Following are citations for articles and reports dealing with Plantower sensor uses and characteristics. There are a number of comparisons with optical particle counters as well as collocated FEM and FRM instrumentation. Several of them describe packaging alternatives to the PurpleAir, although none of these appear to have created the extensive data acquisition and dissemination infrastructure that supports the PurpleAir networks. 
Amoah, N.A., Xu, G., Kumar, A.R., Wang, Y., (2023). Calibration of low-cost particulate matter sensors for coal dust monitoring. Science of the Total Environment, 859, 10.1016/j.scitotenv.2022.160336.
aqicn, (2018). The Plantower PMS5003 and PMS7003 air quality sensor experiment. Beijing, China. https://www.rigacci.org/wiki/lib/exe/fetch.php/doc/appunti/hardware/raspberrypi/pms5003-and-pms7003-experiment.pdf
ARA, (2020). LFR-6 sampler. ARA Instruments, Eugene, OR. LFR-6 Sampler - ARA Instruments
ARA, (2020). FTS Flow Calibrator. ARA Instruments, Eugene, OR. FTS Flow Calibrator - ARA Instruments
Badura, M., Batog, P., Drzeniecka-Osiadacz, A., Modzel, P., (2018). Evaluation of low-cost sensors for ambient PM2.5 monitoring. Journal of Sensors, 10.1155/2018/5096540.
Badura, M., Batog, P., Drzeniecka-Osiadacz, A., Modzel, P., (2018). Optical particulate matter sensors in PM2.5 measurements in atmospheric air, Kazmierczak, B., Kutylowska, M., Piekarska, K., Jadwiszczak, P. (Eds.), Proceedings, 10th Conference on Interdisciplinary Problems in Environmental Protection and Engineering Eko-Dok 2018. https://www.e3s-conferences.org/articles/e3sconf/pdf/2018/19/e3sconf_eko-dok2018_00006.pdf
Badura, M., Batog, P., Drzeniecka-Osiadacz, A., Modzel, P., (2019). Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements. Sn Applied Sciences, 1, 10.1007/s42452-019-0630-1.
Badura, M., Sowka, I., Szymanski, P., Batog, P., (2020). Assessing the usefulness of dense sensor network for PM2.5 monitoring on an academic campus area. Science of the Total Environment, 722, 10.1016/j.scitotenv.2020.137867.
Báthory, C., Dobó, Z., Garami, A., Palotás, Á., Tóth, P., (2022). Low-cost monitoring of atmospheric PM—development and testing. Journal of Environmental Management, 304, 10.1016/j.jenvman.2021.114158.
Bauerova, P., Sindelarova, A., Rychlik, S., Novak, Z., Keder, J., (2020). Low-cost air quality sensors: One-year field comparative measurement of different gas sensors and particle counters with reference monitors at Tusimice observatory. Atmosphere, 11, 10.3390/atmos11050492.
Cho, E.M., Jeon, H.J., Yoon, D.K., Park, S.H., Hong, H.J., Choi, K.Y., Cho, H.W., Cheon, H.C., Lee, C.M., (2019). Reliability of low-cost, sensor-based fine dust measurement devices for monitoring atmospheric particulate matter concentrations. International Journal of Environmental Research and Public Health, 16, 10.3390/ijerph16081430.
Cowell, N., Chapman, L., Bloss, W., Pope, F., (2022). Field calibration and evaluation of an Internet-of-Things-based particulate matter sensor. Frontiers in Environmental Science, 9, 10.3389/fenvs.2021.798485.
Crnosija, N., Zamora, M.L., Rule, A.M., Payne-Sturges, D., (2022). Laboratory chamber evaluation of Flow Air Quality Sensor PM2.5 and PM10 measurements. International Journal of Environmental Research and Public Health, 19, 10.3390/ijerph19127340.
