Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise

Actkinson, Blake; Griffin, Robert J.

doi:https://doi.org/10.5194/amt-16-3547-2023

Articles | Volume 16, issue 14

https://doi.org/10.5194/amt-16-3547-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-16-3547-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 14

Research article

|

25 Jul 2023

Research article |

| 25 Jul 2023

Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise

Blake Actkinson and Robert J. Griffin

Supplement

https://doi.org/10.5194/amt-16-3547-2023-supplement

Data sets

Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01 B. Actkinson and R. Griffin https://doi.org/10.5281/zenodo.6473859

Roadway Inventory Texas Department of Transportation https://www.txdot.gov/data-maps/roadway-inventory.html

TIGER/Line Shapefile, 2018, county, Harris County, TX, All Roads County-based Shapefile U.S. Census Bureau https://catalog.data.gov/dataset/tiger-line-shapefile-2018-county-harris-county-tx-all-roads-county-based-shapefile

Model code and software

bactkinson/Anomaly_Analysis: AMT Preprint Submission (AMT) B. Actkinson https://doi.org/10.5281/zenodo.7700290

bactkinson/Plume_Detection_with_DBSCAN: Plume Detection with DBSCAN - R Shiny App (AMT) B. Actkinson https://doi.org/10.5281/zenodo.7700300

DBSCAN Plume Detection Tool B. Actkinson https://bactkinson.shinyapps.io/plume_detection_with_dbscan/

leafem: "leaflet" Extensions for "mapview" T. Appelhans https://CRAN.R-project.org/package=leafem

data.table: Extension of "Data.Frame" M. Dowle and A. Srinivasan https://CRAN.R-project.org/package=data.table

Ggpubr: "ggplot2" Based Publication Ready Plots A. Kassambara https://CRAN.R-project.org/package=ggpubr

Scattermore: Scatterplots with More Points M. Kratochvil https://CRAN.R-project.org/package=scattermore

caret: Classification and Regression Training M. Kuhn https://CRAN.R-project.org/package=caret

leaflet: Create Interactive Web Maps with the JavaScript "Leaflet" Library J. Cheng, B. Karambelkar, and Y. Xie https://CRAN.R-project.org/package=leaflet

mapview: Interactive Viewing of Spatial Data in R T. Appelhans, F. Detsch, C. Reudenbach, and S. Woellauer https://github.com/r-spatial/mapview

dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms M. Hahsler and M. Piekenbrock https://CRAN.R-project.org/package=dbscan

sf: Simple Features for R E. Pebesma https://CRAN.R-project.org/package=sf

psych: Procedures for Personality and Pscyhological Research W. Revelle https://CRAN.R-project.org/package=psych

Short summary

Data collected using air quality instrumentation deployed on automobiles and driven repeatedly in Houston neighborhoods are analyzed using a novel machine learning technique. The aim is to separate large plumes from the rest of the data in order to identify the sources of the highest levels of the pollutants. The number and nature of these plumes are characterized spatially and can be linked to emissions from different types of motor vehicles.