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.
Introduction
The development of reliable anthropogenic emission inventories is crucial to
understanding air pollution sources and designing effective emission abatement
measures (Day et al., 2019). Emission inventories are also key inputs for
air quality research and forecasting applications and represent one of the
largest source of uncertainty in the air quality modelling chain (Russell
and Dennis, 2000). For their use in atmospheric chemistry models, emissions
need to be spatially distributed over a gridded domain, temporally resolved
with (typically) hourly intervals and mapped to the species defined in the
gas-phase and aerosol chemical mechanisms of the atmospheric chemistry
model.
The last decades have seen large efforts to develop emission inventories for
global and regional scales using a variety of input datasets and approaches.
These inventories have become key in supporting scientific research and
policymaking (e.g. the Air Quality Modelling Evaluation International
Initiative, AQMEII; Pouliot et al., 2015). At a global scale, some of the most
frequently used inventories are the Air Pollutants and Greenhouse Gases
Emission Database for Global Atmospheric Research (EDGAR; Crippa et al.,
2018) and the dataset derived from the Evaluating the Climate and Air
Quality Impacts of Short-Lived Pollutants project (ECLIPSEv5.a; Klimont et
al., 2017). There are also widely known regional emission inventories such
as the European Monitoring and Evaluation Programme (EMEP) (Mareckova et
al., 2019) and the TNO-MACC_III (Kuenen et al., 2014) or the
Regional Emissions inventory in Asia (REAS), which covers China, Japan and
other Asian countries (Kurokawa et al., 2013). More recently, and as part of
the European Copernicus Atmosphere Monitoring Service (CAMS), new updated
global and regional emission datasets covering both anthropogenic and
natural sources have been developed (Granier et al., 2019).
These inventories provide estimates of emissions either globally or
regionally for a variety of sectors, pollutants and years in a consistent
way. However, they are usually limited to high-resolution modelling
applications; assessing urban air quality or the local impact of emission
reduction measures are two good examples (e.g. Timmermans et al., 2013).
Such a limitation is due to the insufficient level of detail of the data
used to estimate the emissions, typically national statistics, the
uncertainties associated with their spatial and temporal distribution and the
lack of flexibility for computing specific scenarios (e.g change in road
speed limits). Emissions are first estimated at the annual and national
level and the spatial proxies assigned to each pollutant source (e.g.
population, city lights, land uses) are usually empirical and may not be
representative of the real-world spatial emission patterns (Andres et al.,
2016; Geng et al., 2017). Hourly emissions are computed through the
application of temporal profiles (i.e. monthly, weekly and diurnal) to the
original annual inventories, which are usually static (i.e. spatially
constant) and do not account for the dynamical component of the emission
processes (e.g. volatilization of ammonia as a function of meteorological
parameters; Backes et al., 2016). Moreover, existing global and regional
emission inventories are reported following sector aggregations (e.g.
Gridded Nomenclature For Reporting, GNFR) that can hinder the application of
detailed speciation profiles. For instance, the EMEP inventory, which
compiles emissions from the parties of the Convention on Long-range and
Trans-boundary Air Pollution (CLRTAP), reports all road transport emissions
under one single category (i.e. GNFR F_RoadTransport) without
discriminating by vehicle type (e.g. passenger cars, heavy duty vehicles),
fuel type (i.e. petrol and diesel vehicles) or EURO (European emission
standards) category. These factors become crucial when assigning the
fraction of total nitrogen oxides (NOx) emitted directly as nitrogen
dioxide (NO2) (e.g. Carslaw and Rhys-Tyler, 2013).
Dedicated emission models combining detailed databases with novel
calculation methods that represent the main factors influencing the emission
processes (e.g. meteorology, soil properties) can overcome these
limitations. Some recent examples are the French VOLT'AIR model
(Hamaoui-Laguel et al., 2014), which simulates ammonia (NH3)
volatilization fluxes after the application of fertilizer taking into
account agro-environmental factors (e.g. meteorology, agricultural
practices, soil properties), the Brazilian VEIN model (Ibarra-Espinosa et
al., 2018), which provides bottom–up exhaust and evaporative vehicular
emissions at street and hourly levels using different sets of emission
factors (e.g. COmputer Programme to calculate Emissions from Road
Transport version 5 (COPERT), EPA), and the Norwegian MetVed model (Grythe et al.,
2019), which estimates residential wood combustion hourly emissions on a
250 m grid resolution considering several influencing factors such as outdoor
temperature, type of available heating technologies and the number, type and
size of dwellings.
Despite presenting highly accurate and detailed modelling methodologies,
there are still some shortcomings associated with these modelling tools,
mainly in terms of their usability for air quality modelling. On the one
hand, each model covers only a specific pollutant sector, which means that
they may have to be manually combined with other existing inventories. On
the other hand, the models are usually designed to provide emissions for
specific frameworks, which results in limitations in terms of the number of
atmospheric chemistry models compatible with the emission outputs, map
projections and type of working domains supported, as well as
regions or countries in which the model can be applied. Finally, these tools
are not always distributed under open-access licenses, which limits their
usage within the scientific community.
This paper is the second part of the description of an open-source,
Python-based, parallel, stand-alone and multi-scale atmospheric emission
modelling framework. The High-Elective Resolution Modelling Emission System
version 3 (HERMESv3) estimates atmospheric emissions for use in multiple air
quality models (i.e. CMAQ, Appel et al., 2017; WRF-Chem, Grell et al., 2005;
and MONARCH, Badia et al., 2017) as well as map projections and model grids
(i.e. regular and rotated latitude–longitude, Lambert conformal conic,
Mercator). The system reflects the learning from previous versions of HERMES
developed by the Earth Sciences Department of the Barcelona Supercomputing
Center (BSC) during the last decade (i.e. Baldasano et al., 2008; Ferreira et
al., 2013; Guevara et al., 2013, 2017).
HERMESv3 is composed of two independent modules named global_regional and
bottom_up. The global_regional module (HERMESv3_GR) is a customizable emission
processing system that combines existing gridded inventories with
user-defined 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. (2019a).
The bottom_up module, (described in this paper and further referred to as
HERMESv3_BU) is an emission model that computes high spatial
(e.g. road link, point source) and temporal (i.e. hourly) resolution
anthropogenic emissions using state-of-the-art calculation methods that
combine local activity and emission factors along with meteorological data.
The model covers the estimation of bottom–up emissions from multiple
sources, including power and manufacturing industries, road transport (i.e.
exhaust and non-exhaust sources), residential and commercial combustion,
other mobile sources (i.e. agricultural machinery, landing and take-off
cycles at airports, shipping activities in ports, and recreational boats), and
agricultural activities (manure management, fertilizer application and crop
operations). The computed pollutants include the main criteria pollutants (i.e.
NOx; CO; NMVOCs; SOx; NH3; PM10 and PM2.5) and
greenhouse gases (i.e. CO2 and CH4, only related to combustion
processes). HERMESv3_BU provides specific estimation
methodologies and emission factors for each source, which are mostly based
on (but not limited to) the calculation methodologies reported by the
European EMEP/EEA air pollutant emission inventory guidebook (EMEP/EEA,
2016). Users are allowed to load their own emission factors or apply
specific tuning factors to the default dataset. With respect to the input
activity data (e.g. geographic location of the industrial facilities and
corresponding activity factors and temporal profiles), the user is
responsible for providing the required information on the region of interest
following the formats established in HERMESv3_BU. The
application of the model is not restricted to a specific country; it can be
run for any European region if the corresponding input data are provided.
HERMESv3_BU includes a variety of global and regional
state-of-the-art datasets to increase the usability of the tool and minimize
the amount of input information that needs to be provided by the user. In
the same line of thinking, HERMESv3_BU includes
functionalities similar to geographic information systems (GISs) for
automatically manipulating and performing spatial operations on geometric
objects (e.g. remap spatial data from one spatial domain to another). The
model counts as well with a flexible speciation mapping functionality, which
allows the user to speciate the original pollutants to any desired chemical
mechanism. Besides the aforementioned mesoscale atmospheric chemistry models
that are compatible with HERMESv3, the road transport emission outputs of
HERMESv3_BU can also be used within the R-LINE research-grade
dispersion model (Snyder et al., 2013).
General descriptionOverview
A schematic of the model structure and execution workflow is shown in Fig. 1. The characteristics of the working domain, execution dates, emission
sectors and pollutants to be included in the calculation process, input data
paths, atmospheric chemistry output format, and number of calculation and
writing processors have to be specified by the user in the general
configuration file (see Sect. 2.2). Once all this
information is compiled, HERMESv3_BU starts the general
initialization process, which includes (i) the creation of the working grid
where emissions will be calculated (grid function), (ii) the creation of the
polygon feature that will be used to perform polygon clipping operations of
the input data when needed (clip function) and (iii) the distribution of the
computational resources among the different emission sectors (sector manager
function). The data generated with the grid and clip functions, which are
specific to each working domain, are stored as auxiliary files by default
after their creation so that they can be reused in subsequent executions.
Schematic representation of the general structure of
HERMESv3_BU. All the parameters described by the user in the
general configuration file (e.g. working domain, execution dates, input data
paths) are used to execute the initialization process (i.e. creation of
working grid, clip polygon and auxiliary files and distribution of the
computational resources). Once the general initialization is finished,
HERMESv3_BU starts the execution of the emission core of the
model. For each pollutant sector, and considering the input data provided by
the user, HERMESv3_BU performs a (i) sector initialization,
(ii) sector calculation, (iii) spatial mapping, (iv) temporal distribution
and (v) speciation. Once the emission estimation of all sectors is finished,
the writing core proceeds to gather all the data and writing it according to
the atmospheric chemistry output format selected by the user.
The input data used by HERMESv3_BU include sector-specific
and general data. The first set of information is divided into individual
folders (<sector>), each one containing the activity
and emission factor files of each sector, and the <profiles> folder, which contains the temporal, vertical and
speciation profiles associated with each sector. Regarding the general data,
this section includes meteorological files, which are used by several
sectors, as well as global and regional datasets such as population maps.
The different types of files used in the model are described in detail in
Sect. 3.
The emission core of the model is composed of individual and independent
submodules that calculate the emissions of each sector following specific
estimation methodologies (see Sect. 3.1 to
3.6). Most of the submodules share a common
procedure, which consist of the following steps: (i) sector initialization,
which creates sector-specific auxiliary files, (ii) sector calculation,
(iii) spatial mapping, (iv) temporal distribution and (v) speciation.
Once the execution of all the sectors is finished, HERMESv3_BU starts the data writing process. This function consists in first
gathering the emissions estimated by each submodule (provided as 4D matrices
with information on emissions across space, time and vertical levels) and
secondly writing the merged data in an output NetCDF file according to the
conventions of the atmospheric chemistry model of interest (see Sect. 2.5).
General configuration file
The general configuration options are passed to HERMESv3_BU
via a configuration file, which is divided into seven different sections.
General: this section defines the main paths of the model (i.e.
input, output, general data, auxiliary files), the name of the output
emission file, time step configuration parameters (i.e. start and end dates
and number of hourly time steps) and the option of removing the existing
auxiliary files at the beginning of the execution.
Domain and output format: this section defines the
characteristics of the working grid (e.g. spatial coverage, horizontal
resolution and vertical description) as well as the structure and naming
convention of the output NetCDF emission file. Currently,
HERMESv3_BU supports four map projections (i.e. regular
lat–long, rotated lat–long, Lambert conformal conic and Mercator) and
three atmospheric chemistry model file conventions (i.e. CMAQ, WRF-Chem and
MONARCH) (see Sect. 2.5).
Clipping: this section defines the polygon feature that will be
used to perform the clipping operation during the general initialization
process (see Sect. 2.4).
Sector management: this section defines the number of
computational processors that will be assigned to each pollutant sector
during the emission calculation process. Sectors can be individually
deactivated setting their corresponding numbers to 0. This section also
defines the number of processors that will be assigned for the writing
process (see Sect. 4).
General shapefiles and raster: this section defines the path to
the general shapefiles (i.e. administrative boundaries) and rasters (i.e.
population, land use, livestock and soil property maps) used in the model.
