to the ongoing discourse on effective strategies for PM2.5 and O3 pollution management and encouraging further research that addresses these emerging challenges with a longer temporal analysis.

Furthermore, we will expand the temporal scope of our analysis by incorporating longer time series data. This will enable us to more accurately identify the underlying trends in PM2.5 and O3 pollution. Future research will seek to disentangle the respective contributions of changes in emissions and meteorological forcing over longer periods, thereby deepening our understanding of the dynamics governing air quality. Exploring the effectiveness of pollution control strategies across different meteorological and geographical contexts is crucial for developing more nuanced and effective approaches to air pollution management.

5 Conclusions

During the study period, globally, 30% and 50% of cities were exposed to high PM2.5 (>70 μg/m3) and O3 (>100 μg/m3) concentrations respectively. Elevated concentrations of PM2.5 and O3 were predominantly observed in cities of developing nations, notably China and India. Furthermore, it was noted that over 80% of global cities encountered peak PM2.5 values during winter, whereas peak O3 values were predominantly identified during summer months. Nearly 50% of cities worldwide were affected by PM2.5-O3 compound pollution. Countries like China, South Korea, Japan, and India suffered the most severe impacts from PM2.5-O3 compound pollution. With the exacerbation of O3 pollution from 2019 to 2022, it was observed that 44.2% of cities globally transitioned from being primarily affected by PM2.5 or other contaminants to a predominant influence of PM2.5-O3 compound pollution. Over 40 cities were identified as areas of high exposure risk to this compound pollution, with exposure risk types classified as Stabilization + Stabilization (29), Stabilization + High Risk (10), and High Risk + High Risk (4). From the perspective of regional economic levels, there is an inequality in exposure risk due to PM2.5-O3 compound pollution. Specifically, cities in developing nations were found to be at higher risk compared to their counterparts in developed countries. Between 2019 and 2022, 52.5% of cities worldwide achieved a coordinated decline in the annual average concentrations of PM2.5 and O3. These cities witnessed an average drop of 13.97% for PM2.5 and 19.18% for O3 concentrations. Notably, there was a significant spatial clustering characteristic in the concentrations of PM2.5 and O3 in these cities, accompanied by a positive spatial correlation. Additionally, nearly 12% of cities saw a synchronized increase in the annual average concentrations of PM2.5 and O3.

CRediT authorship contribution statement

C.H.: supervision, conceptualization, writing–original draft, writing–review editing; J.H.L.: visualization, writing–original draft; Y.Q.Z.: data curation, resources; J.W.Z.: software; L.Z.: methodology; Y.F.W.: Investigation; L.L., S.P.: supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.eehl.2024.04.004.
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References

1 Sicard P. Lesne O. Alexandre N. Mangin A. Collomp R. Air quality trends and potential health effects – development of an aggregate risk index Atmos. Environ. 45 5 2011 1145 1153 10.1016/j.atmosenv.2010.12.052
2 Fleming Z.L. Doherty R.M. von Schneidemesser E. Malley C.S. Cooper O.R. Pinto J.P. Tropospheric Ozone Assessment Report: present-day ozone distribution and trends relevant to human health Elementa: Sci Anthropocene 6 2018 12 10.1525/elementa.273
