## 1 Introduction  
Traffic emissions are the major source of air pollution in urban areas [1], accounting for 90% of primary pollutant emissions (e.g., NO\({}_{\chi}\) and CO) in Korea [2]. Populations living near major roadways have a high risk of acute and chronic health problems due to air pollution exposure [3, 4, 5]. The population living in urban areas accounted for 56% of the global population in 2020, which is expected to increase to up to 68% in 2050 worldwide (the proportion of the urban population was 82% in high-income countries (including Korea) in 2020) [6]. Thus, it is important to assess the health risks associated with being exposed to air pollutants emitted from dense urban road-networks by monitoring the highly spatiotemporally resolved air quality in various urban micro-environments. The air pollutant concentrations in urban areas vary spatially and temporally due to the urban configuration, complicated dense road networks, fleet composition, meteorological conditions, and other factors [7; 8; 9]. The wind fields and dispersion characteristics of the in-canopy atmosphere are modified by the urban built environment, which alters the ventilation capacity and dilution of local emissions, ultimately enhancing or mitigating local concentrations of air pollutants close to sources [7; 10]. It has previously been reported that the higher the traffic density, the less turbulent energy is produced, which affects the capacity for pollutant dispersion [9]. Consequently, complicated urban canopy environments can lead to spatiotemporally heterogeneous distributions of traffic-related air pollutants. This heterogeneity of pollutant distribution causes the extent of pedestrian exposure to air pollutants to vary over time and space. Therefore, spatiotemporally dense air quality monitoring is required, particularly in the center of urban areas, to assess the complicated built environment, dense road-network, and large volume of people moving through the city. Many studies have investigated the spatial distributions of traffic-related air pollutants based on model simulations with computational fluid dynamics (CFD, [11]) and large eddy simulation (LES, [12]), together with observations made from a mobile platform [10; 13]. Mobile measurements provide highly spatially resolved air pollutant distributions; however, simultaneous and continuous monitoring at various locations is limited and on-road measurements can be influenced by intermittently encountered high-emitting vehicles [14]. Many CFD or LES modeling studies of urban micro-environments have targeted an idealized street canyon, while studies of real urban environments at larger spatial scales are relatively rare. In addition, model validation with observations from multiple stations or mobile platforms is not simple because model results represent the typical distributions in certain specific environments, whereas real atmospheric and traffic conditions vary over relatively short periods. To overcome those limitations modeling and mobile measurement studies contain, several recent studies have attempted to build a highly spatially resolved air quality monitoring network, using a number of low-cost sensors in urban areas [15; 16; 17; 18; 19; 20]. One study deployed 35 low-cost sensors in a 10 \(\times\) 10 km domain in Cambridge, UK to monitor CO, NO, and NO\({}_{2}\), showing the potential of a low-cost sensor network for gaseous pollutants [19]. Another study used 32 sensor-nodes (CO, NO, and NO\({}_{2}\)) that were deployed in and around the city of Cambridge, UK, with a particular focus on the spatial distribution of CO between urban and rural areas [17]. To investigate the spatial heterogeneity of particulate matter (PM), a previous study deployed 17 sets of PM sensors in Memphis, USA [16]. Another study deployed 11 sensor-nodes to monitor CO\({}_{2}\) and PM within a 1.8 \(\times\) 1 km spatial domain, to examine local emission sources [18].