Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer

References

• Allen, G. P., R. M. Hodgson, S. R. Marsland, and J. R. Flenley. 2008. Machine vision for automated optical recognition and classification of pollen grains or other singulated microscopic objects. 15th International Conference on Mechatronics and Machine Vision in Practice, M2VIP’08, New York, 221–226. doi: 10.1109/MMVIP.2008.4749537.
   Google Scholar
• American Academy of Allergy Asthma & Immunology. 2019. National Allergy Bureau Pollen Counts. Accessed August 1, 2019. https://www.aaaai.org/global/nab-pollen-counts?ipb=1.
   Google Scholar
• Blaiss, M. 2010. Allergic rhinitis: Direct and indirect costs. Allergy and Asthma Proceedings, OceanSide Publications.31.3329 doi: 10.2500/aap.2010.
   Google Scholar
• Bragg, L. H. 1969. Pollen size variation in selected grass taxa. Ecology 50 (1):124–7. doi: 10.2307/1934670.
   Web of Science ®Google Scholar
• Breiman, L. 2001. Random forests. Mach. Learn. 45 (1):5–32. doi: 10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Buckland, M., and F. Gey. 1994. The relationship between Recall and Precision. J. Am. Soc. Inf. Sci. 45 (1):12–9. doi: 10.1002/(SICI)1097-4571(199401)45:1 < 12::AID-ASI2 > 3.0.CO;2-L.
   Web of Science ®Google Scholar
• Bureau, A., J. Dupuis, B. Hayward, K. Falls, and P. Van Eerdewegh. 2003. Mapping complex traits using random forests. BMC Genet. 4 (Suppl 1):S64. doi: 10.1186/1471-2156-4-S1-S64.
   PubMed Web of Science ®Google Scholar
• Buters, J., C. Antunes, A. Galveias, K. Bergmann, M. Thibaudon, C. Galán, C. Schmidt-Weber, and J. Oteros. 2018. Pollen and spore monitoring in the world. Clin. Transl. Allergy 8 (1):9.doi: 10.1186/s13601-018-0197-8.
   PubMed Web of Science ®Google Scholar
• Calvo, A., D. Baumgardner, A. Castro, D. Fernández-González, A. Vega-Maray, R. Valencia-Barrera, F. Oduber, C. Blanco-Alegre, and R. Faile. 2018. Daily behavior of urban Fluorescing Aerosol Particles in northwest Spain. Atmos. Environ. 184: 262–77. doi: 10.1016/j.atmosenv.2018.04.027.
   Web of Science ®Google Scholar
• Chen, C., E. A. Hendriks, R. P. W. Duin, J. H. C. Reiber, P. S. Hiemstra, L. A. De Weger, and B. C. Stoel. 2006. Feasibility study on automated recognition of allergenic pollen: Grass, birch and mugwort. Aerobiologia (Bologna) 22 (4):275–84. doi: 10.1007/s10453-006-9040-0.
   Web of Science ®Google Scholar
• Conner, J. K., M. Proctor, P. Yeo, and A. Lack. 1997. The natural history of pollination. Ecology 78 (1):327–239. doi: 10.2307/2266004.
   Google Scholar
• Crawford, I., S. Ruske, D. Topping, and M. Gallagher. 2015. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol. Atmos. Meas. Tech. 8 (11):4979–91. doi: 10.5194/amt-8-4979-2015.
   Web of Science ®Google Scholar
• Crouzy, B., M. Stella, T. Konzelmann, B. Calpini, and B. Clot. 2016. All-optical automatic pollen identification: Towards an operational system. Atmos. Environ. 140:202–12. doi: 10.1016/j.atmosenv.2016.05.062.
   Web of Science ®Google Scholar
• Dell’Anna, R. (2010). A critical presentation of innovative techniques for automated pollen identification in aerobiological monitoring networks. In Pollen: Structure, types, and effects, ed. B. J. Kaiser, 273–88. Hauppauge, NY: Nova Science Publishers.
