1. An experience on modelling-based assessment of the air quality within the Air Quality Directive framework
Ana Isabel Miranda, Isabel Ribeiro, Patrícia Fernandes, Alexandra Monteiro, Cristina Monteiro, Carlos Borrego

2. 2008 Air Quality Directive| air quality assessment
Assessment strategy depends on upper and lower assessment thresholds
Those fixed measurements may be supplemented by modelling techniques and/or indicative measurements to provide adequate information on the spatial distribution of the ambient air quality.
Fixed measurements shall be used
Upper assessment threshold
Combination of fixed measurements and modelling techniques and or indicative measurements may be used
Concentration
Lower assessment threshold
Modelling techniques or objective-estimation shall be sufficient
SO2, NO2, NOx, PM10, PM2,5, Pb, C6H6, CO

3. Upper and lower thresholds exceedances for 2006-2010 (5 years period).
Assessment for 2010 and 2011
Upper and lower thresholds exceedances for (5 years period).
Exceedances of upper and lower assessment thresholds shall be determined on the basis of concentration during the previous 5 years where sufficient data are available.
An assessment threshold shall be deemed to have been exceeded if it has beed exceed during at least 3 separate years out of those previous five years.

4. AQ assessment based on a combination of Modelling and Measuring values
The approach
Modelling
Monitoring
NO2, O3, PM10, PM2.5, SO2, CO, C6H6.
1. Monitoring stations selection and data treatment, for the period
2. Comparison with the upper and lower thresholds, for every pollutant
data treatment for the model evaluation
1. Model application to Portugal (5 km x 5 km), 2010 and 2011
2. Bias correction based on the multiplicative ratio adjustment technique
3. Evaluation (using the DELTA tool when possible)
AQ assessment based on a combination of Modelling and Measuring values

5. Meteorological conditions
MM5-EURAD
Emissions
Meteorological conditions

6. Simulation domains
125x125 km2
25x25 km2
5x5 km2

7. Emissions EMEP 2008 for the larger domains
Portuguese inventory 2008 for the portuguese domain
Industrial processes
Transports

8. Monitoring stations

9. Bias-correction techniques
We started to compare…
SUBST
an additive correction of the mean bias
RAT
a multiplicative ratio correction
First we start to compare two simple bias-correction: one using na additive correction and a multiplicative ratio, both tested using different period of previous days (3-4 and 7 days) to correct the forecasted bias

10. Bias-correction techniques RAT & SUBST
(Borrego et al., 2011, Atmospheric Environment)
Bias-correction techniques RAT & SUBST
PM10 O3
What we found is that ratio adustment using previous days was the most successful technique, comparing to the others…
In this sense, RAT will be focus of our work
after BIAS correction, model results have a decrease > 70% on the average systematic error
the multiplicative ratio: better correction technique
synoptic conditions are characterized by a 3-4 day period.

11. RDE = 15%; R = 0.63; bias = -1.01 µg.m-3; MSE = 4.31 µg.m-3
Validation
SO2
Hourly values – Calendário 2010
measured
modelled
RDE = 15%; R = 0.63; bias = µg.m-3; MSE = 4.31 µg.m-3

12. SO2 4th maximum of the daily averages (protection of human health)
Upper
Lower LV
A excedência dos limiares de avaliação superior e inferior deve ser determinada a partir das concentrações dos cinco anos anteriores, caso se encontrem disponíveis dados suficientes (eficiência mínima de 85%). Considera -se que um limiar de avaliação foi ultrapassado se tiver sido excedido em, pelo menos, três desses cinco anos.
Avaliação para os anos 2010 e 2011
Análise excedências LAI e LAS com base nos anos 2006 a 2010
Zones and agglomerations
Threshold values were not exceeded 14

13. SO2 4th maximum of the daily averages (protection of human health)
< LAT
LAT-UAT
> UAT
It was not possible to have the needed data everywhere
2010
2011

14. SO2 Annual average winter period (ecosystem protection) 2010 2011 16
< LAT
LAT-UAT
>UAT
2010
2011 16

15. SO2 GIS Annual average winter period (ecosystems protection)
Modelling results
CORINE Land Cover 2006
GIS 17

16. SO2
25th maximum hourly value LV
2010
2011 18

17. Validation
NO2
Hourly data

18. Validation
NO2
Hourly data

19. Zones and agglomerations
NO2
Annual mean
Upper
Lower LV
Zones and agglomerations 21

20. NO2
Annual mean LV
< LAT
LAT-UAT
> UAT
2010
2011 22

21. Population data at sub-municipality level
NO2
Annual mean
Modelling results
Population data at sub-municipality level
GIS
National roads network

22. Zones and agglomerations
NO2
19th maximum of the hourly averages (protection of human health)
Upper
Lower LV
Zones and agglomerations 24

23. NO2 19th maximum of the hourly averages 2011 2010 25 LV < LAT
LAT-UAT
> UAT
2010
2011 25

24. NO2
19th maximum of the hourly averages

25. NO2
Number of hours exceeding the LV
2010
2011 27

26. Validation
PM10
Daily average

27. Validation
PM10
Daily average

28. PM10
Annual average LV
< LAT
LAT-UAT
> UAT
2010
2011 30

29. PM10
Daily average LV
< LAT
LAT-UAT
> UAT
2010
2011 31

30. Final comments
This work was requested by the Portuguese Agency for the Environment.
It was presented to the national agency and to the different regional entities in charge of the air quality assessment in Portugal. Several comments and feedbacks were received.
They were very interested and willing to do a cost-benefit analysis of using modelling tools and reducing monitoring stations instead of keeping the maintenance costs they’re facing nowadays.
We’re doing 2012 and we’re going to increase spatial resolution.
Notwithstanding this modelling work no modelling-based report to the Comission was delivered.

31. But …
There is a strong difficulty to trust models and people is afraid of using them, because:
they were always working with AQ monitoring networks and that’s what they know
they think models are a kind of “monster” and they do not provide a really added value
February 2009

32. Attitude towards models changed and people are much more receptive to their use for air quality assessment.
Thanks to FAIRMODE!!!!

33. Thank you very much!!!