Evaluation of the effects of air pollutants on lung function using ambulatory air pollution monitor data from the Mobilisense Project

Evaluation of the effects of air pollutants on lung function using ambulatory air pollution monitor data from the Mobilisense Project | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of the effects of air pollutants on lung function using ambulatory air pollution monitor data from the Mobilisense Project Najat Rizk, Basile Chaix, Isabella Annesi-Maesano, Sanjeev Bista, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9022657/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background : Exposure to air pollution negatively impacts respiratory health, but limited research exists on its short-term effects while simultaneously considering several pollutants measured with sensors. Objective : This study investigated the impact of air pollutants on lung function among 199 participants in Paris, France. Participants' exposure to black carbon (BC), nitrogen dioxide (NO 2 ), nitrogen monoxide (NO), carbon monoxide (CO), ozone (O 3 ), and particulate matter (PM 2.5 ) was recorded continuously. Lung function was assessed using spirometry tests conducted twice per day in the morning and evening over three days (N = 2504), measuring forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and the FEV1/FVC ratio. Methods : Air pollution levels were averaged over time windows from 15 minutes to 6 hours before the spirometry tests. Mixed-effect linear models were used to estimate the pollutants' associations with lung function. Results : Results showed that increased exposure to BC and PM2.5 was associated with a reduced lung function. A 1 μg/m 3 increase in BC within 1 or 2 hours prior to testing was associated with a decrease in FEV1 by 0.016 (95% CI -0.024, -0.008) and 0.021 (95% CI -0.034, -0.007) respectively. Similarly, increases in BC exposure over 2 hours to 4 hours were associated with a decrease in the FEV1/FVC ratio. Additionally, PM2.5 exposure 15 or 30 minutes or 1 hour before testing was linked to a 0.60 (95% CI -1.30, -0.03), 0.70 (95% CI -1.39, -0.09) and 0.50 (95% CI -1.10,-0.01) percentage points reduction in the FEV1/FVC ratio. Ozone (O3) was positively associated with FEV1 and FVC. No associations were found for other pollutants or time windows. Significance : This study highlights the detrimental short-term effects of air pollution, particularly BC and PM2.5, on lung function during daily mobility. Impact statement: This study provides evidence that short-term exposure to air pollutants – particularly black carbon (BC) and fine particulate matter (PM2.5) – can impair lung function. The findings demonstrate that even brief increases in BC and PM2.5 during daily mobility are associated with measurable reductions in FEV1 and the FEV1/FVC ratio. By assessing multiple pollutants across short exposure windows (15 minutes to 6 hours), this study strengthens causal inference regarding rapid respiratory effects and underscore the health relevance of transient pollution peaks encountered in urban environments, particularly from traffic emissions. Clinical trial number : not applicable. air pollutants lung function spirometry ambulatory monitors lung diseases Figures Figure 1 1 Introduction Exposure to environmental factors are constantly happening in the natural world (Choo et al., 2023).The expansion of urban areas has brought about various consequences, including issues related to transportation and increased exposure to harmful environmental pollutants (Malagón-Rojas et al., 2022). Nine out of ten individuals worldwide inhale air that is heavily contaminated with pollutants (Cipryan et al., 2020).Air pollutants [particulate matter with an aerodynamic diameter below 10 µm (PM10) or 2.5 µm (PM2.5), nitrogen dioxide (NO 2 ) and sulfur dioxide (SO 2 )] originate from various sources, including vehicle emissions, industrial processes, and power plants (Jion et al., 2023). Additionally, secondary pollutants can form through the interaction of other pollutants: for example, ozone (O 3 ) is formed by the interaction of nitric oxides, volatile organic compounds (VOCs), and sunlight (Tiotiu et al., 2020). Air pollution stands as the fourth most significant factor contributing to the loss of disability-adjusted life years (DALYs) and mortality (Lee et al., 2021), after tobacco, lead exposure, and high body mass index (BMI) (Levine & Marciniuk, 2022). A comprehensive analysis conducted by the Global Burden of Disease initiative revealed that air pollution was accountable for a staggering 6.67 million deaths in 2019 (Lee et al., 2021). Various studies have documented associations between long-term exposure to air pollution and development of lung cancer (Pope Iii, 2002), chronic obstructive pulmonary disease (X. Huang et al., 2019), asthma (Annesi-Maesano et al., 2021), deterioration of lung function (Chen & Hoek, 2020; Thurston et al., 2020) and increased mortality rates (Stafoggia et al., 2022; Teng et al., 2022). Evidence is even stronger for the short-term exposure to outdoor air pollutants in relation to various aspects of respiratory diseases (Lee et al., 2021) such as lung function impairment (Mentz et al., 2019), asthma exacerbations (Garcia et al., 2019; Zuo et al., 2019), chronic obstructive pulmonary disease (Doiron et al., 2019)and respiratory mortality (So et al., 2022). The studies mentioned above have demonstrated a strong correlation between exposure to various air pollutants and lung function, an important marker of respiratory health impairment. Nevertheless, there is a gap in research, particularly in terms of the spatiotemporal assessment of the exposure: research frequently evaluates short-term exposure to outdoor pollutants by using data from the nearest monitoring station, averaging or interpolating data from various stations, or utilizing residential estimates derived from air dispersion models or land use regression models (Tiotiu et al., 2020) . Few studies have specifically investigated the impact of air pollution exposure on respiratory health outcomes, particularly in France, while considering the measured rather than only estimated individual’s short term exposure (Sesé et al., 2023). Although they also have their limitations, there is a general consensus that portable monitors are essential in improving the assessment of personal exposures (Dons et al., 2012). The integration of mobility into exposure research offers an opportunity to improve our understanding of how movement in space and time affects both positive and negative environmental exposures (Pearson et al., 2024). Additionally, passive sensing (such as air pollution or noise monitors, GPS trackers (Chaix et al., 2022)) allows monitoring individuals' environmental conditions (including air pollution exposure) and activities along daily mobility paths and makes it possible to potentially include external geographic data in the analysis (E.-K. Kim et al., 2023). This study seeks to provide insights into the potential health risks posed by the exposure to air pollution during daily life among healthy adults in the Grand Paris region. To address gaps in the literature, this study aims to further investigate the association between time-varying air pollution exposure and lung function measured trough ambulatory air pollution monitors carried during daily activities and spirometry tests. These measures together are able to provide a new perspective for understanding the short-term health effects of exposure in different microenvironments (Chaix, 2018). We first assessed the associations between the short-term exposure to various air pollutants [particulate matter (PM), nitrogen monoxide (NO), nitrogen dioxide (NO 2 ), carbon monoxide (CO), black carbon (BC) and ozone (O 3 )] and lung function [forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC) and FEV1/FVC]. Secondly we evaluated the possible relationships of daily fluctuations in and accumulation of air pollutant levels between morning and evening with lung function. 2 Materials and methods 2.1 Survey and study population: This research was carried out in the Grand Paris region, which includes the city of Paris and several nearby municipalities, in France. The study took place from May 2018 to March 2022 as part of the initial phase of the MobiliSense project (Chaix et al., 2022) which received funding from the European Research Council. The participants were selected using a two-stage stratified random sampling method following the protocol described elsewhere (Chaix et al., 2022). Initially, neighborhoods were randomly chosen from the first and last quartiles of road traffic density within each quartile of area income. The second stage involved the random selection of dwelling units within the pre-selected neighborhoods, using data from the 2013 and 2014 population censuses conducted by the National Institute of Statistics and Economic Studies. A total of 31,970 dwellings were selected from 234 neighborhoods (Bista et al., 2023) as shown in figure 1. Postal invitations were sent twice to the residents of the selected dwellings, resulting in the recruitment of 289 nonsmokers participants aged between 30 and 64 years in the sensor-based MobiliSense study (Bista et al., 2022). Participants had to conduct the same data gathering process, including various sensors and questionnaires, within 1 or 2 years after the initial wave. In the present study we focused on the first wave data. Figure 1 During the 6-day survey, participants were asked to carry different combinations of sensors over the different days (Chaix et al., 2022). In addition to questionnaire data (sociodemographic, health status, behaviors and health habits), the analysis in this study focuses on data collected from days 1 and 2 from portable ambulatory monitors of air pollutants (AE51, PAQM520) and spirometry test (Spirotel 2) as shown in Table1. Table 1 2.2 Spirometry measures Participants underwent spirometry testing with the Spirotel 2 device (MIR, Langlade, France) for three days, both in the morning and in the evening, prior to taking their medications. The Spirotel 2 device is designed to meet the standards set by the applicant tracking system (ATS) and international organization for standardization (ISO). This device is clinically approved for screening and monitoring requirements (Fonseca et al., 2005).It measures various parameters such as peak expiratory flow, forced expiratory volume in 1 second, forced vital capacity (FVC), forced expiratory flow between 25% and 75% of vital capacity, and forced expiratory volume in 6 seconds (FEV6). It is reliable for measuring forced expiratory flows in large and small airways and it has a sensitivity for detecting airway obstruction that is comparable to the laboratory spirometer in both large and small airways (Ezzahir et al., 2005). The Spirotel 2 device automatically transmitted measures to a remote server through a smartphone provided to the participants. Our research assistants underwent extensive training and visited the participants' homes to demonstrate the proper usage of the device. They carefully explained the measurement process and encouraged participants to exert maximum effort during expiration. Each morning and each evening, we performed two spirometry measurements and we selected the best one for each indicator based on the reproducibility test results. For FEV1, the curve is deemed acceptable if the value is less than or equal to 150ml, and for FVC if the value is below or equal to 150ml (Forbes et al., 2009; Smith et al., 2018). The spirometer curves were monitored remotely on a daily basis and if any issues with the quality of the curves was detected, participants were contacted by phone to solve the problem (Chaix et al., 2022). Once the best two curves were identified, the higher value between the two measurements was selected. In our study, FEV1 was considered as our first outcome, as abnormal lung function in patients is often indicated by a forced expiratory volume in 1 second (FEV1) that falls below 80% (Kirkby et al., 2019; Shapira et al., 2021). This measure represents the severity of obstructive lung diseases such as asthma and chronic obstructive pulmonary disease (Langan & Goodbred, 2020). As a second outcome we analyzed the forced vital capacity (FVC). FVC < 60% as categorized as moderate-to-severely reduced lung function (Cohen et al., 2017). We included the FEV1/FVC ratio as a third outcome, as an indicator of possible pulmonary obstruction (for a ratio below 70%) (Wu et al., 2021). Finally, to further analyze the fluctuations between morning and evening measurements, we introduced a fourth variable denominated “delta”. This variable is measured as the difference between the evening and morning measurements for FEV1 (delta FEV1) and FVC (Delta FVC). The delta values were then considered as the fourth outcome in our study. 2.3 Exposure to air pollutants Participants were asked to carry during their activities the PAQM 520 portable device (Table 1) to measure personal exposure to concentrations of gases (O 3 , NO 2 , NO, and CO) as well as particle matter PM 2.5 (Chaix et al., 2022). This portable device was calibrated against reference instruments to ensure accuracy (Bista et al., 2023). Gas measurements were averaged over 10-second intervals. Particle measurements were taken every 1 minute over a 5-second interval and temperature and humidity measured by the device were used to account for any changes in the environment. More details about the measurement of pollutants can be found elsewhere (Chaix et al., 2022). Measurements with the PAQM 520 were conducted on days 1, 2, 5, and 6 of the Mobilisense project, thus including the initial two days of spirometry measurements on days 1 and 2. Participants were also asked to carry the Aethalometer (MicroAeth AE51, AethLabs, CA, USA) to collect data on the personal exposure to black carbon (BC). This device has been utilized in multiple previous epidemiological studies (Louwies et al., 2015; Mirowsky et al., 2015; X. Zhao et al., 2014). Participants carried the device on a belt, positioning the tube's inlet at neck height to capture BC concentration within their breathing zone. To address incorrectly high and low BC values, the Optimized Noise Reduction Averaging (ONA) algorithm (Hagler et al., 2011) was applied to the 10-second measurements, accounting for filter changes. Detailed information regarding the processing steps and algorithm can be found in our previous publication (Bista et al., 2022, 2023; Chaix et al., 2022). 2.4 Covariates Demographic and socioeconomic information was collected prior to the sensor-based assessment through a web questionnaire administered by a research assistant. Sex was coded as a binary variable (male; female), while age was included as a continuous variable. Education (3 categories: less than the baccalaureate; equal to the baccalaureate; higher than the baccalaureate), employment (4 categories: employed; not employed; retired; other) and monthly income per household member (continuous) were included as socioeconomic covariates. Income was then divided into three groups using the tertiles: low ≤ 1600; 1600 < Medium < 2300; and high ≥ 2300 euros. BMI was calculated based on measured height and weight. We also included contextual factors and environmental variables that change over time, including season, pollen level from the French Aerobiology Network stations, and temperature and humidity from the PAQM 520 sensor (the last 3 variables were examined at the day level). Residence area was defined as either in the central city (Paris) or around it. Days were classified into week day and weekend, while time of the day was categorized as morning or afternoon-evening (with a break at noon). Additionally, with the questionnaire, we assessed health habits and behaviors including alcohol consumption (categorized as regular, frequently, occasionally, and never) and smoking status (categorized as non-smoker, ex-smoker, occasional smoker, and regular smoker). 