Forecast of urban air pollution level by expertise
Introduction. A climate pattern with marine features is typical for St. Petersburg. Vagaries of weather and climate conditions in the last decade specify the timeliness of this work, the purpose of which is to outline the expected level of the open air pollution in St. Petersburg by the “decision tr...
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| Format: | Article |
| Language: | Russian |
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Don State Technical University
2017-12-01
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| Series: | Advanced Engineering Research |
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| Online Access: | https://www.vestnik-donstu.ru/jour/article/view/194 |
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| author | Elena O. Lazareva Irina N. Lipovitskaya Elena S. Andreeva Yulia V. Yefimova |
| author_facet | Elena O. Lazareva Irina N. Lipovitskaya Elena S. Andreeva Yulia V. Yefimova |
| author_sort | Elena O. Lazareva |
| collection | DOAJ |
| description | Introduction. A climate pattern with marine features is typical for St. Petersburg. Vagaries of weather and climate conditions in the last decade specify the timeliness of this work, the purpose of which is to outline the expected level of the open air pollution in St. Petersburg by the “decision tree” method. Materials and Methods. Current data of weather observations carried out at station 26063 (St. Petersburg) from 2006 to 2014 are studied and processed. Within the framework of the study, the data were considered on the vertical profile of the atmosphere obtained through radiosounding the atmosphere of St. Petersburg at 00.00 and 12.00 UTC (Universal Time Coordinated) at Voeykovo station. Research Results . In the course of the investigation, the dependence of the atmospheric air pollution level on the synoptic process and the inertial factor was established which made it possible to figure a scheme for forecasting the air pollution level in the form of the decision tree by expertise. Accuracy of the predictive determination of the expected air pollution group in St. Petersburg was calculated on the dependent material and topped 90% (nighttime hours) and 91% (daytime hours) for a cold period; and - 84% (nighttime hours) and 87% (daylight hours) for a warm period of the year. This suggests that the proposed schemes allow obtaining a more efficient prediction of the atmospheric air pollution level in a cold period of the year. Discussion and Conclusions . In conclusion, basic outcomes and inferences are summarized. - Archives of baseline standard meteorological data and data of the atmosphere radiosounding, as well as synoptic situations and information on the level of atmospheric air pollution in St. Petersburg for the period from 2006 to 2014, are formed. - Groups of synoptic processes typical for St. Petersburg from 2006 to 2014 are established. - Schemes for forecasting the atmospheric air pollution level are developed using the “decision tree” method with accuracy of 84-91%. The research results are applicable for forecasting the urban air pollution level. |
| format | Article |
| id | doaj-art-96f2e9c7264a4fa580647a44890ce760 |
| institution | Kabale University |
| issn | 2687-1653 |
| language | Russian |
| publishDate | 2017-12-01 |
| publisher | Don State Technical University |
| record_format | Article |
| series | Advanced Engineering Research |
| spelling | doaj-art-96f2e9c7264a4fa580647a44890ce7602025-08-20T03:57:12ZrusDon State Technical UniversityAdvanced Engineering Research2687-16532017-12-0117414415010.23947/1992-5980-2017-17-4-144-150194Forecast of urban air pollution level by expertiseElena O. Lazareva0Irina N. Lipovitskaya1Elena S. Andreeva2Yulia V. Yefimova3Weather stationSaint-Petersburg Institute of Education in the Sphere of Humanities and Social SciencesDon State Technical UniversityRussian State Hydrometeorological UniversityIntroduction. A climate pattern with marine features is typical for St. Petersburg. Vagaries of weather and climate conditions in the last decade specify the timeliness of this work, the purpose of which is to outline the expected level of the open air pollution in St. Petersburg by the “decision tree” method. Materials and Methods. Current data of weather observations carried out at station 26063 (St. Petersburg) from 2006 to 2014 are studied and processed. Within the framework of the study, the data were considered on the vertical profile of the atmosphere obtained through radiosounding the atmosphere of St. Petersburg at 00.00 and 12.00 UTC (Universal Time Coordinated) at Voeykovo station. Research Results . In the course of the investigation, the dependence of the atmospheric air pollution level on the synoptic process and the inertial factor was established which made it possible to figure a scheme for forecasting the air pollution level in the form of the decision tree by expertise. Accuracy of the predictive determination of the expected air pollution group in St. Petersburg was calculated on the dependent material and topped 90% (nighttime hours) and 91% (daytime hours) for a cold period; and - 84% (nighttime hours) and 87% (daylight hours) for a warm period of the year. This suggests that the proposed schemes allow obtaining a more efficient prediction of the atmospheric air pollution level in a cold period of the year. Discussion and Conclusions . In conclusion, basic outcomes and inferences are summarized. - Archives of baseline standard meteorological data and data of the atmosphere radiosounding, as well as synoptic situations and information on the level of atmospheric air pollution in St. Petersburg for the period from 2006 to 2014, are formed. - Groups of synoptic processes typical for St. Petersburg from 2006 to 2014 are established. - Schemes for forecasting the atmospheric air pollution level are developed using the “decision tree” method with accuracy of 84-91%. The research results are applicable for forecasting the urban air pollution level.https://www.vestnik-donstu.ru/jour/article/view/194безопасность жизнедеятельностиметеорологические характеристикихарактеристики загрязненности атмосферысиноптический процесспараметр рпрогноз загрязнения атмосферного воздуха«дерево принятия решения»статистический анализфизический анализсанкт-петербургhealth safetymeteorological characteristicsair impurity characteristicssynoptic processр parameterforecast of atmospheric air pollution“decision tree” methodstatistical analysisphysical analysisst. petersburg |
| spellingShingle | Elena O. Lazareva Irina N. Lipovitskaya Elena S. Andreeva Yulia V. Yefimova Forecast of urban air pollution level by expertise Advanced Engineering Research безопасность жизнедеятельности метеорологические характеристики характеристики загрязненности атмосферы синоптический процесс параметр р прогноз загрязнения атмосферного воздуха «дерево принятия решения» статистический анализ физический анализ санкт-петербург health safety meteorological characteristics air impurity characteristics synoptic process р parameter forecast of atmospheric air pollution “decision tree” method statistical analysis physical analysis st. petersburg |
| title | Forecast of urban air pollution level by expertise |
| title_full | Forecast of urban air pollution level by expertise |
| title_fullStr | Forecast of urban air pollution level by expertise |
| title_full_unstemmed | Forecast of urban air pollution level by expertise |
| title_short | Forecast of urban air pollution level by expertise |
| title_sort | forecast of urban air pollution level by expertise |
| topic | безопасность жизнедеятельности метеорологические характеристики характеристики загрязненности атмосферы синоптический процесс параметр р прогноз загрязнения атмосферного воздуха «дерево принятия решения» статистический анализ физический анализ санкт-петербург health safety meteorological characteristics air impurity characteristics synoptic process р parameter forecast of atmospheric air pollution “decision tree” method statistical analysis physical analysis st. petersburg |
| url | https://www.vestnik-donstu.ru/jour/article/view/194 |
| work_keys_str_mv | AT elenaolazareva forecastofurbanairpollutionlevelbyexpertise AT irinanlipovitskaya forecastofurbanairpollutionlevelbyexpertise AT elenasandreeva forecastofurbanairpollutionlevelbyexpertise AT yuliavyefimova forecastofurbanairpollutionlevelbyexpertise |