Influence of ambient temperature on the heterogeneity of ambient fine particle chemical composition and disease prevalence

Joint exposure to air pollution, ambient temperature and residential greenness and their association with metabolic syndrome (MetS): A large population-based study among Chinese adults

Previous studies assessing adverse health have traditionally focused on a single environmental exposure, failing to reflect the reality of various exposures present simultaneously. Air pollution, ambient temperature and greenness have been proposed as critical environmental factors associated with metabolic syndrome (MetS). However, evidence exploring their joint relationships with MetS is needed for identifying interactive factors and developing more targeted public health interventions. The baseline data was obtained from China Multi-Ethnic Cohort (CMEC). Environmental data of air pollutants (PM_2.5, O₃) and NDVI for greenness was calculated from satellites data. Ambient temperature data were obtained from European Center for Medium-Range Weather Forecasts (ECMWF). MetS was classified based on National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) using anthropometric measures and biomarkers. Logistic regression models were utilized to examine the combined relationship of MetS with three-year exposure to air pollutants, temperature and NDVI. Relative excess risk due to interaction (RERI) was calculated to evaluate interaction on an additive scale. We found associations between prevalent MetS and interquartile range (IQR) increases in PM_2.5 (OR: 1.38; 95% confidence interval [95% CI]: 1.23, 1.55) and O₃ (OR: 1.15; 95% CI: 1.09, 1.22). Additive and multiplicative interactions were observed between air pollutants and temperature exposure. Compared to low-temperature level, the relationship between PM_2.5 and MetS attenuated (RERI: 0.22, 95% CI: 0.44, -0.04) at high-temperature level, while the relationship between O₃ and MetS enhanced (RERI: 0.05, 95% CI: 0.02, 0.11). At low NDVI 250 m, the association between PM_2.5 and MetS was stronger (RERI: 0.13, 95% CI: 0.05, 0.19) with high NDVI 250 m as the reference group. Our findings showed that ambient temperature and residential greenness could affect the relationship between air pollutants and MetS.

Factors That Influence Climate Change-Related Mortality in the United States: An Integrative Review

Global atmospheric warming leads to climate change that results in a cascade of events affecting human mortality directly and indirectly. The factors that influence climate change-related mortality within the peer-reviewed literature were examined using Whittemore and Knafl’s framework for an integrative review. Ninety-eight articles were included in the review from three databases—PubMed, Web of Science, and Scopus—with literature filtered by date, country, and keywords. Articles included in the review address human mortality related to climate change. The review yielded two broad themes in the literature that addressed the factors that influence climate change-related mortality. The broad themes are environmental changes, and social and demographic factors. The meteorological impacts of climate change yield a complex cascade of environmental and weather events that affect ambient temperatures, air quality, drought, wildfires, precipitation, and vector-, food-, and water-borne pathogens. The identified social and demographic factors were related to the social determinants of health. The environmental changes from climate change amplify the existing health determinants that influence mortality within the United States. Mortality data, national weather and natural disaster data, electronic medical records, and health care provider use of International Classification of Disease (ICD) 10 codes must be linked to identify climate change events to capture the full extent of climate change upon population health.

Microbiological and chemical quality of indoor air in kindergartens in Slovenia

The concentrations of microorganisms, aerosol black carbon and carbon dioxide (CO₂) in indoor and outdoor air of two kindergartens were investigated during four seasons. The highest mean concentrations of aerobic mesophilic microorganisms in indoor air were detected in spring. Cladosporium, Penicillium and Aspergillus were the most common fungi in all air samples. The concentrations of Staphylococci, Enterobacteria and CO₂ had a positive correlation with the number of persons in the rooms. The highest mean concentration of black carbon in indoor and outdoor air was obtained in winter. Concentrations of CO₂ exceeded 1000 ppm in 89.3% of the indoor air measurements. The reduction of the number of children in individual playrooms and more frequent ventilation are recommended for lowering the concentration of CO₂ and the number of microorganisms in the air, especially potential pathogen fungi. The renovation of buildings with installation of effective artificial ventilation is recommended as well.Abbreviations: CFU, Colony-Forming Unit; HPC, Heterotrophic Plate Count (Aerobic Mesophilic Microorganisms); YGC, Yeast Extract Glucose Chloramphenicol Agar; BC, Aerosol Black Carbon Particles; CO₂, Carbon Dioxide; PM, Particulate Matter.

Source apportionment of PM2.5 using positive matrix factorization (PMF) at a rural site in Korea

The sources of different pollutants contributing to ambient fine particles (PM_2.5) on Daebu Island, Korea, were estimated. Twenty four hour integrated filter samples were collected from May 21-November 1, 2016, and analyzed for organic carbon, elemental carbon, ions, and trace elements. Positive matrix factorization was conducted on the PM_2.5 chemical speciation data from the samples to define the pathways and sources of PM_2.5 at the sampling site. A total of 80 samples and 24 chemical species were used to run the model and a total of nine sources were identified: secondary sulfate (29.0%), mobile (22.0%), secondary nitrate (13.2%), oil combustion (10.1%), coal combustion (9.4%), aged sea salt (7.9%), soil (5.6%), non-ferrous smelting (1.7%), and industrial activity (1.1%). Conditional probability and potential source contribution functions were then used to determine whether these sources were local or came from pollutants transported over long-range distances. The anthropogenic sources came from local emissions and originated from both industrialized and metropolitan areas, whereas the secondary inorganic aerosols were strongly influenced by the long-range transport of air pollutants from Shandong and Jiangsu provinces in China.