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RISS 인기검색어
- 검색키워드
- 저자 : Skiles, Matthew J. (검색결과 2 건)
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Bae, Min-Suk,Skiles, Matthew J.,Lai, Alexandra M.,Olson, Michael R.,de Foy, Benjamin,Schauer, James J. Elsevier 2019 Environmental pollution Vol.246 No.-
<P><B>Abstract</B></P> <P>Two hundred sixty-three fine particulate matter (PM<SUB>2.5</SUB>) samples were collected over fourteen months in Fresno and Bakersfield, California. Samples were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and 160 organic molecular markers. Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) source apportionment models were applied to the results in order to understand monthly and seasonal source contributions to PM<SUB>2.5</SUB> OC. Similar source categories were found from the results of the CMB and PMF models to PM<SUB>2.5</SUB> OC across the sites. Six source categories with reasonably stable profiles, including biomass burning, mobile, food cooking, two different secondary organic aerosols (SOAs) (i.e., winter and summer), and forest fires were investigated. Both the CMB and the PMF models showed a strong seasonality in contributions of some sources, as well as dependence on wind transport for both sites. The overall relative source contributions to OC were 24% CMB wood smoke, 19% CMB mobile sources, 5% PMF food cooking, 2% CMB vegetative detritus, 17% PMF SOA summer, 22% PMF SOA winter, and 12% PMF forest fire. Back-trajectories using the Weather Research and Forecasting model combined with the FLEXible PARTicle dispersion model (WRF-FLEXPART) were used to further characterize wind transport. Clustering of the trajectories revealed dominant wind patterns associated with varying concentrations of the different source categories. The Comprehensive Air Quality Model with eXtensions (CAMx) was used to simulate aerosol transport from forest fires and thus confirm the impacts of individual fires, such as the Rough Fire, at the measurement sites.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Comparison of Molecular marker CMB and PMF models to OC. </LI> <LI> Assessment of Forest Fire impacts on OC using PMF. </LI> <LI> Forest fire confirmation from CAMx simulations based on FLAMBE emissions. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
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Health-Care Providers' Perspectives towards Childhood Cancer Treatment in Kenya
Njuguna, F,Burgt, RHM van der,Seijffert, A,Musimbi, J,Langat, S,Skiles, J,Sitaresmi, MN,Ven, PM van de,Kaspers, GJL,Mostert, S Asian Pacific Journal of Cancer Prevention 2016 Asian Pacific journal of cancer prevention Vol.17 No.9
Background: This study explored perspectives of health-care providers on childhood cancer treatment in Kenya. Materials and Methods: A self-administered questionnaire was completed by 104 health-care providers in January and February 2013. Results: Seventy six percent of the health-care providers believed cancer to be curable. More doctors than other health-care providers had this positive opinion (p=0.037). The majority of health-care providers (92%) believed that most children with cancer will not be able to finish their treatment due to financial difficulties. They considered that prosperous highly-educated parents adhere better with treatment (88%) and that doctors adhere better with treatment for prosperous highly-educated parents (79%). According to 74% of health-care providers, quality of care is better for prosperous highly-educated parents (74%). Most health-care providers reported giving more explanation (71%), work with greater accuracy (70%) and use less difficult vocabulary (55%) to prosperous more educated families. Only 34% of health-care providers reported they feel more empathy towards patients from prosperous families. Reasons for non-adherence with the protocol according to health-care providers are: family refuses drugs (85%), inadequate supply of drugs at pharmacy (79%), child looks ill (75%), and financial difficulties of parents (69%). Conclusions: Health-care providers' health beliefs and attitudes differ for patients with families having high versus low socio-economic backgrounds.
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