Type:	Package
Title:	Estimating Public Transport Emissions from General Transit Feed Specification (GTFS) Data
Version:	0.1.1
Description:	A bottom up model to estimate the emission levels of public transport systems based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) Public transport data in the GTFS standard format; and ii) Some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the package estimates several pollutants at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics. A full description of the methods used in the 'gtfs2emis' model is presented in Vieira, J. P. B.; Pereira, R. H. M.; Andrade, P. R. (2022) <doi:10.31219/osf.io/8m2cy>.
URL:	https://ipeagit.github.io/gtfs2emis/ , https://github.com/ipeaGIT/gtfs2emis
BugReports:	https://github.com/ipeaGIT/gtfs2emis/issues
License:	MIT + file LICENSE
Depends:	R (≥ 3.6)
Imports:	checkmate, data.table, furrr, future, gtfs2gps, methods, sf (≥ 0.9-0), sfheaders, terra, units
Suggests:	gtfstools, ggplot2, knitr, lwgeom, progressr, rmarkdown, testthat (≥ 2.1.0)
VignetteBuilder:	knitr
Encoding:	UTF-8
RoxygenNote:	7.3.2
LazyData:	true
NeedsCompilation:	no
Packaged:	2024-12-02 15:20:51 UTC; joao
Author:	Joao Bazzo [aut, cre], Rafael H. M. Pereira [aut], Pedro R. Andrade [aut], Sergio Ibarra-Espinosa [ctb], Ipea - Institute for Applied Economic Research [cph, fnd]
Maintainer:	Joao Bazzo <joao.bazzo@gmail.com>
Repository:	CRAN
Date/Publication:	2024-12-02 15:40:12 UTC

Running exhaust emissions factors for buses from Brazil (CETESB)

Description

Returns a vector or data.table of emission factors for buses based on estimates from the Environment Company of Sao Paulo, Brazil (CETESB) 2019. Emission factor estimates are expressed in units 'g/km'.

Usage

ef_brazil_cetesb(pollutant, veh_type, model_year, as_list = TRUE)

Arguments

Details

The new convention for vehicles names are translated from CETESB report:

The percentage varies of biofuels varies by law.

These emission factors are not exactly the same as the report of CETESB.

1. In this emission factors, there is also NO and NO2 based on split by published in the EMEP/EEA air pollutant emission inventory guidebook.

2. Also, the emission factors were extended till 50 years of use, repeating the oldest value.

Value

data.table. Emission factors in units 'g/km' by model_year.

See Also

Other Emission factor model: ef_europe_emep(), ef_scaled_euro(), ef_usa_emfac(), ef_usa_moves(), emi_europe_emep_wear()

Examples

df <- ef_brazil_cetesb(
          pollutant = c("CO","PM10","CO2","CH4","NOx"),
          veh_type = "BUS_URBAN_D",
          model_year = 2015,
          as_list = TRUE)

Emission factors from Environment Company of Sao Paulo, Brazil (CETESB)

Description

units 'g/km'; Emission factors for buses based on estimates from the Environment Company of Sao Paulo, Brazil (CETESB) 2017, and obtained from vein package. The R script used to organize the CETESB database can be found in the repository <https://github.com/ipeaGIT/gtfs2emis/blob/master/data-raw/ef_brazil_cetesb_db.R>.

Usage

ef_brazil_cetesb_db

Format

A data.table:

pollutant

character; Pollutants: "CH4", "CO2", "PM10", "N2O", "KML", "FC" (Fuel Consumption), "gD/KWH", "gCO2/KWH", "CO", "HC" (Total Hydrocarbon), "NMHC" (Non-Methane Hydrocarbon), "NOx", "NO2", "NO", "RCHO", "ETOH", "FS"(Fuel Sales) and "NH3"

veh_type

character; Vehicle categories by fuel:"BUS_URBAN_D", "BUS_MICRO_D", "BUS_COACH_D" and "BUS_ARTIC_D".

model_year

numeric; Filter the emission factor to start from a specific base year.

as_list

logical; Returns emission factors as a list, instead of data.table format.

See Also

Other emission factor data: ef_europe_emep_db, ef_usa_emfac_db, ef_usa_moves_db

Speed-dependent emission factor from the European Environment Agency (EMEP/EEA) model

Description

Returns a list or data.table of emission factors for buses based on EMEP/EEA air pollutant emission inventory guidebooks. The function uses four emission factor databases published by EMEP/EEA, considering the editions of 2019, 2016, 2013 and 2007. Estimates are expressed in units 'g/km'. See more in @details.

Usage

ef_europe_emep(
  speed,
  veh_type,
  euro,
  pollutant,
  fuel = "D",
  tech = "SCR",
  slope = 0,
  load = 0.5,
  fcorr = 1,
  as_list = TRUE
)

Arguments

Details

The new convention for vehicles names are translated from the EMEP/EEA report:

When the information of vehicle technology does not match the existing database, the function display a message mentioning the returned technology. User can either select an existing data for the combining variables (euro, tech, veh_type and pollutant), or accept the assumed change in vehicle technology.

