Abstract
Abstract. Ambient air pollution poses a major global public health risk. Lower-cost air quality sensors (LCSs) are increasingly being explored as a tool to understand local air pollution problems and develop effective solutions. A barrier to LCS adoption is potentially larger measurement uncertainty compared to reference measurement technology. The technical performance of various LCSs has been tested in laboratory and field environments, and a growing body of literature on uses of LCSs primarily focuses on proof-of-concept deployments. However, few studies have demonstrated the implications of LCS measurement uncertainties on a sensor network's ability to assess spatiotemporal patterns of local air pollution. Here, we present results from a 2-year deployment of 100 stationary electrochemical nitrogen dioxide (NO2) LCSs across Greater London as part of the Breathe London pilot project (BL). We evaluated sensor performance using collocations with reference instruments, estimating ∼ 35 % average uncertainty (root mean square error) in the calibrated LCSs, and identified infrequent, multi-week periods of poorer performance and high bias during summer months. We analyzed BL data to generate insights about London's air pollution, including long-term concentration trends, diurnal and day-of-week patterns, and profiles of elevated concentrations during regional pollution episodes. These findings were validated against measurements from an extensive reference network, demonstrating the BL network's ability to generate robust information about London's air pollution. In cases where the BL network did not effectively capture features that the reference network measured, ongoing collocations of representative sensors often provided evidence of irregularities in sensor performance, demonstrating how, in the absence of an extensive reference network, project-long collocations could enable characterization and mitigation of network-wide sensor uncertainties. The conclusions are restricted to the specific sensors used for this study, but the results give direction to LCS users by demonstrating the kinds of air pollution insights possible from LCS networks and provide a blueprint for future LCS projects to manage and evaluate uncertainties when collecting, analyzing, and interpreting data.
Highlights
Ambient air pollution is a leading contributor to human disease and mortality around the world, causing more than 4 million premature deaths annually, with the greatest health burden in low- and middle-income countries (WHO, 2018; HEI, 2020)
Robust uncertainty characterization and validation against reference instruments equips the user to take full advantage of data, including (i) developing corrections, (ii) excluding measurements during conditions where sensor performance might be compromised, or (iii) ensuring analyses are appropriate based on the data quality
Our findings emphasize the importance of monitoring sensor performance for the duration of a measurement campaign as even pre- and post-campaign sensor evaluations may not have detected the seasonal changes in sensor performance that our repeat (Fig. 2) and long-term (Fig. 3) collocations allowed us to quantify
Summary
Ambient (outdoor) air pollution is a leading contributor to human disease and mortality around the world, causing more than 4 million premature deaths annually, with the greatest health burden in low- and middle-income countries (WHO, 2018; HEI, 2020). Even in many high-income countries, ambient air pollution monitoring is relatively sparse (e.g., Apte et al, 2017; US GAO, 2020). Reference monitoring stations are state of the art in terms of accuracy and reliability and are required for regulatory reporting (EU, 2008).
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