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Abstract Background Chronic obstructive pulmonary disease (COPD) affecting 334 million people in the world remains a major cause of morbidity and mortality. Proper diagnosis of COPD is still a challenge and largely solely based on spirometric criteria. We aimed to investigate the potential of nitrosative/oxidative stress and related metabolic biomarkers in exhaled breath condensate (EBC) to discriminate COPD patients. Methods Three hundred three participants were randomly selected from a 15,000-transit worker cohort within the Respiratory disease Occupational Biomonitoring Collaborative Project (ROBoCoP). COPD was defined using the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria as post-bronchodilator ratio of Forced Expiratory Volume in 1st second to Forced Vital Capacity

Introduction: Health effects after long-term exposure to subway particulate matter (PM) remain unknown due to the lack of individual PM exposure data. This study aimed to apply the job exposure matrix (JEM) approach to retrospectively assess occupational exposure to PM in the Parisian subway. Methods: Job, the line and sector of the transport network, as well as calendar period were four JEM dimensions. For each combination of these dimensions, we generated statistical models to estimate the annual average PM10 concentration using data from an exhaustive inventory of the PM measurement campaigns conducted between 2004 and 2020 in the Parisian subway and historical data from the Parisian air pollution monitoring network. The resulting JEM and its exposure estimates were critically examined by experts using the uncertainty analysis framework. Results: The resulting JEM allows for the assignment of the estimated annual PM10 concentration to three types of professionals working in the subway: locomotive operators, station agents, and security guards. The estimates’ precision and validity depend on the amount and quality of PM10 measurement data used in the job-, line-, and sector-specific models. Models using large amounts of personal exposure measurement data produced rather robust exposure estimates compared to models with lacunary data (i.e., in security guards). The analysis of uncertainty around the exposure estimates allows for the identification of the sources of uncertainty and parameters to be addressed in the future in order to refine and/or improve the JEM. Conclusions: The JEM approach seems relevant for the retrospective exposure assessment of subway workers. When applied to available data on PM10, it allows for the estimation of this exposure in locomotive operators and station agents with an acceptable validity. Conversely, for security guards, the current estimates have insufficient validity to recommend their use in an epidemiological study. Therefore, the current JEM should be considered as a valid prototype, which shall be further improved using more robust measurements for some jobs. This JEM can also be further refined by considering additional exposure determinants.

Abstract Background Underground transportation systems can contribute to the daily particulates and metal exposures for both commuter and subway workers. The redox and metabolic changes in workers exposed to such metal-rich particles have yet to be characterized. We hypothesize that the distribution of nitrosative/oxidative stress and related metabolic biomarkers in exhaled breath condensate (EBC) are modified depending on exposures. Results Particulate number and size as well as mass concentration and airborne metal content were measured in three groups of nine subway workers (station agents, locomotive operators and security guards). In parallel, pre- and post-shift EBC was collected daily during two consecutive working weeks. In this biological matrix, malondialdehyde, lactate, acetate, propionate, butyrate, formate, pyruvate, the sum of nitrite and nitrate (ΣNOx) and the ratio nitrite/nitrate as well as metals and nanoparticle concentrations was determined. Weekly evolution of the log-transformed selected biomarkers as well as their association with exposure variables was investigated using linear mixed effects models with the participant ID as random effect. The professional activity had a strong influence on the pattern of anions and malondialdehyde in EBC. The daily number concentration and the lung deposited surface area of ultrafine particles was consistently and mainly associated with nitrogen oxides variations during the work-shift, with an inhibitory effect on the ΣNOx. We observed that the particulate matter (PM) mass was associated with a decreasing level of acetate, lactate and ΣNOx during the work-shift, suggestive of a build-up of these anions during the previous night in response to exposures from the previous day. Lactate was moderately and positively associated with some metals and with the sub-micrometer particle concentration in EBC. Conclusions These results are exploratory but suggest that exposure to subway PM could affect concentrations of nitrogen oxides as well as acetate and lactate in EBC of subway workers. The effect is modulated by the particle size and can correspond to the body’s cellular responses under oxidative stress to maintain the redox and/or metabolic homeostasis.

Background: Air pollution in subway environments is a growing concern as it often exceeds WHO recommendations for indoor air quality. Ultrafine particles (UFP), for which there is still no regulation nor a standardized exposure monitoring method, are the strongest contributor to this pollution when the number concentration is used as exposure metric. Objectives: We aimed to assess the real-time UFP number concentration in the personal breathing zone (PBZ) of three types of underground Parisian subway professionals and analyze it using a novel Bayesian spline approach. Consecutively, we investigated the effect of job, week day, subway station, worker location, and some further events on UFP number concentrations. Methods: The data collection procedure originated from a longitudinal study and lasted for a total duration of 6 weeks (from October 7 to November 15, 2019, i.e. two weeks per type of subway professionals). Time-series were built from the real-time particle number concentration (PNC) measured in the PBZ of professionals during their work-shifts. Complementarily, contextual information expressed as Station, Environment, and Event variables were extracted from activity logbooks completed for every work-shift. A Bayesian spline approach was applied to model the PNC within a Bayesian framework as a function of the mentioned contextual information. Results: Overall, the Bayesian spline method suited a real-time personal PNC data modeling approach. The model enabled estimating the differences in UFP exposure between subway professionals, stations, and various locations. Our results suggest a higher PNC closer to the subway tracks, with the highest PNC on subway station platforms. Studied event and week day variables had a lesser influence. Conclusion: It was shown that the Bayesian spline method is suitable to investigate individual exposure to UFP in underground subway settings. This method is informative for better documenting the magnitude and variability of UFP exposure, and for understanding the determinants in view of further regulation and control of this exposure.

The regularly reported associations between particulate matter (PM) exposure, and morbidity and mortality due to respiratory, cardiovascular, cancer, and metabolic diseases have led to the reduction in recommended outdoor PM10 and PM2.5 exposure limits. However, indoor PM10 and PM2.5 concentrations in subway systems in many cities are often higher than outdoor concentrations. The effects of these exposures on subway workers and passengers are not well known, mainly because of the challenges in exposure assessment and the lack of longitudinal studies combining comprehensive exposure and health surveillance. To fulfill this gap, we made an inventory of the PM measurement campaigns conducted in the Parisian subway since 2004. We identified 5856 PM2.5 and 18,148 PM10 results from both personal and stationary air sample measurements that we centralized in a database along with contextual information of each measurement. This database has extensive coverage of the subway network and will enable descriptive and analytical studies of indoor PM exposure in the Parisian subway and its potential effects on human health.