3 Unger N. Zheng Y. Yue X. Harper K.L. Mitigation of ozone damage to the world's land ecosystems by source sector Nat. Clim. Change 10 2 2020 134 137 10.1038/s41558-019-0678-3
4 Emberson L. Effects of ozone on agriculture, forests and grasslands Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 378 2020 20190327 10.1098/rsta.2019.0327
5 Zhou L. Chen X. Tian X. The impact of fine particulate matter (PM2.5) on China's agricultural production from 2001 to 2010 J. Clean. Prod. 178 2018 133 141 10.1016/j.jclepro.2017.12.204
6 Kinney P.L. Interactions of climate change, air pollution, and human health Current Environmental Health Reports 5 1 2018 179 186 10.1007/s40572-018-0188-x 29417451
7 Shindell D. Faluvegi G. Lacis A. Hansen J. Ruedy R. Aguilar E. Role of tropospheric ozone increases in 20th-century climate change J. Geophys. Res. Atmos. 111 D8 2006 D08302 10.1029/2005JD006348
8 McDuffie E.E. Martin R.V. Spadaro J.V. Burnett R. Smith S.J. O'Rourke P. Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales Nat. Commun. 12 1 2021 3594 10.1038/s41467-021-23853-y 34127654
9 Atkinson R. Atmospheric chemistry of VOCs and NOx Atmos. Environ. 34 12 2000 2063 2101 10.1016/S1352-2310(99)00460-4
10 Li C. van Donkelaar A. Hammer M.S. McDuffie E.E. Burnett R.T. Spadaro J.V. Reversal of trends in global fine particulate matter air pollution Nat. Commun. 14 1 2023 5349 10.1038/s41467-023-41086-z 37660164
11 Cooper O.R. Schultz M.G. Schröder S. Chang K.-L. Gaudel A. Benítez G.C. Multi-decadal surface ozone trends at globally distributed remote locations Elementa: Science of the Anthropocene 8 2020 23 10.1525/elementa.420
12 Murray C.J.L. Aravkin A.Y. Zheng P. Abbafati C. Abbas K.M. Abbasi-Kangevari M. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 Lancet 396 10258 2020 1223 1249 10.1016/S0140-6736(20)30752-2 33069327
13 Zhu T. Air pollution in China: scientific challenges and policy implications Natl. Sci. Rev. 4 6 2018 800 10.1093/nsr/nwx151
14 Yue H. Huang Q. He C. Zhang X. Fang Z. Spatiotemporal patterns of global air pollution: a multi-scale landscape analysis based on dust and sea-salt removed PM2.5 data J. Clean. Prod. 252 2020 119887 10.1016/j.jclepro.2019.119887
15 Bai Y. Zhao T. Hu W. Zhou Y. Xiong J. Wang Y. Meteorological mechanism of regional PM2.5 transport building a receptor region for heavy air pollution over Central China Sci. Total Environ. 808 2022 151951 10.1016/j.scitotenv.2021.151951
16 Wang S. Yu R. Shen H. Wang S. Hu Q. Cui J. Chemical characteristics, sources, and formation mechanisms of PM2.5 before and during the Spring Festival in a coastal city in Southeast China Environ. Pollut. 251 2019 442 452 10.1016/j.envpol.2019.04.050 31103004
17 Zhao Y. Li Y. Kumar A. Ying Q. Vandenberghe F. Kleeman M.J. Separately resolving NOx and VOC contributions to ozone formation Atmos. Environ. 285 2022 119224 10.1016/j.atmosenv.2022.119224
18 Xu T. Zhang C. Liu C. Hu Q. Variability of PM2.5 and O3 concentrations and their driving forces over Chinese megacities during 2018-2020 J. Environ. Sci. 124 2023 1 10 10.1016/j.jes.2021.10.014
19 Ojha N. Soni M. Kumar M. Gunthe S.S. Chen Y. Ansari T.U. Mechanisms and pathways for coordinated control of fine particulate matter and ozone Current Pollution Reports 8 4 2022 594 604 10.1007/s40726-022-00229-4 35991936
20 Wang Y. Hu J. Zhu J. Li J. Qin M. Liao H. Health Burden and economic impacts attributed to PM2.5 and O3 in China from 2010 to 2050 under different representative concentration pathway scenarios Resour. Conserv. Recycl. 173 2021 105731 10.1016/j.resconrec.2021.105731