   Google Scholar
• Després, V., Huffman, J. A., Burrows, S., Hoose, C., Safatov, A., Buryak, G., Fröhlich-Nowoisky, J., Elbert, W., Andreae, M., Pöschl, U., and Jaenicke, R. (2012). Primary biological aerosol particles in the atmosphere: a review. Chem. Phys. Meteorol. 64 (1):15598. doi: 10.3402/tellusb.v64i0.15598.
   Google Scholar
• Dietterich, T. 1995. Overfitting and undercomputing in machine learning. ACM Comput. Surv. 27 (3):326–7. doi: 10.1145/212094.212114.
   Web of Science ®Google Scholar
• Elangasinghe, M. A., N. Singhal, K. N. Dirks, J. A. Salmond, and S. Samarasinghe. 2014. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering. Atmos. Environ. 94:106–116. doi: 10.1016/j.atmosenv.2014.04.051.
   Web of Science ®Google Scholar
• Erdmann, N., A. Dell'Acqua, P. Cavalli, C. Grüning, N. Omenetto, J. P. Putaud, F. Raes, and R. V. Dingenen. 2005. Instrument characterization and first application of the single particle analysis and sizing system (SPASS) for atmospheric aerosols. Aerosol Sci. Technol. 39 (5):377–93. doi: 10.1080/027868290935696.
   Web of Science ®Google Scholar
• Erdtman, G. 1952. Pollen morphology and plant taxonomy, GFF. Abingdon: Brill Archive.
   Google Scholar
• Fennelly, M. J., G. Sewell, M. B. Prentice, D. J. O’Connor, and J. R. Sodeau. 2017. Review: The use of real-time fluorescence instrumentation to monitor ambient primary biological aerosol particles (PBAP). Atmosphere (Basel) 9 (1):1. doi: 10.3390/atmos9010001.
   Web of Science ®Google Scholar
• Forde, E., M. Gallagher, V. Foot, R. Sarda-Esteve, I. Crawford, P. Kaye, W. Stanley, and D. Topping. 2019. Characterisation and source identification of biofluorescent aerosol emissions over winter and summer periods in the United Kingdom. Atmos. Chem. Phys. 19 (3):1665–84. doi: 10.5194/acp-19-1665-2019.
   Web of Science ®Google Scholar
• Frenz, D. A. 1999. Comparing pollen and spore counts collected with the Rotorod Sampler and Burkard spore trap. Ann. Allergy, Asthma Immunol. 83 (5):341–9. doi: 10.1016/S1081-1206(10)62828-1.
   PubMed Web of Science ®Google Scholar
• Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29 (5):1189–232. doi: 10.1214/aos/1013203451.
   Web of Science ®Google Scholar
• Gabey, A. M., M. W. Gallagher, J. Whitehead, J. R. Dorsey, P. H. Kaye, and W. R. Stanley. 2010. Measurements and comparison of primary biological aerosol above and below a tropical forest canopy using a dual channel fluorescence spectrometer. Atmos. Chem. Phys. 10 (10):4453–66. doi: 10.5194/acp-10-4453-2010.
   Web of Science ®Google Scholar
• Galán, C., H. García-Mozo, P. Cariñanos, P. Alcázar, and E. Domínguez-Vilches. 2001. The role of temperature in the onset of the Olea europaea L. pollen season in southwestern Spain. Int. J. Biometeorol. 45 (1):8–12. doi: 10.1007/s004840000081.
   PubMed Web of Science ®Google Scholar
• Geburek, T., K. Hiess, R. Litschauer, and N. Milasowszky. 2012. Temporal pollen pattern in temperate trees: Expedience or fate? Oikos 121 (10):1603–12. doi: 10.1111/j.1600-0706.2011.20140.x.
   Web of Science ®Google Scholar
• Han, H., X. Guo, and H. Yu. 2017. Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, IESS, Beijing, 219–224. doi: 10.1109/ICSESS.2016.7883053.
   Google Scholar
• Hartigan, J. A., and M. A. Wong. 2006. Algorithm AS 136: A K-means clustering algorithm. Appl. Stat. 28 (1):100–8. doi: 10.2307/2346830.
