Airborne fungal spores, a type of bioaerosols, are significant air pollutants. We conducted a study to determine the spatiotemporal distributions of ambient fungi in the Greater Taipei area and develop land use regression (LUR) models for total and major fungal taxa. Four seasonal sampling campaigns were conducted over a year at 44 representative sites. Multiple regressions were performed to construct the LUR models. Ascospores were the most prevalent category, followed by Aspergillus/Penicillium, basidiospores, and Cladosporium. The highest fungal concentrations were found in spring. According to the LUR models, higher concentrations of Aspergillus/Penicillium and basidiospores were respectively present in residential/commercial areas and in areas with shorter road lengths. Various meteorological factors, particulates with aerodynamic diameters of ≤10 μm, and elevation also had significant relationships with fungal concentrations. The LUR models developed in this study can be used to assess spatiotemporal fungal distribution in the Greater Taipei area.
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http://dx.doi.org/10.1016/j.envpol.2015.04.020 | DOI Listing |
Ecotoxicol Environ Saf
December 2024
Department of Public Health, and Department of Endocrinology of the Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children's Health, Hangzhou 310058, China. Electronic address:
Background: Previous studies have suggested that neighborhoods characterized by higher walkability are related to a reduced risk of ischemic heart disease (IHD), whereas exposure to PM is positively associated with risk of IHD. Nevertheless, their joint impact on IHD warrants further investigation.
Methods: This prospective cohort study was performed in Yinzhou, Ningbo, China, comprising 47,516 participants.
Eur J Intern Med
December 2024
IRCCS Ca' Granda Maggiore Policlinico Hospital Foundation, Italy; Angelo Bianchi Bonomi Hemophilia and Thrombosis Center, Milan, Italy. Electronic address:
Introduction: The association between air pollution and cardiovascular diseases is well established. However, fewer studies focused on the relationship between air pollution and peripheral artery disease (PAD), notwithstanding that not only it is a predictor of CVD mortality but also that incidence is globally rising, particularly in low-middle income countries.
Objectives: The aim of this study is to estimate the association between long-term exposure to air pollutants and the incidence of PAD in the Rome Longitudinal Study (RLS) during 2011-2019.
Environ Sci Pollut Res Int
November 2024
School of Architecture, Technology and Engineering, University of Brighton, Brighton, UK.
Assessing exposure to environmental noise levels at transport corridors remains complex in conditions where no standardized noise prediction model is available. In planning and policy implementation for noise control, noise mapping is an important step. In the present study, land use regression model has been developed to predict the environmental noise levels in Delhi city, India, using previously developed approaches along with machine learning techniques, however improved using new datasets.
View Article and Find Full Text PDFEnviron Int
November 2024
Research Division, California Air Resources Board, Sacramento, CA 95812, the United States of America.
California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO), fine particulate matter (PM), and ozone (O) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data.
View Article and Find Full Text PDFEnviron Monit Assess
October 2024
Department of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, South Korea.
Traffic information is crucial for estimating NO concentrations, but it is static and limited in predicting constantly changing NO levels. To overcome these challenges, this study utilized real-time spatial big data to capture both the spatial and temporal fluctuations in traffic. Digital tachograph (DTG) data, sourced from digital devices in all commercial vehicles, are employed to construct a DTG land use regression (LUR) model, and its performance is compared with that of a non-DTG-LUR model.
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