Quantification of CO emissions from the city of madrid using MOPITT satellite retrievals and WRF simulations
The growth of mega-cities leads to air quality problems directly affecting the citizens. Satellite measurements are becoming of higher quality and quantity, which leads to more accurate satellite retrievals of enhanced air pollutant concentrations over large cities. In this paper, we compare and discuss both an existing and a new method for estimating urban-scale trends in CO emissions using multiyear retrievals from the MOPITT satellite instrument. The first method is mainly based on satellite data, and has the advantage of fewer assumptions, but also comes with uncertainties and limitations as shown in this paper. To improve the reliability of urban-To-regional scale emission trend estimation, we simulate MOPITT retrievals using the Weather Research and Forecast model with chemistry core (WRFChem). The difference between model and retrieval is used to optimize CO emissions in WRF-Chem, focusing on the city of Madrid, Spain. This method has the advantage over the existing method in that it allows both a trend analysis of CO concentrations and a quantification of CO emissions. Our analysis confirms that MOPITT is capable of detecting CO enhancements over Madrid, although significant differences remain between the yearly averaged model output and satellite measurements (R2 D0.75) over the city. After optimization, we find Madrid CO emissions to be lower by 48% for 2002 and by 17% for 2006 compared with the EdgarV4.2 emission inventory. The MOPITT-derived emission adjustments lead to better agreement with the European emission inventory TNO-MAC-III for both years. This suggests that the downward trend in CO emissions over Madrid is overestimated in EdgarV4.2 and more realistically represented in TNO-MACC-III. However, our satellite and model based emission estimates have large uncertainties, around 20% for 2002 and 50% for 2006.
| Main Authors: | , , , , , , , |
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| Format: | Article/Letter to editor biblioteca |
| Language: | English |
| Subjects: | Life Science, |
| Online Access: | https://research.wur.nl/en/publications/quantification-of-co-emissions-from-the-city-of-madrid-using-mopi |
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| Summary: | The growth of mega-cities leads to air quality problems directly affecting the citizens. Satellite measurements are becoming of higher quality and quantity, which leads to more accurate satellite retrievals of enhanced air pollutant concentrations over large cities. In this paper, we compare and discuss both an existing and a new method for estimating urban-scale trends in CO emissions using multiyear retrievals from the MOPITT satellite instrument. The first method is mainly based on satellite data, and has the advantage of fewer assumptions, but also comes with uncertainties and limitations as shown in this paper. To improve the reliability of urban-To-regional scale emission trend estimation, we simulate MOPITT retrievals using the Weather Research and Forecast model with chemistry core (WRFChem). The difference between model and retrieval is used to optimize CO emissions in WRF-Chem, focusing on the city of Madrid, Spain. This method has the advantage over the existing method in that it allows both a trend analysis of CO concentrations and a quantification of CO emissions. Our analysis confirms that MOPITT is capable of detecting CO enhancements over Madrid, although significant differences remain between the yearly averaged model output and satellite measurements (R2 D0.75) over the city. After optimization, we find Madrid CO emissions to be lower by 48% for 2002 and by 17% for 2006 compared with the EdgarV4.2 emission inventory. The MOPITT-derived emission adjustments lead to better agreement with the European emission inventory TNO-MAC-III for both years. This suggests that the downward trend in CO emissions over Madrid is overestimated in EdgarV4.2 and more realistically represented in TNO-MACC-III. However, our satellite and model based emission estimates have large uncertainties, around 20% for 2002 and 50% for 2006. |
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