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The bcsa package provide datasets for source apportionment of light absorbing carbon (LAC) in Blantyre, Malawi. The package contains data on Absorption Angstrom Exponent experiments determination of local pollution sources. The package also contains data on spatial distribution and ambient concentrations of LAC concentrations.

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bcsa

License: CC BY 4.0 R-CMD-check DOI

The goal of bcsa is to provide datasets for source apportionment of light absorbing carbon (LAC) in Blantyre, Malawi. This package combines datasets collected as part of two projects. The first project is on determining Absorption Angstrom Exponent (AAE) values of local pollution sources in Blantyre, Malawi. AAE values can be used to differentiate the LAC from fossil fuel and biomass based sources. The second project is to determine the light absorbing carbon concentrations by mobile, personal and stationary monitoring in Blantyre.

The package includes the following seven datasets:

  1. df_aae: Data of experiments to determine AAE values
  2. df_mm: Mobile monitoring data in eight settlements
  3. df_mm_road_type: Mobile monitoring data classified by highways (main_road) and non-highways (non_main_roads) in eight settlements
  4. df_pm: Personal monitoring data in four settlements
  5. df_pm_trips: Data on times when open waste burning was observed during the personal monitoring
  6. df_sm: Raw data from stationary monitoring in two settlements
  7. df_collocation: Data when the two LAC monitors are placed and run next to each other to check data quality

This study used the MA200 micro-aethalometer to measure the light absorbing carbon (LAC) concentrations. The MA200 measures the LAC concentrations in real-time at five different wavelengths, that allows for source apportionment.

Installation

You can install the development version of bcsa from GitHub with:

# install.packages("devtools")
devtools::install_github("Global-Health-Engineering/bcsa")

Alternatively, you can download the individual datasets as a CSV or XLSX file from the table below.

dataset CSV XLSX
df_aae Download CSV Download XLSX
df_collocation Download CSV Download XLSX
df_mm Download CSV Download XLSX
df_mm_road_type Download CSV Download XLSX
df_pm Download CSV Download XLSX
df_pm_trips Download CSV Download XLSX
df_sm Download CSV Download XLSX

Datasets

This data package has seven datasets: df_aae df_mm df_mm_road_type df_pm df_pm_trips df_sm df_collocation

df_aae

This dataset contains data from experiments to determine AAE values of local pollution sources in Blantyre, Malawi.

  • Vehicular emission
  • Waste Burning (Plastics)
  • Waste Burning (Plastic-based textiles, e.g., polyester)
  • Waste Burning (Garden Waste)
  • Waste Burning (Cardboard and Paper)
  • Mixed waste burning
  • Cooking (Using Solid Biofuels - Wood, Charcoal, Briquettes)

All the data mentioned above were collected over 17 days, from 16th May to 1st June, 2023.

Vehicular emissions: three diesel pick-up trucks were sampled. The exhaust was monitored by the MA200, positioned approximately 3 meters from the vehicle’s exhaust, after the vehicle engine was started. Monitoring was conducted for 20 minutes. Also, mobile monitoring was carried out on three heavily trafficked roads during peak traffic hours, with a duration of 20 minutes on each road.

Open waste burning emissions (individual components burning): various waste components (plastics, textiles, cardboard and paper, wood, and leaves) were burned in a semi-open guard shelter. The shelter, covered on three sides and open on one side, allowed for burning at the edge of the open side. The MA200 monitor was positioned at a 3-meter distance from the open side, with the micro-cyclone attached to MA200 placed 1.5 meters above the ground. The amount of waste components burned was carefully determined through trial and error, ensuring concentrations remained within desired levels while burning for a sufficient duration. A consistent 20-minute burning duration was chosen to align with vehicle monitoring. Each waste type was burned in three replicates, with a minimum half-an-hour gap between each experiment, during which the shelter was ventilated.

Mixed waste burning: six known mixed waste dumps across the city were monitored, each for a 20-minute duration.

