首页|University of Dublin Reports Findings in Machine Learning (Detecting and quantif ying PM2.5 and NO2 contributions from train and road traffic in the vicinity of a major railway terminal in Dublin, Ireland)
University of Dublin Reports Findings in Machine Learning (Detecting and quantif ying PM2.5 and NO2 contributions from train and road traffic in the vicinity of a major railway terminal in Dublin, Ireland)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Dublin, Irel and, by NewsRx correspondents, research stated, “Air pollution from transport hu bs is a recognised health concern for local urban inhabitants. Within the domain of transport hubs, significant attention has been given to larger airport and p ort settings, however concerns have been raised about emissions from urban railw ay hubs, especially those with diesel trains.” Our news editors obtained a quote from the research from the University of Dubli n, “This paper presents an approach that adopts low-cost monitoring (LCM) for fi xed site monitoring (FSM) to quantify and disaggregate PM and NO contributions f rom railway station and road traffic on air quality in the vicinity of railway s tation in Dublin, Ireland. The NO sensor showed larger discrepancies than the PM sensor when compared to the reference monitor. Machine learning models (XGBoost and Random Forest (RF) regression) were applied to calibrate the LCM devices, w ith the XGBoost model (NO R = 0.8 and RSME = 9.1 mg/m & PM, R = 0. 92 and RSME = 2.2 mg/m) deemed more appropriate than the RF model. Local wind co nditions, pressure, PM concentrations, and road traffic significantly impacted N O model results, while raw PM sensor readings greatly influenced the PM model ou tput. This highlights that the NO sensor requires more input data for accurate c alibration, unlike the PM sensor. The monitoring results from the one-month moni toring campaign from May 25, 2023 to June 25, 2023 presented elevated NO and PM concentrations measured at the railway station, which translated to exceedances of the annual WHO limits (PM = 5 mg/m, NO = 10 mg/m) by 1.6-1.8 and 3.2-5.2 time s respectively at the study site. A subsequent data filtering technique based on wind orientation, revealed that the railway station was the main PM source and road traffic was the main NO source when winds come from the railway station.”
DublinIrelandEuropeCyborgsEmergi ng TechnologiesMachine Learning
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