7 Case Study - Alternate PM_2.5 surfaces

The PM2.5 exposure surfaces used in the provided pipeline comes from Surface PM_2.5 V4.GL.02 dataset developed by van Donkelaar, A. et al. (2016). Alternative models of PM_2.5 differing in method, temporal or spatial coverage and/or resolution which are more suited to your particular study may be used as the input exposure rasters.

To use different PM_2.5 surfaces, replace the call to function import_globalgwr_pm25_2010_2015 in _targets.R with targets importing and tidying the new raster data.

A satellite-derived land use regression (SatLUR) modelled PM2.5 surface produced by Luke Knibbs is available on request.

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Input files for van Donkelaar’s PM_2.5 rasters are specified in target input_globalgwr_pm25_2010_2015. These files are subsequently cleaned in target tidy_env_exposure_pm25 with a custom function do_tidy_env_exposure_pm25. The input raster files are read and stacked into a multilayer raster, with names and time attribute of the layers assigned with the appropriate year.

To replace these input data with a set of different source rasters, substitute the input_globalgwr_pm25_2010_2015 with a target pointing to the new input exposure raster files. Read and tidy the data in a function replacing do_tidy_env_exposure_pm25, producing a SpatRaster with time attribute attached. Your code should look similar to the following.

inputs <- list(
  ...,

  # exposure rasters
  tar_files_input(
    infile_pm25,
    file.path(
      "YOUR_DATA_DIR",
      sprintf("SatPM25_2000_2015/data_derived_rasters/satlur_pm25_ug_m3_%s.tif",
              years)
      )
    ),
  ...
)

Change the processing steps in do_tidy_env_exposure_pm25 as needed to tidy the raster input.