Chapter 72
Assessing Potential Climate Change
Impacts on Local Air Quality Using
AERMOD
Jinliang Liu, Congtru Doan, Abby Salb, Yvonne Hall and Chris Charron
Abstract Model results in this study show that the modelled maximum ground level
concentrations could vary significantly with the choice of meteorological data periods
and source configurations, mainly due to changes in the climatology of wind speeds,
their distribution and temperature. Modelled maximum ground-level concentrations
could vary by as much as 30% during the historical period (1996–2016), and could
decrease by over 50% for most low-level sources in the future (2051–2055) with
projected climate change.
72.1 Introduction
Meteorological data is one of the critical inputs to an air dispersion model such as
AERMOD [4]. It is recommended by the US EPA [5] that the most recent, readily
available 5 consecutive years of meteorological data be used for regulatory purpose. Due to climate change, meteorological conditions have been experiencing
dramatic changes in the past and could be in the future at most locations around
the globe; therefore, using different time periods of meteorological data could result
in significant differences in modelled concentrations, and thus impact regulatory
conclusions. A previous similar study [1] focused on point sources using previous
versions of AERMET/AERMOD; this study is an update by including more sources
types/configurations, and using the latest versions of these models and meteorological data.
J. Liu (B) · C. Doan · A. Salb · Y. Hall · C. Charron
Ontario Ministry of the Environment and Climate Change, Toronto, ON M9P 3V6, Canada
e-mail: Jinliang.Liu@Ontario.ca
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_72
451
Assessing Potential Climate Change
Impacts on Local Air Quality Using
AERMOD
Jinliang Liu, Congtru Doan, Abby Salb, Yvonne Hall and Chris Charron
Abstract Model results in this study show that the modelled maximum ground level
concentrations could vary significantly with the choice of meteorological data periods
and source configurations, mainly due to changes in the climatology of wind speeds,
their distribution and temperature. Modelled maximum ground-level concentrations
could vary by as much as 30% during the historical period (1996–2016), and could
decrease by over 50% for most low-level sources in the future (2051–2055) with
projected climate change.
72.1 Introduction
Meteorological data is one of the critical inputs to an air dispersion model such as
AERMOD [4]. It is recommended by the US EPA [5] that the most recent, readily
available 5 consecutive years of meteorological data be used for regulatory purpose. Due to climate change, meteorological conditions have been experiencing
dramatic changes in the past and could be in the future at most locations around
the globe; therefore, using different time periods of meteorological data could result
in significant differences in modelled concentrations, and thus impact regulatory
conclusions. A previous similar study [1] focused on point sources using previous
versions of AERMET/AERMOD; this study is an update by including more sources
types/configurations, and using the latest versions of these models and meteorological data.
J. Liu (B) · C. Doan · A. Salb · Y. Hall · C. Charron
Ontario Ministry of the Environment and Climate Change, Toronto, ON M9P 3V6, Canada
e-mail: Jinliang.Liu@Ontario.ca
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_72
451