Qualar - from near to real-time air pollution data for Portugal to Ozone and PM10 forecast

Qualar - from near to real-time air pollution data for Portugal to Ozone and PM10 forecast

Cláudia Martins, Francisco Ferreira,Ana Teresa Perez, and Jorge Neto

Workshop on Real time air pollution data exchange Workshop on Real time air pollution data exchange and forecast in Europeand forecast in Europe

7-8 April 2005 Copenhagen (EEA)

QUALAR information system

Workshop on Real time air pollution data exchange and forecast in EuropeWorkshop on Real time air pollution data exchange and forecast in Europe 7-8 April 2005 Copenhagen

Process of data transmissionProcess of data transmission


Air Quality Monitoring Stations

QUALAROn-line Data Base

CCDRs – Commission of Coordination and Regional Development

ATMIS Application – collecting the data from AQMS and sending to

the data base server

Validation during weekdays

Environment Institute

Qualar - StationsQualar - Stations


Qualar - MeasurementsQualar - Measurements


Qualar - Air Quality IndexQualar - Air Quality Index


Very Good

Good

Medium

Weak

Bad

The daily index for Portugal is calculated for each zone(an agglomeration is also a zone) and is based on the average for each pollutant between all equipments inthe zone. The index is determined by the worst pollutantconcentration measured in one or more monitoring stations

Qualar – Air Quality IndexQualar – Air Quality Index


The existence of an index for a zone depends on the following conditions: 1. There must be at least one equipment for NO2, SO2, O3 e PM10 . Althoughit is used on the index calculation the existence of CO equipment is not mandatory;2. The efficiency of the measurements must be equal or greater then 75%

- Provisory Index available after 18h00, based on the resultsmeasured between 00h00 and 15h00;

- Definitive Index From the previous day available at the same time.

Qualar – ExceedancesQualar – Exceedances


Air quality forecast for the area ofAir quality forecast for the area ofLisbon, Portugal Lisbon, Portugal


MethodologyMethodology

- Methodologies already used (Casmassi, 1987; EPA, 2003; and others)

- Two years meteorological and air quality database (2001-2002)

- Subjective analysis of surface and 500mb synoptic situations

- Models construction:- Classification and Regression Tree (CART) analysis- multiple linear regression (MR) analysis

- Testing and validating the obtained models

7-8 April 2005 CopenhagenWorkshop on Real time air pollution data exchange and forecast in EuropeWorkshop on Real time air pollution data exchange and forecast in Europe

DataData used used

- Air quality data from the several stations of the case study area (O3, PM10, CO, NO2 and SO2)

- Surface meteorological data – Maximum temperature and average relative humidity from various cities, and pressure difference between Lisbon and other cities

- Altitude meteorological data for Lisbon - geopotencial height, temperature and relative humidity at 1200UTC for several levels

- Subjective analysis of synoptic situations (surface and 500 mb) at 1200UTC

- Day of week, flag “type of day”, and solar duration day


SynopticSynoptic situations situationsFrontal systems1– Frontal systemsLow pressure systems2 – Deepening low pressure (instability)3 – low pressure influenceHigh pressure systems4 – surface calm5 – N/NW circulation6 – High pressure and thermal through from the north of Africa7 – NE/E circulation

Surface

1 – Cut off low2 – Low pressure trough3 – Approaching trough or ridge breakdown4 – Building high pressure ridge or zonal flow5 – High pressure ridge5

00mb


CART analysisCART analysis

O3

Mean=78.755

SD=23.676

N=730

Mean=69.475

SD=15.885

N=541

O3_1<91.500

Mean=105.317

SD=22.173

N=189

Mean=95.416

SD=14.240

N=122

TX734<30.200

Mean=123.346

SD=22.747

N=67

Mean=61.350

SD=12.107

N=238

DD<10.760

Mean=75.856

SD=15.598

N=303

PM10

Mean=42.124

SD=21.446

N=730

Mean=33.811

SD=12.934

N=502

PM10_1<47.600

Mean=60.425

SD=24.871

N=228

Mean=27.376

SD=9.539

N=220

PM10_1<28.600

Mean=38.832

SD=13.025

N=282

Mean=46.011

SD=16.327

N=63

Mean=65.929

SD=25.400

N=165

HR850<59.000


Multiple regression modelsMultiple regression models

PMPM1010

PM10_1<47.6PM10_1<47.6 PM10_1>=47.6PM10_1>=47.6

PM10_1<28.PM10_1<28.66

PM10_1>=28PM10_1>=28.6.6

HR850>=5HR850>=599

HR850<5HR850<599

rr 0.9760.976 0.9730.973 0.9970.997 0.9810.981

rr22 0.9520.952 0.9460.946 0.9940.994 0.9620.962

Std.errorStd.error 6.7696.769 10.05410.054 6.0556.055 15.33815.338

NN 218218 280280 6161 164164

OO33

O3_1<91.5O3_1<91.5 O3_1>=91.5O3_1>=91.5

DD<10.76DD<10.76 DD>=10.76DD>=10.76 T734<30.2T734<30.2 T734>=30.2T734>=30.2

rr 0.9940.994 0.9920.992 0.9970.997 0.9990.999

rr22 0.9880.988 0.9840.984 0.9950.995 0.9970.997

Std.errorStd.error 7.4727.472 10.46910.469 8.3838.383 9.9269.926

NN 238238 303303 121121 6767


Modeling Test Modeling Test Summer 2003Summer 2003

0

20

40

60

80

100

120

19-06-0324-06-03

29-06-0304-07-03

09-07-0314-07-03

19-07-0324-07-03

29-07-0303-08-03

08-08-0313-08-03

18-08-0323-08-03

28-08-03

days

PM

10 [ g

/m3 ]

Observed values Predicted values

r=0.752

0

40

80

120

160

200

19-06-0324-06-03

29-06-0304-07-03

09-07-0314-07-03

19-07-0324-07-03

29-07-0303-08-03

08-08-0313-08-03

18-08-0323-08-03

28-08-03

days

O3

[g

/m3 ]

Observed values Predicted values

r=0.862


ResultsResults

• The model results achieved are very satisfactory (the correlation is always higher than 0.973)

• The model application results in other periods (year 2003) shows inferior correlations but still highly significant;

• A does not exist for PM10; therefore the final results strong relation between O3 and some meteorological parameters exists (temperature). Such a strong relationship with a variable are worse;

• Air quality index is also well predicted, since the two pollutants always determine the final result.

AvailabilityAvailability

• The model uses measurements from air quality and meteorology taken in the last 24 hours between 1500 GMT (the day before) and 1500 GMT (actual day);

• Predicted values for next day come out validated at 1700 GMT;

• Model application results to an out-of-trend year (2003) shows inferior correlations but still highly significant;

• Application is being extended first to Greater Porto Metropolitan Area agglomerations, and then to the all country;

• At the same time, a forecast physico-chemical mathematical model is being developed for the all country (in articulation with Universidade de Aveiro), and the integration of results from both is foreseen.

Qualar - from near to real-time air pollution data for Portugal to Ozone and PM10 forecast

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