TY - JOUR

T1 - Hyperlocal air pollution in an urban environment - measured with low-cost sensors

AU - Frederickson, Louise Bøge

AU - Russell, Hugo Savill

AU - Fessa, Dafni

AU - Khan, Jibran

AU - Schmidt, Johan Albrecht

AU - Johnson, Matthew Stanley

AU - Hertel, Ole

N1 - Funding Information: LBF, HSR, JK, and OH are supported by BERTHA - The Danish Big Data Centre for Environment and Health, funded by the Novo Nordisk Foundation Challenge Programme (grant NNF17OC0027864 ). https://projects.au.dk/bertha/ . We acknowledge infrastructure support from ACTRIS-DK. Funding Information: The authors would like to express their sincerest gratitude to the 24 volunteers who helped conduct the measurement campaign; Anders Johan Johnson, Benedikte Roland, Catriona O'Shea, Christian Moritzen, Christina Bjerre, Christina Nielsen, Christoffer Møller, Emma Baraban, Emma Amalie Petersen-Sonn, Jean-Baptiste Chandelier, Jesper Baldtzer Liisberg, Joachim Bjerre, Kim Michael Lundemo, Maiken Wehlast Jørgensen, Maria Thomsen, Marianna Bissa, Marie Mikkelsen, Megan Elisabeth Davies, Merve Polat, Morten Frausig, Susanna Dedring, Zhaoxi Zhang, Zoé Briesinger shall all be thanked for their contribution, and especially Peter Philip Nyemann for being extra committed and helpful. Professor Zorana Jovanovic Andersen and her group at the Public Health Science at the University of Copenhagen should be thanked for sharing their premises during the measurement campaign. For calibration and comparison, quality-controlled air quality data from the Danish air quality survey program under NOVANA were used (Ellermann et al. 2020). We acknowledge Maria Bech Poulsen for contributing to developing the methodology and helping to organize a pilot study. LBF, HSR, JK, and OH are supported by BERTHA - The Danish Big Data Centre for Environment and Health, funded by the Novo Nordisk Foundation Challenge Programme (grant NNF17OC0027864). https://projects.au.dk/bertha/. We acknowledge infrastructure support from ACTRIS-DK. Publisher Copyright: © 2023 The Authors

PY - 2023/11

Y1 - 2023/11

N2 - Air pollution levels can vary significantly over short distances, particularly in urban areas and near emission sources. This study examined the performance of low-cost sensor devices for monitoring levels of NO2, O3, and PM2.5 along two closely spaced (average 8 m) routes in Copenhagen, Denmark. One route was located near a lake (Route 1) and the other near a busy road (Route 2). The routes were walked in tandem for 84 h. The mode of deployment was determined using an accelerometer, gyroscope, and light sensor, achieving a 97.4 % accuracy rate. Field calibration with multivariate linear regression proved the most robust calibration model across pollutants, yielding mean R2-values of 0.64, 0.79, and 0.48 for NO2, O3, and PM2.5, respectively. The sensor intervariability was generally low, with mean R2-values of 0.84–0.94 for PM2.5 measured with optical particle sensors and 0.88–0.90 for NO2 and O3 measured with metal-oxide sensors. Results showed significantly higher NO2 concentrations on Route 2 (21.6 ± 6.6 ppb) compared to Route 1 (10.1 ± 4.0 ppb) during mornings. However, no significant differences in O3 and PM2.5 concentrations were observed. Our findings demonstrate that low-cost sensors can accurately quantify air pollution exposure in urban areas with high spatiotemporal resolution.

AB - Air pollution levels can vary significantly over short distances, particularly in urban areas and near emission sources. This study examined the performance of low-cost sensor devices for monitoring levels of NO2, O3, and PM2.5 along two closely spaced (average 8 m) routes in Copenhagen, Denmark. One route was located near a lake (Route 1) and the other near a busy road (Route 2). The routes were walked in tandem for 84 h. The mode of deployment was determined using an accelerometer, gyroscope, and light sensor, achieving a 97.4 % accuracy rate. Field calibration with multivariate linear regression proved the most robust calibration model across pollutants, yielding mean R2-values of 0.64, 0.79, and 0.48 for NO2, O3, and PM2.5, respectively. The sensor intervariability was generally low, with mean R2-values of 0.84–0.94 for PM2.5 measured with optical particle sensors and 0.88–0.90 for NO2 and O3 measured with metal-oxide sensors. Results showed significantly higher NO2 concentrations on Route 2 (21.6 ± 6.6 ppb) compared to Route 1 (10.1 ± 4.0 ppb) during mornings. However, no significant differences in O3 and PM2.5 concentrations were observed. Our findings demonstrate that low-cost sensors can accurately quantify air pollution exposure in urban areas with high spatiotemporal resolution.

KW - Hyperlocal air pollution

KW - Low-cost sensors

KW - Personal exposure monitoring

KW - Pollution exposure

KW - Urban air pollution

U2 - 10.1016/j.uclim.2023.101684

DO - 10.1016/j.uclim.2023.101684

M3 - Journal article

AN - SCOPUS:85172196615

VL - 52

JO - Urban Climate

JF - Urban Climate

SN - 2212-0955

M1 - 101684

ER -