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Air quality is one of the most important factors in public health. While outdoor air quality is widely studied, the indoor environment has been less scrutinised, even though time spent indoors is typically much greater than outdoors. The emergence of low-cost sensors can help assess indoor air quality. This study provides a new methodology, utilizing low-cost sensors and source apportionment techniques, to understand the relative importance of indoor and outdoor air pollution sources upon indoor air quality. The methodology is tested with three sensors placed in different rooms inside an exemplar house (bedroom, kitchen and office) and one outdoors. When the family was present, the bedroom had the highest average concentrations for PM and PM (3.9 ± 6.8 ug/m and 9.6 ± 12.7 μg/m respectively), due to the activities undertaken there and the presence of softer furniture and carpeting. The kitchen, while presenting the lowest PM concentrations for both size ranges (2.8 ± 5.9 ug/m and 4.2 ± 6.9 μg/m respectively), presented the highest PM spikes, especially during cooking times. Increased ventilation in the office resulted in the highest PM concentration (1.6 ± 1.9 μg/m), highlighting the strong effect of infiltration of outdoor air for the smallest particles. Source apportionment, via positive matrix factorisation (PMF), showed that up to 95 % of the PM was found to be of outdoor sources in all the rooms. This effect was reduced as particle size increased, with outdoor sources contributing >65 % of the PM, and up to 50 % of the PM, depending on the room studied. The new approach to elucidate the contributions of different sources to total indoor air pollution exposure, described in this paper, is easily scalable and translatable to different indoor locations.
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http://dx.doi.org/10.1016/j.envint.2023.107907 | DOI Listing |
Environ Monit Assess
September 2025
Department of Environment and Life Science, KSKV Kachchh University, Bhuj, Gujarat, 370 001, India.
India's energy demand increased by 7.3% in 2023 compared to 2022 (5.6%), primarily met by coal-based thermal power plants (TPPs) that contribute significantly to greenhouse gas emissions.
View Article and Find Full Text PDFLight Sci Appl
September 2025
Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 230031, Hefei, China.
Marine vessels play a vital role in the global economy; however, their negative impact on the marine atmospheric environment is a growing concern. Quantifying marine vessel emissions is an essential prerequisite for controlling these emissions and improving the marine atmospheric environment. Optical imaging remote sensing is a vital technique for quantifying marine vessel emissions.
View Article and Find Full Text PDFEnviron Res
September 2025
Thrust of Sustainable Energy and Environment, Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 510000, China. Electronic address:
China's aluminum-products industry, a large-scale consumer of industrial paints, is a potentially significant source of full-volatility organic compounds (F-VOCs). However, the emission characteristics of F-VOCs, including VOCs, intermediate-, semi-, and low-volatility organic compounds (I/S/LVOCs), and their role in ozone formation potentials (OFP), and secondary organic aerosol formation potentials (SOAP) remain unclear. In this study, we collected in-field samples from three industrial paints (solvent-based, water-based and powder paints) at spraying and drying processes, and treatment devices to analyze the emission characteristics of F-VOCs, OFP, SOAP.
View Article and Find Full Text PDFJ Environ Manage
September 2025
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Dissolved oxygen (DO) is a key water quality indicator reflecting river health. Modeling and understanding the spatiotemporal dynamics of DO and its influencing factors are crucial for effective river management. Machine learning (ML) models have gained popularity in water quality prediction; however, their accuracy strongly depends on the predictor variables.
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