Chapter 41
Importance of Inventory
Representativeness for Air Quality
Forecasting: A Recent North American
Example
Michael D. Moran, Qiong Zheng, Junhua Zhang, Radenko Pavlovic
and Mourad Sassi
Abstract North American air quality (AQ) forecasts made by the Environment and
Climate Change Canada (ECCC) operational regional AQ prediction system since
2015 have used input emissions files based on Canadian, U.S., and Mexican national
emissions inventories for base years 2010, 2011, and 1999, respectively. Since 2010,
however, emissions of many criteria air pollutants have declined in both Canada
and the U.S.. We recently tested new input emissions files based on a 2013 Canadian inventory, a projected 2017 U.S. inventory, and a 2008 Mexican inventory in
the ECCC regional AQ prediction system. For Canada, the switch from the 2010
inventory to the 2013 inventory reduced SO 2 , NO x , and VOC annual anthropogenic
emissions by 12%, 2%, and 4%, respectively. For the continental U.S., adoption of
the projected 2017 inventory reduced SO 2 , NO x , and VOC annual anthropogenic
emissions relative to the 2011 inventory by 65%, 33%, and 11%, respectively, suggesting the importance of emissions base-year representativeness for AQ forecasting.
Moreover, the use of these new input emissions fields for 2016 and 2017 test periods
improved AQ forecasts in comparison to the operational model for Canada and the
U.S., in particular for summertime ozone forecasts over the eastern U.S.. A new
version of the ECCC forecast system that uses these updated input emissions files
was accepted for operational implementation in mid 2018.
M. D. Moran (B) · Q. Zheng · J. Zhang
Air Quality Research Division, Environment and Climate Change Canada,
Toronto, ON, Canada
e-mail: mike.moran@canada.ca
R. Pavlovic · M. Sassi
Air Quality Policy-Issue Response Section, Environment and Climate Change Canada, Montreal,
QC, Canada
© Crown 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_41
261
Importance of Inventory
Representativeness for Air Quality
Forecasting: A Recent North American
Example
Michael D. Moran, Qiong Zheng, Junhua Zhang, Radenko Pavlovic
and Mourad Sassi
Abstract North American air quality (AQ) forecasts made by the Environment and
Climate Change Canada (ECCC) operational regional AQ prediction system since
2015 have used input emissions files based on Canadian, U.S., and Mexican national
emissions inventories for base years 2010, 2011, and 1999, respectively. Since 2010,
however, emissions of many criteria air pollutants have declined in both Canada
and the U.S.. We recently tested new input emissions files based on a 2013 Canadian inventory, a projected 2017 U.S. inventory, and a 2008 Mexican inventory in
the ECCC regional AQ prediction system. For Canada, the switch from the 2010
inventory to the 2013 inventory reduced SO 2 , NO x , and VOC annual anthropogenic
emissions by 12%, 2%, and 4%, respectively. For the continental U.S., adoption of
the projected 2017 inventory reduced SO 2 , NO x , and VOC annual anthropogenic
emissions relative to the 2011 inventory by 65%, 33%, and 11%, respectively, suggesting the importance of emissions base-year representativeness for AQ forecasting.
Moreover, the use of these new input emissions fields for 2016 and 2017 test periods
improved AQ forecasts in comparison to the operational model for Canada and the
U.S., in particular for summertime ozone forecasts over the eastern U.S.. A new
version of the ECCC forecast system that uses these updated input emissions files
was accepted for operational implementation in mid 2018.
M. D. Moran (B) · Q. Zheng · J. Zhang
Air Quality Research Division, Environment and Climate Change Canada,
Toronto, ON, Canada
e-mail: mike.moran@canada.ca
R. Pavlovic · M. Sassi
Air Quality Policy-Issue Response Section, Environment and Climate Change Canada, Montreal,
QC, Canada
© Crown 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_41
261