Mobile Observations of Air Pollution in an Urban Area: Characteristics and Variability

Urban air pollution exhibits pronounced spatial heterogeneity, yet conventional fixed-site monitoring often cannot resolve fine-scale hotspot patterns. To address this issue, this study conducted a winter intensive observation campaign combining mobile measurements and synchronous fixed-site observations in Chengdu. The mobile observation was used to characterize the spatial distribution of particulate pollution, while fixed-site pollutant and meteorological data were used to provide temporal and background context. Three mobile observation sessions were performed each day at fixed local times (09:00–11:00, 14:00–16:00, and 19:00–21:00). Based on the PM2.5 concentration, the observation period was categorized into two episodes: polluted episodes (PM2.5 > 75 μg m−3) and clean episodes (<75 μg m−3). Polluted episodes were characterized by substantially elevated PM2.5, PM10, NOx, CO, and particle number concentrations, together with relatively weak wind speed, indicating enhanced accumulation under stagnant conditions. In contrast, clean episodes generally occurred under stronger ventilation and lower pollutant levels. The results revealed marked small-scale spatial variability and distinct temporal changes in particulate pollution. PCA was suitable for the dataset (Kaiser–Meyer–Olkin = 0.788; Bartlett’s test, p < 0.001), and the first three principal components explained 82.7% of the total variance. Cluster analysis further identified three pollution regimes among 224 samples: clean/ventilated (34.4%), intermediate accumulation (39.7%), and heavy accumulation (25.9%). These findings demonstrate that short-term intensive mobile monitoring can serve as a cost-effective supplement to conventional monitoring for hotspot identification and targeted urban air-pollution management.