Cooking emissions: the MA200 was placed at a guardian shelter in Queens, known for using firewood for cooking. The micro-cyclone was positioned 1.5 meters above the ground and 3 meters from the front windows section of the shelter. Sampling was conducted for 20 minutes on three days during peak cooking times.

library(bcsa)

The df_aae data set has 21 variables and 1199 observations. For an overview of the variable names, see the following table.

df_aae |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
serial_number session_id date time lat long uv_bcc blue_bcc ir_bcc uv_babs blue_babs ir_babs date_time day_type id date_start date_end start_time end_time exp_type emission_source
MA200-0416 74 2023-05-16 16:59:30 -15.80290 35.01876 443154 260559 164890 8204825.9 3822200.1 1283605.2 2023-05-16 16:59:30 weekday 121 2023-05-16 2023-05-16 2023-05-16 16:55:00 2023-05-16 17:15:00 cooking Wood
MA200-0416 74 2023-05-16 17:00:00 -15.80290 35.01874 210422 137354 88891 3895882.4 2014877.5 691982.2 2023-05-16 17:00:00 weekday 121 2023-05-16 2023-05-16 2023-05-16 16:55:00 2023-05-16 17:15:00 cooking Wood
MA200-0416 74 2023-05-16 17:00:30 -15.80289 35.01873 734252 520106 293355 13594393.4 7629554.9 2283655.8 2023-05-16 17:00:30 weekday 121 2023-05-16 2023-05-16 2023-05-16 16:55:00 2023-05-16 17:15:00 cooking Wood
MA200-0416 74 2023-05-16 17:01:00 -15.80288 35.01872 671815 465730 265236 12438396.3 6831900.8 2064760.2 2023-05-16 17:01:00 weekday 121 2023-05-16 2023-05-16 2023-05-16 16:55:00 2023-05-16 17:15:00 cooking Wood
MA200-0416 74 2023-05-16 17:01:30 -15.80289 35.01872 45410 36755 24193 840748.7 539167.6 188333.2 2023-05-16 17:01:30 weekday 121 2023-05-16 2023-05-16 2023-05-16 16:55:00 2023-05-16 17:15:00 cooking Wood
MA200-0416 74 2023-05-16 17:02:00 -15.80289 35.01872 1112057 700612 365036 20589307.6 10277439.1 2841664.9 2023-05-16 17:02:00 weekday 121 2023-05-16 2023-05-16 2023-05-16 16:55:00 2023-05-16 17:15:00 cooking Wood
variable_name variable_type description
serial_number character serial number of the MA200 monitoring from which the data is collected
session_id double session number of the MA200 monitor (each monitoring session is automatically given a number in the output file of MA200 monitoring data)
date double date of monitoring
time double time of monitoring
lat double latitude of location of monitoring
long double longitude of location of monitoring
uv_bcc double concentration of black carbon at the UV (ultravoilet) wavelength channel in ng/m3
blue_bcc double concentration of black carbon at the Blue wavelength channel in ng/m3
ir_bcc double concentration of black carbon at the IR (infrared) wavelength channel in ng/m3
uv_babs double absorption coefficient of black carbon at the UV (ultravoilet) wavelength channel
blue_babs double absorption coefficient of black carbon at the Blue wavelength channel
ir_babs double absorption coefficient of black carbon at the IR (infrared) wavelength channel
date_time double date and time of monitoring
day_type character type of day of monitoring - weekend or weeday
id double id is a unique identifier given to every monitoring session and experiment given in the data structure
date_start double starting date of the experiment
date_end double end date of the experiment
start_time double starting time of the experiment
end_time double end time of the experiment
exp_type character type of experiment
emission_source character source of emission

df_mm

Mobile monitoring data in eight settlements in Blantyre, Malawi. The data was collected from eight settlements (four planned and four unplanned settlements.

Mobile monitoring utilised a vehicle equipped with a portable MA200 instrument. The micro-cyclone was positioned outside the car’s front window at an elevation of 1.5 meters above the ground. The vehicle was driven at a speed of less than 20 km/h.

The monitoring took place from 3rd May to 14th May, 2023, covering both weekdays and weekends.

