Articles | Volume 16, issue 21
https://doi.org/10.5194/amt-16-5415-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-5415-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spectral analysis approach for assessing the accuracy of low-cost air quality sensor network data
Vijay Kumar
Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
current address: Department of Environmental Health Sciences, Columbia University, New York, NY 10032, USA
Dinushani Senarathna
Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
Supraja Gurajala
Department of Computer Science, State University of New York, Potsdam, NY 13676, USA
William Olsen
Department of Civil and Environmental Engineering, Clarkson University, Potsdam, NY 13699, USA
Shantanu Sur
Department of Biology, Clarkson University, Potsdam, NY 13699, USA
Sumona Mondal
Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
Department of Mechanical & Aerospace Engineering, Clarkson University, Potsdam, NY 13699, USA
Related authors
No articles found.
Dongwook Kim, Pedro Campuzano-Jost, Hongyu Guo, Douglas A. Day, Da Yang, Suresh Dhaniyala, Leah Williams, Philip Croteau, John Jayne, Douglas Worsnop, Rainer Volkamer, and Jose L. Jimenez
Aerosol Research, 3, 371–404, https://doi.org/10.5194/ar-3-371-2025, https://doi.org/10.5194/ar-3-371-2025, 2025
Short summary
Short summary
Quantitative real-time aerosol sampling on board aircraft platforms is challenging, especially at higher altitudes. Herein, we present comprehensive analyses of a new aircraft inlet system and tools for aerosol beam diagnostics for aerosol mass spectrometers (AMSs). The beam focusing of aerodynamic lenses and the thermal decomposition on the vaporizer were investigated. The new inlet system can be operated at higher altitudes while sampling aerosols over a broader size range than previous versions.
Da Yang, Emmanuel Assaf, Roy Mauldin, Suresh Dhaniyala, and Rainer Volkamer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2390, https://doi.org/10.5194/egusphere-2024-2390, 2024
Short summary
Short summary
Sulfuric acid forms particles in the atmosphere, but the airborne sampling faces challenges due to vapor losses in inlet lines. An innovative aircraft sampling system to sample sulfuric acid from the sea surface into the lower stratosphere (0–15 km) is described and characterized. Our results challenge the widely held view that laminar core sampling is the best strategy to sample condensable vapors, and identify better strategies to sample condensable vapors.
Da Yang, Margarita Reza, Roy Mauldin, Rainer Volkamer, and Suresh Dhaniyala
Atmos. Meas. Tech., 17, 1463–1474, https://doi.org/10.5194/amt-17-1463-2024, https://doi.org/10.5194/amt-17-1463-2024, 2024
Short summary
Short summary
This paper evaluates the performance of an aircraft gas inlet. Here, we use computational fluid dynamics (CFD) and experiments to demonstrate the role of turbulence in determining sampling performance of a gas inlet and identify ideal conditions for inlet operation to minimize gas loss. Experiments conducted in a high-speed wind tunnel under near-aircraft speeds validated numerical results. We believe that the results obtained from this work will greatly inform future gas inlet studies.
