# Air pollution and performances in outdoor sports: a systematic review and meta-analysis of short-terms associations

Paquito Bernard, Valérie Bougault, Thomas Deshayes, Pauline Caille, Louis Hognon, Quentin Martinet, Johan Caudroit, Adewale Oyeyemi, Ahmed Jérôme Romain, Tarik Benmarhnia, et al.

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## To cite this version:

Paquito Bernard, Valérie Bougault, Thomas Deshayes, Pauline Caille, Louis Hognon, et al. Air pollution and performances in outdoor sports: a systematic review and meta-analysis of short-terms associations. 2025. hal-04947984

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1 Air pollution and performances in outdoor sports: a systematic review and meta-
2 analysis of short-terms associations
3

3

4 Paquito Bernardª, Valérie Bougault b,c, Thomas Deshayes d,e, Pauline Caillef, Louis Hognon g, Quentin

5 Martinet h, Johan Caudroit i, Adewale Oyeyemi j, Ahmed-Jérôme Romain e,k, Tarik Benmarhnia a,l,

6 Guillaume Chevance a,m

7

8 a Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) -

9 UMR_S, 1085, Rennes, France

10 b Université Côte d'Azur, LAMHESS, France

11 c University of British Columbia, Vancouver, Canada

12 d Montreal Heart Institute, Québec, Canada

13 e École de kinésiologie et des sciences de l'activité physique, Université de Montréal, Québec, Canada

14 f Univ Rennes, UR2, VIPS² (Valeurs, Innovations, Politiques, Socialisations et Sports) – UR3646, F-
15 35000 Rennes, France.

16 g Climate Fresk, research department, Paris, France

17 h Faculty of Sport Sciences, Performance, Health, Metrology, Society Laboratory, Reims, France

18 i Laboratoire sur les vulnérabilités et l'innovation dans le Sport, Université Claude Bernard Lyon 1,
19 Villeurbanne, France

20 j College of Health Solutions, Arizona State University, Phoenix, AZ, USA

21 k Research Centre of the University Institute of Mental Health of Montreal, Montreal, QC, Canada.

22 l Scripps Institution of Oceanography, University of California, San Diego, 8885 Biological, Grade, La
23 Jolla, CA 92037

24 m ISGlobal, Barcelona, Spain

25

26 *Corresponding author: Paquito Bernard. Univ Rennes, Inserm, IRSET (Institut de Recherche en Santé, Environnement et Travail) - UMR_S, Rennes, France. paquito.bernard@inserm.fr

28

29 Keywords

30 Air pollution, marathon, football, ozone, endurance, physical activity, meta-analysis

31
32 **Highlight**

33 • Evidence suggests no significant link between AQI, PM<sub>2.5</sub>, NO<sub>2</sub> exposure, and performance

34 • Higher O<sub>3</sub> concentration could impair sport performances in real-life context

35 • No firm conclusion about the short-term CO, PM<sub>10</sub>, or SO<sub>2</sub> levels performances.

36

37 **Funding:** Thomas A. Deshayes, Ahmed-Jérôme are financially supported by the Fonds de Recherche

38 du Québec—Santé (FRQS).

39

40
41 Abstract

42 Laboratory studies suggest that high concentrations of particles matter (diameter<2.5 mm [PM<sub>2.5</sub>]) and
43 ozone (O<sub>3</sub>) may reduce sport performances at short term. However, a systematic review of literature
44 examining whether air pollution has an effect on sport performances in real situations is missing.

45 Seven databases were searched for articles which examined the association between particulate (PM<sub>2.5</sub>,
46 PM<sub>10</sub>, PM<sub>Coarse</sub>), gaseous (O<sub>3</sub>, sulfur dioxide [SO<sub>2</sub>], nitrogen dioxide [NO<sub>2</sub>], carbon monoxide [CO], and
47 index (Air Quality Index [AQI]) of air pollutants and sport performances in real context. We included
48 observational studies with objective measures of air pollution and performances. They were categorized
49 into endurance, power/speed, technical performances. Harvest plots were used to summarize the
50 findings and guide narrative synthesis. Risk of bias was also assessed. When appropriate, meta-
51 analyses were completed to evaluate the strength of the associations. Subgroup and sensitivity analyses
52 were also conducted. Over 200 reported associations from 21 articles were examined. Participants were
53 primarily elite athletes. The majority of studies examined football (n = 9), and endurance sports (n = 9).
54 Overall, no evidence supported a negative association between performances and the AQI, PM<sub>2.5</sub>, and
55 NO<sub>2</sub> outdoor concentrations. We found that exposure to O<sub>3</sub> was associated with reduced sports
56 performance O<sub>3</sub>, (d = -0.33, 95%CI: -0.65 -0.02 [all performances]; d = -0.54, 95%CI: -1.02 -0.04)
57 [endurance performances]). An absence of significant association between AQI, PM<sub>2.5</sub>, or NO<sub>2</sub>
58 exposure and sports performance is clearly supported by our systematic review and meta-analyses.
59 Higher O<sub>3</sub> concentration could impair sport performances in real context, but the evidence is less
60 consistent. No firm conclusion can be drawn from our work for the short term levels of CO, PM<sub>10</sub>, or
61 SO<sub>2</sub> and their respective association with endurance and technical performances.
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**Introduction**

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Substantial amounts of pollutants are released into the atmosphere daily, and the concentrations of multiple air pollutants are worsening with climate change. Chronic and acute exposure to air pollution have negative effects on various human health outcomes including human physical activity and exercise (Bernard et al., 2021; Tainio et al., 2021). Elevated outdoor air pollution levels are linked to decreased physical activity in the general population and reduced engagement in moderate to vigorous physical activities (Bernard et al., 2021). In competitive contexts, there is growing concern about the potential risks that air pollution poses to athletes' performance (Bougault et al., 2024; Mougin et al., 2024).

Seminal reviews on air pollution and athletes' performance concluded that inhalation of high levels of particulate matter (diameter<10 [PM₁₀] or <2.5 mm [PM₂.₅]) resulted in immediate and subsequent reduced exercise endurance performances in experimental, laboratory, settings (Cutrufello et al., 2012; Rundell, 2012). More recently, two systematic reviews were published including a larger set of air pollutants (ozone [O₃], diesel exhaust, black carbon, carbon monoxide [CO], nitrogen dioxide [NO₂], traffic-related air pollution). Exposure to O₃ and CO while exercising was generally associated with impaired endurance performances (i.e., decreased VO₂max), again in studies performed mostly in controlled laboratory settings (Hung et al., 2022; Qin et al., 2019). The authors did not draw conclusions regarding other air pollutants (e.g., sulfur dioxide [SO₂]) due to the limited number of included articles and the variability of performance outcomes included. These last two reviews also suggested that untrained endurance athletes and women could be more vulnerable to air pollution with physical fitness mediating the effects of air pollution on performance (Hung et al., 2022). Women may exhibit increased hyper-responsiveness to air pollution due to a higher fractional deposition of particulate matter in their airways (Kim and Hu, 1998). This could be attributed to their smaller laryngeal openings and reduced cross-sectional area of the trachea. Consequently, it increases the breath frequency, personal discomfort and is associated with a slower their pace (Marr and Ely, 2010).

