Prediction of the Concentration and Source Contributions of PM<sub>2.5</sub> and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System
The SmartAQ (Smart Air Quality) forecasting system produces high-resolution (1 × 1 km<sup>2</sup>) air quality predictions in an urban area for the next three days using advanced chemical transport modeling. In this study, we evaluated the SmartAQ performance for the urban area of Patras...
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| Format: | Article |
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MDPI AG
2023-12-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/15/1/8 |
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| author | Evangelia Siouti Ksakousti Skyllakou Ioannis Kioutsioukis David Patoulias Ioannis D. Apostolopoulos George Fouskas Spyros N. Pandis |
| author_facet | Evangelia Siouti Ksakousti Skyllakou Ioannis Kioutsioukis David Patoulias Ioannis D. Apostolopoulos George Fouskas Spyros N. Pandis |
| author_sort | Evangelia Siouti |
| collection | DOAJ |
| description | The SmartAQ (Smart Air Quality) forecasting system produces high-resolution (1 × 1 km<sup>2</sup>) air quality predictions in an urban area for the next three days using advanced chemical transport modeling. In this study, we evaluated the SmartAQ performance for the urban area of Patras, Greece, for four months (July 2021, September 2021, December 2021, and March 2022), covering all seasons. In this work, we assess the system’s ability to forecast PM<sub>2.5</sub> levels and the major gas-phase pollutants during periods with different meteorological conditions and local emissions, but also in areas of the city with different characteristics (urban, suburban, and background sites). We take advantage of this SmartAQ application to also quantify the main sources of the pollutants at each site. During the summertime, PM<sub>2.5</sub> model performance was excellent (Fbias < 15%, Ferror < 30%) for all sites both in the city center and suburbs. For the city center, the model reproduced well (MB = −0.9 μg m<sup>−3</sup>, ME = 2.5 μg m<sup>−3</sup>) the overall measured PM<sub>2.5</sub> behavior and the high nighttime peaks due to cooking activity, as well as the transported PM pollution in the suburbs. During the fall, the SmartAQ PM<sub>2.5</sub> performance was good (Fbias < 42%, Ferror < 45%) for the city center and the suburban core, while it was average (Fbias < 50%, Ferror < 54%, MB, ME < 3.3 μg m<sup>−3</sup>) for the suburbs because the model overpredicted the long-range transport of pollution. For wintertime, the system reproduced well (MB = −2 μg m<sup>−3</sup>, ME = 6.5 μg m<sup>−3</sup>) the PM<sub>2.5</sub> concentration in the high-biomass-burning emission area with an excellent model performance (Fbias = −4%, Ferror = 33%) and reproduced well (MB < 1.1 μg m<sup>−3</sup>, ME < 3 μg m<sup>−3</sup>) the background PM<sub>2.5</sub> levels. SmartAQ reproduced well the PM<sub>2.5</sub> concentrations in the urban and suburban core during the spring (Fbias < 40%, Ferror < 50%, MB < 8.5 μg m<sup>−3</sup>, ME < 10 μg m<sup>−3</sup>), while it tended to slightly overestimate the regional pollution. The main local source of fine PM during summer and autumn was cooking, but most of the PM was transported to the city. Residential biomass burning was the dominant particle source of pollution during winter and early spring. For gas-phase pollutants, the system reproduced well the daily nitrogen oxides (NO<sub>x</sub>) concentrations during the summertime. Predicted NO<sub>x</sub> concentrations during the winter were consistent with measurements at night but underestimated the observations during the rest of the day. SmartAQ achieved the US EPA modeling goals for hourly O<sub>3</sub> concentrations indicating good model performance. |
| first_indexed | 2024-03-08T11:06:13Z |
| format | Article |
| id | doaj.art-4a413f7b9b4d4b5cac90139d198c412f |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-08T11:06:13Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-4a413f7b9b4d4b5cac90139d198c412f2024-01-26T15:00:45ZengMDPI AGAtmosphere2073-44332023-12-01151810.3390/atmos15010008Prediction of the Concentration and Source Contributions of PM<sub>2.5</sub> and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting SystemEvangelia Siouti0Ksakousti Skyllakou1Ioannis Kioutsioukis2David Patoulias3Ioannis D. Apostolopoulos4George Fouskas5Spyros N. Pandis6Department of Chemical Engineering, University of Patras, 26504 Patras, GreeceInstitute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology Hellas (FORTH), 26504 Patras, GreeceDepartment of Physics, University of Patras, 26504 Patras, GreeceInstitute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology Hellas (FORTH), 26504 