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PM-carried microorganisms, widely dispersed in the atmosphere, have garnered significant attention for their potential health impacts. However, the impact of spatio-temporal variations on bioaerosol distribution remains unclear. In this study, we collected PM samples across all seasons from industrial, urban, and rural areas of Nanjing, a Chinese megacity, and analyzed the spatio-temporal distribution of microorganisms and their influencing factors. Results revealed significant spatio-temporal variations in microbial diversity and abundance. Bacterial diversity and abundance were higher in autumn and winter, with notable seasonal differences in industrial and rural areas. Fungi exhibited regional variations, with higher diversity and abundance in industrial areas during spring and autumn, and peak abundance in urban and rural areas in winter. Additionally, key genera responded variably to spatio-temporal changes. Broadly adapted genera, such as Achromobacter (bacterium) and Cladosporium (fungus) remained stable across seasons, while taxa like Chloroplast and Epicoccum showed significant abundance fluctuations with environmental changes. Multifactorial analyses revealed bacterial communities were primarily influenced by temperature, wind speed, and air quality index (AQI), while fungi were also significantly impacted by humidity and population density. Correlation network analyses linked metals, carbon and nitrogen contents in PM to microbial abundance. Most bacterial genera were negatively correlated with chemical constituents, while fungi were more adaptive to these constituents and were positively correlated. This study offers insights into the spatio-temporal distribution of PM microbial components and the influence of environmental factors and aerosol chemistry on microbial communities, highlighting implications for air quality and pollution management in public health.
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http://dx.doi.org/10.1016/j.envres.2025.121677 | DOI Listing |
Fungal Biol
October 2025
School of Life Sciences, Nanjing Normal University, Nanjing, 210023, Jiangsu Province, China. Electronic address:
Urban green areas are vital yet underexplored reservoirs of microbial diversity in cities. This study examines myxomycete communities in Zijin Mountain National Forest Park, a subtropical urban forest in Nanjing, China, across four seasons and multiple forest types. Combining field collections and moist chamber cultures, we documented 60 species from 906 occurrence records.
View Article and Find Full Text PDFPrev Vet Med
September 2025
World Organisation for Animal Health (WOAH) Sub-Regional Representation for South East Asia, Bangkok 10400, Thailand.
Foot and mouth disease (FMD) remains endemic in several countries across Southeast Asia, China, and Mongolia (SEACFMD region), posing an ongoing threat to livestock and trade. This study aimed to investigate the epidemiological characteristics and analyze the spatial and temporal distribution of FMD outbreaks reported across the SEACFMD region. FMD outbreak and virus lineage data from 2015 to 2023 were utilized.
View Article and Find Full Text PDFPLoS Negl Trop Dis
September 2025
Université de Montréal, École de Santé Publique, Montreal, Canada.
Background: Dengue, chikungunya, and Zika present significant public health challenges in Colombia. Spatial studies help clarify the distribution and progression of these diseases over time and location. Objective to describe the spatio-temporal distribution and clustering patterns of dengue, chikungunya, and Zika in Medellín, Colombia, between 2013 and 2021, with the aim of providing baseline spatial intelligence to support future epidemiological and policy-oriented analyses.
View Article and Find Full Text PDFRev Peru Med Exp Salud Publica
August 2025
Área Académica de Medicina Veterinaria y Zootecnia, Instituto de Ciencias Agropecuarias, Universidad Autónoma del Estado de Hidalgo, Hidalgo, México.
Background: Motivation for the study. Vector-borne diseases such as Rocky Mountain spotted fever require constant monitoring of their spatial and temporal distribution patterns due to current climate change and atypical conditions. Main findings.
View Article and Find Full Text PDFMath Biosci Eng
July 2025
National Marine Fisheries Service, Northeast Fisheries Science Center, MA 02543-1026, USA.
Fishery stock assessments typically rely on biomass estimates derived from stratified random sampling, where a key assumption is a consistent spatial biomass distribution over time. However, climate-driven movements of marine species may be violating this assumption, potentially introducing biases into biomass estimates. To address this, we develop a spatially explicit data-driven mathematical modeling framework where species-specific movement is driven by environmental variables such as water temperature and geographic habitat preferences.
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