The df_mm data set has 23 variables and 3956 observations. For an overview of the variable names, see the following table.

df_mm |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
serial_number session_id date time lat long uv_bcc blue_bcc ir_bcc uv_babs blue_babs ir_babs date_time day_type id date_start date_end start_time end_time exp_type settlement_id time_of_day type_of_settlement
MA200-0416 54 2023-05-03 07:55:00 -15.79465 35.00386 5133 3451 1863 95035.52 50623.52 14502.74 2023-05-03 07:55:00 weekday 3 2023-05-03 2023-05-03 2023-05-03 07:55:00 2023-05-03 08:41:00 mobile_monitoring Sunnyside Morning Formal
MA200-0416 54 2023-05-03 07:55:30 -15.79464 35.00386 10341 7767 6968 191459.64 113935.92 54243.20 2023-05-03 07:55:30 weekday 3 2023-05-03 2023-05-03 2023-05-03 07:55:00 2023-05-03 08:41:00 mobile_monitoring Sunnyside Morning Formal
MA200-0416 54 2023-05-03 07:56:00 -15.79457 35.00378 13207 7889 5247 244522.53 115725.56 40845.88 2023-05-03 07:56:00 weekday 3 2023-05-03 2023-05-03 2023-05-03 07:55:00 2023-05-03 08:41:00 mobile_monitoring Sunnyside Morning Formal
MA200-0416 54 2023-05-03 07:56:30 -15.79436 35.00269 8863 6042 3702 164095.04 88631.49 28818.65 2023-05-03 07:56:30 weekday 3 2023-05-03 2023-05-03 2023-05-03 07:55:00 2023-05-03 08:41:00 mobile_monitoring Sunnyside Morning Formal
MA200-0416 54 2023-05-03 07:57:00 -15.79510 35.00163 5884 4017 3281 108940.00 58926.30 25541.32 2023-05-03 07:57:00 weekday 3 2023-05-03 2023-05-03 2023-05-03 07:55:00 2023-05-03 08:41:00 mobile_monitoring Sunnyside Morning Formal
MA200-0416 54 2023-05-03 07:57:30 -15.79551 35.00019 5778 4320 4527 106977.45 63371.08 35240.95 2023-05-03 07:57:30 weekday 3 2023-05-03 2023-05-03 2023-05-03 07:55:00 2023-05-03 08:41:00 mobile_monitoring Sunnyside Morning Formal
variable_name variable_type description
serial_number character serial number of the MA200 monitoring from which the data is collected
session_id double session number of the MA200 monitor (each monitoring session is automatically given a number in the output file of MA200 monitoring data)
date double date of monitoring
time double time of monitoring
lat double latitude of location of monitoring
long double longitude of location of monitoring
uv_bcc double concentration of black carbon at the UV (ultravoilet) wavelength channel in ng/m3
blue_bcc double concentration of black carbon at the Blue wavelength channel in ng/m3
ir_bcc double concentration of black carbon at the IR (infrared) wavelength channel in ng/m3
uv_babs double absorption coefficient of black carbon at the UV (ultravoilet) wavelength channel
blue_babs double absorption coefficient of black carbon at the Blue wavelength channel
ir_babs double absorption coefficient of black carbon at the IR (infrared) wavelength channel
date_time double date and time of monitoring
day_type character type of day of monitoring - weekend or weeday
id double id is a unique identifier given to every monitoring session and experiment given in the data structure
date_start double starting date of the experiment
date_end double end date of the experiment
exp_type character type of experiment
settlement_id character settlement name at which the monitoring was conducted
time_of_day character the days are divided into three types - morning, first half and second half
type_of_settlement character type of settlement - formal or informal
start_time double starting time of the experiment
end_time double ending time of the experiment

df_mm_road_type

This dataset is collected during mobile monitoring and is the same as df_mm, except that the highways and non-highways are demarcated in this dataset.