Patras, GreeceInstitute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology Hellas (FORTH), 26504 Patras, GreeceInstitute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology Hellas (FORTH), 26504 Patras, GreeceDepartment of Chemical Engineering, University of Patras, 26504 Patras, GreeceThe SmartAQ (Smart Air Quality) forecasting system produces high-resolution (1 × 1 km<sup>2</sup>) air quality predictions in an urban area for the next three days using advanced chemical transport modeling. In this study, we evaluated the SmartAQ performance for the urban area of Patras, Greece, for four months (July 2021, September 2021, December 2021, and March 2022), covering all seasons. In this work, we assess the system’s ability to forecast PM<sub>2.5</sub> levels and the major gas-phase pollutants during periods with different meteorological conditions and local emissions, but also in areas of the city with different characteristics (urban, suburban, and background sites). We take advantage of this SmartAQ application to also quantify the main sources of the pollutants at each site. During the summertime, PM<sub>2.5</sub> model performance was excellent (Fbias < 15%, Ferror < 30%) for all sites both in the city center and suburbs. For the city center, the model reproduced well (MB = −0.9 μg m<sup>−3</sup>, ME = 2.5 μg m<sup>−3</sup>) the overall measured PM<sub>2.5</sub> behavior and the high nighttime peaks due to cooking activity, as well as the transported PM pollution in the suburbs. During the fall, the SmartAQ PM<sub>2.5</sub> performance was good (Fbias < 42%, Ferror < 45%) for the city center and the suburban core, while it was average (Fbias < 50%, Ferror < 54%, MB, ME < 3.3 μg m<sup>−3</sup>) for the suburbs because the model overpredicted the long-range transport of pollution. For wintertime, the system reproduced well (MB = −2 μg m<sup>−3</sup>, ME = 6.5 μg m<sup>−3</sup>) the PM<sub>2.5</sub> concentration in the high-biomass-burning emission area with an excellent model performance (Fbias = −4%, Ferror = 33%) and reproduced well (MB < 1.1 μg m<sup>−3</sup>, ME < 3 μg m<sup>−3</sup>) the background PM<sub>2.5</sub> levels. SmartAQ reproduced well the PM<sub>2.5</sub> concentrations in the urban and suburban core during the spring (Fbias < 40%, Ferror < 50%, MB < 8.5 μg m<sup>−3</sup>, ME < 10 μg m<sup>−3</sup>), while it tended to slightly overestimate the regional pollution. The main local source of fine PM during summer and autumn was cooking, but most of the PM was transported to the city. Residential biomass burning was the dominant particle source of pollution during winter and early spring. For gas-phase pollutants, the system reproduced well the daily nitrogen oxides (NO<sub>x</sub>) concentrations during the summertime. Predicted NO<sub>x</sub> concentrations during the winter were consistent with measurements at night but underestimated the observations during the rest of the day. SmartAQ achieved the US EPA modeling goals for hourly O<sub>3</sub> concentrations indicating good model performance.https://www.mdpi.com/2073-4433/15/1/8air quality predictionspollutant sourcesPM<sub>2.5</sub>NO<sub>x</sub>O<sub>3</sub>evaluation metrics |
| spellingShingle | Evangelia Siouti Ksakousti Skyllakou Ioannis Kioutsioukis David Patoulias Ioannis D. Apostolopoulos George Fouskas Spyros N. Pandis Prediction of the Concentration and Source Contributions of PM<sub>2.5</sub> and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System Atmosphere air quality predictions pollutant sources PM<sub>2.5</sub> NO<sub>x</sub> O<sub>3</sub> evaluation metrics |
| title | Prediction of the Concentration and Source Contributions of PM<sub>2.5</sub> and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System |
| title_full | Prediction of the Concentration and Source Contributions of PM<sub>2.5</sub> and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System |
| title_fullStr | Prediction of the Concentration and Source Contributions of PM<sub>2.5</sub> and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System |
| title_full_unstemmed | Prediction of the Concentration and Source Contributions of PM<sub>2.5</sub> and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System |
| title_short | Prediction of the Concentration and Source Contributions of PM<sub>2.5</sub> and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System |
| title_sort | prediction of the concentration and source contributions of pm sub 2 5 sub and gas phase pollutants in an urban area with the smartaq forecasting system |
| topic | air quality predictions pollutant sources PM<sub>2.5</sub> NO<sub>x</sub> O<sub>3</sub> evaluation metrics |
| url | https://www.mdpi.com/2073-4433/15/1/8 |
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