## 2 Methods
### Sensor-Nodes and Data Quality  
The sensor-nodes contained cost-effective sensors for air pollutants (O\({}_{3}\), NO\({}_{2}\), CO, PM\({}_{2.5}\), and PM\({}_{10}\)), a weather sensor (temperature and humidity), and a fan for ventilation (ventilation rate > 20 times the volume per minute) [23]. The details and specifications of the sensors incorporated in the sensor-node are shown in our previous study (Table 1 in [PERSON] et al. [23]). CO (CO-B4, Alphasense) and O\({}_{3}\) (SM50, Aeroqual) sensors are based on an electrochemical technique, and the working principle is described in detail in [PERSON] et al. [19]. The NO\({}_{2}\) sensor (MicSS-2714, SGX Sensortech) is a semiconducting metal oxide sensor that measures changes in electrical conductivity [24]. The PM sensor (PMS5003, Plantower) uses a light scattering technique, and a detailed working principle is described in [PERSON] et al. [25]. The sensor-nodes stored data at 10-s intervals on an SD card and communicated with a smartphone via Wi-Fi to display the sensor conditions, pollutant concentrations, and battery levels. A lead-acid battery was used to power the sensor-nodes, and the battery was periodically replaced due to the lack of a power supply in the streets. Further details of the sensor-node configuration are provided in our previous study [23]. \begin{table}
\begin{tabular}{c c c c c} \hline
**Qualitative** & **Site-1** & **Site-2** & **Site-3** & **Site-4** \\
**Characteristics** & **(Eulji-ro)** & **(Sogong-ro)** & **(Namdaemun-ro)** & **(Deogsugung-gil)** \\ \hline \multirow{4}{*}{Surrounding built environments} & Typical urban canyon surrounded by tall buildings on both sides of the road with the road & Large open lot and moderate buildings between several large buildings & Tall buildings on both sides between tall buildings & Pedestrian-oriented one lane road. To build an air quality monitoring network with a large number of sensors, there is a need to quantitatively evaluate the consistency and accuracy of sensor readings for spatial assessment of air pollutants. We conducted intercomparisons by placing the 30 sensor-nodes in the same location (Pukyong National University campus) for 2-4 days before and after the field surveys, in both the summer and winter (under real atmospheric conditions). The readings from the 30 sensor-nodes exhibited high consistency (R\({}^{2}\) > 0.90; Table 3 in [PERSON] et al. [23]) and the linearity between sensor readings did not change before and after the field surveys (Supplementary Information Figure S2) [23]. We corrected the sensor readings from 29 sensor-nodes to those of the reference sensor node (i.e., the sensor node located nearest to the AQMS inlet) (1 st step correction). To assure the accuracy of sensor readings, we placed the reference sensor-node in close proximity to the inlet of an AQMS (Jongro-gu AQMS located 2 km east of the monitoring site) during the summer and winter field intensive periods and conducted intercomparison tests between the reference sensor node and federal reference/equivalent method (FRM/FEM) instruments. For all air pollutants (except NO\({}_{2}\)), the sensor-node revealed a strong linear consistency with the FRM/FEM instruments for both the summer and winter experimental periods (R\({}^{2}\)\(\geq\) 0.80; Table 4 in [PERSON] et al. [23]). Thus, based on these intercomparison results, we corrected the data from the 29 sensor-nodes, which had been initially corrected to correspond to the reference sensor readings, and now corresponded to the officially validated concentrations from the FRM/FEM instruments (2 nd step correction, Supplementary Information Figure S3) [23]. The NO\({}_{2}\) sensors appeared to be sensitive to weather conditions and potential interferences (e.g., O\({}_{3}\)). We therefore additionally corrected NO\({}_{2}\) sensor readings with a multi-variate regression method using O\({}_{3}\), temperature, and humidity as explanatory variables [23]. More details about sensor evaluation and data correction are provided in our previous study [23].