The df_mm_road_type data set has 25 variables and 3540 observations. For an overview of the variable names, see the following table.

df_mm_road_type |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
serial_number session_id date time lat long uv_bcc blue_bcc ir_bcc uv_babs blue_babs ir_babs date_time day_type id date_start date_end exp_type id_road_type settlement_id time_of_day type_of_settlement start_time end_time type_of_road
MA200-0416 56 2023-05-04 07:31:00 -15.78823 35.00268 16027 15219 12866 296733.7 223251.02 100156.86 2023-05-04 07:31:00 weekday 5 2023-05-04 2023-05-04 mobile_monitoring 8 Namiwawa Morning Formal 2023-05-04 07:31:00 2023-05-04 07:59:00 non_main_road
MA200-0416 56 2023-05-04 07:31:30 -15.78809 35.00201 11694 10641 11200 216509.9 156095.28 87187.69 2023-05-04 07:31:30 weekday 5 2023-05-04 2023-05-04 mobile_monitoring 8 Namiwawa Morning Formal 2023-05-04 07:31:00 2023-05-04 07:59:00 non_main_road
MA200-0416 56 2023-05-04 07:32:00 -15.78790 35.00087 9640 8732 7078 178480.9 128091.72 55099.51 2023-05-04 07:32:00 weekday 5 2023-05-04 2023-05-04 mobile_monitoring 8 Namiwawa Morning Formal 2023-05-04 07:31:00 2023-05-04 07:59:00 non_main_road
MA200-0416 56 2023-05-04 07:32:30 -15.78694 35.00091 7644 6563 5915 141525.7 96274.16 46046.00 2023-05-04 07:32:30 weekday 5 2023-05-04 2023-05-04 mobile_monitoring 8 Namiwawa Morning Formal 2023-05-04 07:31:00 2023-05-04 07:59:00 non_main_road
MA200-0416 56 2023-05-04 07:33:00 -15.78651 34.99960 6115 5634 5501 113216.9 82646.45 42823.17 2023-05-04 07:33:00 weekday 5 2023-05-04 2023-05-04 mobile_monitoring 8 Namiwawa Morning Formal 2023-05-04 07:31:00 2023-05-04 07:59:00 non_main_road
MA200-0416 56 2023-05-04 07:33:30 -15.78631 34.99837 5744 4445 4282 106348.0 65204.73 33333.72 2023-05-04 07:33:30 weekday 5 2023-05-04 2023-05-04 mobile_monitoring 8 Namiwawa Morning Formal 2023-05-04 07:31:00 2023-05-04 07:59:00 non_main_road
variable_name variable_type description
serial_number character serial number of the MA200 monitoring from which the data is collected
session_id double session number of the MA200 monitor (each monitoring session is automatically given a number in the output file of MA200 monitoring data)
date double date of monitoring
time double time of monitoring
lat double latitude of location of monitoring
long double longitude of location of monitoring
uv_bcc double concentration of black carbon at the UV (ultravoilet) wavelength channel in ng/m3
blue_bcc double concentration of black carbon at the Blue wavelength channel in ng/m3
ir_bcc double concentration of black carbon at the IR (infrared) wavelength channel in ng/m3
uv_babs double absorption coefficient of black carbon at the UV (ultravoilet) wavelength channel
blue_babs double absorption coefficient of black carbon at the Blue wavelength channel
ir_babs double absorption coefficient of black carbon at the IR (infrared) wavelength channel
date_time double date and time of monitoring
day_type character type of day of monitoring - weekend or weeday
id double id is a unique identifier given to every monitoring session and experiment given in the data structure
date_start double starting date of the experiment
date_end double end date of the experiment
start_time double starting time of the experiment
end_time double end time of the experiment
exp_type character type of experiment
settlement_id character settlement name at which the monitoring was conducted
time_of_day character the days are divided into three types - morning, first half and second half
type_of_settlement character type of settlement - formal or informal
id_road_type double a unique id given to different roads sections
type_of_road character roads are categorised as highways and non-highways

df_pm

Personal monitoring data in four unplanned settlements in Blantyre, Malawi, covering the areas inaccesible by vehicles due to narrow and undefined unpaved roads.

For personal mobile monitoring, an individual carried the monitoring equipment and conducted on-foot surveys within the informal settlements. The micro-cyclone was attached at the collar of the person.

This method was implemented twice in each settlement, from 9:00 a.m. to 11:30 a.m. and from 2:00 p.m. to 4:30 p.m., between the 19th to 25th of May, 2023, covering weekdays.

