Exploring the Holiday Effect on Elevated Traffic-Related Air Pollution with Hyperlocal Measurements in Chengdu, China

Abstract

Traffic-related air pollutants (TRAPs) pose significant health risks in megacities, yet fixed monitoring sites often fail to capture their complexity. To characterize the TRAP concentrations which fixed sites cannot address, we employed a mobile platform to effectively capture real-time hyperlocal-scale TRAP variations in Chengdu, China. A 17-day sampling campaign was conducted covering the National Holiday of China and collected ~1.2 × 105 1 Hz paired data. We measured particle number concentration (PNC), black carbon (BC), and nitrogen oxides (NOx) across urban and rural freeway environments to assess the impact of reduced heavy-duty diesel vehicles (HDDVs) during the holiday (i.e., holiday effect). No clear impact of wind direction on TRAP concentrations was found in this study. However, substantial differences (two times) were observed when comparing non-holiday to holiday campaigns. Spearman correlations (0.21–0.56) between TRAPs persistently exceeded Pearson correlations (0.14–0.41), indicating non-linear relationships and suggesting the necessity for data transformations (e.g., logarithms) in TRAP analysis. The comparison of the background subtracted TRAPs concentrations between non-holiday and holidays, revealing approximately a 50% reduction in TRAPs across microenvironments. Among the TRAPs, NOx emerged as a reliable indicator of HDDV emissions. The study provides insights into vehicle fleet composition impacts, paving the way for enhanced exposure assessment strategies.