2.5 Statistical analyses Given the longitudinal nature of the data as repeated measurements related to the time-varying exposures and the outcomes, linear mixed models were utilized to assess the relationship between average pollutant exposure and spirometry measurements (FEV1, FVC, FEV1/FVC, delta FEV1 and delta FVC). For the first 3 outcomes (FEV1, FVC, and FEV1/FVC), both the morning measurement and the evening measurement on each day were pooled and analyzed together as distinct observations in the same dataset. Models assessed time-varying exposures and covariates across a range of time windows from 15 minutes to 6 hours prior to the spirometry measurements. To account for the correlation within subjects of repeated outcome measures, two random intercepts were specified at the individual and day levels in all models. Covariables retained in the models were selected according to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. Based on this selection of variables, we adjusted the models for the following confounders: sex, age, income, day of the week, time of day, and smoking status. Each pollutant was individually tested with these covariates in a separate model in relation to the outcomes. Subsequently, a comprehensive model was estimated that included all the associated pollutants and the selected covariates that improved model fit. With the last two outcomes (delta FEV1 and FVC), we explored whether and how air pollutant exposure affected changes in lung function during the day between the morning and evening (“delta” outcomes). These linear mixed models included a random intercept at the individual level (no random effect at the day level as the outcome was at the day level). As an explanatory variable, we analyzed the difference in pollutant exposure between the evening and the morning measurement (each exposure calculated in the time windows before the measurement). Based on our selection of variables, we adjusted these last two models for the following confounders: sex, age, income, alcohol consumption, and smoking status. To investigate possible interaction effects between the selected pollutants we performed a moderation analysis, by including product terms between two pollutants. Finally, we tested our models considering individuals as fixed effects in order to estimate associations only on the basis of within-individual differences. The fixed effect model specifies for each individual a fixed intercept or effect by adding k-1 dummy variables for the k individuals (Schempf & Kaufman, 2012). This approach permits to neutralize all individual-level confounders. This methodology was applied to all the outcomes considered (FEV1, CVF, FEV1/CVF ratio, delta FEV1 and delta CVF). All the analyses were performed using the R software version 4.3.3 and the "lme4" package. 3 Results 3.1 Analytical sample We collected data from 289 participants. A total of 90 participants were excluded from the present study due to the unavailability of valid spirometry measurements. Consequently, the analysis was conducted on 199 participants for a total 2504 spirometry observations. In Table 2 we provide a concise overview of the participants’ sociodemographic characteristics, health-related behaviours, and lung function. Among the participants, 52.8% were female Participants had had an average age of 51.2 years. Overall, 20% resided in Paris, while the remaining majority lived in the suburbs. The majority of the sample (60.8%) reported frequent alcohol consumption, were non-smoker (57.8%) and did not take any medication or have hypertension (94%). On average, spirometry tests on participants resulted in 3.28L for FEV1 and 4.16L for FVC. The average daily pollutant concentration between morning and evening (considering all the time between the morning measurement and the evening measurement) were 24 ppb for NO, 12 ppb for NO 2 , 870 ppb for CO, 17 ppb for O 3 , 1600 ng/m 3 for BC, and 26μg/m 3 for PM 2.5 . Detailed statistics about the outcomes (FEV1, FVC, FEV1/FVC, delta FEV1, and delta FVC) are presented in Appendix Table A. Statistics on exposure to targeted air pollutants, ranging from 15 minutes to 6 hours before the morning and evening measurements, are outlined in Appendix Table B. Table 2 3.2 Associations between air pollutants and lung function assessments Table 3 shows the final models with the pollutants that were independently associated with FEV1, FVC, and the FEV1/FVC ratio. As shown at the top of the Table, BC and O 3 were independently associated with FEV1. A 1 μg/m 3 increase in BC exposure over the preceding 1 hour and 2 hours was associated with a decrease of 0.016 (95% CI: -0.024, -0.008) and 0.021 (95% CI: -0.034, -0.007) in FEV1. Moreover, after controlling for BC and other covariates, a 1 ppb increase in O 3 over the preceding 1 hour and 2 hours was related to a 0.43 (95% CI: 0.04, 0.81) and 0.45 (95% CI: 0.08, 0.83) increase in FEV1. No association with FEV1 was found with other pollutants or at different time intervals. Results also showed (Table 3) that 1 ppb rise in O 3 exposure within the 15 minutes to 2 hours prior to the spirometry test led to an increase in FVC by 0.64 (95% CI: 0.01, 1.29), 0.62 (95% CI: 0.02, 1.22), 0.64 (95% CI: 0.009, 0.11), and 0.67 (95% CI: 0.01, 1.12), respectively. No associations with FVC were found with other pollutants or at different time intervals. Finally, Table 3 shows that an increase of 1 μg/m 3 in the PM2.5 exposure during the 15 minutes, 30 minutes, and 1 hour before spirometry measurements resulted in a decrease in the FEV1/CVF ratio by 0.60 (95%CI -1.30,-0.03), 0.70 (95%CI -1.39, -0.09), and 0.50 (95% CI -1.10,-0.01) percentage points, respectively. Similarly, a rise of 1 μg/m 3 in BC exposure within the 2 to 4 hours before spirometry measurements lead to a reduction in the FEV1/CVF ratio of 0.26 (95%CI -0.44, -0.09), 0.15 (95%CI -0.29, -0.01), and 0.17 (95%CI -0.32, -0.01) percentage points. No associations were found for the FEV1/FVC ratio with other pollutants or at different time intervals. 3.3 Associations between air pollutants and change in lung function between morning and evening. The exposure to air pollutants between the morning and evening measurements were not associated with the change between morning and evening in FEV1 and FVC. Similarly, the exposure to air pollutants in time windows before the evening measurement were not associated with the change in lung function between morning and evening. 3.4 Sensitivity analyses The fixed effect model results are presented in appendix Tables C to E. For FEV1, the findings of the fixed effect models aligned with those of the random effect models (the 95% CI for the association between O3 over the past hour and FEV1 just overlapped value 0, but the pattern of association was similar). For FVC, there were also positive associations with O3, however most (but not all) of the 95% CIs overlapped value 0. Regarding the FEV1/FVC ratio, the patterns of negative associations between PM 2.5 or BC were exactly the same, with the only exception that for the 1 hour window of exposure, the two pollutants were simultaneously negatively associated with the outcome. We did not document any relationship of air pollutants with the morning to evening change in lung function. We did not observe any interaction of effects between air pollutants on the lung function outcome. Table 3 4 Discussion This study investigated the association of the time-varying air pollution exposure with repeated measurements of lung function in 199 healthy adults. The main results show that the exposure to BC 1h to 2h before the spirometry test was negatively associated with both FEV1 and the FEV1/FVC ratio. Regarding the FEV1/FVC ratio, the most acute associations were observed in relation to the exposure to PM 2.5 in the 15 minutes to 1 hour before the spirometry test. In contrast to these results, our study revealed that short-term exposure to O 3 in the 15 minutes to 2 hours before the test contributed to an increase in FEV1 and in FVC. It is also important to note that we did not find any associations between individual exposure to nitrogen monoxide (NO), carbon monoxide (CO), or nitrogen dioxide (NO 2 ) and the measured lung function parameters. 4.1 Black Carbon (BC) The observed association between short-term exposure to BC and lung function aligns with findings from previous population-based studies involving both healthy and unhealthy individuals. Evidences in the literature show that black carbon exposure negatively impacts lung function of individuals, particularly affecting their middle and upper airways (Gardiner et al., 2001 ) and could impair lung function in healthy individuals (Bessagnet et al., 2022 ). For example, a research conducted in Beijing found 1 µg/m 3 increase in BC linked to a 0.18% (95% CI -0.34,-0.03%) decrease in FEV1 during 2 hours of exposure to air pollutants (J. Huang et al., 2016 ). In accordance with these results our study found that a 1 µg/m 3 rise in BC was associated with a 0.021 reduction (95% CI -0.034, -0.007) in FEV1 after 2 hours of exposure. The BAMSE prospective birth cohort study in Sweden (Yu et al., 2023 ) revealed that decreasing BC concentrations were consistently linked to higher annual growth rates of FEV1 and FVC in children after 24 years of follow ups (Yu et al., 2023 ). The respiratory health study on BC and European workers (Gardiner et al., 2001 ) also demonstrated a correlation between exposure to BC and declines in FEV1 and the FEV1/FVC ratio. Similar results were also observed in China, France, Spain and Japan (J. Huang et al., 2016 ; Paunescu et al., 2019 ; Yoda et al., 2017 ). 4.2 Particle matter (PM 2.5 ) PM 2.5 has been described as the air pollutant that poses the highest risk to respiratory health (Ilenič et al., 2024 ). In our research, the average PM 2.5 concentration falls between 26.5 and 34 µg/m 3 , exceeding the standard mean established by European air quality regulations that aligns with WHO guidelines of 15 µg/m 3 (European Environmental Agency ambient air quality and cleaner air 2024 .). We did not observe association between PM exposure and FEV1 or FVC. However, our results that showed that PM 2.5 reduced FEV1/FVC ratio aligns with recent researches (Bo et al., 2021 ; Cai et al., 2020 ; Doiron et al., 2019 ; Elbarbary et al., 2020 ; Panigrahi & Padhi, 2018 ; Schikowski et al., 2005 ), that demonstrated a negative association between particulate matter (PM) exposure and the FEV1/FVC ratio in both children and adults. 4.3 Carbon monoxide (CO) The main consequence of exposure to high outdoor concentrations of CO is hypoxia, leading to symptoms such as confusion, headache, and nausea (Canova et al., 2010 ). Our research did not reveal any link between CO levels and spirometry measurements, which aligns with a panel study carried out in Rome (Lagorio et al., 2006 ). Conversely, a study in China (Song et al., 2023 ) demonstrated positive relationships between ambient CO exposure and the risk of hospitalization for various respiratory conditions such as chronic obstructive pulmonary disease, asthma, and influenza-pneumonia, as well as a decrease in FEV1, FVC, and FEV1/FVC (Wei et al., 2023 ). It is important to note that the average concentration of CO measured in our research did not exceed 1000 ppb (equivalent to 1.15 mg/m 3 ), with a 95th percentile of 1929.7 ppb. This value is notably lower than the European standards and the WHO's Air Quality Guidelines target of 4 mg/m 3 for outdoor air (European Environmental Agency ambient air quality and cleaner air 2024 .; WHO air quality guidelines, 2021 .).This discrepancy may explain why no association was detected between the spirometry measures and CO in our study. 4.4 Nitrogen dioxide (NO 2 ) and nitrogen monoxide (NO) Similar to a Korean cohort study (Kwon et al., 2020 ), our research did not uncover a link between NO 2 levels and lung function, which is in contrast to prior studies that linked short-term NO 2 exposure to decreased FEV1 and FVC or FEV1/FVC ratio in adults (Adam et al., 2015 ; Elbarbary et al., 2020 ; Lim et al., 2022 ; Schindler et al., 2001 ; Strassmann et al., 2021 ). Studies have shown that NO 2 increases the risk of developing and exacerbating asthma (Tiotiu et al., 2020 ), and heightens the likelihood of chronic obstructive pulmonary disease attacks (Wu et al., 2021 ). It is possible that our study lacked the necessary power to identify a potential small association between moderate NO 2 exposure and lung function in healthy individuals. For example, we identified an association between NO 2 and FVC after adjusting for covariates, however this association disappeared when O 3 was included as well in the model. Furthermore, the average of NO 2 level in our study was 24.5 µg/m³ (13 ppb), so it was lower than the European standard and the WHO Air Quality Guidelines target of 25 µg/m³ (European Environmental Agency ambient air quality and cleaner air 2024 , ; WHO air quality guidelines, 2021 ). In accordance with a study conducted in Los Angeles (Hao et al., 2022 ), exposure to NO was found not to be associated with lung function (Hao et al., 2022 ). 4.5 Ozone O3 In contrast to previous studies (Brown et al., 2008 ; Feng et al., 2024 ; C. S. Kim et al., 2011 ; Paulin et al., 2020; T. Zhao et al., 2023), we found a positive association between the O 3 exposure and FEV1 and FVC. This finding contradicts the general understanding that ozone is negatively associated with lung function. However, it is important to note that different exposure durations can yield different outcomes. Additionally, variations in study populations, including individual physical conditions, lifestyles, education, and activity patterns (Guo et al., 2023 ), may contribute to the differences in results. Comparing the documented association directly with other studies is challenging due to variations in study design, target participants, and statistical methods employed (Feng et al., 2024 ). In our research, we found that the average O 3 concentration was around 17 ppb, equivalent to approximately 33 µg/m³. This value is largely below the European standard and the WHO Air Quality Guidelines target of 100 µg/m³ over an 8-hour averaging time (European Environmental Agency ambient air quality and cleaner air 2024 .; WHO air quality guidelines, 2021 .). This suggests that the positive association observed could be attributed to hyperventilation, as noted in previous studies (Tarlo, 2000 ; You et al., 2022 ). Additionally, an association between higher O 3 levels and an increase in daily visits for Hyperventilation Syndrome was observed in one of these studies with a correlation coefficient equal to 0,133 (You et al., 2022 ). This hyperventilation is characterized by breathing excessively beyond the body's typical metabolic needs (Boulding et al., 2016 ; You et al., 2022 ). 4.6 Limitation This study has limitations that should be taken into consideration. Firstly, the sample size included only 199 participants due to missing spirometry data. Additionally, the monitoring period was limited to 3 days, which may not accurately reflect the participants' regular behavior and variability in lung function. It is important to note that a monitoring period of only 3 days may be insufficient to detect changes in lung function and spirometry measures (De Paula Santos1 et al., 2021). Furthermore, the absence of data on gases and PM 2.5 the third day resulted in the exclusion of the corresponding spirometry data. The analysis was therefore based solely on the data from the first 2 days. Lastly, it is worth mentioning that the concentrations of air pollutants in our study were relatively low, compared to heavily polluted countries in Asia for example. As a result, the findings from our research may not be directly applicable to individuals exposed to higher levels of air pollution. However, these findings could potentially be relevant to other European countries with lower air pollution levels. Additionally, our work was based on a relatively healthy population, so the results cannot be generalized to patients suffering from diseases. 