Cited articles
Afrifa-Yamoah, E., Mueller, U. A., Taylor, S., and Fisher, A.: Missing data imputation of high-resolution temporal climate time series data, Meteorol. Appl., 27, e1873, https://doi.org/10.1002/met.1873, 2020. a
Bi, J., Wildani, A., Chang, H. H., and Liu, Y.: Incorporating low-cost sensor measurements into high-resolution PM2.5 modeling at a large spatial scale, Environ. Sci. Technol., 54, 2152–2162, 2020. a
Blanchard, C. L., Tanenbaum, S., and Lawson, D. R.: Differences between weekday and weekend air pollutant levels in Atlanta; Baltimore; Chicago; Dallas–Fort Worth; Denver; Houston; New York; Phoenix; Washington, DC; and surrounding areas, J. Air Waste Manage., 58, 1598–1615, 2008. a
Bureau, U. C.: US Census Bureau: Public Database, https://www.census.gov/geo/maps-data/data/tallies/tractblock.html (last access: 9 January 2023), 2021. a
Chaipitakporn, C., Athavale, P., Kumar, V., Sathiyakumar, T., Budisic, M., Sur, S., and Mondal, S.: COVID-19 in the United States during pre-vaccination period: Shifting impact of sociodemographic factors and air pollution, Front. Epidemiol., 2, 48, ISSN 2674-1199, https://doi.org/10.3389/fepid.2022.927189, 2022. a
Commodore, A., Wilson, S., Muhammad, O., Svendsen, E., and Pearce, J.: Community-based participatory research for the study of air pollution: A review of motivations, approaches, and outcomes, Environ. Monit. Assess., 189, 1–30, 2017. a
Dilmaghani, S.: Spectral analysis of air quality data, Ph.D. thesis, University of Southern California, 2007. a
EPA: US Environmental Protection Agency (EPA): Publically available air quality data API, https://aqs.epa.gov/aqsweb/documents/data_api.html (last access: 20 October 2022), 2021. a
Eskridge, R. E., Ku, J. Y., Rao, S. T., Porter, P. S., and Zurbenko, I. G.: Separating different scales of motion in time series of meteorological variables, B. Am. Meteorol. Soc., 78, 1473–1484, 1997. a
ESRI: Esri. “Navigation” [basemap], Scale Not Given, “World Navigation Map”, http://www.arcgis.com/home/item.html?id=30e5fe3149c34df1ba922e6f5bbf808f (last access: 9 January 2023), 2021. a
Fang, C., Qiu, J., Li, J., and Wang, J.: Analysis of the meteorological impact on PM2.5 pollution in Changchun based on KZ filter and WRF-CMAQ, Atmos. Environ., 271, 118924, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2021.118924, 2022. a
Giordano, M. R., Malings, C., Pandis, S. N., Presto, A. A., McNeill, V. F., Westervelt, D. M., Beekmann, M., and Subramanian, R.: From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors, J. Aerosol Sci., 158, 105833, https://doi.org/10.1016/j.jaerosci.2021.105833, 2021. a
Gupta, P., Doraiswamy, P., Levy, R., Pikelnaya, O., Maibach, J., Feenstra, B., Polidori, A., Kiros, F., and Mills, K.: Impact of California fires on local and regional air quality: the role of a low-cost sensor network and satellite observations, GeoHealth, 2, 172–181, 2018. a
Hadeed, S. J., O'Rourke, M. K., Burgess, J. L., Harris, R. B., and Canales, R. A.: Imputation methods for addressing missing data in short-term monitoring of air pollutants, Sci. Total Environ., 730, 139–140, 2020. a
Hirabayashi, S. and Kroll, C. N.: Single imputation method of missing air quality data for i-tree eco analyses in the conterminous united states, Retrieved 1 January 2021, http://www.itreetools.org/eco/resources/Single_imputation_method_of_missing_air_quality_data_for_i-Tree_Eco_analyses_in_the_conterminous_United_States.pdf (last access: 1 December 2021), 2017. a
Hogrefe, C., Rao, S. T., Zurbenko, I. G., and Porter, P. S.: Interpreting the information in ozone observations and model predictions relevant to regulatory policies in the eastern United States, B. Am. Meteorol. Soc., 81, 2083–2106, 2000. a
Hollaway, M., Wild, O., Yang, T., Sun, Y., Xu, W., Xie, C., Whalley, L., Slater, E., Heard, D., and Liu, D.: Photochemical impacts of haze pollution in an urban environment, Atmos. Chem. Phys., 19, 9699–9714, https://doi.org/10.5194/acp-19-9699-2019, 2019. a
