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Mobile on-line DOAS trace-gases monitoring system with fiber spectroscopy

机译:具有纤维光谱学的移动式在线DOAS痕量气体监测系统

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The article mainly focused on mobile on-line air quality monitoring system. By deeply analyzed DOAS theory, we designed this new air quality monitoring system. It is mobile and may monitoring many pollution sources on-line everyday. The Differential Optical Absorption Spectroscopy (DOAS), based on the work by U. Platt and co-workers, is becoming increasingly popular for environment monitoring. DOAS may measure many trace gases like NO2, O3, BrO, NO3 , and SO2. It is designed for the measurement of primary and secondary urban air pollutants with high precision and little cross interference. In the DOAS technique, the spectrum of an artificial light source within a given bandwidth is measured after passing through the open atmosphere for between 100 m and 10 km. After removing the emission spectrum of the light source, the remaining differential absorption features are compared with the absorption cross sections of relevant trace gases. This allows both the qualitative and quantitative determination of their concentration in the light path. After deeply research, we design this new system. It uses fiber spectroscopy, and it is mobile. User may monitor many pollution sources in a car. This makes auto-monitoring more easily. Our DOAS system has these merits: New Light Emission-Receiver Unit which united emission, receiver and collimation lenses. New Background Elimination Fiber. And dynamic-feedback self-adapting program. In our experiment by this design idea, we get accurate data.
机译:本文主要关注移动在线空气质量监测系统。通过深入分析DOAS理论,我们设计了这种新的空气质量监测系统。它是可移动的,可以每天在线监测许多污染源。基于U. Platt及其同事的工作的差分光吸收光谱法(DOAS)在环境监测中正变得越来越受欢迎。 DOAS可以测量许多微量气体,例如NO2,O3,BrO,NO3和SO2。它是专为高精度,无交叉干扰的城市一级和二级空气污染物的测量而设计的。在DOAS技术中,在给定带宽内的人造光源的光谱是在穿过露天大气100 m至10 km之后进行测量的。在去除光源的发射光谱之后,将其余的差分吸收特征与相关痕量气体的吸收截面进行比较。这允许定性和定量确定它们在光路上的浓度。经过深入研究,我们设计了这个新系统。它使用光纤光谱仪,并且可以移动。用户可以监视汽车中的许多污染源。这使自动监视更加容易。我们的DOAS系统具有以下优点:新型发光接收器单元,它将发射,接收器和准直透镜组合在一起。新的背景消除光纤。以及动态反馈自适应程序。在此设计思想的实验中,我们获得了准确的数据。

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