Past reviews have focused on endurance-related outcomes (Hung et al., 2022; Qin et al., 2019). Endurance athletes are thought to be more exposed to air pollution due to increased ventilation rates, mainly through mouth breathing (bypassing the normal nasal filtration mechanisms), which carries air pollutants deeper into the respiratory tract (Rundell et al., 2015) compared to other performances that
99 would focus on strength (e.g., javeling) or technical abilities (e.g., pass accuracy, goal scored). 100 Impairment of respiratory, cardiovascular, immune and endocrine functions are crucial factors affecting 101 endurance performance, while cognitive changes associated with air pollution may also impact overall 102 performance as well as other less physically demanding sports (Mo et al., 2023). For instance, higher 103 indoor air pollution levels have been correlated with reduced risk-taking in chess tournaments(Klingen 104 and Van Ommeren, 2022), less effective strategic decisions in e-sports (Mo et al., 2023), and increased 105 time to solve escape room challenges (Lohmann et al., 2024). In baseball, research has indicated that 106 rising levels of fine particulate matter and carbon monoxide significantly increased umpiring errors 107 (Archsmith et al., 2018).

108 In summary, evidence from these previous reviews is largely derived from studies conducted in labora- 109 tory contexts where pollutants were generated for the experiments and with a main focus on endurance- 110 related outcomes. This reduces the generalizability of their conclusions to real-world performances and 111 limits their relevance beyond endurance-related outcomes. Recent studies suggest that air pollution 112 could have an impact on other type of performances (e.g., pass accuracy, long jump) (Archsmith et al., 113 2018; Picchio and Van Ours, 2024). While randomized controlled trials are impractical during sports 114 competitions, observational cohort studies offer a valuable opportunity to explore the short-term associ- 115 ations (i.e., the day of the competition or the next few days before) between air pollutants and various 116 types of sports performances. Although laboratory findings highlight the negative effects of O₃ and CO 117 on endurance performance, observational data allow for the investigation of additional contextual vari- 118 ables (e.g., home advantage, weather, acclimatization) and non-endurance sports performances. To our 119 knowledge, a systematic review examining the short-term associations between outdoor air pollution 120 and sport performances in real-world context is missing. Furthermore, no previous work has quantified 121 these associations via a meta-analysis. 122

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Built on top of the existing literature, the purpose of this systematic review and meta-analysis was: (1) 123 to summarize the evidence on the short-term associations regarding outdoor air pollutants and sport 124 performances based on objective data collected in real-world sport competitions, and (2) to provide 125 quantitative estimates of the short term association between air pollutants and sport performances 126 through a meta-analysis.
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131 The current systematic review and meta-analysis were conducted following the PRISMA guideline
132 adapted for research in exercise, rehabilitation, sport medicine and sports science (Ardern et al., 2022).
133 The study protocol was registered in PROSPERO (CRD42023485789) and all the study materials and
134 data collected as part of the present study are made available on the Open Science Framework
135 (https://osf.io/cb6fu/).

136

137 Eligibility criteria

138 Our inclusion criteria were based on the Population, Exposure, Comparator, and Outcomes framework
139 (Morgan et al., 2018). Population: Studies were eligible when they focused on adult, professional or
140 amateur athletes, performing in a real competitive setting. Exposures: Studies had to report objective
141 air pollution measures the day or previous days before the competition(s). Comparators: Studies were
142 eligible if they provided information on competition level, sex, type of performance, varying concentra-
143 tions of air pollution exposure. Outcomes: Studies were eligible irrespective of objective measure of
144 sport performance collected during a competition (e.g., race time, number of scored goals). The designs
145 of included studies were observational studies quantifying the exposure-outcome relationship. Studies
146 were excluded if they meet the following criteria: laboratory-based studies, indoor sport competition,
147 performance(s) done outside a competitive context, subjective measure of air pollution, or examining
148 volatile organic compounds, polycyclic aromatic hydrocarbons, or airborne metals. Some indirect per-
149 formance outcomes were not included such as foul, yellow cards, corners, because they are more asso-
150 ciated with referees' decisions or tactical choices (e.g., percentage of possession, red card, long passes,
151 corner during a football game) (Li et al., 2024; Zhou et al., 2019).

152

153 *Search strategy*

154 We conducted online research in the following databases: Pubmed, CINAHL, SportDiscus, GreenFILE,
155 GeoRef, Scopus, JSTOR, for relevant publications up to April 2024. To reduce the risk of publication
156 bias, three databases dedicated to gray literature were used (Open Grey, SportRxiv and ProQuest
157 Dissertations). Furthermore, to find unpublished studies, we used two additional strategies. First, we e-
158 mailed the first authors of the included published papers to know whether they possessed any
159 unpublished data or were aware of unpublished studies by other researchers. Second, studies were
160 sourced from six relevant reviews (Carlisle and Sharp, 2001; Cutrufello et al., 2012; Fitch, 2016;
Gashaw and Yitayal, 2019; Giles and Koehle, 2014; Salonen et al., 2020). The search strategy was adapted for each database (further details are available in Supplementary file 1). Titles and abstracts of the initially identified articles were scanned by three reviewers for exclusion criteria (GC, QM, PB). Then, full-text articles were screened by GC, PC and PB.

## Data Collection Process

Data were extracted on a pre-piloted spreadsheet by one author (PB) and cross-checked by six authors (QM, GC, QM, VB, CP, AO). All relevant studies were scrutinized to extract the following data: Authors, year of publication, study location, sample size, age, sex ratio, study design, air pollution outcome(s), duration of exposition, method of air pollution exposure assessment, nature of sport performance outcome(s), category of sport performance (endurance, speed/power, technical), number of competition examined, category of sport, elite level (based on definitions presented in (McKay et al., 2022)), weather confounders measured and adjusted for, other relevant confounders, multi-pollutant models, nature of previous short term exposition (e.g., lag in terms of hours, days), statistical analysis conducted, use of non-linear approach, and author's conclusion.

## Outcomes

The respective associations between particulate (PM<sub>2.5</sub>, PM<sub>10</sub>, PM<sub>Coarse</sub>), gaseous (O<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO, black carbon, and index (Air Quality Index [AQI]) of air pollutants and sport performances were examined. Sport performances are described in supplemental materials (see Supplementary file 2), they include, for example, race time, pass accuracy, or first serve successful rate in tennis.

