HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 1: global and regional module

Guevara, Marc; Tena, Carles; Porquet, Manuel; Jorba, Oriol; García‐Pando, Carlos Pérez

doi:10.5194/gmd-12-1885-2019

Cited by 44 publications

(37 citation statements)

References 59 publications

(63 reference statements)

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“…The original annual CAMS-REG-APv3.1 emission inventory was processed using the HERMESv3 system, an open source, stand-alone multi-scale atmospheric emission modelling framework developed at the BSC that computes gaseous and aerosol emissions for use in atmospheric chemistry models (Guevara et al, 2019). The HERMESv3 system was used to remap the original CAMS-REG-AP data (0.1x0.05 degrees) onto the MONARCH modelling domain and to derive hourly and speciated emissions.…”

Section: Hermesv3 Emission Systemmentioning

confidence: 99%

Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns

Guevara¹,

Jorba²,

Soret³

et al. 2020

Preprint

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Abstract. We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow modelling and identifying the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open access and near-real time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalised electricity consumption reductions. The time period covered is from 21 February, when the first European localised lockdown was implemented in the region of Lombardy (Italy), until 26 April 2020. This period includes five weeks (23 March until 26 April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted emission temporal profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be −33 % for NOx, −8 % for NMVOC, −7 % for SOx and −7 % for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85 % of the total reduction is attributable to road transport, except SOx. The reductions reached −50 % (NOx), −14 % (NMVOC), −12 % (SOx) and −15 % (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to −58 % at urban background locations (Madrid, Spain) and −44 % at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86 % and 93 %, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced with more than 90 %. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial/residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the supplementary material.

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Section: Hermesv3 Emission Systemmentioning

confidence: 99%

Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns

Guevara¹,

Jorba²,

Soret³

et al. 2020

Preprint

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“…Code and data availability. The Python package "emiproc" (Jähn et al, 2020) for emission pre-processing is publicly available through the C2SM GitHub organization (https://github.com/C2SM-RCM/cosmo-emission-processing, last access: 15 May 2020). The repository also includes the vertical and temporal profiles used here.…”

Section: Discussionmentioning

confidence: 99%

“…A prominent example of an emission pre-processing model is the Sparse Matrix Operator Kernel Emissions model (SMOKE, https://www.cmascenter.org/smoke/, last access: 15 May 2020), which has been used extensively with different atmospheric chemistry models in the United States (McHenry et al, 2004;Wong et al, 2012), Europe (Borge et al, 2008;Bieser et al, 2011a), and Asia (Wang et al, 2011). More recent examples are the pre-processor PREP-CHEM-SRC (Freitas et al, 2011), the High-Elective Resolution Modelling Emission System (HERMES; Guevara et al, 2019), and the Harvard-NASA Emission Component v1.0 (HEMCO; Keller et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1

Jähn

Kuhlmann

et al. 2020

Geosci. Model Dev.

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Abstract. Emission inventories serve as crucial input for atmospheric chemistry transport models. To make them usable for a model simulation, they have to be pre-processed and, traditionally, provided as input files at discrete model time steps. In this paper, we present an “online” approach, which produces a minimal number of input data read-in at the beginning of a simulation and which handles essential processing steps online during the simulation. For this purpose, a stand-alone Python package “emiproc” was developed, which projects the inventory data to the model grid and generates temporal and vertical scaling profiles for individual emission categories. The package is also able to produce “offline” emission files if desired. Furthermore, we outline the concept of the online emission module (written in Fortran 90) and demonstrate its implementation in two different atmospheric transport models: COSMO-GHG and COSMO-ART. Simulation results from both modeling systems show the equivalence of the online and offline procedure. While the model run time is very similar for both approaches, input size and pre-processing time are greatly reduced when online emissions are utilized.

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“…vertical, temporal and speciation profiles for the generation of global and regional air quality model-ready emission files. A complete description of HERMESv3_GR can be found in Guevara et al (2019).…”

Section: Introductionmentioning

confidence: 99%

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

Guevara¹,

Tena²,

Porquet³

et al. 2019

Preprint

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Abstract. We describe the bottom-up module of the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), a python-based and multiscale 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., 2019) 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 main criteria pollutants (i.e. NOx, CO, NMVOC, 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. Meteorological-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/region where the required input data is 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.

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HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 1: global and regional module

Cited by 44 publications

References 59 publications

Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns

Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns

An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1

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

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