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- Montazeri, AmirSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca NY, USA (författare)
A spatial land use clustering framework for investigating the role of land use in mediating the effect of meteorology on urban air quality
- Artikel/kapitelEngelska2021
Förlag, utgivningsår, omfång ...
- Elsevier,2021
- printrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:oru-95986
- https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-95986URI
- https://doi.org/10.1016/j.aeaoa.2021.100126DOI
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- Språk:engelska
- Sammanfattning på:engelska
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- Funding agencies:David R. Atkinson Center for a Sustainable Future (ACSF) at Cornell UniversityUnited States Department of Energy (DOE) DE-AR0000749
- Air pollution in urban areas is driven by emission sources and modulated by local meteorology, including the effects of urban form on wind speed and ventilation, and thus varies markedly in space and time. Recently, mobile measurement campaigns have been conducted in urban areas to measure the spatial distribution of air pollutant concentrations. While the main focus of these studies has been revealing spatial patterns in mean (or median) concentrations, they have mostly ignored the temporal aspects of air pollution. However, assessing the temporal variability of air pollution is essential in understanding the integrated exposure of individuals to pollutants above critical thresholds. Here, we examine the role of urban land use in mediating the effect of regional meteorology on Nitrogen Dioxide (NO2) concentrations measured in different regions of Oakland, CA. Inspired by Land Use Regression (LUR) models, we cluster 30-m road segments in the urban area based on their land use. The concentration data from the resulting clusters are stratified based on seasonality and conditionally averaged based on concurrent wind speeds. The clustering analysis yielded 7 clusters, with 4 of them chosen for further statistical analysis due to their large sample sizes. Two of the four clusters demonstrated in winter a strong negative linear relationship between NO2 concentration and wind speed (R-2 > 0.87) with a slope of approximately 3 ppb/m s(-1). A weaker correlation and flatter slope was found for the cluster representing road segments belonging to interstate highways (R-2 > 0.73 and slope < 2 ppb/m s(-1)). No significant relationship was found during the summer season. These findings are consistent with the concept of strong vertical mixing due to highway traffic and increased surface heat fluxes during summer weakening the relationship between wind speed and NO2 concentrations. In summary, the clustering analysis framework presented here provides a novel tool for use with large-scale mobile measurements to reveal the effect of urban land form on the temporal dynamics of pollutant concentrations and ultimately human exposure.
Ämnesord och genrebeteckningar
- NATURVETENSKAP Geovetenskap och miljövetenskap Miljövetenskap hsv//swe
- NATURAL SCIENCES Earth and Related Environmental Sciences Environmental Sciences hsv//eng
- Air pollution profiles
- Cluster analysis
- Mobile monitoring
- Land use effects
- K-means
- Exceedance probabilities
- Unsupervised learning
- Machine learning
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Lilienthal, Achim,1970-Örebro universitet,Institutionen för naturvetenskap och teknik,Centre for Applied Autonomous Sensor Systems (AASS)(Swepub:oru)amll (författare)
- Albertson, John D.School of Civil and Environmental Engineering, Cornell University, Ithaca NY, USA (författare)
- Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca NY, USAInstitutionen för naturvetenskap och teknik (creator_code:org_t)
Sammanhörande titlar
- Ingår i:Atmospheric Environment: X: Elsevier122590-1621
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