21 Im U. Bauer S.E. Frohn L.M. Geels C. Tsigaridis K. Brandt J. Present-day and future PM2.5 and O3-related global and regional premature mortality in the EVAv6.0 health impact assessment model Environ. Res. 216 2023 114702 10.1016/j.envres.2022.114702
22 Zhao C. Pan J. Zhang L. Spatio-temporal patterns of global population exposure risk of PM2.5 from 2000–2016 Sustainability 13 2021 7427 10.3390/su13137427
23 Lim C.-H. Ryu J. Choi Y. Jeon S.W. Lee W.-K. Understanding global PM2.5 concentrations and their drivers in recent decades (1998–2016) Environ. Int. 144 2020 106011 10.1016/j.envint.2020.106011
24 Zhou Y. Duan W. Chen Y. Yi J. Wang B. Di Y. Exposure risk of global surface O3 during the Boreal spring season Exposure and Health 14 2 2022 431 446 10.1007/s12403-022-00463-7 35128147
25 Zhang Y. West J.J. Emmons L.K. Flemming J. Jonson J.E. Lund M.T. Contributions of world regions to the global tropospheric ozone burden change from 1980 to 2010 Geophys. Res. Lett. 48 1 2021 e2020GL089184 10.1029/2020GL089184
26 Lyu X. Li K. Guo H. Morawska L. Zhou B. Zeren Y. A synergistic ozone-climate control to address emerging ozone pollution challenges One Earth 6 8 2023 964 977 10.1016/j.oneear.2023.07.004
27 He C. Hong S. Zhang L. Mu H. Xin A. Zhou Y. Global, continental, and national variation in PM2.5, O3, and NO2 concentrations during the early 2020 COVID-19 lockdown Atmos. Pollut. Res. 12 3 2021 136 145 10.1016/j.apr.2021.02.002 33584105
28 Zhao S. Yin D. Yu Y. Kang S. Qin D. Dong L. PM2.5 and O3 pollution during 2015–2019 over 367 Chinese cities: spatiotemporal variations, meteorological and topographical impacts Environ. Pollut. 264 2020 114694 10.1016/j.envpol.2020.114694
29 Sicard P. Khaniabadi Y.O. Leca S. De Marco A. Relationships between ozone and particles during air pollution episodes in arid continental climate Atmos. Pollut. Res. 14 8 2023 101838 10.1016/j.apr.2023.101838
30 Zhang Y. West J.J. Mathur R. Xing J. Hogrefe C. Roselle S.J. Long-term trends in the ambient PM2.5- and O3-related mortality burdens in the United States under emission reductions from 1990 to 2010 Atmos. Chem. Phys. 18 20 2018 15003 15016 https://acp.copernicus.org/articles/18/15003/2018/ 30930942
31 de Hoogh K. Chen J. Gulliver J. Hoffmann B. Hertel O. Ketzel M. Spatial PM2.5, NO2, O3 and BC models for western europe–evaluation of spatiotemporal stability Environ. Int. 120 2018 81 92 10.1016/j.envint.2018.07.036 30075373
32 Sicard P. Agathokleous E. De Marco A. Paoletti E. Calatayud V. Urban population exposure to air pollution in Europe over the last decades Environ. Sci. Eur. 33 1 2021 28 10.1186/s12302-020-00450-2 33717794
33 Li K. Jacob D.J. Liao H. Zhu J. Shah V. Shen L. A two-pollutant strategy for improving ozone and particulate air quality in China Nat. Geosci. 12 11 2019 906 910 10.1038/s41561-019-0464-x
34 Qu Y. Wang T. Yuan C. Wu H. Gao L. Huang C. The underlying mechanisms of PM2.5 and O3 synergistic pollution in East China: photochemical and heterogeneous interactions Sci. Total Environ. 873 2023 162434 10.1016/j.scitotenv.2023.162434
35 Sicard P. Agathokleous E. Anenberg S.C. De Marco A. Paoletti E. Calatayud V. Trends in urban air pollution over the last two decades: a global perspective Sci. Total Environ. 858 2023 160064 10.1016/j.scitotenv.2022.160064
36 Agathokleous E. Feng Z. Sicard P. Surge in nocturnal ozone pollution Science 382 2023 1131 10.1126/science.adm7628
37 Wang F. Wang W. Wang Z. Zhang Z. Feng Y. Russell A.G. Drivers of PM(2.5)-O(3) co-pollution: from the perspective of reactive nitrogen conversion pathways in atmospheric nitrogen cycling Sci. Bull. 67 18 2022 1833 1836 10.1016/j.scib.2022.08.016
