School of Architecture, Planning and Environmental Policy, University College Dublin, Dublin, Ireland.
School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland.
Environ Sci Pollut Res Int. 2024 Sep;31(44):56114-56129. doi: 10.1007/s11356-024-34903-5. Epub 2024 Sep 10.
Mobile monitoring provides high-resolution observation on temporal and spatial scales compared to traditional fixed-site measurement. This study demonstrates the use of high spatio-temporal resolution of air pollution data collected by Google Air View vehicles to identify hotspots and assess compliance with WHO Air Quality Guidelines (AQGs) in Dublin City. The mobile monitoring was conducted during weekdays, typically from 7:00 to 19:00, between 6 May 2021 and 6 May 2022. One-second data were aggregated to 377,113 8 s road segments, and 8 s rolling medians were aggregated to hourly and daily levels for further analysis. We assessed the temporal variability of fine particulate matter (PM), nitrogen monoxide (NO), nitrogen dioxide (NO), ozone (O), carbon monoxide (CO), and carbon dioxide (CO) concentrations at hyperlocal levels. The average daytime median concentrations of NO (28.4 ± 15.7 µg/m) and PM (7.6 ± 4.7 µg/m) exceeded the WHO twenty-four hours (24 h) Air Quality Guidelines in 49.4% and 9% of the 1-year sampling time, respectively. For the diurnal variation of measured pollutants, the morning (8:00) and early evening (18:00) showed higher concentrations for NO and PM, mostly happening in the winter season, while the afternoon is the least polluted time except for O. The low-percentile approach along with 1-h and daytime minima method allowed for decomposing pollutant time series into the background and local contributions. Background contributions for NO and PM changed along with the seasonal variation. Local contributions for PM changed slightly; however, NO showed significant diurnal and seasonal variability related to traffic emissions. Short-lived event enhancement (1 min to 1 h) accounts for 36.0-40.6% and 20.8-42.2% of the total concentration for NO and PM. The highly polluted days account for 56.3% of total NO, highlighting local traffic is the dominant contributor to short-term NO concentrations. The longer-lived events (> 8 h) enhancement accounts for 25% of the monitored concentrations. Additionally, conducting optimal hotspot analysis enables mapping the spatial distribution of "hot" spots for PM and NO on highly polluted days. Overall, this investigation suggests both background and local emissions contribute to PM and NO pollution in urban areas and emphasize the urgent need for mitigating NO from traffic pollution in Dublin.
与传统的固定站点测量相比,移动监测在时间和空间分辨率上提供了更高的观测能力。本研究展示了利用谷歌 Air View 车辆收集的高时空分辨率空气污染数据来识别热点,并评估都柏林市的世界卫生组织空气质量准则(AQG)的达标情况。移动监测于 2021 年 5 月 6 日至 2022 年 5 月 6 日期间的工作日进行,通常在 7:00 至 19:00 之间进行。1 秒的数据被汇总为 377,113 个 8 秒路段,8 秒滚动中位数被汇总为每小时和每日水平,以进行进一步分析。我们评估了细颗粒物(PM)、氮氧化物(NO)、二氧化氮(NO)、臭氧(O)、一氧化碳(CO)和二氧化碳(CO)浓度在超局部水平的时间变化。NO(28.4±15.7µg/m)和 PM(7.6±4.7µg/m)的日平均中位数浓度在 1 年采样时间的 49.4%和 9%中超过了世界卫生组织 24 小时(24 h)空气质量准则。对于测量污染物的日变化,早晨(8:00)和傍晚(18:00)的 NO 和 PM 浓度较高,主要发生在冬季,而下午是除 O 以外污染最少的时间。采用低百分位数方法和 1 小时和白天最小值方法,可以将污染物时间序列分解为背景和本地贡献。NO 和 PM 的背景贡献随季节变化而变化。PM 的本地贡献变化不大;然而,NO 显示出与交通排放有关的显著昼夜和季节性变化。短期事件增强(1 分钟至 1 小时)占 NO 和 PM 总浓度的 36.0-40.6%和 20.8-42.2%。高污染天数占总 NO 的 56.3%,突出了当地交通是短期 NO 浓度的主要贡献者。较长寿命事件(>8 小时)增强占监测浓度的 25%。此外,进行最佳热点分析可以绘制高污染日 PM 和 NO 的“热点”空间分布。总的来说,本研究表明背景和本地排放都对城市地区的 PM 和 NO 污染有贡献,并强调都柏林迫切需要减少交通污染中的 NO。