In order to cover more pollutants, vehicle technologies, and fuel consumption data, the function uses four emission factor databases published by EMEP/EEA, considering the editions of 2019, 2016, 2013 and 2007.

The R scripts used to download and pre-process 4 EMEP/EEA editions (2019, 2016, 2013 and 2007) can be accessed in the 'gtfs2emis' GitHub repository at <https://github.com/ipeaGIT/gtfs2emis/blob/master/data-raw/ef_europe_emep_db.R>

EMEP/EEA data and reports can be accessed in the following links:

• 2019 edition https://www.eea.europa.eu/themes/air/air-pollution-sources-1/emep-eea-air-pollutant-emission-inventory-guidebook,

• 2016 edition https://www.eea.europa.eu/publications/emep-eea-guidebook-2016/,

• 2013 edition https://www.eea.europa.eu/publications/emep-eea-guidebook-2013/, and

• 2007 edition https://www.eea.europa.eu/publications/EMEPCORINAIR5/.

Value

List. emission factors in units 'g/km' (list or a data.table).

See Also

Other Emission factor model: ef_brazil_cetesb(), ef_scaled_euro(), ef_usa_emfac(), ef_usa_moves(), emi_europe_emep_wear()

Examples

ef_europe_emep( speed = units::set_units(1:100,"km/h"),
                veh_type = c("Ubus Midi <=15 t","Ubus Std 15 - 18 t","Ubus Artic >18 t"),
                euro = c("IV","V"),
                fuel = "D",
                pollutant = c("CO","PM10","CH4","NOx"),
                as_list = FALSE) 

Emission factors from European Environment Agency — EMEP/EEA

Description

Hot exhaust emission factors are speed dependent functions and are expressed in g/km. It varies by fuel, vehicle segment, euro standard, pollutant, and after treatment technology. These variables are consolidated in different EF equations, given by:

Usage

ef_europe_emep_db

Format

A data.table with 6431 rows and 22 variables:

Category

Buses.

Fuel

Fuel type, classified in "D" (Diesel),"DHD" (Diesel Hybrid ~ Diesel), "DHE" (Diesel Hybrid ~ Electricity), "CNG" (Compressed Natural Gas), "BD" (Biodiesel).

Segment

character; Bus type, classified in "Ubus Midi <=15 t","Ubus Std 15 - 18 t","Ubus Artic >18 t", "Coaches Std <=18 t","Coaches Artic >18 t".

Euro

character; Euro period of vehicle, classified in "Conventional", "I", "II", "III", "IV", "V", "VI", and "EEV".

Technology

character; After treatment technology, classified in "SCR" (Selective Catalytic Reduction), "EGR" (Exhaust Gas Recirculation), and "DPF+SCR" (Diesel Particulate Filter + SCR, for Euro VI). Default is "SCR" for "IV" and "V". There are no available after treatment technology associated with euro standards "Conventional", "I", "II" and "III".

Pol

character; Pollutant, classified in "FC","CO2","CO","NOx","VOC","PM10","EC","CH4","NH3","N2O". "FC" means Fuel Consumption.

Vmin

Minimum speed for emission factor estimation, in km/h.

Vmax

Maximum speed for emission factor estimation, in km/h.

Alpha, Beta, Gamma, Delta, Epsilon, Zita, Hta, Thita

Constant parameters.

RF

Reduction Factor; In percentage (%) units.

k

Constant factor.

Details

EF = EF(Alpha, Beta, Gamma, Delta, Epsilon, Zita, Hta, RF, Speed, Function_ID, k, fcorr),

where Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Eta are constant parameters; RF is the Reduction Factor, Speed in the average speed, Function_ID is the equation (function of on the year of the inventory and the pollutant); k is a constant value, and fcorr is the fuel correction factor.

The emissions factors are derived from the EMEP/EEA air pollutant emission inventory guidebook (formerly called the EMEP CORINAIR emission inventory guidebook). The document provides guidance on estimating emissions from both anthropogenic and natural emission sources.

The package presents a combination of emission factors from EMEP/EEA guidelines of 2007, 2013, 2016, and 2019, aiming to cover a greater number of pollutants and vehicle segments. The script used to process the raw EMEP/EEA databases can be found in the repository <https://github.com/ipeaGIT/gtfs2emis/blob/master/data-raw/ef_europe_emep_db.R>.

Source

More information can be found at https://www.eea.europa.eu//publications/emep-eea-guidebook-2019, https://www.eea.europa.eu/publications/emep-eea-guidebook-2016/, https://www.eea.europa.eu/publications/emep-eea-guidebook-2013/, and https://www.eea.europa.eu/publications/EMEPCORINAIR5/.

