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. The development of low-cost sensors and novel calibrationalgorithms provides new hints to complement conventional ground-basedobservation sites to evaluate the spatial and temporal distribution ofpollutants on hyperlocal scales (tens of meters). Here we use sensorsdeployed on a taxi fleet to explore the air quality in the road network ofNanjing over the course of a year (October 2019–September 2020). Based on GIStechnology, we develop a grid analysis method to obtain 50 m resolution mapsof major air pollutants (CO, NO2, and O3). Through hotspotidentification analysis, we find three main sources of air pollutantsincluding traffic, industrial emissions, and cooking fumes. We find that COand NO2 concentrations show a pattern: highways &gt; arterialroads &gt; secondary roads &gt; branch roads &gt;residential streets, reflecting traffic volume. The O3concentrations in these five road types are in opposite order due to thetitration effect of NOx. Combined the mobile measurements and the stationarystation data, we diagnose that the contribution of traffic-relatedemissions to CO and NO2 are 42.6 % and 26.3 %, respectively.Compared to the pre-COVID period, the concentrations of CO and NO2during the COVID-lockdown period decreased for 44.9 % and 47.1 %,respectively, and the contribution of traffic-related emissions to them bothdecreased by more than 50 %. With the end of the COVID-lockdown period,traffic emissions and air pollutant concentrations rebounded substantially,indicating that traffic emissions have a crucial impact on the variation ofair pollutant levels in urban regions. This research demonstrates the sensing power of mobile monitoring for urban air pollution, which provides detailedinformation for source attribution, accurate traceability, and potentialmitigation strategies at the urban micro-scale.

Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: impacts of COVID-19 pandemic lockdown