{"doi":"10.1111/ppe.70008","title":"Clearing the Air on Reproductive Health: Unpacking the Impact of <scp>PM<sub>2</sub></scp><sub>.5</sub> Constituents on Fecundability","abstract":"Infertility remains a pressing public health challenge, affecting 10%–17% of couples globally in their lifetime [1]. While characteristics such as age and lifestyle behaviours are well-documented risk factors, emerging evidence points to environmental exposures—specifically, air pollution—as significant contributors to fertility outcomes. Previous studies examining the association between particulate matter (PM) with a diameter less than or equal to 2.5 μm (PM2.5) and fertility provided mixed results in spontaneous conceptions and IVF populations [2-4]. One of the possible explanations for these mixed results is the difference in PM2.5 composition, which varies over both time and space [5]. In this issue of Paediatric and Perinatal Epidemiology, Wesselink and colleagues [6] offer a critical advancement in this area by investigating how specific chemical constituents of PM2.5, rather than total PM2.5 mass alone, are associated with fecundability, a robust marker of fertility. They used data from an internet-based preconception cohort study of 5905 Danish couples trying to conceive linked with PM2.5 constituents from spatiotemporal models across each menstrual cycle. Among seven constituents, primary organic aerosol (POA) had the strongest association with fecundability (fecundability ratio (FR) 0.93, 95% confidence interval [CI] 0.87, 0.99). Notably, the study adds to the ongoing discussion regarding whether the health effects of PM2.5 are driven primarily by particle size, chemical toxicity, or both. This commentary will focus on the additional considerations and analytical challenges to infer causality beyond those encountered in research focused solely on PM2.5 mass. The data used to assign exposures to PM2.5 constituents are subject to measurement errors, generally larger than those for total PM2.5 mass, potentially influencing point estimates and standard errors in health outcome analyses. The extent of measurement error can also vary between pollutants, which may lead to stronger and more precise associations for certain constituents simply because they are measured more accurately than others. When a constituent's daily levels are extremely low, they may fall below the detection limit, leading to unstable concentration estimates. Additionally, PM2.5 mass is often correlated with both constituent concentrations and health outcomes, making the estimation of the constituents' causal effect complex (Figure 1). If a constituent makes up a substantial portion of PM2.5 mass or shares a similar exposure pattern with PM2.5 (e.g. due to a common source), it may appear more strongly linked to health effects—not because of its inherent toxicity, but due to its association with PM2.5. Since constituents often serve as indicators of pollutant mixtures from the same source, findings related to a specific constituent reflect not only its effects but also those of other co-occurring pollutants with similar exposure patterns. Although many constituents contribute minimally to total PM2.5 mass, associations with fecundability may still be observed because, despite their small mass, they can exhibit high toxicity individually or in combination with other co-pollutants. Additionally, the concentrations of many constituents are influenced by the same meteorological conditions, leading to strong correlations in their daily fluctuations. Some constituents also act as tracers for specific pollution sources. As a result, health outcome estimates for a given constituent reflect both its direct effects and those of related constituents with similar sources or exposure patterns. Exposure misclassification in pollution studies mainly stems from two types of measurement error [7]. Berkson-type errors occur when pollutant concentrations averaged to the available spatial scale (e.g. county, zip code) are used as proxies for individual exposures. While these errors typically introduce minimal or no bias when the true dose–response is linear, they can dec","journal":"Paediatric and Perinatal Epidemiology","year":2025,"id":562337,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9484,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":18642,"name":"Stefania I. Papatheodorou","orcid":"0000-0002-9451-9094","position":0,"is_corresponding":true}],"reference_count":11,"raw_metadata":null,"created_at":"2026-07-19T02:56:05.550545Z","pmid":"39976174","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}