5 Conclusion In conclusion, our study provides valuable insights into the specific effects of different air pollutants on lung function in healthy adults in Paris, France, considering personal exposure assessment. Regarding implications, the findings of our study highlight the importance of minimizing the short-term exposure to BC and PM in order to protect respiratory well-being, especially in urban settings like Paris. These findings suggest that implementing measures to reduce the levels of these air pollutants in places where people conduct their daily activities can have a positive impact on the lung health of individuals. A positive impact could also be attained also by modifying exposure behaviors at the population level. Further investigations are needed to understand the role of air pollution on lung function considering personal assessments during daily activities. Declarations All authors confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. All authors have approved the manuscript and agree with its submission. Each author has participated sufficiently in the work to believe in its overall validity Ethical approval: The sampling and data collection protocol was approved by the National Council for Statistical Information, the French Data Protection Authority and the Ethical Committee of Inserm. Competing Interests: The authors declare no competing interests Funding : This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 647000, 2014 ERC Consolidator grant, MobiliSense project). Author Contribution Najat Rizk: Formal analysis; writing original draft; methodology. Basile Chaix: Funding acquisition; data collection; Methodology; assisting in statistical modelling and manuscript revision. Isabella Annesi-Maesano: assisting in statistical modelling and manuscript revision. Sanjeev Bista: Writing - review & editing. Giovanna Fancello: Conceptualization; supervision; Methodology; assisting in statistical modelling and manuscript revision. Data Availability The Mobilisense data that support the findings of this study are available upon request and with the permission of INSERM and the Nemesis team, Institut Pierre Louis d’Epidémiologie et de Santé Publique. References Adam, M., Schikowski, T., Carsin, A. E., Cai, Y., Jacquemin, B., Sanchez, M., Vierkötter, A., Marcon, A., Keidel, D., Sugiri, D., Al Kanani, Z., Nadif, R., Siroux, V., Hardy, R., Kuh, D., Rochat, T., Bridevaux, P.-O., Eeftens, M., Tsai, M.-Y., … Probst-Hensch, N. (2015). Adult lung function and long-term air pollution exposure. ESCAPE : A multicentre cohort study and meta-analysis. European Respiratory Journal , 45 (1), 38‑50. https://doi.org/10.1183/09031936.00130014 Annesi-Maesano, I., Forastiere, F., Balmes, J., Garcia, E., Harkema, J., Holgate, S., Kelly, F., Khreis, H., Hoffmann, B., Maesano, C. N., McConnell, R., Peden, D., Pinkerton, K., Schikowski, T., Thurston, G., Van Winkle, L. S., & Carlsten, C. (2021). The clear and persistent impact of air pollution on chronic respiratory diseases : A call for interventions. European Respiratory Journal , 57 (3), 2002981. https://doi.org/10.1183/13993003.02981-2020 Bessagnet, B., Allemand, N., Putaud, J.-P., Couvidat, F., André, J.-M., Simpson, D., Pisoni, E., Murphy, B. N., & Thunis, P. (2022). Emissions of Carbonaceous Particulate Matter and Ultrafine Particles from Vehicles—A Scientific Review in a Cross-Cutting Context of Air Pollution and Climate Change. Applied Sciences , 12 (7), 3623. https://doi.org/10.3390/app12073623 Bista, S., Chatzidiakou, L., Jones, R. L., Benmarhnia, T., Postel-Vinay, N., & Chaix, B. (2023). Associations of air pollution mixtures with ambulatory blood pressure : The MobiliSense sensor-based study. Environmental Research , 227 , 115720. https://doi.org/10.1016/j.envres.2023.115720 Bista, S., Dureau, C., & Chaix, B. (2022). Personal exposure to concentrations and inhalation of black carbon according to transport mode use : The MobiliSense sensor-based study. Environment International , 158 , 106990. https://doi.org/10.1016/j.envint.2021.106990 Bo, Y., Chang, L., Guo, C., Lin, C., Lau, A. K. H., Tam, T., & Lao, X. Q. (2021). Reduced ambient PM2.5, better lung function, and decreased risk of chronic obstructive pulmonary disease. Environment International , 156 , 106706. https://doi.org/10.1016/j.envint.2021.106706 Boulding, R., Stacey, R., Niven, R., & Fowler, S. J. (2016). Dysfunctional breathing : A review of the literature and proposal for classification. European Respiratory Review , 25 (141), 287‑294. https://doi.org/10.1183/16000617.0088-2015 Brown, J. S., Bateson, T. F., & McDonnell, W. F. (2008). Effects of Exposure to 0.06 ppm Ozone on FEV 1 in Humans : A Secondary Analysis of Existing Data. Environmental Health Perspectives , 116 (8), 1023‑1026. https://doi.org/10.1289/ehp.11396 Cai, Y., Hansell, A. L., Granell, R., Blangiardo, M., Zottoli, M., Fecht, D., Gulliver, J., Henderson, A. J., & Elliott, P. (2020). Prenatal, Early-Life, and Childhood Exposure to Air Pollution and Lung Function : The ALSPAC Cohort. American Journal of Respiratory and Critical Care Medicine , 202 (1), 112‑123. https://doi.org/10.1164/rccm.201902-0286OC Canova, C., Torresan, S., Simonato, L., Scapellato, M. L., Tessari, R., Visentin, A., Lotti, M., & Maestrelli, P. (2010). Carbon monoxide pollution is associated with decreased lung function in asthmatic adults. European Respiratory Journal , 35 (2), 266‑272. https://doi.org/10.1183/09031936.00043709 Chaix, B. (2018). Mobile Sensing in Environmental Health and Neighborhood Research. Annual Review of Public Health , 39 (1), 367‑384. https://doi.org/10.1146/annurev-publhealth-040617-013731 Chaix, B., Bista, S., Wang, L., Benmarhnia, T., Dureau, C., & Duncan, D. T. (2022). MobiliSense cohort study protocol : Do air pollution and noise exposure related to transport behaviour have short-term and longer-term health effects in Paris, France? BMJ Open , 12 (3), e048706. https://doi.org/10.1136/bmjopen-2021-048706 Chen, J., & Hoek, G. (2020). Long-term exposure to PM and all-cause and cause-specific mortality : A systematic review and meta-analysis. Environment International , 143 , 105974. https://doi.org/10.1016/j.envint.2020.105974 Choo, E. L. W., Janhavi, A., Koo, J. R., Yim, S. H. L., Dickens, B. L., & Lim, J. T. (2023). Association between ambient air pollutants and upper respiratory tract infection and pneumonia disease burden in Thailand from 2000 to 2022 : A high frequency ecological analysis. BMC Infectious Diseases , 23 (1), 379. https://doi.org/10.1186/s12879-023-08185-0 Cipryan, L., Kutac, P., Dostal, T., Zimmermann, M., Krajcigr, M., Jandackova, V., Sram, R., Jandacka, D., & Hofmann, P. (2020). Regular running in an air-polluted environment : Physiological and anthropometric protocol for a prospective cohort study (Healthy Aging in Industrial Environment Study – Program 4). BMJ Open , 10 (12), e040529. https://doi.org/10.1136/bmjopen-2020-040529 Cohen, K. E., Buelow, M. W., Dixon, J., Brazauskas, R., Cohen, S. B., Earing, M. G., & Ginde, S. (2017). Forced vital capacity predicts morbidity and mortality in adults with repaired tetralogy of Fallot : COHEN et al. Congenital Heart Disease , 12 (4), 435‑440. https://doi.org/10.1111/chd.12470 De Paula Santos1, U., Abdo Arbex2,3, M., Ferreira Braga3,4, A. L., Futoshi Mizutani5, R., Delfini Cançado6, J. E., Terra-Filho7, M., & Chatkin8,9, J. M. (2021). Environmental air pollution : Respiratory effects. Jornal Brasileiro de Pneumologia , e20200267. https://doi.org/10.36416/1806-3756/e20200267 Doiron, D., De Hoogh, K., Probst-Hensch, N., Fortier, I., Cai, Y., De Matteis, S., & Hansell, A. L. (2019). Air pollution, lung function and COPD : Results from the population-based UK Biobank study. European Respiratory Journal , 54 (1), 1802140. https://doi.org/10.1183/13993003.02140-2018 Dons, E., Int Panis, L., Van Poppel, M., Theunis, J., & Wets, G. (2012). Personal exposure to Black Carbon in transport microenvironments. Atmospheric Environment , 55 , 392‑398. https://doi.org/10.1016/j.atmosenv.2012.03.020 Elbarbary, M., Oganesyan, A., Honda, T., Kelly, P., Zhang, Y., Guo, Y., Morgan, G., Guo, Y., & Negin, J. (2020). Ambient air pollution, lung function and COPD : Cross-sectional analysis from the WHO Study of AGEing and adult health wave 1. BMJ Open Respiratory Research , 7 (1), e000684. https://doi.org/10.1136/bmjresp-2020-000684 European Environmental Agency ambient air quality and cleaner air 2024, Https://www.europarl.europa.eu/news/fr/press-room/20240219IPR17816/pollution-de-l-air-accord-avec-le-conseil-pour-ameliorer-la-qualite-de-l-air. Ezzahir, N., Leske, V., Peiffer, C., & Trang, H. (2005). Relevance of a portable spirometer for detection of small airways obstruction. Pediatric Pulmonology , 39 (2), 178‑184. https://doi.org/10.1002/ppul.20148 Feng, S., Yang, L., Dou, S., Li, X., Wen, S., Yan, L., Huang, W., Zhang, Y., Ma, B., Yuan, L., Li, S., Lu, P., & Guo, Y. (2024). Associations between long-term ozone exposure and small airways function in Chinese young adults : A longitudinal cohort study. Respiratory Research , 25 (1), 105. https://doi.org/10.1186/s12931-024-02679-4 Fonseca, J. A., Costa-Pereira, A., Delgado, L., Silva, L. N., Magalhães, M., Castel-Branco, M. G., & Vaz, M. (2005). Pulmonary Function Electronic Monitoring Devices. Chest , 128 (3), 1258‑1265. https://doi.org/10.1378/chest.128.3.1258 Forbes, L. J. L., Kapetanakis, V., Rudnicka, A. R., Cook, D. G., Bush, T., Stedman, J. R., Whincup, P. H., Strachan, D. P., & Anderson, H. R. (2009). Chronic exposure to outdoor air pollution and lung function in adults. Thorax , 64 (8), 657‑663. https://doi.org/10.1136/thx.2008.109389 Garcia, E., Berhane, K. T., Islam, T., McConnell, R., Urman, R., Chen, Z., & Gilliland, F. D. (2019). Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014. JAMA , 321 (19), 1906. https://doi.org/10.1001/jama.2019.5357 Gardiner, K., Van Tongeren, M., & Harrington, M. (2001). Respiratory health effects from exposure to carbon black : Results of the phase 2 and 3 cross sectional studies in the European carbon black manufacturing industry. Occupational and Environmental Medicine , 58 (8), 496‑503. https://doi.org/10.1136/oem.58.8.496 Guo, Q., Zhao, Y., Zhao, J., Bian, M., Qian, L., Xue, T., Zhang, J. (Jim), & Duan, X. (2023). Acute change of lung function to short-term exposure to ambient air pollutants with and without physical activity : A real-world crossover study. Environmental Pollution , 316 , 120481. https://doi.org/10.1016/j.envpol.2022.120481 Hagler, G. S. W., Yelverton, T. L. B., Vedantham, R., Hansen, A. D. A., & Turner, J. R. (2011). Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data. Aerosol and Air Quality Research , 11 (5), 539‑546. https://doi.org/10.4209/aaqr.2011.05.0055 Hao, H., Eckel, S. P., Hosseini, A., Van Vliet, E. D. S., Dzubur, E., Dunton, G., Chang, S. Y., Craig, K., Rocchio, R., Bastain, T., Gilliland, F., Okelo, S., Ross, M. K., Sarrafzadeh, M., Bui, A. A. T., & Habre, R. (2022). Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit. International Journal of Environmental Research and Public Health , 19 (6), 3578. https://doi.org/10.3390/ijerph19063578 Huang, J., Deng, F., Wu, S., Zhao, Y., Shima, M., Guo, B., Liu, Q., & Guo, X. (2016). Acute effects on pulmonary function in young healthy adults exposed to traffic-related air pollution in semi-closed transport hub in Beijing. Environmental Health and Preventive Medicine , 21 (5), 312‑320. https://doi.org/10.1007/s12199-016-0531-5 Huang, X., Mu, X., Deng, L., Fu, A., Pu, E., Tang, T., & Kong, X. (2019). The etiologic origins for chronic obstructive pulmonary disease. International Journal of Chronic Obstructive Pulmonary Disease , Volume 14 , 1139‑1158. https://doi.org/10.2147/COPD.S203215 Ilenič, A., Pranjić, A. M., Zupančič, N., Milačič, R., & Ščančar, J. (2024). Fine particulate matter (PM2.5) exposure assessment among active daily commuters to induce behaviour change to reduce air pollution. Science of The Total Environment , 912 , 169117. https://doi.org/10.1016/j.scitotenv.2023.169117 Jion, Most. M. M. F., Jannat, J. N., Mia, Md. Y., Ali, Md. A., Islam, Md. S., Ibrahim, S. M., Pal, S. C., Islam, A., Sarker, A., Malafaia, G., Bilal, M., & Islam, A. R. M. T. (2023). A critical review and prospect of NO2 and SO2 pollution over Asia : Hotspots, trends, and sources. Science of The Total Environment , 876 , 162851. https://doi.org/10.1016/j.scitotenv.2023.162851 Kim, C. S., Alexis, N. E., Rappold, A. G., Kehrl, H., Hazucha, M. J., Lay, J. C., Schmitt, M. T., Case, M., Devlin, R. B., Peden, D. B., & Diaz-Sanchez, D. (2011). Lung Function and Inflammatory Responses in Healthy Young Adults Exposed to 0.06 ppm Ozone for 6.6 Hours. American Journal of Respiratory and Critical Care Medicine , 183 (9), 1215‑1221. https://doi.org/10.1164/rccm.201011-1813OC Kim, E.-K., Conrow, L., Röcke, C., Chaix, B., Weibel, R., & Perchoux, C. (2023). Advances and challenges in sensor-based research in mobility, health, and place. Health & Place , 79 , 102972. https://doi.org/10.1016/j.healthplace.2023.102972 Kirkby, J., Nenna, R., & McGowan, A. (2019). Changes in FEV 1 over time in COPD and the importance of spirometry reference ranges : The devil is in the detail. Breathe , 15 (4), 337‑339. https://doi.org/10.1183/20734735.0252-2019 Kwon, S. O., Hong, S. H., Han, Y.-J., Bak, S. H., Kim, J., Lee, M. K., London, S. J., Kim, W. J., & Kim, S.-Y. (2020). Long-term exposure to PM10 and NO2 in relation to lung function and imaging phenotypes in a COPD cohort. Respiratory Research , 21 (1), 247. https://doi.org/10.1186/s12931-020-01514-w Lagorio, S., Forastiere, F., Pistelli, R., Iavarone, I., Michelozzi, P., Fano, V., Marconi, A., Ziemacki, G., & Ostro, B. D. (2006). Air pollution and lung function among susceptible adult subjects : A panel study. Environmental Health , 5 (1), 11. https://doi.org/10.1186/1476-069X-5-11 Langan, R. C., & Goodbred, A. J. (2020). Office Spirometry:Indications and Interpretation . 101 (6). Lee, Y.-G., Lee, P.-H., Choi, S.-M., An, M.-H., & Jang, A.-S. (2021). Effects of Air Pollutants on Airway Diseases. International Journal of Environmental Research and Public Health , 18 (18), 9905. https://doi.org/10.3390/ijerph18189905 Levine, S. M., & Marciniuk, D. D. (2022). Global Impact of Respiratory Disease. Chest , 161 (5), 1153‑1154. https://doi.org/10.1016/j.chest.2022.01.014 Lim, Y.-H., Hersoug, L.-G., Lund, R., Bruunsgaard, H., Ketzel, M., Brandt, J., Jørgensen, J. T., Westendorp, R., Andersen, Z. J., & Loft, S. (2022). Inflammatory markers and lung function in relation to indoor and ambient air pollution. International Journal of Hygiene and Environmental Health , 241 , 113944. https://doi.org/10.1016/j.ijheh.2022.113944 Louwies, T., Nawrot, T., Cox, B., Dons, E., Penders, J., Provost, E., Panis, L. I., & De Boever, P. (2015). Blood pressure changes in association with black carbon exposure in a panel of healthy adults are independent of retinal microcirculation. Environment International , 75 , 81‑86. https://doi.org/10.1016/j.envint.2014.11.006 Malagón-Rojas, J. N., Parra-Barrera, E. L., Toloza-Pérez, Y. G., Soto, H., Lagos, L. F., Mendez, D., Rico, A., Almentero, J. E., Quintana-Cortes, M. A., Pinzón-Silva, D. C., García, A., Benavides-Piracón, J. A., Zona-Rubio, D. C., Portilla, C., Wilches-Mogollon, M. A., Romero-Díaz, S. A., Hernández-Florez, L. J., Morales, R., & Sarmiento, O. L. (2022). Assessment of Factors Influencing Personal Exposure to Air Pollution on Main Roads in Bogota : A Mixed-Method Study. Medicina , 58 (8), 1125. https://doi.org/10.3390/medicina58081125 Mentz, G., Robins, T. G., Batterman, S., & Naidoo, R. N. (2019). Effect modifiers of lung function and daily air pollutant variability in a panel of schoolchildren. Thorax , 74 (11), 1055‑1062. https://doi.org/10.1136/thoraxjnl-2017-211458 Mirowsky, J. E., Peltier, R. E., Lippmann, M., Thurston, G., Chen, L.-C., Neas, L., Diaz-Sanchez, D., Laumbach, R., Carter, J. D., & Gordon, T. (2015). Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults. Environmental Health , 14 (1), 66. https://doi.org/10.1186/s12940-015-0049-0 Panigrahi, A., & Padhi, B. K. (2018). Chronic bronchitis and airflow obstruction is associated with household cooking fuel use among never-smoking women : A community-based cross-sectional study in Odisha, India. BMC Public Health , 18 (1), 924. https://doi.org/10.1186/s12889-018-5846-2 Paulin, L. M., Gassett, A. J., Alexis, N. E., Kirwa, K., Kanner, R. E., Peters, S., Krishnan, J. A., Paine, R., Dransfield, M., Woodruff, P. G., Cooper, C. B., Barr, R. G., Comellas, A. P., Pirozzi, C. S., Han, M., Hoffman, E. A., Martinez, F. J., Woo, H., Peng, R. D., … for SPIROMICS investigators. (2020). Association of Long-term Ambient Ozone Exposure With Respiratory Morbidity in Smokers. JAMA Internal Medicine , 180 (1), 106‑115. https://doi.org/10.1001/jamainternmed.2019.5498 Paunescu, A.-C., Casas, M., Ferrero, A., Pañella, P., Bougas, N., Beydon, N., Just, J., Lezmi, G., Sunyer, J., Ballester, F., & Momas, I. (2019). Associations of black carbon with lung function and airway inflammation in schoolchildren. Environment International , 131 , 104984. https://doi.org/10.1016/j.envint.2019.104984 Pearson, A. L., Tribby, C., Brown, C. D., Yang, J.-A., Pfeiffer, K., & Jankowska, M. M. (2024). Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research. BMJ Open , 14 (2), e077036. https://doi.org/10.1136/bmjopen-2023-077036 Pope Iii, C. A. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. JAMA , 287 (9), 1132. https://doi.org/10.1001/jama.287.9.1132 Schempf, A. H., & Kaufman, J. S. (2012). Accounting for context in studies of health inequalities : A review and comparison of analytic approaches. Annals of Epidemiology , 22 (10), 683‑690. https://doi.org/10.1016/j.annepidem.2012.06.105 Schikowski, T., Sugiri, D., Ranft, U., Gehring, U., Heinrich, J., Wichmann, H.