Pollutant sector data: this section contains individual
subsections for each pollutant sector, in which the user defines (i) the
list of pollutants to be calculated, (ii) the data paths that point to
the specific-sector input files (see Table B1) used for the emission
calculation process (i.e. users can freely define their own file data storage
convention) and (iii) an optional subset of pollutant categories to be
considered for the calculation process and only available for certain
sectors (i.e. list of vehicle categories in the road transport sector, list
of crop categories in the fertilizer application sector, list of animal
categories in the livestock sector, list of airport codes in the aircraft
sector, list of port codes in the shipping sector, list of fuels in the
residential and commercial sector). This last option can be very useful when
studying the contribution of certain pollutant categories to total sectoral
emissions (e.g. diesel vehicles in road transport) or when performing source
attribution modelling studies (e.g. Pay et al., 2019).
Meteorology: this section define the paths to the gridded
meteorological files used as input.
Input files and cross-referencing
HERMESv3_BU uses four types of input file formats.
Esri shapefiles (points, polygons and polylines): used to provide spatial
georeferenced information, including road transport networks, collections of
point source facilities, infrastructure boundaries (i.e. airport, port) and
administrative boundaries.
Geotiff raster files: used to provide spatially gridded information,
including land use information, population and livestock distributions, and
soil properties (i.e. pH and cation exchange capacity).
CSV files: used to store non-georeferenced activity data and emission
factors as well as sets of temporal, vertical and speciation profiles.
NetCDF files: used to provide modelled meteorological data (e.g.
temperature, wind speed). HERMESv3_BU can currently use
gridded meteorological data provided by MONARCH and ERA5 (C3S, 2017).
Depending on the pollutant sector one or more types of data are combined
during the emission calculation process. For each sector, cross-references
and spatial relationships between files are used for matching all the
different input data. Figure 2 shows an example of the files used in the
point source sector (see Sect. 3.1) and how all
the information is linked. Most of the point source's input information
(e.g. activity and emission factors, stack parameters, geographical
coordinates) used by the model is provided in a multipoint shapefile, each
row containing the information on a specific facility. The shapefile
includes temporal and speciation profile IDs (e.g. “MXXX” for the monthly
profiles, “XXX” being a three-digit numeric code that starts at “001”), which are cross-referenced with temporal and speciation CSV files where the
numeric profiles are stored. On the other hand, the geographical coordinates
of each facility are used to identify the closest grid cell of the
meteorological NetCDF file and subsequently associate with them the required
meteorological information.
Illustration of how the different input files used by the point
source sector are linked and cross-referenced. The multipoint shapefile
(point_source_facilities.shp), contains
temporal and speciation profile IDs for each facility (e.g. “MXXX” for the
monthly profiles, “XXX” being a three-digit numeric code that starts at
“001”), which are cross-referenced with the corresponding temporal and
speciation CSV files (month.csv, week.csv, hour.csv,
speciation_cb05_aero5.csv). The shapefile also
contains the geographical coordinates of each facility (long, lat), which are
used to identify the closest grid cell of the meteorological NetCDF file
(meteo_file.nc) and derive the surface temperature data.
The input data required to run HERMESv3_BU can be classified
into three main categories.
User-dependent data files: files that contain local information for the
domain of study (e.g. energy consumption statistics, cultivated crop areas)
and that need to be provided by the user.
Built-in data files: store information that is not tied to a specific domain
(e.g. emission factors, temporal profiles) and that is provided by default
with the model. Users can modify the files provided by default if needed or
add new ones (e.g. speciation profiles for a new chemical mechanism) but it
is not mandatory in order to correctly run the model.
External data files: open-source files reported by third parties (e.g. Joint
Research Centre, Copernicus Land Monitoring Service) that contain global or
regional information (e.g. population density, land use map) and that allow
minimizing the amount of local information that needs to be provided by the
user
All the model input data (user-defined, built-in and external) are needed to
correctly execute the emission core of the system, as illustrated in Fig. 1. A classification of all the input files according to the three categories
described above can be found in Table B1. Section 3
provides more information on the input datasets used for each pollutant
sector. A complete description of all the input files and corresponding
information fields can be found in the wiki of the model (see Sect. 6).
Spatial operations
HERMESv3_BU includes multiple functionalities for
manipulating and performing spatial operations on geometric objects that are
spatially referenced and have associated attributes (i.e. shapefiles and
raster files) without requiring the users to have a GIS software. This allows the model to automatically manipulate
georeferenced input data files, as well as to create spatial surrogates that
have lately been used to map the estimated emissions onto the grid cells of the
desired working domain. The following operations are implemented in the
model:
read, write or create – reads, writes and creates vector-based spatial data
including Esri shapefiles and Geotiff raster files. These operations also
allow changing the map projection of the original files.
clip – overlays a polygon on a target feature and extracts from it only the
data that lie within the area outlined by the clip polygon. By default, the
clip polygon is defined as the outline of the user-defined working domain,
but users can optionally use an existing shapefile (e.g. administrative
boundary) or define a costume polygon providing a set of latitude–longitude
coordinates. The clipped data become a new feature.
unary union – returns a representation of the union of the given geometric
objects. This function is used to create the outline of the user-defined
gridded working domain.
data conversion – converts raster files to a polygon feature class.
spatial intersection – computes a geometric intersection of two features. The
operation returns only those geometries that are contained by both targeted
features. Unlike the clip operation, in the spatial intersection the
associated attribute values from the input feature classes are copied to the
output feature class.
spatial difference – as opposed to the spatial intersection, in this case the
operation returns only those geometries that are not contained by both
targeted features.
spatial join – joins attributes from one feature to another based on the
spatial relationship. The target features and the joined attributes from the
join features are written to the output feature class. Unlike the spatial
intersection, the spatial join does not modify the geometry of the target
feature.
nearest point – Calculate the nearest point in a pair of geometries. This
operation is used to assign to each emission source (e.g. point source, road
link) the closest meteorological data reported in the NetCDF input files.
As an illustration, Fig. 3 shows the steps performed by
HERMESv3_BU to generate the gridded fuel consumption data
used by the residential and commercial combustion emission submodule (see
Sect. 3.4). In the example, Spanish natural gas
and wood consumption data obtained at the province level (IDAE, 2018;
MITECO, 2018) are mapped onto a 4 km by 4 km regional Lambert conformal conic
grid covering the Iberian Peninsula. For creating the gridded data, the
model uses the population maps reported by the Global Human Settlement Layer
(GHSL) project (JRC and CIESIN, 2015; Florczyk et al., 2019). The GHSL provides global Geotiff
raster files at a resolution of 1 km by 1 km on the distribution and density
of population, expressed as the number of people per cell (Schiavina et al.,
2019), and on the classification of human settlements on the basis of the
built-up area and population density, expressed as high- and low-density clusters
(i.e. large and small urban areas, here remapped under a single category
expressed as urban areas) and rural areas (Pesaresi et al., 2019). In the
example, a clip of the original GHSL population density raster is performed
using a shapefile of the administrative borders of Spain (Fig. 3a). The
resulting clipped raster is converted to a polygon feature (Fig. 3b, zoom
over the region of Madrid), to which new information is appended performing
two spatial joins: one with a shapefile of the Spanish Nomenclature of
Territorial Units for Statistics level 3 (NUTS3) administrative boundaries
to append the province code to each source grid cell and another one with
the GHSL settlement classification layer (that has also been previously
converted from raster to shapefile) to append the population type
information (Fig. 3c). Once each grid cell of the polygon has information
on the population, the NUTS3 code and type of settlement,
HERMESv3_BU spatially distributes the annual fuel consumption
input data, which are provided by the user in a CSV file. For that, the
following expression is applied (Eq. 1):
FCf(x‾)=FCf,n⋅Popn,t(x‾)∑x=1NPopn,t(x‾),
where FCf(x‾) is the annual fuel consumption (GJ yr-1) of
fuel f on the source grid cell x‾; FCf,n is the total annual
fuel consumption (GJ yr-1) of fuel f in the NUTS3 n; and
Pop(x‾)n is the amount of population (inhabitant per cell) of
type t (urban, rural) from NUTS3 n on the source grid cell x‾. In the
example provided, urban and rural population are considered for the
distribution of natural gas consumption (Fig. 3d), and only rural
population for the distribution of wood consumption (Fig. 3e).
Examples of the spatial operations performed by
HERMESv3_BU during the initialization of the residential and
commercial combustion emission sector, including (a) a clip of the original
GHSL population density raster (population per pixel) using a shapefile of the
administrative borders of Spain, (b) a conversion of the clipped raster to a
polygon feature (population per cell) (zoom over the area of Madrid), (c) the
spatial joins performed to append the NUTS3 administrative boundary codes
(ES300, Madrid; ES424, Guadalajara and ES425, Toledo) and the GHSL
settlement categories (urban, rural) to each source grid cell, and the
spatial intersections applied to remap the fuel consumption data (natural
gas in urban and rural areas and wood in rural areas) (GJ per cell per year) from the source (d, f) domain to the user-defined (e, g) destination domain (4 km by 4 km Lambert conformal conic grid).
In the final step, the resulting polygon features are spatially intersected
with the 4 km by 4 km gridded domain in order to remap the fuel consumption
data from the source domain to the destination domain (Fig. 3f and g). The remapping is performed taking into account the ratio of the area
of the region of intersection between the source and destination grid cells
(A(x‾x)) to the total area of the source grid cell (A(x‾)), as
expressed in Eq. (2):
FCf(x)=FCf(x‾)⋅A(x‾,x)A(x‾).
Similar operations are applied for the spatial manipulation of other
georeferenced datasets such as land use categories, livestock maps or
digitalized traffic networks.
Air quality model-ready files
HERMESv3_BU creates NetCDF emission output files in a format
that is compatible with the conventions used by a number of air quality
models, including CMAQ, WRF-Chem and MONARCH. For each model, an independent
writing function has been implemented (e.g. writing_cmaq.py) to perform the required
conversion of units and inclusion of mandatory global attributes. This
modular approach allows us to easily extend the writing capabilities of the
model to other atmospheric chemistry model conventions. Alternatively, the
user can also estimate the emissions in a so-called DEFAULT format, which
stores the emissions (g h-1) in a NetCDF file that follows
the Climate and Forecast (CF1.6) Metadata Conventions. In the case of road
transport emissions, a dedicated writing function was designed so that the
computed link-level emissions can be used by the R-LINE Gaussian dispersion
model. This functionality allows HERMESv3_BU to be used for
modelling air pollution at the urban (street level) scale (Benavides et al.,
2019).
Emission sectors
Table 1 summarizes the major characteristics of each pollutant sector
considered in HERMESv3_BU, including source type, categories
and processes considered, pollutants involved and temporal, and vertical
distribution. The following subsections provide a detailed description of
the emission estimation methodologies implemented within
HERMESv3_BU for each pollutant sector. Unless otherwise
stated, all the equations reported in the following subsections are derived
from the emission estimation expressions reported by EMEP/EEA (2016).
Original expressions have been reformulated to produce high-resolution
emissions (i.e. gridded or source-specific and hourly) instead of total
national annual emissions. Some illustrative examples of the outputs that
can be generated with the tool are also presented and compared against other
existing emission datasets. In all the cases, the presented results were
estimated for Spain or a Spanish region or city. The reason for this is mainly
because the access to the required local, regional and national data is easy
for the authors in this country. Compiling data with the same level of
detail for other countries is beyond the scope of this work. Nevertheless,
and as mentioned before, HERMESv3_BU is designed so that it
can be applicable to other European countries or regions where similar input
data are available.
Summary of the main characteristics of each pollutant
sector included in HERMESv3_BU.