See Also

Other emission factor data: ef_brazil_cetesb_db, ef_usa_emfac_db, ef_usa_moves_db

Scale local emission factors in order to make emission estimates a function of speed.

Description

Scale emission factors to account for vehicle speed based on values from the emission factor model by the European Environment Agency (EMEP/EEA). Emission factor estimates are expressed in units 'g/km'.

Usage

ef_scaled_euro(
  ef_local,
  speed,
  veh_type,
  euro,
  pollutant,
  fuel = "D",
  tech = "SCR",
  SDC = 19,
  slope = 0,
  load = 0.5,
  fcorr = 1
)

Arguments

Details

The scaled emission factor is related to speed by the expression

EF_scaled (V) = EF_local * ( EF(V) / EF(SDC)),

where EF_scaled(V) is the scaled emission factors for each street link, EF_local is the local emission factor, EF(V) and EF(SDC) are the EMEP/EEA emission factor the speed of V and the average urban driving speed 'SDC', respectively.

Please note that the function reads the vector arguments in the same order as informed by the user. For instance, if the pollutant input is c("CO","PM10") input in the local emission factor function, the order needs to be the same for the pollutant in the ef_scaled_euro function.

In the case of vehicle type, which generally changes according to the emission factor source, the input argument in the ef_scaled_euro needs to be consistent with the order adopted in the local emission factor function.

For example, if the vector of local vehicle type is c("BUS_URBAN_D","BUS_MICRO_D"), the related vector for EMEP/EEA model needs to be c("Ubus Std 15 - 18 t","Ubus Midi <=15 t"). The same approach applies for other input arguments. See more in the examples.

Value

list. Emission factors in units 'g/km'.

See Also

Other Emission factor model: ef_brazil_cetesb(), ef_europe_emep(), ef_usa_emfac(), ef_usa_moves(), emi_europe_emep_wear()

Examples

temp_ef_br <- ef_brazil_cetesb(
                    pollutant = c("CO","PM10","CO2","CH4","NOx"),
                    veh_type = c("BUS_URBAN_D","BUS_MICRO_D"),
                    model_year = c(2015,2015),
                    as_list = TRUE
                    )

temp_ef_scaled <- ef_scaled_euro(
                    ef_local = temp_ef_br,
                    speed = units::set_units(1:100,"km/h"),
                    veh_type = c("Ubus Std 15 - 18 t","Ubus Midi <=15 t"),
                    euro = c("IV","IV"),
                    fuel = c("D","D"),
                    tech = c("SCR","SCR"),
                    pollutant = c("CO","PM10","CO2","CH4","NOx")
                    )

Running exhaust emissions factors for buses from United States (EMFAC2017 model)

Description

Returns a vector or data.frame of emission factors for buses based on the California EMission Factor model (EMFAC2017). The model considers emission factors (EF) of urban buses in California (United States), considering different pollutants, years of reference, model year, fuel, speed ranges, type of regions, model version, and type of season. The gtfs2emis package currently supports EF only for "Statewide" region type, and "Annual" season. Specific data of these variables can be download at <https://arb.ca.gov/emfac/emissions-inventory>.

Usage

ef_usa_emfac(
  pollutant,
  reference_year = 2020,
  fuel = "D",
  model_year,
  speed,
  as_list = TRUE
)

Arguments

Value

List or data.table. Emission factors in units 'g/km' by speed and model_year.

Source

https://arb.ca.gov/emfac/

See Also

Other Emission factor model: ef_brazil_cetesb(), ef_europe_emep(), ef_scaled_euro(), ef_usa_moves(), emi_europe_emep_wear()

Examples

df <- ef_usa_emfac(
        pollutant = c("CO","PM10"),
        reference_year = 2019,
        model_year = 2015,
        speed = units::set_units(10:100,"km/h"),
        fuel = "D",
        as_list = TRUE
        )

Emission factors from California Air Resources Board (EMFAC Model)

Description

Running exhaust emissions factors from EMFAC2017 model. The model generates emission factors (EF) of urban buses in California (United States), considering different pollutants, years of reference, model year, fuel, speed ranges, type of regions, model version, and type of season. Currently, the package supports EFs only for "Statewide" region type, and "Annual" season. Specific data of other regions and seasons can be download at <https://arb.ca.gov/emfac/emissions-inventory>.

