3.2.1 Hot and cold exhaust

Hot-exhaust emissions are estimated following Eq. (4):

where Ehot_l,i(h) is the hourly hot-exhaust emissions of pollutant i at road link l and hour h (g h⁻¹); AADT_v,l is the annual average daily traffic for vehicle category v at road link l (no. vehicles per day); L_l 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):

where Ecold_l,i(h) is the hourly cold-exhaust emissions of pollutant i at road link l and hour h (g h⁻¹); ${\mathrm{Ehot}}_{l,i,v}\left(h\right)$ is the hourly hot-exhaust emissions of pollutant i for vehicle category v at road link l and hour h (g h⁻¹); β_i,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 ${\mathrm{Qcold}}_{l,i,v}\left(V\right(h{)}_l$, 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 β_i,v(T(h)_l ) and ${\mathrm{Qcold}}_{l,i,v}\left(V\right(h{)}_l,T(h{)}_l$) 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 NO_x 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 PM_2.5 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 (1 km×1 km) (kg h ⁻¹, Fig. 5b). The total annual NO_x 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 NO_x and PM₁₀, respectively), (iii) the use of COPERT 5 real-world adjusted NO_x 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.

Figure 5Hourly PM_2.5 road transport emissions estimated for an area of Barcelona city (09:00 UTC) at (a) the road link level (kg km⁻¹ h⁻¹) and (b) grid cell level (1 km×1 km) (kg h⁻¹) (sources of the ArcGIS Online basemap: Esri, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo and the GIS user community). (c) Barcelona city total annual NO_x and PM₁₀ road transport emissions (t yr⁻¹) estimated by HERMESv3_BU and reported by the Barcelona City Council (AB, 2015). Maps in (a) and (b) were created using ArcGIS^® software by Esri. ArcGIS^® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights reserved. For more information about Esri^® software, please visit https://www.esri.com (last access: February 2020).

3.2.2 Non-exhaust (wear and resuspension)

HERMESv3_BU also estimates non-exhaust PM₁₀ and PM_2.5 traffic emissions, including road surface, tyre and brake wear, and resuspension. Emissions derived from processes of abrasion are estimated following Eq. (6):

where Ewear_l,i(h) is the hourly emissions of pollutant i at road link l and hour h (g h⁻¹); AADT_v,l is the annual average daily traffic for vehicle category v at road link l (n d⁻¹); L_l is the length of the road link l (km); EFwear_v,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):

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) (EFresus_v,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):

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).

3.2.3 Gasoline evaporation

NMVOC evaporative diurnal emissions are considered in HERMESv3_BU as follows (Eq. 9):

where E_i(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.

3.3.1 Fertilizer application

Hourly and spatially disaggregated NH₃ emissions from agricultural fertilizers are estimated following the expression reported by Paulot et al. (2014) (Eq. 10):

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); C_c 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 (A_c(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):

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); β_a,c is the fraction of fertilizer applied to crop c at stage a (1: planting; 2: at growth; 3: after harvest); τ_c,a is the optimal application date for crop c at stage a (Julian day, $\mathrm{1}:\mathrm{365}/\mathrm{366}$); σ_c,a is the deviation around date τ_c,a (number of days); and d is the day of the year (Julian day, $\mathrm{1}:\mathrm{365}/\mathrm{366}$).

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).

Figure 6Spanish 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).

3.3.2 Livestock

Hourly gridded NH₃ emissions derived from manure management activities are estimated according to the expression reported by Paulot et al. (2014) (Eq. 12):

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); Γ_l is the nitrogen (N) excretion rate for livestock l (grams of N per head); β_l is the fraction of total ammoniacal nitrogen (TAN) content of the excreta from livestock l (0 to 1); γ_p,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); EF_a,l is the emission factor for livestock l and activity a (g NH₃ g N⁻¹); $\mathrm{FD}(x,d{)}_{a,l}$ 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 ($\mathrm{FD}(x,d{)}_{a,l}$) 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):

where $T(x,d{)}_{\mathrm{housing},l}$ is the barn temperature associated with the housing of livestock category l at destination grid cell x and day d (^∘C) and $V(x,d{)}_{\mathrm{housing},l}$ 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 PM_2.5 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 4 km×4 km 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.

3.3.3 Crop operations

Particulate matter emissions released during soil cultivation and crop harvesting activities are estimated following Eq. (14):

where E(x,h) is the hourly PM₁₀orPM_2.5 emissions at destination grid cell x and hour h (g h⁻¹); A_c(x) is the annual cultivated area of crop c at destination grid cell x (ha yr⁻¹); EF_c,o is the emission factor for crop c and operation o (g PM₁₀orPM_2.5 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).

