|This study provides spatially resolved sectorial adjustment factors (AFs) of emissions during the COVID lockdown. As pointing out by the authors, this database is expected to be directly applied in emission changes which can be further used in global or regional inventories in air quality modelling. |
While this database is useful and desires for a publication in ESSD, I think it is necessary and helpful to clarify:
1) For road transport, while the information from Google or Baidu maps provides generally the transport intensities on road, emissions from different vehicle types and gasoline/diesel are different. Were this considered in the emission AFs?
2) Industrial sector- workplace change might be an indicator, as it is hard to get accurate and reliable data on this. But this estimation is expected to have much higher uncertainties, and the steel production activities are rather different from many others, for example, petroleum industry facilities most of which are not shut down during the COVID.
3) For power plants, were data for electricity plants using different fuels- coal-fired, nuclear power, hydroelectric power etc.,
4) Residential sector-I agreed with the authors that there was an increase in residential emissions, which could be also found in some recent studies finding high indoor air pollution (also leading to higher overall exposure) during the COVID in rural area. But in urban homes, the increased electricity contributed small to the increase of emissions (in fact in most emission inventories, residential electricity associated emissions are not counted in the residential sector), and gas burning for cooking increased very small. People had three meals per day, no matter it is during the COVID or not. However, the AFs based on the increased electricity data for London are not representative for other countries using solid fuels. Why not referring to information from other developing countries, especially those using solid fuels in rural area?
5) Besides changes in individual residential homes, there are significant changes in commercial emissions for example restaurants and the mall. Was this considered and available from some data?
6) Emissions are different from different fuels- fossil and biomass ones. This is more obvious in residential sector, where multiple different fuels are used. Residential coal and biomass use contribute largely to the primary emissions of PM2.5, BC and OC. Were different AFs for different fuels types, and differences in pollutant species, considered in the development of database?
7) Validation of results is always an important concern. It is accepted that global or regional emission inventory itself is difficult to be validated unless in couple with the air transport chemical models and validated in comparison to the monitoring data. This should be discussed in the manuscript, if it is presently not in the study scope.