-E., & Krämer, U. (2005). Long-term air pollution exposure and living close to busy roads are associated with COPD in women. Respiratory Research , 6 (1), 152. https://doi.org/10.1186/1465-9921-6-152 Schindler, C., Künzli, N., Bongard, J.-P., Leuenberger, P., Karrer, W., Rapp, R., Monn, C., Ackermann-Liebrich, U., & The Swiss Study on Air Pollution and Lung Diseases In Adults Investigators. (2001). Short-Term Variation in Air Pollution and in Average Lung Function Among Never-Smokers : The Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA). American Journal of Respiratory and Critical Care Medicine , 163 (2), 356‑361. https://doi.org/10.1164/ajrccm.163.2.9911116 Sesé, L., Gille, T., Pau, G., Dessimond, B., Uzunhan, Y., Bouvry, D., Hervé, A., Didier, M., Kort, F., Freynet, O., Rotenberg, C., Jeny, F., Khamis, W., Hindre, R., Maesano, C. N., Planes, C., Nunes, H., & Annesi-Maesano, I. (2023). Low-cost air quality portable sensors and their potential use in respiratory health. The International Journal of Tuberculosis and Lung Disease , 27 (11), 803‑809. https://doi.org/10.5588/ijtld.23.0197 Shapira, U., Brezinski, R. Y., Rogowski, O., Zeltser, D., Berliner, S., Shapira, I., Shenhar-Tsarfaty, S., & Fireman, E. (2021). Association between elevated serum bilirubin levels with preserved lung function under conditions of exposure to air pollution. BMC Pulmonary Medicine , 21 (1), 119. https://doi.org/10.1186/s12890-021-01488-5 Smith, M. P., Standl, M., Berdel, D., von Berg, A., Bauer, C.-P., Schikowski, T., Koletzko, S., Lehmann, I., Krämer, U., Heinrich, J., & Schulz, H. (2018). Handgrip strength is associated with improved spirometry in adolescents. PloS One , 13 (4), e0194560. https://doi.org/10.1371/journal.pone.0194560 So, R., Andersen, Z. J., Chen, J., Stafoggia, M., De Hoogh, K., Katsouyanni, K., Vienneau, D., Rodopoulou, S., Samoli, E., Lim, Y.-H., Jørgensen, J. T., Amini, H., Cole-Hunter, T., Mahmood Taghavi Shahri, S., Maric, M., Bergmann, M., Liu, S., Azam, S., Loft, S., … Mehta, A. J. (2022). Long-term exposure to air pollution and mortality in a Danish nationwide administrative cohort study : Beyond mortality from cardiopulmonary disease and lung cancer. Environment International , 164 , 107241. https://doi.org/10.1016/j.envint.2022.107241 Song, J., Qiu, W., Huang, X., Guo, Y., Chen, W., Wang, D., & Zhang, X. (2023). Association of ambient carbon monoxide exposure with hospitalization risk for respiratory diseases : A time series study in Ganzhou, China. Frontiers in Public Health , 11 , 1106336. https://doi.org/10.3389/fpubh.2023.1106336 Stafoggia, M., Oftedal, B., Chen, J., Rodopoulou, S., Renzi, M., Atkinson, R. W., Bauwelinck, M., Klompmaker, J. O., Mehta, A., Vienneau, D., Andersen, Z. J., Bellander, T., Brandt, J., Cesaroni, G., De Hoogh, K., Fecht, D., Gulliver, J., Hertel, O., Hoffmann, B., … Janssen, N. A. H. (2022). Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people : Results from seven large European cohorts within the ELAPSE project. The Lancet Planetary Health , 6 (1), e9‑e18. https://doi.org/10.1016/S2542-5196(21)00277-1 Strassmann, A., De Hoogh, K., Röösli, M., Haile, S. R., Turk, A., Bopp, M., Puhan, M. A., & for the Swiss National Cohort Study Group. (2021). NO2 and PM2.5 Exposures and Lung Function in Swiss Adults : Estimated Effects of Short-Term Exposures and Long-Term Exposures with and without Adjustment for Short-Term Deviations. Environmental Health Perspectives , 129 (1), 017009. https://doi.org/10.1289/EHP7529 Tarlo, S. M. (2000). Workplace respiratory irritants and asthma. Occupational Medicine (Philadelphia, Pa.) , 15 (2), 471‑484. Teng, J., Li, J., Yang, T., Cui, J., Xia, X., Chen, G., Zheng, S., Bao, J., Wang, T., Shen, M., Zhang, X., Meng, C., Wang, Z., Wu, T., Xu, Y., Wang, Y., Ding, G., Duan, H., & Li, W. (2022). Long-term exposure to air pollution and lung function among children in China : Association and effect modification. Frontiers in Public Health , 10 , 988242. https://doi.org/10.3389/fpubh.2022.988242 Thurston, G. D., Balmes, J. R., Garcia, E., Gilliland, F. D., Rice, M. B., Schikowski, T., Van Winkle, L. S., Annesi-Maesano, I., Burchard, E. G., Carlsten, C., Harkema, J. R., Khreis, H., Kleeberger, S. R., Kodavanti, U. P., London, S. J., McConnell, R., Peden, D. B., Pinkerton, K. E., Reibman, J., & White, C. W. (2020). Outdoor Air Pollution and New-Onset Airway Disease. An Official American Thoracic Society Workshop Report. Annals of the American Thoracic Society , 17 (4), 387‑398. https://doi.org/10.1513/AnnalsATS.202001-046ST Tiotiu, A. I., Novakova, P., Nedeva, D., Chong-Neto, H. J., Novakova, S., Steiropoulos, P., & Kowal, K. (2020). Impact of Air Pollution on Asthma Outcomes. International Journal of Environmental Research and Public Health , 17 (17), 6212. https://doi.org/10.3390/ijerph17176212 Wei, T., Chen, C., Yang, Y., Li, L., Wang, J., Ye, M., Kan, H., Yang, D., Song, Y., Cai, J., & Hou, D. (2023). Associations between short-term exposure to ambient air pollution and lung function in adults. Journal of Exposure Science & Environmental Epidemiology . https://doi.org/10.1038/s41370-023-00550-0 WHO air quality guidelines, 2021, Https://www.who.int/news-room/feature-stories/detail/what-are-the-who-air-quality-guidelines. Wu, D.-W., Chen, S.-C., Tu, H.-P., Wang, C.-W., Hung, C.-H., Chen, H.-C., Kuo, T.-Y., Wang, C.-F., Lai, B.-C., Chen, P.-S., & Kuo, C.-H. (2021). The Impact of the Synergistic Effect of Temperature and Air Pollutants on Chronic Lung Diseases in Subtropical Taiwan. Journal of Personalized Medicine , 11 (8), 819. https://doi.org/10.3390/jpm11080819 Yoda, Y., Takagi, H., Wakamatsu, J., Ito, T., Nakatsubo, R., Horie, Y., Hiraki, T., & Shima, M. (2017). Acute effects of air pollutants on pulmonary function among students : A panel study in an isolated island. Environmental Health and Preventive Medicine , 22 (1), 33. https://doi.org/10.1186/s12199-017-0646-3 You, D.-F., Qiao, Q.-G., Lu, J.-S., Wei, M., Tan, W.-Y., Wang, C.-H., Liu, Y.-G., Zheng, M.-Q., & Liu, G. (2022). Study on the correlation between hyperventilation syndrome and climate and air quality. Health Policy and Technology , 11 (3), 100655. https://doi.org/10.1016/j.hlpt.2022.100655 Yu, Z., Merid, S. K., Bellander, T., Bergström, A., Eneroth, K., Georgelis, A., Hallberg, J., Kull, I., Ljungman, P., Klevebro, S., Stafoggia, M., Wang, G., Pershagen, G., Gruzieva, O., & Melén, E. (2023). Associations of improved air quality with lung function growth from childhood to adulthood : The BAMSE study. The European Respiratory Journal , 61 (5), 2201783. https://doi.org/10.1183/13993003.01783-2022 Zhao, T., Markevych, I., Fuertes, E., De Hoogh, K., Accordini, S., Boudier, A., Casas, L., Forsberg, B., Garcia Aymerich, J., Gnesi, M., Holm, M., Janson, C., Jarvis, D., Johannessen, A., Jörres, R. A., Karrasch, S., Leynaert, B., Maldonado Perez, J. A., Malinovschi, A., … Heinrich, J. (2023). Impact of long-term exposure to ambient ozone on lung function over a course of 20 years (The ECRHS study) : A prospective cohort study in adults. The Lancet Regional Health - Europe , 34 , 100729. https://doi.org/10.1016/j.lanepe.2023.100729 Zhao, X., Sun, Z., Ruan, Y., Yan, J., Mukherjee, B., Yang, F., Duan, F., Sun, L., Liang, R., Lian, H., Zhang, S., Fang, Q., Gu, D., Brook, J. R., Sun, Q., Brook, R. D., Rajagopalan, S., & Fan, Z. (2014). Personal Black Carbon Exposure Influences Ambulatory Blood Pressure : Air Pollution and Cardiometabolic Disease (AIRCMD-China) Study. Hypertension , 63 (4), 871‑877. https://doi.org/10.1161/HYPERTENSIONAHA.113.02588 Zuo, B., Liu, C., Chen, R., Kan, H., Sun, J., Zhao, J., Wang, C., Sun, Q., & Bai, H. (2019). Associations between short-term exposure to fine particulate matter and acute exacerbation of asthma in Yancheng, China. Chemosphere , 237 , 124497. https://doi.org/10.1016/j.chemosphere.2019.124497 Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Table3.docx Appendix.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 03 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9022657","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609970126,"identity":"a864b5c2-b12f-4ec0-a2c6-fd28fa2dae48","order_by":0,"name":"Najat Rizk","email":"","orcid":"","institution":"Lebanese university","correspondingAuthor":false,"prefix":"","firstName":"Najat","middleName":"","lastName":"Rizk","suffix":""},{"id":609970127,"identity":"f524d5b6-283d-4176-a461-0a6b42a8d959","order_by":1,"name":"Basile Chaix","email":"","orcid":"","institution":"Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, NEMESIS","correspondingAuthor":false,"prefix":"","firstName":"Basile","middleName":"","lastName":"Chaix","suffix":""},{"id":609970128,"identity":"127748dd-a1f7-46ea-9768-975f2b91bca8","order_by":2,"name":"Isabella Annesi-Maesano","email":"","orcid":"","institution":"IDESP, University of Montpellier and INSERM","correspondingAuthor":false,"prefix":"","firstName":"Isabella","middleName":"","lastName":"Annesi-Maesano","suffix":""},{"id":609970130,"identity":"808ca731-621e-4184-a701-206f0b9a3d7f","order_by":3,"name":"Sanjeev Bista","email":"","orcid":"","institution":"École de Santé Publique de l’Université de Montréal (ESPUM)","correspondingAuthor":false,"prefix":"","firstName":"Sanjeev","middleName":"","lastName":"Bista","suffix":""},{"id":609970132,"identity":"6564b51e-832f-43ad-82e9-9ff74c8afb71","order_by":4,"name":"Giovanna Fancello","email":"data:image/png;base64,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","orcid":"","institution":"Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, IPLESP, NEMESIS","correspondingAuthor":true,"prefix":"","firstName":"Giovanna","middleName":"","lastName":"Fancello","suffix":""}],"badges":[],"createdAt":"2026-03-03 17:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9022657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9022657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105217867,"identity":"912e9636-ac59-4ec0-8fbe-ce65602c9567","added_by":"auto","created_at":"2026-03-23 15:06:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1013776,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of neighborhoods in the MobiliSense Study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9022657/v1/2aed53db27f7cb49f969cb53.png"},{"id":105569167,"identity":"5ebf5379-4477-4f80-a0f0-caf56de2a41a","added_by":"auto","created_at":"2026-03-27 13:11:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1680265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9022657/v1/ad3ab0bd-4402-47ee-abb8-963e8ee82a8b.pdf"},{"id":105217868,"identity":"bc2f406c-76c8-4de3-b86d-12d1100d04a9","added_by":"auto","created_at":"2026-03-23 15:06:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20208,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9022657/v1/bbeac224210a84731e6e0f2c.docx"},{"id":105217869,"identity":"3710afd9-0871-49e8-a1fa-681876627605","added_by":"auto","created_at":"2026-03-23 15:06:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23126,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9022657/v1/972ceb91eb131cb643530c98.docx"},{"id":105217871,"identity":"a99f5c73-6c94-41eb-a9c9-96f89065b14c","added_by":"auto","created_at":"2026-03-23 15:06:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21548,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9022657/v1/e1accde9830752738dde7ab1.docx"},{"id":105563815,"identity":"fc2225b9-a1de-469a-9d0c-bd82b40b318f","added_by":"auto","created_at":"2026-03-27 12:47:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":24716,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9022657/v1/bcdded57db21900927c5d235.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of the effects of air pollutants on lung function using ambulatory air pollution monitor data from the Mobilisense Project","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eExposure to environmental factors are constantly happening in the natural world (Choo et al., 2023).The expansion of urban areas has brought about various consequences, including issues related to transportation and increased exposure to harmful environmental pollutants (Malagón-Rojas et al., 2022). Nine out of ten individuals worldwide inhale air that is heavily contaminated with pollutants (Cipryan et al., 2020).Air pollutants [particulate matter with an aerodynamic diameter below 10 µm (PM10) or 2.5 µm (PM2.5), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) and sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e)] originate from various sources, including vehicle emissions, industrial processes, and power plants (Jion et al., 2023). Additionally, secondary pollutants can form through the interaction of other pollutants: for example, ozone (O\u003csub\u003e3\u003c/sub\u003e) is formed by the interaction of nitric oxides, volatile organic compounds (VOCs), and sunlight (Tiotiu et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAir pollution stands as the fourth most significant factor contributing to the loss of disability-adjusted life years (DALYs) and mortality (Lee et al., 2021), after tobacco, lead exposure, and high body mass index (BMI) (Levine \u0026amp; Marciniuk, 2022). A comprehensive analysis conducted by the Global Burden of Disease initiative revealed that air pollution was accountable for a staggering 6.67 million deaths in 2019 (Lee et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVarious studies have documented associations between long-term exposure to air pollution and development of lung cancer (Pope Iii, 2002), chronic obstructive pulmonary disease (X. Huang et al., 2019), asthma (Annesi-Maesano et al., 2021), deterioration of lung function (Chen \u0026amp; Hoek, 2020; Thurston et al., 2020) and increased mortality rates (Stafoggia et al., 2022; Teng et al., 2022). Evidence is even stronger for the short-term exposure to outdoor air pollutants in relation to various aspects of respiratory diseases (Lee et al., 2021) such as lung function impairment (Mentz et al., 2019), asthma exacerbations (Garcia et al., 2019; Zuo et al., 2019), chronic obstructive pulmonary disease (Doiron et al., 2019)and respiratory mortality (So et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe studies mentioned above have demonstrated a strong correlation between exposure to various air pollutants and lung function, an important marker of respiratory health impairment.\u0026nbsp;Nevertheless, there is a gap in research, particularly in terms of the spatiotemporal assessment of the exposure: research frequently evaluates short-term exposure to outdoor pollutants by using data from the nearest monitoring station, averaging or interpolating data from various stations, or utilizing residential estimates derived from air dispersion models or land use regression models (Tiotiu et al., 2020)\u003cstrong\u003e.\u003c/strong\u003e Few studies have specifically investigated the impact of air pollution exposure on respiratory health outcomes, particularly in France, while considering the measured rather than only estimated individual’s short term exposure\u0026nbsp;(Sesé et al., 2023).\u003c/p\u003e\n\u003cp\u003eAlthough they also have their limitations, there is a general consensus that portable monitors are essential in improving the assessment of personal exposures (Dons et al., 2012). The integration of mobility into exposure research offers an opportunity to improve our understanding of how movement in space and time affects both positive and negative environmental exposures (Pearson et al., 2024). Additionally, passive sensing (such as air pollution or noise monitors, GPS trackers (Chaix et al., 2022)) allows monitoring individuals' environmental conditions (including air pollution exposure) and activities along daily mobility paths and makes it possible to potentially include external geographic data in the analysis (E.-K. Kim et al., 2023).\u003c/p\u003e\n\u003cp\u003eThis study seeks to provide insights into the potential health risks posed by the exposure to air pollution during daily life among healthy adults in the Grand Paris region. To address gaps in the literature, this study aims to further investigate the association between time-varying air pollution exposure and lung function measured trough ambulatory air pollution monitors carried during daily activities and spirometry tests. These measures together are able to provide a new perspective for understanding the short-term health effects of exposure in different microenvironments (Chaix, 2018). We first assessed the associations between the short-term exposure to various air pollutants [particulate matter (PM), nitrogen monoxide (NO), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), carbon monoxide (CO), black carbon (BC) and ozone (O\u003csub\u003e3\u003c/sub\u003e)] and lung function [forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC) and FEV1/FVC]. Secondly we evaluated the possible relationships of daily fluctuations in and accumulation of air pollutant levels between morning and evening with lung function.