Source attribution of air pollution using a generalized additive model and particle trajectory clusters

Detecting and quantifying PM2.5 and NO2 contributions from train and road traffic in the vicinity of a major railway terminal in Dublin, Ireland

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 May 25, 2023 to June 25, 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.

Back-trajectory analyses for evaluating the transboundary transport effect to the aerosol pollution in South Korea

This study performed a back-trajectory analysis to determine the influence of transboundary transport on the extent of aerosol pollution in South Korea, based on 5-year PM2.5 measurements (2015-2019) in five cities covering South Korea. A transboundary transport case was selected if a back trajectory passed over a dedicated region (BOX 1 and BOX 2) in the Yellow Sea. First, we found that the frequency of transboundary transport largely increases in the high pollution case, and this pattern is almost consistent for all months and all five cities, indicating the importance of investigating the horizontal direction of air mass movement associated with PM2.5, which has been discussed extensively in previous studies. In this study, we also examined the altitude change and straight moving distance (defined as travel distance) of back trajectories regarding the extent of local PM2.5. Consequently, we found that back trajectories in high aerosol pollution showed much lower altitudes and shorter travel differences, implying a significant contribution of surface emissions and stagnant air conditions to severe aerosol pollution. As a result, the local PM2.5 level was not significantly enhanced when the air mass passed over the Yellow Sea if transboundary transport occurred at high altitudes with rapid movement (i.e., high altitude and long travel distance back-trajectory). Based on these results, we suggest utilizing the combined information of the horizontal direction, altitude variation, and length of back trajectories to better evaluate transboundary transport.

Impact of shipping emissions regulation on urban aerosol composition changes revealed by receptor and numerical modelling

Various shipping emissions controls have recently been implemented at both local and national scales. However, it is difficult to track the effect of these on PM2.5 levels, owing to the non-linear relationship that exists between changes in precursor emissions and PM components. Positive Matrix Factorisation (PMF) identifies that a switch to cleaner fuels since January 2020 results in considerable reductions in shipping-source-related PM2.5, especially sulphate aerosols and metals (V and Ni), not only at a port site but also at an urban background site. CMAQ sensitivity analysis reveals that the reduction of secondary inorganic aerosols (SIA) further extends to inland areas downwind from ports. In addition, mitigation of secondary organic aerosols (SOA) in coastal urban areas can be anticipated either from the results of receptor modelling or from CMAQ simulations. The results in this study show the possibility of obtaining human health benefits in coastal cities through shipping emission controls.

The investigations on organic sources and inorganic formation processes and their implications on haze during late winter in Seoul, Korea

This study investigated the sources and formation processes of particulate matter (PM) with an aerodynamic diameter ≤1 μm (PM₁) and black carbon (BC) in Seoul during late winter via high-resolution time-of-flight aerosol mass spectrometry (HR-ToF-AMS) and positive matrix factorization (PMF) analysis. In this study, secondary aerosols (75.1%) exhibited higher contributions than did primary aerosols (24.9%), suggesting the importance of secondary aerosol formation over primary aerosol emissions for NR-PM₁+BC during late winter. Frequent haze episodes were observed and these were found to proceed in two distinct stages each with different pattern of sulfur oxidation ratio (SOR), nitrogen oxidation ratio (NOR) and meteorological conditions, such as the wind speed, direction and relative humidity (RH). Haze formation during stage 1 was caused mainly by local accumulation of primary aerosols and formation of local secondary aerosols under stagnant conditions. However, there were some impacts of down mixing of regional transport. Stage 2 took place during the night following stage 1 and was characterized by enhanced secondary aerosol formation. Enhancement of SOR might be due to accelerated aqueous phase reactions under higher RH and enhanced NOR is probably because of the heterogeneous uptake of N₂O₅ by ammonium sulfate aerosols ensued after sulfate formation. These findings suggest that the winter air quality in Seoul depends on complex processes, from not only emissions and transport from upwind areas but also from significant impacts of meteorological condition.