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Original Articles

Evaluation and calibration of low-cost particulate matter sensors for respirable coal mine dust monitoring

, ORCID Icon, , , &
Pages 158-169 | Received 10 May 2023, Accepted 15 Nov 2023, Published online: 12 Dec 2023

References

  • Amoah, N. A., G. Xu, A. R. Kumar, and Y. Wang. 2023. Calibration of low-cost particulate matter sensors for coal dust monitoring. Sci. Total Environ. 859:160336. doi: 10.1016/j.scitotenv.2022.160336.
  • Amoah, N. A., G. Xu, Y. Wang, J. Li, Y. Zou, and B. Nie. 2022. Application of low-cost particulate matter sensors for air quality monitoring and exposure assessment in underground mines: A review. Int. J. Miner. Metall. Mater. 29 (8):1475–90. doi: 10.1007/s12613-021-2378-z.
  • Anlimah, F., V. Gopaldasani, C. MacPhail, and B. Davies. 2023. A systematic review of the effectiveness of dust control measures adopted to reduce workplace exposure. Environ. Sci. Pollut. Res. Int. 30 (19):54407–28. doi: 10.1007/s11356-023-26321-w.
  • Badura, M., P. Batog, A. Drzeniecka-Osiadacz, and P. Modzel. 2018. Evaluation of low-cost sensors for ambient PM2.5 monitoring. J. Sens. 2018:1–16. doi: 10.1155/2018/5096540.
  • Bai, L., L. Huang, Z. Wang, Q. Ying, J. Zheng, X. Shi, and J. Hu. 2020. Long-term field evaluation of low-cost particulate matter sensors in Nanjing. Aerosol Air Qual. Res. 20 (2):242–53. doi: 10.4209/aaqr.2018.11.0424.
  • Calvert, G. M., M. Moore, and S. M. Hessl. 1991. Ventilatory function after exposure to various respirable hazards in a population of former coal miners. Br. J. Ind. Med. 48 (1):38–40. doi: 10.1136/oem.48.1.38.
  • Chao, C.-Y., H. Zhang, M. Hammer, Y. Zhan, D. Kenney, R. V. Martin, and P. Biswas. 2021. Integrating fixed monitoring systems with low-cost sensors to create high-resolution air quality maps for the northern China plain region. ACS Earth Space Chem. 5 (11):3022–35. doi: 10.1021/acsearthspacechem.1c00174.
  • Chase, O. A., M. B. Teles, M. d J. dos Santos Rodrigues, J. F. Souza de Almeida, W. N. Macedo, and C. T. da Costa Junior. 2018. A low-cost, stand-alone sensory platform for monitoring extreme solar overirradiance events. Sensors 18 (8):2685. doi: 10.3390/s18082685.
  • Chen, C., N. Wang, and M. Chen. 2021. Prediction model of end-point phosphorus content in consteel electric furnace based on PCA-extra tree model. ISIJ Int. 61 (6):1908–14. doi: 10.2355/isijinternational.ISIJINT-2020-615.
  • Chen, T., and C. Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. Paper presented at the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA.
  • Cheng, S., L. Wu, S. Zhang, D. Zhang, F. Liu, H. Wang, and P. Xie. 2023. A model for lumbar EMG signal recognition based on stacking integration learning. IEEE Sensors J. 23 (4):3766–75. doi: 10.1109/JSEN.2022.3229363.
  • Di Antonio, A., O. A. M. Popoola, B. Ouyang, J. Saffell, and R. L. Jones. 2018. Developing a relative humidity correction for low-cost sensors measuring ambient particulate matter. Sensors 18 (9):2790. doi: 10.3390/s18092790.
  • Duvall, R., A. Clements, G. Hagler, A. Kamal, V. Kilaru, L. Goodman, S. Frederick, K. Barkjohn, I. VonWald, and D. Greene. 2021. Performance testing protocols, metrics, and target values for fine particulate matter air sensors: Use in ambient, outdoor, fixed sites, non-regulatory supplemental and informational monitoring applications. Washington, DC: US EPA Office of Research and Development.
  • Geurts, P., D. Ernst, and L. Wehenkel. 2006. Extremely randomized trees. Mach. Learn. 63 (1):3–42. doi: 10.1007/s10994-006-6226-1.
  • Go, L. H. T., S. D. Krefft, R. A. Cohen, and C. S. Rose. 2016. Lung disease and coal mining: What pulmonologists need to know. Curr. Opin. Pulm. Med. 22 (2):170–8. doi: 10.1097/MCP.0000000000000251.
  • Halterman, A., S. Sousan, and T. M. Peters. 2018. Comparison of respirable mass concentrations measured by a personal dust monitor and a personal dataRAM to gravimetric measurements. Ann. Work Expo. Health 62 (1):62–71. doi: 10.1093/annweh/wxx083.
  • He, R., T. Han, D. Bachman, D. J. Carluccio, R. Jaeger, J. Zhang, S. Thirumurugesan, C. Andrews, and G. Mainelis. 2020. Evaluation of two low-cost PM monitors under different laboratory and indoor conditions. Aerosol Sci. Technol. 55 (3):316–31. doi: 10.1080/02786826.2020.1851649.