SectorSource typeCategories and processesPollutantsVertical and temporal distributionPoint sourcesPointEnergy and manufacturing facilities and waste incinerators: – combustion processes – production processesNOx, CO, NMVOC, SOx, NH3, PM10, PM2.5, CO2 and CH4– vertical distribution according to stack height or plume rise calculation – monthly, weekly and diurnal time factorsRoad transportLine (non-evaporative), area (evaporative)COPERT 5 vehicle categories:1 – exhaust (hot and cold start) – non-exhaust (wear, resuspension and evaporation)NOx, CO, NMVOC, SOx, NH3, PM10, PM2.5, CO2 and CH4– ground-based emissions – monthly, weekly and diurnal time factorsResidential and commercial combustionAreaNatural gas, liquefied petroleum gas, heating diesel oil, wood and coal: – combustion processesNOx, CO, NMVOC, SOx, NH3, PM10, PM2.5, CO2 and CH4– ground-based emissions – day-of-year time distribution using heating degree-day approachShipping in portsAreaEMEP/EEA (2016) ship categories:2 – manoeuvring – hotellingNOx, CO, NMVOC, SOx, NH3, PM10, PM2.5, CO2 and CH4– ground-based emissions – monthly, weekly and diurnal time factorsAviation (LTO cycle)AreaEMEP/EEA (2016) plane categories:3 – land-based operations: taxi-in, taxi-out, take-off – air operations: climb out and approachNOx, CO, NMVOC, SOx, NH3, PM10, PM2.5, CO2 and CH4– vertical distribution according to LTO cycle – monthly, weekly and diurnal time factorsRecreational boatsAreaEMEP/EEA (2016) pleasure boat categories:4 – combustion processesNOx, CO, NMVOC, SOx, NH3, PM10, PM2.5, CO2 and CH4– ground-based emissions – monthly, weekly and diurnal time factorsLivestockAreaPigs, cattle, poultry, goats and sheep: – housing, yarding, storage, grazingNOx, NMVOC, NH3, PM10, PM2.5– ground-based emissions – day-of-year time distribution using Skjøth et al. (2011) parameterizationAgricultural crop operationsAreaWheat, rye, barley and oat: – soil cultivation – crop harvestingPM10, PM2.5– ground-based emissions – monthly, weekly and diurnal time factorsAgricultural machineryAreaTwo-wheel tractors, agricultural tractors and harvesters: – combustion processesNOx, CO, NMVOC, SOx, NH3, PM10, PM2.5, CO2 and CH4– ground-based emissions – monthly, weekly and diurnal time factorsAgricultural fertilizersAreaAlfalfa, almond, apple, apricot, barley, cherry, cotton, fig, grape, lemon, maize, melon, oats, olive, orange, pea, peach, pear, potato, rice, rye, sunflower, tangerine, tomato, triticale, vetch, watermelon, wheat: – mineral fertilizers and manure applicationNH3– ground-based emissions – day-of-year time distribution using Skjøth et al. (2011) parameterization
This submodule estimates hourly emissions from process and combustion
activities occurring in energy and manufacturing industrial point sources
Eq. (3):
Ep,i(h)=AFp⋅EFp,i⋅FM(m)p⋅FW(d)p⋅FH(h)p,
where Ep,i(h) is the hourly emissions of pollutant i at point source
p and hour h (g h-1); AFp is the annual activity factor
(energy or material produced, fuel consumed) associated with point source p (GWh yr-1 or GJ yr-1 or grams of product yr-1); EFp,i is
the emission factor linked to point source p and pollutant i (g GWh-1 or
g GJ-1 or gram per gram of product); FM(m)p is the
monthly factor associated with month m and point source p (0 to 1); FW(d)p is the weekly factor associated with day d and point source p
(0 to 1); and FH(h)p is the hourly factor associated with hour h and point
source p (0 to 1).
As previously mentioned, most of the input data are provided in a
georeferenced multipoint shapefile, each row containing the information for
each specific facility (see Sect. 2.4). Emission
factors are derived from facility-level emission reports when available, as
recommended by the Tier 3 approach of EMEP/EEA (2016) (chap. 1.A.1 and
1.A.2). Alternatively, Tier 2 technology and fuel-dependent emission factors
provided by the European guidelines are proposed. For each point source,
emissions are horizontally allocated to the nearest grid cell of the
destination working domain. Regarding the vertical allocation,
HERMESv3_BU explicitly uses plume rise calculations to
determine for each hour and each point source the effective emission
heights. For this, the plume rise formulas as described by Gordon et al. (2018) are implemented. The algorithm takes into account stack and
meteorological parameters, including stack height, stack diameter, exit
temperature at the stack outlet, stack emission exhaust velocity, air
temperature at stack height, wind speed at stack height, surface
temperature, boundary-layer height, friction velocity and Obukhov length.
Emissions are uniformly allocated across all the vertical layers that are
included between the top and the bottom of the calculated plume. The plume
rise function can be deactivated in HERMESv3_BU. In that
case, the model will allocate the emissions to the layer closest to the
stack height.
Alternatively to Eq. (3), HERMESv3_BU can directly ingest
measured hourly emissions if available. For this, the user needs to provide
a separate CSV file that contains the point source's measured emission
fluxes per hour of the day (g h-1) and to define the AFp
parameter in the shapefile as “-1”. This functionality becomes very relevant
when assessing the impact of point source's plumes for specific days and
under specific meteorological conditions (e.g. Baldasano et al., 2014).
Figure 4 shows an example of hourly and vertically distributed SO2
emissions (kg h-1) estimated by HERMESv3_BU for the As
Pontes coal-fired power plant (Spain) during the months of January and July 2015. As Pontes is the largest power plant in Spain (1468.5 MW) and its
exhaust stack (356 m) is the largest in the country and the second largest
in Europe. The emission fluxes are directly derived from measurements
reported by the Spanish Research Centre for Energy, Environment and
Technology (CIEMAT, personal communication, 2018). The meteorological parameters
for the plume rise calculations are obtained from the MONARCH model. It can
be seen that there are significant differences between the vertical profiles
obtained for January (winter) and July (summer), the emissions being
injected to lower altitudes in the first case. The average plume thickness
and plume top in January are 219.3 and 685.5 m, respectively, while in July
the values are 259.4 and 745.7 m (18.3 % and 8.8 % larger,
respectively). This is mainly due to meteorological differences between July
and January in terms of air temperature at the stack height (+6.5∘C) and boundary-layer height (-430.5 m). The results are
in line with other plume rise calculations performed in other facilities
located in similar climate zones (Bieser et al., 2011).
Hot-exhaust emissions are estimated following Eq. (4):
Ehotl,i(h)=∑v=1nAADTv,l⋅Ll⋅EFhot(V(h)l)v,i⋅Mcorr(V(h)l)v⋅FM(m)l⋅FW(d)l⋅FH(h)l,
where Ehotl,i(h) is the hourly hot-exhaust emissions of pollutant
i at road link l and hour h (g h-1); AADTv,l is the annual average
daily traffic for vehicle category v at road link l (no. vehicles per day); Ll is the length of the road link l (km);
EF(V(h)l)v,i is the hot-exhaust emission factor linked to
vehicle category v and pollutant i (grams per kilometre per no. of vehicles) as a function of the hourly mean vehicle travelling speed
(V(h)l at road link l and hour h (km h-1); Mcorr(V(h)l)v is the mileage correction factor associated with
vehicle category v, also estimated as a function of the hourly mean travelling
speed; FM(m)l is the monthly factor associated with month
m and road link l (0 to 12); FW(d)l is the weekly factor
associated with day d and link l (0 to 1); and FH(h)l is the
hourly factor associated with hour h and link l (0 to 1). The number of vehicle
categories is n.
Most of the activity input data (e.g. average daily traffic flow, mean
vehicle speed) are provided in a multiline shapefile, each row containing the
information on a specific road link. The shapefile includes vehicle fleet
composition, temporal and speciation profile IDs, which are cross-referenced
with the corresponding CSV files where all the numeric profiles are stored
(similarly to the example shown for point sources; see Sect. 2.3). These profiles are used to distribute the
total traffic flow among the different vehicle categories, temporally
disaggregate the traffic flow and average speed at the hourly level and
speciate the estimated emissions, respectively.
Both the emission and mileage correction factors implemented in
HERMESv3_BU are the ones reported by the Tier 3 methodology
of EMEP/EEA (2016) (chap. 1.A.3.b.i–iv), which correspond to the values
reported by the European COPERT 5 (https://copert.emisia.com/, last access: February 2020). A
total of 491 vehicle categories are considered, discriminated by vehicle
type (i.e. mopeds, motorcycles, passenger cars, light duty vehicles, heavy
duty vehicles, buses), fuel type (i.e. diesel, gasoline, liquefied petroleum gas (LPG), hybrid,
electric), EURO category, engine power and gross weight class.
In the case of cold-start emissions, the calculation expression used is the
following one (Eq. 5):
Ecoldl,i(h)=∑v=1nEhotl,i,v(h)⋅βi,v(T(h)l)⋅(Qcoldl,i,v(V(h)l,T(h)l)-1),
where Ecoldl,i(h) is the hourly cold-exhaust emissions of pollutant
i at road link l and hour h (g h-1); Ehotl,i,v(h) is the hourly hot-exhaust emissions of pollutant i for vehicle category v at road
link l and hour h (g h-1); βi,v(T(h)l) is
the fraction of mileage driven with a cold-engine pollutant i and vehicle
category k (0 to 1) as a function of the hourly outdoor temperature
T(h)l for hour h and road link l (∘C); and
Qcoldl,i,v(V(h)l, T(h)l) is the
cold or hot emission quotient for pollutant i and vehicles category v [≥1]
as a function of the hourly outdoor temperature T(h)l
(∘C) and hourly mean vehicle travelling speed (V(h)l
for hour h and road link l (km h-1). The number of vehicle categories is
n.
As in the case of the hot-exhaust emissions, the βi,v(T(h)l) and Qcoldl,i,v(V(h)l,T(h)l) parameters are estimated following the expressions
and constants reported by the EMEP/EEA (2016) Tier 3 methodology (chap. 1.A.3.b.i–iv).
Besides the COPERT 5 constants used to calculate vehicle- and pollutant-specific hot and cold emission factors, HERMESv3_BU also
includes scaling factor parameters (defined as 1 by default) that the user
can modify to tune the original emission factors. This functionality can be
useful for adjusting the default factors based on the insights reported by
measurements performed under real-world driving conditions (e.g.
underestimation of COPERT NH3 cold-start emissions according to
Suarez-Bertoa et al., 2017). The mileage correction factors reported by
COPERT 5, which only apply to gasoline vehicles, are expanded to diesel
vehicles (i.e. deterioration of tailpipe NOx emissions of 22 % and
10 % on EURO 2 and 3 diesel passenger cars), following the results
reported by Chen and Borken-Kleefeld (2016).
The estimated link-level vehicle emissions are mapped onto the user-defined
gridded working domain by applying a spatial intersection. Once the
intersection is performed, emissions are automatically gathered at the grid
cell level and the total sum is computed. Figure 5 shows an example of the
hourly PM2.5 road transport emissions estimated for an area of Barcelona
city (09:00 UTC), both at the road link level (kg km -1 h-1,
Fig. 5a) and grid cell level (1km×1km) (kg h -1, Fig. 5b). The total
annual NOx and PM10 road transport emissions were estimated for the
city of Barcelona using HERMESv3_BU and the results were
compared against the latest available local emission inventory developed by
the Barcelona City Council (AB, 2015) (Fig. 5c). Information on the
traffic flow data was obtained from the local automatic traffic counting
network (Barcelona city council, mobility and transport department, personal
communication, 2017) and the TomTom historical average speed profiles product
(https://www.tomtom.com, last access: February 2020), whereas vehicle fleet composition
profiles were derived from a remote-sensing campaign (RACC, 2017). It is
observed that HERMESv3_BU results are 60 % and 39 %
higher than the ones reported by Barcelona City Council (AB). This is due to a combination of
several factors, including (i) the different years of reference (2017 for
HERMESv3_BU and 2013 for AB), (ii) the inclusion of Barcelona's port-area-associated road transport in HERMESv3_BU (large amount of heavy duty vehicles that contribute more than 250
and 15 t yr-1 of NOx and PM10, respectively), (iii) the use of
COPERT 5 real-world adjusted NOx emission factors for EURO 5 and 6
diesel vehicles in HERMESv3_BU (AB inventory is based on
COPERT 4, which does not consider the dieselgate effect), and (iv) the
consideration of deterioration factors on old diesel passenger cars in
HERMESv3_BU.
