Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Building Services Engineering | en_US |
| dc.contributor.advisor | Wong, Ling Tim (BSE) | en_US |
| dc.creator | Cheng, Ka Man | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/11245 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Forecasting indoor radon concentration using Box-Jenkins ARIMA modeling approach | en_US |
| dcterms.abstract | The World Health Organization (WHO) has updated the guidelines for indoor air quality (IAQ) on selected pollutants in 2010 and presents to protect public health. In Hong Kong, the Environmental Protection Department (HKEPD) updated the guidance notes for IAQ management in 2019. The HKEPD has been promoted the concept of IAQ and launched a relevant management program since 2000. Various types of indoor air pollutants have been mentioned together with the management advice. The IAQ control strategy is necessary to know the contaminant concentration ahead before it comes to uncontrollable levels. The strategies are taken using the data-driven analysis technique to modeling and predicting for simulating and monitoring the level of pollutants. In this study, modeling and forecasting radon concentration are investigated since it is the primary source of indoor air pollution. The measurement was addressed the time-related information of radon accumulation in time series. The SPSS statistics program is enhanced for inspection. The historical indoor radon data was collected on office building and used mechanical ventilation system for fresh air intake. The observations of indoor radon concentration data from June 1998 to May 1999 are evaluated. The Box-Jenkins ARIMA method approach simulates the best-fitted and appropriate model to analyze the future trend of the radon level in specific (indoor) office areas. The methodology applied for modeling from June 1998 to February 1999 and forecasting from March 1999 to May 1999. The modeling and forecasting process consisting of model identification, parameter estimation and diagnostic checking for the modeling part; forecasting and accuracy evaluation are followed in forecasting part. Results found from the Box-Jenkins method presented that the seasonal ARIMA (2, 0, 2) (2, 1, 1)8 is capable of being used to forecasting the future trends for regulation of indoor radon. From a forecasting perspective, 22.46% MAPE was computed by comparing the forecasting result with the actual observations. The seasonal ARIMA (2, 0, 2) (2, 1, 1)8 model is appropriate for presenting the dataset of indoor radon level and predicting the future data precisely. | en_US |
| dcterms.extent | 136 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2021 | en_US |
| dcterms.educationalLevel | M.Eng. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Indoor air quality | en_US |
| dcterms.LCSH | Indoor air pollution | en_US |
| dcterms.LCSH | Air -- Pollution | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5721.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 4.68 MB | Adobe PDF | View/Open |
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