Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis
A simple urban air quality model [MODelo de Dispersión Atmosférica Ubana – Generic Reaction Set (DAUMOD-GRS)] was recently developed. One-hour peak O3 concentrations in the Metropolitan Area of Buenos Aires (MABA) during the summer estimated with the DAUMOD-GRS model have shown values lower than 20...
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2016
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13522310_v141_n_p422_PinedaRojas http://hdl.handle.net/20.500.12110/paper_13522310_v141_n_p422_PinedaRojas |
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paper:paper_13522310_v141_n_p422_PinedaRojas2023-06-08T16:11:00Z Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis Pineda Rojas, Andrea L. Venegas, Laura Esperanza Mazzeo, Nicolás Antonio Air quality Model uncertainty Monte Carlo analysis Ozone Sensitivity Air quality Input output programs Linear regression Monte Carlo methods Ozone Quality control Regression analysis Background concentration Generic reaction set Model uncertainties Monte carlo analysis Multiple linear regression analysis Relative contribution Sensitivity Urban air quality Uncertainty analysis nitrogen oxide oxygen ozone volatile organic compound air quality concentration (composition) data assimilation error analysis metropolitan area Monte Carlo analysis ozone uncertainty analysis air quality air temperature Article controlled study Monte Carlo method multiple linear regression analysis photolysis priority journal sensitivity analysis solar radiation titrimetry urban area wind Argentina Buenos Aires [Argentina] A simple urban air quality model [MODelo de Dispersión Atmosférica Ubana – Generic Reaction Set (DAUMOD-GRS)] was recently developed. One-hour peak O3 concentrations in the Metropolitan Area of Buenos Aires (MABA) during the summer estimated with the DAUMOD-GRS model have shown values lower than 20 ppb (the regional background concentration) in the urban area and levels greater than 40 ppb in its surroundings. Due to the lack of measurements outside the MABA, these relatively high ozone modelled concentrations constitute the only estimate for the area. In this work, a methodology based on the Monte Carlo analysis is implemented to evaluate the uncertainty in these modelled concentrations associated to possible errors of the model input data. Results show that the larger 1-h peak O3 levels in the MABA during the summer present larger uncertainties (up to 47 ppb). On the other hand, multiple linear regression analysis is applied at selected receptors in order to identify the variables explaining most of the obtained variance. Although their relative contributions vary spatially, the uncertainty of the regional background O3 concentration dominates at all the analysed receptors (34.4–97.6%), indicating that their estimations could be improved to enhance the ability of the model to simulate peak O3 concentrations in the MABA. © 2016 Elsevier Ltd Fil:Pineda Rojas, A.L. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Venegas, L.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Mazzeo, N.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13522310_v141_n_p422_PinedaRojas http://hdl.handle.net/20.500.12110/paper_13522310_v141_n_p422_PinedaRojas |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Air quality Model uncertainty Monte Carlo analysis Ozone Sensitivity Air quality Input output programs Linear regression Monte Carlo methods Ozone Quality control Regression analysis Background concentration Generic reaction set Model uncertainties Monte carlo analysis Multiple linear regression analysis Relative contribution Sensitivity Urban air quality Uncertainty analysis nitrogen oxide oxygen ozone volatile organic compound air quality concentration (composition) data assimilation error analysis metropolitan area Monte Carlo analysis ozone uncertainty analysis air quality air temperature Article controlled study Monte Carlo method multiple linear regression analysis photolysis priority journal sensitivity analysis solar radiation titrimetry urban area wind Argentina Buenos Aires [Argentina] |
| spellingShingle |
Air quality Model uncertainty Monte Carlo analysis Ozone Sensitivity Air quality Input output programs Linear regression Monte Carlo methods Ozone Quality control Regression analysis Background concentration Generic reaction set Model uncertainties Monte carlo analysis Multiple linear regression analysis Relative contribution Sensitivity Urban air quality Uncertainty analysis nitrogen oxide oxygen ozone volatile organic compound air quality concentration (composition) data assimilation error analysis metropolitan area Monte Carlo analysis ozone uncertainty analysis air quality air temperature Article controlled study Monte Carlo method multiple linear regression analysis photolysis priority journal sensitivity analysis solar radiation titrimetry urban area wind Argentina Buenos Aires [Argentina] Pineda Rojas, Andrea L. Venegas, Laura Esperanza Mazzeo, Nicolás Antonio Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis |
| topic_facet |
Air quality Model uncertainty Monte Carlo analysis Ozone Sensitivity Air quality Input output programs Linear regression Monte Carlo methods Ozone Quality control Regression analysis Background concentration Generic reaction set Model uncertainties Monte carlo analysis Multiple linear regression analysis Relative contribution Sensitivity Urban air quality Uncertainty analysis nitrogen oxide oxygen ozone volatile organic compound air quality concentration (composition) data assimilation error analysis metropolitan area Monte Carlo analysis ozone uncertainty analysis air quality air temperature Article controlled study Monte Carlo method multiple linear regression analysis photolysis priority journal sensitivity analysis solar radiation titrimetry urban area wind Argentina Buenos Aires [Argentina] |
| description |
A simple urban air quality model [MODelo de Dispersión Atmosférica Ubana – Generic Reaction Set (DAUMOD-GRS)] was recently developed. One-hour peak O3 concentrations in the Metropolitan Area of Buenos Aires (MABA) during the summer estimated with the DAUMOD-GRS model have shown values lower than 20 ppb (the regional background concentration) in the urban area and levels greater than 40 ppb in its surroundings. Due to the lack of measurements outside the MABA, these relatively high ozone modelled concentrations constitute the only estimate for the area. In this work, a methodology based on the Monte Carlo analysis is implemented to evaluate the uncertainty in these modelled concentrations associated to possible errors of the model input data. Results show that the larger 1-h peak O3 levels in the MABA during the summer present larger uncertainties (up to 47 ppb). On the other hand, multiple linear regression analysis is applied at selected receptors in order to identify the variables explaining most of the obtained variance. Although their relative contributions vary spatially, the uncertainty of the regional background O3 concentration dominates at all the analysed receptors (34.4–97.6%), indicating that their estimations could be improved to enhance the ability of the model to simulate peak O3 concentrations in the MABA. © 2016 Elsevier Ltd |
| author |
Pineda Rojas, Andrea L. Venegas, Laura Esperanza Mazzeo, Nicolás Antonio |
| author_facet |
Pineda Rojas, Andrea L. Venegas, Laura Esperanza Mazzeo, Nicolás Antonio |
| author_sort |
Pineda Rojas, Andrea L. |
| title |
Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis |
| title_short |
Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis |
| title_full |
Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis |
| title_fullStr |
Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis |
| title_full_unstemmed |
Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis |
| title_sort |
uncertainty of modelled urban peak o3 concentrations and its sensitivity to input data perturbations based on the monte carlo analysis |
| publishDate |
2016 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_13522310_v141_n_p422_PinedaRojas http://hdl.handle.net/20.500.12110/paper_13522310_v141_n_p422_PinedaRojas |
| work_keys_str_mv |
AT pinedarojasandreal uncertaintyofmodelledurbanpeako3concentrationsanditssensitivitytoinputdataperturbationsbasedonthemontecarloanalysis AT venegaslauraesperanza uncertaintyofmodelledurbanpeako3concentrationsanditssensitivitytoinputdataperturbationsbasedonthemontecarloanalysis AT mazzeonicolasantonio uncertaintyofmodelledurbanpeako3concentrationsanditssensitivitytoinputdataperturbationsbasedonthemontecarloanalysis |
| _version_ |
1768546267451883520 |