HERMESv3_BU also estimates non-exhaust PM10 and
PM2.5 traffic emissions, including road surface, tyre and brake wear, and resuspension. Emissions derived from processes of abrasion are estimated
following Eq. (6):
Ewearl,i(h)=∑v=1nAADTv,l⋅Ll⋅EFwearv,i⋅S(V(h)l)⋅FM(m)l⋅FW(d)l⋅FH(h)l,
where Ewearl,i(h) is the hourly emissions of pollutant i at road link
l and hour h (g h-1); AADTv,l is the annual average daily traffic
for vehicle category v at road link l (n d-1); Ll is the length of
the road link l (km); EFwearv,i is the emission factor linked to
vehicle category v and pollutant i (g km-1); S(V(h)l)
is the correction factor (0.902 to 1.39) estimated as a function of the hourly
mean vehicle travelling speed (V(h)l at road link l and hour h (km h-1); FM(m)l is the monthly factor associated with
month m and road link l (0 to 12); FW(d)l is the weekly
factor associated with day d and link l (0 to 1); and FH(h)l is
the hourly factor associated with hour h and link l (0 to 1). Both the emission and
correction factors are derived from the Tier 2 methodology proposed by
EMEP/EEA (2016) (chap. 1.A.3.b.vi, Tables 3-4, 3-6 and 3-8). The number of
vehicle categories is n.
In the case of resuspension, emissions are estimated as follows (Eq. 7):
Eresusl,i(h)=∑v=1nAADTv,l⋅Ll⋅EFresusv,i⋅S(Hrain(h)l)⋅FM(m)l⋅FW(d)l⋅FH(h)l.
All the parameters used in the expression are the same as the ones defined
in Eq. (6) except for the resuspension emission factor linked to vehicle
category v and pollutant i (g km-1) (EFresusv,i) and the
correction factor S(Hrain(h)l) (0 to 1). The resuspension
emission factors proposed by default are vehicle type dependent (i.e.
motorcycles, passenger cars, light duty vehicles, heavy duty vehicles) and
derived from a measurement campaign performed in Barcelona (Amato et al.,
2012a). The correction factor is estimated as a function of the number of
hours after a precipitation event at road link l and hour h
(Hrain(h)l), following the expression reported by Amato et al. (2012b) (Eq. 8):
S(Hrain(h)l)=(1-e-r⋅Hrain(h)l).
The formula, which is based on measurements undertaken in Barcelona (Spain)
and Utrecht (the Netherlands), indicates that after a rainfall (when the
mobility particles drop to values close to zero), the loading of mobile
road dust mobility increases exponentially tending to reach again the
maximum emission strength. The equation depends on a recovery rate (r)
that varies according to the traffic characteristics and local climatic
conditions. By default, HERMESv3_BU uses the recovery rate
value derived from the Barcelona measurements, but the user can change it to
other values if desired. The effect of precipitation on resuspension
emissions is only applied when at least a 0.254 mm h-1 rainfall occurs
(US EPA, 2011).
Gasoline evaporation
NMVOC evaporative diurnal emissions are considered in
HERMESv3_BU as follows (Eq. 9):
Ei(x,h)=∑v=1nN(x)v⋅EF(T(x,d))v⋅FH(T(x,h)),
where Ei(x,h) is the hourly emissions of NMVOC at the destination grid
cell x and hour h (g h-1); N(x)v is the number of registered
vehicles of category v in the destination grid cell x (no.
vehicles); EF(T(x,d))v is the emission factor for
vehicle category v (grams per no. of vehicles) as a function of the
daily mean outdoor temperature T(x,d) for day d and
destination grid cell x (∘C); and FH(T(x,h)) is the
hourly factor associated with hour h as a function of the hourly mean outdoor
temperature T(x,h) for hour h and destination grid cell x. The
number of vehicle categories is n.
The gridded number of registered vehicles (N(x)v) is obtained
combining the GHSL gridded population map with information provided by the
user on registered gasoline vehicles at NUTS level 3, following the spatial
operations showed in Sect. 2.4. Contrary to the
exhaust and wear emissions, evaporative emissions are considered as an area
source and directly computed at the grid cell level.
Emission factors are derived from the Tier 2 method of EMEP/EEA (2016)
(chap. 1.A.3.b.v, Tables 3-5 and 3-6). Neither running loss nor hot-soak
emissions are currently considered in HERMESv3_BU. This is
due to the fact that these emissions mainly occur in gasoline vehicles with
carburettors, and the fraction of European passenger cars and light duty
vehicles post-EURO 1 with this technology is almost zero.
AgricultureFertilizer application
Hourly and spatially disaggregated NH3 emissions from agricultural
fertilizers are estimated following the expression reported by Paulot et al. (2014) (Eq. 10):
E(x,h)=∑c=1nA(x)c⋅Cc⋅Γ(x)c⋅EF(x)c⋅FD(x,d)c⋅FH(h),
where E(x,h) is the hourly NH3 emissions at destination
grid cell x and hour h (g h-1); A(x)c is the annual cultivated area
of crop c at destination grid cell x (ha yr-1); Cc is the ratio
of cultivated to fertilized area for crop c (0 to 1); Γ(x)c is the
fertilizer application rate for crop c at destination grid cell x (kg N ha-1); EF(x)c is the emission factor for crop c at destination
grid cell x (g NH3 kg N-1); FD(x,d)c is the daily factor
for crop c at destination grid cell x and day d (0 to 1); and FH(h) is
the hourly factor associated with hour h (0 to 1). The number of crop categories
is n.
The distribution of the cultivated crop areas onto the destination grid
cells (Ac(x)) is performed using the spatial operation
capabilities of HERMESv3_BU (see Sect. 2.4). The model combines the land use Geotiff raster
reported by the CORINE Land Cover (CLC) inventory 2018 version 18 at
250×250 m (CLMS, 2018) with cultivated crop area statistics at NUTS level 2
provided by the user. HERMESv3_BU performs a mapping between
the different CLC and crop categories (Table A1) in order to spatially
distribute the statistics across the space. One limitation of this approach
is that the number of agricultural land use categories in CLC is limited and
therefore certain crops are assigned to the same CLC category (e.g. maize,
barley, wheat, oat and rye crop categories are all mapped to the
“Permanently irrigated land” CLC category). Future work will include
exploring the use of more detailed datasets such as the crop type map
product included in the Sentinel-2 for Agriculture portfolio (http://www.esa-sen2agri.org, last access: February 2020), which provides maps of the main crop types
at 10 m resolution based on Sentinel-2 and Landsat-8 imagery.
The emission factors (EF(x)c) are calculated following the
methodology proposed by Bouwman and Boumans (2002), which determines that
the NH3 volatilization is driven by soil pH and cation exchange
capacity (CEC), the type of fertilizer used (e.g. urea, ammonium, ammonium
sulfate, manure), type of crop (i.e. upland, flooded), and application mode
(i.e. broadcast or injection). HERMESv3_BU takes the soil
parameters from the International Soil Reference and Information Centre
(ISRIC) World Soil Information database (Hengl et al., 2017), which reports
global pH soil and CEC maps at 250 m resolution. Original maps are remapped
onto the user-defined grid applying a spatial intersection operation. All
crops are assumed to be upland except for rice, and the application mode is
assumed to be broadcast in all cases, following Paulot et al. (2014). The
input on the type of fertilizer used can be distinguished by crop type and
NUTS level 2.
The daily factors (FD(x,d)c) are estimated following the dynamical
ammonia emission parameterization reported Gyldenkærne (2005) and
Skjøth et al. (2004, 2011), which is dependent on the outdoor air
temperature, wind speed and timing of the fertilizer application, the last
parameter being described with a Gauss function (Eq. 11):
FD(x,d)c=e0.0223⋅T(x,d)+0.0419⋅WS(x,d)⋅∑a=13βa,cσc,a⋅2⋅π⋅e(d-τc,a)2-2⋅σc,a2,
where T(x,d) is the 2 m outdoor temperature at destination grid cell
x and day d (∘C); WS(x,d) is the 10 m wind speed at
destination grid cell x and day d (m s-1); βa,c is the fraction
of fertilizer applied to crop c at stage a (1: planting; 2: at growth; 3:
after harvest); τc,a is the optimal application date for crop c at
stage a (Julian day, 1:365/366); σc,a is the deviation around date
τc,a (number of days); and d is the day of the year (Julian day,
1:365/366).
Concerning the hourly distribution of emissions, the fixed temporal profile
for the agriculture sector reported by Denier van der Gon et al. (2011) is
proposed by default.
Figure 6a shows the results for the Spanish total annual NH3
fertilizer emissions calculated on a Lambert conformal conic grid of 4 km by
4 km resolution. Cultivated crop area statistics were obtained from MAPA
(2017a), and information on the fertilizer application rate and type of
fertilizer used by crop type were derived from both MAPA (2011, 2017b)
and Mueller et al. (2012). The time series of daily NH3 emissions for
the region of Aragon and Catalonia is plotted in Fig. 6b. To calculate the
daily distribution, the fraction of fertilizers applied to each crop and
stage are obtained from Paulot et al. (2014), and the application dates and
deviation values are derived from multiple sources, including
Gyldenkærne et al. (2005), Sacks et al. (2010) and Skjøth et al. (2011). In the particular case of barley, rye, wheat, oats and maize, the
growth application dates are determined using the accumulated growing degree
days (GDDs) since planting (McMaster and Wilhelm, 1997). Meteorological
parameters were derived from the ERA5 dataset (C3S, 2017). As shown in the
example, HERMESv3_BU is able to discriminate the emission
estimation by crop type, which allows quantifying the contribution of each
source to the total NH3. The annual emissions estimated for this
Spanish region, which is considered to be one of the Europe's main NH3
hotspots, are compared against the emission fluxes derived from Infrared
Atmospheric Sounding Interferometer (IASI) satellite observations (Van Damme
et al., 2018) (Fig. 6d). It is observed that results are in agreement, the
emissions reported by HERMESv3_BU being just -6 % lower to
the ones derived from the IASI instrument. It is important to note that for
this comparison, livestock emissions estimated by HERMESv3_BU
have also been considered (see Sect. 3.3.2).
Spanish annual NH3(a) fertilizer
and (c) livestock emissions (t yr-1) calculated on
a Lambert conformal conic grid of 4 km by 4 km resolution and corresponding
time series of daily NH3 (t d-1) for the regions of (b) Aragon and Catalonia per
crop type and (d) Murcia per livestock category; (e) the total
NH3 emissions (fertilizers+livestock)
estimated for these two hot spots (t yr-1) are
compared against IASI satellite-derived NH3 emission
fluxes (Van Damme et al., 2018).
Livestock
Hourly gridded NH3 emissions derived from manure management activities
are estimated according to the expression reported by Paulot et al. (2014)
(Eq. 12):
E(x,h)=∑a=1a=4∑l=1l=nD(x)l⋅Γl⋅βl⋅γa,l⋅EFa,l⋅FD(x,d)a,l⋅FH(h),
where E(x,h) is the hourly NH3 emissions at destination
grid cell x and hour h (g h-1); D(x)l is the animal density for
livestock category l at destination grid cell x (no. of heads per cell); Γl is the nitrogen (N) excretion rate for livestock l (grams of N per head); βl is the fraction of total ammoniacal nitrogen
(TAN) content of the excreta from livestock l (0 to 1); γp,l is the
fraction of total excreta associated with activity a (1: housing; 2: yarding; 3:
storage; 4: grazing) for livestock l (0 to 1); EFa,l is the emission
factor for livestock l and activity a (g NH3 g N-1);
FD(x,d)a,l is the daily factor for livestock l and activity a at
destination grid cell x and day d (0 to 1); and FH(h) is the hourly
factor associated with hour h (0 to 1). The number of livestock categories is n.
The estimation methodology and emission factors follow the Tier 2 approach
proposed by EMEP/EEA (2016) (chap. 3.B, Table 3.9), which uses a mass-flow
approach based on the concept of a flow of TAN through the manure management
system.
