Department of Civil, Structural, and Environmental Engineering, Trinity College Dublin, University of Dublin, College Green, Dublin, 2, Ireland.
Department of Civil, Structural, and Environmental Engineering, Trinity College Dublin, University of Dublin, College Green, Dublin, 2, Ireland; Centre for Transport Research and Innovation for People, Trinity College Dublin, University of Dublin, College Green, Dublin, 2, Ireland.
Environ Pollut. 2024 Nov 15;361:124903. doi: 10.1016/j.envpol.2024.124903. Epub 2024 Sep 6.
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 PM and NO contributions from railway station and road traffic on air quality in the vicinity of railway station 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, with the XGBoost model (NO R = 0.8 and RSME = 9.1 μg/m & PM, R = 0.92 and RSME = 2.2 μg/m) deemed more appropriate than the RF model. Local wind conditions, pressure, PM concentrations, and road traffic significantly impacted NO model results, while raw PM sensor readings greatly influenced the PM model output. This highlights that the NO sensor requires more input data for accurate calibration, unlike the PM sensor. The monitoring results from the one-month monitoring 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 μg/m, NO = 10 μg/m) 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 PM source and road traffic was the main NO 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.
交通枢纽的空气污染是当地城市居民公认的健康问题。在交通枢纽领域，人们已经对大型机场和港口环境给予了高度关注，但人们也开始关注城市铁路枢纽的排放问题，尤其是那些使用柴油火车的铁路枢纽。本文提出了一种方法，该方法采用低成本监测（LCM）替代固定站点监测（FSM），以量化和分解都柏林爱尔兰火车站附近空气质量中来自火车站和道路交通的 PM 和 NO 贡献。与参考监测仪相比，NO 传感器的测量结果与 PM 传感器相比存在较大差异。机器学习模型（XGBoost 和随机森林（RF）回归）被应用于校准 LCM 设备，其中 XGBoost 模型（NO R = 0.8 和 RSME = 9.1μg/m & PM，R = 0.92 和 RSME = 2.2μg/m）比 RF 模型更合适。当地的风况、压力、PM 浓度和道路交通对 NO 模型结果有显著影响，而原始 PM 传感器读数对 PM 模型输出有很大影响。这表明与 PM 传感器不同，NO 传感器需要更多的输入数据进行准确校准。2023 年 5 月 25 日至 6 月 25 日进行的为期一个月的监测活动的监测结果表明，火车站测量的 NO 和 PM 浓度较高，分别超过了世界卫生组织（WHO）年度限值（PM = 5μg/m，NO = 10μg/m）的 1.6-1.8 倍和 3.2-5.2 倍。随后，根据风向进行的数据过滤技术表明，当风从火车站吹来时，火车站是 PM 的主要来源，道路交通是 NO 的主要来源。本研究强调了 LCM 设备与强大的机器学习技术相结合在捕捉城市环境空气质量方面的价值。
