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...
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| Format: | Article |
| Language: | English |
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Thai Society of Higher Eduction Institutes on Environment
2017-01-01
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| Series: | EnvironmentAsia |
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| Online Access: | http://tshe.org/ea/pdf/vol10no1-14.pdf |
| _version_ | 1818514351469887488 |
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| author | Noor Zaitun Yahaya Nurul Adyani Ghazali Sabri Ahmad Mohammad Akmal Mohammad Asri Zul Fahdli Ibrahim Nor Azam Ramli |
| author_facet | Noor Zaitun Yahaya Nurul Adyani Ghazali Sabri Ahmad Mohammad Akmal Mohammad Asri Zul Fahdli Ibrahim Nor Azam Ramli |
| author_sort | Noor Zaitun Yahaya |
| collection | DOAJ |
| 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 and nighttime 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. |
| first_indexed | 2024-12-11T00:14:34Z |
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| institution | Directory Open Access Journal |
| issn | 1906-1714 |
| language | English |
| last_indexed | 2024-12-11T00:14:34Z |
| publishDate | 2017-01-01 |
| publisher | Thai Society of Higher Eduction Institutes on Environment |
| record_format | Article |
| series | EnvironmentAsia |
| spelling | doaj.art-8af798d67ccd4d37b879247ed6f5bbf62022-12-22T01:28:00ZengThai Society of Higher Eduction Institutes on EnvironmentEnvironmentAsia1906-17142017-01-0110111812910.14456/ea.2017.14Analysis of Daytime and Nighttime Ground Level Ozone Concentrations Using Boosted Regression Tree TechniqueNoor Zaitun Yahaya 0Nurul Adyani Ghazali 1Sabri Ahmad 2Mohammad Akmal Mohammad Asri 3Zul Fahdli Ibrahim 4Nor Azam Ramli 5School of Ocean Engineering, Universiti Malaysia Terengganu, 21030, Kuala Terengganu, MalaysiaSchool of Ocean Engineering, Universiti Malaysia Terengganu, 21030, Kuala Terengganu, MalaysiaSchool of Mathematic and Informatic, Universiti Malaysia Terengganu, 21030, Kuala Terengganu, MalaysiaUniversiti Malaysia Terengganu, Terengganu, MalaysiaUniversiti Malaysia Terengganu, Terengganu, MalaysiaSchool of Civil Engineering, Universiti Sains Malaysia, Penang, MalaysiaThis 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 and nighttime 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.http://tshe.org/ea/pdf/vol10no1-14.pdfstochasticalgorithmozoneR softwareinteractions |
| spellingShingle | Noor Zaitun Yahaya Nurul Adyani Ghazali Sabri Ahmad Mohammad Akmal Mohammad Asri Zul Fahdli Ibrahim Nor Azam Ramli Analysis of Daytime and Nighttime Ground Level Ozone Concentrations Using Boosted Regression Tree Technique EnvironmentAsia stochastic algorithm ozone R software interactions |
| 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 | stochastic algorithm ozone R software interactions |
| url | http://tshe.org/ea/pdf/vol10no1-14.pdf |
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