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003ch2\u003e2.1 Survey and study population:\u003c/h2\u003e\n\u003cp\u003eThis research was carried out in the Grand Paris region, which includes the city of Paris and several nearby municipalities, in France. The study took place from May 2018 to March 2022 as part of the initial phase of the MobiliSense project (Chaix et al., 2022) which received funding from the European Research Council. The participants were selected using a two-stage stratified random sampling method following the protocol described elsewhere (Chaix et al., 2022). Initially, neighborhoods were randomly chosen from the first and last quartiles of road traffic density within each quartile of area income. The second stage involved the random selection of dwelling units within the pre-selected neighborhoods, using data from the 2013 and 2014 population censuses conducted by the National Institute of Statistics and Economic Studies. A total of 31,970 dwellings were selected from 234 neighborhoods (Bista et al., 2023) as shown in figure 1. Postal invitations were sent twice to the residents of the selected dwellings, resulting in the recruitment of 289 nonsmokers participants aged between 30 and 64 years in the sensor-based MobiliSense study (Bista et al., 2022). Participants had to conduct the same data gathering process, including various sensors and questionnaires, within 1 or 2 years after the initial wave. In the present study we focused on the first wave data.\u003c/p\u003e\n\u003cp\u003eFigure 1 \u003c/p\u003e\n\u003cp\u003eDuring the 6-day survey, participants were asked to carry different combinations of sensors over the different days (Chaix et al., 2022). In addition to questionnaire data (sociodemographic, health status, behaviors and health habits), the analysis in this study focuses on data collected from days 1 and 2 from portable ambulatory monitors of air pollutants (AE51, PAQM520) and spirometry test (Spirotel 2) as shown in Table1.\u003c/p\u003e\n\u003cp\u003eTable 1 \u003c/p\u003e\n\u003ch2\u003e2.2 Spirometry measures\u003c/h2\u003e\n\u003cp\u003eParticipants underwent spirometry testing with the Spirotel 2 device (MIR, Langlade, France) for three days, both in the morning and in the evening, prior to taking their medications. The Spirotel 2 device is designed to meet the standards set by the applicant tracking system (ATS) and international organization for standardization (ISO). This device is clinically approved for screening and monitoring requirements (Fonseca et al., 2005).It measures various parameters such as peak expiratory flow, forced expiratory volume in 1 second, forced vital capacity (FVC), forced expiratory flow between 25% and 75% of vital capacity, and forced expiratory volume in 6 seconds (FEV6). It is reliable for measuring forced expiratory flows in large and small airways and it has a sensitivity for detecting airway obstruction that is comparable to the laboratory spirometer in both large and small airways (Ezzahir et al., 2005).\u003c/p\u003e\n\u003cp\u003eThe Spirotel 2 device automatically transmitted measures to a remote server through a smartphone provided to the participants. Our research assistants underwent extensive training and visited the participants' homes to demonstrate the proper usage of the device. They carefully explained the measurement process and encouraged participants to exert maximum effort during expiration. Each morning and each evening, we performed two spirometry measurements and we selected the best one for each indicator based on the reproducibility test results. For FEV1, the curve is deemed acceptable if the value is less than or equal to 150ml, and for FVC if the value is below or equal to 150ml (Forbes et al., 2009; Smith et al., 2018). The spirometer curves were monitored remotely on a daily basis and if any issues with the quality of the curves was detected, participants were contacted by phone to solve the problem (Chaix et al., 2022). Once the best two curves were identified, the higher value between the two measurements was selected.\u003c/p\u003e\n\u003cp\u003eIn our study, FEV1 was considered as our first outcome, as abnormal lung function in patients is often indicated by a forced expiratory volume in 1 second (FEV1) that falls below 80% (Kirkby et al., 2019; Shapira et al., 2021). This measure represents the severity of obstructive lung diseases such as asthma and chronic obstructive pulmonary disease (Langan \u0026amp; Goodbred, 2020).\u003c/p\u003e\n\u003cp\u003eAs a second outcome we analyzed the forced vital capacity (FVC). FVC \u0026lt; 60% as categorized as moderate-to-severely reduced lung function (Cohen et al., 2017). We included the FEV1/FVC ratio as a third outcome, as an indicator of possible pulmonary obstruction (for a ratio below 70%) (Wu et al., 2021).\u003c/p\u003e\n\u003cp\u003eFinally, to further analyze the fluctuations between morning and evening measurements, we introduced a fourth variable denominated “delta”. This variable is measured as the difference between the evening and morning measurements for FEV1 (delta FEV1) and FVC (Delta FVC). The delta values were then considered as the fourth outcome in our study.\u003c/p\u003e\n\u003ch2\u003e2.3 Exposure to air pollutants\u003c/h2\u003e\n\u003cp\u003eParticipants were asked to carry during their activities the PAQM 520 portable device (Table 1) to measure personal exposure to concentrations of gases (O\u003csub\u003e3\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, NO, and CO) as well as particle matter PM\u003csub\u003e2.5 \u003c/sub\u003e(Chaix et al., 2022). This portable device was calibrated against reference instruments to ensure accuracy (Bista et al., 2023). Gas measurements were averaged over 10-second intervals. Particle measurements were taken every 1 minute over a 5-second interval and temperature and humidity measured by the device were used to account for any changes in the environment. More details about the measurement of pollutants can be found elsewhere (Chaix et al., 2022). Measurements with the PAQM 520 were conducted on days 1, 2, 5, and 6 of the Mobilisense project, thus including the initial two days of spirometry measurements on days 1 and 2.\u003c/p\u003e\n\u003cp\u003eParticipants were also asked to carry the Aethalometer (MicroAeth AE51, AethLabs, CA, USA) to collect data on the personal exposure to black carbon (BC). This device has been utilized in multiple previous epidemiological studies (Louwies et al., 2015; Mirowsky et al., 2015; X. Zhao et al., 2014). Participants carried the device on a belt, positioning the tube's inlet at neck height to capture BC concentration within their breathing zone. To address incorrectly high and low BC values, the Optimized Noise Reduction Averaging (ONA) algorithm (Hagler et al., 2011) was applied to the 10-second measurements, accounting for filter changes. Detailed information regarding the processing steps and algorithm can be found in our previous publication (Bista et al., 2022, 2023; Chaix et al., 2022).\u003c/p\u003e\n\u003ch2\u003e2.4 Covariates\u003c/h2\u003e\n\u003cp\u003eDemographic and socioeconomic information was collected prior to the sensor-based assessment through a web questionnaire administered by a research assistant. Sex was coded as a binary variable (male; female), while age was included as a continuous variable. Education (3 categories: less than the baccalaureate; equal to the baccalaureate; higher than the baccalaureate), employment (4 categories: employed; not employed; retired; other) and monthly income per household member (continuous) were included as socioeconomic covariates. Income was then divided into three groups using the tertiles: low ≤ 1600; 1600 \u0026lt; Medium \u0026lt; 2300; and high ≥ 2300 euros. BMI was calculated based on measured height and weight.\u003c/p\u003e\n\u003cp\u003eWe also included contextual factors and environmental variables that change over time, including season, pollen level from the French Aerobiology Network stations, and temperature and humidity from the PAQM 520 sensor (the last 3 variables were examined at the day level). Residence area was defined as either in the central city (Paris) or around it. Days were classified into week day and weekend, while time of the day was categorized as morning or afternoon-evening (with a break at noon). Additionally, with the questionnaire, we assessed health habits and behaviors including alcohol consumption (categorized as regular, frequently, occasionally, and never) and smoking status (categorized as non-smoker, ex-smoker, occasional smoker, and regular smoker).\u003c/p\u003e\n\u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e\n\u003cp\u003eGiven the longitudinal nature of the data as repeated measurements related to the time-varying exposures and the outcomes, linear mixed models were utilized to assess the relationship between average pollutant exposure and spirometry measurements (FEV1, FVC, FEV1/FVC, delta FEV1 and delta FVC). \u003c/p\u003e\n\u003cp\u003eFor the first 3 outcomes (FEV1, FVC, and FEV1/FVC), both the morning measurement and the evening measurement on each day were pooled and analyzed together as distinct observations in the same dataset. Models assessed time-varying exposures and covariates across a range of time windows from 15 minutes to 6 hours prior to the spirometry measurements. To account for the correlation within subjects of repeated outcome measures, two random intercepts were specified at the individual and day levels in all models. Covariables retained in the models were selected according to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. Based on this selection of variables, we adjusted the models for the following confounders: sex, age, income, day of the week, time of day, and smoking status. Each pollutant was individually tested with these covariates in a separate model in relation to the outcomes. Subsequently, a comprehensive model was estimated that included all the associated pollutants and the selected covariates that improved model fit.\u003c/p\u003e\n\u003cp\u003eWith the last two outcomes (delta FEV1 and FVC), we explored whether and how air pollutant exposure affected changes in lung function during the day between the morning and evening (“delta” outcomes). These linear mixed models included a random intercept at the individual level (no random effect at the day level as the outcome was at the day level). As an explanatory variable, we analyzed the difference in pollutant exposure between the evening and the morning measurement (each exposure calculated in the time windows before the measurement). Based on our selection of variables, we adjusted these last two models for the following confounders: sex, age, income, alcohol consumption, and smoking status. To investigate possible interaction effects between the selected pollutants we performed a moderation analysis, by including product terms between two pollutants. \u003c/p\u003e\n\u003cp\u003eFinally, we tested our models considering individuals as fixed effects in order to estimate associations only on the basis of within-individual differences. The fixed effect model specifies for each individual a fixed intercept or effect by adding k-1 dummy variables for the k individuals (Schempf \u0026amp; Kaufman, 2012). This approach permits to neutralize all individual-level confounders. This methodology was applied to all the outcomes considered (FEV1, CVF, FEV1/CVF ratio, delta FEV1 and delta CVF).\u003c/p\u003e\n\u003cp\u003eAll the analyses were performed using the R software version 4.3.3 and the \"lme4\" package.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 Analytical sample\u003c/h2\u003e\n\u003cp\u003eWe collected data from 289 participants. A total of 90 participants were excluded from the present study due to the unavailability of valid spirometry measurements. Consequently, the analysis was conducted on 199 participants for a total 2504 spirometry observations.\u003c/p\u003e\n\u003cp\u003eIn Table 2 we provide a concise overview of the participants’ sociodemographic characteristics, health-related behaviours, and lung function. Among the participants, 52.8% were female Participants had had an average age of 51.2 years. Overall, 20% resided in Paris, while the remaining majority lived in the suburbs. The majority of the sample (60.8%) reported frequent alcohol consumption, were non-smoker (57.8%) and did not take any medication or have hypertension (94%). On average, spirometry tests on participants resulted in 3.28L for FEV1 and 4.16L for FVC. The average daily pollutant concentration between morning and evening (considering all the time between the morning measurement and the evening measurement) were 24 ppb for NO, 12 ppb for NO\u003csub\u003e2\u003c/sub\u003e, 870 ppb for CO, 17 ppb for O\u003csub\u003e3\u003c/sub\u003e, 1600 ng/m\u003csup\u003e3 \u003c/sup\u003efor BC, and 26μg/m\u003csup\u003e3 \u003c/sup\u003efor PM\u003csub\u003e2.5\u003c/sub\u003e. Detailed statistics about the outcomes (FEV1, FVC, FEV1/FVC, delta FEV1, and delta FVC) are presented in Appendix Table A. Statistics on exposure to targeted air pollutants, ranging from 15 minutes to 6 hours before the morning and evening measurements, are outlined in Appendix Table B.\u003c/p\u003e\n\u003cp\u003eTable 2 \u003c/p\u003e\n\u003ch2\u003e3.2 Associations between air pollutants and lung function assessments\u003c/h2\u003e\n\u003cp\u003eTable 3 shows the final models with the pollutants that were independently associated with FEV1, FVC, and the FEV1/FVC ratio. As shown at the top of the Table, BC and O\u003csub\u003e3\u003c/sub\u003e were independently associated with FEV1. A 1 μg/m\u003csup\u003e3\u003c/sup\u003e increase in BC exposure over the preceding 1 hour and 2 hours was associated with a decrease of 0.016 (95% CI: -0.024, -0.008) and 0.021 (95% CI: -0.034, -0.007) in FEV1. Moreover, after controlling for BC and other covariates, a 1 ppb increase in O\u003csub\u003e3\u003c/sub\u003e over the preceding 1 hour and 2 hours was related to a 0.43 (95% CI: 0.04, 0.81) and 0.45 (95% CI: 0.08, 0.83) increase in FEV1. No association with FEV1 was found with other pollutants or at different time intervals.\u003c/p\u003e\n\u003cp\u003eResults also showed (Table 3) that 1 ppb rise in O\u003csub\u003e3\u003c/sub\u003e exposure within the 15 minutes to 2 hours prior to the spirometry test led to an increase in FVC by 0.64 (95% CI: 0.01, 1.29), 0.62 (95% CI: 0.02, 1.22), 0.64 (95% CI: 0.009, 0.11), and 0.67 (95% CI: 0.01, 1.12), respectively. No associations with FVC were found with other pollutants or at different time intervals.\u003c/p\u003e\n\u003cp\u003eFinally, Table 3 shows that an increase of 1 μg/m\u003csup\u003e3\u003c/sup\u003e in the PM2.5 exposure during the 15 minutes, 30 minutes, and 1 hour before spirometry measurements resulted in a decrease in the FEV1/CVF ratio by 0.60 (95%CI -1.30,-0.03), 0.70 (95%CI -1.39, -0.09), and 0.50 (95% CI -1.10,-0.01) percentage points, respectively. Similarly, a rise of 1 μg/m\u003csup\u003e3\u003c/sup\u003e in BC exposure within the 2 to 4 hours before spirometry measurements lead to a reduction in the FEV1/CVF ratio of 0.26 (95%CI -0.44, -0.09), 0.15 (95%CI -0.29, -0.01), and 0.17 (95%CI -0.32, -0.01) percentage points. No associations were found for the FEV1/FVC ratio with other pollutants or at different time intervals.\u003c/p\u003e\n\u003ch2\u003e3.3 Associations between air pollutants and change in lung function between morning and evening.\u003c/h2\u003e\n\u003cp\u003eThe exposure to air pollutants between the morning and evening measurements were not associated with the change between morning and evening in FEV1 and FVC. Similarly, the exposure to air pollutants in time windows before the evening measurement were not associated with the change in lung function between morning and evening.