Usage

ef_usa_emfac_db

Format

A data.table with 79198 rows and 8 variables:

pol

Character; Pollutants: CH4(Methane), CO(Carbon Monoxide), CO2(Carbon Dioxide), N2O(Nitrous Oxide), NOx(Oxides of Nitrogen), PM10(Primary Exhaust PM10 - Total), PM25(Primary Exhaust PM2.5 - Total), SOX(Oxides of Sulfur), TOG(Total Organic Gases), ROG (Reactive Organic Gases)

reference_year

Numeric; Year of reference between 2010 - 2020

fuel

character; Type of fuel: 'D' (Diesel),'G' (Gasoline),'CNG' (Compressed Natural Gas).

model_year

Model year.

speed

Units; Speed in 'km/h'; Emission factor are returned in speed intervals such as "5-10", "10-15", "15-20", "20-25", "25-30", "30-35", "35-40", "40-45", "45-50" "50-55", "55-60", "60-65", "65-70", "70-75", "75-80", "80-85", "85-90", ">90" mph (miles/h)

Details

The function returns the data in a data.frame format. The R script used to process the raw EMFAC database can be found in the repository <https://github.com/ipeaGIT/gtfs2emis/blob/master/data-raw/ef_usa_emfac_db.R>.

Source

https://arb.ca.gov/emfac/emissions-inventory

See Also

Other emission factor data: ef_brazil_cetesb_db, ef_europe_emep_db, ef_usa_moves_db

Running exhaust emissions factors for buses from United States (MOVES3 model)

Description

Returns a vector or data.frame of emission factors for urban buses based on values from the MOVES3 Model. Emission factor estimates are expressed in units 'g/km'.

Usage

ef_usa_moves(
  pollutant,
  model_year,
  reference_year = 2020,
  speed,
  fuel = "D",
  as_list = TRUE
)

Arguments

Details

Users can view the pre-processed database in data(ef_usa_moves_db) function.

Value

List. Emission factors in units 'g/km' by speed and model_year.

See Also

Other Emission factor model: ef_brazil_cetesb(), ef_europe_emep(), ef_scaled_euro(), ef_usa_emfac(), emi_europe_emep_wear()

Examples

df <- ef_usa_moves(
         pollutant = c("CO","PM10"),
         model_year = 2015,
         speed = units::set_units(10:100,"km/h"),
         reference_year = 2016,
         fuel = "D",
         as_list = TRUE
        )

Emission factors from MOtor Vehicle Emission Simulator (MOVES) Data.frame of emission factors for buses based on values from the MOVES3 Model. Estimates expressed in units 'g/km'.

Description

Emission factors from MOtor Vehicle Emission Simulator (MOVES)

Data.frame of emission factors for buses based on values from the MOVES3 Model. Estimates expressed in units 'g/km'.

Usage

ef_usa_moves_db

Format

A data.table:

pollutant

character; Pollutants: CH4 (Methane), CO (Carbon Monoxide), CO2 (Carbon Dioxide), EC (Energy Consumption), HONO (Nitrous Acid), N2O (Nitrous Oxide), NH3 (Ammonia), NH4 (Ammonium), NO (Nitrogen Oxide), NO2 (Nitrogen Dioxide), NO3 (Nitrate), NOx (Oxides of Nitrogen), PM10 (Primary Exhaust PM10 - Total), PM25 (Primary Exhaust PM2.5 - Total), SO2 (Sulfur Dioxide), THC (Total Gaseous Hydrocarbons ), TOG (Total Organic Gases) and VOC (Volatile Organic Compounds)

fuel_type

character; Type of fuel: 'D' (Diesel),'G' (Gasoline),'CNG' (Compressed Natural Gas).

reference_year

Numeric; Calendar Year between 2015 - 2022. Year in which the emissions inventory is estimated.

model_year

numeric; Model year of vehicle.

lower_speed_interval

units 'km/h'; Represents the lower value of the speed intervals; The speed intervals are " - 2.5", "2.5 - 7.5", "7.5 - 12.5" , "12.5 - 17.5", "17.5 - 22.5", "22.5 - 27.5","27.5 - 32.5","32.5 - 37.5" ,"37.5 - 42.5","42.5 - 47.5","47.5 - 52.5","52.5 - 57.5", "57.5 - 62.5" , "62.5 - 67.5", "67.5 - 72.5", and ">72.5" mph (miles/h).

upper_speed_interval

units in km/h; Represents the upper value of the speed intervals. The speed intervals are analogous to lower_speed_interval above.

source_type

character; Type of vehicle, which currently has only "Transit Bus".

id_speed

integer;it caracterizes the types of vehicle speeds.

Source

https://www.epa.gov/moves

See Also

Other emission factor data: ef_brazil_cetesb_db, ef_europe_emep_db, ef_usa_emfac_db

Emissions from road vehicle tyre, brake, and surface wear from the European Environment Agency (EMEP/EEA) model

Description

Returns a list or data.table of emissions for urban buses based on Tier 2 of EMEP/EEA air pollutant emission inventory guidebooks (2019). The function concerns the emissions of particulate matter (PM), encompassing black carbon (BC) (1), which arises from distinct sources, namely, road vehicle tire and brake wear (NFR code 1.A.3.b.vi), and road surface wear (NFR code 1.A.3.b.vii). It is important to note that PM emissions exhaust from vehicle exhaust are excluded. The focus is on primary particles, which refer to those that are directly emitted due to surface wear, rather than those generated from the resuspension of previously deposited material. See more in @details.