The df_pm data set has 23 variables and 1108 observations. For an overview of the variable names, see the following table.

df_pm |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
serial_number session_id date time lat long uv_bcc blue_bcc ir_bcc uv_babs blue_babs ir_babs date_time day_type id date_start date_end start_time end_time exp_type settlement_id time_of_day type_of_settlement
MA200-0416 80 2023-05-24 09:53:00 -15.78791 35.08536 2238 1178 1722 41435.71 17280.35 13405.11 2023-05-24 09:53:00 weekday 94 2023-05-24 2023-05-24 2023-05-24 09:53:00 2023-05-24 11:17:00 personal_monitoring Kacheri first_half Informal
MA200-0416 80 2023-05-24 09:53:30 -15.78809 35.08543 4617 2166 2059 85481.98 31773.55 16028.52 2023-05-24 09:53:30 weekday 94 2023-05-24 2023-05-24 2023-05-24 09:53:00 2023-05-24 11:17:00 personal_monitoring Kacheri first_half Informal
MA200-0416 80 2023-05-24 09:54:00 -15.78815 35.08557 2981 1892 1681 55192.07 27754.18 13085.94 2023-05-24 09:54:00 weekday 94 2023-05-24 2023-05-24 2023-05-24 09:53:00 2023-05-24 11:17:00 personal_monitoring Kacheri first_half Informal
MA200-0416 80 2023-05-24 09:54:30 -15.78822 35.08584 2725 1547 1769 50452.33 22693.30 13770.98 2023-05-24 09:54:30 weekday 94 2023-05-24 2023-05-24 2023-05-24 09:53:00 2023-05-24 11:17:00 personal_monitoring Kacheri first_half Informal
MA200-0416 80 2023-05-24 09:55:00 -15.78835 35.08589 2037 1174 1549 37714.27 17221.68 12058.37 2023-05-24 09:55:00 weekday 94 2023-05-24 2023-05-24 2023-05-24 09:53:00 2023-05-24 11:17:00 personal_monitoring Kacheri first_half Informal
MA200-0416 80 2023-05-24 09:55:30 -15.78856 35.08579 1833 966 1641 33937.29 14170.48 12774.55 2023-05-24 09:55:30 weekday 94 2023-05-24 2023-05-24 2023-05-24 09:53:00 2023-05-24 11:17:00 personal_monitoring Kacheri first_half Informal
variable_name variable_type description
serial_number character serial number of the MA200 monitoring from which the data is collected
session_id double session number of the MA200 monitor (each monitoring session is automatically given a number in the output file of MA200 monitoring data)
date double date of monitoring
time double time of monitoring
lat double latitude of location of monitoring
long double longitude of location of monitoring
uv_bcc double concentration of black carbon at the UV (ultravoilet) wavelength channel in ng/m3
blue_bcc double concentration of black carbon at the Blue wavelength channel in ng/m3
ir_bcc double concentration of black carbon at the IR (infrared) wavelength channel in ng/m3
uv_babs double absorption coefficient of black carbon at the UV (ultravoilet) wavelength channel
blue_babs double absorption coefficient of black carbon at the Blue wavelength channel
ir_babs double absorption coefficient of black carbon at the IR (infrared) wavelength channel
date_time double date and time of monitoring
day_type character type of day of monitoring - weekend or weeday
id double id is a unique identifier given to every monitoring session and experiment given in the data structure
date_start double starting date of the experiment
date_end double end date of the experiment
start_time double starting time of the experiment
end_time double end time of the experiment
exp_type character type of experiment
settlement_id character settlement name at which the monitoring was conducted
time_of_day character the days are divided into three types - morning, first half and second half
type_of_settlement character type of settlement - formal or informal

df_pm_trips

While conducting the personal monitoring, the individual also recorded the times when the open waste burning. The times when the individual observed the burning events is given in this dataset.

