Enhancing the understanding of Glasgow's emissions through inverse modeling

In the era of climate change, major cities around the world are striving to reduce greenhouse gases to achieve net zero goals. The city of Glasgow has set an ambitious climate goal of becoming net zero carbon by 2030. This preliminary study aims to better understand city level emission sources using high resolution inverse modelling and observations.
This study utilises a Bayesian inverse modelling technique to quantify carbon dioxide emissions by using a dense atmospheric CO2 sensor network to update an existing national emissions inventory in Glasgow. Glasgow Environmental Monitoring of Indoor and Outdoor Air (GEMINOA) has established a high density network of moderate cost sensors in collaboration with the University of California’s Berkeley Environmental Air-Quality and CO2 (BEACO2N) Network, to measure CO2, gaseous and particulate pollutants. Data from 10-15 sensor nodes with high-frequency CO2 measurements have been assimilated using a Bayesian optimization algorithm to estimate posterior emissions for 2022. The Stochastic Time-Inverted Lagrangian Transport (STILT) model was utilized to trace atmospheric observations back to their emission sources. The STILT model uses a high-resolution meteorological data generated by the Weather Research and Forecasting (WRF) model set up for Glasgow region. The footprints were used to update prior datasets from the available inventory and tall tower flux measurement data.

It is observed that emission estimates changed significantly with the added observational evidence. Spatial and temporal analysis indicate an underestimation of prior inventory emission estimates. Clear distinctions in the emission between weekdays and weekends highlight the major influence of traffic emissions in the city. The domestic combustion and transport sectors have a major share in annual carbon emissions. We characterize the seasonal and diurnal changes in dynamic and stationary anthropogenic emission sectors. To better understand these processes and analyse interannual variability will require extensive observation and modelling work. The results from this study will be used to take informed decisions and public engagement activities on emission controls in several sectors of Glasgow.