Regarding the animal density data (D(x)l), HERMESv3_BU
uses as a basis the gridded livestock population from the Gridded Livestock
of the World version 3 (GLWv3; Gilbert et al., 2018), which provides raster
maps of global population densities of cattle, buffaloes, horses, sheep,
goats, pigs, chickens and ducks for 2010 at a spatial resolution of 0.083333∘. HERMESv3_BU adjusts the original data to match
province-level official records from most recent years. These official
statistics, which need to be provided by the user, are also used to
distribute each general livestock GLWv3 group (e.g. pigs) into specific
categories (e.g. fattening pigs between 50 and 80 kg, boars, sows not yet
covered). This disaggregation is relevant due to the different types of
feeding received by each animal type, which subsequently affect the levels
of N and TAN content in their excreta. The remapping and adjustment of the
GLWv3 original data to the destination gridded domain is performed using the
spatial operation tools described in Sect. 2.4.
HERMESv3_BU currently considers a total of 36 livestock
categories, which are grouped into five main groups: pigs (10), cattle (11),
poultry (2), goats (6) and sheep (7). Other livestock groups (i.e.
buffaloes, horses and ducks) are not currently included due to their low
contribution to NH3 emissions in Europe.
Daily factors (FD(x,d)a,l) are estimated following the dynamical
emission parameterization reported in Gyldenkærne et al. (2005) and Skjøth et
al. (2004, 2011). For housing operations, the daily factors are assumed
to be dependent on the barn air temperature and ventilation rate, following
Eq. (13):
FD(x,d)housing,l=(T(x,d)housing,l0.89+V(x,d)housing,l0.26),
where T(x,d)housing,l is the barn temperature associated with the
housing of livestock category l at destination grid cell x and day d
(∘C) and V(x,d)housing,l is the ventilation rate
associated with the housing of livestock category l at destination grid cell
x and day d (m s-1). Both parameters are calculated as a function of the
outdoor 2 m temperature and 10 m wind speed considering the parameterizations
reported in Gyldenkærne et al. (2005), which take into account whether the livestock
are kept in open or closed barns. HERMESv3_BU assumes that
pigs and poultry are kept in closed barns, while cattle, sheep and goats are
kept in open barns, following Backes et al. (2016). The daily factors for
yarding and storage activities also follow Eq. (13), but using wind speed
and air temperature. Finally, for grazing activities the temporal
variability is linked to the availability of grass and to its growing
period. Therefore, the daily distribution is estimated using Eq. (11) and
considering only the growing stage of grass. Concerning the hourly
distribution of emissions, the same profile proposed for fertilizer
application is proposed.
Emissions from NMVOC, PM10 and PM2.5 are estimated by multiplying
the animal density (D(x)l) by the default Tier 1 annual emission
factors reported per livestock category in EMEP/EEA (2016) (chap. 3.B,
Tables 3.4 and 3.5). As noted in the guidelines, only housing emissions are
considered due to the large uncertainties and lack of available information
from other sources. A flat temporal distribution is assumed for both
pollutants since emissions are related to feeding processes.
Figure 6c shows the results of the Spanish annual NH3 livestock
emissions calculated on a Lambert conformal conic grid of 4km×4km
resolution. Animal number statistics at NUTS level 3 were obtained from MAPA (2017c), and information on the N excretion rate for each livestock category
was derived from MAPA (2017b). The TAN content data were obtained from
Antezana et al. (2016) for pig categories and EMEP/EEA (2016) (chap. 3.B,
Table 3.9) for the remaining animals. The time series of daily NH3
emissions for the region of Murcia is plotted in Fig. 6d. Meteorology is
derived from ERA5. As shown in the example, HERMESv3_BU is
able to discriminate the emission estimation by livestock group, which
allows quantifying the contribution of each source to the total NH3.
The annual emissions estimated for this Spanish region, also considered a
major European NH3 hotspot, are again compared against the emission
fluxes derived from IASI (Van Damme et al., 2018) (Fig. 6e). The
HERMESv3_BU results include both livestock and fertilizers
emissions. It is observed that results are in the same order of magnitude,
HERMESv3_BU reporting 21 % less emissions.
Crop operations
Particulate matter emissions released during soil cultivation and crop
harvesting activities are estimated following Eq. (14):
E(x,h)=∑o=1m∑c=1nAc(x)⋅EFc,o⋅FM(m)c,o⋅FW(d)⋅FH(h),
where E(x,h) is the hourly PM10orPM2.5 emissions at
destination grid cell x and hour h (g h-1); Ac(x) is the
annual cultivated area of crop c at destination grid cell x (ha yr-1);
EFc,o is the emission factor for crop c and operation o (g PM10orPM2.5 ha-1); FM(m)c,o is the monthly factor
associated with crop c, operation o and month m (0 to 1); FW(d) is the
weekly factor associated with day d (0 to 1); and FH(h) is the
hourly factor associated with hour h (0 to 1). The number of crop categories is
n.
The model uses the emission factors reported by the EMEP/EEA (2016) Tier 2
methodology (chap. 3.D, Tables 3.6 and 3.8). The methodology takes into
account emissions happening in four different types of crops (i.e. wheat,
rye, barley and oat). Gridded crop areas are estimated using the same
approach described in the fertilizers sector (see Sect. 3.3.1). Considering the monthly distribution of
emissions, specific weight factors are applied based on the soil cultivation
and harvesting calendars reported by Sacks et al. (2010). The daily and
hourly profiles used by default are the ones recommended by EMEP/EEA (2016)
for temporally allocating agricultural machinery activities (chap. 1.A.4.c
ii, Table 5.1).
Residential and commercial combustion
Emissions from residential and commercial small combustion plants are
estimated as follows (Eq. 15):
Ei(x,h)=∑f=1nFCf(x)⋅EFf,i⋅FD(x,d)f⋅FH(h)f,
where Ei(x,h) is the hourly emissions of pollutant i and hour h (g h-1); FCf(x) is the annual gridded fuel consumption
data for fuel category f and destination grid cell x (GJ yr-1);
EFf,i is the emission factor linked to the consumption of fuel f and
pollutant i (g GJ-1); FD(d,x)f is the gridded daily factor
associated with fuel type f, destination grid cell x and day d (0 to 1); and
FH(h)f is the hourly factor associated with fuel type f and hour h (0 to 1).
The number of fuel categories is n.
The gridded consumption data are calculated combining the fuel statistic
consumptions at NUTS level 3 provided by the user with the GHSL population
data, as previously described in Sect. 2.4.
HERMESv3_BU considers the following fuel types: natural gas,
LPG, heating diesel oil, wood and coal. For all of
them, emission factors are obtained from the EMEP/EEA (2016) Tier 2 approach
(chap. 1.A.4, Tables 3-10, 3-18, 3-19, 3-21, 3-31 and 3-37). In the
particular case of wood, the proposed emission factors are obtained as an
average of the values reported for the different types of appliances (i.e.
fireplace, conventional stove, conventional boiler, eco-labelled boiler and
pellet stove; Tables 3-14, 3-17, 3-18, 3-24 and 3-25). The average emission
factors are obtained considering the appliance shares reported by Denier
van der Gon et al. (2015). It is also important to note that wood-related
PM10 and PM2.5 emission factors take into account the condensable
fraction of PM.
The temporal distribution of annual emissions is performed using gridded
daily temporal profiles, which are derived according to the heating degree
day (HDD) concept. The HDD is an indicator used as a proxy variable to
reflect the daily energy demand for heating a building (Quayle and Diaz,
1980). The original expression is reformulated according to Mues et al. (2014) to consider those combustion processes that are not only related to
space heating but also to other activities that remain constant throughout
the year (i.e. water heating, cooking) (Eq. 16):
FDf(x,d)=HDD(x,d)+ff⋅HDD‾(x)(1+ff)⋅HDD‾(x),
where HDD(x,d) is the heating degree-day factor for grid cell
x and day of the year d (0 to 1); HDD‾(x) is the yearly
average of the heating degree-day factor per grid cell x; and ff is a
constant offset that indicates the share of the fuel f that is used for
activities not related to space heating (0 to 1). By default, ff is
considered to be 0 for wood and 0.2 for the other fuels, following the
European household energy statistics reported by Eurostat (2018). HDD(x,d) and HDD‾(x) are estimated as shown in Eqs. (17) and (18) (Quayle and Diaz, 1980):
17HDD(x,d)=max(Tb-T2m(x,d),1)18HDD‾(x)=∑1NHDD(x,d)N,
where Tb is the threshold temperature above which a building needs no
heating (i.e. heating appliances will be switched off) (∘C), T2m(x,d) is the daily mean 2 m outdoor temperature for
grid cell x and day d (∘C), and N is the number of days of the
simulation year (365 or 366). Following Spinoni et al. (2015), who developed
gridded European degree-day climatologies, we assume Tb=15.5∘C, a value also suggested by the UK Met Office. The
HDD(x,d) value increases with increasing difference between
the outdoor and base temperatures. Note that a minimum value of 1 is assumed
instead of 0 to avoid numerical problems.
Two profiles are proposed in HERMESv3_BU for the hourly
distribution of emissions. The first one applies only to residential wood
burning emissions, and it is a combination of existing profiles derived from
citizen interviews performed in Norway and Finland (Finstad et al., 2004;
Gröndahl et al., 2010) as well as from long-term measurements of the
wood burning fraction of black carbon in Athens (Athanasopoulou et al.,
2017). The second profile (applicable to all other fuel types) is equivalent
to the one proposed by Denier van der Gon et al. (2011) for residential
combustion emissions. The wood-related profile presents an intense peak in
the evening hours but not during the morning (Grythe et al., 2019). This
fact is related to a common practice in Europe of using fireplaces and other
types of wood-burning appliances mainly in the evening.
Other mobile sourcesShipping in port areas
Hourly emissions related to fuel combustion processes occurring in main and
auxiliary maritime engines during manoeuvring and hotelling operations in
port areas are estimated as follows (Eq. 19):
Ep,f,i(x,h)=∑v=1n∑e=12Np,v⋅S(x)p,f⋅tp,v,f⋅Pv,e⋅LFv,e,f⋅EFv,e,f,i⋅FM(m)v,p⋅FW(d)p⋅FH(h)v,p,
where Ep,f,i(x,h) is the hourly emissions of pollutant i at port p,
destination grid cell x and hour h during phase f (i.e. manoeuvring, hotelling) (g h-1); Np,v,f(x) is the number of annual operations associated with vessel v (i.e. liquid bulk ship, dry bulk carrier, general cargo, ro-ro,
cruiser, ferry, container, tug or others) at port p (operation yr-1);
S(x)p,f is the spatial weight factor associated with port p and
destination grid cell x during phase f (0 to 1); tp,v,f is the time spent by
vessel v to complete phase f at port p (h); Pv,e is the average power of
vessel v's engine e (1: main; 2: auxiliary) (kW); LFv,e,f is the
average load factor of vessel v's engine e of during phase f (0 to 1);
EFv,e,f,i is the emission factor for pollutant i of vessel v's engine
e during phase f (g kWh-1); FM(m)v,p is the monthly
factor associated with month m, vessel v and port p (0 to 1); FW(d)p is the weekly factor associated with day of the week d and port
p (0 to 1); and FH(h)p is the hourly factor associated with hour h and port
p (0 to 1). The number of vessel categories is n.
The estimation methodology and corresponding emission factors are derived
from the EMEP/EEA (2016) Tier 3 approach (chap. 1.A.3.d, Table 3-10).
Information on the vessel's technical characteristics are obtained from
Trozzi (2010), including (i) the average power of a vessel's engines (as a
function of the gross tonnage) and (ii) the engine type (i.e. slow-speed
diesel, medium-speed diesel, high-speed diesel, gas turbine, steam turbine)
and fuel class (i.e. marine diesel oil, marine gas oil and bunker fuel oil)
assigned to each vessel category. The values of engine load factors and
operation times per phase and vessel are obtained from Entec (2002). The
remaining information, which is port-dependent, needs to be provided by the
user.