The df_pm_trips data set has 11 variables and 81 observations. For an overview of the variable names, see the following table.

df_pm_trips |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
id serial_number session_id date_start date_end start_time end_time exp_type event time settlement_id
89 MA200-0420 59 2023-05-19 2023-05-19 2023-05-19 09:25:00 2023-05-19 10:54:00 personal_monitoring burning 09:26:00 Ndirande
89 MA200-0420 59 2023-05-19 2023-05-19 2023-05-19 09:25:00 2023-05-19 10:54:00 personal_monitoring burning 09:28:00 Ndirande
89 MA200-0420 59 2023-05-19 2023-05-19 2023-05-19 09:25:00 2023-05-19 10:54:00 personal_monitoring burning 09:30:00 Ndirande
89 MA200-0420 59 2023-05-19 2023-05-19 2023-05-19 09:25:00 2023-05-19 10:54:00 personal_monitoring burning 09:39:00 Ndirande
89 MA200-0420 59 2023-05-19 2023-05-19 2023-05-19 09:25:00 2023-05-19 10:54:00 personal_monitoring burning 09:43:00 Ndirande
89 MA200-0420 59 2023-05-19 2023-05-19 2023-05-19 09:25:00 2023-05-19 10:54:00 personal_monitoring burning 09:46:00 Ndirande
variable_name variable_type description
serial_number character serial number of the MA200 monitoring from which the data is collected
session_id double session number of the MA200 monitor (each monitoring session is automatically given a number in the output file of MA200 monitoring data)
date double date of monitoring
time double time when a burning event was observed
id double id is a unique identifier given to every monitoring session and experiment given in the data structure
date_start double starting date of the experiment
date_end double ending date of the experiment
start_time double starting time of the experiment
end_time double ending time of the experiment
exp_type character type of experiment
settlement_id character settlement name at which the monitoring was conducted

df_sm

To capture the ambient concentration and diurnal pattern of LAC, stationary monitoring was conducted in two specific areas, one planned and one unplanned. The monitoring campaign spanned from 13th July to 22nd August during the winter season.

The df_sm data set has 21 variables and 20756 observations. For an overview of the variable names, see the following table.

df_sm |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
serial_number session_id date time lat long uv_bcc blue_bcc ir_bcc uv_babs blue_babs ir_babs date_time day_type id date_start date_end start_time end_time exp_type settlement_id
MA200-0416 107 2023-07-13 08:35:00 0 0 2696 2345 2339 49915.40 34399.35 18208.22 2023-07-13 08:35:00 weekday 131 2023-07-13 2023-07-14 2023-07-13 08:25:00 2023-07-14 08:30:00 stationary_monitoring Ndirande
MA200-0416 107 2023-07-13 08:40:00 0 0 2412 2091 2006 44657.25 30673.36 15615.94 2023-07-13 08:40:00 weekday 131 2023-07-13 2023-07-14 2023-07-13 08:25:00 2023-07-14 08:30:00 stationary_monitoring Ndirande
MA200-0416 107 2023-07-13 08:45:00 0 0 2334 2035 2009 43213.11 29851.88 15639.29 2023-07-13 08:45:00 weekday 131 2023-07-13 2023-07-14 2023-07-13 08:25:00 2023-07-14 08:30:00 stationary_monitoring Ndirande
MA200-0416 107 2023-07-13 08:50:00 0 0 1795 1437 1323 33233.73 21079.68 10299.05 2023-07-13 08:50:00 weekday 131 2023-07-13 2023-07-14 2023-07-13 08:25:00 2023-07-14 08:30:00 stationary_monitoring Ndirande
MA200-0416 107 2023-07-13 08:55:00 0 0 2253 1755 1565 41713.43 25744.50 12182.92 2023-07-13 08:55:00 weekday 131 2023-07-13 2023-07-14 2023-07-13 08:25:00 2023-07-14 08:30:00 stationary_monitoring Ndirande
MA200-0416 107 2023-07-13 09:00:00 0 0 2888 2094 1765

About

The bcsa package provide datasets for source apportionment of light absorbing carbon (LAC) in Blantyre, Malawi. The package contains data on Absorption Angstrom Exponent experiments determination of local pollution sources. The package also contains data on spatial distribution and ambient concentrations of LAC concentrations.

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