References
- Bulot, F. M. J., S. J. Johnston, P. J. Basford, N. H. C. Easton, M. Apetroaie-Cristea, G. L. Foster, A. K. R. Morris, S. J. Cox, and M. Loxham. 2019. Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment. Sci. Rep. 9 (1):7497. doi: 10.1038/s41598-019-43716-3.
- Dubey, P., and S. Dhaniyala. 2013. Improved inversion of scanning electrical mobility spectrometer data using a new multiscale expectation maximization algorithm. Aerosol. Sci. Technol. 47 (1):69–80. doi: 10.1080/02786826.2012.728014.
- Garda, B., and Z. Galias. 2014. Tikhonov regularization and constrained quadratic programming for magnetic coil design problems. Int. J. Appl. Math. Comput. Sci. 24 (2):249–57. doi: 10.2478/amcs-2014-0018.
- Hansen, P. C. 2008. Regularization tools: A matlab package for analysis and solution of discrete ill-posed problems. Version 4.1 for Matlab 7.3. Informatics and Mathematical Modelling. Technical University of Denmark, Lyngby, Denmark. doi: 10.1007/BF02149761.
- He, M., and S. Dhaniyala. 2012. Vertical and horizontal concentration distributions of ultrafine particles near a highway. Atmos. Environ. 46:225–36. doi: 10.1016/j.atmosenv.2011.09.076.
- Jayaratne, R., X. Liu, P. Thai, M. Dunbabin, and L. Morawska. 2018. The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog. Atmos. Meas. Tech. 11 (8):4883–90. doi: 10.5194/amt-11-4883-2018.
- Kelly, K. E., J. Whitaker, A. Petty, C. Widmer, A. Dybwad, D. Sleeth, R. Martin, and A. Butterfield. 2017. Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environ. Pollut. 221:491–500. doi: 10.1016/j.envpol.2016.12.039.
- Knutson, E. O., and K. T. Whitby. 1975. Aerosol classification by electric mobility: Apparatus, theory, and applications. J. Aerosol Sci. 6 (6):443–51. doi: 10.1016/0021-8502(75)90060-9.
- Lazer, D., R. Kennedy, G. King, and A. Vespignani. 2014. Big data. The parable of Google Flu: Traps in big data analysis. Science 343 (6176):1203–5. doi: 10.1126/science.1248506.
- Malings, C., R. Tanzer, A. Hauryliuk, P. K. Saha, A. L. Robinson, A. A. Presto, and R. Subramanian. 2019. Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation. Aerosol Sci. Technol.:1–40. doi: 10.1080/02786826.2019.1623863.
- Mätzler, C. 2002. Matlab functions for Mie scattering and absorption. Research Report No. 2002-08, University of Bern, Institute of Applied Physics, Bern.
- Snyder, E. G., T. H. Watkins, P. A. Solomon, E. D. Thoma, R. W. Williamas, G. S. W. Hagler, D. Shelow, D. A. Hindin, V. J. Kilaru, and P. W. Preuss. 2013. The changing paradigm of air pollution monitoring. Environ. Sci. Technol. 47 (20):11369–77. doi: 10.1021/es4022602.
- Tikhonov, A., and V. Arsenin. 1977. Solutions of ill-posed problems. Washington, DC: John Wiley & Sons.
- Tryner, J., N. Good, A. Wilson, M. L. Clark, J. L. Peel, and J. Volckens. 2019. Variation in gravimetric correction factors for nephelometer-derived estimates of personal exposure to PM2.5. Environ. Pollut. 250:251–61. doi: 10.1016/j.envpol.2019.03.121.
- Vlasenko, S. S., H. Su, U. Pöschl, M. O. Andreae, and E. F. Mikhailov. 2017. Tandem configuration of differential mobility and centrifugal particle mass analysers for investigating aerosol hygroscopic properties. Atmos. Meas. Tech. 10 (3):1269–80. doi: 10.5194/amt-10-1269-2017.
- Wang, Y., and C. Yang. 2008. Regularizing active set method for retrieval of the atmospheric aerosol particle size distribution function. J. Opt. Soc. Am. A 25 (2):348–56. doi: 10.1364/JOSAA.25.000348.
- Wolfenbarger, J. K., and J. H. Seinfeld. 1990. Inversion of aerosol size distribution data. J. Aerosol. Sci. 21 (2):227–47. doi: 10.1016/0021-8502(90)90007-K.
- Zimmerman, N., A. A. Presto, S. P. N. Kumar, J. Gu, A. Hauryliuk, E. S. Robinson, and A. L. Robinson, and R. Subramanian. 2018. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmos. Meas. Tech. 11 (1):291–313. doi: 10.5194/amt-11-291-2018.