README.Rmd

---
output: github_document
always_allow_html: true
editor_options: 
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  markdown: 
    wrap: 72
execute:
  echo: false
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<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  message = FALSE,
  warning = FALSE,
  fig.retina = 2,
  fig.align = 'center'
)

library(tidyverse)
library(bcsa)
```

# bcsa

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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](https://github.com/) with:

```{r installation, eval=FALSE, echo=TRUE}
# 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.

```{r, echo=FALSE, message=FALSE, warning=FALSE}

extdata_path <- "https://github.com/Global-Health-Engineering/bcsa/raw/main/inst/extdata/"

read_csv("data-raw/dictionary.csv") |> 
  distinct(file_name) |> 
  dplyr::mutate(file_name = str_remove(file_name, ".rda")) |> 
  dplyr::rename(dataset = file_name) |> 
  mutate(
    CSV = paste0("[Download CSV](", extdata_path, dataset, ".csv)"),
    XLSX = paste0("[Download XLSX](", extdata_path, dataset, ".xlsx)")
  ) |> 
  knitr::kable()

```

# 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.

```{r}
library(bcsa)
```

The `df_aae` data set has `r ncol(df_aae)` variables and
`r nrow(df_aae)` observations. For an overview of the variable names,
see the following table.

```{r, eval=TRUE}
df_aae |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
```

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "df_aae.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling() |> 
  kableExtra::scroll_box(height = "400px")
```

## 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 `r ncol(df_mm)` variables and `r nrow(df_mm)`
observations. For an overview of the variable names, see the following
table.

```{r, eval=TRUE}
df_mm |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
```

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "df_mm.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling() |> 
  kableExtra::scroll_box(height = "400px")
```

## 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 `r ncol(df_mm_road_type)` variables
and `r nrow(df_mm_road_type)` observations. For an overview of the
variable names, see the following table.

```{r, eval=TRUE}
df_mm_road_type |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
```

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "df_mm_road_type.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling() |> 
  kableExtra::scroll_box(height = "400px")
```

## 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 `r ncol(df_pm)` variables and `r nrow(df_pm)`
observations. For an overview of the variable names, see the following
table.

```{r, eval=TRUE}
df_pm |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
```

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "df_pm.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling() |> 
  kableExtra::scroll_box(height = "400px")
```

## 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 `r ncol(df_pm_trips)` variables and
`r nrow(df_pm_trips)` observations. For an overview of the variable
names, see the following table.

```{r, eval=TRUE}
df_pm_trips |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
```

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "df_pm_trips.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling() |> 
  kableExtra::scroll_box(height = "400px")
```

## 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 `r ncol(df_sm)` variables and `r nrow(df_sm)`
observations. For an overview of the variable names, see the following
table.

```{r, eval=TRUE}
df_sm |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
```

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "df_sm.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling() |> 
  kableExtra::scroll_box(height = "400px")
```

## df_collocation

For quality assurance, the two sensors were collocated multiple times
throughout the study: twice during mobile monitoring, twice during AAE
experiments, and before and after stationary monitoring.

The `df_collocation` data set has `r ncol(df_collocation)` variables and
`r nrow(df_collocation)` observations. For an overview of the variable
names, see the following table.

```{r, eval=TRUE}
df_collocation |> 
  head() |> 
  gt::gt() |>
  gt::as_raw_html()
```


```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |> 
  dplyr::filter(file_name == "df_collocation.rda") |> 
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling() |> 
  kableExtra::scroll_box(height = "400px")
```

## License

Data are available as
[CC-BY](https://github.com/Global-Health-Engineering/bcsa/blob/main/LICENSE.md).

## Citation

Please cite this package using:

```{r}
citation("bcsa")
```

## Additional data use information