DfRobot, (2021). Air Quality Monitor (PM 2.5, Formaldehyde, Temperature & Humidity Sensor). Air Quality Monitor (PM 2.5, Formaldehyde, Temperature & Humidity Sensor) - DFRobot
Air_Quality_Monitor__PM_2.5,_Formaldehyde,_Temperature_&_Humidity_Sensor__SKU__SEN0233-DFRobot
Gramsch, E., Oyola, P., Reyes, F., Vásquez, Y., Rubio, M.A., Soto, C., Pérez, P., Moreno, F., Gutiérrez, N., (2021). Influence of particle composition and size on the accuracy of low cost PM sensors: Findings from field campaigns. Frontiers in Environmental Science, 9, 10.3389/fenvs.2021.751267.
He, M.L., Kuerbanjiang, N., Dhaniyala, S., (2020). Performance characteristics of the low-cost plantower PMS optical sensor. Aerosol Science and Technology, 54, 232-241. 10.1080/02786826.2019.1696015.
Hegde, S., Min, K.T., Moore, J., Lundrigan, P., Patwari, N., Collingwood, S., Balch, A., Kelly, K.E., (2020). Indoor household particulate matter measurements using a network of low-cost sensors. Aerosol and Air Quality Research, 20, 381-394. 10.4209/aaqr.2019.01.0046.
Hofstetter, D., Lorenzoni, G., Fabian, E., (2021). Dust generator for maintaining a set indoor airborne dust concentration for poultry health research, Proceedings, 2021 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2021, American Society of Agricultural and Biological Engineers, pp. 1795-1804.
Hong, G.H., Le, T.C., Tu, J.W., Wang, C., Chang, S.C., Yu, J.Y., Lin, G.Y., Aggarwal, S.G., Tsai, C.J., (2021). Long-term evaluation and calibration of three types of low-cost PM2.5 sensors at different air quality monitoring stations. Journal of Aerosol Science, 157, 10.1016/j.jaerosci.2021.105829.
Huang, C.H., He, J., Austin, E., Seto, E., Novosselov, I., (2021). Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements. Plos One, 16, 10.1371/journal.pone.0259745.
Jayaratne, R., Liu, X.T., Thai, P., Dunbabin, M., Morawska, L., (2018). The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog. Atmospheric Measurement Techniques, 11, 4883-4890. 10.5194/amt-11-4883-2018.
Jayaratne, R., Liu, X.T., Ahn, K.H., Asumadu-Sakyi, A., Fisher, G., Gao, J., Mabon, A., Mazaheri, M., Mullins, B., Nyaku, M., Ristovski, Z., Scorgie, Y., Thai, P., Dunbabin, M., Morawska, L., (2020). Low-cost PM2.5 sensors: An assessment of their suitability for various applications. Aerosol and Air Quality Research, 20, 520-532. 10.4209/aaqr.2018.10.0390.
Jiang, Y.X., Zhu, X.L., Chen, C., Ge, Y.H., Wang, W.D., Zhao, Z.H., Cai, J., Kan, H.D., (2021). On-field test and data calibration of a low-cost sensor for fine particles exposure assessment. Ecotoxicology and Environmental Safety, 211, 10.1016/j.ecoenv.2021.111958.
Johnston, S.J., Basford, P.J., Bulot, F.M.J., Apetroaie-Cristea, M., Foster, G.L., Loxham, M., Cox, S.J., (2018). IoT deployment for city scale air quality monitoring with Low-Power Wide Area Networks, Proceedings, Global IoT Summit, Global IoT Summit, Bilbao, Spain. https://eprints.soton.ac.uk/420732/1/LoraForAQ_StevenJohnstonPDFexpressCompatiable.pdf
Kaliszewski, M., Włodarski, M., Młyńczak, J., Kopczyński, K., (2020). Comparison of low-cost particulate matter sensors for indoor air monitoring during covid-19 lockdown. Sensors (Switzerland), 20, 1-17. 10.3390/s20247290.
Kumar, V., Sahu, M., (2021). Evaluation of nine machine learning regression algorithms for calibration of low-cost PM2.5 sensor. Journal of Aerosol Science, 157, 10.1016/j.jaerosci.2021.105809.
Kuula, J., Makela, T., Aurela, M., Teinila, K., Varjonen, S., Gonzalez, O., Timonen, H., (2020). Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors. Atmospheric Measurement Techniques, 13, 2413-2423. 10.5194/amt-13-2413-2020.
