Black carbon and its relationship with PM2.5 and NO2 in a high-traffic urban environment of Kuala Lumpur

Urban air is highly affected by various air pollutants such as carbonaceous aerosols, particulate matter and gaseous emission. The accumulation of these pollutants impacts the densely populated urban population. This study utilised high-time resolution data of black carbon (BC), PM 2.5 and NO 2 in the city centre of Kuala Lumpur using UV-IR black carbon monitor equipped with particulate and NO 2 sensors from January 2025 to early April 2025. The analysis aims to characterise the concentrations of these pollutants and evaluate their sensitivity to traffic-related events. During the study period, average concentrations were 2187 ± 782 ng/m 3 for BC, 15.45 ± 5.85 μg/m 3 for PM 2.5 and 13.60 ± 2.34 ppb for NO 2 . A comparison between weekdays and weekends concentrations revealed significant differences for BC and NO 2 ( p < 0.05) with higher levels observed on weekdays, whereas PM 2.5 showed no significant difference ( p > 0.05). A K-means clustering analysis successfully categorised the BC, PM 2.5 and NO 2 for the overall monitoring data into three distinct clusters: high, moderate, and low pollution concentrations. For predictive analysis, a random forest (RF) model achieved the highest predictive accuracy with an R 2 value of 0.77, and one-hour lag of BC concentration (BClag1) is highly influential predictor in predicting BC levels. The findings from this study suggest that BC and NO 2 are more effective indicators of city traffic emissions than PM 2.5 as PM 2.5 concentrations exhibited lower sensitivity to changes in traffic and urban activities. This research provides crucial insights for developing targeted air quality management strategies in urban environments. • High-time resolution monitoring of black carbon, PM 2.5 , and NO 2 was conducted in central Kuala Lumpur. • Weekday vs weekend analysis revealed significantly higher black carbon and NO 2 on weekdays. • One-hour lagged black carbon as the strongest predictor of black carbon concentration. • Black carbon and NO 2 as better traffic emission indicators compared to PM 2.5 .