time is another variable of interest. As the time of exposure to air pollutants increases, health risks increase, too. Still, for low or moderate levels of pollution (PM 2.5 background level of 50 µg/m 3 ), there is a time threshold (300 min of bicycle riding) above which risks can out- weigh benefits (Targino et al., 2018). Another potential pollutant, mainly in cit- ies, is noise. Since 1972, the World Health Organization (WHO) has declared noise a pollutant (WHO, 2019; Wothge & Niemann, 2020). Noise is one of the most common urban environmental risk factors and road trac is the primary source of community noise in urban areas (Okokon et al., 2017; Wothge & Niemann, 2020). Noise can cause hearing loss, sleeping disorders, annoyance, cardiovascular disease, stress (increasing serum cortisol levels have been demonstrated after exposure to noise levels >55 dB), and other health impairments (Gilani & Mir, 2021; Wallas et al., 2018; Wothge & Nie- mann, 2020). Currently, WHO recommends that expo- sure to road trac noise not exceed L den = 53 dB or L night = 45 dB outdoors (WHO, 2019), with L den being a long-time exposure indica- tor considering sound pressure level at dif- ferent times during the day and L night being the equivalent A-weighted sound pressure level for 9 hr during the night, usually from 10 p.m. to 7 a.m. Moreover, active transport users are more exposed to trac noise than other transport users in several European cit- ies, which can increase health risks in these individuals (Okokon et al., 2017). Montevideo, the capital city of Uruguay, has a population of more than 1 million inhabitants and shares with other cities of similar size several environmental risk fac- tors such as noise and other pollutants. For surveillance of these factors, the city has an urban network of air monitoring stations (Montevideo Municipal Administration, n.d.). There is a dierence, however, between the air quality at street level and measure- ments recorded at a greater height. Thus, existing records of regular monitoring of air pollutants might not be accurate reflections of air quality at the street level, and potential risk exposure of the general population and especially of people who use active means of transport might be underestimated. Our study is part of a wider project aimed at contributing to active travel planning in
Montevideo, including air quality manage- ment in decision-making processes (D’Angelo et al., 2023). The objective of our study was to measure exposure to environmental pol- lutants (PM 2.5 , PM 10 , NO 2 , and noise) by assessing a group of cyclists in an urban ride of up to 30 min in Montevideo and the eect of this exposure on the cyclists’ carbon mon- oxide (CO) exhaled at the end of the trip. Methods We selected two sampling routes, Cen- tral Route and Boulevard Route (hereafter referred to as Route 1 and Route 2, respec- tively). Route 1 had narrow streets, high buildings, and medium trac flow. Route 2 had broad avenues, high and middle-height buildings, and heavy trac flow. These routes were chosen based on the following criteria: • Routes were frequently used by cyclists in Montevideo. • Routes were located in high-mobility areas of the city. •Routes surrounded air quality stations belonging to the Montevideo Air Quality Monitoring Network. • Routes had areas with high and low values of the environmental variables to be evalu- ated as part of the recorded environmen- tal exposure (e.g., vehicular flow, building height, street width, presence of cycling infrastructure). We used a flyer to recruit broad partici- pation of volunteer cyclists. Overall, >100 cyclists contacted our research team. Before participating, all participants involved in the study signed informed consent forms. Our study was conducted per the Declara- tion of Helsinki and approved by the Ethics Committee of the Faculty of Medicine at the University of the Republic (EXP No. 070153- 000585-18, 01/11/2018). Sample size calculations were reported in D’Angelo et al. (2023). We used the method- ology developed by Van den Bossche et al. (2015) to estimate the minimum number of measurements to be done along each moni- toring route to obtain representative atmo- spheric concentrations of the pollutants. It was determined that 30 measurements would be needed along each monitoring route. Measurements from each participant were included once and an equal number of participants were measured in each route. Measurements were done periodically from
February 2021 to December 2021, depend- ing on participant availability and always at the same time (i.e., morning rush hour) on working days. The assembly of the necessary equipment to measure clinical and environ- mental variables included the installation of a basket (fixed to the bicycle) with devices to measure the concentration of contaminants, attachment of GPS equipment to the bicycle, placement of the noise dosimeter sensor on the cyclist’s shoulder, and placement of the heart rate sensor on the cyclist using a chest strap design. Before starting the cycling trip, all partici- pants were asked to answer a questionnaire that included information on demographic variables, smoking habits, active lifestyle, height and weight, comorbidities, blood pres- sure, and CO-oximetry. After completion of the trip, a final CO-oximetry was performed. Descriptive statistics of the main char- acteristics of the participants were done. Bivariate analysis with some selected vari- ables in each sampling route was conducted, and parametric or nonparametric tests were used to compare continuous variables in each route depending on the normal distri- bution of data. The percentage of time per trip and route during which PM 10 and PM 2.5 concentrations were either >10 μg/m 3 or above concentra- tions recorded simultaneously at a monitor- ing station placed on the roof of a nearby building were measured according to recom- mendations by Orellano et al. (2020). The percentage of time per trip and route during which the noise levels were >70 dBA was also counted (WHO, 2019). The potential inhaled dose for a specific air pollutant (D) was determined according to the following equation (Targino et al., 2018), with C being air pollutant concentrations and V being cyclist ventilation rates: D (μg/s) = C (μg/m 3 ) × V (m 3 /s). Lastly, multivariate models were done to assess the association between levels of CO in cyclists’ expired air before and after the cycling trip. The models were adjusted for poten- tial confounders. We conducted our analysis using R statistical software version 3.6.1. Results A total of 64 participants were recruited—32 for each route—and 60% of participants self- reported their sex as male ( n = 38). For the
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