Lagesse, B., Wang, S., Larson, T.V., Kim, A.A., (2022). Performing indoor PM2.5 prediction with low-cost data and machine learning. Facilities, 40, 495-514. 10.1108/F-05-2021-0046.
Lambey, V., Prasad, A.D., (2023). Measurement of PM10, PM2.5, NO2, and SO2 using sensors, Springer Geography, Springer Science and Business Media Deutschland GmbH, pp. 89-99.
Liu, X.T., Jayaratne, R., Thai, P., Kuhn, T., Zing, I., Christensen, B., Lamont, R., Dunbabin, M., Zhu, S.C., Gao, J., Wainwright, D., Neale, D., Kan, R., Kirkwood, J., Morawska, L., (2020). Low-cost sensors as an alternative for long-term air quality monitoring. Environmental Research, 185, 10.1016/j.envres.2020.109438.
Markowicz, K.M., Chilinski, M.T., (2020). Evaluation of two low-cost optical particle counters for the measurement of ambient aerosol scattering coefficient and Angstrom exponent. Sensors, 20, 10.3390/s20092617.
Marques, G., Ferreira, C.R., Pitarma, R., (2018). A system based on the Internet of Things for real-time particle monitoring in buildings. International Journal of Environmental Research and Public Health, 15, 10.3390/ijerph15040821.
Masic, A., Bibic, D., Pikula, B., Blazevic, A., Huremovic, J., Zero, S., (2020). Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution. Atmospheric Measurement Techniques, 13, 6427-6443. 10.5194/amt-13-6427-2020.
Mei, H., Han, P.F., Wang, Y.N., Zeng, N., Liu, D., Cai, Q.X., Deng, Z.Z., Wang, Y.H., Pan, Y.P., Tang, X., (2020). Field evaluation of low-cost particulate matter sensors in Beijing. Sensors, 20, 10.3390/s20164381.
Nelson, K.N., Boehmler, J.M., Khlystov, A.Y., Moosmüller, H., Samburova, V., Bhattarai, C., Wilcox, E.M., Watts, A.C., (2019). A multipollutant smoke emissions sensing and sampling instrument package for unmanned aircraft systems: Development and testing. Fire, 2, 32. Fire | Free Full-Text | A Multipollutant Smoke Emissions Sensing and Sampling Instrument Package for Unmanned Aircraft Systems: Development and Testing
Patton, A., Datta, A., Zamora, M.L., Buehler, C., Xiong, F., Gentner, D.R., Koehler, K., (2022). Non-linear probabilistic calibration of low-cost environmental air pollution sensor networks for neighborhood level spatiotemporal exposure assessment. Journal of Exposure Science and Environmental Epidemiology, 32, 908-916. 10.1038/s41370-022-00493-y.
PlanTower, (2016). Digital universal particle concentration sensor: PMS5003 series manual. http://www.aqmd.gov/docs/default-source/aq-spec/resources-page/plantower-pms5003-manual_v2-3.pdf
Plantower, (2019). PMS 5003. Plantower, Beijing, China. http://www.plantower.com/en/content/?108.html
Plantower, (2020). PMS 3003. Plantower, Beijing, China. http://www.plantower.com/en/content/?107.html
Plantower, (2022). PMS 7003. Plantower, Beijing, China. PMS7003---Laser PM2.5 Sensor-Plantower Technology
Reisen, F., Cooper, J., Powell, J.C., Roulston, C., Wheeler, A.J., (2021). Performance and deployment of low-cost particle sensor units to monitor biomass burning events and their application in an educational initiative. Sensors, 21, 10.3390/s21217206.
Sayahi, T., Butterfield, A., Kelly, K.E., (2019). Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors. Environmental Pollution, 245, 932-940. 10.1016/j.envpol.2018.11.065.
Sayahi, T., Kaufman, D., Becnel, T., Kaur, K., Butterfield, A.E., Collingwood, S., Zhang, Y., Gaillardon, P.E., Kelly, K.E., (2019). Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors. Environmental Pollution, 255, 10.1016/j.envpol.2019.113131.
Si, M.X., Xiong, Y., Du, S., Du, K., (2020). Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods. Atmospheric Measurement Techniques, 13, 1693-1707. 10.5194/amt-13-1693-2020.