The gridded weight factors (S(x)p,f), which are used to spatially
allocate the emissions, are calculated by the model through a spatial
intersection between the destination domain and two shapefiles representing
the areas of manoeuvring and hotelling of each port. Both shapefiles need to
be provided by the user. Figure 7a shows an example of the total NOx
annual emissions (t yr-1) estimated for the Port of Barcelona on a
1km×1km regular grid. Both the manoeuvring and hotelling shapefiles used for
the spatial distribution were digitalized using as a basis an infrastructure
map provided by the Barcelona's Port Authority (APB, personal
communication, 2017). The hotelling layer (in red) consists of a multipolygon
shapefile, each polygon representing one of the docks in which the ships
operate. A specific weight was assigned to each dock as a function of its
usage, allowing a more realistic distribution of the total emissions. On the
other hand, manoeuvring emissions were distributed homogeneously between all
the cells intersected by the corresponding shapefile (light blue layer).
Annual emissions are compared against the results reported by the APB 2013
inventory (APB, 2016) (Fig. 7b). Results show that HERMESv3_BU estimates lower emissions both for NOx and PM10. The difference
can be related to the different years of reference and subsequently the number
of vessel operations considered, as well as to the fact that the APB
inventory assumes a main engine load factor of 20 % during hotelling
operations, while in HERMESv3_BU the load factor is null
following the recommendations in Entec (2002).
Emissions derived from pleasure boat activities can be estimated in
HERMESv3_BU following Eq. (20):
Ei(x,h)=∑b=1nNb(x)⋅Tb⋅Pb⋅LFb⋅EFb,i⋅FM(m)⋅FW(d)⋅FH(h),
where Ei(x,h) is the hourly recreational boat emissions of pollutant
i at destination grid cell x and hour h (g h-1); Nb(x) is the number
of pleasure boats associated with category b and destination grid cell x
(no. boats); Tb is the number of hours that the pleasure
boat of category b is used during 1 year (h); Pb is the engine nominal
power associated with the pleasure boat of category b (kW); LFb is the
engine load factor associated with the pleasure boat of category b (0 to 1);
EFb,i is the emission factor linked to the pleasure boat of category b and
pollutant i (g kWh-1); FM(m) is the monthly factor
associated with month m (0 to 1); FW(d) is the weekly factor
associated with day of the week d (0 to 1); and FH(h) is the hourly
factor associated with hour h (0 to 1). The number of recreational boat categories
is n.
The parameters EFb,i, LFb, Tb and Pb are derived
from the EMEP/EEA (2016) Tier 3 approach (chap. 1.A.5.b, Table 3-11). The
gridded number of pleasure boats (Nb(x)) is computed by combining
official statistics of registered recreational crafts with a raster file
that simulates the distribution of recreational boat activities along the
coast. Both datasets need to be provided by the user. Figure 7c shows the
spatial distribution of Spanish recreational boats along the Costa Brava
region (coast of Catalonia). The spatial proxy is based on the location of
each Spanish marina and associated number of docks, which are also
represented in the map (Fondear, 2019). A raster interpolation was performed
to simulate the activities of recreational boats nearby the marinas,
considering the number of docks as a weight and assuming that no operations
are happening beyond the territorial waters (i.e. 12 nautical miles away
from the coastline). A hot spot region is observed near Cap de Creus
(headland located at the far northeast of Catalonia) due to the presence of
the largest Spanish marina (Empuriabrava, with 5000 docks). The total number of
recreational boats per type of boat were derived from ICOMINA (2016). Figure 7d shows the total annual emissions estimated with HERMESv3_BU for Spain. To the authors' knowledge, no other national emission
inventory is currently available for this pollutant source. Emissions are
contrasted against an inventory of pleasure boats estimated for the Baltic
Sea (Johansson et al., 2020) to assess that the results are within the same
range of magnitude. HERMESv3_BU reports higher NOx (4.1
times) and lower CO and NMVOC (0.7 and 0.5 times, respectively) emissions.
This is probably due to the different fleet characteristics of each region.
While in Spain more than 40 % of the boats are related to large diesel
motor sail boats, in the Baltic Sea region this category only accounts for
less than 15 % of the total fleet.
Agricultural machinery
Emissions related to the use of agricultural equipment (i.e. two-wheel
tractors, agricultural tractors and harvesters) are estimated following Eq. (21):
Ei(x,h)=∑e=1nNe(x)⋅Te⋅Pe⋅LFe⋅EFe,i(Pe)⋅(1+DFe,i)⋅FMe(m)⋅FW(d)⋅FH(h),
where Ei(x,h) is the hourly emissions of pollutant i at destination
grid cell x and hour h (g h-1); Ne(x) is the number of
agricultural equipment associated with category e and destination grid cell x
(no. equipment); Te is the number of hours that the
equipment of category e is used during 1 year (h); Pe is the engine
nominal power associated with the equipment of category e (kW); LFe is
the engine load factor associated with the equipment of category e (0 to 1);
EFe,i(Pe) is the emission factor linked to the agricultural
equipment of category e and pollutant i (g kWh-1) as a function of the
engine nominal power Pe; DFe,i is the deterioration adjustment
factor for the equipment of category e and pollutant i; FMe(m) is the monthly factor associated with month m and equipment category
e (0 to 1); FW(d) is the weekly factor associated with day d (0 to 1);
and FH(h) is the hourly factor associated with hour h (0 to 1). The
number of agricultural equipment categories is n.
HERMESv3_BU takes as an input the total number of
agricultural equipment and corresponding engine nominal power registered at
the NUTS 3 level. The spatial allocation of this data onto the destination
domain is performed considering the CLC non-irrigated arable land category
as a proxy and applying the spatial operations described in Sect. 2.4. Both the emission and deterioration adjustment
factors are derived from the EMEP/EEA (2016) Tier 3 methodology (chap. 1.A.4, Tables 3-6 and 3-11). The load factor adjustments proposed by
default are the ones reported by Winther and Nielsen (2006) (i.e. 0.4 for
two-wheel tractors, 0.5 for agricultural tractors and 0.8 for harvesters).
Emissions from two-wheel tractors and agricultural tractors are
disaggregated at the monthly level considering the crop calendars associated with the cultivation operation. In the case of the harvester emissions, the
monthly factors are associated with the harvesting period of the non-irrigated
arable crops (Sacks et al., 2010).
Landing and take-off cycles at airports
Hourly aircraft landing and take-off (LTO) emissions occurring at airports
are estimated according to Eq. (22):
Ea,f,i(x,h)=∑p=1nN(m)a,p,f⋅S(x)a,f⋅EFp,f,i⋅FW(d)a⋅FH(h)a,f,
where Ea,f,i(x,h) is the hourly emissions of pollutant i at airport
a, destination grid cell x and hour h during phase f (i.e. approach, landing,
taxi-in, post taxi-in, pre taxi-out, taxi-out, take-off and climb-out) of
the LTO cycle (g h-1); N(m)a,p,f is the number of monthly
operations associated with aircraft p at airport a during phase f for month m (operations per month); S(x)a,f is the spatial weight factor associated with
airport a and destination grid cell x during phase f (0 to 1); EFp,f,i is
the emission factor for pollutant i associated with aircraft p and phase f (grams per operation); FW(d)a is the weekly factor associated with
day d and airport a (0 to 1); and FH(h)a,f is the hourly factor associated with hour h phase f and airport a (0 to 1).
Depending on the LTO phase, different emission processes and subsequently
emission factors are considered. For taxi-in, taxi-out, take-off, climb out
and approach operations the emission factors correspond to the fuel
combustion in the main engines (EFmainp,f,i) (Eq. 23):
EFmainp,f,i=Ep⋅ta,p,f⋅EFenginep,f,i,
where Ep is the number of engines associated with aircraft p (engine);
ta,p,f is the time spent by aircraft p to complete phase f at airport a
(s); and EFenginep,f,i is the emission factor for pollutant i of the
main engine associated with aircraft p and phase f (grams per second per engine per operation). The number of engines and emission factors
associated with each aircraft category are derived from the EMEP/EEA (2016)
Tier 3 approach (Annex 5 spreadsheets to the chap. 1.A.3.a). For taxi-in
and taxi-out, times are also obtained from the same source, whereas for the
other operations (take-off, climb out and approach) different values are
assumed as a function of the type of aircraft (i.e. wide-body planes,
narrow-body planes, business planes and light planes with piston engines)
(Dellaert and Hulskotte, 2017).
For landing operations, particulate-matter emission factors
(EFwearp,i) related to the wear of the aircraft brakes and tyres are
considered (Eq. 24) (Morris, 2006):
EFwearp,i=MTOWp⋅EFweari,
where MTOWp is the maximum take-off weight associated with aircraft p (t)
and EFweari is the emission factor for pollutant i (grams per tonne per operation), which is taken from Morris (2006).
Finally, emissions during the pre taxi-out and post taxi-in operations are
linked to the fuel combustion in the auxiliary power units (APUs). The
corresponding emission factors (EFauxp,i) are estimated as follows
(Eq. 25) (Watterson et al., 2004):
EFauxp,i=t_apua,p,f⋅EFapup,i,f,
where t_apua,p,f is the APU running time associated with
aircraft p, phase f and airport a (s) and EFapup,i is the emission
factor for pollutant i of the APU engine associated with aircraft p and phase
f (grams per second per operation). Data on the type of APU installed in
each type of aircraft and corresponding emission factors are obtained from
Watterson et al. (2004).
The spatial weight factors (S(x)a,f) to spatially distribute the
estimated emissions also vary according to the LTO cycle's phase. Taxi-in,
post taxi-in, pre taxi-out and taxi-out emissions are assigned at the ground
level; taking into account digitized airport areas. Emissions are mapped to
the grid cells that spatially intersect with the airport polygons, which
need to be provided by the user in a shapefile. Take-off and landing
emissions are also allocated at the ground level, but they are horizontally
distributed across the grid cells that intersect with the digitized runways
(also provided by the user in a shapefile). The user has the option of
defining specific weights to each runway as a function of its usage (for
departures, arrivals or both). Finally, emissions from approach and
climb-out operations are allocated on a 3D basis, considering the trajectory
outlined between the origin or end of the runway and 1000 m of altitude. This
operation is performed in HERMESv3_BU using as input a
shapefile representing the air trajectories and information on the approach
and take-off angles for each runway.
Speciation
This process disaggregates the calculated primary pollutants (i.e. NOx,
NMVOC, PM2.5) into the more detailed species defined by the chemical
mechanism of interest. Specific speciation CSV files, which contain a set
of profiles with numerical factors for converting the primary pollutants
(e.g. NOx) to output model species (e.g. NO, NO2 and HONO), are
assigned to each pollutant sector. The number of speciation profiles
considered varies according to the pollutant sector of interest. For
instance, in the case of residential combustion the number of speciation
profiles proposed is equal to the number of fuel types considered (e.g.
natural gas, biomass), whereas in the case of road transport, specific
profiles are assigned to each vehicle category. The assignment between
profiles and pollutant categories is performed following the
cross-referencing system described in Sect. 2.3.
The speciation factors are mass-based (i.e. grams of chemical species per gram of source pollutant) for NOx and PM2.5. For NOx, emissions are divided into 90 % NO and 10 % NO2 for all sectors
(Houyoux et al., 2000) except for road transport, where vehicle-dependent
speciation factors are derived from EMEP/EEA (2016) (chap. 1.A.3.b.i–iv,
Table 3.87) and the work by Carslaw et al. (2016) and Rappenglueck et al. (2013). For PM2.5, the speciation factors are derived from multiple
sources including EMEP/EEA (2016) and the SPECIATE (Simon et al., 2010) and
SPECIEUROPE (Pernigotti et al., 2016) databases. For the rest of the
calculated primary pollutants (i.e. CO, NH3, SO2, PM10) a
default speciation factor of 1 is proposed for all sources. All computed
gas-phase species are converted from mass to moles using a molecular weight
CSV file that is included in the model database (built-in data file).
