JEP  Vol.7 No.10 , September 2016
Portrait and Classification of Individual Haze Particulates
Abstract: Haze (known as “Mai” 霾 in Chinese) threatens the health of billions of people across the globe. To begin solving this problem without severely slowing down the economy, one has to mechanistically and geographically pinpoint the sources of these pollutants, the key of which is to thoroughly characterize and fingerprint the particulates. Here we present a broad survey and classification of thousands of individual airborne particu-lates by using the Scanning Electron Microscope (SEM) to measure their diverse mor-phologies and chemistries, which could eventually be organized into a “haze finger-print database”. For instance, one collection in Xi’an City, China during March-April 2014 yielded 494 airborne particulates that settled on silicon wafers placed outside the window of a 3rd floor office. These 494 particulates were manually imaged with high resolution (down to 2 nm), elementally mapped using Energy-dispersive X-ray Spec-troscopy (EDS), and were identified and categorized into presumed source classes such as construction activities (Ca, Al, Si-O), coal burning (sulfates), biologic (pollen, bac-teria), automotive, mining, steel making, and etc. About 20% of the particulates have mysterious origins, as it is still unclear how they were formed, and a fraction of them contained clearly hazardous elements such as lead and chromium. For future work, we propose using unmanned aerial vehicles with a special “rolling film” substrate that can autonomously collect airborne particulates, a customized SEM auto-imaging system, and machine learning software to establish an online open-access database. The end goal would be to monitor and analyze the particulate pollutants that are pumped into our atmosphere every day, and precisely track down their sources so we can better model and police the quality of the air around us.
Cite this paper: Li, C. , Ding, M. , Yang, Y. , Zhang, P. , Li, Y. , Wang, Y. , Huang, L. , Yang, P. , Wang, M. , Sha, X. , Xu, Y. , Guo, C. and Shan, Z. (2016) Portrait and Classification of Individual Haze Particulates. Journal of Environmental Protection, 7, 1355-1379. doi: 10.4236/jep.2016.710118.

[1]   World Health Organization (2016) WHO Global Urban Ambient Air Pollution Database (Update 2016).

[2]   Donkelaar, A.V. and Villeneuve, P.J. (2010) Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application. Environmental Health Perspectives, 118, 847-855.

[3]   China National Environmental Monitoring Centre (2013) Air Quality Report in 74 Chinese Cities in March and the First Quarter 2013.

[4]   Huang, R.-J., et al. (2014) High Secondary Aerosol Contribution to Particulate Pollution during Haze Events in China. Nature, 514, 218-222.

[5]   Zhang, Q., He, K. and Huo, H. (2012) Policy: Cleaning China’s Air. Nature, 484, 161-162.

[6]   Chen, R., Zhao, Z. and Kan, H. (2013) Heavy Smog and Hospital Visits in Beijing, China. American Journal of Respiratory and Critical Care Medicine, 188, 1170-1171.

[7]   Raaschou-Nielsen, O., et al. (2013) Air Pollution and Lung Cancer Incidence in 17 European Cohorts: Prospective Analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). The Lancet Oncology, 14, 813-822.

[8]   Wang, Y., Zhang, R. and Saravanan, R. (2014) Asian Pollution Climatically Modulates Mid-Latitude Cyclones Following Hierarchical Modelling and Observational Analysis. Nature Communications, 5, 3098.

[9]   Chinese State Council (2013) Atmospheric Pollution Prevention and Control Action Plan.

[10]   China State Media Defends “APEC Blue” Skies (2014).

[11]   US Environmental Protection Agency (2016) Particulate Matter Pollution.

[12]   Liu, J., et al. (2014) Source Apportionment Using Radiocarbon and Organic Tracers for PM2. 5 Carbonaceous Aerosols in Guangzhou, South China: Contrasting Local-and Regional-Scale Haze Events. Environmental Science & Technology, 48, 12002-12011.

[13]   Ding, M., Han, W., Li, J., Ma, E. and Shan, Z. (2015) In Situ Study of the Mechanical Properties of Airborne Haze Particles. Science China Technological Sciences, 58, 2046-2051.

[14]   Feng, J., et al. (2012) Source and Formation of Secondary Particulate Matter in PM2. 5 in Asian Continental Outflow. Journal of Geophysical Research: Atmospheres, 117, Article ID: D03302.

[15]   Yu, L., et al. (2013) Characterization and Source Apportionment of PM2. 5 in an Urban Environment in Beijing. Aerosol and Air Quality Research, 13, 574-583.

[16]   Cao, J.-J., et al. (2012) Winter and Summer PM2. 5 Chemical Compositions in Fourteen Chinese Cities. Journal of the Air & Waste Management Association, 62, 1214-1226.

[17]   Pachauri, T., Singla, V., Satsangi, A., Lakhani, A. and Kumari, K.M. (2013) SEM-EDX Characterization of Individual Coarse Particles in Agra, India. Aerosol and Air Quality Research, 13, 523-536.

[18]   Li, W. and Shao, L. (2009) Transmission Electron Microscopy Study of Aerosol Particles from the Brown Hazes in Northern China. Journal of Geophysical Research: Atmospheres, 114, 9.

[19]   Geng, H., Ryu, J., Maskey, S., Jung, H.-J. and Ro, C.-U. (2011) Characterisation of Individual Aerosol Particles Collected during a Haze Episode in Incheon, Korea Using the Quantitative ED-EPMA Technique. Atmospheric Chemistry and Physics, 11, 1327-1337.

[20]   Wagner, J., Naik-Patel, K., Wall, S. and Harnly, M. (2012) Measurement of Ambient Particulate Matter Concentrations and Particle Types near Agricultural Burns Using Electron Microscopy and Passive Samplers. Atmospheric Environment, 54, 260-271.

[21]   Cao, J., Chow, J.C., Lee, F.S. and Watson, J.G. (2013) Evolution of PM2. 5 Measurements and Standards in the US and Future Perspectives for China. Aerosol and Air Quality Research, 13, 1197-1211.

[22]   Si, S., Tao, D. and Geng, B. (2010) Bregman Divergence-Based Regularization for Transfer Subspace Learning. IEEE Transactions on Knowledge and Data Engineering, 22, 929-942.

[23]   Goldberg, A.B., Zhu, X., Singh, A., Xu, Z. and Nowak, R.D. (2009) Multi-Manifold Semi-Supervised Learning. Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), 169-176.

[24]   Liu, X., Lu, H. and Li, W. (2010) Multi-Manifold Modeling for Head Pose Estimation. 2010 IEEE International Conference on Image Processing, 26-29 September 2010, 3277-3280.