162 research outputs found
Particle-Size Characteristics of Levoglucosan in Ambient Aerosols from Rice Straw Burning
No full textObservations of ozone and carbon monoxide at Mei-Feng mountain site (2269 m a.s.l.) in Central Taiwan: Seasonal variations and influence of Asian continental outflow
No full textPhysico-Chemical Characteristics of Particulate Emissions from Diesel Engines Fuelled with Waste Cooking Oil Derived Biodiesel and Ultra Low Sulphur Diesel
No full textShort text authorship attribution via sequence kernels, Markov chains and author unmasking: An investigation
No full textWe present an investigation of recently proposed character and word sequence kernels for the task of authorship attribution based on relatively short texts. Performance is compared with two corresponding probabilistic approaches based on Markov chains. Several configurations of the sequence kernels are studied on a relatively large dataset (50 authors), where each author covered several topics. Utilising Moffat smoothing, the two probabilistic approaches obtain similar performance, which in turn is comparable to that of character sequence kernels and is better than that of word sequence kernels. The results further suggest that when using a realistic setup that takes into account the case of texts which are not written by any hypothesised authors, the amount of training material has more influence on discrimination performance than the amount of test material. Moreover, we show that the recently proposed author unmasking approach is less useful when dealing with short texts
Short Text Authorship Attribution via Sequence Kernels, Markov Chains and Author Unmasking: An Investigation
No full textWe present an investigation of recently proposed character and word sequence kernels for the task of authorship attribution based on relatively short texts. Performance is compared with two corresponding probabilistic approaches based on Markov chains. Several configurations of the sequence kernels are studied on a relatively large dataset (50 authors), where each author covered several topics. Utilising Moffat smoothing, the two probabilistic approaches obtain similar performance, which in turn is comparable to that of character sequence kernels and is better than that of word sequence kernels. The results further suggest that when using a realistic setup that takes into account the case of texts which are not written by any hypothesised authors, the amount of training material has more influence on discrimination performance than the amount of test material. Moreover, we show that the recently proposed author unmasking approach is less useful when dealing with short texts.
Short text authorship attribution via sequence kernels, Markov chains and author unmasking: An investigation
No full textWe present an investigation of recently proposed character and word sequence kernels for the task of authorship attribution based on relatively short texts. Performance is compared with two corresponding probabilistic approaches based on Markov chains. Several configurations of the sequence kernels are studied on a relatively large dataset (50 authors), where each author covered several topics. Utilising Moffat smoothing, the two probabilistic approaches obtain similar performance, which in turn is comparable to that of character sequence kernels and is better than that of word sequence kernels. The results further suggest that when using a realistic setup that takes into account the case of texts which are not written by any hypothesised authors, the amount of training material has more influence on discrimination performance than the amount of test material. Moreover, we show that the recently proposed author unmasking approach is less useful when dealing with short texts
Physico-Chemical Characteristics of Particulate Emissions from Diesel Engines Fuelled with Waste Cooking Oil Derived Biodiesel and Ultra Low Sulphur Diesel
No full textHazardous aerosol emissions during agriculture biomass burning season in Son La and Ba Vi regions, Vietnam
No full textMajor national emission sources are assessed by characterization of smoke pollution arising due to traditional agriculture, domestic, and cooking activities in the regions of the biggest biomass burning. Measurement campaigns were carried in Son La and Ba Vi regions, Vietnam, during the dry seasons of 2013 and 2015-2016. PM and BC monitoring, aerosol sampling, chemical speciation were conducted to evaluate ambient smoke level, to relate the characteristics of local on-field emissions to regional aerosols, and to identify the dangerous components of smoke composition. The regions Son La and Ba Vi in February-June faced severe levels of air pollution, with critical PM2.5 and PM10 concentrations up to 130 and 167 µg/m3, respectively, significantly exceeding the air quality standards. A wide range of PM mass concentrations was categorized according to the smoke level, supported by the evolution of carbon (OC, EC) fractions as well as ionic species and molecular markers. The level of PM and BC concentrations was seen to be dependent on factors such as weather conditions and precipitation. Non-acid carbonyls, carboxylates, and aliphatic carbon compounds were evolved with increasing smoke intensity, together with carbonates in coarse size fractions, indicating a large impact of smoke emissions and soil lifted up by the intense fires. On-field emissions in both smoldering and flaming phases were assessed in near-source measurements
A highly resolved anion-exchange chromatographic method for determination of saccharidic tracers for biomass combustion and primary bio-particles in atmospheric aerosol
No full textDetermination of PM<sub>1</sub> Sources at a Prague Background Site during the 2012–2013 Period Using PMF Analysis of Combined Aerosol Mass Spectra
No full textTwo intensive measurement campaigns using a compact time-of-flight aerosol mass spectrometer were carried out at the suburban site in Prague (Czech Republic) in summer (2012) and winter (2013). The aim was to determine the aerosol sources of the NR-PM1 fraction by PMF analysis of organic (OA) and inorganic aerosol mass spectra. Firstly, an analysis of the OA mass spectra was performed. Hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and two types of oxygenated OA (OOA1) and (OOA2) were identified in summer. In winter, HOA, BBOA, long-range oxygenated OA (LROOA), and local oxygenated OA (LOOA) were determined. The identified HOA and BBOA factors were then used as additional input for the subsequent ME-2 analysis of the combined organic and inorganic spectra. This analysis resulted in six factors in both seasons. All of the previously reported organic factors were reidentified and expanded with the inorganic part of the spectra in both seasons. Two predominantly inorganic factors ammonium sulphate (AMOS) and ammonium nitrate (AMON) were newly identified in both seasons. Despite very similar organic parts of the mass profiles, the daily cycles of HOA and LOOA differed significantly in winter. It appears that the addition of the inorganic part of the mass profile, in some cases, reduces the ability of the model to identify physically meaningful factors
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