## 4 Conclusions  
In summary, the polar Mle scattering intensity patterns from pure water droplets, contaminated water droplets and black carbon aggregates were simulated for various size parameters determined by the 1.064-\(\upmu\)m, 0.532-\(\upmu\)m, and 0.355-\(\upmu\)m wavelengths of the illuminating laser light and the 0.1-\(\upmu\)m, 0.5-\(\upmu\)m, and 1.25-\(\upmu\)m radii of the scattering particle. The effect of the scatterer's complex refractive index was also considered in the simulations. As the size parameter increases because of the particle becoming bigger and the laser wavelength becoming shorter, the scattering patterns from pure water, contaminated water, and black carbon exhibit distinguishable features that become increasingly unique. Likewise, the scattering intensity profiles are unique to the scattering particle. The unique scattering patterns and intensity profiles could serve as fingerprints that can be used to identify the scattering particle. To capture the actual scattering patterns in-situ, a Mle Scattering Time-gated multi-Static LIDAR (MISTS-LIDAR) scheme was proposed. The MISTS-LIDAR consists of a pulsed laser illumination source and a series of telescope detectors positioned around a scattering center. By scanning the detectors through a range of angles, the angular dependence of the scattering cross-section can be obtained. The scattering pattern that was collected using MISTS-LIDAR will then be scanned against the scattering intensity patterns in the database to identify the particles present in the scattering center. The pulsed laser operation serves as a time gate that would exclude the detection of scattering from other scattering centers. Although the simulations focused on pure water, contaminated water and black carbon, MISTS-LIDAR could be extended to other types of pollutants, enabling the development of a database of atmospheric pollutants and their corresponding scattering patterns. The database could then be used in the in  
Figure 5: Back scattering intensity from (**a**) pure water, (**b**) contaminated water and (**c**) black carbon as a function of the wavelength of the incident laser for various particle radii \(a\) = 0.1 \(\upmu\)m, 0.5 \(\upmu\)m, and 1.25 \(\upmu\)m.  
situ identification of various pollutants in the atmosphere during practical environmental monitoring events. The present work used Mie theory as a crude approximation as a first step in verifying the proposed time-gated multi-static scheme. In future work, the Mie theory can be extended to coated and multilayer particles such as droplets with surface and/or internal contaminants. The T-Matrix method for aggregates and colloid particles, the Discrete Dipole Approximation for irregularly shaped particles, and Hybrid approaches for facet particles will also be considered.  
Conceptualization, N.S., H.S., A.K., Y.S. and D.V.T.; methodology, N.S., K.Y. and L.V.M.; software, L.V.M.; validation, T.N.H., K.S., K.Y., T.S., N.S., B.V.H., D.V.N., P.H.M. and M.C.-R.; formal analysis, L.V.M., T.N.H., K.S., K.Y., T.S., N.S., B.V.H., D.V.N., P.H.M. and M.C.-R.; investigation, L.V.M., T.N.H., K.S., K.Y., T.S., N.S., B.V.H., D.V.N., P.H.M. and M.C.-R.; resources, N.S., H.S., A.K., Y.S. and D.V.T.; data curation, L.V.M., T.N.H., N.S. and M.C.-R.; writing--original draft preparation, L.V.M. and M.C.-R.; writing--review and editing, T.N.H., K.S., K.Y., T.S., N.S., H.S., A.K., Y.S., B.V.H., D.V.N., P.H.M. and D.V.T.; visualization, L.V.M. and M.C.-R.; supervision, N.S., H.S., A.K., Y.S., M.C.-R. and D.V.T.; project administration, N.S., H.S., A.K., Y.S. and D.V.T.; funding acquisition, N.S., H.S., A.K., Y.S. and D.V.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Osaka University ASEAN Campuses, the Vietnam Academy of Science and Technology under Project No. VAST01.03/22-23 and the Institute of Laser Engineering Collaborative Research Fund (2020B1-003 and 2022B1-004).  
Not applicable.  
The authors declare no conflict of interest.