Usage

emi_europe_emep_wear(
  dist,
  speed,
  pollutant,
  veh_type,
  fleet_composition,
  load = 0.5,
  process = "tyre",
  as_list = TRUE
)

Arguments

Details

The following equation is employed to evaluate emissions originating from tyre and brake wear

TE(i) = dist x EF_tsp(j) x mf_s(i) x sc(speed),

where:

• TE(i) = total emissions of pollutant i (g),

• dist = distance driven by each vehicle (km),

• EF_tsp(j) = TSP mass emission factor for vehicles of category j (g/km),

• mf_s(i) = mass fraction of TSP that can be attributed to particle size class i,

• sc(speed) = correction factor for a mean vehicle travelling at a given speed (-)

Tyre

In the case of heavy-duty vehicles, the emission factor needs the incorporation of vehicle size, as determined by the number of axles, and load. These parameters are introduced into the equation as follows:

EF_tsp_tyre_hdv = 0.5 x N_axle x LCF_tyre x EF_tsp_tyre_pc

where

• EF_tsp_tyre_hdv = TSP emission factor for tyre wear from heavy-duty vehicles (g/km),

• N_axle = number of vehicle axles (-),

• LCF_t = a load correction factor for tyre wear (-),

• EF_tsp_tyre_pc = TSP emission factor for tyre wear from passenger car vehicles (g/km).

and LCF_tyre = 1.41 + (1.38 x LF),

where:

• LF = load factor (-), ranging from 0 for an empty bus to 1 for a fully laden one.

The function considers the following look-up table for number of vehicle axes:

The size distribution of tyre wear particles are given by:

Finally, the speed correction is:

sc_tyre(speed) = 1.39, when V < 40 km/h; sc_tyre(speed) = -0.00974 x speed + 1.78, when 40 <= speed <= 90 km/h; sc_tyre(speed) = 0.902, when speed > 90 km/h.

Brake

The heavy-duty vehicle emission factor is derived by modifying the passenger car emission factor to conform to experimental data obtained from heavy-duty vehicles.

EF_tsp_brake_hdv = 1.956 x LCF_brake x EF_tsp_brake_pc

where:

• EF_tsp_brake_hdv = heavy-duty vehicle emission factor for TSP,

• LCF_brake = load correction factor for brake wear,

• EF_tsp_brake_pc = passenger car emission factor for TSP,

and LCF_brake = 1 + (0.79 x LF),

where:

• LF = load factor (-), ranging from 0 for an empty bus to 1 for a fully laden one.

The size distribution of brake wear particles are given by:

Finally, the speed correction is:

sc_brake(speed) = 1.67, when V < 40 km/h; sc_brake(speed) = -0.0270 x speed + 2.75, when 40 <= speed <= 95 km/h; sc_brake(speed) = 0.185, when speed > 95 km/h.

Road Wear

Emissions are calculated according to the equation:

TE(i) = dist x EF_tsp_road(j) x mf_road

where:

• TE = total emissions of pollutant i (g),

• dist = total distance driven by vehicles in category j (km),

• EF_tsp_road = TSP mass emission factor from road wear for vehicles j (0.0760 g/km),

• mf_road = mass fraction of TSP that can be attributed to particle size class i (-).

The following table shows the size distribution of road surface wear particles

References

#' EMEP/EEA data and reports can be accessed in the following links:

• 2019 edition https://www.eea.europa.eu/publications/emep-eea-guidebook-2019/part-b-sectoral-guidance-chapters/1-energy/1-a-combustion/1-a-3-b-vi/view.

Value

List. emission in units 'g' (list or a data.table).

See Also

Other Emission factor model: ef_brazil_cetesb(), ef_europe_emep(), ef_scaled_euro(), ef_usa_emfac(), ef_usa_moves()

Examples

emi_europe_emep_wear(dist = units::set_units(1,"km"),
                     speed =  units::set_units(30,"km/h"),
                     pollutant = c("PM10","TSP","PM2.5"),
                     veh_type = c("Ubus Std 15 - 18 t","Ubus Artic >18 t"),
                     fleet_composition = c(0.5,0.5),
                     load = 0.5,
                     process = c("brake","tyre","road"),
                     as_list = TRUE)

Spatial aggregation of emission estimates into a grid

Description

Aggregate emissions proportionally in an sf polygon grid, by performing an intersection operation between emissions data in ⁠sf linestring⁠ format and the input grid cells. User can also aggregate the emissions in the grid by time of the day.

Usage

emis_grid(
  emi_list,
  grid,
  time_resolution = "day",
  quiet = TRUE,
  aggregate = FALSE
)

Arguments

Value

An ⁠"sf" "data.frame"⁠ object with emissions estimates per grid cell.

