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Use Case 23: Air Quality at Continental Scale

License: MIT License

R 7.09% Python 92.91%

usecase_aircon's Introduction

OEMC Use Case 23: Air Quality Monitoring at Continental Scale

The goal is to create annual, monthly, daily, and potentially hourly maps of four key air quality indicators (NO2, O3, SO2, PM10, PM2.5). Hourly data from about 7,000 EEA measurement stations throughout the EU serves as basis for interpolation. Supplementary variables such as population density, land cover, climatology are further inputs to a universal Kriging method.

ToDo

Preprocess AQ

write download functions
write function to filter by quality flags
write function to filter for rural/urban/suburban & background/traffic
write function to join pollutant tables by country
download station data for all countries (2015-2023)
preprocess all station data

Gapfill hourly PM2.5

gapfill all PM2.5 with predictors according to EEA
- PM10
- coordinates
- population density
- sun shine duration
- ERA5 alternative: surface_net_solar_radiation

Temporal aggregates (annual, monthly, daily)

Supplementary data

Station-level (static)

Measurement-level (hourly)

CAMS data (atmospheric transport model outputs for each pollutant, hourly)
- reanalysis for 2015-2022
  - validated reanalysis: 2015-2020
  - interim reanalysis: 2021-2022
- forecasts for 2023 (3-year rolling archive)
- download
  - 2015-2022
  - 2023
ECWMF ERA5 Land data (hourly):
- wind speed (from u and v)
- surface net solar radiation
- temperature
- relative humidity (from temp. and dew point temp.)
- download
- calculate wind speed/direction & humidity
- temporal aggregates (daily, monthly, annual)
  - mean
  - percentiles
Sentinel-5P TROPOMI (daily)
- annual
- monthly
- daily

Interpolation

function to read aq data
function to read and warp covariates to a common grid
function wrapping lm
function for residual kriging in parallel

create 3 separate map layers based on…

rural background stations
urban/suburban background stations
urban/suburban traffic station
run interpolation test for one annual datasets (2015-2023)

Map merging

rasterize GRIP road data (area impacted by traffic)
- buffer and rasterize GRIP vector data for road classes 1-3
- distance to nearest road (by type)
function to merge the 3 map layers

Potential improvements to the current method

interpolation using
- standard Random Forest
- Random Forest with awareness for spatial correlation (RF-GLS)

Codeflow

Step	File	Description
1	`ACS_CAMS_access.R` `CCS_ERA5_access.R` `download_nc_job.R` `rename_nc.R`	Request hourly ERA5 weather and CAMS pollution data. Copy to URLs from the web interface and store in .txt file to iterate over for downloading. Rename files according to metadata.
2	`EEA_stations.qmd`	Create a spatial dataset with all AQ stations and supplement them with static covariates (elevation, population, land cover).
3	`EEA_AQ_data_access.qmd` `EEA_AQ_data_access_all_countries.R`	Download & pre-process hourly AQ measurements. This includes reading, filtering, and joining up to 4 pollutant time series per station for 2015-2023.
4	`xarray_extract_station_SSR.ipynb` `EEA_PM25_gapfilling_all_countries.qmd`	Extract hourly Surface Solar Radiation before gapfilling PM2.5 (only where PM10 is measured) using linear regression.
5	`xarray_dask_rel_humidity.ipynb` `xarray_dask_ws_wd.ipynb`	Process ERA5 wind vectors and temperature data to wind speed & direction and relative humidity.
6	`EEA_AQ_data_temporal_aggregation.qmd` `xarray_temp_aggregate.ipynb` `xarray_temp_aggregate_rolling.ipynb`	Aggregate AQ measurements and CAMS/ERA5 hourly data to annual, monthly, daily means/quantiles.
7	`AQ_interpolation_demo.qmd`	Interpolate AQ data using environmental and socio-economic covariates.

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