# Exposure to NO\({}_{2}\) and PM\({}_{2.5}\) While Commuting: Utility of Low-Cost Sensor
## 2 Materials and Methods
### Study Design  
The study was designed to assess the real-time exposure of two participants (male--person 1; female--person 2) to PM\({}_{2.5}\) and NO\({}_{2}\) while commuting to the University campus by three transportation modes: bicycle, vehicle, and walking. The measurements were systematically conducted on each regular working day during the study period (from January to November 2022), ensuring comprehensive coverage of typical daily conditions. The participants were not instructed or constrained in their choice of transportation mode; instead, they independently selected the means of transport based on their personal needs, preferences, circumstances, and weather conditions on a given day. This methodological decision was deliberate, aiming to capture natural variations in exposure levels under real-world conditions, thereby enhancing the ecological validity and practical applicability of the findings. Figure 1 presents the routes of journeys. During cycling, the participants used the available cycle paths. Gliwice currently offers access to a network comprising 126.5 km of dedicated cycling routes and pathways [25], corresponding to 0.71 m of cycle routes per capita. The distance puts Gliwice at the top of Polish cities; however, it is far from the European leaders, exceeding 1 m of cycle routes per capita [26; 27]. The measurements of personal exposure to NO\({}_{2}\) and PM\({}_{2.5}\) were performed by a low-cost Flow 2 personal sensor (produced by Plume Labs, Paris, France). The device has a light-scattering laser counter that measures particulate concentrations and a metal oxide sensor that measures gas concentrations. An application on the phone enables data recording as graphs and levels that can be uploaded. NO\({}_{2}\) concentrations are presented in ppb. Hence, in the article, NO\({}_{2}\) concentrations are converted to \(\upmu\)g/m\({}^{3}\) (the conversion factor is 1.91), while PM\({}_{2.5}\) is measured in \(\upmu\)g/m\({}^{3}\). The sampling frequency is fixed at 60 s. Self-calibration with cutting-edge machine-learning algorithms is contained in Flow's proprietary firmware [28]. According to the manufacturer, the correlation coefficient for NO\({}_{2}\) with the reference device APNA370 NOx monitor (produced  
Figure 1: Location of Gliwice in Poland, a map with bicycle infrastructure in the area, a monitoring station, and an air quality laboratory were used in the study, as well as the dominant journeys of the participants (green dots indicate the starting and ending points of the journeys). by HORIBA Corp., Kyoto, Japan) is in the range from 84 to 99% (average 96%), while for PM\({}_{2.5}\) the reference device was an AeroTrak 9306 Handheld Particle Counter (produced by TSI Incorporated, Shoreview, MN, USA) with correlation in the range from 87.3% to 97.0% (average 90.9%) [29]. It should be noted that manufacturers often achieve high correlations with reference-grade sensors under strictly controlled conditions, while the details of the calibration procedures are typically protected as trade secrets. However, under real-world conditions, the accuracy of many low-cost sensors generally falls within a \(\pm\)10% range for most air pollutants. Furthermore, regardless of the precision of the calibration process, low-cost sensors are highly sensitive to meteorological factors and require an acclimatization period when the monitoring environment changes [30; 31]. Among the studies published to date, [PERSON] et al. [32] proved in their study that a Flow air quality sensor (the same model as used in our study) can be a helpful instrument for monitoring air quality, particularly in places where air pollution concentrations are higher. Linear regression (\(R^{2}\)) between Flow Sensors and reference sensor Plantower A003 (produced by Nanchang Panteng Technology Co., Ltd., Nanchang, China) for PM\({}_{2.5}\) was 0.76.