## Risks of bias

A validated tool to assess the risk of bias for studies examining the association between air pollution and health/performance outcomes is missing (Lam et al., 2020). We therefore used the six following criteria from a previous investigation (Liu et al., 2020a): "Is the sample generalizable to the population of interest?", "Did the study a priori quantify sample and power?", "Was missing data addressed and tested?", "Was a dose-response relationship seen in any outcome?", "Did the study design or analysis account for important confounding and modifying variables?", "Did the study design or analysis adjust or control for other environmental exposures that were anticipated to bias results?", "Did the study conclusions match the results?". For each criterion, we indicated whether study met the requirement
191 (i.e., binary outcome, yes/no), or not applicable. The risk of bias assessment was conducted by PB and
192 GC, then checked by OA and AJR. Any disagreements were resolved by discussion.

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194 *Data synthesis*

195 A narrative synthesis has been carried out to synthesize the findings from included studies. Harvest plot
196 matrices presenting the associations between each air pollutant and performances categories
197 (endurance, speed/power, technical) to which association was supported (negative, positive, not
198 significant) have been generated (Crowther et al., 2011). Additionally, a methodological quality score
199 (i.e., the inverted weighted mean of risk of bias) was assigned to each reported association in the
200 harvest plots. These plots were used to facilitate the interpretation of findings and to illustrate the
201 heterogeneity of the reported results.

202

203 *Meta-analysis*

204 Pearson's r or (un)standardized beta were used as effect size, and transformed in Cohen's d. All effect
205 sizes were pooled for a fixed increment in pollutant concentration (per 10 µg/m³), other reported
206 quantities or units (such as parts per billion and parts per million) were converted (see details in
207 Supplementary file 1) when appropriate. We assumed a linear relation between air pollutants and
208 performances.

209 When results were available from both single- and multi-pollutant models, only the multipollutant
210 model results were included because mixed exposure is more representative of real-life situations.

211 Magnitude of effect sizes (d) were interpreted as small >0.20, medium >0.50, and large >0.80 (Cohen,
212 2013). Meta-analyses employed a robust variance estimation to account for dependencies of having
213 multiple effect sizes come from each study. Statistical dependency is very common in environmental
214 research (Liu et al., 2020b). This method allows simultaneous analysis of multiple effect sizes per study
215 with accurate estimates and standard errors, even when information on the covariance of these effect
216 sizes is unavailable. As indicated by (Voracek et al., 2019), a meta-analysis was performed only if 4 or
217 more associations were reported from different studies. Separate analyses were carried out for each
218 pollutant. When the number of available estimates was equal or higher to 4, a subgroup analysis for
219 each performance category (i.e., endurance, speed/power, technical) was also performed.

220 Heterogeneity was quantified with the I² statistic (Higgins and Green, 2008). We also report the method
221 of moments estimate of τ², the between-studies variance component, as a measure of between-study
222 heterogeneity in effect sizes. If an association below *p*<0.10 was found a prediction interval was 223 provided, possible outlier and publication bias were also explored. The prediction interval provides a 224 range within which we can expect the associations of future studies to fall based on the current 225 evidence.

226 Traditional influence or publication bias analyses are not implemented in the *robumeta* package used to 227 perform the robust variance estimation. To overcome this limitation, we followed the method used in 228 previous meta-analyses using similar approaches (Chevance et al., 2019). First, univariate outliers were 229 inspected with the Grubbs test on all effect sizes. Then, we conducted publication bias analysis using 230 the *PublicationBias* package and calculated corrected effect sizes assuming significant results were 231 times more likely to be published than non-significant results. These effect sizes conservatively reflect 232 estimates of publication bias greater than those found in a meta-meta-analysis.

233

234 *Sensitivity analyses*

235 As recommended (Iyengar and Greenhouse, 2009), a set of sensitivity analyses were also carried out to 236 verify the robustness of ours estimated effect sizes. Results from the robust variance estimation meta- 237 analyses were compared with those from multi-level meta-analyses (Assink and Wibbelink, 2016).

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239 *Open science*

240 All analyses were carried out in R 4.3 using the *robumeta* package (Fisher and Tipton, 2015) for the 241 robust variance estimation meta-analysis, the *metafor* package (Viechtbauer, 2010) for the multi-level 242 meta-analysis. Analyses were performed by PB and independently replicated by GC and LH. The 243 dataset and annotated R code are available as supplemental materials on the Open Science Framework 244 page of the project (see: https://osf.io/mgv82/).

245

246 **Results**

247

248 *Study selection*

249 The initial electronic searches identified 1172 references, of which 107 were duplicates. After a review 250 of titles and abstracts, 1019 were excluded because they did not meet all inclusion criteria. Hence, 46 251 full-text articles were assessed for eligibility, and 19 were further included in the review (see Figure 1 252 in Supplementary file 1). Additionally, two studies were provided by authors who were contacted. No
other additional studies were identified through hand search of relevant articles. The corresponding au-
thors of articles with missing information were contacted.

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256 *Study description*

Table 1 provides a summary of the 21 included studies. More details are presented in supplemental file
2. The oldest article retrieved was published in 1967 and the most recent one 60 years later, in 2024.
Football was the most studied sport (n = 9) follows by short (e.g., 5 km running) and long endurance
sports (e.g., marathon, ironman, n = 9). Performances data from US football, tennis, and track and field
practices (i.e., sports including running, jumping, and throwing practices) were examined in one study,
respectively. All performances were characterized at the individual level, except for two studies ex-
amining collective performances (Wayne et al., 1967; Xu et al., 2024). Fifteen studies were conducted
with data from elite athletes (e.g., top 3 marathon finisher, professional football player), one only with
amateur athletes, and six with both. The number of performances reported within-study ranged from 20
to 480,000.

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Table 1

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Figure 1 illustrates the countries and cities in which the studies were carried out, China was the most represented country. Most studies were conducted in high-income countries according to World Bank classification (62%), the remaining studies were conducted in low- and middle-income countries (38%).

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Figure 1

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Regarding the measures of air pollution, data were provided by the environmental authorities of the cit-
ies or countries concerned. Analyses from 16 studies (76%) were based on local air pollution stations.
Meteorological conditions were not always detailed, 16 studies included weather related variables in
their models (most frequent were: temperature, humidity and wind speed). Ten studies (47%) per-
formed multipollutant statistic model. Additionally, some data of pollutants are also missing in multi-
center studies. For instance, some pollution sensors have been moved or did not measure all the same
parameters, as is the case for PM₁₀ in the study led by Helou et al. (2012), which is not available for all
cities. In general, the risk of bias was relatively low, the items assessing the missing data management and a priori power analyses had the higher rate of missing information. Table 2 presents the scoring of studies' risk of bias.

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Type of air pollutants studied

The pollutants reported in the studies were PM<sub>coarse</sub>, PM<sub>10</sub> and PM<sub>2.5</sub>, O<sub>3</sub>, NO<sub>2</sub>, CO and SO<sub>2</sub>. AQI was also considered among included studies. Five studies classified air quality during events into categories ranging from very good or good to hazardous or very poor, based on the pollutant with the worst AQI value (Cusick et al., 2023; Guo and Fu, 2019; Zacharko et al., 2021; Zhang, 2024; Zhou et al., 2019).