   Google Scholar
• Healy, D. A., J. A. Huffman, D. J. O'Connor, C. Pöhlker, U. Pöschl, J. R. Sodeau. 2014. Ambient measurements of biological aerosol particles near Killarney, Ireland: A comparison between real-time fluorescence and microscopy techniques. Atmos. Chem. Phys. 14:8055–8069. doi: 10.5194/acp-14-8055-2014.
   Google Scholar
• Healy, D. A., D. J. O'Connor, A. M. Burke, and J. R. Sodeau. 2012. A laboratory assessment of the Waveband Integrated Bioaerosol Sensor (WIBS-4) using individual samples of pollen and fungal spore material. Atmos. Environ. 60:534. doi: 10.1016/j.atmosenv.2012.06.052.
   Web of Science ®Google Scholar
• Hernandez, M., Perring, A., McCabe, K., Kok, G., Granger, G., and Baumgardner, D. (2016). Chamber catalogues of optical and fluorescent signatures distinguish bioaerosol classes. Atmos. Meas. Tech. 9(7):3283. doi: 10.5194/amt-9-3283-2016.
   Google Scholar
• Hill, S. C., M. W. Mayo, and R. K. Chang. 2009. Fluorescence of bacteria, pollens, and naturally occurring airborne particles: Excitation/emission spectra. Army Res. Lab. Appl. Phys. (No. ARL-TR-4722): 62.
   Google Scholar
• Hill, S. C., R. G. Pinnick, S. Niles, N. F. Fell, Y.-L. Pan, J. Bottiger, B. V. Bronk, S. Holler, and R. K. Chang. 2001. Fluorescence from airborne microparticles: dependence on size, concentration of fluorophores, and illumination intensity: erratum. Appl. Opt. 40 (18):3005–3013. doi: 10.1364/AO.40.003005.
   PubMed Web of Science ®Google Scholar
• Hill, S., R. Pinnick, S. Niles, and Y. Pan. 1999. Real‐time measurement of fluorescence spectra from single airborne biological particles. F. Anal. 3:221–239. doi: 10.1002/(SICI)1520-6521(1999)3:4/5 < 221::AID-FACT2 > 3.0.CO;2-7.
   Google Scholar
• Hothorn, T., B. Lausen, A. Benner, and M. Radespiel-Tröger. 2004. Bagging survival trees. Stat. Med. 23 (1):77–91. doi: 10.1002/sim.1593.
   PubMed Web of Science ®Google Scholar
• Hothorn, T., A. Zeileis, E. Cheng, and S. Ong. 2015. partykit: A modular toolkit for recursive partytioning in R. J. Mach. Learn. Res. 16 (1):3905–3909.
   Google Scholar
• Huang, H. C., Y.-L. Pan, S. C. Hill, and R. G. Pinnick. 2011. Fluorescence-based classification with selective collection and identification of individual airborne bioaerosol particles. In Optical Processes in Microparticles and Nanostructures: A Festschrift Dedicated to Richard Kounai Chang on His Retirement from Yale University, , ed. A. Serpengüzel and A. W. Poon, 153–67. Singapore: World Scientific. doi: 10.1142/9789814295789_0009.
   Google Scholar
• Huffman, J., A. Perring, N. Savage, B. Clot, B. Crouzy, F. Tummon, O. Shoshanim, B. Damit, J. Schneider, V. Sivaprakasam, et al. 2019. Real-time sensing of bioaerosols: Review and current perspectives. Aerosol Sci. Technol. 1–56. doi: 10.1080/02786826.2019.1664724.
   Web of Science ®Google Scholar
• Huffman, D., B. Swanson, and J. A. Huffman. 2016. A wavelength-dispersive instrument for characterizing fluorescence and scattering spectra of individual aerosol particles on a substrate. Atmos. Meas. Tech. 9 (8):3987–3998. 2016. doi: 10.5194/amt-9-3987-2016.
   Web of Science ®Google Scholar
• Huffman, D. R., and Huffman. J. A. 2019. A wavelength dispersive microscope spectrofluorometer for characterizing multiple particles simultaneously. University of Denver. US Patent US20160320306A1. filed January 8, 2014, and issued December 13, 2018
   Google Scholar
• James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. An introduction to statistical learning. New York: springer. doi: 10.1007/978-1-4614-7138-7.
