Mathematical Insights into Indoor Air Quality and Pollutants Behavior

Air pollution is a key threat to human life as we spend about 90% of time in indoor. Mathematical analysis is a tool to analyze and predict air quality and its distribution. This paper investigates the air pollution dynamics in room, focusing on their impact on indoor air quality. We have explored the role of the air pollutants to affect the indoor air quality of the room. In two sets of environment, we observed and compared the correlation among particular matters of size 1, 2.5 and 10 microns; PM1, PM2.5, PM10 and AQIUS. It is observed that PM1 and PM2.5 have high impact on AQI with correlation coefficient 0.99 and 1 respectively whereas PM10 has less than that of them with correlation coefficients 0.97. This study leads to a recommendation to policy makers and users to focus more in reducing the PM2.5 concentrations by identifying sources and using suitable technologies.

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Publication Details

Journal
RR Interdisciplinary Journal
Published
2024-12-31
DOI
https://doi.org/10.3126/rrij.v5i5.78959
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
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article

Mathematical Insights into Indoor Air Quality and Pollutants Behavior

Buddhi Prasad Sapkota
1 links
RR Interdisciplinary Journal
Air Quality Monitoring and Forecasting
article

Mathematical Insights into Indoor Air Quality and Pollutants Behavior

Buddhi Prasad Sapkota
article en

Abstract

Air pollution is a key threat to human life as we spend about 90% of time in indoor. Mathematical analysis is a tool to analyze and predict air quality and its distribution. This paper investigates the air pollution dynamics in room, focusing on their impact on indoor air quality. We have explored the role of the air pollutants to affect the indoor air quality of the room. In two sets of environment, we observed and compared the correlation among particular matters of size 1, 2.5 and 10 microns; PM1, PM2.5, PM10 and AQIUS. It is observed that PM1 and PM2.5 have high impact on AQI with correlation coefficient 0.99 and 1 respectively whereas PM10 has less than that of them with correlation coefficients 0.97. This study leads to a recommendation to policy makers and users to focus more in reducing the PM2.5 concentrations by identifying sources and using suitable technologies.

RR Interdisciplinary JournalVol. 5(5)
Tribhuvan University (NP)
Openalex Percentile: Top 75%
Air Quality Monitoring and Forecasting
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