2015
DOI: 10.1016/j.atmosenv.2015.07.003
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Mass loading and episodic variation of molecular markers in PM2.5 aerosols over a rural area in eastern central India

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Cited by 47 publications
(33 citation statements)
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“…Therefore, we presume a negligible input of ma- rine sources to sugar alcohols in Alaskan fine aerosol samples. Arabitol and mannitol were also detected in aerosol particles derived from BB (Fu et al, 2012;Yang et al, 2012;Nirmalkar et al, 2015). We found insignificant correlations of arabitol (r = 0.16) and mannitol (r = 0.27) with levoglucosan ( Fig.…”
Section: Sugar Compounds: Tracers Of Primary Biological Particlesmentioning
confidence: 75%
See 1 more Smart Citation
“…Therefore, we presume a negligible input of ma- rine sources to sugar alcohols in Alaskan fine aerosol samples. Arabitol and mannitol were also detected in aerosol particles derived from BB (Fu et al, 2012;Yang et al, 2012;Nirmalkar et al, 2015). We found insignificant correlations of arabitol (r = 0.16) and mannitol (r = 0.27) with levoglucosan ( Fig.…”
Section: Sugar Compounds: Tracers Of Primary Biological Particlesmentioning
confidence: 75%
“…Although early Arctic explorers had noticed atmospheric haze (Nordenskiold, 1883), the remote Arctic atmosphere was believed to be extremely clean. Pilots flying over the North American Arctic in the 1950s observed widespread haze that could be seen every winter and early spring (Mitchell, 1957).…”
Section: Introductionmentioning
confidence: 99%
“…We presume a negligible input of marine sources to sugar alcohols in Alaskan fine aerosol samples. Arabitol and mannitol were also detected in aerosol particles derived from BB (Fu et al, 2012;Yang et al, 2012;Nirmalkar et al, 2015). We found insignificant correlations of arabitol (r = 0.16) and mannitol (r = 0.27) with levoglucosan ( Fig.…”
Section: Sugar Compounds: Tracers Of Primary Biological Particlesmentioning
confidence: 75%
“…Principal component analysis (PCA) is a useful tool for reducing the dimensionality of large aerosol datasets to principal components using varimax rotation for source identification (Cao et al, 2005;Lin et al, 2018;Nirmalkar et al, 2019). Four principal components (PCs) in winter and three in spring were identified with eigenvalues > 1 after varimax rotation, explaining 96 % and 92 %, respectively, of the total variance (Tables 2 and 3).…”
Section: Principal Component Analysismentioning
confidence: 99%