Imtiaz, S. A. and Shah, S. L.: Treatment of missing values in process data analysis, The Can. J. Chem. Eng., 86, 838–858, 2008. a
IQAIR: Air quality in Chicago: Public Database, https://www.iqair.com/us/usa/illinois/chicago (last access: 20 October 2022), 2020. a
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An introduction to statistical learning, Vol. 112, Springer, 2013. a
Jia, M., Zhao, T., Cheng, X., Gong, S., Zhang, X., Tang, L., Liu, D., Wu, X., Wang, L., and Chen, Y.: Inverse relations of PM2.5 and O3 in air compound pollution between cold and hot seasons over an urban area of east China, Atmosphere, 8, 59, https://doi.org/10.3390/atmos8030059, 2017. a
Kang, D., Mathur, R., Rao, S. T., and Yu, S.: Bias adjustment techniques for improving ozone air quality forecasts, J. Geophys. Res.-Atmos., 113, D23308, https://doi.org/10.1029/2008jd010151, 2008. a
Kim, T., Kim, J., Yang, W., Lee, H., and Choo, J.: Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks, Int. J. Environ. Res. Pu., 18, 12213, https://doi.org/10.3390/ijerph182212213, 2021. a
Kumar, V.: vijaykumar18/Airquality-Spectral-Analysis,
Airquality-Spectral-Analysis, GitHub [data set], https://github.com/vijaykumar18/Airquality-Spectral-Analysis (last access: 29 February 2023), 2023. a
Airquality-Spectral-Analysis, GitHub [data set], https://github.com/vijaykumar18/Airquality-Spectral-Analysis (last access: 29 February 2023), 2023. a
Kuula, J., Mäkelä, T., Hillamo, R., and Timonen, H.: Response characterization of an inexpensive aerosol sensor, Sensors, 17, 2915, https://doi.org/10.3390/s17122915, 2017. a
Kuula, J., Mäkelä, T., Aurela, M., Teinilä, K., Varjonen, S., González, Ó., and Timonen, H.: Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors, Atmos. Meas. Tech., 13, 2413–2423, https://doi.org/10.5194/amt-13-2413-2020, 2020. a
Landrigan, P. J., Fuller, R., Acosta, N. J. R., Adeyi, O., Arnold, R., Basu, N. N., Baldé, A. B., Bertollini, R., Bose-O’Reilly, S., Boufford, J. I., Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L., Fobil, J., Fuster, V., Greenstone, M., Haines, A., Hanrahan, D., Hunter, D., Khare, M., Krupnick, A., Lanphear, B., Lohani, B., Martin, K., Mathiasen, K. V., McTeer, M. A., Murray, C. J. L., Ndahimananjara, J. D., Perera, F., Potoˇcnik, J., Preker, A. S., Ramesh, J., Rockström, J., Salinas, C., Samson, L. D., Sandilya, K., Sly, P. D., Smith, K. R., Steiner, A., Stewart, R. B., Suk, W. A., van Schayck, O. C. P., Yadama, G. N., Yumkella, K., and Zhong, M.: The Lancet Commission on pollution and health, The Lancet, 391, 462–512, https://doi.org/10.1016/s0140-6736(17)32345-0, 2018. a
Lewis, T. C., Robins, T. G., Dvonch, J. T., Keeler, G. J., Yip, F. Y., Mentz, G. B., Lin, X., Parker, E. A., Israel, B. A., Gonzalez, L., and Hill, Y.: Air Pollution–Associated Changes in Lung Function among Asthmatic Children in Detroit, Environ. Health Perspect., 113, 1068–1075, https://doi.org/10.1289/ehp.7533, 2005. a
Li, L., Losser, T., Yorke, C., and Piltner, R.: Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter PM2.5 in the contiguous US using parallel programming and kd tree, Int. J. Environ. Res. Pub. Health, 11, 9101–9141, 2014. a
Li, T., Hu, R., Chen, Z., Li, Q., Huang, S., Zhu, Z., and Zhou, L.-F.: Fine particulate matter (PM2.5): The culprit for chronic lung diseases in China, Chronic Diseases and Translational Medicine, 4, 176–186, 2018. a
Lian, L. and Ma, H.: FDI and economic growth in western region of China and dynamic mechanism: Based on time-series data from 1986 to 2010, International Business Research, 6, 180, 2013. a
Magi, B. I., Cupini, C., Francis, J., Green, M., and Hauser, C.: Evaluation of PM2.5 measured in an urban setting using a low-cost optical particle counter and a Federal Equivalent Method Beta Attenuation Monitor, Aerosol Sci. Technol., 54, 147–159, 2020. a
