Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique
This paper investigated the use of boosted regression trees (BRTs) to draw an inference about daytime and nighttime ozone formation in a coastal environment. Hourly ground-level ozone data for a full calendar year in 2010 were obtained from the Kemaman (CA 002) air quality monitoring station. A BRT...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Thai Society of Higher Eduction Institutes on Environment
2017
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| Online Access: | http://eprints.usm.my/38317/1/Analysis_of_Daytime_and_Nighttime_Ground_Level_Ozone_Concentrations.pdf |
| _version_ | 1797010007943806976 |
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| author | Yahaya, Noor Zaitun Ghazali, Nurul Adyani Ahmad, Sabri Mohammad Asri, Mohammad Akmal Ibrahim, Zul Fahdli Ramli, Nor Azman |
| author_facet | Yahaya, Noor Zaitun Ghazali, Nurul Adyani Ahmad, Sabri Mohammad Asri, Mohammad Akmal Ibrahim, Zul Fahdli Ramli, Nor Azman |
| author_sort | Yahaya, Noor Zaitun |
| collection | USM |
| description | This paper investigated the use of boosted regression trees (BRTs) to draw an inference about daytime and nighttime ozone formation in a coastal environment. Hourly ground-level ozone data for a full calendar year in 2010 were obtained from the Kemaman (CA 002) air quality monitoring station. A BRT model was developed using hourly ozone data as a response variable and nitric oxide (NO), Nitrogen Dioxide (NO2) and Nitrogen Dioxide (NOx) and meteorological parameters as explanatory variables. The ozone BRT algorithm model was constructed from multiple regression models, and the ‘best iteration’ of BRT model was performed by optimizing prediction performance. Sensitivity testing of the BRT model was conducted to determine the best parameters and good explanatory variables. Using the number of trees between 2,500-3,500, learning rate of 0.01, and interaction depth of 5 were found to be the best setting for developing the ozone boosting model. The performance of the O3 boosting models were assessed, and the fraction of predictions within two factor (FAC2), coefficient of determination (R²) and the index of agreement (IOA) of the model developed for day andnighttime are 0.93, 0.69 and 0.73 for daytime and 0.79, 0.55 and 0.69 for nighttime respectively. Results showed that the model developed was within the acceptable range and could be used to understand ozone formation and identify potential sources of ozone for estimating O3 concentrations during daytime and nighttime Results indicated that the wind speed, wind direction, relative humidity, and temperature were the most dominant variables in terms of influencing ozone formation. Finally, empirical evidence of the production of a high ozone level by wind blowing from coastal areas towards the interior region, especially from industrial areas, was obtained.
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| first_indexed | 2024-03-06T15:13:39Z |
| format | Article |
| id | usm.eprints-38317 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:13:39Z |
| publishDate | 2017 |
| publisher | Thai Society of Higher Eduction Institutes on Environment |
| record_format | dspace |
| spelling | usm.eprints-383172018-01-09T09:31:22Z http://eprints.usm.my/38317/ Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique Yahaya, Noor Zaitun Ghazali, Nurul Adyani Ahmad, Sabri Mohammad Asri, Mohammad Akmal Ibrahim, Zul Fahdli Ramli, Nor Azman TA1-2040 Engineering (General). Civil engineering (General) This paper investigated the use of boosted regression trees (BRTs) to draw an inference about daytime and nighttime ozone formation in a coastal environment. Hourly ground-level ozone data for a full calendar year in 2010 were obtained from the Kemaman (CA 002) air quality monitoring station. A BRT model was developed using hourly ozone data as a response variable and nitric oxide (NO), Nitrogen Dioxide (NO2) and Nitrogen Dioxide (NOx) and meteorological parameters as explanatory variables. The ozone BRT algorithm model was constructed from multiple regression models, and the ‘best iteration’ of BRT model was performed by optimizing prediction performance. Sensitivity testing of the BRT model was conducted to determine the best parameters and good explanatory variables. Using the number of trees between 2,500-3,500, learning rate of 0.01, and interaction depth of 5 were found to be the best setting for developing the ozone boosting model. The performance of the O3 boosting models were assessed, and the fraction of predictions within two factor (FAC2), coefficient of determination (R²) and the index of agreement (IOA) of the model developed for day andnighttime are 0.93, 0.69 and 0.73 for daytime and 0.79, 0.55 and 0.69 for nighttime respectively. Results showed that the model developed was within the acceptable range and could be used to understand ozone formation and identify potential sources of ozone for estimating O3 concentrations during daytime and nighttime Results indicated that the wind speed, wind direction, relative humidity, and temperature were the most dominant variables in terms of influencing ozone formation. Finally, empirical evidence of the production of a high ozone level by wind blowing from coastal areas towards the interior region, especially from industrial areas, was obtained. Thai Society of Higher Eduction Institutes on Environment 2017 Article PeerReviewed application/pdf en http://eprints.usm.my/38317/1/Analysis_of_Daytime_and_Nighttime_Ground_Level_Ozone_Concentrations.pdf Yahaya, Noor Zaitun and Ghazali, Nurul Adyani and Ahmad, Sabri and Mohammad Asri, Mohammad Akmal and Ibrahim, Zul Fahdli and Ramli, Nor Azman (2017) Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique. EnvironmentAsia, 10 (1). pp. 118-129. ISSN 1906-1714 https://doi.org/10.14456/ea.2017.14 |
| spellingShingle | TA1-2040 Engineering (General). Civil engineering (General) Yahaya, Noor Zaitun Ghazali, Nurul Adyani Ahmad, Sabri Mohammad Asri, Mohammad Akmal Ibrahim, Zul Fahdli Ramli, Nor Azman Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique |
| title | Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique |
| title_full | Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique |
| title_fullStr | Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique |
| title_full_unstemmed | Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique |
| title_short | Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique |
| title_sort | analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique |
| topic | TA1-2040 Engineering (General). Civil engineering (General) |
| url | http://eprints.usm.my/38317/1/Analysis_of_Daytime_and_Nighttime_Ground_Level_Ozone_Concentrations.pdf |
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