* Corresponding Author
Low-Cost Sensor Based on Internet of Things for PM₂¸₅ Air Quality Monitoring
https://doi.org/10.22146/ijg.104159 Dian Hudawan Santoso(1), Sri Juari Santosa(2), Andung Bayu Sekaranom(3*)
(1) Doctoral Program in Environmental Science, The Graduate School of Universitas Gadjah Mada, Yogyakarta, Indonesia and Department of Environmental Engineering, Faculty of Mineral Technology and Energy, UPN Veteran Yogyakarta, Indonesia
(2) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Bulaksumur, Yogyakarta, Indonesia
(3) Department of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, Bulaksumur, Yogyakarta, Indonesia
(*) Corresponding Author
Abstract
The issue of air pollution, particularly that of particulate matter (PM2.5), has recently garnered significant global attention. However, the implementation of effective air quality management is frequently impeded by a dearth of adequate monitoring and measurement equipment. In Yogyakarta City and its surrounding areas, monitoring ambient air concentration, particularly PM2.5, remains difficult due to the limitations of monitoring tools such as Air Quality Monitoring System (AQMS). These tools are costly to operate, which further worsens the challenges. Therefore, this research aimed to design Internet of Things (IoT)-based Low-Cost Sensor (LCS) as an economical and reliable alternative to PM2.5 monitoring tools. Research and Development method was used with Plomp development model, which included investigation, design, calibration, as well as implementation. The results showed that IoT-based LCS followed the SNI 9178: 2023 standard with precision (SD 0.659 µg/m³; CV 23.59%), bias (slope 0.94; intercept 0.65 µg/m³), linearity (R² = 0.9), and RMSE 1.43 µg/m³. Moreover, the regression relationship between IoT-based LCS and AQMS was shown by the equation Y = 0.8633X + 2.7604, signifying a strong correlation between the two tools. During the analysis, IoT-based LCS appeared to be a promising solution for air quality monitoring, offering both effectiveness and affordability, with real-time data relevant to environmental management.. The IoT-based LCS has been designed simply, meets the calibration standards of SNI 9178:2023, and can be applied in suburban areas.
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Aboosaedi, Z., Naddafi, K., Nodehi, R. N., Hassanvand, M. S., Faridi, S., & Aliannejad, R. (2023). Investigating the performance of urban air quality monitoring station in measuring PM2.5 and PM10: A case study in Tehran, Iran. Environmental Health Engineering and Management, 10(4), 451–458. https://doi.org/10.34172/EHEM.2023.48
Adedeji, O. H., Oluwafunmilayo, O., & Oluwaseun, T. A. O. (2016). Mapping of traffic-related air pollution using GIS techniques in Ijebu-Ode, Nigeria. The Indonesian Journal of Geography, 48(1), 73. https://doi.org/10.22146/ijg.12488
Ahmed, S. (2024). Environmental Pollution and Global Issues (With special regard to air pollution). Procedure International Journal of Science and Technology, 1(3), 1–13. https://doi.org/10.62796/pijst.2024v1i301
Akbar, R. Z. (2023). Analisis Tingkat Pencemaran Udara Kendaraan Bermotor di Area Parkir Selatan Universitas Muhammadiyah Yogyakarta. Media Ilmiah Teknik Lingkungan, 8(1), 25–33. https://doi.org/10.33084/mitl.v8i1.4680
Alfano, B., Barretta, L., Giudice, A. Del, De Vito, S., Francia, G. Di, Esposito, E., Formisano, F., Massera, E., Miglietta, M. L., & Polichetti, T. (2020). A review of low-cost particulate matter sensors from the developers’ perspectives. Sensors (Switzerland). 20 (23), 1–56. https://doi.org/10.3390/s20236819
Ali, S., Glass, T., Parr, B., Potgieter, J., & Alam, F. (2021). Low-cost sensor with IoT LoRaWAN Connectivity and Machine Learning-Based Calibration for Air Pollution Monitoring. IEEE Transactions on Instrumentation and Measurement, 70, 1-11. https://doi.org/10.1109/TIM.2020.3034109
Anggraeni, R. A. Y., & Lestari, K. S. (2023). The impact of PM2.5 air pollutant exposure on human respiratory health: A literature review. World Journal of Advanced Research and Reviews, 19(2), 1057–1064. https://doi.org/10.30574/wjarr.2023.19.2.1680
Asif, M., Haq, R. A. U., Gulfreen, E., Arshad, S., Tasleem, M. W., Rajpoot, S. R., Munir, S., Waseem, M., Yar, M. A., Sohail, M., Abbas, M., Fatima, K., & Maqbool, M. (2022). Particulate Matter Emission Sources And Theircontrol Technologies. Pollution Research, 696–706. https://Doi.Org/10.53550/Pr.2022.V41i02.043
Asim, I.A.B., Kamarudin, A.M.B., Onn, M., Azli, S.A., Zainuddin, A., Zain, N.M., & Hasan, K.M. (2018). Low-Cost Real-Time Mobile Air-Quality Monitoring System (AQMS). International Journal of Engineering & Technology, 7(4.22), 101-105. https://doi.org/10.14419/ijet.v7i4.22.27859
Badura, M., Batog, P., Osiadacz, A.D., & Modzel, P. (2018). Evaluation of low-cost sensors for ambient PM2.5 monitoring. Journal of Sensors, 2018 (1) : 5096540. https://doi.org/10.1155/2018/5096540