38 Dai H. An J. Huang C. Wang H. Zhou M. Qiao L. Roadmap of coordinated control of PM2.5 and ozone in Yangtze River Delta Chin. Sci. Bull. 67 18 2022 2100 2112 10.1360/TB-2021-0774
39 Faridi S. Shamsipour M. Krzyzanowski M. Künzli N. Amini H. Azimi F. Long-term trends and health impact of PM2.5 and O3 in Tehran, Iran, 2006–2015 Environ. Int. 114 2018 37 49 10.1016/j.envint.2018.02.026 29477017
40 Rentschler J. Leonova N. Global air pollution exposure and poverty Nat. Commun. 14 1 2023 4432 10.1038/s41467-023-39797-4 37481598
41 Jbaily A. Zhou X. Liu J. Lee T.-H. Kamareddine L. Verguet S. Air pollution exposure disparities across US population and income groups Nature 601 7892 2022 228 233 10.1038/s41586-021-04190-y 35022594
42 Juginović A. Vuković M. Aranza I. Biloš V. Health impacts of air pollution exposure from 1990 to 2019 in 43 European countries Sci. Rep. 11 1 2021 22516 10.1038/s41598-021-01802-5
43 Lelieveld J. Evans J.S. Fnais M. Giannadaki D. Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale Nature 525 7569 2015 367 371 10.1038/nature15371 26381985
44 World Health Organization (WHO) WHO Global Air Quality Guidelines: ParticulateMatter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide 2021 World Health Organization https://www.who.int/publications/i/item/9789240034228
45 Guan W.-J. Zheng X.-Y. Chung K.F. Zhong N.-S. Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action Lancet 388 10054 2016 1939 1951 10.1016/S0140-6736(16)31597-5 27751401
46 C. Guerreiro, F. de Leeuw, V. Foltescu, Johannes, J. Aardenne, A. Luekewille, et al., Air quality in Europe—2012 report. European Environment Agency, 2012. https://www.researchgate.net/publication/260600055_Air_Quality_in_Europe_2012_report.
47 Mann H.B. Nonparametric tests against trend Econometrica 13 1945 245 http://www.jstor.org/stable/1907187
48 He C. Niu X. Ye Z. Wu Q. Liu L. Zhao Y. Black carbon pollution in China from 2001 to 2019: patterns, trends, and drivers Environ. Pollut. 324 2023 121381 10.1016/j.envpol.2023.121381
49 Strak M. Weinmayr G. Rodopoulou S. Chen J. de Hoogh K. Andersen Z.J. Long term exposure to low level air pollution and mortality in eight European cohorts within the ELAPSE project: pooled analysis Bmj 374 2021 n1904 10.1136/bmj.n1904 34470785
50 Lu B. Hu Y. Yang D. Liu Y. Liao L. Yin Z. GWmodelS: a software for geographically weighted models SoftwareX 21 2023 101291 10.1016/j.softx.2022.101291
51 Ren J. Guo F. Xie S. Diagnosing ozone–NOx–VOC sensitivity and revealing causes of ozone increases in China based on 2013–2021 satellite retrievals Atmos. Chem. Phys. 22 22 2022 15035 15047 10.5194/acp-22-15035-2022
52 Wang Y. Yang X. Wu K. Mei H. De Smedt I. Wang S. Long-term trends of ozone and precursors from 2013 to 2020 in a megacity (Chengdu), China: evidence of changing emissions and chemistry Atmos. Res. 278 2022 106309 10.1016/j.atmosres.2022.106309
53 Zhu L. Liu M. Song J. Spatiotemporal variations and influent factors of tropospheric ozone concentration over China based on OMI data Atmosphere 13 2 2022 253 10.3390/atmos13020253
54 Huo M. Yamashita K. Chen F. Sato K. Spatial-temporal variation in health impact attributable to PM2.5 and ozone pollution in the Beijing metropolitan region of China Atmosphere 13 11 2022 1813 10.3390/atmos13111813
55 Zhang L. Zhao N. Zhang W. Wilson J.P. Changes in long-term PM2.5 pollution in the urban and suburban areas of China's three largest urban agglomerations from 2000 to 2020 Rem. Sens. 14 7 2022 1716 10.3390/rs14071716
