Comparison of air quality model

results with urban

measurements in Budapest, Hungary

Ádám Leelőssy, Attila Kovács, István Lagzi, Adrienn Balogh, Róbert Mészáros

Department of Meteorology, Eötvös Loránd University, Budapest

Problem statement

Problem:

High and frequent PM10 air pollution in the winters in Budapest

Public and political focus on forecasting & understanding

Challenges:

Very sensitive on local sources (domestic heating)

Emissions are very uncertain and variable (domestic heating)

Large intra-urban variability of air quality

Low-level inversions

Possibilities:

Good observed data availability (12 sites within Budapest with hourly samples)

Temporal stationarity of smog events (several days of anticyclonic weather)

Are smog events predictable by CAMS models?

Is the CAMS model error smaller than intra-urban variability?

Is there a „best” CAMS model for Budapest?

Is CAMS better than naive (persistence) forecasting?

Problem statement

Problem:

High and frequent PM10 air pollution in the winters in Budapest

Public and political focus on forecasting & understanding

Challenges:

Very sensitive on local sources (domestic heating)

Emissions are very uncertain and variable (domestic heating)

Large intra-urban variability of air quality

Low-level inversions

Possibilities:

Good observed data availability (12 sites within Budapest with hourly samples)

Temporal stationarity of smog events (several days of anticyclonic weather)

Are smog events predictable by CAMS models?

Is the CAMS model error smaller than intra-urban variability?

Is there a „best” CAMS model for Budapest?

Is CAMS better than naive (persistence) forecasting?

Problem statement

Problem:

High and frequent PM10 air pollution in the winters in Budapest

Public and political focus on forecasting & understanding

Challenges:

Very sensitive on local sources (domestic heating)

Emissions are very uncertain and variable (domestic heating)

Large intra-urban variability of air quality

Low-level inversions

Possibilities:

Good observed data availability (12 sites within Budapest with hourly samples)

Temporal stationarity of smog events (several days of anticyclonic weather)

Are smog events predictable by CAMS models?

Is the CAMS model error smaller than intra-urban variability?

Is there a „best” CAMS model for Budapest?

Is CAMS better than naive (persistence) forecasting?

Data sources

Period: 2018.12.01. – 2019.02.28.

Observed hourly PM10 data from 12 sites of the HungarianAir Quality Monitoring Network (OLM)

2% missing data

concentrations >200 μg/m3 removed (7 obs.)

1st January 0-1 UTC removed

Predicted hourly surface PM10 data of the CAMS models

0-23-hour forecasts

nearest gridpoint (values from 6 gridpoints used)

Data sources

Period: 2018.12.01. – 2019.02.28.

Observed hourly PM10 data from 12 sites of the HungarianAir Quality Monitoring Network (OLM)

2% missing data

concentrations >200 μg/m3 removed (7 obs.)

1st January 0-1 UTC removed

Predicted hourly surface PM10 data of the CAMS models

0-23-hour forecasts

nearest gridpoint (values from 6 gridpoints used)

Data sources

Period: 2018.12.01. – 2019.02.28.

Observed hourly PM10 data from 12 sites of the HungarianAir Quality Monitoring Network (OLM)

2% missing data

concentrations >200 μg/m3 removed (7 obs.)

1st January 0-1 UTC removed

Predicted hourly surface PM10 data of the CAMS models

0-23-hour forecasts

nearest gridpoint (values from 6 gridpoints used)

Winter air pollution in Budapest, 2018-2019

Histograms of hourly PM10 concentrations

Scatter plots of

hourly PM10

concentrations

Pearson correlation (incl. seasonality)

Spearman correlation (incl. seasonality)

RMSE

BIAS

Ratio of observations >50 μg/m3

CAQI accuracy

Community Air Quality Index (CAQ) PM10 concentration [μg/m3]

Very low 0-25

Low 25-50

Medium 50-75

High 75-100

Very high >100

0

0,1

0,2

0,3

0,4

0,5

Very low Low Medium High Very high

Relative frequencies of CAQI categories in Budapest during the winter 2018-2019

CAQI accuracy

Exceedance sensitivity(predicted vs. all exceedances above the 50 μg/m3 threshold)

Conclusions

SILAM forecasts were better than persistance

SILAM uncertainties were comparable to intra-urban variability

Serious underestimation of polluted events in the other models

Best CAQI accuracies and exceedance sensitivities didn’t reach 60%

Only a case study of one winter

Conclusions

SILAM forecasts were better than persistance

SILAM uncertainties were comparable to intra-urban variability

Serious underestimation of polluted events in the other models

Best CAQI accuracies and exceedance sensitivities didn’t reach 60%

Only a case study of one winter

Thank you for your attention!