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Concentrations of air pollutants and sampling frequency

<caption>Table 2</caption>
The details of PM and O₃, CO, NO₂ m AQI and SO₂ concentrations are given in supplemental materials (S1. Table 1-4). The hourly values of local pollution data were used by all authors. The data were mostly reported as monitored and averaged over either the event time for each athlete, the time a team spent on the field or the duration of the match (Beavan et al., 2023; Guo and Fu, 2019; Hodgson et al., 2021; Humphreys and Ruseski, 2023; Lichter et al., 2017; Zacharko et al., 2021; Zhou et al., 2019). El Helou et al. (2012) used an average of 4 hours from the start of each marathon (El Helou et al., 2012), and Humphreys et al., (2023) calculated average air pollutants from 12:00 to 18:00 during NFL games (Humphreys and Ruseski, 2023). Hodgson et al., (2022) considered two fixed air quality values between 10:00 and 12:00 and between 10:00 and 15:00 depending on the group of runners (elite vs amateur) (Hodgson et al., 2022). Some authors used daily averages during the day of the event (Naidenova et al., 2022; Picchio and Van Ours, 2024; Zhang, 2024), and Marr et al., (2010) used 2-hour averages for gaseous pollutants during the race and 24-hour averages on the day of the race for PM (Marr and Ely, 2010). Zacharko et al. (2023) used three measurements of air pollution during the game, i.e. at the beginning, during the break and at the end of the game (Zacharko et al., 2022). Finally, few authors characterized exposure to air pollutants prior to the sporting event until the day of the meet/match. For instance, Cusick et al (2023) averaged exposure to air pollutants 20 days before the event (Cusick et al., 2023), Mullins (2018) 7 days before the start (Mullins, 2018), Wayne et al., (1967) 3 hours before the event (Wayne et al., 1967) and Qin et al., (2022) 3 months before the event (Qin et al., 2022).

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317 Weather conditions
The range of temperature reported varies from -12.2°C to 36.1°C, relative humidity from 5.6 to 100% and wind speed from 0 to 68 km.h⁻¹ (see S1. Table 5). Most data sources were obtained from official meteorological services while a few authors did not specify the source of the weather data (Mullins, 2018; Qin et al., 2022) or use specific data sources such as the one from the National Football Ligue (Humphreys and Ruseski, 2023). Hourly weather conditions were mainly averaged over the duration of the sporting event (Beavan et al., 2023; Cusick et al., 2023; Guo and Fu, 2019; Hodgson et al., 2022; Humphreys and Ruseski, 2023; Marr and Ely, 2010; Qin et al., 2022; Zhou et al., 2024). As for air pollutants, El Helou et al. (2022) used the 4 hours average after the start of each race (El Helou et al., 2012), and Hodgson et al. (2022) averaged the values from 10:00 to 12:00 and from 10:00 to 15:00, depending on the group (elite or amateur). Picchio et al. (2024) used the temperature at 15:00 two meters above the tennis court and Zhang et al. (2024) used a daily average.

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### *Sport related covariates*

Eleven studies included sport specific related variables in their multivariate models. For instance, Cusik et al. (2023) added the 5 km running athlete's personal record, and days since the last race. In football, player position, stadium attendance, home advantage, team market value, play time, opponent strength have also been used as covariates.

335

### *Amateur versus elite status*

Studies examining how the relationship between air pollution and performance varies by athlete status (amateur vs. elite) have found mixed results and the literature is rather inconclusive. For example, high AQI levels were linked to longer running times for both amateur and elite runners (Guo and Fu, 2019). High O₃ levels were associated with slower running times, but only for the slowest runners and elite female runners (Hodgson et al., 2022). In contrast, elevated O₃ levels negatively impacted running times for elite runners (Top 20) but not running time for the slowest runners (El Helou, 2011). High NO₂ levels correlated with longer race times for all runners, including the top quartile of finishers (El Helou et al., 2012); however, this effect was not observed in analyses focusing on the top 1% of runners. Another study analysed the effects of O₃ and PM₂.₅ on both professional and amateur Ironman athletes. Authors found no association between overall race or running times and O₃ and PM₂.₅ levels across all Ironman athletes. However, a significant negative association was identified between O₃ levels and bicycle riding time, but only among professional athletes (Naidenova et al., 2022).
349

350 *Sex differences*

351 The evidence regarding sex-specific differences in the effects of air pollution is inconclusive. In terms of sex differences, four studies found comparable negative associations between air pollution levels and performance for both male and female athletes. Specifically, AQI (Guo et al., 2019) and NO₂ (El Helou et al., 2012) were associated to longer marathon race times for both male and female athletes, and O₃ levels were associated with decreased athletic performance for both the slowest amateur runners and elite athletes (Mullins et al., 2018; Hodgson et al., 2022). Higher PM₁₀ concentrations were associated with longer race times in female, but not in male, athletes in one study.

358

359 *Associations between outdoor air pollution and sport performances*

360

361 *Harvest plots*

362 In general, a consistent pattern of association was found for AQI, PM₂.₅, and NO₂ with endurance performances (see Figure 2). However, the directions of PM₁₀ associations were less consistent. Only five studies examined the relationship between air pollution and speed/power performance outcomes, making it difficult to identify a clear pattern of association for these outcomes.

366

367 *Air quality index*

368 Of the fourteen-association tested between AQI and endurance-related performances, height showed a significant negative association, with a low risk of bias. No significant association was found between AQI and technical performances, with six of nine associations showing no significant association (moderate risk of bias).

372 *PM₁₀ and PM₂.₅*

373 Studies examining the association between PM₂.₅ and endurance performances showed a relatively similar pattern, with twenty height associations reporting no significant effects (ranging from low to high risk of bias). The evidence from ten tested associations between PM₂.₅ and technical performances was inconclusive. Similarly, findings on the association between PM₁₀ and performance outcomes did not indicate a consistent pattern.

378 *O₃*
No clear pattern of association was found between O₃ and endurance performances. Eleven associations were significantly negative (low risk of methodological bias), while nineteen associations showed no significant effects (ranging from low to high risk of bias).

NO₂

No significant association was observed between NO₂ and endurance performances, with twelve reported no significant associations moderate to high risk of bias (see Figure 2).

SO₂, CO and PMcoarse

No significant association was found for SO₂ and endurance performances, with eight of nine associations showing no effect (moderate risk of bias). Due to the limited number of studies examining associations between CO, PMcoarse, and performance outcomes, it is challenging to establish a global pattern of results for these pollutants.

