International Journal on Advanced Science, Engineering and Information Technology, Vol. 13 (2023) No. 5, pages: 1628-1634, DOI:10.18517/ijaseit.13.5.18361

Prediction of Particulate Matter (PM) Concentration of Wooden Houses in the Highlands by Two Statistical Modelling Methods

Abstract

Wooden houses can potentially contain high levels of Particulate Matter (PM), which can cause lung disease in residents. Wooden houses have advantages in terms of maintaining the sustainability of building materials. Building design needs to pay attention to PM predictions in residential homes to avoid sick building syndrome. This study aimed to investigate and find predictive models for PM 10, PM2.5, and PM1.0 in wooden houses based on PM content in outdoor spaces. The study used quantitative methods by measuring PM10, PM2.5, and PM1.0 indoors and outdoors in wooden houses in Wonosobo Regency. The number of samples is 100 wooden houses. Measurements were carried out for one full day for each residential house. Data recording is done every 15 minutes—prediction model development using linear regression test and structural equation modeling (SEM). The results obtained three equations based on PM10, PM2.5, and PM1.0. PM10indoor = 53.202 + 0.406 PM10outdoor; PM2.5indoor = 36.865 + 0.373 PM2.5outdoor; PM1.0indoor = 34.143 + 0.194 PM1.0outdoor. The difference with the results from SEM is PM10indoor = 52.89 + 0.41 PM10outdoor, PM2.5indoor = 38.31 + 0.37 PM2.5outdoor, PM1.0indoor = 26.58 + 0.19.PM1.0outdoor. There is no significant difference in the prediction results, so it can be concluded that the Prediction Model is valid. The implications of this research can provide input for improving the standard of PM content in wooden houses. The study results become input for the government in monitoring PM content in simple houses.

Keywords:

Wooden house; highland; particulate matter (PM); prediction model

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