HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 2: The bottom–up module

Abstract

Abstract. We describe the bottom–up module of the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), a Python-based and multi-scale modelling tool intended for the processing and computation of atmospheric emissions for air quality modelling. HERMESv3 is composed of two separate modules: the global_regional module and the bottom_up module. In a companion paper (Part 1, Guevara et al., 2019a) we presented the global_regional module. The bottom_up module described in this contribution is an emission model that estimates anthropogenic emissions at high spatial- (e.g. road link level,) and temporal- (hourly) resolution using state-of-the-art calculation methods that combine local activity and emission factors along with meteorological data. The model computes bottom–up emissions from point sources, road transport, residential and commercial combustion, other mobile sources, and agricultural activities. The computed pollutants include the main criteria pollutants (i.e. NOx, CO, NMVOCs (non-methane volatile organic compounds), SOx, NH3, PM10 and PM2.5) and greenhouse gases (i.e. CO2 and CH4, only related to combustion processes). Specific emission estimation methodologies are provided for each source and are mostly based on (but not limited to) the calculation methodologies reported by the European EMEP/EEA air pollutant emission inventory guidebook. Meteorologically dependent functions are also included to take into account the dynamical component of the emission processes. The model also provides several functionalities for automatically manipulating and performing spatial operations on georeferenced objects (shapefiles and raster files). The model is designed so that it can be applicable to any European country or region where the required input data are available. As in the case of the global_regional module, emissions can be estimated on several user-defined grids, mapped to multiple chemical mechanisms and adapted to the input requirements of different atmospheric chemistry models (CMAQ, WRF-Chem and MONARCH) as well as a street-level dispersion model (R-LINE). Specific emission outputs generated by the model are presented and discussed to illustrate its capabilities.

Meteorology-normalized impact of the COVID-19 lockdown upon NO<sub>2</sub> pollution in Spain

“…Meteorological data are taken from the ERA5 reanalysis dataset (Hersbach et al, 2020). ERA5 data have a spatial resolution of about 31 km.…”

Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling

“…In addition to the meteorological-dependent profiles described above, we created a specific monthly profile for NMVOC evaporative emissions (GNFR_F4) based on recent results obtained with the High-Elective Resolution Modelling Emission System (HERMESv3) (Guevara et al, 2020c). The HERMESv3 model computes hourly gasoline evaporative emissions from standing cars (diurnal losses) using the "Tier 2" approach reported in the EMEP/EEA emission inventory guidelines 2016.…”

“…Several datasets even provide emission maps for selected pollutants or study regions with resolutions as fine as 1 × 1 km (e.g. ODIAC2016, Open-source Data Inventory for Anthropogenic CO 2 , version 2016, Oda et al, 2018; Hestia-LA, Hestia fossil fuel CO 2 emissions data product for the Los Angeles megacity; Gurney et al, 2019;Super et al, 2020). This improvement is largely due to the emergence of new detailed, satellitebased and open-access spatial proxies such as the population maps at 1 × 1 km proposed by the Global Human Settlement Layer (GHSL) project (Florczyk et al, 2019), the global land cover maps at 300 × 300 m provided by the European Space Agency Climate Change Initiative (ESA CCI, https: //www.esa-landcover-cci.org/, last access: February 2021) or the georeferenced road traffic network distributed by OpenStreetMap (OSM, http://www.openstreetmap.org, last access: February 2021).…”

High-Resolution Assessment of Air Quality in Urban Areas—A Business Model Perspective

“…However, ultimately, the problem is that a top-down approach does not work well no matter how well the information is disaggregated because the correct and real emission indices must be known. What is required are a greater number of ambient air measurements for source apportionment and inverse dispersion modelling methods, i.e., a bottom-up approach [62]. This can be seen in the most recent academic review of 48 US cities in which it was found that they were, on average, under reporting their emissions by 18.3%, with a range of −145.5% to +63.5%.…”