{"title":"Home Air Quality Monitoring System","authors":"K. S. Kumari","doi":"10.22214/ijraset.2024.63566","DOIUrl":null,"url":null,"abstract":"Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.","PeriodicalId":13718,"journal":{"name":"International Journal for Research in Applied Science and Engineering Technology","volume":"25 10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Research in Applied Science and Engineering Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22214/ijraset.2024.63566","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.
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家庭空气质量监测系统
摘要:室内空气质量是决定健康和幸福的关键因素,特别是考虑到个人在室内投入的大量时间。认识到空气质量的关键作用,本文介绍了一种新型的家庭空气质量监测系统(HAQMS),旨在对住宅环境中的空气质量进行实时、准确的评估。HAQMS 集成了先进的传感器和物联网技术,可检测和量化各种空气污染物,包括颗粒物(PM2.5 和 PM10)、挥发性有机化合物(VOC)、二氧化碳(CO2)、一氧化碳(CO)和臭氧(O3)。系统架构分为三个主要部分:用于污染物检测的传感器阵列、采用先进算法进行实时数据分析的数据处理单元,以及用于显示空气质量指标和提供健康建议的用户界面。利用机器学习技术,该系统不仅能报告当前的空气质量,还能根据历史数据和趋势分析预测未来的空气质量水平。这一预测功能对于采取积极措施保持室内空气质量至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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