Air pollution sources of PM10 in Buenos Aires City
To elucidate the sources of PM10 air pollution from the experimental information collected in a local air quality monitoring campaign we have applied two methods, effective variance and genetic algorithms, in the solution of the chemical mass balance. The comparison of these two mathematical approac...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | JOUR |
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_01676369_v155_n1-4_p191_Reich |
| Aporte de: |
| id |
todo:paper_01676369_v155_n1-4_p191_Reich |
|---|---|
| record_format |
dspace |
| spelling |
todo:paper_01676369_v155_n1-4_p191_Reich2023-10-03T15:04:58Z Air pollution sources of PM10 in Buenos Aires City Reich, S. Robledo, F. Gomez, D. Smichowski, P. Aerosol Effective variance Genetic algorithm Receptor model Screening procedure Air quality monitoring Buenos Aires Chemical mass balance Effective variance Key elements Major and trace elements Mathematical approach Receptor model Search spaces SIMPLE method Air quality Atmospheric aerosols Genetic algorithms Permanent magnets Trace elements Mathematical models trace element air quality atmospheric pollution genetic algorithm mass balance numerical model particulate matter pollutant source pollution monitoring urban pollution air monitoring air pollution air quality Argentina article controlled study genetic algorithm intermethod comparison mathematical analysis particulate matter standardization variance Aerosols Air Pollutants Argentina Environmental Monitoring Models, Theoretical Particulate Matter Argentina Buenos Aires [Federal District] Federal District [Argentina] South America To elucidate the sources of PM10 air pollution from the experimental information collected in a local air quality monitoring campaign we have applied two methods, effective variance and genetic algorithms, in the solution of the chemical mass balance. The comparison of these two mathematical approaches show that the identification of the possible sources and the evaluation of its contributions are quite independent of them. The role of possible different sources for major and trace elements and the significance of standardizing available data is also addressed. We also present a simple method for identifying the number of candidate sources, a key element defining the dimension of the search space. © Springer Science+Business Media B.V. 2008. Fil:Reich, S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_01676369_v155_n1-4_p191_Reich |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Aerosol Effective variance Genetic algorithm Receptor model Screening procedure Air quality monitoring Buenos Aires Chemical mass balance Effective variance Key elements Major and trace elements Mathematical approach Receptor model Search spaces SIMPLE method Air quality Atmospheric aerosols Genetic algorithms Permanent magnets Trace elements Mathematical models trace element air quality atmospheric pollution genetic algorithm mass balance numerical model particulate matter pollutant source pollution monitoring urban pollution air monitoring air pollution air quality Argentina article controlled study genetic algorithm intermethod comparison mathematical analysis particulate matter standardization variance Aerosols Air Pollutants Argentina Environmental Monitoring Models, Theoretical Particulate Matter Argentina Buenos Aires [Federal District] Federal District [Argentina] South America |
| spellingShingle |
Aerosol Effective variance Genetic algorithm Receptor model Screening procedure Air quality monitoring Buenos Aires Chemical mass balance Effective variance Key elements Major and trace elements Mathematical approach Receptor model Search spaces SIMPLE method Air quality Atmospheric aerosols Genetic algorithms Permanent magnets Trace elements Mathematical models trace element air quality atmospheric pollution genetic algorithm mass balance numerical model particulate matter pollutant source pollution monitoring urban pollution air monitoring air pollution air quality Argentina article controlled study genetic algorithm intermethod comparison mathematical analysis particulate matter standardization variance Aerosols Air Pollutants Argentina Environmental Monitoring Models, Theoretical Particulate Matter Argentina Buenos Aires [Federal District] Federal District [Argentina] South America Reich, S. Robledo, F. Gomez, D. Smichowski, P. Air pollution sources of PM10 in Buenos Aires City |
| topic_facet |
Aerosol Effective variance Genetic algorithm Receptor model Screening procedure Air quality monitoring Buenos Aires Chemical mass balance Effective variance Key elements Major and trace elements Mathematical approach Receptor model Search spaces SIMPLE method Air quality Atmospheric aerosols Genetic algorithms Permanent magnets Trace elements Mathematical models trace element air quality atmospheric pollution genetic algorithm mass balance numerical model particulate matter pollutant source pollution monitoring urban pollution air monitoring air pollution air quality Argentina article controlled study genetic algorithm intermethod comparison mathematical analysis particulate matter standardization variance Aerosols Air Pollutants Argentina Environmental Monitoring Models, Theoretical Particulate Matter Argentina Buenos Aires [Federal District] Federal District [Argentina] South America |
| description |
To elucidate the sources of PM10 air pollution from the experimental information collected in a local air quality monitoring campaign we have applied two methods, effective variance and genetic algorithms, in the solution of the chemical mass balance. The comparison of these two mathematical approaches show that the identification of the possible sources and the evaluation of its contributions are quite independent of them. The role of possible different sources for major and trace elements and the significance of standardizing available data is also addressed. We also present a simple method for identifying the number of candidate sources, a key element defining the dimension of the search space. © Springer Science+Business Media B.V. 2008. |
| format |
JOUR |
| author |
Reich, S. Robledo, F. Gomez, D. Smichowski, P. |
| author_facet |
Reich, S. Robledo, F. Gomez, D. Smichowski, P. |
| author_sort |
Reich, S. |
| title |
Air pollution sources of PM10 in Buenos Aires City |
| title_short |
Air pollution sources of PM10 in Buenos Aires City |
| title_full |
Air pollution sources of PM10 in Buenos Aires City |
| title_fullStr |
Air pollution sources of PM10 in Buenos Aires City |
| title_full_unstemmed |
Air pollution sources of PM10 in Buenos Aires City |
| title_sort |
air pollution sources of pm10 in buenos aires city |
| url |
http://hdl.handle.net/20.500.12110/paper_01676369_v155_n1-4_p191_Reich |
| work_keys_str_mv |
AT reichs airpollutionsourcesofpm10inbuenosairescity AT robledof airpollutionsourcesofpm10inbuenosairescity AT gomezd airpollutionsourcesofpm10inbuenosairescity AT smichowskip airpollutionsourcesofpm10inbuenosairescity |
| _version_ |
1807320586371727360 |