\u003c/p\u003e\n\u003ch2\u003e3.4 Sensitivity analyses\u003c/h2\u003e\n\u003cp\u003eThe fixed effect model results are presented in appendix Tables C to E. For FEV1, the findings of the fixed effect models aligned with those of the random effect models (the 95% CI for the association between O3 over the past hour and FEV1 just overlapped value 0, but the pattern of association was similar). For FVC, there were also positive associations with O3, however most (but not all) of the 95% CIs overlapped value 0. Regarding the FEV1/FVC ratio, the patterns of negative associations between PM\u003csub\u003e2.5\u003c/sub\u003e or BC were exactly the same, with the only exception that for the 1 hour window of exposure, the two pollutants were simultaneously negatively associated with the outcome. We did not document any relationship of air pollutants with the morning to evening change in lung function.\u003c/p\u003e\n\u003cp\u003eWe did not observe any interaction of effects between air pollutants on the lung function outcome.\u003c/p\u003e\n\u003cp\u003eTable 3\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study investigated the association of the time-varying air pollution exposure with repeated measurements of lung function in 199 healthy adults. The main results show that the exposure to BC 1h to 2h before the spirometry test was negatively associated with both FEV1 and the FEV1/FVC ratio. Regarding the FEV1/FVC ratio, the most acute associations were observed in relation to the exposure to PM\u003csub\u003e2.5\u003c/sub\u003e in the 15 minutes to 1 hour before the spirometry test. In contrast to these results, our study revealed that short-term exposure to O\u003csub\u003e3\u003c/sub\u003e in the 15 minutes to 2 hours before the test contributed to an increase in FEV1 and in FVC. It is also important to note that we did not find any associations between individual exposure to nitrogen monoxide (NO), carbon monoxide (CO), or nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) and the measured lung function parameters.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Black Carbon (BC)\u003c/h2\u003e \u003cp\u003eThe observed association between short-term exposure to BC and lung function aligns with findings from previous population-based studies involving both healthy and unhealthy individuals. Evidences in the literature show that black carbon exposure negatively impacts lung function of individuals, particularly affecting their middle and upper airways (Gardiner et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and could impair lung function in healthy individuals (Bessagnet et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, a research conducted in Beijing found 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in BC linked to a 0.18% (95% CI -0.34,-0.03%) decrease in FEV1 during 2 hours of exposure to air pollutants (J. Huang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In accordance with these results our study found that a 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e rise in BC was associated with a 0.021 reduction (95% CI -0.034, -0.007) in FEV1 after 2 hours of exposure. The BAMSE prospective birth cohort study in Sweden (Yu et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) revealed that decreasing BC concentrations were consistently linked to higher annual growth rates of FEV1 and FVC in children after 24 years of follow ups (Yu et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The respiratory health study on BC and European workers (Gardiner et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) also demonstrated a correlation between exposure to BC and declines in FEV1 and the FEV1/FVC ratio. Similar results were also observed in China, France, Spain and Japan (J. Huang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Paunescu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yoda et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Particle matter (PM\u003csub\u003e2.5\u003c/sub\u003e)\u003c/h2\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e has been described as the air pollutant that poses the highest risk to respiratory health (Ilenič et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our research, the average PM\u003csub\u003e2.5\u003c/sub\u003e concentration falls between 26.5 and 34 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, exceeding the standard mean established by European air quality regulations that aligns with WHO guidelines of 15 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (European Environmental Agency ambient air quality and cleaner air \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e.). We did not observe association between PM exposure and FEV1 or FVC. However, our results that showed that PM\u003csub\u003e2.5\u003c/sub\u003e reduced FEV1/FVC ratio aligns with recent researches (Bo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Doiron et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Elbarbary et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Panigrahi \u0026amp; Padhi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schikowski et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), that demonstrated a negative association between particulate matter (PM) exposure and the FEV1/FVC ratio in both children and adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Carbon monoxide (CO)\u003c/h2\u003e \u003cp\u003eThe main consequence of exposure to high outdoor concentrations of CO is hypoxia, leading to symptoms such as confusion, headache, and nausea (Canova et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Our research did not reveal any link between CO levels and spirometry measurements, which aligns with a panel study carried out in Rome (Lagorio et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Conversely, a study in China (Song et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated positive relationships between ambient CO exposure and the risk of hospitalization for various respiratory conditions such as chronic obstructive pulmonary disease, asthma, and influenza-pneumonia, as well as a decrease in FEV1, FVC, and FEV1/FVC (Wei et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is important to note that the average concentration of CO measured in our research did not exceed 1000 ppb (equivalent to 1.15 mg/m\u003csup\u003e3\u003c/sup\u003e), with a 95th percentile of 1929.7 ppb. This value is notably lower than the European standards and the WHO's Air Quality Guidelines target of 4 mg/m\u003csup\u003e3\u003c/sup\u003e for outdoor air (European Environmental Agency ambient air quality and cleaner air \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e.; WHO air quality guidelines, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.).This discrepancy may explain why no association was detected between the spirometry measures and CO in our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) and nitrogen monoxide (NO)\u003c/h2\u003e \u003cp\u003eSimilar to a Korean cohort study (Kwon et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), our research did not uncover a link between NO\u003csub\u003e2\u003c/sub\u003e levels and lung function, which is in contrast to prior studies that linked short-term NO\u003csub\u003e2\u003c/sub\u003e exposure to decreased FEV1 and FVC or FEV1/FVC ratio in adults (Adam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Elbarbary et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schindler et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Strassmann et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies have shown that NO\u003csub\u003e2\u003c/sub\u003e increases the risk of developing and exacerbating asthma (Tiotiu et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and heightens the likelihood of chronic obstructive pulmonary disease attacks (Wu et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is possible that our study lacked the necessary power to identify a potential small association between moderate NO\u003csub\u003e2\u003c/sub\u003e exposure and lung function in healthy individuals. For example, we identified an association between NO\u003csub\u003e2\u003c/sub\u003e and FVC after adjusting for covariates, however this association disappeared when O\u003csub\u003e3\u003c/sub\u003e was included as well in the model. Furthermore, the average of NO\u003csub\u003e2\u003c/sub\u003e level in our study was 24.5 \u0026micro;g/m\u0026sup3; (13 ppb), so it was lower than the European standard and the WHO Air Quality Guidelines target of 25 \u0026micro;g/m\u0026sup3; (European Environmental Agency ambient air quality and cleaner air \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, ; WHO air quality guidelines, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn accordance with a study conducted in Los Angeles (Hao et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), exposure to NO was found not to be associated with lung function (Hao et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Ozone O3\u003c/h2\u003e \u003cp\u003eIn contrast to previous studies (Brown et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; C. S. Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Paulin et al., 2020; T. Zhao et al., 2023), we found a positive association between the O\u003csub\u003e3\u003c/sub\u003e exposure and FEV1 and FVC. This finding contradicts the general understanding that ozone is negatively associated with lung function. However, it is important to note that different exposure durations can yield different outcomes. Additionally, variations in study populations, including individual physical conditions, lifestyles, education, and activity patterns (Guo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), may contribute to the differences in results. Comparing the documented association directly with other studies is challenging due to variations in study design, target participants, and statistical methods employed (Feng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our research, we found that the average O\u003csub\u003e3\u003c/sub\u003e concentration was around 17 ppb, equivalent to approximately 33 \u0026micro;g/m\u0026sup3;. This value is largely below the European standard and the WHO Air Quality Guidelines target of 100 \u0026micro;g/m\u0026sup3; over an 8-hour averaging time (European Environmental Agency ambient air quality and cleaner air \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e.; WHO air quality guidelines, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.). This suggests that the positive association observed could be attributed to hyperventilation, as noted in previous studies (Tarlo, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; You et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, an association between higher O\u003csub\u003e3\u003c/sub\u003e levels and an increase in daily visits for Hyperventilation Syndrome was observed in one of these studies with a correlation coefficient equal to 0,133 (You et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This hyperventilation is characterized by breathing excessively beyond the body's typical metabolic needs (Boulding et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; You et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Limitation\u003c/h2\u003e \u003cp\u003eThis study has limitations that should be taken into consideration. Firstly, the sample size included only 199 participants due to missing spirometry data. Additionally, the monitoring period was limited to 3 days, which may not accurately reflect the participants' regular behavior and variability in lung function. It is important to note that a monitoring period of only 3 days may be insufficient to detect changes in lung function and spirometry measures (De Paula Santos1 et al., 2021). Furthermore, the absence of data on gases and PM\u003csub\u003e2.5\u003c/sub\u003e the third day resulted in the exclusion of the corresponding spirometry data. The analysis was therefore based solely on the data from the first 2 days. Lastly, it is worth mentioning that the concentrations of air pollutants in our study were relatively low, compared to heavily polluted countries in Asia for example. As a result, the findings from our research may not be directly applicable to individuals exposed to higher levels of air pollution. However, these findings could potentially be relevant to other European countries with lower air pollution levels. Additionally, our work was based on a relatively healthy population, so the results cannot be generalized to patients suffering from diseases.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, our study provides valuable insights into the specific effects of different air pollutants on lung function in healthy adults in Paris, France, considering personal exposure assessment. Regarding implications, the findings of our study highlight the importance of minimizing the short-term exposure to BC and PM in order to protect respiratory well-being, especially in urban settings like Paris. These findings suggest that implementing measures to reduce the levels of these air pollutants in places where people conduct their daily activities can have a positive impact on the lung health of individuals. A positive impact could also be attained also by modifying exposure behaviors at the population level. Further investigations are needed to understand the role of air pollution on lung function considering personal assessments during daily activities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. All authors have approved the manuscript and agree with its submission. Each author has participated sufficiently in the work to believe in its overall validity\u003c/p\u003e\n\u003ch2\u003eEthical approval:\u003c/h2\u003e\n\u003cp\u003eThe sampling and data collection protocol was approved by the National Council for Statistical Information, the French Data Protection Authority and the Ethical Committee of Inserm.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003ch2\u003eFunding :\u003c/h2\u003e\n\u003cp\u003e This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 647000, 2014 ERC Consolidator grant, MobiliSense project).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eNajat Rizk: Formal analysis; writing original draft; methodology. Basile Chaix: Funding acquisition; data collection; Methodology; assisting in statistical modelling and manuscript revision. Isabella Annesi-Maesano: assisting in statistical modelling and manuscript revision. Sanjeev Bista: Writing - review \u0026amp; editing. Giovanna Fancello:\u0026nbsp;Conceptualization; supervision; Methodology; assisting in statistical modelling and manuscript revision.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe Mobilisense data that support the findings of this study are available upon request and with the permission of INSERM and the Nemesis team, Institut Pierre Louis d’Epidémiologie et de Santé Publique.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdam, M., Schikowski, T., Carsin, A. E., Cai, Y., Jacquemin, B., Sanchez, M., Vierk\u0026ouml;tter, A., Marcon, A., Keidel, D., Sugiri, D., Al Kanani, Z., Nadif, R., Siroux, V., Hardy, R., Kuh, D., Rochat, T., Bridevaux, P.-O., Eeftens, M., Tsai, M.-Y., \u0026hellip; Probst-Hensch, N. (2015). Adult lung function and long-term air pollution exposure. ESCAPE : A multicentre cohort study and meta-analysis. \u003cem\u003eEuropean Respiratory Journal\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(1), 38‑50. https://doi.org/10.1183/09031936.00130014\u003c/li\u003e\n\u003cli\u003eAnnesi-Maesano, I., Forastiere, F., Balmes, J., Garcia, E., Harkema, J., Holgate, S., Kelly, F., Khreis, H., Hoffmann, B., Maesano, C. N., McConnell, R., Peden, D., Pinkerton, K., Schikowski, T., Thurston, G., Van Winkle, L. S., \u0026amp; Carlsten, C. (2021). The clear and persistent impact of air pollution on chronic respiratory diseases : A call for interventions. \u003cem\u003eEuropean Respiratory Journal\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(3), 2002981. https://doi.org/10.1183/13993003.02981-2020\u003c/li\u003e\n\u003cli\u003eBessagnet, B., Allemand, N., Putaud, J.-P., Couvidat, F., Andr\u0026eacute;, J.-M., Simpson, D., Pisoni, E., Murphy, B. N., \u0026amp; Thunis, P. (2022). Emissions of Carbonaceous Particulate Matter and Ultrafine Particles from Vehicles\u0026mdash;A Scientific Review in a Cross-Cutting Context of Air Pollution and Climate Change. \u003cem\u003eApplied Sciences\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(7), 3623. https://doi.org/10.3390/app12073623\u003c/li\u003e\n\u003cli\u003eBista, S., Chatzidiakou, L., Jones, R. L., Benmarhnia, T., Postel-Vinay, N., \u0026amp; Chaix, B. (2023). Associations of air pollution mixtures with ambulatory blood pressure : The MobiliSense sensor-based study. \u003cem\u003eEnvironmental Research\u003c/em\u003e, \u003cem\u003e227\u003c/em\u003e, 115720. https://doi.org/10.1016/j.envres.2023.115720\u003c/li\u003e\n\u003cli\u003eBista, S., Dureau, C., \u0026amp; Chaix, B. (2022). Personal exposure to concentrations and inhalation of black carbon according to transport mode use : The MobiliSense sensor-based study. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e158\u003c/em\u003e, 106990. https://doi.org/10.1016/j.envint.2021.106990\u003c/li\u003e\n\u003cli\u003eBo, Y., Chang, L., Guo, C., Lin, C., Lau, A. K. H., Tam, T., \u0026amp; Lao, X. Q. (2021). Reduced ambient PM2.5, better lung function, and decreased risk of chronic obstructive pulmonary disease. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e156\u003c/em\u003e, 106706. https://doi.org/10.1016/j.envint.2021.106706\u003c/li\u003e\n\u003cli\u003eBoulding, R., Stacey, R., Niven, R., \u0026amp; Fowler, S. J. (2016). Dysfunctional breathing : A review of the literature and proposal for classification. \u003cem\u003eEuropean Respiratory Review\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(141), 287‑294. https://doi.org/10.1183/16000617.0088-2015\u003c/li\u003e\n\u003cli\u003eBrown, J. S., Bateson, T. F., \u0026amp; McDonnell, W. F. (2008). Effects of Exposure to 0.06 ppm Ozone on FEV \u003csub\u003e1\u003c/sub\u003e in Humans : A Secondary Analysis of Existing Data. \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e(8), 1023‑1026. https://doi.org/10.1289/ehp.11396\u003c/li\u003e\n\u003cli\u003eCai, Y., Hansell, A. L., Granell, R., Blangiardo, M., Zottoli, M., Fecht, D., Gulliver, J., Henderson, A. J., \u0026amp; Elliott, P. (2020). Prenatal, Early-Life, and Childhood Exposure to Air Pollution and Lung Function : The ALSPAC Cohort. \u003cem\u003eAmerican Journal of Respiratory and Critical Care Medicine\u003c/em\u003e, \u003cem\u003e202\u003c/em\u003e(1), 112‑123. https://doi.org/10.1164/rccm.201902-0286OC\u003c/li\u003e\n\u003cli\u003eCanova, C., Torresan, S., Simonato, L., Scapellato, M. L., Tessari, R., Visentin, A., Lotti, M., \u0026amp; Maestrelli, P. (2010). Carbon monoxide pollution is associated with decreased lung function in asthmatic adults. \u003cem\u003eEuropean Respiratory Journal\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(2), 266‑272. https://doi.org/10.1183/09031936.00043709\u003c/li\u003e\n\u003cli\u003eChaix, B. (2018). Mobile Sensing in Environmental Health and Neighborhood Research. \u003cem\u003eAnnual Review of Public Health\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(1), 367‑384. https://doi.org/10.1146/annurev-publhealth-040617-013731\u003c/li\u003e\n\u003cli\u003eChaix, B., Bista, S., Wang, L., Benmarhnia, T., Dureau, C., \u0026amp; Duncan, D. T. (2022). MobiliSense cohort study protocol : Do air pollution and noise exposure related to transport behaviour have short-term and longer-term health effects in Paris, France? \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), e048706. https://doi.org/10.1136/bmjopen-2021-048706\u003c/li\u003e\n\u003cli\u003eChen, J., \u0026amp; Hoek, G. (2020). Long-term exposure to PM and all-cause and cause-specific mortality : A systematic review and meta-analysis. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e143\u003c/em\u003e, 105974. https://doi.org/10.1016/j.envint.2020.105974\u003c/li\u003e\n\u003cli\u003eChoo, E. L. W., Janhavi, A., Koo, J. R., Yim, S. H. L., Dickens, B. L., \u0026amp; Lim, J. T. (2023). Association between ambient air pollutants and upper respiratory tract infection and pneumonia disease burden in Thailand from 2000 to 2022 : A high frequency ecological analysis. \u003cem\u003eBMC Infectious Diseases\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 379. https://doi.org/10.1186/s12879-023-08185-0\u003c/li\u003e\n\u003cli\u003eCipryan, L., Kutac, P., Dostal, T., Zimmermann, M., Krajcigr, M., Jandackova, V., Sram, R., Jandacka, D., \u0026amp; Hofmann, P. (2020). Regular running in an air-polluted environment : Physiological and anthropometric protocol for a prospective cohort study (Healthy Aging in Industrial Environment Study \u0026ndash; Program 4). \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(12), e040529. https://doi.org/10.1136/bmjopen-2020-040529\u003c/li\u003e\n\u003cli\u003eCohen, K. E., Buelow, M. W., Dixon, J., Brazauskas, R., Cohen, S. B., Earing, M. G., \u0026amp; Ginde, S. (2017). Forced vital capacity predicts morbidity and mortality in adults with repaired tetralogy of Fallot : COHEN et al. \u003cem\u003eCongenital Heart Disease\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 435‑440. https://doi.org/10.1111/chd.12470\u003c/li\u003e\n\u003cli\u003eDe Paula Santos1, U., Abdo Arbex2,3, M., Ferreira Braga3,4, A. L., Futoshi Mizutani5, R., Delfini Can\u0026ccedil;ado6, J. E., Terra-Filho7, M., \u0026amp; Chatkin8,9, J. M. (2021). Environmental air pollution : Respiratory effects. \u003cem\u003eJornal Brasileiro de Pneumologia\u003c/em\u003e, e20200267. https://doi.org/10.36416/1806-3756/e20200267\u003c/li\u003e\n\u003cli\u003eDoiron, D., De Hoogh, K., Probst-Hensch, N., Fortier, I., Cai, Y., De Matteis, S., \u0026amp; Hansell, A. L. (2019). Air pollution, lung function and COPD : Results from the population-based UK Biobank study. \u003cem\u003eEuropean Respiratory Journal\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(1), 1802140. https://doi.org/10.1183/13993003.02140-2018\u003c/li\u003e\n\u003cli\u003eDons, E., Int Panis, L., Van Poppel, M., Theunis, J., \u0026amp; Wets, G. (2012). Personal exposure to Black Carbon in transport microenvironments. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 392‑398. https://doi.org/10.1016/j.atmosenv.2012.03.020\u003c/li\u003e\n\u003cli\u003eElbarbary, M., Oganesyan, A., Honda, T., Kelly, P., Zhang, Y., Guo, Y., Morgan, G., Guo, Y., \u0026amp; Negin, J. (2020). Ambient air pollution, lung function and COPD : Cross-sectional analysis from the WHO Study of AGEing and adult health wave 1. \u003cem\u003eBMJ Open Respiratory Research\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), e000684. https://doi.org/10.1136/bmjresp-2020-000684\u003c/li\u003e\n\u003cli\u003eEuropean Environmental Agency ambient air quality and cleaner air 2024, Https://www.europarl.europa.eu/news/fr/press-room/20240219IPR17816/pollution-de-l-air-accord-avec-le-conseil-pour-ameliorer-la-qualite-de-l-air.\u003c/li\u003e\n\u003cli\u003eEzzahir, N., Leske, V., Peiffer, C., \u0026amp; Trang, H. (2005). Relevance of a portable spirometer for detection of small airways obstruction. \u003cem\u003ePediatric Pulmonology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 178‑184. https://doi.org/10.1002/ppul.20148\u003c/li\u003e\n\u003cli\u003eFeng, S., Yang, L., Dou, S., Li, X., Wen, S., Yan, L., Huang, W., Zhang, Y., Ma, B., Yuan, L., Li, S., Lu, P., \u0026amp; Guo, Y. (2024). Associations between long-term ozone exposure and small airways function in Chinese young adults : A longitudinal cohort study. \u003cem\u003eRespiratory Research\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 105. https://doi.org/10.1186/s12931-024-02679-4\u003c/li\u003e\n\u003cli\u003eFonseca, J. A., Costa-Pereira, A., Delgado, L., Silva, L. N., Magalh\u0026atilde;es, M., Castel-Branco, M. G., \u0026amp; Vaz, M. (2005). Pulmonary Function Electronic Monitoring Devices. \u003cem\u003eChest\u003c/em\u003e, \u003cem\u003e128\u003c/em\u003e(3), 1258‑1265. https://doi.org/10.1378/chest.128.3.1258\u003c/li\u003e\n\u003cli\u003eForbes, L. J. L., Kapetanakis, V., Rudnicka, A. R., Cook, D. G., Bush, T., Stedman, J. R., Whincup, P. H., Strachan, D. P., \u0026amp; Anderson, H. R. (2009). Chronic exposure to outdoor air pollution and lung function in adults. \u003cem\u003eThorax\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(8), 657‑663. https://doi.org/10.1136/thx.2008.109389\u003c/li\u003e\n\u003cli\u003eGarcia, E., Berhane, K. T., Islam, T., McConnell, R., Urman, R., Chen, Z., \u0026amp; Gilliland, F. D. (2019). Association of Changes in Air Quality With Incident Asthma in Children in California, 1993-2014. \u003cem\u003eJAMA\u003c/em\u003e, \u003cem\u003e321\u003c/em\u003e(19), 1906. https://doi.org/10.1001/jama.2019.5357\u003c/li\u003e\n\u003cli\u003eGardiner, K., Van Tongeren, M., \u0026amp; Harrington, M. (2001). Respiratory health effects from exposure to carbon black : Results of the phase 2 and 3 cross sectional studies in the European carbon black manufacturing industry. \u003cem\u003eOccupational and Environmental Medicine\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(8), 496‑503. https://doi.org/10.1136/oem.58.8.496\u003c/li\u003e\n\u003cli\u003eGuo, Q., Zhao, Y., Zhao, J., Bian, M., Qian, L., Xue, T., Zhang, J. (Jim), \u0026amp; Duan, X. (2023). Acute change of lung function to short-term exposure to ambient air pollutants with and without physical activity : A real-world crossover study. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, \u003cem\u003e316\u003c/em\u003e, 120481. https://doi.org/10.1016/j.envpol.2022.120481\u003c/li\u003e\n\u003cli\u003eHagler, G. S. W., Yelverton, T. L. B., Vedantham, R., Hansen, A. D. A., \u0026amp; Turner, J. R. (2011). Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data. \u003cem\u003eAerosol and Air Quality Research\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(5), 539‑546. https://doi.org/10.4209/aaqr.2011.05.0055\u003c/li\u003e\n\u003cli\u003eHao, H., Eckel, S. P., Hosseini, A., Van Vliet, E. D. S., Dzubur, E., Dunton, G., Chang, S. Y., Craig, K., Rocchio, R., Bastain, T., Gilliland, F., Okelo, S., Ross, M. K., Sarrafzadeh, M., Bui, A. A. T., \u0026amp; Habre, R. (2022). Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(6), 3578. https://doi.org/10.3390/ijerph19063578\u003c/li\u003e\n\u003cli\u003eHuang, J., Deng, F., Wu, S., Zhao, Y., Shima, M., Guo, B., Liu, Q., \u0026amp; Guo, X. (2016). Acute effects on pulmonary function in young healthy adults exposed to traffic-related air pollution in semi-closed transport hub in Beijing. \u003cem\u003eEnvironmental Health and Preventive Medicine\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(5), 312‑320. https://doi.org/10.1007/s12199-016-0531-5\u003c/li\u003e\n\u003cli\u003eHuang, X., Mu, X., Deng, L., Fu, A., Pu, E., Tang, T., \u0026amp; Kong, X. (2019). The etiologic origins for chronic obstructive pulmonary disease. \u003cem\u003eInternational Journal of Chronic Obstructive Pulmonary Disease\u003c/em\u003e, \u003cem\u003eVolume 14\u003c/em\u003e, 1139‑1158. https://doi.org/10.2147/COPD.S203215\u003c/li\u003e\n\u003cli\u003eIlenič, A., Pranjić, A. M., Zupančič, N., Milačič, R., \u0026amp; \u0026Scaron;čančar, J. (2024). Fine particulate matter (PM2.5) exposure assessment among active daily commuters to induce behaviour change to reduce air pollution. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e912\u003c/em\u003e, 169117. https://doi.org/10.1016/j.scitotenv.2023.169117\u003c/li\u003e\n\u003cli\u003eJion, Most. M. M. F., Jannat, J. N., Mia, Md. Y., Ali, Md. A., Islam, Md. S., Ibrahim, S. M., Pal, S. C., Islam, A., Sarker, A., Malafaia, G., Bilal, M., \u0026amp; Islam, A. R. M. T. (2023). A critical review and prospect of NO2 and SO2 pollution over Asia : Hotspots, trends, and sources. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e876\u003c/em\u003e, 162851. https://doi.org/10.1016/j.scitotenv.2023.162851\u003c/li\u003e\n\u003cli\u003eKim, C. S., Alexis, N. E., Rappold, A. G., Kehrl, H., Hazucha, M. J., Lay, J. C., Schmitt, M. T., Case, M., Devlin, R. B., Peden, D. B., \u0026amp; Diaz-Sanchez, D. (2011). Lung Function and Inflammatory Responses in Healthy Young Adults Exposed to 0.06 ppm Ozone for 6.6 Hours. \u003cem\u003eAmerican Journal of Respiratory and Critical Care Medicine\u003c/em\u003e, \u003cem\u003e183\u003c/em\u003e(9), 1215‑1221. https://doi.org/10.1164/rccm.201011-1813OC\u003c/li\u003e\n\u003cli\u003eKim, E.-K., Conrow, L., R\u0026ouml;cke, C., Chaix, B., Weibel, R., \u0026amp; Perchoux, C. (2023). Advances and challenges in sensor-based research in mobility, health, and place. \u003cem\u003eHealth \u0026amp; Place\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e, 102972. https://doi.org/10.1016/j.healthplace.2023.102972\u003c/li\u003e\n\u003cli\u003eKirkby, J., Nenna, R., \u0026amp; McGowan, A. (2019). Changes in FEV \u003csub\u003e1\u003c/sub\u003e over time in COPD and the importance of spirometry reference ranges : The devil is in the detail. \u003cem\u003eBreathe\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 337‑339. https://doi.org/10.1183/20734735.0252-2019\u003c/li\u003e\n\u003cli\u003eKwon, S. O., Hong, S. H., Han, Y.-J., Bak, S. H., Kim, J., Lee, M. K., London, S. J., Kim, W. J., \u0026amp; Kim, S.-Y. (2020). Long-term exposure to PM10 and NO2 in relation to lung function and imaging phenotypes in a COPD cohort. \u003cem\u003eRespiratory Research\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 247. https://doi.org/10.1186/s12931-020-01514-w\u003c/li\u003e\n\u003cli\u003eLagorio, S., Forastiere, F., Pistelli, R., Iavarone, I., Michelozzi, P., Fano, V., Marconi, A., Ziemacki, G., \u0026amp; Ostro, B. D. (2006). Air pollution and lung function among susceptible adult subjects : A panel study. \u003cem\u003eEnvironmental Health\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 11. https://doi.org/10.1186/1476-069X-5-11\u003c/li\u003e\n\u003cli\u003eLangan, R. C., \u0026amp; Goodbred, A. J. (2020). \u003cem\u003eOffice Spirometry:Indications and Interpretation\u003c/em\u003e. \u003cem\u003e101\u003c/em\u003e(6).\u003c/li\u003e\n\u003cli\u003eLee, Y.-G., Lee, P.-H., Choi, S.-M., An, M.-H., \u0026amp; Jang, A.-S. (2021). Effects of Air Pollutants on Airway Diseases. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(18), 9905. https://doi.org/10.3390/ijerph18189905\u003c/li\u003e\n\u003cli\u003eLevine, S. M., \u0026amp; Marciniuk, D. D. (2022). Global Impact of Respiratory Disease. \u003cem\u003eChest\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e(5), 1153‑1154. https://doi.org/10.1016/j.chest.2022.01.014\u003c/li\u003e\n\u003cli\u003eLim, Y.-H., Hersoug, L.-G., Lund, R., Bruunsgaard, H., Ketzel, M., Brandt, J., J\u0026oslash;rgensen, J. T., Westendorp, R., Andersen, Z. J., \u0026amp; Loft, S. (2022). Inflammatory markers and lung function in relation to indoor and ambient air pollution. \u003cem\u003eInternational Journal of Hygiene and Environmental Health\u003c/em\u003e, \u003cem\u003e241\u003c/em\u003e, 113944. https://doi.org/10.1016/j.ijheh.2022.113944\u003c/li\u003e\n\u003cli\u003eLouwies, T., Nawrot, T., Cox, B., Dons, E., Penders, J., Provost, E., Panis, L. I., \u0026amp; De Boever, P. (2015). Blood pressure changes in association with black carbon exposure in a panel of healthy adults are independent of retinal microcirculation. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e, 81‑86. https://doi.org/10.1016/j.envint.2014.11.006\u003c/li\u003e\n\u003cli\u003eMalag\u0026oacute;n-Rojas, J. N., Parra-Barrera, E. L., Toloza-P\u0026eacute;rez, Y. G., Soto, H., Lagos, L. F., Mendez, D., Rico, A., Almentero, J. E., Quintana-Cortes, M. A., Pinz\u0026oacute;n-Silva, D. C., Garc\u0026iacute;a, A., Benavides-Pirac\u0026oacute;n, J. A., Zona-Rubio, D. C., Portilla, C., Wilches-Mogollon, M. A., Romero-D\u0026iacute;az, S. A., Hern\u0026aacute;ndez-Florez, L. J., Morales, R., \u0026amp; Sarmiento, O. L. (2022). Assessment of Factors Influencing Personal Exposure to Air Pollution on Main Roads in Bogota : A Mixed-Method Study. \u003cem\u003eMedicina\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(8), 1125. https://doi.org/10.3390/medicina58081125\u003c/li\u003e\n\u003cli\u003eMentz, G., Robins, T. G., Batterman, S., \u0026amp; Naidoo, R. N. (2019). Effect modifiers of lung function and daily air pollutant variability in a panel of schoolchildren. \u003cem\u003eThorax\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(11), 1055‑1062. https://doi.org/10.1136/thoraxjnl-2017-211458\u003c/li\u003e\n\u003cli\u003eMirowsky, J. E., Peltier, R. E., Lippmann, M., Thurston, G., Chen, L.-C., Neas, L., Diaz-Sanchez, D., Laumbach, R., Carter, J. D., \u0026amp; Gordon, T. (2015). Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults. \u003cem\u003eEnvironmental Health\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 66. https://doi.org/10.1186/s12940-015-0049-0\u003c/li\u003e\n\u003cli\u003ePanigrahi, A., \u0026amp; Padhi, B. K. (2018). Chronic bronchitis and airflow obstruction is associated with household cooking fuel use among never-smoking women : A community-based cross-sectional study in Odisha, India. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 924. https://doi.org/10.1186/s12889-018-5846-2\u003c/li\u003e\n\u003cli\u003ePaulin, L. M., Gassett, A. J., Alexis, N. E., Kirwa, K., Kanner, R. E., Peters, S., Krishnan, J. A., Paine, R., Dransfield, M., Woodruff, P. G., Cooper, C. B., Barr, R. G., Comellas, A. P., Pirozzi, C. S., Han, M., Hoffman, E. A., Martinez, F. J., Woo, H., Peng, R. D., \u0026hellip; for SPIROMICS investigators. (2020). Association of Long-term Ambient Ozone Exposure With Respiratory Morbidity in Smokers. \u003cem\u003eJAMA Internal Medicine\u003c/em\u003e, \u003cem\u003e180\u003c/em\u003e(1), 106‑115. https://doi.org/10.1001/jamainternmed.2019.5498\u003c/li\u003e\n\u003cli\u003ePaunescu, A.-C., Casas, M., Ferrero, A., Pa\u0026ntilde;ella, P., Bougas, N., Beydon, N., Just, J., Lezmi, G., Sunyer, J., Ballester, F., \u0026amp; Momas, I. (2019). Associations of black carbon with lung function and airway inflammation in schoolchildren. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e131\u003c/em\u003e, 104984. https://doi.org/10.1016/j.envint.2019.104984\u003c/li\u003e\n\u003cli\u003ePearson, A. L., Tribby, C., Brown, C. D., Yang, J.-A., Pfeiffer, K., \u0026amp; Jankowska, M. M. (2024). Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research. \u003cem\u003eBMJ Open\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), e077036. https://doi.org/10.1136/bmjopen-2023-077036\u003c/li\u003e\n\u003cli\u003ePope Iii, C. A. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. \u003cem\u003eJAMA\u003c/em\u003e, \u003cem\u003e287\u003c/em\u003e(9), 1132. https://doi.org/10.1001/jama.287.9.1132\u003c/li\u003e\n\u003cli\u003eSchempf, A. H., \u0026amp; Kaufman, J. S. (2012). Accounting for context in studies of health inequalities : A review and comparison of analytic approaches. \u003cem\u003eAnnals of Epidemiology\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(10), 683‑690. https://doi.org/10.1016/j.annepidem.2012.06.105\u003c/li\u003e\n\u003cli\u003eSchikowski, T., Sugiri, D., Ranft, U., Gehring, U., Heinrich, J., Wichmann, H.-E., \u0026amp; Kr\u0026auml;mer, U. (2005). Long-term air pollution exposure and living close to busy roads are associated with COPD in women. \u003cem\u003eRespiratory Research\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 152. https://doi.org/10.1186/1465-9921-6-152\u003c/li\u003e\n\u003cli\u003eSchindler, C., K\u0026uuml;nzli, N., Bongard, J.-P., Leuenberger, P., Karrer, W., Rapp, R., Monn, C., Ackermann-Liebrich, U., \u0026amp; The\u0026ensp;Swiss\u0026ensp;Study\u0026ensp;on\u0026ensp;Air\u0026ensp;Pollution and\u0026ensp;Lung\u0026ensp;Diseases\u0026ensp;In\u0026ensp;Adults\u0026ensp;Investigators. (2001). Short-Term Variation in Air Pollution and in Average Lung Function Among Never-Smokers : The Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA). \u003cem\u003eAmerican Journal of Respiratory and Critical Care Medicine\u003c/em\u003e, \u003cem\u003e163\u003c/em\u003e(2), 356‑361. https://doi.org/10.1164/ajrccm.163.2.9911116\u003c/li\u003e\n\u003cli\u003eSes\u0026eacute;, L., Gille, T., Pau, G., Dessimond, B., Uzunhan, Y., Bouvry, D., Herv\u0026eacute;, A., Didier, M., Kort, F., Freynet, O., Rotenberg, C., Jeny, F., Khamis, W., Hindre, R., Maesano, C. N., Planes, C., Nunes, H., \u0026amp; Annesi-Maesano, I. (2023). Low-cost air quality portable sensors and their potential use in respiratory health. \u003cem\u003eThe International Journal of Tuberculosis and Lung Disease\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(11), 803‑809. https://doi.org/10.5588/ijtld.23.0197\u003c/li\u003e\n\u003cli\u003eShapira, U., Brezinski, R. Y., Rogowski, O., Zeltser, D., Berliner, S., Shapira, I., Shenhar-Tsarfaty, S., \u0026amp; Fireman, E. (2021). Association between elevated serum bilirubin levels with preserved lung function under conditions of exposure to air pollution. \u003cem\u003eBMC Pulmonary Medicine\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 119. https://doi.org/10.1186/s12890-021-01488-5\u003c/li\u003e\n\u003cli\u003eSmith, M. P., Standl, M., Berdel, D., von Berg, A., Bauer, C.-P., Schikowski, T., Koletzko, S., Lehmann, I., Kr\u0026auml;mer, U., Heinrich, J., \u0026amp; Schulz, H. (2018). Handgrip strength is associated with improved spirometry in adolescents. \u003cem\u003ePloS One\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), e0194560. https://doi.org/10.1371/journal.pone.0194560\u003c/li\u003e\n\u003cli\u003eSo, R., Andersen, Z. J., Chen, J., Stafoggia, M., De Hoogh, K., Katsouyanni, K., Vienneau, D., Rodopoulou, S., Samoli, E., Lim, Y.-H., J\u0026oslash;rgensen, J. T., Amini, H., Cole-Hunter, T., Mahmood Taghavi Shahri, S., Maric, M., Bergmann, M., Liu, S., Azam, S., Loft, S., \u0026hellip; Mehta, A. J. (2022). Long-term exposure to air pollution and mortality in a Danish nationwide administrative cohort study : Beyond mortality from cardiopulmonary disease and lung cancer. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e164\u003c/em\u003e, 107241. https://doi.org/10.1016/j.envint.2022.107241\u003c/li\u003e\n\u003cli\u003eSong, J., Qiu, W., Huang, X., Guo, Y., Chen, W., Wang, D., \u0026amp; Zhang, X. (2023). Association of ambient carbon monoxide exposure with hospitalization risk for respiratory diseases : A time series study in Ganzhou, China. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 1106336. https://doi.org/10.3389/fpubh.2023.1106336\u003c/li\u003e\n\u003cli\u003eStafoggia, M., Oftedal, B., Chen, J., Rodopoulou, S., Renzi, M., Atkinson, R. W., Bauwelinck, M., Klompmaker, J. O., Mehta, A., Vienneau, D., Andersen, Z. J., Bellander, T., Brandt, J., Cesaroni, G., De Hoogh, K., Fecht, D., Gulliver, J., Hertel, O., Hoffmann, B., \u0026hellip; Janssen, N. A. H. (2022). Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people : Results from seven large European cohorts within the ELAPSE project. \u003cem\u003eThe Lancet Planetary Health\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), e9‑e18. https://doi.org/10.1016/S2542-5196(21)00277-1\u003c/li\u003e\n\u003cli\u003eStrassmann, A., De Hoogh, K., R\u0026ouml;\u0026ouml;sli, M., Haile, S. R., Turk, A., Bopp, M., Puhan, M. A., \u0026amp; for the Swiss National Cohort Study Group. (2021). NO2 and PM2.5 Exposures and Lung Function in Swiss Adults : Estimated Effects of Short-Term Exposures and Long-Term Exposures with and without Adjustment for Short-Term Deviations. \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, \u003cem\u003e129\u003c/em\u003e(1), 017009. https://doi.org/10.1289/EHP7529\u003c/li\u003e\n\u003cli\u003eTarlo, S. M. (2000). Workplace respiratory irritants and asthma. \u003cem\u003eOccupational Medicine (Philadelphia, Pa.)\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 471‑484.\u003c/li\u003e\n\u003cli\u003eTeng, J., Li, J., Yang, T., Cui, J., Xia, X., Chen, G., Zheng, S., Bao, J., Wang, T., Shen, M., Zhang, X., Meng, C., Wang, Z., Wu, T., Xu, Y., Wang, Y., Ding, G., Duan, H., \u0026amp; Li, W. (2022). Long-term exposure to air pollution and lung function among children in China : Association and effect modification. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 988242. https://doi.org/10.3389/fpubh.2022.988242\u003c/li\u003e\n\u003cli\u003eThurston, G. D., Balmes, J. R., Garcia, E., Gilliland, F. D., Rice, M. B., Schikowski, T., Van Winkle, L. S., Annesi-Maesano, I., Burchard, E. G., Carlsten, C., Harkema, J. R., Khreis, H., Kleeberger, S. R., Kodavanti, U. P., London, S. J., McConnell, R., Peden, D. B., Pinkerton, K. E., Reibman, J., \u0026amp; White, C. W. (2020). Outdoor Air Pollution and New-Onset Airway Disease. An Official American Thoracic Society Workshop Report. \u003cem\u003eAnnals of the American Thoracic Society\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(4), 387‑398. https://doi.org/10.1513/AnnalsATS.202001-046ST\u003c/li\u003e\n\u003cli\u003eTiotiu, A. I., Novakova, P., Nedeva, D., Chong-Neto, H. J., Novakova, S., Steiropoulos, P., \u0026amp; Kowal, K. (2020). Impact of Air Pollution on Asthma Outcomes. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(17), 6212. https://doi.org/10.3390/ijerph17176212\u003c/li\u003e\n\u003cli\u003eWei, T., Chen, C., Yang, Y., Li, L., Wang, J., Ye, M., Kan, H., Yang, D., Song, Y., Cai, J., \u0026amp; Hou, D. (2023). Associations between short-term exposure to ambient air pollution and lung function in adults. \u003cem\u003eJournal of Exposure Science \u0026amp; Environmental Epidemiology\u003c/em\u003e. https://doi.org/10.1038/s41370-023-00550-0\u003c/li\u003e\n\u003cli\u003eWHO air quality guidelines, 2021, Https://www.who.int/news-room/feature-stories/detail/what-are-the-who-air-quality-guidelines.\u003c/li\u003e\n\u003cli\u003eWu, D.-W., Chen, S.-C., Tu, H.-P., Wang, C.-W., Hung, C.-H., Chen, H.-C., Kuo, T.-Y., Wang, C.-F., Lai, B.-C., Chen, P.-S., \u0026amp; Kuo, C.-H. (2021). The Impact of the Synergistic Effect of Temperature and Air Pollutants on Chronic Lung Diseases in Subtropical Taiwan. \u003cem\u003eJournal of Personalized Medicine\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(8), 819. https://doi.org/10.3390/jpm11080819\u003c/li\u003e\n\u003cli\u003eYoda, Y., Takagi, H., Wakamatsu, J., Ito, T., Nakatsubo, R., Horie, Y., Hiraki, T., \u0026amp; Shima, M. (2017). Acute effects of air pollutants on pulmonary function among students : A panel study in an isolated island. \u003cem\u003eEnvironmental Health and Preventive Medicine\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 33. https://doi.org/10.1186/s12199-017-0646-3\u003c/li\u003e\n\u003cli\u003eYou, D.-F., Qiao, Q.-G., Lu, J.-S., Wei, M., Tan, W.-Y., Wang, C.-H., Liu, Y.-G., Zheng, M.-Q., \u0026amp; Liu, G. (2022). Study on the correlation between hyperventilation syndrome and climate and air quality. \u003cem\u003eHealth Policy and Technology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(3), 100655. https://doi.org/10.1016/j.hlpt.2022.100655\u003c/li\u003e\n\u003cli\u003eYu, Z., Merid, S. K., Bellander, T., Bergstr\u0026ouml;m, A., Eneroth, K., Georgelis, A., Hallberg, J., Kull, I., Ljungman, P., Klevebro, S., Stafoggia, M., Wang, G., Pershagen, G., Gruzieva, O., \u0026amp; Mel\u0026eacute;n, E. (2023). Associations of improved air quality with lung function growth from childhood to adulthood : The BAMSE study. \u003cem\u003eThe European Respiratory Journal\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(5), 2201783. https://doi.org/10.1183/13993003.01783-2022\u003c/li\u003e\n\u003cli\u003eZhao, T., Markevych, I., Fuertes, E., De Hoogh, K., Accordini, S., Boudier, A., Casas, L., Forsberg, B., Garcia Aymerich, J., Gnesi, M., Holm, M., Janson, C., Jarvis, D., Johannessen, A., J\u0026ouml;rres, R. A., Karrasch, S., Leynaert, B., Maldonado Perez, J. A., Malinovschi, A., \u0026hellip; Heinrich, J. (2023). Impact of long-term exposure to ambient ozone on lung function over a course of 20 years (The ECRHS study) : A prospective cohort study in adults. \u003cem\u003eThe Lancet Regional Health - Europe\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, 100729. https://doi.org/10.1016/j.lanepe.2023.100729\u003c/li\u003e\n\u003cli\u003eZhao, X., Sun, Z., Ruan, Y., Yan, J., Mukherjee, B., Yang, F., Duan, F., Sun, L., Liang, R., Lian, H., Zhang, S., Fang, Q., Gu, D., Brook, J. R., Sun, Q., Brook, R. D., Rajagopalan, S., \u0026amp; Fan, Z. (2014). Personal Black Carbon Exposure Influences Ambulatory Blood Pressure : Air Pollution and Cardiometabolic Disease (AIRCMD-China) Study. \u003cem\u003eHypertension\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(4), 871‑877. https://doi.org/10.1161/HYPERTENSIONAHA.113.02588\u003c/li\u003e\n\u003cli\u003eZuo, B., Liu, C., Chen, R., Kan, H., Sun, J., Zhao, J., Wang, C., Sun, Q., \u0026amp; Bai, H. (2019). Associations between short-term exposure to fine particulate matter and acute exacerbation of asthma in Yancheng, China. \u003cem\u003eChemosphere\u003c/em\u003e, \u003cem\u003e237\u003c/em\u003e, 124497. https://doi.org/10.1016/j.chemosphere.2019.124497\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"air pollutants, lung function, spirometry, ambulatory monitors, lung diseases","lastPublishedDoi":"10.21203/rs.3.rs-9022657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9022657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Exposure to air pollution negatively impacts respiratory health, but limited research exists on its short-term effects while simultaneously considering several pollutants measured with sensors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study investigated the impact of air pollutants on lung function among 199 participants in Paris, France. Participants' exposure to black carbon (BC), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), nitrogen monoxide (NO), carbon monoxide (CO), ozone (O\u003csub\u003e3\u003c/sub\u003e), and particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) was recorded continuously. Lung function was assessed using spirometry tests conducted twice per day in the morning and evening over three days (N = 2504), measuring forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and the FEV1/FVC ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Air pollution levels were averaged over time windows from 15 minutes to 6 hours before the spirometry tests. Mixed-effect linear models were used to estimate the pollutants' associations with lung function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Results showed that increased exposure to BC and PM2.5 was associated with a reduced lung function. A 1 μg/m\u003csup\u003e3\u003c/sup\u003e increase in BC within 1 or 2 hours prior to testing was associated with a decrease in FEV1 by 0.016 (95% CI -0.024, -0.008) and 0.021 (95% CI -0.034, -0.007) respectively. Similarly, increases in BC exposure over 2 hours to 4 hours were associated with a decrease in the FEV1/FVC ratio. Additionally, PM2.5 exposure 15 or 30 minutes or 1 hour before testing was linked to a 0.60 (95% CI -1.30, -0.03), 0.70 (95% CI -1.39, -0.09) and 0.50 (95% CI -1.10,-0.01) percentage points reduction in the FEV1/FVC ratio. Ozone (O3) was positively associated with FEV1 and FVC. No associations were found for other pollutants or time windows.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e: This study highlights the detrimental short-term effects of air pollution, particularly BC and PM2.5, on lung function during daily mobility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides evidence that short-term exposure to air pollutants – particularly black carbon (BC) and fine particulate matter (PM2.5) – can impair lung function. The findings demonstrate that even brief increases in BC and PM2.5 during daily mobility are associated with measurable reductions in FEV1 and the FEV1/FVC ratio. By assessing multiple pollutants across short exposure windows (15 minutes to 6 hours), this study strengthens causal inference regarding rapid respiratory effects and underscore the health relevance of transient pollution peaks encountered in urban environments, particularly from traffic emissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e","manuscriptTitle":"Evaluation of the effects of air pollutants on lung function using ambulatory air pollution monitor data from the Mobilisense Project","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-23 15:06:04","doi":"10.21203/rs.3.rs-9022657/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T04:41:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T21:06:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T18:33:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T12:22:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67846729016539243771957843149520836103","date":"2026-03-24T19:22:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139720903345879689703540709011468712476","date":"2026-03-21T19:58:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11244530155228693242920506854656293691","date":"2026-03-19T14:51:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T07:07:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T08:29:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T08:27:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2026-03-03T17:13:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a560bfc-8f34-4124-a9d3-520bffa427f8","owner":[],"postedDate":"March 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T04:55:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-23 15:06:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9022657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9022657","identity":"rs-9022657","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}