See Also

Other emission analysis: emis_summary(), emis_to_dt()

Examples

if (requireNamespace("gtfstools", quietly=TRUE)) {
library(sf)

# read GTFS
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file) 

# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")
  
# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
                            spatial_resolution = 100,
                            parallel = FALSE)

# Fleet data, using Brazilian emission model and fleet
fleet_data_ef_cetesb <- data.frame(veh_type = "BUS_URBAN_D",
                                   model_year = 2010:2019,
                                   fuel = "D",
                                   fleet_composition = rep(0.1,10)
                                   )
# Emission model
emi_list <- emission_model(
                tp_model = tp_model,
                ef_model = "ef_brazil_cetesb",
                fleet_data = fleet_data_ef_cetesb,
                pollutant = c("CO","PM10","CO2","CH4","NOx")
                )

# create spatial grid
grid <- sf::st_make_grid(
  x = sf::st_make_valid(emi_list$tp_model)
  , cellsize = 0.25 / 200
  , crs= 4326
  , what = "polygons"
  , square = FALSE
  )

emi_grid <- emis_grid( emi_list,grid,'day')

plot(grid)
plot(emi_grid["PM10_2010"],add = TRUE)
plot(st_geometry(emi_list$tp_model), add = TRUE,col = "black")
}

Summarize emissions estimates

Description

Summarize emissions estimates, aggregating emissions by pollutant, time of the day, vehicle.

Usage

emis_summary(
  emi_list,
  by = "pollutant",
  veh_vars = "veh_type",
  segment_vars = NULL,
  process_vars = "process"
)

Arguments

Value

data.table with pollutants units ('g') aggregated by pollutant, time, or vehicle type.

See Also

Other emission analysis: emis_grid(), emis_to_dt()

Examples

if (requireNamespace("gtfstools", quietly=TRUE)) {

# read GTFS
gtfs_file <- system.file("extdata/irl_dub_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file)

# Keep a single trip
gtfs <- gtfstools::filter_by_trip_id(gtfs
                                     , trip_id = c('238.2.60-118-b12-1.59.I'
                                                   ,"7081.2.60-X27-b12-1.106.I"))
# Transport model
tp_model <- transport_model(gtfs_data = gtfs,
                            spatial_resolution = 100,
                            parallel = FALSE)

# fleet data
fleet_df <- read.csv(system.file("extdata/irl_dub_fleet.txt"
                                 , package = "gtfs2emis"))
# emission model
emi_list <- emission_model(tp_model = tp_model
                           , ef_model = "ef_europe_emep"
                           , fleet_data = fleet_df
                           , pollutant = c("CO2","PM10"))

# Aggregate total emissions by 'pollutant'
emis_summary(emi_list) 

# by vehicle type
emis_summary(emi_list, by = "vehicle")
             
emis_summary(emi_list
             , by = "vehicle"
             , veh_vars = c("euro"))

emis_summary(emi_list
             , by = "vehicle"
             , veh_vars = c("fuel"))

emis_summary(emi_list
             , by = "vehicle"
             , veh_vars = c("veh_type","euro","tech","fuel"))
             
# by time of the day
emis_summary(emi_list
             , by = "time"
             , segment_vars = "slope") 
}

Convert emission estimates from list to data.table format

Description

Read emission estimates generated by the emission_model or from emission factor functions (e.g. ef_brazil_cetesb) and convert them into a data.table format.

Usage

emis_to_dt(
  emi_list,
  emi_vars = "emi",
  veh_vars = "veh_type",
  pol_vars = "pollutant",
  process_vars = "process",
  segment_vars = NULL
)

Arguments

Value

data.table.

See Also

Other emission analysis: emis_grid(), emis_summary()

Examples

 if (requireNamespace("gtfstools", quietly=TRUE)) {

# read GTFS
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file) 

# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")
  
# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
                            min_speed = 2,
                            max_speed = 80,
                            new_speed = 20,
                            spatial_resolution = 100,
                            parallel = FALSE)

# Example using Brazilian emission model and fleet
fleet_data_ef_cetesb <- data.frame(veh_type = "BUS_URBAN_D",
                                   model_year = 2010:2019,
                                   fuel = "D",
                                   fleet_composition = rep(0.1,10)
                                   )
                                   
emi_list <- emission_model(
                tp_model = tp_model,
                ef_model = "ef_brazil_cetesb",
                fleet_data = fleet_data_ef_cetesb,
                pollutant = c("CO","PM10","CO2","CH4","NOx")
                )

# convert emission list to data.table
dt <- emis_to_dt(emi_list)
}

Emission model

Description

Estimate hot-exhaust emissions of public transport systems. This function must be used together with transport_model.

