1. Air Quality Assessment and Management
Land use patterns and spatial interpolation of air pollution monitoring data
Stijn Janssen1, Clemens Mensink1,
Gerwin Dumont2 and Frans Fierens2
(1) VITO, Belgium
(2) IRCEL, Belgium
11th EIONET Workshop on
Air Quality Assessment and Management
La Rochelle, October 26-27, 2006

2. Introduction
Belgium is a rather small country with highly urbanized regions
Air quality is sampled by a dense network of monitoring sites
e.g. more than 50 stations for NO2 and PM10 in Belgium
Air quality is forecasted by statistical models
forecasts for monitoring sites only
For monitoring and forecast data: pollution levels are only representative for POINT locations

3. Introduction
Real-time measurements and daily forecasts are published one-line by IRCEL
Need for reliable maps to inform the public
How to interpolate point values to an air quality map? ?

4. Introduction

5. RIO-model: Methodology
Observation:
Sampling values depend on land use in (direct) vicinity of the monitoring site
Conclusion:
Interpolation schema needs to know this relation between land use and air quality levels
Approach in RIO-model:
Quantify this at a statistical level (mean and standard deviation)
Create land use indicator to express relation

6. RIO-model: Land use indicator
2 km
Land use indicator
For each station:
Determine buffer (e.g. 2km radius)
Characterize land use by CORINE class distribution inside buffer

7. RIO-model: Land use indicator
Land use indicator is based on CORINE class distribution
Calibration of coefficients ai:
multi-regression to optimize trend for mean and standard dev. of monitoring data
<NO2>

8. RIO-model: Trends Trends in mean and standard dev. of sampling values:
sNO2
<O3>
<NO2>
<PM10>
sO3
sPM10

9. week/weekend variations
RIO-model: Trends
Hourly and week/weekend variations in trends
hourly variations
week/weekend variations

10. RIO-model: Detrending
Use relation between land use indicator and AQ statistics to “detrend” monitoring data:
Remove local character of sampling values

11. RIO-model: Interpolation
Detrended sampling values are interpolated with ordinary Kriging
Correlation function R(r) based on historical time series
Much more information than in standard Kriging

12. RIO-model: Spatial correlation
Spatial correlation functions depend on:
Pollutant (O3, NO2, PM10,…)
Aggregation value (day avg, max1h, max8h, hourly value,…)

13. RIO-model: Methodology
For each grid cell:
Interpolate detrended values with Kriging
Determine local bCORINE-value
Get corresponding trend shift
Add trend to interpolation result

14. RIO-model: Validation
Validation: leaving-one-out. Compare with standard IDW O3
NO2
PM10

15. RIO: Results Real-time NO2 concentrations for 25/03/2003
(max 1h values)
IDW
RIO

16. RIO-model: Results Real-time PM10 concentrations for 24/03/2005
(day average values)
IDW
RIO

17. RIO-model: Results
Year average O3 concentrations for 2002
IDW
RIO

18. RIO-model: Results
Year average NO2 concentrations for 2002
IDW
RIO

19. RIO-model: Results
Year average PM10 concentrations for 2002
IDW
RIO

20. RIO-model: Results
Year average PM10 concentrations for 2005
IDW
RIO

21. MODIS AOD 2003, Koelemeijer et al, Atm. Env. 40, 5304, 2006
RIO-model: Validation
Validation: Compare PM10-map with Aerosol Optical Depth satellite measurements (2003)
MODIS AOD 2003, Koelemeijer et al, Atm. Env. 40, 5304, 2006

22. RIO-model: Extensions
Relation between land use indicator and air quality statistics can be used for:
Assessment of spatial representativeness of monitoring locations
Downscaling of model results: redistribution of model concentration inside grid cell
Land use indicator can be optimized with satellite data
Aerosol Optical Depth for PM10

23. Conclusion
RIO is an interpolation scheme for ambient air pollution (O3, NO2,PM10 ,… )
Applicable for historical, real-time or forecast values
Kriging is used as interpolation tool
Land use model is applied to incorporate local patterns
Detrending is essential step for interpolation of air quality values