RTEII : A new high-resolution (0.1° × 0.1°) road transport emission inventory for India of 74 speciated NMVOCs, CO, NOx, NH3, CH4, CO2, PM2.5 reveals massive overestimation of NOx and CO and missing nitromethane emissions by existing inventories

21 of 30 most polluted cities for particulate matter (PM2.5) are in India, yet the distribution, identity and emissions of volatile organic compounds (VOCs) from traffic, which are PM2.5 and ozone precursors, remain unknown. Here, we measured emission factors (EFs) of 74 VOCs from a range of Indian vehicle-technology and fuel types. When combined with 0.1 ° × 0.1 ° spatially resolved activity data for the year 2015, toluene (137 ± 39 Gg yr1), isopentane (111 ± 38 Ggyr−1), and acetaldehyde (41 ± 6 Ggyr−1) were top 3-VOC emissions. Petrol-2-wheelers and LPG-3-wheelers emitted the highest VOCs (EFs> 50 gVOC/L) and had highest secondary pollutant formation potential, so their replacement with electric vehicles would improve air quality. EDGARv4.3.2 and REASv.2.1 emission inventories overestimated total road sector emitted VOCs due to obsolete EFs and activity data, in particular over-estimating ethene, propene, ethyl benzene, 2,2- dimethyl butane, CO, NOx while significantly under-estimating acetaldehyde. Nitromethane emissions were missing from previous inventories and with isocyanic acid and benzene contributed significantly to toxic emissions (summed total ~41 ± 4 Ggyr−1). Knowledge of key VOCs emitted from the world's third largest road-network provides critical new data for mitigating secondary pollutant formation over India and will enable more accurate modelling of atmospheric composition over South Asia

A Sensitivity Study of a Bayesian Inversion Model Used to Estimate Emissions of Synthetic Greenhouse Gases at the European Scale

To address and mitigate the environmental impacts of synthetic greenhouse gases it’s crucial to quantify their emissions to the atmosphere on different spatial scales. Atmospheric Inverse modelling is becoming a widely used method to provide observation-based estimates of greenhouse gas emissions with the potential to provide an independent verification tool for national emission inventories. A sensitivity study of the FLEXINVERT+ model for the optimisation of the spatial and temporal emissions of long-lived greenhouse gases at the regional-to-country scale is presented. A test compound HFC-134a, the most widely used refrigerant in mobile air conditioning systems, has been used to evaluate its European emissions in 2011 to be compared with a previous study. Sensitivity tests on driving factors like—observation selection criteria, prior data, background mixing ratios, and station selection—assessed the model’s performance in replicating measurements, reducing uncertainties, and estimating country-specific emissions. Across all experiments, good prior (0.5–0.8) and improved posterior (0.6–0.9) correlations were achieved, emphasizing the reduced sensitivity of the inversion setup to different a priori information and the determining role of observations in constraining the emissions.The posterior results were found to be very sensitive to background mixing ratios, with even slight increases in the baseline leading to significant decrease of emissions