56 Ma Y. Zhang Y. Wang W. Qin P. Li H. Jiao H. Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China Sci. Rep. 13 1 2023 17717 10.1038/s41598-023-45062-x
57 Zhu C. Zhu C. Qiu M. Gai Y. Li R. Li L. Health burden and driving force changes due to exposure to PM2.5 and O3 from 2014 to 2060 in a typical industrial province, China Atmosphere 14 11 2023 1672 10.3390/atmos14111672
58 Zhang X. Xiao X. Wang F. Brasseur G. Chen S. Wang J. Observed sensitivities of PM2.5 and O3 extremes to meteorological conditions in China and implications for the future Environ. Int. 168 2022 107428 10.1016/j.envint.2022.107428
59 Liu T. Wang X. Hu J. Wang Q. An J. Gong K. Driving forces of changes in air quality during the COVID-19 lockdown period in the Yangtze River Delta Region, China Environ. Sci. Technol. Lett. 7 11 2020 779 786 10.1021/acs.estlett.0c00511 37566315
60 Wang Y. Zhu S. Ma J. Shen J. Wang P. Wang P. Enhanced atmospheric oxidation capacity and associated ozone increases during COVID-19 lockdown in the Yangtze River Delta Sci. Total Environ. 768 2021 144796 10.1016/j.scitotenv.2020.144796
61 Le T. Wang Y. Liu L. Yang J. Yung Y.L. Li G. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China Science 369 6504 2020 702 706 10.1126/science.abb7431 32554754
62 Chen Y. Beig G. Archer-Nicholls S. Drysdale W. Acton W.J.F. Lowe D. Avoiding high ozone pollution in Delhi, India Faraday Discuss 226 0 2021 502 514 10.1039/D0FD00079E 33244555
63 Pandey A. Brauer M. Cropper M.L. Balakrishnan K. Mathur P. Dey S. Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019 Lancet Planet. Health 5 1 2021 e25 e38 10.1016/S2542-5196(20)30298-9 33357500
64 Muhammad S. Pan Y. Agha M.H. Umar M. Chen S. Industrial structure, energy intensity and environmental efficiency across developed and developing economies: the intermediary role of primary, secondary and tertiary industry Energy 247 2022 123576 10.1016/j.energy.2022.123576
65 Sahoo M. Sethi N. The dynamic impact of urbanization, structural transformation, and technological innovation on ecological footprint and PM2.5: evidence from newly industrialized countries Environ. Dev. Sustain. 24 3 2022 4244 4277 10.1007/s10668-021-01614-7
66 Chen J. Zhou C. Wang S. Li S. Impacts of energy consumption structure, energy intensity, economic growth, urbanization on PM2.5 concentrations in countries globally Appl. Energy 230 2018 94 105 10.1016/j.apenergy.2018.08.089
67 Zhu R. Tang Z. Chen X. Liu X. Jiang Z. Rapid O3 assimilations – Part 2: tropospheric O3 changes accompanied by declining NOx emissions in the USA and Europe in 2005–2020 Atmos. Chem. Phys. 23 17 2023 9745 9763 10.5194/acp-23-9745-2023
68 Schnell J.L. Prather M.J. Josse B. Naik V. Horowitz L.W. Zeng G. Effect of climate change on surface ozone over North America, Europe, and East Asia Geophys. Res. Lett. 43 7 2016 3509 3518 10.1002/2016GL068060 32818004
69 Nawaz M.O. Henze D.K. Anenberg S.C. Ahn D.Y. Goldberg D.L. Tessum C.W. Sources of air pollution-related health impacts and benefits of radially applied transportation policies in 14 US cities Front. Sustain. Cities 5 2023 1102493 10.3389/frsc.2023.1102493
70 Querol X. Pérez N. Reche C. Ealo M. Ripoll A. Tur J. African dust and air quality over Spain: is it only dust that matters? Sci. Total Environ. 686 2019 737 752 10.1016/j.scitotenv.2019.05.349 31195282
71 Wang Y. Gao W. Wang S. Song T. Gong Z. Ji D. Contrasting trends of PM2.5 and surface-ozone concentrations in China from 2013 to 2017 Natl. Sci. Rev. 7 8 2020 1331 1339 10.1093/nsr/nwaa032 34692161
