Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland

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_2.5), nitrogen monoxide (NO), nitrogen dioxide (NO₂), ozone (O₃), carbon monoxide (CO), and carbon dioxide (CO₂) concentrations at hyperlocal levels. The average daytime median concentrations of NO₂ (28.4 ± 15.7 µg/m³) and PM_2.5 (7.6 ± 4.7 µg/m³) 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 NO₂ and PM_2.5, mostly happening in the winter season, while the afternoon is the least polluted time except for O₃. 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 NO₂ and PM_2.5 changed along with the seasonal variation. Local contributions for PM_2.5 changed slightly; however, NO₂ 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 NO₂ and PM_2.5. The highly polluted days account for 56.3% of total NO₂, highlighting local traffic is the dominant contributor to short-term NO₂ 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_2.5 and NO₂ on highly polluted days. Overall, this investigation suggests both background and local emissions contribute to PM_2.5 and NO₂ pollution in urban areas and emphasize the urgent need for mitigating NO₂ from traffic pollution in Dublin.