A Mobile Air Pollution Monitoring Data Set

Air pollution was observed in Hamilton, Ontario, Canada using monitors installed in a mobile platform from November 2005 up to November 2016. The dataset is an aggregation of several project specific monitoring days, which attempted to quantify air pollution spatial variation under varying conditions or in specific regions. Pollutants observed included carbon monoxide, nitric oxide, nitrogen dioxide, total nitrogen oxides, ground-level ozone, particulate matter concentrations for size cuts of 10 µm, 2.5 µm and 1 µm, and sulfur dioxide. Observations were collected over 114 days, which occurred in varying seasons and months. During sampling, the mobile platform travelled at an average speed of 27 km/h. The samples were collected as one-minute integrated samples and are prepared as line-segments, which include an offset for instrument response time. Sampling occurred on major freeways, highways, arterial and residential roads. This dataset is shared in hopes of supporting research on how to best utilize air pollution observations obtained with mobile air pollution platforms, which is a growing technique in the field of urban air pollution monitoring. We conclude with limitations in the data capture technique and recommendations for future mobile monitoring studies.

[1]  Nazmul Sohel,et al.  Estimation of sulfur dioxide air pollution concentrations with a spatial autoregressive model , 2013 .

[2]  R. Burnett,et al.  A GIS–Environmental Justice Analysis of Particulate Air Pollution in Hamilton, Canada , 2001 .

[3]  P. Kanaroglou,et al.  Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models. , 2016, Journal of environmental management.

[4]  Bart Elen,et al.  The Aeroflex: A Bicycle for Mobile Air Quality Measurements , 2012, Sensors.

[5]  B. Brunekreef,et al.  Air pollution and health , 2002, The Lancet.

[6]  J. Eyles,et al.  Factors influencing health behaviours in response to the air quality health index: a cross-sectional study in Hamilton, Canada , 2016 .

[7]  H. Farid.  A picture tells a thousand lies , 2003 .

[8]  P. Kanaroglou,et al.  Topographic and spatial impacts of temperature inversions on air quality using mobile air pollution surveys. , 2010, The Science of the total environment.

[9]  Julie Wallace,et al.  Mobile monitoring of air pollution in cities: the case of Hamilton, Ontario, Canada. , 2009, Journal of environmental monitoring : JEM.

[10]  Inge F. Goldstein,et al.  Analysis of air pollution patterns in New York City—II. Can one aerometric station represent the area surrounding it? , 1977 .

[11]  Inge F. Goldstein,et al.  Analysis of air pollution patterns in New York City—I. Can one station represent the large metropolitan area? , 1977 .

[12]  Michael Jerrett,et al.  The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies , 2007 .

[13]  K. Newbold,et al.  Air Quality in Hamilton: Who is Concerned? Perceptions from Three Neighbourhoods , 2012 .

[14]  Denis Corr,et al.  Mobile Air Monitoring: Measuring Change in Air Quality in the City of Hamilton, 2005–2010 , 2012 .

[15]  Alexandra Schneider,et al.  Health effects of particulate air pollution: A review of epidemiological evidence , 2011, Inhalation toxicology.

[16]  A. Peters,et al.  Long-term air pollution exposure and cardio- respiratory mortality: a review , 2013, Environmental Health.

[17]  Julie Wallace,et al.  Effective mitigation efforts to reduce road dust near industrial sites: assessment by mobile pollution surveys. , 2012, Journal of environmental management.

[18]  M. Brauer,et al.  High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. , 2017, Environmental science & technology.

[19]  A statistical assessment of saturation and mobile sampling strategies to estimate long-term average concentrations across urban areas. , 2007, Journal of the Air & Waste Management Association.

[20]  Michael Brauer,et al.  Mobile monitoring of particle light absorption coefficient in an urban area as a basis for land use regression. , 2009, Environmental science & technology.

[21]  Michael Brauer,et al.  A picture tells a thousand … exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology , 2018, Environment international.

[22]  P. Kanaroglou,et al.  Air pollution exposure: An activity pattern approach for active transportation , 2016 .