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393 **Meta-analyses**

All associations reported in the harvest plots are not included in the meta-analyses because details were missing from the included manuscripts to be meta-analyzed. Three studies were excluded (Beavan et al., 2023; Zacharko et al., 2022, 2021), and three others were only partially used (El Helou, 2011; Mullins, 2018; Naidenova et al., 2022). In total, we used 150 out of the 225 associations retrieved (67%). Our estimates for AQI, CO, NO₂, O₃, PM₁₀, PM₂.₅, and SO₂ were based on the following proportions of reported findings from included studies: 26/28 for AQI (93%), 9/9 for CO (100%), 19/37 for NO₂ (52%), 32/56 for O₃ (58%), 14/31 for PM₁₀ (45%), 35/45 for PM₂.₅ (78%), and 11/13 for SO₂ (85%). The meta-analyses incorporated between 280,000 and 1.6 million individual observations.

402

Regarding pooled and endurance performances, an analysis has been conducted for AQI, CO, NO₂, O₃, PM₁₀, PM₂.₅, and SO₂. For technical performances, a subgroup analysis has been carried out for PM₂.₅. No statistically significant associations were found using robust variance estimations. Then, multi-level meta-analysis models were carried out as sensitivity analyses. Two statistically significant and negative effect sizes have been found for O₃ and all performances (d = -0.33, 95%CI: -0.65 -0.02) and endurance performances (d = -0.54, 95%CI: -1.02 -0.04). No statistically significant associations were obtained for the remaining five air pollutants and performance outcomes. Results from the robust

Figure 2
410 variance estimation are available in Table 3. Further details about the sensitivity analyses as well as
411 forest plots are available in supplemental materials (see Supplementary file 3).

412

413

<caption>Table 3</caption>

414

415 **Discussion**

416

417 *Principal findings*

418 This systematic review and meta-analysis offer the most comprehensive evaluation to date of observa-
419 tional studies examining the associations between short-term exposure to ambient air pollution and
420 sports performance. Overall, we found no evidence of a negative association between outdoor concen-
421 trations of AQI, PM<sub>2.5</sub>, and NO<sub>2</sub> and sports performance in our harvest plots and meta-analyses. Short-
422 term O<sub>3</sub> exposure was associated with lower performances of all types (moderate effect size) and en-
423 durance performances (low effect size). However, these associations were observed only with the
424 multi-level meta-analyses, and contrast with the results reported in our Harvest plot. Other meta-ana-
425 lyses for CO, PM<sub>10</sub>, and SO<sub>2</sub> showed non-statistical significant results and the associations presented in
426 harvest plots were inconclusive.

427

428 Our findings for O₃, based on the multi-level meta-analysis, align with a previous systematic review by
429 Hung et al. (2022), which concluded that O₃ exposure during physical exercise in laboratory settings
430 can impair endurance performance in both trained and untrained athletes (Hung et al., 2022). A major
431 distinction however, that could partially explain the lack of association between O₃ and performances,
432 based on the robust variance estimation and our harvest plots, is concentration level tested in laboratory
433 setting versus in real-life context. In laboratories, O₃ concentrations typically ranged from 118 to 1472
434 µg/m³ (60–750 ppb), whereas measured concentrations in actual sports contexts did not exceed 111
435 µg/m³ (Xu et al., 2024).

436

437 Regarding PM, PM<sub>2.5</sub>, and PM<sub>10</sub>, our results do not align with the conclusions of previous reviews
438 which suggested that short-term exposure to PM impairs performances (Cutrufello et al., 2012;
439 Rundell, 2012). Only a limited number of studies reported associations between PM<sub>10</sub>, PM<sub>coarse</sub> and
440 performance outcomes in our review. Notably, 35 reported associations (77%) between PM<sub>2.5</sub> and
performances were not significant, and our meta-analysis was based on more than 1.6 millions
observations. Therefore, and although narrative reviews have suggested that PM exposure may affect
exercise performance (Cutrufello et al., 2012; Rundell, 2012), possibly by impairing organ's function
or immunity, short-term exposure to PM did not appear to reduce performance in the present review
that includes performances in real-life competition. However, these findings should be interpreted with
caution, especially in cases of severe pollution. For example, during the 2014 Beijing Marathon, visibly
high PM levels (e.g., smog) may have influenced athlete behavior, leading some to withdraw, wear
masks, or forgo participation altogether (Gittings, 2014). This may mean that only less sensitive
athletes and/or already locally exposed residents participated in polluted conditions. Similarly,
respiratory symptoms have impaired performance during events affected by mega-fires, such as the
2019 Australian Open tennis tournament, held during Australia's "Black summer" (BBC, 2020). In
some cases, events may be postponed due to hazardous air quality, as during the 2023 Canadian mega-
fires (Boucher, 2023). While it is safer to cancel sporting events when pollution levels are too high, this
impedes the investigation of high levels of air pollution on performances in real-life competitions.

Our analyses reveal that no firm conclusions can be drawn regarding the statistically imprecise
associations between CO, NO₂, and SO₂ due to the limited number of tested associations. Our results
are confirmed by studies carried out in laboratory settings. The authors concluded that the level of
evidence for a negative effect of these pollutants on endurance performance was low (Hung et al.,
2022; Qin et al., 2019). Regarding AQI, our findings cannot be directly compared with previous
laboratory investigations, as AQI is a composite index combining air pollution measures which cannot
be replicated in laboratory settings. Furthermore, a potential non-linear relationship between AQI and
performance (Guo and Fu, 2019) could partly account for the absence of statistically significant
associations observed in our meta-analyses.

Strengths and limitations

This review has several strengths. It used a reproducible, systematic and standardized approach to
produce both qualitative and quantitative syntheses of the findings, including data from 47 countries
across all continents. Additionally, our literature synthesis is the first to include AQI and five other air
pollutants. Our meta-analyses were conducted with a large number of observations, which increases the
robustness of our findings.
472
473 The primary limitation of the present review is related to the scope: because we focused on
474 observational studies, conclusions about potential causal relationships between air pollution and sports
475 performance cannot be drawn. Second, most investigations used air pollutant monitoring data or
476 modeled concentrations instead of hyper-local or personal exposure measurements, which may
477 misrepresent actual exposure and potentially underestimate negative effects (Ribalta et al., 2024).
478 Third, half of all studies reported adjusted estimates, the number of covariates varied among different
479 studies and important covariates were not considered (e.g., wind) or characterized with different
480 approaches in many studies. Fourth, air pollution early warning systems are increasingly deployed in
481 urban context, it could be associated with canceled sport competitions or coping strategies development
482 among athletes (e.g., medications, antioxidant supplementation). These systems and strategies were
483 never reported in reviewed studies. Five, the number of associations available between air pollutants
484 and speed/power performances was insufficient to perform meta-analyses.
485

