TY - JOUR

T1 - Urban vertical air pollution gradient and dynamics investigated with low-cost sensors and large-eddy simulations

AU - Frederickson, Louise B.

AU - Russell, Hugo S.

AU - Raasch, Siegfried

AU - Zhang, Zhaoxi

AU - Schmidt, Johan A.

AU - Johnson, Matthew S.

AU - Hertel, Ole

N1 - Funding Information: The project was carried out as an activity under the Big Data Center for Environment and Health (BERTHA) supported by the Novo Nordisk Foundation . https://projects.au.dk/bertha/ (grant NNF17OC0027864 ). The authors acknowledge Jibran Khan for his help with generating the building geometries used for the PALM simulations. We also thank Sophia Pettitt-Kenney for helping with deploying the low-cost sensors node. All PALM simulations have been carried out on a cluster system of the Northern German Supercomputing Alliance (HLRN). We thank the students of the course Atmospheric Environmental Chemistry at the University of Copenhagen for their assistance in counting vehicles. We thank ACTRIS-DK for infrastructure used to support this work. We express our sincere gratitude to Christian Tortzen and Heino Theodor Langtoft for their assistance in allowing the study and installing the electronics for the low-cost sensor node deployment. Publisher Copyright: © 2023 The Authors

PY - 2024

Y1 - 2024

N2 - A network of five low-cost air pollution sensor (LCS) nodes was deployed vertically on the exterior of the H. C. Ørsted Institute at the University of Copenhagen, Denmark, to investigate the transport of pollution from the road below. All LCS nodes measured PM2.5, NO2, and O3 at 1-min time resolution, and one of them also measured noise. Traffic was monitored with a webcam, where traffic type and levels were derived using a machine-learning algorithm. We investigated how well traffic-related air pollution, noise, and real-time traffic counts serve as proxies for one another. The correlations between NO2, noise, and traffic count exhibited relatively low values when considering all the data. However, these correlations significantly increased under southwesterly wind direction and low wind speed, reaching R2 = 0.40 for NO2 and noise, R2 = 0.51 for NO2 and traffic volume, and R2 = 0.70 for noise and traffic volume. These results indicate a common source, namely traffic, for all three parameters. The five LCS nodes spanning 25 m vertically had extremely low intervariability with minimum R2-values of 0.98 for PM2.5, 0.89 for NO2, and 0.97 for O3. The system could not detect a vertical gradient in pollution levels. Large-eddy simulation model runs using the PALM model system generally supported the lack of gradient observed in measured observations. Under slightly unstable stratification, concentration remained relatively constant with height for southwesterly and southerly winds. Conversely, winds from the north, west, and northwest showed an increase in concentration with height. For other wind directions, the concentration decreased with height by approximately 40 % to 50 %, which is not as strong as for neutral stratification, attributed to enhanced vertical mixing under unstable stratification. Based on the measurements and modeling, we conclude that the vertical concentration profile is very sensitive to stratification, and under these conditions, the concentration outside the window of a fifth-floor office is almost the same as for an office on the ground floor.

AB - A network of five low-cost air pollution sensor (LCS) nodes was deployed vertically on the exterior of the H. C. Ørsted Institute at the University of Copenhagen, Denmark, to investigate the transport of pollution from the road below. All LCS nodes measured PM2.5, NO2, and O3 at 1-min time resolution, and one of them also measured noise. Traffic was monitored with a webcam, where traffic type and levels were derived using a machine-learning algorithm. We investigated how well traffic-related air pollution, noise, and real-time traffic counts serve as proxies for one another. The correlations between NO2, noise, and traffic count exhibited relatively low values when considering all the data. However, these correlations significantly increased under southwesterly wind direction and low wind speed, reaching R2 = 0.40 for NO2 and noise, R2 = 0.51 for NO2 and traffic volume, and R2 = 0.70 for noise and traffic volume. These results indicate a common source, namely traffic, for all three parameters. The five LCS nodes spanning 25 m vertically had extremely low intervariability with minimum R2-values of 0.98 for PM2.5, 0.89 for NO2, and 0.97 for O3. The system could not detect a vertical gradient in pollution levels. Large-eddy simulation model runs using the PALM model system generally supported the lack of gradient observed in measured observations. Under slightly unstable stratification, concentration remained relatively constant with height for southwesterly and southerly winds. Conversely, winds from the north, west, and northwest showed an increase in concentration with height. For other wind directions, the concentration decreased with height by approximately 40 % to 50 %, which is not as strong as for neutral stratification, attributed to enhanced vertical mixing under unstable stratification. Based on the measurements and modeling, we conclude that the vertical concentration profile is very sensitive to stratification, and under these conditions, the concentration outside the window of a fifth-floor office is almost the same as for an office on the ground floor.

KW - LES

KW - Low-cost sensors

KW - PALM

KW - TRAP

KW - Urban air pollution

KW - Vertical gradient

U2 - 10.1016/j.atmosenv.2023.120162

DO - 10.1016/j.atmosenv.2023.120162

M3 - Journal article

AN - SCOPUS:85175257879

VL - 316

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

M1 - 120162

ER -