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., & Bartonova, A. (2017). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment International, 99, 293–302. https://doi.org/10.1016/j.envint.2016.12.007
Cheepsattayakorn, A., & Cheepsattayakorn, R. (2019). PM2.5, PM2.5-related air pollutants, health hazards and impacts on respiratory and cardiovascular disorders and diseases: systematic review and meta-analysis. Journal of Lung, Pulmonary & Respiratory Research, 6(3), 40–48. https://doi.org/10.15406/jlprr.2019.06.00205
Chen, H. Y., & Chen, C. (2022). Evaluation of Calibration Equations by Using Regression Analysis: An Example of Chemical Analysis. Sensors, 22(2). https://doi.org/10.3390/s22020447
Chen, J., & Hoek, G. (2020). Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis. Environment International, 143: 105974. https://doi.org/10.1016/j.envint.2020.105974
Chen, Z., Chen, D., Zhao, C., Kwan, M. po, Cai, J., Zhuang, Y., Zhao, B., Wang, X., Chen, B., Yang, J., Li, R., He, B., Gao, B., Wang, K., & Xu, B. (2020). Influence of meteorological conditions on PM2.5 concentration across China: A review of methodology and mechanism. Environment International, 139:105558. https://doi.org/10.1016/j.envint.2020.105558
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. https://doi.org/10.3389/fenvs.2021.798485
Darquenne, C. (2020). Deposition Mechanisms. Journal of Aerosol Medicine and Pulmonary Drug Delivery, 33(4), 181–185. https://doi.org/10.1089/jamp.2020.29029.cd
Estuhono., Festiyed., & Bentri, A. (2019). Preliminary research of developing a research-based learning model integrated by scientific approach on physics learning in senior high school. Journal of Physics: Conference Series, 1185(1). https://doi.org/10.1088/1742-6596/1185/1/012041
Falcon-Rodriguez, C. I., Osornio-Vargas, A. R., Sada-Ovalle, I., & Segura-Medina, P. (2016). Aeroparticles, composition, and lung diseases. Frontiers in Immunology (Vol. 7, Issue JAN). https://doi.org/10.3389/fimmu.2016.00003
Giordano, M. R., Malings, C., Pandis, S. N., Presto, A. A., Mcneill, V. F., Westervelt, D. M., Beekmann, M., & Subramanian, R. (2021). From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors. Journal of Aerosol Science, 158, 105833. https://doi.org/10.1016/j.jaerosci.2021.105833ï
Glass, T., Ali, S., Parr, B., Potgieter, J., & Alam, F. (2020). IoT Enabled Low-Cost Air Quality Sensor. 2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings. https://doi.org/10.1109/SAS48726.2020.9220079
Holstius, D. M., Pillarisetti, A., Smith, K. R., & Seto, E. (2014). Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmospheric Measurement Techniques, 7(4), 1121–1131. https://doi.org/10.5194/amt-7-1121-2014
Hou, K., & Xu, X. (2022). Evaluation of the influence between local meteorology and air quality in Beijing using generalized additive models. Atmosphere, 13(1), 24. https://doi.org/10.3390/atmos13010024
Irsyada, N.A., & Oktapatika, S. (2023). Peran Pemerintah Dalam Mewujudkan Smart City Di Kota Tanjungpinang. Jurnal Wilayah, Kota Dan Lingkungan Berkelanjutan, 2(2), 109–116. https://Doi.Org/10.58169/Jwikal.V2i2.260
Jiao, W., Hagler, G., Williams, R., Sharpe, R., Brown, R., Garver, D., Judge, R., Caudill, M., Rickard, J., Davis, M., Weinstock, L., Zimmer-Dauphinee, S., & Buckley, K. (2016). Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States. Atmospheric Measurement Techniques, 9(11), 5281–5292. https://doi.org/10.5194/amt-9-5281-2016
Jin, S., Guo, J., Wheeler, S., Kan, L., & Che, S. (2014). Evaluation of impacts of trees on PM2.5 dispersion in urban streets. Atmospheric Environment, 99, 277–287. https://doi.org/10.1016/j.atmosenv.2014.10.002
Kang, Y., Aye, L., Ngo, T. D., & Zhou, J. (2022). Performance evaluation of low-cost air quality sensors: A review. Science of the Total Environment, 818. https://doi.org/10.1016/j.scitotenv.2021.151769
Kusuma, M. (2024). Pemodelan Polusi Udara Akibat Pengalihan Lalu Lintas Dari Pembangunan Fly Over Aloha Sidoarjo. Jurnal Ilmu Lingkungan, 22(4), 923–932. https://doi.org/10.14710/jil.22.4.923-932
Larson, P. S., Espira, L., Glenn, B. E., Larson, M. C., Crowe, C. S., Jang, S., & O’neill, M. S. (2022). Long-Term PM2.5 Exposure Is Associated with Symptoms of Acute Respiratory Infections among Children under Five Years of Age in Kenya, 2014. International Journal of Environmental Research and Public Health, 19(5). https://doi.org/10.3390/ijerph19052525
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. https://doi.org/10.5194/amt-2020-237
Mesra, R., Salem, V. E. T., Goretti, M., Polii, M., Daniel, Y., Santie, A., Made, N., Wisudariani, R., Sarwandi, R. P., Sari, R., Yulianti, A., Nasar, Y., Yenita, D., Putu, N., & Santiari, L. (2023). Research & Development Dalam Pendidikan. PT. Mifandi Mandiri Digital.
