Human exposure to particles depends on particle loss mechanisms such as deposition and filtration. Fine and ultrafine particles (FP and UFP) were measured continuously over seven consecutive days during summer and winter inside 74 homes in Edmonton, Canada. Daily average air exchange rates were also measured. FP were also measured outside each home and both FP and UFP were measured at a central monitoring station. A censoring algorithm was developed to identify indoor-generated concentrations, with the remainder representing particles infiltrating from outdoors. The resulting infiltration factors were employed to determine the continuously changing background of outdoor particles infiltrating the homes. Background-corrected indoor concentrations were then used to determine rates of removal of FP and UFP following peaks due to indoor sources. About 300 FP peaks and 400 UFP peaks had high-quality (median R(2) value >98%) exponential decay rates lasting from 30 min to 10 h. Median (interquartile range (IQR)) decay rates for UFP were 1.26 (0.82-1.83) h(-1); for FP 1.08 (0.62-1.75) h(-1). These total decay rates included, on average, about a 25% contribution from air exchange, suggesting that deposition and filtration accounted for the major portion of particle loss mechanisms in these homes. Models presented here identify and quantify effects of several factors on total decay rates, such as window opening behavior, home age, use of central furnace fans and kitchen and bathroom exhaust fans, use of air cleaners, use of air conditioners, and indoor-outdoor temperature differences. These findings will help identify ways to reduce exposure and risk.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1021/es402580t | DOI Listing |
Alzheimers Dement (Amst)
January 2025
Introduction: Cross-sectional resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed altered complexity with advanced Alzheimer's disease (AD) stages. The current study conducted longitudinal rsfMRI complexity analyses in AD.
Methods: Linear mixed-effects (LME) models were implemented to evaluate altered rates of disease progression in complexity across disease groups.
Sci Total Environ
January 2025
Institute of Agrochemistry and Food Technology, IATA-CSIC, Av. Agustín Escardino 7, Paterna, Valencia 46980, Spain. Electronic address:
Human enteric viruses and emerging viruses such as severe acute respiratory syndrome coronavirus 2, influenza virus and monkeypox virus, are frequently detected in wastewater. Human enteric viruses are highly persistent in water, but there is limited information available for non-enteric viruses. The present study evaluated the stability of hepatitis A virus (HAV), murine norovirus (MNV), influenza A virus H3N2 (IAV H3N2), human coronavirus (HCoV) 229E, and vaccinia virus (VACV) in reference water (RW), effluent wastewater (EW) and drinking water (DW) under refrigeration and room temperature conditions.
View Article and Find Full Text PDFFront Antibiot
April 2024
The Science Academy, Istanbul, Türkiye.
The aim of this study was to reveal the microbial and kinetic impacts of acute and chronic exposure to one of the frequently administered antibiotics, i.e., sulfamethoxazole, on an activated sludge biomass.
View Article and Find Full Text PDFInorg Chem
January 2025
Department of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada.
Photothermal therapy is a promising strategy for treating tumors and bacterial infections by using light irradiation to locally heat tissues. Metalloisoporphyrinoid materials have been investigated for their use as singlet oxygen photosensitizers for photodynamic therapy but remain underexplored as photothermal agents. Recently, two metallophlorin and two metalloisocorrole materials were found to have strong near-infrared absorbance, with low photoluminescent quantum yields, suggesting high rates of nonradiative decay.
View Article and Find Full Text PDFClin Exp Nephrol
January 2025
Kawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan.
Background: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinical practice. We aimed to investigate whether deep learning techniques, especially those suitable for processing missing values, can improve the accuracy of predicting future renal function compared to traditional statistical method, using the Japan Chronic Kidney Disease Database (J-CKD-DB), a nationwide multicenter CKD registry.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!