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

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 hyper-local 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 (Oct. 2019–Sep. 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 hotspots 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. While 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 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 pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors

“…A neutral stratification is also considered as the most representative condition because stable and unstable conditions are either unfavorable or favorable for pollutant dispersion (Kurppa et al, 2018). The fifthorder upwind scheme of Wicker and Skamarock (2002) is used for both momentum and tracer advection. We use CO as a representative pollutant as it has a relatively long lifetime (months to years) (Jaffe, 1968).…”

“…We evaluate the model results with observations collected from a mobile platform that is performed during Septem-ber 2019-October 2020. The details of the platform instrument and its deployment are described in Wang et al (2020). Briefly, we use two XHAQSN-508 instruments (dimensions: 290 × 81 × 55 mm; weight: 1.0 kg) produced by Hebei Sailhero Environmental Protection High-tech Co., Ltd. (Hebei, China), which include an internal CO gas sensor (detectable CO range: 0 to 50 mg m −3 ) and are installed on the top of two Nanjing taxis (∼ 1.5 m above ground).…”

“…One major advantage of this mobile platform is the minimum maintenance cost, as samples are automatically collected during the operation of the taxis. An analysis of the sensing power, defined as the fraction of city road network sampled by a taxi fleet, also demonstrates that a remarkably small number of taxis can scan a large number of streets (O'Keeffe et al, 2019;Wang et al, 2020). The instrument is calibrated once per month against a stationary instrument (T300 CO Analyzer by Teledyne API) at the SORPES observation station in the Xianlin Campus of Nanjing University (https://as.nju.edu.cn/as_en/obsplatform/ list.htm, last access: 1 November 2020).…”

“…During calibration, the instrument is taken back to the campus and placed backto-back to the calibrating instrument in the station. The calibration lasts for at least seven days, and the parameters for the sensor retrieval algorithm are adjusted to make sure the difference between the sensor-retrieved data and the station data is < 1 % (Wang et al, 2020). As only traffic-related emissions are considered in the PALM, we add the model results to the background concentrations of Nanjing for comparison to the observed data by the mobile platform (but the pure model output is used for other analyses).…”

“…One drawback of the taxi platform is that the popular streets are easily covered and sampled repeatedly, but unpopular segments are rarely visited (O'Keeffe et al, 2019;Wang et al, 2020). The sensor data used in this study mainly cover the highway and arterial roads but generally leave the model results for residential streets unevaluated.…”