Akeem Olawale Olaniyia, A. M. Abdullaha, Mohammad Firuz Ramlia, Hosea Kato Mandea, Deborah Babarinsab
{"title":"Estimating Trends of Mean Monthly Ozone Emission in Urbanised Areas of Malaysia","authors":"Akeem Olawale Olaniyia, A. M. Abdullaha, Mohammad Firuz Ramlia, Hosea Kato Mandea, Deborah Babarinsab","doi":"10.1515/avutgs-2017-0011","DOIUrl":null,"url":null,"abstract":"Abstract A 21 year (1992 – 2012) daily ozone emission data of a highly urbanised district in Malaysia was analysed with the aim of estimating the trend of ozone emission and relating this trend to the socio – economic and climatic characteristics of the area. Daily ozone emission dataset used in this study were obtained from the World Ozone and Ultraviolet Data Centre (WOUDC). The data were aggregated to obtain the mean monthly emission data. Descriptive and inferential statistical analyses were conducted to describe the datasets. Trend of the ozone emission was estimated with the use of MANN - KENDALL test. The magnitude of the trend was derived by the use of ordinary least-square fitting and the significance of trend was also tested with the use of MANN-KENDALL tool. The results of the statistical analysis indicated that the highest ozone emission occurred during the south western monsoon (May to August) period and these mean monthly ozone emission differed significantly over the study period. The trend analysis indicated a yearly decrease of between 0.069 ppt to 9.45 ppt for all the months except for the month of June when the predicted ozone concentration increased between 0.403 ppt and 0.414 ppt over 2020 to 2100. Furthermore, the results indicated that the ozone emission datasets yielded good estimates (predictive power of over 90%) with polynomial regression model. It could be concluded that the results of this study provided useful evidence for the importance of the climatic factors such as ambient air temperature, relative humidity on ozone formation. More so, this study could be useful in developing baseline information for assessing the health impact of ozone emission and for urban airshed modelling.","PeriodicalId":250092,"journal":{"name":"Annals of Valahia University of Targoviste, Geographical Series","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Valahia University of Targoviste, Geographical Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/avutgs-2017-0011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract A 21 year (1992 – 2012) daily ozone emission data of a highly urbanised district in Malaysia was analysed with the aim of estimating the trend of ozone emission and relating this trend to the socio – economic and climatic characteristics of the area. Daily ozone emission dataset used in this study were obtained from the World Ozone and Ultraviolet Data Centre (WOUDC). The data were aggregated to obtain the mean monthly emission data. Descriptive and inferential statistical analyses were conducted to describe the datasets. Trend of the ozone emission was estimated with the use of MANN - KENDALL test. The magnitude of the trend was derived by the use of ordinary least-square fitting and the significance of trend was also tested with the use of MANN-KENDALL tool. The results of the statistical analysis indicated that the highest ozone emission occurred during the south western monsoon (May to August) period and these mean monthly ozone emission differed significantly over the study period. The trend analysis indicated a yearly decrease of between 0.069 ppt to 9.45 ppt for all the months except for the month of June when the predicted ozone concentration increased between 0.403 ppt and 0.414 ppt over 2020 to 2100. Furthermore, the results indicated that the ozone emission datasets yielded good estimates (predictive power of over 90%) with polynomial regression model. It could be concluded that the results of this study provided useful evidence for the importance of the climatic factors such as ambient air temperature, relative humidity on ozone formation. More so, this study could be useful in developing baseline information for assessing the health impact of ozone emission and for urban airshed modelling.