Detecting and quantifying PM2.5 and NO2 contributions from train and road traffic in the vicinity of a major railway terminal in Dublin, Ireland - Caulfield, Brian; O'Mahony, Margaret (My University, 2024)
Air pollution from transport hubs is a recognised health concern for local urban inhabitants. Within the domain of transport hubs, significant attention has been given to larger airport and port settings, however concerns have been raised about emissions from urban railway hubs, especially those with diesel trains. This paper presents an approach that adopts low-cost monitoring (LCM) for fixed site monitoring (FSM) to quantify and disaggregate PM2.5 and NO2 contributions from railway station and road traffic on air quality in the vicinity of railway station in Dublin, Ireland. The NO2 sensor showed larger discrepancies than the PM2.5 sensor when compared to the reference monitor. Machine learning models (XGBoost and Random Forest (RF) regression) were applied to calibrate the LCM devices, with the XGBoost model (NO2, R2 = 0.8 and RSME = 9.1 μg/m3 & PM2.5, R2 = 0.92 and RSME = 2.2 μg/m3) deemed more appropriate than the RF model. Local wind conditions, pressure, PM2.5 concentrations, and road traffic significantly impacted NO2 model results, while raw PM2.5 sensor readings greatly influenced the PM2.5 model output. This highlights that the NO2 sensor requires more input data for accurate calibration, unlike the PM2.5 sensor. The monitoring results from the one-month monitoring campaign from 25 May 2023 to 25 June 2023 presented elevated NO2 and PM2.5 concentrations measured at the railway station, which translated to exceedances of the annual WHO limits (PM2.5 = 5 μg/m3, NO2 = 10 μg/m3) by 1.6-1.8 and 3.2-5.2 times respectively at the study site. A subsequent data filtering technique based on wind orientation, revealed that the railway station was the main PM2.5 source and road traffic was the main NO2 source when winds come from the railway station. This study highlights the value of LCM devices alongside robust machine learning techniques to capture air quality in urban settings.
PUBLISHED
Keywords: air quality sensors, bivariate polar plot, machine learning, railway station, transport hubs; Smart & Sustainable Planet; Environmental aspects of transportation; Transport