PCLM-D-26-00004

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

PLOS Climate

Dear Dr. Bernard,

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Academic Editor

PLOS Climate

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Reviewers' comments:

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-->Comments to the Author

1. Does this manuscript meet PLOS Climate’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This is a well-written systematic review and meta-analysis of the effects of short-term exposure to air pollutants on sports performance. Overall, the authors found no significant association between AQI, PM2.5, or NO2 and performance, though O3 may impair performance. I have methodological/framing concerns that I detail below. If these can be addressed, this study will be a nice addition to the literature.

Methods:

1) Risks of Bias: Maybe not exactly in this section, but the authors should discuss the potential for bias in air pollution measurement/estimation, as some studies used models while others used monitoring stations.

2) Which sports are categorized as endurance, speed/power, or technical?

3) Please clearly specify how findings were harmonized across studies. For example, studies report findings for different increases in PM2.5 (e.g., 1 ug/m3 vs. a quartile) and parameterize performance differently (e.g., directly modeling finish time in seconds vs. modeling ln(finish time)), which cannot always straightforwardly be converted to the same scale.

4) Please state how many unpublished studies, if any, were included in these analyses. If none were included, consider removing lines 142:145.

Results:

1) Table 1: The authors state that the Cussick paper found no association between race-day pollution and finish time. To my knowledge, this manuscript did not model race-day pollution separately from lagged effects. In addition, in the manuscript, in Figure 2.a, lag day 0 PM2.5 (race day, to my understanding) is significantly associated with slower performance. I see the note about re-running the author’s data, but this is a little concerning to me. If the authors wish to include this finding, they need include more information, possibly in the supplement, about this analysis.

2) Table 1: Please indicate which category the sport was classified as (speed, technical, etc.)

3) Table 1: Please specify which distance is meant by “Cross-country” for Wayne et al. (5km?)

4) Line 357:359: The authors report that twenty height associations found no significant effect between PM2.5 and endurance performance, though only 11 studies could be counted as endurance studies. I see that the authors draw a distinction between a tested association and a study, but this distinction could be elaborated. Perhaps the authors can specify in Table 1 how many associations each study is contributing. I found it in Supplemental File 3, but readers without a background in R or without the time might have a hard time with it. Moreover, they should specify what each association corresponds to (e.g., men vs. women, top 3 finishers, etc.). Finally, it can feel misleading to present things in terms of associations alone since the reader may interpret this as independent studies (when in fact the associations are not independent and may share sources of bias); perhaps the authors can phrase it as XX number of associations from XX number of studies found no association.

5) Harvest Plot: The authors need to indicate which associations come from which studies, because, as I have already discussed, interpreting associations as independent is misleading, and it is difficult to determine how many associations each study represents. This could be achieved by adding numbers on top of each bar that correspond to the study it represents. Moreover, please consider coloring the number of observations continuously rather than by tertile. If the range is too large, the authors can log-transform.

6) Supplemental File Forrest plot does not align with the harvest plot. Looking at the endurance PM2.5 plot, I am surmising that bars with the same height and color likely represent contributions from the same study. How is one study contributing 9 associations? I can’t find 9 associations with the same quality scores in Supplemental File 3.

7) Briefly looking at Supplemental File 3 PM2.5 and the Harvest Plot, since that is what I am most well-versed in, I am confused about how some of these studies are classified as finding no association when they, in fact, found significant associations (e.g., Guo and Naidenova). Am I misunderstanding how these are meant to relate to each other?

8) Harvest Plot: What formula was used to calculate the inverted weighted mean of risk of bias?

Discussion:

1) As was done for the O3 paragraph, please compare the PM2.5 range in the present study with that in laboratory settings.

2) Line 45 in the Abstract states: An absence of significant association between AQI, PM2.5, and NO2 exposure and performance is clearly supported by our systematic review […]. However, regarding PM2.5, line 431 states, “These findings should be interpreted with caution,” and lines 441:442 state, “Our analyses indicate that no definitive conclusions can be drawn regarding the associations between CO, NO2, and SO2, due to the limited number of tested associations.” Finally, the conclusion states, “While no consistent associations were observed for AQI, PM2.5, or NO2.” Please align the messaging, especially since some may only read the abstract •

3) Line 426: I am still concerned about the distinction between studies and associations. Because multiple associations originate from the same study, these estimates are not independent and could share sources of bias. Therefore, stating that 35 associations (77%) between PM2.5 and performances were not significant could be misleading if readers aren’t reading closely. See my recommendation above.

Supplemental File 3:

1) I am confused about why the Guo study is included as showing no association between PM2.5 and finish time. From the manuscript, it seems that a 1% increase in PM2.5 is significantly associated with 0.027% longer finish time (Guo, Table 7). Similar with the Naidenova study.

Reviewer #2: The manuscript examines the influence of several pollutants/air quality variables on real-world athletic performance via a meta-analysis. Inclusion criteria for studies in real world settings are comprehensive and sensible to carry out a meta-analysis. Methodology for analysis is well explained and supported by cited literature. The findings and conclusions drawn are accurate and interesting to read. Obviously a reason for the discrepancy between real-world and lab-based studies is the difference in pollutant concentrations as highlighted in the manuscript. However, a few sentences/a paragraph on some of the difficulties in obtaining representative pollution exposure measurements for athletes in competition and potential methods to overcome this would be helpful for future research recommendations to progress the field further.

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Reviewer #1: No

Reviewer #2: No

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

PCLM-D-26-00004R1

Dear Dr. Bernard,

We are pleased to inform you that your manuscript 'Air pollution and performances in outdoor sports: a systematic review and meta-analysis of short-terms associations in amateur and elite athletes' has been provisionally accepted for publication in PLOS Climate.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Climate.

Best regards,

Rebecca K. Saari

Academic Editor

PLOS Climate

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