Proposal of a Methodology for Prediction of Indoor PM<sub>2.5</sub> Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model
This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM<sub>2.5</sub> concentration through a multiple li...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/11/6/526 |
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| author | Shin-Young Park Dan-Ki Yoon Si-Hyun Park Jung-In Jeon Jung-Mi Lee Won-Ho Yang Yong-Sung Cho Jaymin Kwon Cheol-Min Lee |
| author_facet | Shin-Young Park Dan-Ki Yoon Si-Hyun Park Jung-In Jeon Jung-Mi Lee Won-Ho Yang Yong-Sung Cho Jaymin Kwon Cheol-Min Lee |
| author_sort | Shin-Young Park |
| collection | DOAJ |
| description | This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM<sub>2.5</sub> concentration through a multiple linear regression model. The atmospheric conditions and air pollution detected in one-minute intervals using sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside and outside houses from May 2019 to April 2021 were used to develop the prediction model. By dividing the multiple linear regression model into one-hour increments, we attempted to overcome the limitation of not representing the multiple linear regression model’s characteristics over time and limited input variables. The multiple linear regression (MLR) model classified by time unit showed an improvement in explanatory power by up to 9% compared to the existing model, and some hourly models had an explanatory power of 0.30. These results indicated that the model needs to be subdivided by time period to more accurately predict indoor PM<sub>2.5</sub> concentrations. |
| first_indexed | 2024-03-11T01:52:27Z |
| format | Article |
| id | doaj.art-cb250b5501c24c7b8fcb19b2776e3815 |
| institution | Directory Open Access Journal |
| issn | 2305-6304 |
| language | English |
| last_indexed | 2024-03-11T01:52:27Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj.art-cb250b5501c24c7b8fcb19b2776e38152023-11-18T12:54:50ZengMDPI AGToxics2305-63042023-06-0111652610.3390/toxics11060526Proposal of a Methodology for Prediction of Indoor PM<sub>2.5</sub> Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression ModelShin-Young Park0Dan-Ki Yoon1Si-Hyun Park2Jung-In Jeon3Jung-Mi Lee4Won-Ho Yang5Yong-Sung Cho6Jaymin Kwon7Cheol-Min Lee8Department of Chemical and Environmental Engineering, Seokyeong University, Seoul 02713, Republic of KoreaDepartment of Nano and Biological Engineering, Seokyeong University, Seoul 02713, Republic of KoreaDepartment of Nano and Biological Engineering, Seokyeong University, Seoul 02713, Republic of KoreaDepartment of Chemical and Environmental Engineering, Seokyeong University, Seoul 02713, Republic of KoreaDepartment of Chemical and Environmental Engineering, Seokyeong University, Seoul 02713, Republic of KoreaDepartment of Occupational Health, Daegu Catholic University, Gyeongsan 38430, Republic of KoreaDepartment of Nano, Chemical and Biological Engineering, Seokyeong University, Seoul 02713, Republic of KoreaDepartment of Public Health, California State University, Fresno, CA 93740, USADepartment of Nano, Chemical and Biological Engineering, Seokyeong University, Seoul 02713, Republic of KoreaThis study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM<sub>2.5</sub> concentration through a multiple linear regression model. The atmospheric conditions and air pollution detected in one-minute intervals using sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside and outside houses from May 2019 to April 2021 were used to develop the prediction model. By dividing the multiple linear regression model into one-hour increments, we attempted to overcome the limitation of not representing the multiple linear regression model’s characteristics over time and limited input variables. The multiple linear regression (MLR) model classified by time unit showed an improvement in explanatory power by up to 9% compared to the existing model, and some hourly models had an explanatory power of 0.30. These results indicated that the model needs to be subdivided by time period to more accurately predict indoor PM<sub>2.5</sub> concentrations.https://www.mdpi.com/2305-6304/11/6/526indoor PM<sub>2.5</sub>dwellingoutdoor variablestimemultiple linear regressionprediction model |
| spellingShingle | Shin-Young Park Dan-Ki Yoon Si-Hyun Park Jung-In Jeon Jung-Mi Lee Won-Ho Yang Yong-Sung Cho Jaymin Kwon Cheol-Min Lee Proposal of a Methodology for Prediction of Indoor PM<sub>2.5</sub> Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model Toxics indoor PM<sub>2.5</sub> dwelling outdoor variables time multiple linear regression prediction model |
| title | Proposal of a Methodology for Prediction of Indoor PM<sub>2.5</sub> Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model |
| title_full | Proposal of a Methodology for Prediction of Indoor PM<sub>2.5</sub> Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model |
| title_fullStr | Proposal of a Methodology for Prediction of Indoor PM<sub>2.5</sub> Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model |
| title_full_unstemmed | Proposal of a Methodology for Prediction of Indoor PM<sub>2.5</sub> Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model |
| title_short | Proposal of a Methodology for Prediction of Indoor PM<sub>2.5</sub> Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model |
| title_sort | proposal of a methodology for prediction of indoor pm sub 2 5 sub concentration using sensor based residential environments monitoring data and time divided multiple linear regression model |
| topic | indoor PM<sub>2.5</sub> dwelling outdoor variables time multiple linear regression prediction model |
| url | https://www.mdpi.com/2305-6304/11/6/526 |
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