Version 1
: Received: 20 April 2021 / Approved: 21 April 2021 / Online: 21 April 2021 (16:49:09 CEST)
Version 2
: Received: 30 April 2021 / Approved: 5 May 2021 / Online: 5 May 2021 (12:41:17 CEST)
How to cite:
Cummings, L.; Stewart, J.; Kremer, P.; Shakya, K. Associations between the Structure of Urban Landscape and Particulate Matter: A STURLA Case Study in Philadelphia, PA . Preprints2021, 2021040588. https://doi.org/10.20944/preprints202104.0588.v1.
Cummings, L.; Stewart, J.; Kremer, P.; Shakya, K. Associations between the Structure of Urban Landscape and Particulate Matter: A STURLA Case Study in Philadelphia, PA . Preprints 2021, 2021040588. https://doi.org/10.20944/preprints202104.0588.v1.
Cite as:
Cummings, L.; Stewart, J.; Kremer, P.; Shakya, K. Associations between the Structure of Urban Landscape and Particulate Matter: A STURLA Case Study in Philadelphia, PA . Preprints2021, 2021040588. https://doi.org/10.20944/preprints202104.0588.v1.
Cummings, L.; Stewart, J.; Kremer, P.; Shakya, K. Associations between the Structure of Urban Landscape and Particulate Matter: A STURLA Case Study in Philadelphia, PA . Preprints 2021, 2021040588. https://doi.org/10.20944/preprints202104.0588.v1.
Abstract
Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportionsin which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM2.5 concentrations on average (16.60 ± 4.29 µg/m3), while tgbwphosted the highest BC concentrations (2.31 ± 1.94 µg/m3). Furthermore, STURLA combined with machine learning modeling was able to correlate PM2.5(R2= 0.68, RMSE 2.82 µg/m3) and BC (R2 = 0.64, RMSE 0.75 µg/m3) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate theefficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.
Keywords
Air Pollution; STURLA; Urban Structure; Mobile Monitoring; Spatial Prediction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
