Particle emission during manufacturing processes, whether chemical, physical or mechanical can represent a serious danger for environment and for occupational safety. Especially machining processes, particle emission ...Particle emission during manufacturing processes, whether chemical, physical or mechanical can represent a serious danger for environment and for occupational safety. Especially machining processes, particle emission could have an important impact on shop floor air quality and might jeopardise workers’ health. It is therefore important to find ways of reducing the particle emission at the source of manufacturing processes. To do so, there is a need to know the size, the quantity and the distribution of particles produced by processes currently used in industry. In this study, investigations are done to compare the particle emission (PM2.5) when polishing two granites (black and white). The black granite contained low Si concentration (about 10% Si) and the white granite contained high Si concentration (about 50% Si). Particle emission was monitored using the DustTrak II equipment with 2.5 μm impactor. The particle grain size was evaluated using X-ray diffraction techniques. Machining conditions leading to the generation of finer particles were identified.展开更多
Epidemiological studies have demonstrated that chronic exposure to polluted concentration of fine ambient particulate matter(PM2.5)can induce markedly harmful effects on human health,however,an enormous research effor...Epidemiological studies have demonstrated that chronic exposure to polluted concentration of fine ambient particulate matter(PM2.5)can induce markedly harmful effects on human health,however,an enormous research effort is still need to the comprehensive understanding of PM2.5 induction of new negative health outcomes.Recently,Maher and colleges[1]from Environmental Magnetism and Paleomagnetism at Lancaster University展开更多
考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统...考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.展开更多
文摘Particle emission during manufacturing processes, whether chemical, physical or mechanical can represent a serious danger for environment and for occupational safety. Especially machining processes, particle emission could have an important impact on shop floor air quality and might jeopardise workers’ health. It is therefore important to find ways of reducing the particle emission at the source of manufacturing processes. To do so, there is a need to know the size, the quantity and the distribution of particles produced by processes currently used in industry. In this study, investigations are done to compare the particle emission (PM2.5) when polishing two granites (black and white). The black granite contained low Si concentration (about 10% Si) and the white granite contained high Si concentration (about 50% Si). Particle emission was monitored using the DustTrak II equipment with 2.5 μm impactor. The particle grain size was evaluated using X-ray diffraction techniques. Machining conditions leading to the generation of finer particles were identified.
文摘Epidemiological studies have demonstrated that chronic exposure to polluted concentration of fine ambient particulate matter(PM2.5)can induce markedly harmful effects on human health,however,an enormous research effort is still need to the comprehensive understanding of PM2.5 induction of new negative health outcomes.Recently,Maher and colleges[1]from Environmental Magnetism and Paleomagnetism at Lancaster University
文摘考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.
