Scholarlink

<p>As people spend most of their time indoors, it is vital to assess their exposure to air pollutants considering their adverse impacts on human health. The use of low-cost sensors (LCS) for measuring air quality is becoming increasingly important for providing widespread, real-time exposure assessment, particularly in resource-limited settings. As the accuracy of LCS data is influenced by meteorological factors, data correction using co-location with a reference instrument is essential to ensure good quality data. In this study, we collocated these sensors with a Beta Attenuation Monitor (BAM) reference instrument over a two-month period. Analysis of the co-located data revealed that the sensors tend to overestimate PM<sub>2.5</sub> concentrations as relative humidity (RH) increases. To remove this effect from the data, multiple linear regression technique was applied to adjust the sensor data for RH effects. Post correction, the sensors demonstrated Mean Absolute Bias (MAB) ranging from 6.8 to 7.2&#xa0;µg/m<sup>3</sup>, normalized MAB 7.3–7.5%, Root Mean Squared Error (RMSE) of ~ 11&#xa0;µg/m<sup>3</sup>, normalized RMSE of ~ 11%, and a Coefficient of Determination (R<sup>2</sup>) between 0.917 and 0.937 for PM<sub>2.5</sub>. To estimate particulate matter exposure in various indoor microenvironments and its relationship with ambient air quality, the sensors were deployed at an outdoor and multiple indoor locations across an academic campus for a semester. We found that air quality in indoor spaces without active emission sources was worse than ambient air quality for half of the times. Indoor air quality in spaces with sources was mostly poorer than the ambient air quality. The ratio of indoor to outdoor PM<sub>2.5</sub> was in the range of 0.6 to 1.2, and 1.0 to 1.9 for the indoor spaces without sources and with sources, respectively. Correlation between indoor and ambient air quality was good, with indoor air quality lagging behind ambient conditions by ~ 90&#xa0;min. However, indoor environments with active emission sources, such as cooking, soldering, and welding, exhibited poor air quality, posing health concerns. This study underscores the importance of accurate air quality data from LCS and highlights the need for effective indoor air quality management, particularly in environments with emission sources.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Indoor air quality assessment using low-cost sensors, and impact of outdoors

  • 期刊论文

  • 发表日期：

    2025年10月30日

查看全文
  • Yash Dahima,
  • Aditya Vaishya

摘要

As people spend most of their time indoors, it is vital to assess their exposure to air pollutants considering their adverse impacts on human health. The use of low-cost sensors (LCS) for measuring air quality is becoming increasingly important for providing widespread, real-time exposure assessment, particularly in resource-limited settings. As the accuracy of LCS data is influenced by meteorological factors, data correction using co-location with a reference instrument is essential to ensure good quality data. In this study, we collocated these sensors with a Beta Attenuation Monitor (BAM) reference instrument over a two-month period. Analysis of the co-located data revealed that the sensors tend to overestimate PM2.5 concentrations as relative humidity (RH) increases. To remove this effect from the data, multiple linear regression technique was applied to adjust the sensor data for RH effects. Post correction, the sensors demonstrated Mean Absolute Bias (MAB) ranging from 6.8 to 7.2 µg/m3, normalized MAB 7.3–7.5%, Root Mean Squared Error (RMSE) of ~ 11 µg/m3, normalized RMSE of ~ 11%, and a Coefficient of Determination (R2) between 0.917 and 0.937 for PM2.5. To estimate particulate matter exposure in various indoor microenvironments and its relationship with ambient air quality, the sensors were deployed at an outdoor and multiple indoor locations across an academic campus for a semester. We found that air quality in indoor spaces without active emission sources was worse than ambient air quality for half of the times. Indoor air quality in spaces with sources was mostly poorer than the ambient air quality. The ratio of indoor to outdoor PM2.5 was in the range of 0.6 to 1.2, and 1.0 to 1.9 for the indoor spaces without sources and with sources, respectively. Correlation between indoor and ambient air quality was good, with indoor air quality lagging behind ambient conditions by ~ 90 min. However, indoor environments with active emission sources, such as cooking, soldering, and welding, exhibited poor air quality, posing health concerns. This study underscores the importance of accurate air quality data from LCS and highlights the need for effective indoor air quality management, particularly in environments with emission sources.