   Google Scholar
• Jones, M. D., and L. C. Newell. 1948. Size, variability, and identification of grass pollen. J. Am. Soc. Agron. 40:136–143. doi: 10.2134/agronj1948.00021962004000020004x.
   Web of Science ®Google Scholar
• Kawashima, S., B. Clot, T. Fujita, Y. Takahashi, and K. Nakamura. 2007. An algorithm and a device for counting airborne pollen automatically using laser optics. Atmos. Environ. 41 (36):7987–7993. doi: 10.1016/j.atmosenv.2007.09.019.
   Web of Science ®Google Scholar
• Kawashima, S., M. Thibaudon, S. Matsuda, T. Fujita, N. Lemonis, B. Clot, and G. Oliver. 2017. Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen. Aerobiologia (Bologna) 33 (3):351–362. doi: 10.1007/s10453-017-9474-6.
   Web of Science ®Google Scholar
• Kaye, P., W. R. Stanley, E. Hirst, E. V. Foot, K. L. Baxter, and S. J. Barrington. 2005. Single particle multichannel bio-aerosol fluorescence sensor. Opt. Express 13 (10):3583–3593. doi: 10.1364/OPEX.13.003583.
   PubMed Web of Science ®Google Scholar
• Kiselev, D., L. Bonacina, and J. P. Wolf. 2011. Individual bioaerosol particle discrimination by multi-photon excited fluorescence. Opt. Express 19 (24):24516–24521. doi: 10.1364/OE.19.024516.
   PubMed Web of Science ®Google Scholar
• Kiselev, D., L. Bonacina, and J. P. Wolf. 2013. A flash-lamp based device for fluorescence detection and identification of individual pollen grains. Rev. Sci. Instrum. 84 (3):033302. doi: 10.1063/1.4793792.
   PubMed Web of Science ®Google Scholar
• Knobelspiesse, K. D., C. Pietras, G. S. Fargion, M. Wang, R. Frouin, M. A. Miller, A. Subramaniam, and W. M. Balch. 2004. Maritime aerosol optical thickness measured by handheld sun photometers. Remote Sens. Environ. 93:87–106. doi: 10.1016/j.rse.2004.06.018.
   PubMed Web of Science ®Google Scholar
• Könemann, T., F. Ditas, D. Walter, J. Brooks, E. Rodriguez-Caballero, M. Charlotte, M. D. Dewald, F. Drewnick, A. Edtbauer, F. Fachinger, et al. 2019. Online analysis of single aerosol particle fluorescence spectra during the AQABA research cruise around the Arabian Peninsula. Atmos. Chem. Phys. In Preparation.
   Google Scholar
• Lee, M., O. Yaglidere, and A. Ozcan. 2011. Field-portable reflection and transmission microscopy based on lensless holography. Biomed. Opt. Express 2 (9):2721–2730. doi: 10.1364/BOE.2.002721.
   PubMed Web of Science ®Google Scholar
• Liu, Y., Z. Li, H. Xiong, X. Gao, and J. Wu. 2010. Understanding of internal clustering validation measures. Proceedings - IEEE International Conference on Data Mining, ICDM, Sydney, Australia, 911–16. doi: 10.1109/ICDM.2010.35.
   Google Scholar
• Madonna, A. J., K. J. Voorhees, and T. L. Hadfield. 2001. Rapid detection of taxonomically important fatty acid methyl ester and steroid biomarkers using in situ thermal hydrolysis/methylation mass spectrometry (THM-MS): implications for bioaerosol detection. J. Anal. Appl. Pyrolysis 61 (1-2):65–89. doi: 10.1016/S0165-2370(01)00136-X.
   PubMed Web of Science ®Google Scholar
• Miki, K., S. Kawashima, B. Clot, and K. Nakamura. 2019. Comparative efficiency of airborne pollen concentration evaluation in two pollen sampler designs related to impaction and changes in internal wind speed. Atmos. Environ. 203:18–27. doi: 10.1016/j.atmosenv.2019.01.039.
