rhap
is a package designed to estimate health impacts attributable to household air pollution (HAP) associated with alternative scenarios simulated using the Global Change Analysis Model (GCAM).
You can install the rhap
package in two ways:
Make sure the following software is installed:
Open RStudio
Run the following in the R Console:
install.packages("devtools") # Skip if already installed
devtools::install_github("bc3LC/rhap")
Cloning the repository is recommended if you want full access to the project structure, input files, and examples.
Make sure the following software is installed:
The rhap
package includes a built-in .dat
file that lets you run a test case based on a GCAM baseline scenario. This is a great way to verify that the package is working correctly and to explore its core functionality.
You can run this example with or without cloning the full repository, as the required data is included in the package:
test_rhap <- rhap::test_rhap
For a step-by-step walkthrough of the example, visit the official guide: Run a baseline scenario
The primary function offered by this package is calc_hap_impacts
, designed to estimate health impacts attributable to household air pollution (HAP) across a range of alternative GCAM scenarios. This function estimates scenario-specific health outcomes by seamlessly extracting and processing relevant data from GCAM databases or project files. The extracted data encompasses critical socioeconomic and environmental parameters, enabling comprehensive analysis of HAP impacts. These parameters include per capita GDP, emissions of key pollutants such as primary PM2.5 (BC+OC), NOx, and VOCs, and per capita floorspace metrics. By integrating these diverse factors, calc_hap_impacts
facilitates detailed assessments of how variations in economic development, pollutant emissions, and living conditions influence human health under different policy or technological scenarios.
The package also includes an ancillary function, calc_ResidEm_grp
, which determines the contribution of each within-region consumer group to household air pollution (HAP), with results categorized by region, year, and pollutant. Users can customize their analysis by specifying the desired region, time period, and pollutant through adjustable parameters when calling the function. This flexibility enables targeted assessments of how various consumer groups influence HAP across specific contexts, providing valuable insights for research and policy development.