72 Lee C.J. Martin R.V. Henze D.K. Brauer M. Cohen A. Donkelaar A.V. Response of global particulate-matter-related mortality to changes in local precursor emissions Environ. Sci. Technol. 49 7 2015 4335 4344 10.1021/acs.est.5b00873 25730303
73 Zhang H. Luo T. Chen Y. Liu K. Li H. Pensa E. Highly efficient decomposition of perfluorocarbons for over 1000 hours via active site regeneration Angew. Chem. Int. Ed. 62 2023 e202305651 10.1002/anie.202305651
74 Chen Y. Qu W. Luo T. Zhang H. Fu J. Li H. Promoting C–F bond activation via proton donor for CF4 decomposition Proc. Natl. Acad. Sci. USA 120 2023 e2312480120 10.1073/pnas.2312480120
75 Zhao S. Feng T. Tie X. Long X. Li G. Cao J. Impact of climate change on siberian high and wintertime air pollution in China in past two decades Earth's Future 6 2018 118 133 10.1002/2017EF000682
76 Sun X. Wang K. Li B. Zong Z. Shi X. Ma L. Exploring the cause of PM2.5 pollution episodes in a cold metropolis in China J. Clean. Prod. 256 2020 120275 10.1016/j.jclepro.2020.120275
77 Dong J. Liu P. Song H. Yang D. Yang J. Song G. Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the “2+26” cities of northern China Environ. Pollut. 315 2022 120392 10.1016/j.envpol.2022.120392
78 Deng C. Qin C. Li Z. Li K. Spatiotemporal variations of PM2.5 pollution and its dynamic relationships with meteorological conditions in Beijing-Tianjin-Hebei region Chemosphere 301 2022 134640 10.1016/j.chemosphere.2022.134640
79 Gong S. Zhang L. Liu C. Lu S. Pan W. Zhang Y. Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 2020 2. Key weather elements and emissions Sci. Total Environ. 824 2022 153847 10.1016/j.scitotenv.2022.153847
80 Lan Z. Zhang B. Huang X. Zhu Q. Yuan J. Zeng L. Source apportionment of PM2.5 light extinction in an urban atmosphere in China J. Environ. Sci. 63 2018 277 284 10.1016/j.jes.2017.07.016
81 Li C. Chen P. Kang S. Yan F. Hu Z. Qu B. Concentrations and light absorption characteristics of carbonaceous aerosol in PM2.5 and PM10 of Lhasa city, the Tibetan Plateau Atmos. Environ. 127 2016 340 346 10.1016/j.atmosenv.2015.12.059
82 Chan C.Y. Chan L.Y. Effect of meteorology and air pollutant transport on ozone episodes at a subtropical coastal Asian city, Hong Kong J. Geophys. Res. Atmos. 105 2000 20707 20724 10.1029/2000JD900140
83 Yadav R.K. Gadhavi H. Arora A. Mohbey K.K. Kumar S. Lal S. Relation between PM2.5 and O3 over different urban environmental regimes in India Urban Science 7 2023 9 10.3390/urbansci7010009
84 Sharma A. Mandal T.K. Sharma S.K. Shukla D.K. Singh S. Relationships of surface ozone with its precursors, particulate matter and meteorology over Delhi J. Atmos. Chem. 74 2017 451 474 10.1007/s10874-016-9351-7
85 Bran S.H. Srivastava R. Investigation of PM2.5 mass concentration over India using a regional climate model Environ. Pollut. 224 2017 484 493 10.1016/j.envpol.2017.02.030 28237305
86 Hata H. Inoue K. Yoshikado H. Genchi Y. Tsunemi K. Impact of introducing net-zero carbon strategies on tropospheric ozone (O3) and fine particulate matter (PM2.5) concentrations in Japanese region in 2050 Sci. Total Environ. 891 2023 164442 10.1016/j.scitotenv.2023.164442
87 Yeo M.J. Kim Y.P. Long-term trends and affecting factors in the concentrations of criteria air pollutants in South Korea J. Environ. Manag. 317 2022 115458 10.1016/j.jenvman.2022.115458
88 Kassomenos P.A. Vardoulakis S. Chaloulakou A. Paschalidou A.K. Grivas G. Borge R. Study of PM10 and PM2.5 levels in three European cities: analysis of intra and inter urban variations Atmos. Environ. 87 2014 153 163 10.1016/j.atmosenv.2014.01.004