Teriús-Padrón, J.G., García-Betances, R.I., Liappas, N., Cabrera-Umpiérrez, M.F., Waldmeyer, M.T.A., (2019). Design, development and initial validation of a wearable particulate matter monitoring solution, Springer International Publishing, Cham, pp. 190-196.
Trejo, R.E.G., Rossainz, B.B., Torres, J.A.G., Zavala, A.H., (2022). A study on the behavior of different low-cost particle counter sensors for PM10 and PM2.5 suspended air particles, Mata-Rivera, M.F., Zagal-Flores, R., Barria-Huidobro, C. (Eds.), Proceedings, 11th International Congress of Telematics and Computing, WITCOM 2022, Springer Science and Business Media Deutschland GmbH, pp. 33-50.
Tryner, J., Mehaffy, J., Miller-Lionberg, D., Volckens, J., (2020). Effects of aerosol type and simulated aging on performance of low-cost PM sensors. Journal of Aerosol Science, 150, 10.1016/j.jaerosci.2020.105654.
Vogt, M., Schneider, P., Castell, N., Hamer, P., (2021). Assessment of low-cost particulate matter sensor systems against optical and gravimetric methods in a field co-location in Norway. Atmosphere, 12, 10.3390/atmos12080961.
Wang, K., Chen, F.E., Au, W., Zhao, Z.H., Xia, Z.L., (2019). Evaluating the feasibility of a personal particle exposure monitor in outdoor and indoor microenvironments in Shanghai, China. International Journal of Environmental Health Research, 29, 209-220. 10.1080/09603123.2018.1533531.
Wang, X.L., Zhou, H., Arnott, W.P., Meyer, M.E., Taylor, S., Firouzkouhi, H., Moosmuller, H., Chow, J.C., Watson, J.G., (2020). Evaluation of gas and particle sensors for detecting spacecraft-relevant smoke. Fire Safety Journal, 113, 1-12. 10.1016/j.firesaf.2020.102977. Evaluation of gas and particle sensors for detecting spacecraft-relevant fire emissions - ScienceDirect
Wang, W.C.V., Lung, S.C.C., Liu, C.H., Wen, T.Y.J., Hu, S.C., Chen, L.J., (2021). Evaluation and application of a novel low-cost wearable sensing device in assessing real-time PM2.5 exposure in major Asian Transportation modes. Atmosphere, 12, 10.3390/atmos12020270.
Wendt, E.A., Quinn, C., L’Prange, C., Miller-Lionberg, D.D., Ford, B., Pierce, J.R., Mehaffy, J., Cheeseman, M., Jathar, S.H., Hagan, D.H., Rosen, Z., Long, M., Volckens, J., (2021). A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth - Part 3: Automation and design improvements. Atmospheric Measurement Techniques, 14, 6023-6038. 10.5194/amt-14-6023-2021. AMT - A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements
Zamora, M.L., Xiong, F.L.Z., Gentner, D., Kerkez, B., Kohrman-Glaser, J., Koehler, K., (2019). Field and laboratory evaluations of the low-cost Plantower particulate matter sensor. Environmental Science & Technology, 53, 838-849. 10.1021/acs.est.8b05174.
Zheng, T.S., Bergin, M.H., Johnson, K.K., Tripathi, S.N., Shirodkar, S., Landis, M.S., Sutaria, R., Carlson, D.E., (2018). Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments. Atmospheric Measurement Techniques, 11, 4823-4846. 10.5194/amt-11-4823-2018.
Zou, Y., Clark, J.D., May, A.A., (2021). Laboratory evaluation of the effects of particle size and composition on the performance of integrated devices containing Plantower particle sensors. Aerosol Science and Technology, 55, 848-858. 10.1080/02786826.2021.1905148. https://www.tandfonline.com/doi/full/10.1080/02786826.2021.1905148
Zusman, M., Schumacher, C.S., Gassett, A.J., Spalt, E.W., Austin, E., Larson, T.V., Carvlin, G., Seto, E., Kaufman, J.D., Sheppard, L., (2020). Calibration of low-cost particulate matter sensors: Model development for a multi-city epidemiological study. Environment International, 134, 10.1016/j.envint.2019.105329.