In the case of NMVOC, the speciation factors used by HERMESv3_BU are mol-based (mol of chemical species per gram of source pollutant) and are estimated following the expression proposed by Li
et al. (2014) (Eq. 26):
SFe‾,p=∑j=1nXj,pMWj⋅Cj,e‾,
where Xj,p is the mass fraction of chemical compound j to total NMVOC
emissions for speciation profile p, MWj is the molecular weight of
chemical compound j and Cj,e‾ is the mole-based conversion factor
of chemical compound j to destination chemical species e‾. Xj,p
values are obtained from multiple sources including EMEP/EEA (2016) and the
NMVOC SPECIATE database (Simon et al., 2010), and MWj and
Cj,e‾ are obtained from the mechanism-dependent mapping tables
proposed in Carter (2015). The number of individual chemical compounds
considered in the total NMVOC is n.
For reference, the HERMESv3_BU test case input database
currently includes speciation profiles for the Carbon Bond 05 (CB05)
(Whitten et al., 2010) gas-phase mechanism and the fifth-generation aerosol
module (AERO5) (Roselle et al., 2008). Following the formats of the
associated input files, the user can create its own speciation profiles for
other mechanisms of interest, also using alternative sources of
information. As an illustration, Table 2 shows some examples of proposed
CB05 and AERO5 speciation profiles for different pollutant sectors. As
mentioned before, HERMESv3_BU allows using specific profiles
for each of the source categories included in the different sectors. This
feature enables the user to consider key factors influencing the splitting
of primary pollutants into chemical mechanism species such as (i) fuel type
(wood, natural gas) for NMVOC emissions in the residential sector (Simon et
al., 2010), (ii) vehicle type (passenger car, motorcycle) and vehicle fuel
(diesel, gasoline) for road transport NOx (Carslaw et al., 2016;
Rappenglueck et al., 2013), (iii) type of process (combustion, road wear)
for road transport PM2.5 emissions (EMEP/EEA, 2016) (chap. 1.A.3.b.i–iv, Table 3.88 and chap 1.A.3.b.vi, Table 3-11), and (iv) animal
type (pigs, cattle) for livestock NMVOC emissions (EMEP/EEA, 2016) (chap. 3.B, Table A1.2).
Example of speciation profiles included in
HERMESv3_BU for speciating primary emissions to CB05 and
AERO5 mechanisms. Different pollutant sectors and categories are shown to
illustrate the degree of specificity allowed by the model. Speciation
factors are mole-based (mol of chemical species per gram of source pollutant) for NMVOC and mass-based (grams of chemical species per gram of source pollutant) for NOx, CO, SOx,
NH3 and PM2.5. Symbol “–“
denotes that no primary emissions are considered for that pollutant. All
CB05 and AERO5 species are defined in Table A2 of Guevara et al. (2019a).
Residential and Road Livestock commercial combustion transport PrimaryCB05/AERO5BiomassNaturalPassenger carPassenger carPetrol motorcycleRoadCattlePigsemissionsspeciesgaspetrol Euro 5diesel Euro 5Euro 4wearNOxNO0.90.90.970.670.96–11NO20.10.10.0130.3130.023–00HONO000.0080.0170.008–00COCO11111–––SOxSO211111–––NH3NH311111–11NMVOCPAR1.6E–034.5E–023.1E–022.9E–022.6E–02–3.9E–023.3E–02OLE9.5E–040.0E+001.5E–031.7E–031.8E–03–2.3E–050.0E+00TOL5.6E–054.9E–041.6E–033.1E–042.1E–03–2.4E–046.2E–04XYL6.1E–030.0E+001.4E–038.8E–041.3E–03–0.0E+000.0E+00FORM2.2E–046.1E–039.7E–044.3E–031.4E–03–0.0E+000.0E+00ALD25.4E–050.0E+000.0E+000.0E+000.0E+00–1.6E–033.0E–03ETH0.0E+000.0E+002.6E–033.9E–033.1E–03–0.0E+000.0E+00ISOP0.0E+000.0E+000.0E+000.0E+000.0E+00–0.0E+000.0E+00MEOH0.0E+000.0E+000.0E+000.0E+000.0E+00–0.0E+000.0E+00ETOH0.0E+000.0E+000.0E+000.0E+000.0E+00–2.2E–050.0E+00ETHA0.0E+000.0E+001.1E–031.1E–045.5E–04–0.0E+000.0E+00IOLE0.0E+000.0E+003.0E–049.3E–052.6E–04–0.0E+000.0E+00ALDX5.6E–040.0E+001.2E–041.7E–032.5E–04–1.0E–041.0E–03TERP0.0E+000.0E+000.0E+000.0E+000.0E+00–7.3E–060.0E+00BENZENE0.0E+001.2E–037.2E–042.5E–048.7E–04–3.8E–052.6E–05PM2.5POC0.430.490.450.5250.6250.13500PEC0.070.0670.150.150.250.010600PNO300000000PSO400000000PMFINE0.50.4430.40.3250.1250.854411Technical implementation
HERMESv3_BU is coded using Python 3.7.X and requires numpy
(> D1.16.0), NetCDF4 (> D1.3.1) under HDF5 in parallel
mode, pandas (> D0.22.0), geopandas (> D0.4.0), pyproj
(> D1.9.5.1), configargparse (> D0.11.0),
cf_units (> D1.1.3), timezonefinder (> D2.1.0), mpi4py (> D3.0.0), pytest (> D3.6.1), shapely
(D >1.6.4), scipy (D >0.14.1) and rasterio
(D >1.0.21) Python libraries.
The emission core of each pollutant sector (i.e. calculation, temporal and
spatial distribution and speciation) is parallelized using one of the
following strategies:
road link partition – this strategy is applied to the road transport sector.
The original digitalized road transport network is split according to the
number of processors to be used. Each processor computes the emissions of
the road links subgroup that has been assigned with. The partition of the
data is automatically balanced with respect to the number of processors
used.
point source partition – this strategy is used for the point source sector.
The approach is equal to the one described above, but in this case the
partition is applied to the point source facilities.
polygon partition – this strategy is applied to the shipping and aviation
sectors. Each processor is responsible for calculating the emissions of a
subset of the total number of infrastructures (i.e. ports or airports)
included in the working domain.
grid partition – this strategy is applied to all the sectors that are
computed at the destination grid cell level once the original input activity
data have been mapped onto the working domain (i.e. agricultural sources,
residential and commercial combustion, recreational boats and gasoline
evaporative emissions). The destination working domain is divided into
subgroups of row-major consecutive cells. Each subgroup or chunk contains
only those grid cells that have activity data information. The number of
divisions is balanced and equal to the number of processors to be used.
In all cases, emissions are computed independently in each processor and for
each partition (i.e. group of road links, point sources, polygons or grid
cells). As a result of the emission calculation process, each individual
computational processor ends up containing the gridded hourly estimated
emissions for the corresponding section of the working domain. Once this
status is reached for all the processors (i.e. emissions have been estimated
for all partitions and pollutant sectors), HERMESv3_BU starts
the execution of the writing process. The model can perform this task in
serial or in parallel mode, regardless of the processors used for the
emission calculation process. If the user configures the model to run with
more than one writing processor, HERMESv3_BU will decompose
the destination working domain into horizontal sections (as many as
processors selected), maintaining each row as indivisible. Each processor will
first gather the estimated emissions of the corresponding writing section
and then compute the total sum per grid cell, vertical layer and time step.
During this process, unit conversions will also take place. Finally, each
individual section will write the emission 4D array (hour, vertical layer,
longitude, latitude) onto the corresponding memory section of the NetCDF
output file. When only one writing processor is used, the same process is
performed but without applying the aforementioned domain partition.
The number of processors used to calculate each pollutant sector and to
perform the writing function are defined by the user in the general
configuration file (see Sect. 2.2). The
distribution of the defined computational resources is performed using a
sector manager function under the framework of the Message-Passing Interface
(MPI) protocol (mpi4py Python library). MPI creates a global communicator
and the sector manager splits it into a group of sub-communicators (as many
as pollutant sectors involved). This allows HERMESv3_BU to
exclusively assign each group of processors to a specific sector and
therefore isolate the corresponding calculation processes.
Figure 8 shows an illustrative example of the emission calculation and
writing parallel implementations. The example considers four pollutant
sectors: a point-type sector (e.g. point sources), a line-type sector (e.g.
road transport), a polygon-type sector (e.g. aviation) and a grid-type
sector (e.g. fertilizers). A total of 17 processors are defined for the
emission calculation process (Fig. 8a), which are split as follows: seven for
point sources, three for road transport, three for aviation and four for fertilizers.
Each processor is assigned to an individual element (or group of elements)
of the corresponding pollutant sector and performs the calculation of the
gridded hourly emissions (Fig. 8b). Those cells that are not intersected
with any emission source are left without information. Once all the emission
calculation processors have finished, the writing process begins, in this
case using a total of three processors. The working domain is accordingly split
into three horizontal sections, the first two consisting of four completed rows and
the last one of two (Fig. 8c). For each grid cell of each writing section,
the corresponding processor gathers and sums the emissions estimated for all
the different sources (Fig. 8d). The information stored in each writing
processor is then written in the corresponding section of the output NetCDF
file.
Illustrative example of how HERMESv3_BU
parallelizes the calculation and writing processes and distributes the
computational resources. (a) A total of 17 processors are selected to
estimate emissions from point sources, road transport, aviation and an area
source sector (e.g. residential combustion). (b) The calculation processors
are distributed as follows: seven for the point sources (one processor per
facility), three for road transport (one processor per road link), three for aviation
(one processor per polygon) and four for residential combustion (one processor per
subgroup of cells equally balanced, i.e. 10 cells per subgroup). Each
processor estimates the emissions of the corresponding partitioned element
and maps them onto the intersected grid cells (highlighted in grey). (c) During the writing process, HERMESv3_BU decompose the
destination working domain into three horizontal sections (equivalent to the
number of writing processors selected), maintaining each row as indivisible.
(d) For each horizontal section, the model gathers the estimated gridded
emissions of all the sources involved in that section and then computes the
total sum per grid cell. Once this operation is finished, the resulting
emissions are dumped into the corresponding section of the output file.
The supercomputer MareNostrum4, hosted by the BSC (http://www.bsc.es/marenostrum/marenostrum, last access: February 2020), was used to test the capability of
HERMESv3_BU to scale up the emission calculation processes.
HERMESv3_BU was executed using a number of cores from 1 to
256, doubling the number in each successive test. The proposed testing
domain was a Lambert conformal conic domain of 4 km by 4 km with 397 rows,
397 columns and 15 vertical layers covering the Iberian Peninsula (Fig. 6a). Hourly emissions for Spain were estimated for 24 time steps and for
each one of the pollutant sectors described in Sect. 3.
As shown in Fig. 9, for all sectors the computational time decreases as the
number of cores is increased. Traffic is the pollutant sector that requires
the largest amount of time to be completed, the total execution time
decreasing from 48 585.5 to 450.3 s when changing from 1 to 256 processors.
Similar time reduction rates are observed for other sectors when comparing
the results obtained with 1 and 256 processors: livestock (from 17 530.2
to 53.2 s), point sources (from 5107.7 to 75.4 s), residential and
commercial combustion (from 3230.7 to 27.0 s), and crop fertilizers (from
2814.7 to 39.1 s). For the rest of the sectors (i.e. agricultural
machinery, crop operations, recreational boats, shipping in ports),
execution times are already considerably low when only using one processor
(between 19.5 and 326.5 s). Note that for aviation and shipping in port
areas, the number of processors with which HERMESv3_BU can be
executed is limited to the number of infrastructures considered in the
domain of study (i.e. 47 airports and 48 ports in this example). Traffic is by far the most time-demanding pollutant sector
due to a combination of several facts:
a large amount of sources – the
sector considers a road network with more than 100 000 road links and
includes 491 vehicle categories;
multiple emission processes – the
sector estimates emissions derived from hot- and cold-exhaust gases, tyre,
brake and road wear, resuspension, and gasoline evaporation;
meteorologically dependent functions – three of these processes are estimated
using meteorologically dependent parameterizations (i.e. cold-exhaust and
gasoline evaporation take into account outdoor temperature, while
resuspension considers precipitation information).