89 Karamchandani P. Long Y. Pirovano G. Balzarini A. Yarwood G. Source-sector contributions to European ozone and fine PM in 2010 using AQMEII modeling data Atmos. Chem. Phys. 17 2017 5643 5664 10.5194/acp-17-5643-2017
90 Fanizza C. De Berardis B. Ietto F. Soggiu M.E. Schirò R. Inglessis M. Analysis of major pollutants and physico-chemical characteristics of PM2.5 at an urban site in Rome Sci. Total Environ. 616–617 2018 1457 1468 10.1016/j.scitotenv.2017.10.168
91 Kuenen J.J.P. Visschedijk A.J.H. Jozwicka M. Denier van der Gon H.A.C. TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling Atmos. Chem. Phys. 14 2014 10963 10976 10.5194/acp-14-10963-2014
92 Diao B. Ding L. Cheng J. Fang X. Impact of transboundary PM2.5 pollution on health risks and economic compensation in China J. Clean. Prod. 326 2021 129312 10.1016/j.jclepro.2021.129312
93 Baxter L.K. Duvall R.M. Sacks J. Examining the effects of air pollution composition on within region differences in PM2.5 mortality risk estimates J. Expo. Sci. Environ. Epidemiol. 23 2013 457 465 10.1038/jes.2012.114 23250195
94 Wang Y.-S. Chang L.-C. Chang F.-J. Explore regional PM2.5 features and compositions causing health effects in taiwan Environ Manage 67 2021 176 191 10.1007/s00267-020-01391-5 33201258
95 Masselot P. Sera F. Schneider R. Kan H. Lavigne E. Tobias A. Differential mortality risks associated with PM2.5 components: a multi-country, multi-city study Epidemiol. 33 2 2021 167 175 10.1097/EDE.0000000000001455
96 Liao K.-J. Tagaris E. Napelenok S.L. Manomaiphiboon K. Woo J.-H. Amar P. Current and future linked responses of ozone and PM2.5 to emission controls Environ. Sci. Technol. 42 2008 4670 4675 10.1021/es7028685 18677989
97 Pollack I.B. Ryerson T.B. Trainer M. Neuman J.A. Roberts J.M. Parrish D.D. Trends in ozone, its precursors, and related secondary oxidation products in Los Angeles, California: a synthesis of measurements from 1960 to 2010 J. Geophys. Res. Atmos. 118 2013 5893 5911 10.1002/jgrd.50472
98 Warneke C. de Gouw J.A. Holloway J.S. Peischl J. Ryerson T.B. Atlas E. Multiyear trends in volatile organic compounds in Los Angeles, California: five decades of decreasing emissions J. Geophys. Res. 117 2012 D00V17 10.1029/2012JD017899
99 Stanimirova I. Rich D.Q. Russell A.G. Hopke P.K. Common and distinct pollution sources identified from ambient PM2.5 concentrations in two sites of Los Angeles Basin from 2005 to 2019 Environ. Pollut. 340 2024 122817 10.1016/j.envpol.2023.122817
100 Gutiérrez-Avila I. Riojas-Rodriguez H. Colicino E. Rush J. Tamayo-Ortiz M. Borja-Aburto V. Short-term exposure to PM2.5 and 1.5 million deaths: a time-stratified case-crossover analysis in the Mexico City Metropolitan Area Environ. Health 22 2023 70 10.1186/s12940-023-01024-4 37848890
101 Sakthi J.S. Jonathan M.P. Gnanachandrasamy G. Morales-García S.S. Rodriguez-Espinosa P.F. Escobedo-Urias D.C. Atmospheric changes and ozone increase in Mexico City during 2020: recommended remedial measures Li P. Elumalai V. Recent Advances in Environmental Sustainability 2023 Springer International Publishing Cham 209 236 10.1007/978-3-031-34783-2_11
102 Broome R.A. Powell J. Cope M.E. Morgan G.G. The mortality effect of PM2.5 sources in the greater metropolitan region of Sydney, Australia Environ. Int. 137 2020 105429 10.1016/j.envint.2019.105429
103 Ulpiani G. Ranzi G. Santamouris M. Local synergies and antagonisms between meteorological factors and air pollution: a 15-year comprehensive study in the Sydney region Sci. Total Environ. 788 2021 147783 10.1016/j.scitotenv.2021.147783
