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Spatial varying profiling of air PM constituents using paper-based microfluidics.
Biomicrofluidics ( IF 3.2 ) Pub Date : 2019-09-17 , DOI: 10.1063/1.5119910 Yuan Jia , Wenyu Wu 1 , Jianping Zheng 2 , Zhonghua Ni , Hao Sun
Biomicrofluidics ( IF 3.2 ) Pub Date : 2019-09-17 , DOI: 10.1063/1.5119910 Yuan Jia , Wenyu Wu 1 , Jianping Zheng 2 , Zhonghua Ni , Hao Sun
Affiliation
Accurate and quantitative profiling of air particulate matter (PM) compositions is essential for assessing local pollution information. The method combining mobile aerial sampling using unmanned aerial vehicles (UAVs) and prompt analysis excels in this regard as it allows spatiotemporal mapping of air pollution, especially in the vertical direction. However, applications of the method are still scarce as it is limited by a lack of sampling reliability due to insufficient aerial sampling time and a lack of accurate, portable quantification techniques. In this work, by integrating mobile aerial sampling with in-flight tethered charging and smartphone-based colorimetric analysis in a cost-effective paper microfluidic device, we present a method for quantitative, reliable profiling of spatiotemporal variation in air PM compositions. The method extends aerial sampling time to 12-15 flight hours per deployment, thereby significantly improving sampling reliability while maintaining the maneuverability of the UAVs. Also, smartphone-based colorimetric analysis combined with paper-based microfluidics enables portable, economically efficient analysis and is well-suited for using in low-resource settings. We demonstrated the utility of the method by carrying out a spatiotemporal variation study of air PM trace metal components (Fe, Ni, and Mn) at 4 geographical locations in Fuzhou, China, for a period of 21 days, and the results were in good agreement with results obtained from using a commercial instrument. Beside air PM composition study, this method is universally applicable and holds great potential to be extended to multipollutant analysis, such as prompt detection of airborne viruses, bacteria, and others.
更新日期：2019-11-01
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