Comment on essd-2022-162

Abstract

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first the traffic volume and congestion (travel time delay) relation is applied to model the 24-hour traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning-evening rush hours. We estimated an annual emission of 1.82 Gg for PME (Exhaust particulate matter), 0.94 Gg for BC (Black Carbon), 0.75 Gg for OM (Organic matter), 221 Gg for CO (Carbon monoxide), 56 Gg for NOx (Oxide of Nitrogen), 64 Gg for VOC (Volatile Organic Carbon), 0.28 Gg for NH3 (Ammonia), 0.26 Gg for N2O (Nitrous Oxide) and 11.38 Gg for CH4 (Methane) for 2018. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (> 50 %) across all pollutants except PME, BC and NOx, and within them the 2W (Two-wheeler motorcycles) are the major contributors. Diesel fuelled vehicles contribute most of the PME emission. Diesel and CNG vehicles have a substantial contribution in NOx emission. This study provides very detailed spatio-temporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022).