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Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England
(1) Background: Poor air quality affects health and causes premature death and disease. Outdoor air quality has received significant attention, but there has been less focus on indoor air quality and what drives levels of diverse pollutants in the home, such as particulate matter, and the impact thi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858683/ https://www.ncbi.nlm.nih.gov/pubmed/36673833 http://dx.doi.org/10.3390/ijerph20021075 |
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author | Johnes, Christopher Sharpe, Richard A. Menneer, Tamaryn Taylor, Timothy Nestel, Penelope |
author_facet | Johnes, Christopher Sharpe, Richard A. Menneer, Tamaryn Taylor, Timothy Nestel, Penelope |
author_sort | Johnes, Christopher |
collection | PubMed |
description | (1) Background: Poor air quality affects health and causes premature death and disease. Outdoor air quality has received significant attention, but there has been less focus on indoor air quality and what drives levels of diverse pollutants in the home, such as particulate matter, and the impact this has on health; (2) Methods: This study conducts analysis of cross-sectional data from the Smartline project. Analyses of data from 279 social housing properties with indoor sensor data were used to assess multiple factors that could impact levels of particulate matter. T-Tests and Anova tests were used to explore associations between elevated PM(2.5) and building, household and smoking and vaping characteristics. Binary logistic regression was used to test the association between elevated particulate matter and self-reported health; (3) Results: Of the multiple potential drivers of the particulate matter investigated, smoking and vaping were significantly associated with mean PM(2.5). Following multivariate analysis, only smoking remained significantly associated with higher mean concentrations. Properties in which <15 cigarettes/day were smoked were predicted to have PM(2.5) concentrations 9.06 µg/m(3) higher (95% CI 6.4, 12.82, p ≤ 0.001) than those in which residents were non-smokers and 11.82 µg/m(3) higher (95% CI 7.67, 18.19, p ≤ 0.001) where >15 cigarettes were smoked; (4) Conclusions: A total of 25% of social housing properties in this study experienced levels of indoor PM greater than WHO guideline levels for ambient air pollution. Although there are many factors that impact air quality, in this study the main driver was smoking. This highlights the importance of targeting smoking in indoor environments in future smoking cessation and control policy and practice and of understanding how pollutants interact in the home environment. There is also a need for further research into the impact on indoor air quality of vaping, particularly due to the rise in use and uncertainty of its long-term impact. |
format | Online Article Text |
id | pubmed-9858683 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98586832023-01-21 Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England Johnes, Christopher Sharpe, Richard A. Menneer, Tamaryn Taylor, Timothy Nestel, Penelope Int J Environ Res Public Health Article (1) Background: Poor air quality affects health and causes premature death and disease. Outdoor air quality has received significant attention, but there has been less focus on indoor air quality and what drives levels of diverse pollutants in the home, such as particulate matter, and the impact this has on health; (2) Methods: This study conducts analysis of cross-sectional data from the Smartline project. Analyses of data from 279 social housing properties with indoor sensor data were used to assess multiple factors that could impact levels of particulate matter. T-Tests and Anova tests were used to explore associations between elevated PM(2.5) and building, household and smoking and vaping characteristics. Binary logistic regression was used to test the association between elevated particulate matter and self-reported health; (3) Results: Of the multiple potential drivers of the particulate matter investigated, smoking and vaping were significantly associated with mean PM(2.5). Following multivariate analysis, only smoking remained significantly associated with higher mean concentrations. Properties in which <15 cigarettes/day were smoked were predicted to have PM(2.5) concentrations 9.06 µg/m(3) higher (95% CI 6.4, 12.82, p ≤ 0.001) than those in which residents were non-smokers and 11.82 µg/m(3) higher (95% CI 7.67, 18.19, p ≤ 0.001) where >15 cigarettes were smoked; (4) Conclusions: A total of 25% of social housing properties in this study experienced levels of indoor PM greater than WHO guideline levels for ambient air pollution. Although there are many factors that impact air quality, in this study the main driver was smoking. This highlights the importance of targeting smoking in indoor environments in future smoking cessation and control policy and practice and of understanding how pollutants interact in the home environment. There is also a need for further research into the impact on indoor air quality of vaping, particularly due to the rise in use and uncertainty of its long-term impact. MDPI 2023-01-07 /pmc/articles/PMC9858683/ /pubmed/36673833 http://dx.doi.org/10.3390/ijerph20021075 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Johnes, Christopher Sharpe, Richard A. Menneer, Tamaryn Taylor, Timothy Nestel, Penelope Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England |
title | Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England |
title_full | Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England |
title_fullStr | Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England |
title_full_unstemmed | Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England |
title_short | Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England |
title_sort | using sensor data to identify factors affecting internal air quality within 279 lower income households in cornwall, south west of england |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858683/ https://www.ncbi.nlm.nih.gov/pubmed/36673833 http://dx.doi.org/10.3390/ijerph20021075 |
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