Mei, H., Han, P., Wang, Y., Zeng, N., Liu, D., Cai, Q., Deng, Z., Wang, Y., Pan, Y., and Tang, X.: Field evaluation of low-cost particulate matter sensors in Beijing, Sensors, 20, 4381, https://doi.org/10.3390/s20164381, 2020. a
Milanchus, M. L., Rao, S. T., and Zurbenko, I. G.: Evaluating the effectiveness of ozone management efforts in the presence of meteorological variability, J. Air Waste Manage., 48, 201–215, 1998. a
Milando, C., Huang, L., and Batterman, S.: Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago, Atmos. Environ., 129, 197–209, 2016. a
Mondal, S., Chaipitakporn, C., Kumar, V., Wangler, B., Gurajala, S., Dhaniyala, S., and Sur, S.: COVID-19 in New York state: Effects of demographics and air quality on infection and fatality, Sci. Total Environ., 807, 150536, https://doi.org/10.1016/j.scitotenv.2021.150536, 2022. a
Noble, C. A., Vanderpool, R. W., Peters, T. M., McElroy, F. F., Gemmill, D. B., and Wiener, R. W.: Federal reference and equivalent methods for measuring fine particulate matter, Aerosol Sci. Technol., 34, 457–464, 2001. a
Ostro, B., Broadwin, R., Green, S., Feng, W.-Y., and Lipsett, M.: Fine particulate air pollution and mortality in nine California counties: results from CALFINE, Environ. Health Persp., 114, 29–33, 2006. a
Ouimette, J. R., Malm, W. C., Schichtel, B. A., Sheridan, P. J., Andrews, E., Ogren, J. A., and Arnott, W. P.: Evaluating the PurpleAir monitor as an aerosol light scattering instrument, Atmos. Meas. Tech., 15, 655–676, https://doi.org/10.5194/amt-15-655-2022, 2022. a, b
Ouimette, J. R., Malm, W. C., Schichtel, B. A., Sheridan, P. J., Andrews, E., Ogren, J. A., and Arnott, W. P.: Evaluating the PurpleAir monitor as an aerosol light scattering instrument, Atmospheric Measurement Techniques, 15, 655–676, 2022. a
PA: Purple Air: Public Database of sensors installed in entire world, https://map.purpleair.com/1/mAQI/a10/p604800/cC0#11.44/41.8363/-87.6973 (last access: 10 February 2023), 2021. a
PurpleAir: PurpleAir: PublicLab, https://publiclab.org/wiki/purpleair (last access: 10 February 2023), 2020. a
Raaschou-Nielsen, O., Andersen, Z. J., Beelen, R., Samoli, E., Stafoggia, M., Weinmayr, G., Hoffmann, B., Fischer, P., Nieuwenhuijsen, M.J., Brunekreef, B., Xun, W. W., Katsouyanni, K., Dimakopoulou, K., Sommar, J., Forsberg, B., Modig, L., Oudin, A., Oftedal, B., Schwarze, P. E., and Nafstad, P.: Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE). The Lancet Oncology, [online] 14, 813–822, https://doi.org/10.1016/s1470-2045(13)70279-1, 2013. a
Rivera-Muñoz, L. M., Gallego-Villada, J. D., Giraldo-Forero, A. F., and Martinez-Vargas, J. D.: Missing data estimation in a low-cost sensor network for measuring air quality: A case study in Aburrá Valley, Water, Air, Soil Pollut., 232, 1–15, 2021. a
Sá, E., Tchepel, O., Carvalho, A., and Borrego, C.: Meteorological driven changes on air quality over Portugal: a KZ filter application, Atmos. Pollut. Res., 6, 979–989, 2015. a
Samoli, E., Analitis, A., Touloumi, G., Schwartz, J., Anderson, H. R., Sunyer, J., Bisanti, L., Zmirou, D., Vonk, J. M., Pekkanen, J., Goodman, P., Paldy, A., Schindler, C., and Klea, K.: Estimating the exposure–response relationships between particulate matter and mortality within the APHEA multicity project, Environ. Health Perspect., 113, 88–95, https://doi.org/10.1289/ehp.7387, 2005 a
Saputra, M. D., Hadi, A. F., Riski, A., and Anggraeni, D.: Handling Missing Values and Unusual Observations in Statistical Downscaling Using Kalman Filter, J. Phys. Conference Series, 1863, 012 035, https://doi.org/10.1088/1742-6596/1863/1/012035, 2021. a
Sayahi, T., Kaufman, D., Becnel, T., Kaur, K., Butterfield, A., Collingwood, S., Zhang, Y., Gaillardon, P.-E., and Kelly, K.: Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors, Environ. Pollut., 255, 113131, https://doi.org/10.1016/j.envpol.2019.113131, 2019. a