Usage

emission_model(
  tp_model,
  ef_model,
  fleet_data,
  pollutant,
  reference_year = 2020,
  process = "hot_exhaust",
  heightfile = NULL,
  parallel = TRUE,
  ncores = NULL,
  output_path = NULL,
  continue = FALSE,
  quiet = TRUE
)

Arguments

Details

The fleet_data must be a data.frame organized according to the desired ef_model. The required columns is organized as follows (see @examples for real data usage).

• veh_type: character; Bus type, classified according to the @param ef_model . For ef_emep_europe, use "Ubus Midi <=15 t","Ubus Std 15 - 18 t", "Ubus Artic >18 t", "Coaches Std <=18 t" or "Coaches Artic >18 t"; For ef_usa_moves or ef_usa_emfac, use "BUS_URBAN_D"; For ef_brazil_cetesb, use "BUS_URBAN_D", "BUS_MICRO_D", "BUS_COACH_D" or "BUS_ARTIC_D".

• type_name_eu: character; Bus type, used only for @param ef_model ef_scaled_euro are selected. The classes can be "Ubus Midi <=15 t","Ubus Std 15 - 18 t", "Ubus Artic >18 t", "Coaches Std <=18 t" or "Coaches Artic >18 t".

• reference_year: character; Base year of the emission factor model input. Required only when ef_usa_moves or ef_usa_emfac are selected.

• tech: character; After treatment technology. This is required only when emep_europe is selected. Check ?ef_emep_europe for details.

• euro: character; Euro period of vehicle, classified in "Conventional", "I", "II", "III", "IV", "V", "VI", and "EEV". This is required only when ef_emep_europe is selected. Check ef_europe_emep for details.

• fuel: character; Required when ef_usa_moves, ef_usa_emfac and ef_europe_emep are selected.

• fleet_composition: Numeric. Scaled composition of fleet. In most cases, the user might not know which vehicles run on each specific routes. The composition is used to attribute a probability of a specific vehicle to circulate in the line. The probability sums one. Required for all emission factors selection. Users can check the gtfs2emis fleet data vignette, for more examples.

Based on the input height data, the function returns the slope class between two consecutive bus stop positions of a LineString Simple Feature (transport model object). The slope is given by the ratio between the height difference and network distance from two consecutive public transport stops. The function classifies the slope into one of the seven categories available on the European Environmental Agency (EEA) database, which is -0.06, -0.04,-0.02, 0.00, 0.02, 0.04, and 0.06.

Value

A list with emissions estimates or NULL with output files saved locally at output_path.

See Also

Other Core function: transport_model()

Examples

 if (requireNamespace("gtfstools", quietly=TRUE)) {

# read GTFS
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file) 

# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")
  
# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
                            min_speed = 2,
                            max_speed = 80,
                            new_speed = 20,
                            spatial_resolution = 100,
                            parallel = FALSE)

# Example using Brazilian emission model and fleet
fleet_data_ef_cetesb <- data.frame(veh_type = "BUS_URBAN_D",
                                   model_year = 2010:2019,
                                   fuel = "D",
                                   fleet_composition = rep(0.1,10)
                                   )
                                   
emi_cetesb <- progressr::with_progress(emission_model(
                tp_model = tp_model,
                ef_model = "ef_brazil_cetesb",
                fleet_data = fleet_data_ef_cetesb,
                pollutant = c("CO","PM10","CO2","CH4","NOx")
                ))
                            
# Example using European emission model and fleet
fleet_data_ef_europe <- data.frame(  veh_type = c("Ubus Midi <=15 t",
                                                  "Ubus Std 15 - 18 t",
                                                  "Ubus Artic >18 t")
                                   , euro = c("III","IV","V")
                                   , fuel = rep("D",3)
                                   , tech = c("-","SCR","SCR")
                                   , fleet_composition = c(0.4,0.5,0.1))
                                   
emi_emep <- progressr::with_progress(emission_model(tp_model = tp_model
                          , ef_model = "ef_europe_emep"
                          , fleet_data = fleet_data_ef_europe
                          , pollutant = c("PM10","NOx")))
emi_emep_wear <- progressr::with_progress(emission_model(tp_model = tp_model
                          , ef_model = "ef_europe_emep"
                          , fleet_data = fleet_data_ef_europe
                          , pollutant = "PM10"
                          , process = c("tyre","road","brake")))
raster_cur <- system.file("extdata/bra_cur-srtm.tif", package = "gtfs2emis")                           
emi_emep_slope <- progressr::with_progress(emission_model(tp_model = tp_model
                          , ef_model = "ef_europe_emep"
                          , fleet_data = fleet_data_ef_europe
                          , heightfile = raster_cur
                          , pollutant = c("PM10","NOx")))  
                                                  
