A mobile sensor network to map CO₂ emissions in urban environments
There are a variety methods that can characterize carbon dioxide (CO₂) mixing ratios in the atmosphere at coarse scales, however mapping CO₂ emissions and sequestration at fine scales remains a challenge. In this research, a new method for mapping microscale CO₂ emissions in cities was developed. Fi...
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ndltd-UBC-oai-circle.library.ubc.ca-2429-578532018-01-05T17:28:56Z A mobile sensor network to map CO₂ emissions in urban environments Lee, Joseph K. There are a variety methods that can characterize carbon dioxide (CO₂) mixing ratios in the atmosphere at coarse scales, however mapping CO₂ emissions and sequestration at fine scales remains a challenge. In this research, a new method for mapping microscale CO₂ emissions in cities was developed. First, a compact, mobile CO₂ sensor system was built using open source hardware and an infrared gas analyzer. Second, a measurement campaign was carried out in which 5 mobile sensors were deployed within a 12.7 km² study area in the City of Vancouver, BC, Canada for 3.5 hours to map CO₂ mixing ratios to a grid resolution of 100 m × 100 m. The CO₂ mixing ratios ranged from 382 ppm to 518 ppm and averaged 417.1 ppm and were highest in the downtown and arterial roads and lowest in well vegetated and residential areas. Third, an aerodynamic resistance approach to calculating emissions was used to derive CO₂ emissions from the mobile CO₂ mixing ratio measurements in conjunction with data collected from a 24 m tall meteorological tower in the study area. The measured emissions showed a range of -12 to 225 kg CO₂ ha‑¹ hr‑¹ and averaged 36.39 kg CO₂ ha‑¹ hr‑¹. Fourth, an emissions inventory was developed for the study area using emissions estimates derived from buildings energy use and traffic counts. The emissions inventory averaged 25.88 kg CO₂ ha‑¹ hr‑¹ and was used to compare against the measured emissions. The results showed strong linearity between median CO₂ mixing ratios and the total emissions inventory (R² >0.9) binned at equal intervals. The results also indicated that the measured emissions and the total emissions inventory were positively correlated by 78.43% with 99.43% of the measured emissions within ± 1 order of magnitude of the emissions inventory. Ultimately, this research demonstrates the possibility of using a network of mobile sensors and an aerodynamic resistance approach to map emissions at high spatial resolution across a city. While further research is necessary, microscale emissions maps may be used to better inform urban policy and design as well as engage citizens about emissions reductions strategies. Arts, Faculty of Geography, Department of Graduate 2016-04-25T18:08:19Z 2016-04-26T02:02:51 2016 2016-05 Text Thesis/Dissertation http://hdl.handle.net/2429/57853 eng Attribution-ShareAlike 4.0 International http://creativecommons.org/licenses/by-sa/4.0/ University of British Columbia |
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English |
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There are a variety methods that can characterize carbon dioxide (CO₂) mixing ratios in the atmosphere at coarse scales, however mapping CO₂ emissions and sequestration at fine scales remains a challenge. In this research, a new method for mapping microscale CO₂ emissions in cities was developed. First, a compact, mobile CO₂ sensor system was built using open source hardware and an infrared gas analyzer. Second, a measurement campaign was carried out in which 5 mobile sensors were deployed within a 12.7 km² study area in the City of Vancouver, BC, Canada for 3.5 hours to map CO₂ mixing ratios to a grid resolution of 100 m × 100 m. The CO₂ mixing ratios ranged from 382 ppm to 518 ppm and averaged 417.1 ppm and were highest in the downtown and arterial roads and lowest in well vegetated and residential areas. Third, an aerodynamic resistance approach to calculating emissions was used to derive CO₂ emissions from the mobile CO₂ mixing ratio measurements in conjunction with data collected from a 24 m tall meteorological tower in the study area. The measured emissions showed a range of -12 to 225 kg CO₂ ha‑¹ hr‑¹ and averaged 36.39 kg CO₂ ha‑¹ hr‑¹. Fourth, an emissions inventory was developed for the study area using emissions estimates derived from buildings energy use and traffic counts. The emissions inventory averaged 25.88 kg CO₂ ha‑¹ hr‑¹ and was used to compare against the measured emissions. The results showed strong linearity between median CO₂ mixing ratios and the total emissions inventory (R² >0.9) binned at equal intervals. The results also indicated that the measured emissions and the total emissions inventory were positively correlated by 78.43% with 99.43% of the measured emissions within ± 1 order of magnitude of the emissions inventory. Ultimately, this research demonstrates the possibility of using a network of mobile sensors and an aerodynamic resistance approach to map emissions at high spatial resolution across a city. While further research is necessary, microscale emissions maps may be used to better inform urban policy and design as well as engage citizens about emissions reductions strategies. === Arts, Faculty of === Geography, Department of === Graduate |
author |
Lee, Joseph K. |
spellingShingle |
Lee, Joseph K. A mobile sensor network to map CO₂ emissions in urban environments |
author_facet |
Lee, Joseph K. |
author_sort |
Lee, Joseph K. |
title |
A mobile sensor network to map CO₂ emissions in urban environments |
title_short |
A mobile sensor network to map CO₂ emissions in urban environments |
title_full |
A mobile sensor network to map CO₂ emissions in urban environments |
title_fullStr |
A mobile sensor network to map CO₂ emissions in urban environments |
title_full_unstemmed |
A mobile sensor network to map CO₂ emissions in urban environments |
title_sort |
mobile sensor network to map co₂ emissions in urban environments |
publisher |
University of British Columbia |
publishDate |
2016 |
url |
http://hdl.handle.net/2429/57853 |
work_keys_str_mv |
AT leejosephk amobilesensornetworktomapco2emissionsinurbanenvironments AT leejosephk mobilesensornetworktomapco2emissionsinurbanenvironments |
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1718585196624740352 |