Post on 22-Jan-2016
description
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
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
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
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
Qualar - MeasurementsQualar - Measurements
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
Qualar - Air Quality IndexQualar - Air Quality Index
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
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
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
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
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
Air quality forecast for the area ofAir quality forecast for the area ofLisbon, Portugal Lisbon, Portugal
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
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
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
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
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
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
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
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
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
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
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
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.
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
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.