51 citations as recorded by crossref.

1. Spectral Intensity Bioaerosol Sensor (SIBS): an instrument for spectrally resolved fluorescence detection of single particles in real time T. Könemann et al. 10.5194/amt-12-1337-2019
2. 基于1D-CNN的生物气溶胶衰减全反射傅里叶变换红外光谱识别 汪. Wang Yang et al. 10.3788/AOS231963
3. Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses A. May & H. Li 10.1080/02786826.2022.2114312
4. Detection of Airborne Biological Particles in Indoor Air Using a Real-Time Advanced Morphological Parameter UV-LIF Spectrometer and Gradient Boosting Ensemble Decision Tree Classifiers I. Crawford et al. 10.3390/atmos11101039
5. Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications M. Kwaśny et al. 10.3390/s23063339
6. Size-Resolved Single-Particle Fluorescence Spectrometer for Real-Time Analysis of Bioaerosols: Laboratory Evaluation and Atmospheric Measurements M. Zhang et al. 10.1021/acs.est.9b01862
7. Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement A. Rahman et al. 10.1021/acsestengg.4c00262
8. Systematic characterization and fluorescence threshold strategies for the wideband integrated bioaerosol sensor (WIBS) using size-resolved biological and interfering particles N. Savage et al. 10.5194/amt-10-4279-2017
9. Virtual Impactor-Based Label-Free Pollen Detection using Holography and Deep Learning Y. Luo et al. 10.1021/acssensors.2c01890
10. Quantifying bioaerosol concentrations in dust clouds through online UV-LIF and mass spectrometry measurements at the Cape Verde Atmospheric Observatory D. Morrison et al. 10.5194/acp-20-14473-2020
11. Recent Advances in Monitoring, Sampling, and Sensing Techniques for Bioaerosols in the Atmosphere E. Kabir et al. 10.1021/acssensors.9b02585
12. Towards a UK Airborne Bioaerosol Climatology: Real-Time Monitoring Strategies for High Time Resolution Bioaerosol Classification and Quantification I. Crawford et al. 10.3390/atmos14081214
13. Real-time detection of airborne fluorescent bioparticles in Antarctica I. Crawford et al. 10.5194/acp-17-14291-2017
14. Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques N. Savage & J. Huffman 10.5194/amt-11-4929-2018
15. Recent progress in online detection methods of bioaerosols T. An et al. 10.1016/j.fmre.2023.05.012
16. Development and characterization of an inexpensive single-particle fluorescence spectrometer for bioaerosol monitoring B. Swanson & J. Huffman 10.1364/OE.26.003646
17. Label-Free Bioaerosol Sensing Using Mobile Microscopy and Deep Learning Y. Wu et al. 10.1021/acsphotonics.8b01109
18. RealForAll: real-time system for automatic detection of airborne pollen D. Tešendić et al. 10.1080/17517575.2020.1793391
19. Bioaerosol Sensor for In Situ Measurement: Real-Time Measurement of Bioaerosol Particles in a Real Environment and Demonstration of the Effectiveness of Air Purifiers to Reduce Bioaerosol Particle Concentrations at Hot Spots U. Yanagi et al. 10.3390/atmos14111656
20. Comprehensive insights into advances in ambient bioaerosols sampling, analysis and factors influencing bioaerosols composition B. Sajjad et al. 10.1016/j.envpol.2023.122473
21. STRAPS v1.0: evaluating a methodology for predicting electron impact ionisation mass spectra for the aerosol mass spectrometer D. Topping et al. 10.5194/gmd-10-2365-2017
22. Contribution of fluorescent primary biological aerosol particles to low-level Arctic cloud residuals G. Pereira Freitas et al. 10.5194/acp-24-5479-2024
23. Bioaerosol field measurements: Challenges and perspectives in outdoor studies T. Šantl-Temkiv et al. 10.1080/02786826.2019.1676395
24. Identification of new particle formation events with deep learning J. Joutsensaari et al. 10.5194/acp-18-9597-2018
25. Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry J. Xu et al. 10.1016/j.saa.2022.121841
26. Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning K. Lamb 10.5194/amt-12-3885-2019
27. Machine learning for improved data analysis of biological aerosol using the WIBS S. Ruske et al. 10.5194/amt-11-6203-2018
28. Pollen clustering strategies using a newly developed single-particle fluorescence spectrometer B. Swanson & J. Huffman 10.1080/02786826.2019.1711357
29. Laboratory validation of rapid discrimination of single microbial cells via SPAMS with machine learning C. Liu et al. 10.1016/j.ijms.2020.116340
30. Potential of laser‐induced fluorescence‐light detection and ranging for future stand‐off virus surveillance O. Owoicho et al. 10.1111/1751-7915.13698
31. Determination of composition of mixed biological samples using laser-induced fluorescence and combined classification/regression models L. Fellner et al. 10.1140/epjp/s13360-021-02019-1
32. Regionally sourced bioaerosols drive high-temperature ice nucleating particles in the Arctic G. Pereira Freitas et al. 10.1038/s41467-023-41696-7
33. A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy S. S K & A. P 10.1007/s10916-017-0853-x
34. Spatial Differentiation Characteristics of Rural Areas Based on Machine Learning and GIS Statistical Analysis—A Case Study of Yongtai County, Fuzhou City Z. Wang 10.3390/su15054367
35. Airborne Bacterial and Eukaryotic Community Structure across the United Kingdom Revealed by High-Throughput Sequencing H. Song et al. 10.3390/atmos11080802
36. Investigating the impact of attenuated fluorescence spectra on protein discrimination J. Xu et al. 10.1364/OE.499362
37. Analysis of single‐cell microbial mass spectra profiles from single‐particle aerosol mass spectrometry C. Liu et al. 10.1002/rcm.9069
38. Emission of primary bioaerosol particles from Baltic seawater G. Freitas et al. 10.1039/D2EA00047D
39. Impact of different classification schemes on discrimination of proteins with noise-contaminated spectra using laboratory-measured fluorescence data J. Xu et al. 10.1016/j.saa.2023.122646
40. Multiplex Detection of Foodborne Pathogens using 3D Nanostructure Swab and Deep Learning‐Based Classification of Raman Spectra H. Kang et al. 10.1002/smll.202308317
41. 一种基于特征提取的生物气溶胶遥测识别算法研究 杨. Yang Rong et al. 10.3788/CJL230847
42. Dual-wavelength-excitation aerosol fluorescence spectra detection using combined spectrometer with Czerny-Turner design S. Chen et al. 10.1016/j.saa.2022.121260
43. Characterisation and source identification of biofluorescent aerosol emissions over winter and summer periods in the United Kingdom E. Forde et al. 10.5194/acp-19-1665-2019
44. Real-time sensing of bioaerosols: Review and current perspectives J. Huffman et al. 10.1080/02786826.2019.1664724
45. Review: The Use of Real-Time Fluorescence Instrumentation to Monitor Ambient Primary Biological Aerosol Particles (PBAP) M. Fennelly et al. 10.3390/atmos9010001
46. Pollen classification using a single particle fluorescence spectroscopy technique B. Swanson et al. 10.1080/02786826.2022.2142510
47. Improved real-time bio-aerosol classification using artificial neural networks M. Leśkiewicz et al. 10.5194/amt-11-6259-2018
48. Evaluation of regional industrial cluster innovation capability based on particle swarm clustering algorithm and multi-objective optimization Y. Yan et al. 10.1007/s40747-021-00521-8
49. Verifying the viable particle counts of biofluorescent particle counters by using inkjet aerosol generators K. Iida et al. 10.1080/02786826.2024.2316836
50. Quantification and Characterization of Microbial Emissions over the Northeastern Atlantic Using Mesocosm Experiments J. Zinke et al. 10.1021/acsestair.3c00017
51. Fluorescent bioaerosol particle, molecular tracer, and fungal spore concentrations during dry and rainy periods in a semi-arid forest M. Gosselin et al. 10.5194/acp-16-15165-2016