Computational times (s) obtained from the scalability test
performed with HERMESv3_BU over Spain. Each line represents
the amount of time needed to complete the emission estimation process of
each pollutant sector as a function of the number of processors used
(x axis, represented with a base 2 logarithmic scale).
The execution of each sector is performed in parallel (not sequentially).
Therefore, the total execution time of HERMESv3_BU for the
emission calculation process is equal to the sector's largest execution
time. Following with the proposed example, if the user wants to perform the
emission calculation process in less than 8 min, a total of 298 processors should be used with the following distribution: 256 for traffic,
16 for point sources, 12 for livestock, 4 for residential and commercial
combustion, 4 for crop fertilizers and 1 for each one of the other sectors.
The computational time needed to gather and write all the estimated
emissions into a NetCDF output file is very low compared to the emission
estimation time (i.e. between 13 and 25 s depending on the number of
writing processors selected, not shown).
Conclusions
This paper presents HERMESv3_BU, a stand-alone and
open-source emission model that estimates high-resolution spatial and hourly
bottom–up anthropogenic emissions for air quality modelling. The tool
combines state-of-the-art estimation approaches with local activity and
emission factors along with meteorological data.
The main characteristics of HERMESv3_BU are as follows:
multiple map projections and model grids – the model supports emission
calculations in regular and rotated lat–long, Lambert conformal conic and
Mercator map projections. Users can freely define their working domain
introducing information on the starting x–y coordinates, number of grid
cells in each direction and physical size of the grid cells.
multiple pollutant sources – the model includes bottom–up emission estimation
methodologies for several anthropogenic emission sources, including point
sources, road transport (hot- and cold-exhaust, wear and resuspension,
gasoline evaporation), agriculture (i.e. fertilizer application, livestock,
crop operations), residential and commercial combustion, and other mobile
sources (i.e. shipping in port areas, recreational boats, agricultural
machinery, landing and take-off cycles at airports). Users can estimate
emissions for each individual source or a combination thereof.
subsetting of pollutant categories and regions – for each pollutant sector,
users can select individual pollutant categories to be considered during the
emission calculation process (e.g. 491 vehicle categories for road
transport, 28 crop types for fertilizer application). Similarly, users can
define a region of interest within the working domain (i.e. administrative
areas or self-defined polygons) for which the emissions should be
calculated. These functionalities can become useful when studying the
contribution of certain categories or regions to total emissions (e.g. diesel
vehicles from a selected province) or when performing source attribution
modelling studies.
emission estimation approaches – HERMESv3_BU integrates
estimation methodologies and emission factors that are mostly based on (but
not limited to) the approaches reported by the European EMEP/EEA (2016)
emission inventory guidebook. The model also includes
meteorologically dependent functions to take into account the dynamical
component of the emission processes, both in terms of spatial and temporal
allocation (e.g. plume rise calculations for point sources, effect of
temperature and wind speed on the volatilization of agricultural NH3).
applicability – HERMESv3_BU is designed so that it can be
applicable to any European country or region where the required input data are
available. Global and regional state-of-the-art datasets are considered in
the model to decrease the amount of information that needs to be provided by
the user, including land use data (CORINE Land Cover inventory), livestock
distribution (Gridded Livestock of the World), population maps (Global Human
Settlement Layer) and soil properties (World Soil Information database). The
application of the tool to non-European regions could be also possible, but
it would involve reviewing in much more detail the emission factors databases
and estimation methodologies that are proposed by default in the model.
spatial operations – HERMESv3_BU contains a set of GIS
functionalities that helps users manipulate and generate georeferenced data
files related to emissions modelling (i.e. Esri shapefiles and Geotiff
rasters). The operations included in the model allow developing
automatically individual spatial surrogates or remapping spatial data from
one gridded domain to another, among other things.
emission outputs compatible with multiple air quality models – the output
NetCDF emission files follow the convention of multiple chemical transport
models, including CMAQ, WRF-CHEM and MONARCH. In the particular case of road
transport, the resulting link-level emissions can be also used as input for
the R-LINE Gaussian dispersion model.
parallel implementation – the parallelization of the emission calculation and
writing processes allows scaling up the execution time of the model, which
may be relevant when using the model for air quality forecasting
applications.
Several emission outputs obtained with HERMESv3_BU are
provided in this paper and compared to other existing emission datasets to
illustrate its potential. Future work will focus on performing a full
evaluation of the emission results obtained for Spain through comparisons
with other inventories (e.g. EDGAR, EMEP) and using air quality modelling.
HERMESv3_BU currently experiences some limitations. The first
one is the non-inclusion of a submodule to estimate NMVOC emissions related
to the use of solvents. For this source category, activity data are very
uncertain and typically not recorded in statistics (e.g. domestic and
industrial use of solvent products). For now, and as an alternative,
emissions reported by other existing inventories will be used through the
application of HERMESv3_GR. The second limitation is related
to the suitability of the proposed emission estimation approaches for its
application over different regions. For instance, in the case of residential
combustion emissions, HERMESv3_BU assumes that wood
combustion only occurs in rural areas, which is the case for Spain.
Nevertheless, this assumption may be wrong for other countries such as
Norway (López-Aparicio et al., 2017). Similarly, for the agriculture sector
it is assumed that the temporal distribution of fertilizer application is
not affected by any policy restriction (i.e. closed periods). While this is
again true for Spain, there are some European countries (e.g. Germany) in
which the application of fertilizer is prohibited during certain periods
(Backes et al., 2016). In this sense, it is expected that the emission
approaches will be refined in future versions of HERMESv3_BU.
Regarding greenhouse gases, estimated emissions are currently only related
to combustion processes. Emissions from non-combustion sources (e.g.
CH4 emissions from enteric fermentation processes in the livestock
sector) are governed by complex processes that need to be described using
specific estimation approaches, such as the ones reported in the
Intergovernmental Panel on Climate Change (IPCC) inventory guidelines (IPCC,
2006). The implementation of such processes in HERMESv3_BU is
a task that we plan to address in future versions of the system. One last
aspect to consider is the availability and accessibility of all the required
input data needed to run the model, which may limit the usability of the
tool in certain regions. It is important to note that some of the required
data can be obtained from European homogenized databases such as Eurostat
(https://ec.europa.eu/eurostat/, last access: February 2020). Moreover, the continuous
growth of the open data movement can also help to overcome this limitation.
One example for this is the Open Transport Map (http://opentransportmap.info/, last access: February 2020), an application for accessing daily traffic
volumes for the main European road network.
HERMESv3_BU represents an effort to integrate and combine in
a flexible and transparent way state-of-the-art methods for estimating high-resolution bottom–up emissions from multiple anthropogenic sources. The
purpose of the model is to serve as an emission tool for multiple
applications, including air quality research and environmental management.
Relationship between crop types and CORINE Land Cover
(CLC) land use categories.
Crop categoryCLC codeCLC descriptionAlfalfa12Non-irrigated arable landAlmond16Fruit trees and berry plantationsApple16Fruit trees and berry plantationsApricot16Fruit trees and berry plantationsBarley12Non-irrigated arable landCherry16Fruit trees and berry plantationsCotton13Permanently irrigated landFig16Fruit trees and berry plantationsGrape15VineyardsLemon16Fruit trees and berry plantationsMaize12Permanently irrigated landMelon16Fruit trees and berry plantationsOats12Non-irrigated arable landOlive17Olive grovesOrange16Fruit trees and berry plantationsPea12Non-irrigated arable landPeach16Fruit trees and berry plantationsPear16Fruit trees and berry plantationsPotato13Permanently irrigated landRice14Rice fieldsRye12Non-irrigated arable landSunflower12Non-irrigated arable landTangerine16Fruit trees and berry plantationsTomato13Permanently irrigated landTriticale12Non-irrigated arable landVetch12Non-irrigated arable landWatermelon16Fruit trees and berry plantationsWheat12Non-irrigated arable land
Classification of HERMESv3_BU input data
files per pollutant source.
SectorUser-dependent filesBuilt-in filesExternal filesPoint sources– Point sources1 – Temporal profiles – Speciation profiles – Meteorological files (only if plume rise is activated) (hourly 4D temperature, 4D U and V wind components, PBL height, Obukhov length, friction velocity)2Road transport– Road network – Temporal profiles – Fleet composition profiles– Emission factors – Speciation profiles– ERA5 meteorological files (hourly 2 m temperature and precipitation)3Residential and commercial combustion– Energy consumption at NUTS3– Emission factors – Temporal profiles – Speciation profiles– JRC global human settlement population grid – JRC global human settlement city model grid – ERA5 meteorological files (daily 2 m temperature)3Shipping in ports– Hotelling and manoeuvring – Vessel operations– Emission factors – Vessel technology – Load factorAviation (LTO)– Airports, runways and air trajectories – Plane operations – Temporal profiles– Emission factors – Speciation profilesRecreational boats– Recreational boat units, load factor, working hours, nominal engine power – Spatial distribution– Emission factorsLivestock– Livestock split and adjusting factors– Emission factors – Speciation profilesFAO gridded livestock of the world version 3 – ERA5 meteorological files (daily 2 m temperature and 10 m wind speed)3Agricultural crop operations– Emission factors – Temporal profiles – Speciation profiles– CORINE Land Cover land usesAgricultural machinery– Equipment units and nominal engine power and working hours– Emission factors – Deterioration factors – Temporal profiles – Speciation profiles– CORINE Land Cover land usesAgricultural fertilizers– Fertilizer rate – Crop calendar – Ratio of cultivated to fertilized area – Share of fertilizer type per crop– Fertilizer-related emission factor parameter – Temporal profiles – Speciation profiles– ISRIC soil pH and CEC data – CORINE Land Cover land uses – ERA5 meteorological files (daily 2 m temperature and 10 m wind speed)3
1 Fonts are used to specify the file format of each dataset: shapefile, CSV, NetCDF, raster. 2 These meteorological parameters are not provided by ERA5 and therefore need to be derived from other models. 3 ERA5 is proposed since it is open data. Users can alternatively use the outputs from other meteorological models
Code availability
The HERMESv3_BU code package is available at the following
gitlab repository: https://earth.bsc.es/gitlab/es/hermesv3_bu (last access: February 2020) (10.5281/zenodo.3521897, Guevara et al., 2019b). A wiki of the model with further instructions, a
detailed description of the configuration and input files needed to run the
model, and a test case is also included in the gitlab repository. The
required libraries need to be installed by the user in the computer
infrastructure where the model is planned to be run.
Author contributions
MG conceived and coordinated the development of
HERMESv3_BU. MG and MP prepared the
input databases, performed software tests and ran the experiments to obtain
the emission results presented. CT developed the
HERMESv3_BU code and ran the experiments to test the
performance of the parallel implementation. OJ and CPG helped conceive HERMESv3_BU and
supervised the work. MG prepared the paper with contributions
from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The research leading to these results has received funding from the Ministerio
de Ciencia, Innovación y Universidades (MICINN) as part of the PAISA
project CGL2016-75725-R and the BROWNING project RTI2018-099894-B-I00.
Carlos Pérez García-Pando acknowledges long-term support from the
AXA Research Fund, as well as the support received through the Ramón y
Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy
and Competitiveness. The authors are thankful to the Spanish Research Centre
for Energy, Environment and Technology (CIEMAT) and the Royal Automobile
Club of Spain (RACC) for sharing the databases of power plants and road
transport emission measurements, respectively. All the simulations were
performed in the Marenostrum4 supercomputer, hosted by the Barcelona
Supercomputing Center.
Financial support
This research has been supported by the Ministerio de Ciencia, Innovación y Universidades (grant no. CGL2016-75725-R), the Ministerio de Ciencia, Innovación y Universidades (grant no. RTI2018-099894-B-I00), the Ministerio de Ciencia, Innovación y Universidades (grant no. RYC-2015-18690), and the AXA Research Fund (grant no. AXA Research Fund).
Review statement
This paper was edited by Havala Pye and reviewed by Bok H. Baek and one anonymous referee.
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