485

486 *Future research*

487 The reviewed literature demonstrates substantial variability in methodology and findings, which future
488 research should address. First, researchers should develop large-scale, collaborative projects to examine
489 the short-term associations between air pollution and a specific set of comparable performance
490 indicators within the same sport (e.g., home runs or slugging percentage in baseball). Minimal research
491 has been conducted on technical performance, and no studies have examined motorized sports, cycling,
492 bat-and-ball games, or archery/shooting sports. Additionally, more studies focusing exclusively on
493 female and amateur athletes are needed to explore their potential air pollution vulnerability. Second, a
494 direct measurement of air pollution and weather outcomes in sports settings (e.g., stadiums) allow for
495 better temporal modelling. A dynamic air pollution monitoring system should be systematically used
496 during sport competition (e.g., see an example during marathons (Ribalta et al., 2024)). Third, future
497 studies should examine the effects of air pollution exposure not only on the day of the competition
498 (acute exposure), but also a few days before for visiting athletes, or longer periods for home athletes.
499 For national to international events, it would also be helpful to modelize the chronic exposure of
500 athletes during their daily lives. Two previous studies have suggested that soccer teams unaccustomed
501 to high levels of air pollution had lower performance when competing against teams regularly exposed
502 to higher pollutant concentrations (Li et al., 2024; Qin et al., 2022). Fourth, potential non-linear
associations between air pollution and performances should be more systematically tested. More specifically, both official and empirical thresholds of air pollutants should be examined. For example, the official alert threshold for PM₁₀ in Germany is 70 µg/m³ (Adamkiewicz et al., 2021), while the data-based threshold associated with reduced performance in elite German soccer players was 55 µg/m³ (Lichter et al., 2017). This combined approach will help sport stakeholders and coaches to identify sport-specific pollutant thresholds for amateur and elite athletes. Fifth, more sport-specific, contextual mechanistic studies are needed to better understand the potential mediating roles of air pollutants on performances related outcomes. For instance, high air pollution concentration has been associated with lower spectator attendance or more frequent referee mistakes which might also explain, in part, why air pollution has an impact on sport performances.

513

514 Conclusion

An absence of significant association between AQI, PM<sub>2.5</sub>, or NO<sub>2</sub> exposure and sports performance is clearly supported by our systematic review and meta-analyses. Higher O<sub>3</sub> concentration could impair sport performances in real-life context, but the evidence is less consistent across methods. No firm conclusion can be drawn from our work for the short-term levels of CO, PM<sub>10</sub>, or SO<sub>2</sub> and their respective associations with endurance and technical performances. In addition to the lack of studies on the subject, the failure to consider individual characteristics, in particular the sensitivity of the subjects to pollutants or their behaviour in the presence of predicted air pollution, are all parameters likely to influence our results. Recommendations to conduct future solid empirical studies in real-life competition contexts are drawn from the existing literature.

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<figure><img src="image_3.png" /><figcaption>Figure 1. World map of included studies.</figcaption></figure>

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Notes. Multi-center studies are represented here at the national (colored areas) and local (city circles) scale.
<caption>Table 1 Short summary of included studies</caption>

