Statistical downscaling of ambient air PM concentrations using AOD from TROPOMI data

M-GEO
M-SE
ACQUAL
Staff Involved
M-SE Core knowledge areas
Spatial Information Science (SIS)
Additional Remarks

No fieldwork is required.

Topic description

The growing public health concerns about ambient air quality such as particulate matter (PM) concentrations suggest the need for a holistic monitoring to guide interventions. However, monitoring stations are spatially sparse and preferentially located in urban communities, and often with missing data problems. The growing interest on the use of satellite-retrieved aerosol optical depth (AOD) for monitoring ambient concentrations for particulate matter is well placed. Unlike ground monitoring stations, satellite data have the advantage of board spatial coverage. Calibrating stellate retrieved AOD through statistical downscaling can essentially provide high spatial and temporal resolution information on ambient PM concentrations. AOD retrieved from MODIS has often been used for calibration. The TROPOspheric Monitoring Instrument (TROPOMI) is the satellite instrument on board the Copernicus Sentinel-5 Precursor satellite presents a higher potential for AOD calibration due to its with higher spatial resolution. But AOD retrieved from TROPOMI is yet to be tested for predicting PM concentrations.

Topic objectives and methodology

The objective is to develop a space-time statistical downscaling method for predicting PM concentrations from AOD retrieved from TROPOMI at the European scale. Data required include PM observations from European monitoring stations and AOD from TROPOMI. The statistical methodology will involve decomposing PM concentrations into space-time deterministic and stochastic components. The deterministic component will include space-time varying slopes while the stochastic component will include space-time time varying intercepts. The modelling structure should be hierarchical, hence estimation using Bayesian’s approach is advised.