   Web of Science ®Google Scholar
• Mitsumoto, K., K. Yabusaki, K. Kobayashi, and H. Aoyagi. 2010. Development of a novel real-time pollen-sorting counter using species-specific pollen autofluorescence. Aerobiologia (Bologna) 26 (2):99–111. doi: 10.1007/s10453-009-9147-1.
   Web of Science ®Google Scholar
• Mohri, M., A. Rostamizadeh, and A. Talwalkar. 2018. Foundations of machine learning.. Cambridge, MA: MIT press.
   Google Scholar
• O’Connor, D., D. Healy, S. Hellebust, J. Buters, and J. Sodeau. 2014. Using the WIBS-4 (Waveband Integrated Bioaerosol Sensor) technique for the on-line detection of pollen grains. Aerosol Sci. Technol. 48 (4):341–349. doi: 10.1080/02786826.2013.872768.
   Web of Science ®Google Scholar
• Oteros, J., G. Pusch, I. Weichenmeier, U. Heimann, R. Möller, S. Röseler, C. Traidl-Hoffmann, C. Schmidt-Weber, and J. Buters. 2015. Automatic and online pollen monitoring. Int. Arch. Allergy Immunol. 167 (3):158–166. doi: 10.1159/000436968.
   PubMed Web of Science ®Google Scholar
• Pan, Y. L., J. Hartings, R. G. Pinnick, S. C. Hill, J. Halverson, and R. K. Chang. 2003. Single-particle fluorescence spectrometer for ambient aerosols. Aerosol Sci. Technol. 37 (8):628–639. doi: 10.1080/02786820300904.
   Web of Science ®Google Scholar
• Pan, Y. L., S. C. Hill, R. G. Pinnick, J. M. House, R. C. Flagan, and R. K. Chang. 2011. Dual-excitation-wavelength fluorescence spectra and elastic scattering for differentiation of single airborne pollen and fungal particles. Atmos. Environ. 45 (8):1555–1563. doi: 10.1016/j.atmosenv.2010.12.042.
   Web of Science ®Google Scholar
• Pan, Y. L., H. Huang, and R. K. Chang. 2012. Clustered and integrated fluorescence spectra from single atmospheric aerosol particles excited by a 263- and 351-nm laser at New Haven, CT, and Adelphi, MD. J. Quant. Spectrosc. Radiat. Transf. 113 (17):2213–2221. doi: 10.1016/j.jqsrt.2012.07.028.
   Web of Science ®Google Scholar
• Pauling, A., Contributors to the European Aeroallergen Network (EAN), Rotach, M. Gehrig, R. Clot, B. Jäger, S. Cerny, M. Schmidt, R. Bortenschlager, S. Brosch, et al. 2012. A method to derive vegetation distribution maps for pollen dispersion models using birch as an example. Int. J. Biometeorol. 56 (5):949–958. doi: 10.1007/s00484-011-0505-7.
   PubMed Web of Science ®Google Scholar
• Percy, D. F., and B. S. Everitt. 2006. Cluster analysis. 3rd ed. J. Oper. Res. Soc. 34:845–846. doi: 10.2307/2584293.
   Web of Science ®Google Scholar
• Perring, A., J. Schwarz, D. Baumgardner, M. Hernandez, D. Spracklen, C. Heald, R. Gao, G. Kok, G. McMeeking, J. McQuaid, et al. 2015. Airborne observations of regional variation in fluorescent aerosol across the United States. J. Geophys. Res. Atmos. 120 (3):1153–1170. doi: 10.1002/2014JD022495.
   Web of Science ®Google Scholar
• Pinnick, R. G., S. C. Hill, Y. L. Pan, and R. K. Chang. 2004. Fluorescence spectra of atmospheric aerosol at Adelphi, Maryland, USA: Measurement and classification of single particles containing organic carbon. Atmos. Environ. 38 (11):1657–1672. doi: 10.1016/j.atmosenv.2003.11.017.