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533

<table><thead><tr><th>Author</th><th>Sex (%men)</th><th>Nb observations</th><th>Years</th><th>Elite</th><th>Amateur</th><th>Sport</th><th>Air pollution outcomes</th><th>Performances</th><th>Major findings</th></tr></thead><tbody><tr><td>Beavan et al., 2023 Germany</td><td>100</td><td>3 425</td><td>2018-19</td><td>Y</td><td>N</td><td>football</td><td>NO<sub>2</sub>, O<sub>3</sub> PM<sub>10</sub></td><td>Number of accelerations in speed zones, distance in speed zones, Distance/min, total distance, number of sprint</td><td>No significant correlation between PM<sub>10</sub> concentration and the number of accelerations in the speed zone. Increases in PM<sub>10</sub> and NO<sub>2</sub>, have significant association with decreasing total distance run.</td></tr><tr><td>Cusicket al., 2023 US</td><td>100</td><td>1 104</td><td>2010-14</td><td>Y</td><td>N</td><td>5-km running</td><td>AQI, PM<sub>2.5</sub>, O<sub>3</sub></td><td>Race time</td><td>Higher level of O<sub>3</sub> during the race was associated with slower race time. No significant associations were found between PM<sub>2.5</sub> and AQI (during the race) and race time**. Cumulative PM<sub>2.5</sub> exposure at levels above 8µg/m<sup>3</sup> (14 or 21 days) were associated with longer total race times (~12sec) A positive cumulative association (14 or 21 days) between O<sub>3</sub> exposure and increased race time at a range of levels above 36 ppm (~11.5 sec)</td></tr><tr><td>El Helou et al., 2012, International</td><td>NR</td><td>60</td><td>2001-10</td><td>Y</td><td>Y</td><td>Marathon</td><td>NO<sub>2</sub>, O<sub>3</sub> SO<sub>2</sub> PM<sub>10</sub></td><td>Median of arrival times / marathon</td><td>Only a significant and negative correlation between NO<sub>2</sub> concentration and the median of arrival time was found.</td></tr><tr><td>El Helou, 2010 International</td><td>NR</td><td>480 000</td><td>2001-11</td><td>Y</td><td>Y</td><td>Marathon</td><td>NO<sub>2</sub>, O<sub>3</sub> SO<sub>2</sub></td><td>Speed</td><td>O<sub>3</sub> was negatively associated with speed race only in the elite group. For all runners, there was a significant and negative association between NO<sub>2</sub>, and speed race.</td></tr><tr><td>Guoet al., 2019</td><td>81</td><td>314 341</td><td>2014-15</td><td>Y</td><td>Y</td><td>(half)marathon</td><td>AQI, CO, NO<sub>2</sub></td><td>Race time</td><td>A 10% increase in AQI increases finishing time by about 1.1 min. Higher AQI had a negative impact on performance of female,</td></tr></tbody></table>
<table><tr><td>China</td><td></td><td></td><td></td><td></td><td></td><td>PM<sub>2.5</sub>, O<sub>3</sub>, PM<sub>10</sub>, SO<sub>2</sub></td><td>young, old, top 10 runners. Other air pollutants had also a non-negligible, negative effect on runners' performance.</td></tr><tr><td>Hodgson et al., 2022 UK</td><td>63</td><td>668</td><td>2006-19</td><td>Y</td><td>Y</td><td>Half-marathon</td><td>NO<sub>2</sub>, O<sub>3</sub>, PM<sub>2.5</sub>, Race time</td><td>Female elite athletes and the slowest amateur participants were adversely affected by elevated O<sub>3</sub> levels, whilst male elite athletes were not. NO<sub>2</sub>, PM<sub>2.5</sub>, concentrations were not associated with finishing time.</td></tr><tr><td>Hodgson et al., 2021 International</td><td>76</td><td>459</td><td>2010-18</td><td>Y</td><td>N</td><td>5-km running</td><td>NO<sub>2</sub>, PM<sub>2.5</sub>, O<sub>3</sub>, PM<sub>10</sub></td><td>Race time</td><td>High O<sub>3</sub> and NO<sub>2</sub>, concentration were associated with faster finish times.</td></tr><tr><td>Humphreys et al., 2023 US</td><td>100</td><td>77 383</td><td>2008-16</td><td>Y</td><td>N</td><td>US Football</td><td>PM<sub>2.5</sub> O<sub>3</sub></td><td>Overall Yards, rush, pass</td><td>Yards gained per offensive play increase with increase in PM<sub>2.5</sub>, especially for passing plays but not for rushing plays. Production of defensive teams was more sensitive to PM<sub>2.5</sub> than offensive teams.</td></tr><tr><td>Li et al., 2024 China</td><td>100</td><td>24</td><td>2014-20</td><td>Y</td><td>N</td><td>Football</td><td>AQI O<sub>3</sub>, PM<sub>2.5</sub>, PM<sub>10</sub></td><td>Number of passes, number of successful passes, number of tackles</td><td>A 1% increase in the AQI corresponded to a 0.101% reduction in the number of passes. The effects of specific air pollutants, such as PM<sub>2.5</sub> and PM<sub>10</sub>, show similar patterns.</td></tr><tr><td>Lichter et al., 2017 Germany</td><td>100</td><td>75 163</td><td>1999-11</td><td>Y</td><td>N</td><td>Football</td><td>O<sub>3</sub> PM<sub>10</sub></td><td>Total number of passes per game, passing accuracy</td><td>The concentration of PM<sub>10</sub> in the ambient air has a significant and negative impact on the total number of passes made by players, and on their passing accuracy.</td></tr><tr><td>Marr et al., 2010 US</td><td>50</td><td>139</td><td>1980-07</td><td>Y</td><td>N</td><td>Marathon</td><td>CO, NO<sub>2</sub>, PM<sub>2.5</sub>, O<sub>3</sub>, PM<sub>10</sub>, SO<sub>2</sub></td><td>Finish time as a % of course record</td><td>Only PM<sub>10</sub> was associated with a decrease in performance in women. For every 10 µg/m<sup>3</sup> increase in PM<sub>10</sub>, a 1.4% decrease in performance can be expected.</td></tr><tr><td>Mulins et al., 2018 US</td><td>51</td><td>255 819</td><td>2005-13</td><td>Y</td><td>Y</td><td>Endurance (≥800m), sprint (≥100), strength (javeling)</td><td>CO, NO<sub>2</sub>, PM<sub>coarse</sub>, O<sub>3</sub>, PM<sub>2.5</sub>, SO<sub>2</sub></td><td>Standardized measure/ world record</td><td>There is a robust and negative significant effect of O<sub>3</sub> exposure on endurance performance in track and field athletes. Acclimatization effect: Athletes exposed to higher variability in O<sub>3</sub> levels could suffer more from the same level of mean exposure. NO<sub>2</sub> was associated with a higher level of performance.</td></tr></table>
<table><tr><td>Naidenova et al., 2022 USA</td><td>74</td><td>3 735</td><td>2012-19</td><td>Y</td><td>Y</td><td>Triathlon ironman</td><td>PM<sub>2.5</sub> O<sub>3</sub></td><td>Finish time (overall and for each triathlon segment)</td><td>An increase of 1 ppb in O<sub>3</sub>, increased the total finish time by 0.8 min (0.1% of the average time). An increase of 1 ug/m<sup>3</sup> in PM<sub>2.5</sub> increased the total finish time by 0.12% of the average time. Professional athletes were less sensitive to O<sub>3</sub> for total time. The O<sub>3</sub> concentration increased the finishing time in the swimming segment (slightly less for female athletes). The negative effect of O<sub>3</sub> is lower for professional athletes during the cycling segment. A negative effect of PM<sub>2.5</sub> was found for cycling, which was twice as strong for professional athletes.</td></tr><tr><td>Pichio et al., 2024 International</td><td>42</td><td>1 728</td><td>2003-21</td><td>Y</td><td>N</td><td>Tennis</td><td>PM<sub>2.5</sub> O<sub>3</sub></td><td>First serve successful rate, second serve successful rate, double fault rate</td><td>A significant and negative association was found between O<sub>3</sub> and tennis performance.</td></tr><tr><td>Qin et al., 2022 China</td><td>100</td><td>12 535</td><td>2015-17</td><td>Y</td><td>N</td><td>Football</td><td>PM<sub>2.5</sub></td><td>Number of passes, Success rate of passes</td><td>The PM<sub>2.5</sub> levels during the match had a significant and negative effect on performance. Also, non-adapted air pollution teams had a lower pass and success rate passes.</td></tr><tr><td>Wayne et al., 1967, US</td><td>100</td><td>20</td><td>1959-64</td><td>No</td><td>Y</td><td>Cross-country</td><td>O<sub>3</sub>, PM, CO</td><td>% of the group members who did not improve their running time / previous race</td><td>The percentage of team members who failed to improve their performance is highly correlated with O<sub>3</sub> concentration.</td></tr><tr><td>Xu et al., 2024 China</td><td>100</td><td>1 190</td><td>2014-18</td><td>Y</td><td>N</td><td>Football</td><td>AQI CO SO<sub>2</sub> O<sub>3</sub> PM<sub>2.5</sub>, PM<sub>10</sub></td><td>Goals scored by home team, goals scored by visiting team</td><td>AQI, PM<sub>2.5</sub> concentration was associated with both home and visiting goal scored. Home goal scored was negatively associated with PM<sub>10</sub> and SO<sub>2</sub>.</td></tr><tr><td>Zacharko et al., 2021 Germany</td><td>100</td><td>461</td><td>2017-19</td><td>Y</td><td>N</td><td>Football</td><td>NO<sub>2</sub>, AQI, O<sub>3</sub> PM<sub>10</sub></td><td>Total distance covered, high intensity effort</td><td>No significant correlation between individual air pollution parameters (NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub>) and performance measures. Deterioration of AQI was associated with reduced performance.</td></tr><tr><td>Zacharko et al., 2023,</td><td>100</td><td>362</td><td>2019-20</td><td>Y</td><td>N</td><td>Football</td><td>PM<sub>10</sub></td><td>Total distance, distance at high-speed running</td><td>Players showed a significantly longer average total distance in the northern region, where PM<sub>10</sub> concentrations are significantly lower than in central and southern regions.</td></tr></table>
<table><thead><tr><th>Poland</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Zhang et al.,<br>2024<br>China</td><td>100</td><td>15 061</td><td>2014-<br>16</td><td>Y</td><td>N</td><td>Football</td><td>AQI<br>PM<sub>2.5</sub>,<br>PM<sub>10</sub></td><td>Number of<br>passes, running<br>distances<br>One SD increase in AQI was associated with<br>a 2.5% decrease in the number of passes.<br>PM<sub>2.5</sub>, PM<sub>10</sub> were also negatively associated<br>with this performance.</td></tr><tr><td>Zhou et al.,<br>2019<br>China</td><td>100</td><td>478</td><td>2015</td><td>Y</td><td>N</td><td>Football</td><td>AQI</td><td>Shot, shot on<br>target, pass<br>number, pass<br>accuracy, total<br>distance, sprint<br>distance, high<br>(speed) intensity<br>distance<br>AQI was not associated with technical skills.<br>Higher level of AQI was associated with<br>better endurance and technical performance.</td></tr></tbody></table>