Stavroulas, I., Grivas, G., Michalopoulos, P., Liakakou, E., Bougiatioti, A., Kalkavouras, P., Fameli, K. M., Hatzianastassiou, N., Mihalopoulos, N., and Gerasopoulos, E.: Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece, Atmosphere, 11, 926, https://doi.org/10.3390/atmos11090926, 2020. a
Sun, L.: Spectral and time-frequency analyses of freeway traffic flow, J. Adv. Transport., 48, 821–857, 2014. a
Tryner, J., Mehaffy, J., Miller-Lionberg, D., and Volckens, J.: Effects of aerosol type and simulated aging on performance of low-cost PM sensors, J. Aerosol Sci., 150, 105654, https://doi.org/10.1016/j.jaerosci.2020.105654, 2020. a
Wallace, L., Bi, J., Ott, W. R., Sarnat, J., and Liu, Y.: Calibration of low-cost PurpleAir outdoor monitors using an improved method of calculating PM2.5, Atmos. Environ., 256, 118432, https://doi.org/10.1016/j.atmosenv.2021.118432, 2021. a
Wang, X., Wang, L., Liu, Y., Hu, S., Liu, X., and Dong, Z.: A data-driven air quality assessment method based on unsupervised machine learning and median statistical analysis: The case of China, J. Clean. Product., 328, 129531, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2021.129531, 2021. a
Wang, Y., Li, J., Jing, H., Zhang, Q., Jiang, J., and Biswas, P.: Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement, Aerosol Sci. Technol., 49, 1063–1077, 2015. a
Wijesekara, W. M. L. K. N. and Liyanage, L.: Comparison of imputation methods for missing values in air pollution data: Case study on Sydney air quality index, in: Advances in Information and Communication: Proceedings of the 2020 Future of Information and Communication Conference (FICC), Volume 2, 257–269 pp., Springer International Publishing, 2020. a
Woodall, G. M., Hoover, M. D., Williams, R., Benedict, K., Harper, M., Soo, J.-C., Jarabek, A. M., Stewart, M. J., Brown, J. S., Hulla, J. E., Caudill, M., Clements, A. L., Kaufman, A., Parker, A. J., Keating, M., Balshaw, D., Garrahan, K., Burton, L., Batka, S., Limaye, V. S., Hakkinen, P. J., and Thompson, B.: Interpreting mobile and handheld air sensor readings in relation to air quality standards and health effect reference values: Tackling the challenges, Atmosphere, 8, 182, https://doi.org/10.3390/atmos8100182, 2017. a
Wu, X., Nethery, R. C., Sabath, B. M., Braun, D., and Dominici, F.: Exposure to air pollution and COVID-19 mortality in the United States. MedRxiv, https://doi.org/10.1101/2020.04.05.20054502, 2020. a
Xing, Y.-F., Xu, Y.-H., Shi, M.-H., and Lian, Y.-X.: The impact of PM2.5 on the human respiratory system, J. Thorac. Dis., 8, E69, https://doi.org/10.3978/j.issn.2072-1439.2016.01.19, 2016. a
Zhang, Z., Kim, S.-J., and Ma, Z.: Significant decrease of PM2.5 in Beijing based on long-term records and Kolmogorov-Zurbenko filter approach, https://doi.org/10.4209/aaqr.2017.01.0011, 2018. a, b
Zheng, T., Bergin, M. H., Johnson, K. K., Tripathi, S. N., Shirodkar, S., Landis, M. S., Sutaria, R., and Carlson, D. E.: Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments, Atmos. Meas. Tech., 11, 4823–4846, https://doi.org/10.5194/amt-11-4823-2018, 2018. a
Zhou, X., Josey, K., Kamareddine, L., Caine, M. C., Liu, T., Mickley, L. J., Cooper, M., and Dominici, F.: Excess of COVID-19 cases and deaths due to fine particulate matter exposure during the 2020 wildfires in the United States, Sci. Adv., 7, eabi8789, https://doi.org/10.1126/sciadv.abi8789, 2021. a
Short summary
Low-cost sensors are becoming increasingly important in air quality monitoring due to their affordability and ease of deployment. While low-cost sensors have the potential to democratize air quality monitoring, their use must be accompanied by careful interpretation and validation of the data. Analysis of their long-term data record clearly shows that the reported data from low-cost sensors may not be equally sensitive to all emission sources, which can complicate policy-making.
Low-cost sensors are becoming increasingly important in air quality monitoring due to their...