   Web of Science ®Google Scholar
• Pöhlker, C., J. A. Huffman, J.-D. Förster, and U. Pöschl. 2013. Autofluorescence of atmospheric bioaerosols: Spectral fingerprints and taxonomic trends of pollen. Atmos. Meas. Tech. 6 (12):3369–3392. doi: 10.5194/amt-6-3369-2013.
   Web of Science ®Google Scholar
• Pöhlker, C., J. A. Huffman, and U. Pöschl. 2012. Autofluorescence of atmospheric bioaerosols - Fluorescent biomolecules and potential interferences. Atmos. Meas. Tech. 5 (1):37–71. doi: 10.5194/amt-5-37-2012.
   Web of Science ®Google Scholar
• Ranzato, M., P. E. Taylor, J. M. House, R. C. Flagan, Y. LeCun, and P. Perona. 2007. Automatic recognition of biological particles in microscopic images. Pattern Recognit. Lett. 28 (1):31–39. doi: 10.1016/j.patrec.2006.06.010.
   Web of Science ®Google Scholar
• Rebotier, T. P., and K. A. Prather. 2007. Aerosol time-of-flight mass spectrometry data analysis: A benchmark of clustering algorithms. Anal. Chim. Acta doi: 10.1016/j.aca.2006.12.009.
   PubMed Web of Science ®Google Scholar
• Richter, R., U. Berger, S. Dullinger, F. Essl, M. Leitner, M. Smith, and G. Vogl. 2013. Spread of invasive ragweed: Climate change, management and how to reduce allergy costs. J. Appl. Ecol. 50 (6):1422–1430. doi: 10.1111/1365-2664.12156.
   Web of Science ®Google Scholar
• Robinson, N., J. Allan, J. Huffman, P. Kaye, V. Foot, and M. Gallagher. 2013. Cluster analysis of WIBS single-particle bioaerosol data. Atmos. Meas. Tech. 6 (2):337–347. doi: 10.5194/amt-6-337-2013.
   Web of Science ®Google Scholar
• Rokach, L. 2010. Ensemble-based classifiers. Artif. Intell. Rev. 33 (1-2):1–39. doi: 10.1007/s10462-009-9124-7.
   Web of Science ®Google Scholar
• Ruske, S., D. Topping, V. Foot, P. Kaye, W. Stanley, I. Crawford, A. Morse, and M. Gallagher. 2017. Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer. Atmos. Meas. Tech. 10 (2):695–708. doi: 10.5194/amt-10-695-2017.
   Web of Science ®Google Scholar
• Ruske, S., D. O. Topping, V. E. Foot, A. P. Morse, and M. W. Gallagher. 2018. Machine learning for improved data analysis of biological aerosol using the WIBS. Atmos. Meas. Tech. 11 (11):6203–6230. doi: 10.5194/amt-11-6203-2018.
   Web of Science ®Google Scholar
• Šantl-Temkiv, T., F. Carotenuto, B. Sikoparija, T. Maki, P. Amato, M. Yao, P. DeMott, T. Hill, C. Morrism, C. Pöhlker, et al. 2019. Bioaerosol field measurements: Challenges and perspectives in outdoor studies. Aerosol Sci. Technol. 1–27. doi: 10.1080/02786826.2019.1676395.
   Web of Science ®Google Scholar
• Satterwhite, M. B. 1990. Spectral luminescence of plant pollen. 10th Annual International Symposium on Geoscience and Remote Sensing, College Park, MD, 1945–48. doi: 10.1109/igarss.1990.688906.
   Google Scholar
• Šaulienė, I., L. Šukienė, G. Daunys, G. Valiulis, L. Vaitkevičius, P. Matavulj, S. Brdar, M. Panic, B. Sikoparija, B. Clot, et al. 2019. Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps. Atmos. Meas. Tech. Discuss 2:1–33. doi: 10.5194/amt-2018-432.
   Google Scholar
• Sauvageat, E., Y. Zeder, K. Auderset, B. Calpini, B. Clot, B. Crouzy, T. Konzelmann, G. Lieberherr, F. Tummon, and K. Vasilatou. 2019. Real-time pollen monitoring using digital holography. Preprint. doi: 10.5194/amt-2019-427.
