High-resolution modelling of atmospheric methane in the Greater Toronto Area using WRF-GEOS-Chem

Methane (CH4), a potent greenhouse gas, is emitted in large quantities from urban sources including landfills, wastewater treatment facilities, and natural gas distribution networks, and can make up a significant fraction of a city's total carbon budget. Reducing urban CH4 emissions is therefore an important part of anthropogenic climate change mitigation strategies. Toronto, Canada's largest city, has set significant carbon emission reduction goals, aiming to reach net-zero carbon emissions by 2040. The TAME (Toronto Atmospheric Monitoring of Emissions) Project has been established to help track the city's progress towards these goals using both observation networks and atmospheric modelling techniques. As part of TAME, we perform high-resolution (1km) simulations of atmospheric CH4 across the Greater Toronto Area (GTA) using the WRF-GEOS-Chem (WRF-GC) model coupled with two versions of the high-resolution (1km) FLAME-GTA urban methane emission inventory. We use the model to quantify the variability of CH4 across the city in terms of a correlation length scale. We also calculate spatial correlation footprints for multiple observation sites and use these footprints to assess the spatial coverage of different urban observation network configurations. We then compare the modelled CH4 with in-situ surface measurements and remote sensing retrievals at two urban sites and one rural background site. These comparisons show that urban landfill CH4 emissions were likely overestimated in the original FLAME-GTA inventory but have been significantly improved in the updated version. Measured versus modelled spatial gradients of CH4 suggest a possible overestimate of CH4 emissions in Downtown Toronto in both versions of FLAME-GTA. Low biases associated with specific wind directions may indicate regions of underestimated CH4 emissions in the model. These results demonstrate how high-resolution modelling can be combined with observations to assess and improve emission inventories in urban environments. Future work for the TAME project will extend this analysis to include other pollutants including CO2, CO, PM2.5, NOx, and O3.