# Exposure to NO\({}_{2}\) and PM\({}_{2.5}\) While Commuting: Utility of Low-Cost Sensor
## 2 Materials and Methods
### Data Analysis and Statistical Treatment  
Based on the time and traveling patterns of each participant, the speed ranges for each route mode were determined. For cycling, a speed range of 10-40 km/h was selected, while for walking and driving vehicles, \(<\)10 km/h and \(>\)40 km/h, respectively. In order to separate the measurement data, relative to the recorded location, into separate paths, a Python script was created. It validated the continuity of the measurements in relation to the geographical location within 5 min forward and backward of the time under study. The initial time was set to 5 min back from the surveyed time, and then the script looked to see if another measurement occurred within 10 min. If a location has been recorded in the data, the script writes the location to the track and checks the next location. If no measurement occurs within 10 min, the entire path with stored locations is saved in the database. Measurements of pollutant concentrations recorded by the Flow sensor were then assigned to each path, and measurement data from the monitoring station and the mobile laboratory were collected for the same periods. If data points were missing (recorded as zeros in the database), estimation of these values was conducted during data recording. The first and last measurements along a given path were excluded from consideration, as the estimation procedure involved calculating the average of the preceding and subsequent measurements relative to the missing point. Given that subsequent analyses were based on the calculation of mean values and other statistical parameters, this approach was deemedthe most appropriate, as the inclusion of approximated values could reasonably represent the actual measurements. After separating the pollutant concentration measurements into separate pathways, it was possible to calculate the data. For each path, the distance was also calculated using the GeoPy library. The speed V was calculated using the formula V = S/t, where S is the distance, and t is the time of the pathway. These steps were repeated between consecutive locations. All calculated distances were then added together, reaching the final route distance, while the list with speeds was sorted, and the average speed was determined from the 30% fastest speeds obtained and set as the speed for the route. Data on pollutant concentrations and route analyses, including mean, maximum, and minimum values, standard deviation, and 95% confidence interval for each pollutant, were calculated by Python version 3. Statistical tests were carried out with package Statistica 13.1 (StatSoft) at the significance level \(p<0.05\). The results were presented by using MS Office 365, 2019 (Microsoft Corporation, King County, WA, USA) and \(R\) programming language. In order to present a concise characterization of the analyzed concentrations, the basic parameters of descriptive statistics were calculated for each station, i.e., mean, median, standard deviation, maximum and minimum values, and the first and third quartiles. The similarity of the distribution of the analyzed variables (for heating and non-heating seasons) to the normal distribution was examined using the Shapiro-Wilk test. For PM\({}_{2.5}\) concentrations during the heating season, the Shapiro-Wilk test yielded a statistic of \(W=0.6522\) with \(p<0.0001\), whereas for the non-heating season, the statistic was \(W=0.4059\) with \(p<0.0001\). These results indicate a significant deviation from normality in both cases. Similarly, for NO\({}_{2}\) concentrations, the Shapiro-Wilk test produced a statistic of \(W=0.9108\) with \(p<0.0001\) during the heating season and \(W=0.9112\) with \(p<0.0001\) during the non-heating season, confirming the non-normality of the distribution.