Exploring the discrepancy between top-down and bottom-up approaches of fine spatio-temporal vehicular CO2 emission in an urban road network.

Road transport emissions of high spatial and temporal resolution are useful for greenhouse gas emission assessment in local action plans. However, estimating these high-resolution emissions is not straightforward, and different indirect approaches exist. The main aim of this study is to examine the differences in CO2 emissions obtained with different methods within a street canyon network in Helsinki, Finland, where a mobile laboratory campaign to quantify traffic emissions has been conducted. We compared three aerodynamic resistance based top-down methods (MOST1, MOST2 and BHT) and three activity based bottom-up microscopic emission models (NGM, HBEFAv4.2 and PHEMlight). The resulted CO2 fluxes using different methods could vary a few orders of magnitude. The combination of MOST1 and NGM model leads to the smallest discrepancy (sMAPE = 16.90 %) and the highest correlation coefficient (r = 0.78) among the rest. We evaluated the discrepancies in terms of different spatial (microenvrionments, local climate zones LCZs and grid sizes) and temporal features (seasons and periods of day). Measurements taken in LCZs of open high-rise regions and microenvironments of main road tend to have larger discrepancies between the two approaches. Using a coarser grid would lead to a relatively small discrepancy and high correlation in the wintertime, yet a loss in distinctive spatial variation. The discrepancies were also elevated on winter evenings. Among all explanatory variables, relative humidity shows the strongest relative importance for the discrepancy of the two approaches, followed by LCZs. Therefore, we stress the importance of choosing a suitable model for vehicular CO2 emission calculation based on meteorological conditions and LCZs. Such model comparison made on a local scale directly supports environmental organisations and cities' climate action plans where detailed information of CO2 emissions are needed.