3.5.1 Shipping 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):

where $E_{p,f,i}(x,h)$ 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); $N_{p,v,f}\left(x\right)$ 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); $t_{p,v,f}$ is the time spent by vessel v to complete phase f at port p (h); P_v,e is the average power of vessel v's engine e (1: main; 2: auxiliary) (kW); ${\mathrm{LF}}_{v,e,f}$ is the average load factor of vessel v's engine e of during phase f (0 to 1); ${\mathrm{EF}}_{v,e,f,i}$ 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 NO_x annual emissions (t yr⁻¹) estimated for the Port of Barcelona on a 1 km×1 km 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 NO_x 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).

Figure 7(a) Annual NO_x emissions (t yr⁻¹) for the Port of Barcelona calculated on a regular 1 km by 1 km grid. Dark red and light blue layers represent the spatial proxies used to allocate hotelling and manoeuvring emissions, respectively. (b) Comparison between annual NO_x and PM₁₀ of Barcelona's port emissions (t yr⁻¹) estimated with HERMESv3_BU and reported by the Barcelona Port Authority inventory (APB, 2016). (c) Spatial distribution of Spanish marinas (with the associated number of docks) and recreational boats along the Costa Brava region (coast of Catalonia). (d) Comparison between annual NO_x, CO, NMVOC and PM_2.5 pleasure boat emissions (t yr⁻¹) estimated for Spain (HERMESv3_BU) and the Baltic Sea region (Johansson et al., 2020). (Sources of the ArcGIS Online basemap: Esri, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo and the GIS user community. Maps in (a) and (b) were created using ArcGIS^® software by Esri. ArcGIS^® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights reserved. For more information about Esri^® software, please visit https://www.esri.com (last access: February 2020).)

3.5.2 Recreational boats

Emissions derived from pleasure boat activities can be estimated in HERMESv3_BU following Eq. (20):

where E_i(x,h) is the hourly recreational boat emissions of pollutant i at destination grid cell x and hour h (g h⁻¹); N_b(x) is the number of pleasure boats associated with category b and destination grid cell x (no. boats); T_b is the number of hours that the pleasure boat of category b is used during 1 year (h); P_b is the engine nominal power associated with the pleasure boat of category b (kW); LF_b is the engine load factor associated with the pleasure boat of category b (0 to 1); EF_b,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 EF_b,i, LF_b, T_b and P_b are derived from the EMEP/EEA (2016) Tier 3 approach (chap. 1.A.5.b, Table 3-11). The gridded number of pleasure boats (N_b(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 NO_x (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.

3.5.3 Agricultural machinery

Emissions related to the use of agricultural equipment (i.e. two-wheel tractors, agricultural tractors and harvesters) are estimated following Eq. (21):

where E_i(x,h) is the hourly emissions of pollutant i at destination grid cell x and hour h (g h⁻¹); N_e(x) is the number of agricultural equipment associated with category e and destination grid cell x (no. equipment); T_e is the number of hours that the equipment of category e is used during 1 year (h); P_e is the engine nominal power associated with the equipment of category e (kW); LF_e is the engine load factor associated with the equipment of category e (0 to 1); EF_e,i(P_e) 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 P_e; DF_e,i is the deterioration adjustment factor for the equipment of category e and pollutant i; FM_e(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).

3.5.4 Landing and take-off cycles at airports

Hourly aircraft landing and take-off (LTO) emissions occurring at airports are estimated according to Eq. (22):

where $E_{a,f,i}(x,h)$ 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); ${\mathrm{EF}}_{p,f,i}$ 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 (${\mathrm{EFmain}}_{p,f,i}$) (Eq. 23):

where E_p is the number of engines associated with aircraft p (engine); $t_{a,p,f}$ is the time spent by aircraft p to complete phase f at airport a (s); and ${\mathrm{EFengine}}_{p,f,i}$ 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 (EFwear_p,i) related to the wear of the aircraft brakes and tyres are considered (Eq. 24) (Morris, 2006):

where MTOW_p is the maximum take-off weight associated with aircraft p (t) and EFwear_i 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 (EFaux_p,i) are estimated as follows (Eq. 25) (Watterson et al., 2004):

where $t\mathit{\_}{\mathrm{apu}}_{a,p,f}$ is the APU running time associated with aircraft p, phase f and airport a (s) and EFapu_p,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.