   Google Scholar
• Savage, N., and J. A. Huffman. 2018. Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques. Atmos. Meas. Tech. 11 (8):4929–4942. doi: 10.5194/amt-10-4279-2017.
   Web of Science ®Google Scholar
• Savage, N., C. Krentz, T. Könemann, T. Han, G. Mainelis, C. Pöhlker, and J. A. Huffman. 2017. Systematic characterization and fluorescence threshold strategies for the wideband integrated bioaerosol sensor (WIBS) using size-resolved biological and interfering particles. Atmos. Meas. Tech. 10 (11):4279–4302. doi: 10.5194/amt-10-4279-2017.
   Web of Science ®Google Scholar
• Sivaprakasam, V., H. Lin, A. L. Huston, and J. D. Eversole. 2011. Spectral characterization of biological aerosol particles using two-wavelength excited laser- induced fluorescence and elastic scattering measurements. Opt. Express 19 (7):6191–6208. doi: 10.1364/OE.19.006191.
   PubMed Web of Science ®Google Scholar
• Songnuan, W. 2013. Wind-pollination and the roles of pollen allergenic proteins. Asian Pacific J. Allergy Immunol. 31 (4):261. doi: 10.12932/AP0287.31.4.2013.
   PubMed Web of Science ®Google Scholar
• Stach, A., M. Smith, J. C. Prieto Baena, and J. Emberlin. 2008. Long-term and short-term forecast models for Poaceae (grass) pollen in Poznań, Poland, constructed using regression analysis. Environ. Exp. Bot. 62 (3):323–332. doi: 10.1016/j.envexpbot.2007.10.005.
   Web of Science ®Google Scholar
• Strobl, C., A.-L. Boulesteix, A. Zeileis, and T. Hothorn. 2007. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform. 8 (1):25. doi: 10.1186/1471-2105-8-25.
   PubMed Web of Science ®Google Scholar
• Swanson, B., and J. A. Huffman. 2018. Development and characterization of an inexpensive single-particle fluorescence spectrometer for bioaerosol monitoring. Opt. Express 26 (3):3646–3660. doi: 10.1364/OE.26.003646.
   PubMed Web of Science ®Google Scholar
• Tello-Mijares, S., and F. Flores. 2016. A novel method for the separation of overlapping pollen species for automated detection and classification. Comput. Math. Methods Med. 2016:1–12. doi: 10.1155/2016/5689346.
   Web of Science ®Google Scholar
• Tseng, Y.-T., and S. Kawashima. 2019. Applying a pollen forecast algorithm to the Swiss Alps clarifies the influence of topography on spatial representativeness of airborne pollen data. Atmos. Environ. 212:153–162. doi: 10.1016/j.atmosenv.2019.05.052.
   Web of Science ®Google Scholar
• Weber, R. W. 2010. Pollen identification. Ann. Allergy, Asthma Immunol. 80 (2):141–148. doi: 10.1016/S1081-1206(10)62947-X.
   Web of Science ®Google Scholar
• Wlodarski, M., M. Kaliszewski, M. Kwasny, K. Kopczynski, Z. Zawadzki, Z. Mierczyk, J. Mlynczak, E. Trafny, and M. Szpakowska. 2006. Fluorescence excitation-emission matrices of selected biological materials. Optically Based Biological and Chemical Detection for Defence III, Stockholm, Sweden, 639806. doi: 10.1117/12.687872.
   Google Scholar
• Wu, Y., A. Calis, Y. Luo, C. Chen, M. Lutton, Y. Rivenson, X. Lin, H. C. Koydemir, Y. Zhang, H. Wang, et al. 2018. Label-free bioaerosol sensing using mobile microscopy and deep learning. ACS Photonics 5 (11):4617–4627. doi: 10.1021/acsphotonics.8b01109.
   Web of Science ®Google Scholar
• Zhu, W., Q. Liu, and Y. Wu. 2015. Aerosol absorption measurement at SWIR with water vapor interference using a differential photoacoustic spectrometer. Opt. Express 23 (18):23108–23116. doi: 10.1364/OE.23.023108.
   PubMed Web of Science ®Google Scholar