# Example using US EMFAC emission model and fleet
fleet_data_ef_moves <- data.frame(  veh_type = "BUS_URBAN_D"
                                  , model_year = 2010:2019
                                  , fuel = "D"
                                  , reference_year = 2020
                                  , fleet_composition = rep(0.1,10))
                                  
fleet_data_ef_emfac <- data.frame(  veh_type =  "BUS_URBAN_D"
                                  , model_year = 2010:2019
                                  , fuel = "D"
                                  , reference_year = 2020
                                  , fleet_composition = rep(0.1,10))
                                  
# Example using US MOVES emission model and fleet
emi_moves <- emission_model(tp_model = tp_model
                          , ef_model = "ef_usa_moves"
                          , fleet_data = fleet_data_ef_moves
                          , pollutant = c("CO","PM10","CO2","CH4","NOx")
                          , reference_year = 2020)
                          
emi_emfac <- emission_model(tp_model = tp_model
                          , ef_model = "ef_usa_emfac"
                          , fleet_data = fleet_data_ef_emfac
                          , pollutant = c("CO","PM10","CO2","CH4","NOx")
                          , reference_year = 2020)
}

Correction factor equation

Description

Relations between emissions and fuel properties for diesel heavy-duty vehicles based on EMEP/EEA. Function based on values from the EMEP/EEA air pollutant emission inventory guidebook 2019. Estimates are given by the ratio between correction factor of improved fuel by the correction factor of base fuel.

Usage

fuel_correction(
  pollutant,
  euro_stage,
  improved_fuel = c(den = 835, s = 40, pah = 5, cn = 53, t95 = 320)
)

Arguments

Value

numeric. A fuel correction factor.

Multiply emission factors by distances

Description

Calculate hot exhaust emissions by multiplying emission factors by distances weighted by fleet composition profile.

Usage

multiply_ef(
  fleet_composition,
  dist,
  ef,
  aggregate = TRUE,
  prefix = NULL,
  as_list = TRUE
)

Arguments

Value

units ('g'); emissions per link.

Add slope class into the transport model (LineString Simple Feature object)

Description

Based on the input height data, the function returns the slope class between two consecutive bus stop positions of a LineString Simple Feature (transport model object). The slope is given by the ratio between the height difference and network distance from two consecutive public transport stops. The function classifies the slope into one of the seven categories available on the European Environmental Agency (EEA) database, which is -0.06, -0.04,-0.02, 0.00, 0.02, 0.04, and 0.06. The classifications is described in @details .

Usage

slope_class_europe_emep(tp_model, heightfile, keep = FALSE)

Arguments

Value

The transport model with slope information.

Slopes classification:

| slope interval | slope class | | slope <= -0.070 | -0.06 | | slope > -0.070 & slope <= -0.050 | -0.06 | | slope > -0.050 & slope <= -0.030 | -0.04 | | slope > -0.030 & slope <= -0.010 | -0.02 | | slope > -0.010 & slope <= +0.010 | +0.00 | | slope > +0.010 & slope <= +0.030 | +0.02 | | slope > +0.030 & slope <= +0.050 | +0.04 | | slope > +0.050 & slope <= +0.070 | +0.06 | | slope > +0.070 | -0.06 |

Examples

 if (requireNamespace("gtfstools", quietly=TRUE)) {
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file) 

# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")
  
# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
                            min_speed = 2,
                            max_speed = 80,
                            new_speed = 20,
                            spatial_resolution = 100,
                            parallel = FALSE)

# read raster file
raster_cur <- system.file("extdata/bra_cur-srtm.tif", package = "gtfs2emis")

tp_model_slope <- slope_class_europe_emep(tp_model,raster_cur)
}

Transport model

Description

This function converts a public transport data set in GTFS format into a GPS-like table with the space-time positions and speeds of public transport vehicles. The function also allow users to set the spatial resolution of the output and to adjust the speed of public transport vehicles given a min_speed and max_speed range.

Usage

transport_model(
  gtfs_data,
  min_speed = 2,
  max_speed = 80,
  new_speed = NULL,
  parallel = TRUE,
  ncores = NULL,
  spatial_resolution = 100,
  output_path = NULL,
  continue = FALSE
)

Arguments

Value

A ⁠data.table sf_linestring⁠ object or NULL.

See Also

Other Core function: emission_model()

Examples

if (requireNamespace("gtfstools", quietly=TRUE)) {

# read GTFS
gtfs_file <- system.file("extdata/bra_cur_gtfs.zip", package = "gtfs2emis")
gtfs <- gtfstools::read_gtfs(gtfs_file) 

# keep a single trip_id to speed up this example
gtfs_small <- gtfstools::filter_by_trip_id(gtfs, trip_id ="4451136")
  
# run transport model
tp_model <- transport_model(gtfs_data = gtfs_small,
                            min_speed = 2,
                            max_speed = 80,
                            new_speed = 20,
                            spatial_resolution = 100,
                            parallel = FALSE)
  }