Abstract
Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyperlocal scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (October 2019–September 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspot identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. The O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during the COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased 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 of air pollutant levels in urban regions. This research demonstrates the sensing power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at the urban micro-scale.
Highlights
Urban air pollution poses a serious health threat with > 80 % of the world’s urban residents exposed to air pollution levels that exceed the World Health Organization (WHO) guidelines (WHO, 2016)
There is a trade-off between the resolution of an air pollutant concentration map and its reproducibility; i.e., highresolution maps are subject to large randomness due to the limited number of samples in each grid
By comparing the time series of the air pollutant concentrations with that from nearby stateoperated air quality observation stations (A and E, with repeated frequencies > 500), we find that the results are consistent (Fig. S1 in the Supplement), which shows the stability and reliability of our data
Summary
Urban air pollution poses a serious health threat with > 80 % of the world’s urban residents exposed to air pollution levels that exceed the World Health Organization (WHO) guidelines (WHO, 2016). We illustrate an approach to obtain a high-resolution urban air quality map using low-cost sensors deployed on a routinely operating taxi fleet. High spatiotemporal resolution air quality data are critical to urban air quality management, exposure assessment, epidemiology study, and environmental equity (Apte et al, 2011, 2017; Boogaard et al, 2010). Numerous methodologies have been developed to obtain urban air pollutant concentrations, including stationary monitoring networks (Cavellin et al, 2016), near-roadway sampling (Karner et al, 2010; Zhu et al, 2009; Padro-Martinez et al, 2012), satellite remote sensing
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