Miskell, G., Alberti, K., Feenstra, B., Henshaw, G. S., Papapostolou, V., Patel, H., Polidori, A., Salmond, J. A., Weissert, L., & Williams, D. E. (2019). Reliable data from low cost ozone sensors in a hierarchical network. Atmospheric Environment, 214. https://doi.org/10.1016/j.atmosenv.2019.116870
Nan, N., Yan, Z., Zhang, Y., Chen, R., Qin, G., & Sang, N. (2023). Overview of PM2.5 and health outcomes: Focusing on components, sources, and pollutant mixture co-exposure. Chemosphere, 323. https://doi.org/10.1016/j.chemosphere.2023.138181
Nazarenko, Y., Pal, D., & Ariya, P. A. (2021). Air quality standards for the concentration of particulate matter 2.5, global descriptive analysis. Bulletin of the World Health Organization, 99(2), 125–137. https://doi.org/10.2471/BLT.19.245704
Nicolaou, L., & Checkley, W. (2021). Inequities in air pollution exposure and gaps in air quality monitoring. Journal of Allergy and Clinical Immunology, 148 (1), 64–66. https://doi.org/10.1016/j.jaci.2021.04.014
Plantower, https://www.plantower.com/en/products_33/74.html, accessed on June 16, 2025
Raheja, G., Nimo, J., Appoh, E. K. E., Essien, B., Sunu, M., Nyante, J., Amegah, M., Quansah, R., Arku, R. E., Penn, S. L., Giordano, M. R., Zheng, Z., Jack, D., Chillrud, S., Amegah, K., Subramanian, R., Pinder, R., Appah-Sampong, E., Tetteh, E. N., … Westervelt, D. M. (2023). Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana. Environmental Science and Technology, 57(29), 10708–10720. https://doi.org/10.1021/acs.est.2c09264
Raysoni, A. U., Pinakana, S. D., Mendez, E., Wladyka, D., Sepielak, K., & Temby, O. (2023). A Review of Literature on the Usage of Low-Cost Sensors to Measure Particulate Matter. In Earth (Switzerland) (Vol. 4, Issue 1, pp. 168–186). MDPI. https://doi.org/10.3390/earth4010009
Reddy, C. R., Mukku, T., Dwivedi, A., Rout, A., Chaudhari, S., Vemuri, K., Rajan, K. S., & Hussain, A. M. (2020). Improving Spatio-Temporal Understanding of Particulate Matter using Low-Cost IoT Sensors. 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, 1-7. https://doi.org/10.1109/PIMRC48278.2020.9217109
Ruiter, S., Bard, D., Jeddi, H. Ben, Saunders, J., Snawder, J., Warren, N., Gorce, J. P., Cauda, E., Kuijpers, E., & Pronk, A. (2023). Exposure Monitoring Strategies for Applying Low-Cost PM Sensors to Assess Flour Dust in Industrial Bakeries. Annals of Work Exposures and Health, 67(3), 379–391. https://doi.org/10.1093/annweh/wxac088
Santoso, D. H., Santosa, S. J., & Sekaranom, A. B. (2024). Analysis and Trends Research Publications on Air Quality (PM 2.5) Management Strategies. IOP Conference Series: Earth and Environmental Science, 1339(1). https://doi.org/10.1088/1755-1315/1339/1/012042
Sejati, S.P. 2019. Perbandingan Akurasi Metode idw dan kriging dalam Pemetaan Muka Tanah. Majalah Geografi Indonesia. 33(2), 49 – 57. https://doi.org/10.22146/mgi.51228
Shaddick, G., Thomas, M. L., Mudu, P., Ruggeri, G., & Gumy, S. (2020). Half the world’s population are exposed to increasing air pollution. NPJ Climate and Atmospheric Science, 3(1), 1-5. https://doi.org/10.1038/s41612-020-0124-2
Shukla, K., Kumar, P., Mann, G. S., & Khare, M. (2019). Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi. Sustainable Cities & Society Journal, 54, 101997. https://doi.org/10.1016/j.scs.2019.101997
Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R. W., Hagler, G. S. W., Shelow, D., Hindin, D. A., Kilaru, V. J., & Preuss, P. W. (2013). The changing paradigm of air pollution monitoring. Environmental Science and Technology, 47(20), 11369–11377. https://doi.org/10.1021/es4022602
Sousan, S., Koehler, K., Hallett, L., & Peters, T. M. (2016). Evaluation of the Alphasense optical particle counter (OPC-N2) and the Grimm portable aerosol spectrometer (PAS-1.108). Aerosol Science and Technology, 50(12), 1352–1365. https://doi.org/10.1080/02786826.2016.1232859
Sugita, K., Kin, Y., Yagishita, M., Ikemori, F., Kumagai, K., Ohara, T., Kinoshita, M., Nishimura, K., Takagi, Y., & Nakajima, D. (2019). Evaluation of the genotoxicity of PM2.5 collected by a high-volume air sampler with impactor. Genes and Environment, 41(1). https://doi.org/10.1186/s41021-019-0120-0
Suresh, K., & Palaniraj, N. (2018). Impact of Air Pollution on Human Health. International Review of Business and Economics, 1(3), 187–191. https://doi.org/10.56902/irbe.2018.1.3.48
Swamy, G. S. N. V. K. S. N., Nagendra, S. M., & Schlink, U. (2020). Impact of urban heat island on meteorology and air quality at microenvironments. Journal of the Air and Waste Management Association, 70(9), 876–891. https://doi.org/10.1080/10962247.2020.1783390
Tatavarti, R. (2021). Ambient Air Quality Monitoring: Impetus, Complexities, Challenges and Solutions. Global Journal of Science Frontier Research. 21 (H4).41-58. https://doi.org/10.34257/GJSFRHVOL21IS4PG41
Thangavel, P., Park, D., & Lee, Y. C. (2022). Recent Insights into Particulate Matter (PM2.5)-Mediated Toxicity in Humans: An Overview. International Journal of Environmental Research and Public Health, 19(12), 7511. https://doi.org/10.3390/ijerph19127511
Ufe, M. M., Iorliam, A. Y., & Odeh, O. B. (2023). Validation of Rayleigh-Ritz Deflection Equation for a Tapered Cantilever Beam using ANSYS Finite Element Software. FUOYE Journal of Engineering and Technology, 8(1). https://doi.org/10.46792/fuoyejet.v8i1.970
Xu, X., Shi, K., Huang, Z., & Shen, J. (2023). What Factors Dominate the Change of PM2.5 in the World from 2000 to 2019? A Study from Multi-Source Data. International Journal of Environmental Research and Public Health, 20(3), 2282. https://doi.org/10.3390/ijerph20032282
Yan, G., Yu, Z., Wu, Y., Liu, J., Wang, Y., Zhai, J., Cong, L., & Zhang, Z. (2020). Understanding PM2.5 concentration and removal efficiency variation in urban forest park-Observation at human breathing height. PeerJ, 2020(3). https://doi.org/10.7717/peerj.8988
Yan, R., Ma, D., Liu, Y., Wang, R., Fan, L., Yan, Q., Chen, C., Wang, W., Ren, Z., Ku, T., Ning, X., & Sang, N. (2024). Developmental Toxicity of Fine Particulate Matter: Multifaceted Exploration from Epidemiological and Laboratory Perspectives. Toxics, 12(4), 274. https://doi.org/10.3390/toxics12040274
Yang, L., Li, C., & Tang, X. (2020). The Impact of PM2.5 on the Host Defense of Respiratory System. Frontiers in Cell and Developmental Biology, 8, 91. https://doi.org/10.3389/fcell.2020.00091
Zakaria, N. A., Abidin, Z.Z., Harum, N., Hau, L.C., Ali, N.B., & Jafar, F. A. (2018). Wireless Internet of Things-based Air Quality Device for Smart Pollution Monitoring. International Journal of Advanced Computer Science and Applications, 9(11). http://dx.doi.org/10.14569/IJACSA.2018.091110
DOI: https://doi.org/10.22146/ijg.104159
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