535

536 Notes. ** = findings based on re-runed analyses of author's data, SD = stand deviation, OLS = Ordinary least squares, MLM = Multilevel

537 linear model, Corr = Correlation, WBGT = Wet-Bulb Globe Temperature, grey = not included in meta-analyses, Y = yes, N = No, NR = Not

538 reported
<caption>Table 2 Methodological quality of included studies</caption>

<table><thead><tr><th>Author</th><th>Year</th><th>Is the sample generalizable to the population of interest?</th><th>Did the study a priori quantify sample and power?</th><th>Was missing data addressed and tested?</th><th>Was a dose-response relation seen in any outcome?</th><th>Did the study design or analysis account for important confounding and modifying variables?</th><th>Did the study design or analysis adjust or control for other environmental exposures that were anticipated to bias results?</th><th>Did the study conclusions match the results?</th></tr></thead><tbody><tr><td>Beavan</td><td>2023</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>Y</td></tr><tr><td>Cusick</td><td>2023</td><td>Y</td><td>N</td><td>N</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>El Helou</td><td>2012</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>Y</td></tr><tr><td>El Helou</td><td>2010</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>Y</td></tr><tr><td>Guo</td><td>2019</td><td>Y</td><td>N</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Hodgson</td><td>2022</td><td>Y</td><td>N</td><td>NA</td><td>Y</td><td>N</td><td>N</td><td>Y</td></tr><tr><td>Hodgson</td><td>2021</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>Y</td></tr><tr><td>Humphreys</td><td>2023</td><td>N</td><td>N</td><td>NA</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Li</td><td>2024</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Lichter</td><td>2017</td><td>Y</td><td>N</td><td>NA</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Marr</td><td>2010</td><td>Y</td><td>N</td><td>NA</td><td>NA</td><td>N</td><td>Y</td><td>Y</td></tr><tr><td>Mulins</td><td>2017</td><td>Y</td><td>N</td><td>NA</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Naidenova</td><td>2022</td><td>NA</td><td>Y</td><td>N</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Pichio</td><td>2024</td><td>Y</td><td>N</td><td>N</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>
<table><tr><td>Qin</td><td>2022</td><td>N</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Wayne</td><td>1967</td><td>N</td><td>N</td><td>N</td><td>NA</td><td>N</td><td>N</td><td>Y</td></tr><tr><td>Xu</td><td>2024</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Zacharko</td><td>2021</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td></tr><tr><td>Zacharko</td><td>2023</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td><td>N</td></tr><tr><td>Zhang</td><td>2024</td><td>N</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Zhou</td><td>2019</td><td>Y</td><td>N</td><td>N</td><td>N</td><td>Y</td><td>Y</td><td>Y</td></tr></table>
AQI

<figure><img src="image_4.png" /></figure>

PM10

<figure><img src="image_5.png" /></figure>

NO2

<figure><img src="image_6.png" /></figure>

PMCoarse

<figure><img src="image_7.png" /></figure>

PM2.5

<figure><img src="image_8.png" /></figure>

O3

<figure><img src="image_9.png" /></figure>

CO

<figure><img src="image_10.png" /></figure>

SO2

<figure><img src="image_11.png" /></figure>

Number of observation (tertiles)

<figure><img src="image_12.png" /></figure>
545 **Figure 2** Harvest plots of tested associations between air pollution and sport performances

546

547 Notes. Each bar represents a tested association; the height of the bar represents the article's

548 methodological quality, and the colour of the bar represents the number of used observations. Findings

549 from Xu et al (2024) were not represented in this plot because they examined the number of scored

550 goals. NA=not applicable.
<table><caption>Table 3. Findings from RVE meta-analyses.</caption><thead><tr><th></th><th>Performances</th><th>d</th><th>95%CI</th><th>I<sup>2</sup></th><th>Tau<sup>2</sup></th><th>N study</th><th>K</th><th>N observations</th></tr></thead><tbody><tr><td rowspan="2">AQI</td><td>All</td><td>-0.32</td><td>-0.94 0.30</td><td>99</td><td>0.77</td><td>6</td><td>20</td><td>734 401</td></tr><tr><td>Endurance</td><td>-0.001</td><td>-0.05 0.04</td><td>0</td><td>0.00</td><td>4</td><td>6</td><td>645 322</td></tr><tr><td rowspan="2">CO</td><td>All</td><td>-0.40</td><td>-1.70 0.92</td><td>99</td><td>0.01</td><td>5</td><td>9</td><td>938 637</td></tr><tr><td>Endurance</td><td>-0.0001</td><td>-0.01 0.01</td><td>0</td><td>0.00</td><td>4</td><td>5</td><td>455 282</td></tr><tr><td rowspan="2">NO<sub>2</sub></td><td>All</td><td>-0.04</td><td>-0.19 0.11</td><td>96</td><td>0.01</td><td>6</td><td>19</td><td>1 124 823</td></tr><tr><td>Endurance</td><td>-0.04</td><td>-0.33 0.24</td><td>97</td><td>0.01</td><td>5</td><td>15</td><td>641 468</td></tr><tr><td rowspan="2">O<sub>3</sub></td><td>All*</td><td>-0.24</td><td>-0.54 0.06</td><td>99</td><td>0.03</td><td>11</td><td>29</td><td>1 214 016</td></tr><tr><td>Endurance*</td><td>-0.40</td><td>-0.92 0.11</td><td>99</td><td>0.66</td><td>7</td><td>19</td><td>882 884</td></tr><tr><td rowspan="2">PM<sub>10</sub></td><td>All</td><td>-0.31</td><td>-1.10 0.47</td><td>99</td><td>0.37</td><td>7</td><td>14</td><td>502 455</td></tr><tr><td>Endurance</td><td>-0.0008</td><td>-0.05 0.04</td><td>0</td><td>0.01</td><td>4</td><td>6</td><td>285 634</td></tr><tr><td rowspan="3">PM<sub>2.5</sub></td><td>All</td><td>-0.06</td><td>-0.15 0.03</td><td>99</td><td>0.02</td><td>12</td><td>34</td><td>1 665 453</td></tr><tr><td>Endurance</td><td>-0.0009</td><td>-0.01 0.01</td><td>0</td><td>0.00</td><td>8</td><td>21</td><td>688 635</td></tr><tr><td>Technical</td><td>-0.24</td><td>-0.94 0.46</td><td>99</td><td>0.13</td><td>4</td><td>9</td><td>493 463</td></tr><tr><td>SO<sub>2</sub></td><td>All</td><td>-0.08</td><td>-0.41 0.24</td><td>97</td><td>0.01</td><td>5</td><td>12</td><td>1 122 376</td></tr></tbody></table>

Notes. * = after outlier exclusion