  • Hegde, S., K. T. Min, J. Moore, P. Lundrigan, N. Patwari, S. Collingwood, A. Balch, and K. E. Kelly. 2020. Indoor household particulate matter measurements using a network of low-cost sensors. Aerosol Air Qual. Res. 20 (2):381–94. doi: 10.4209/aaqr.2019.01.0046.
  • Jayaratne, R., X. Liu, K.-H. Ahn, A. Asumadu-Sakyi, G. Fisher, J. Gao, A. Mabon, M. Mazaheri, B. Mullins, M. Nyaku, et al. 2020. Low-cost PM2.5 sensors: An assessment of their suitability for various applications. Aerosol Air Qual. Res. 20 (3):520–32. doi: 10.4209/aaqr.2018.10.0390.
  • 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.
  • Knight, D., R. Ehrlich, A. Cois, K. Fielding, A. D. Grant, and G. Churchyard. 2020. Predictors of silicosis and variation in prevalence across mines among employed gold miners in South Africa. BMC Public Health. 20 (1):829. doi: 10.1186/s12889-020-08876-2.
  • Kuempel, E. D., L. T. Stayner, M. D. Attfield, and C. R. Buncher. 1995. Exposure-response analysis of mortality among coal miners in the United States. Am. J. Ind. Med. 28 (2):167–84. doi: 10.1002/ajim.4700280203.
  • Kumar, V., and M. Sahu. 2021. Evaluation of nine machine learning regression algorithms for calibration of low-cost PM2.5 sensor. J. Aerosol Sci. 157:105809. doi: 10.1016/j.jaerosci.2021.105809.
  • Laney, A. S., and D. N. Weissman. 2014. Respiratory diseases caused by coal mine dust. J. Occup. Environ. Med. 56 (Suppl. 10):S18–S22. doi: 10.1097/JOM.0000000000000260.
  • Liang, R. Y., C. Q. Dong, L. Yuan, B. Y. Jiang, D. M. Wang, and W. H. Chen. 2022. Progress in the epidemiological studies on coal mine dust exposure with workers’ health damage." Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi =. Chin. J. Ind. Hyg. Occup. Dis. 40 (6):476–80.
  • Liu, D., Q. Zhang, J. Jiang, and D.-R. Chen. 2017. Performance calibration of low-cost and portable particular matter (PM) sensors. J. Aerosol Sci. 112:1–10. doi: 10.1016/j.jaerosci.2017.05.011.
  • Malings, C., R. Tanzer, A. Hauryliuk, P. K. Saha, A. L. Robinson, A. A. Presto, and R. Subramanian. 2020. Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation. Aerosol Sci. Technol. 54 (2):160–74. doi: 10.1080/02786826.2019.1623863.
  • Manikonda, A., N. Zikova, P. K. Hopke, and A. R. Ferro. 2016. Laboratory assessment of low-cost PM monitors. J. Aerosol Sci. 102:29–40. doi: 10.1016/j.jaerosci.2016.08.010.
  • Massaoudi, M., S. S. Refaat, I. Chihi, M. Trabelsi, F. S. Oueslati, and H. Abu-Rub. 2021. A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for short-term load forecasting. Energy 214:118874. doi: 10.1016/j.energy.2020.118874.
  • Migos, T., I. Christakis, K. Moutzouris, and I. Stavrakas. 2019. On the evaluation of low-cost PM sensors for air quality estimation. Paper presented at the 8th International Conference on Modern Circuits and Systems Technologies (MOCAST), Aristotle Univ Res Disseminat Ctr, Thessaloniki, Greece.
  • Mine Safety and Health Administration (MSHA). 2014. Final rule: Lowering miners’ exposure to respirable coal mine dust, including continuous personal dust monitors. Fed. Regist. 79 (84):24814–994.
  • Mine Safety and Health Administration (MSHA). 2016. Major provisions and effective dates MSHA’s final rule to lower miners’ exposure to respirable coal mine dust.
  • Reid, S., and G. Grudic. 2009. Regularized linear models in stacked generalization. 8th International Workshop on Multiple Classifier Systems, Univ Iceland, Reykjavik, ICELAND. Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Ren, Y., and Y. Qin. 2013. Research on health effects and governance of coal dust. Paper presented at the International Conference on Advances in Energy and Environmental Science (ICAEES), Guangzhou, Peoples Republic of China.
  • Ruiter, S., D. Bard, H. Ben Jeddi, J. Saunders, J. Snawder, N. Warren, J.-P. Gorce, E. Cauda, E. Kuijpers, and A. Pronk. 2023. Exposure monitoring strategies for applying low-cost PM sensors to assess flour dust in industrial bakeries. Ann. Work Expo. Health 67 (3):379–91. doi: 10.1093/annweh/wxac088.
  • Sayahi, T., D. Kaufman, T. Becnel, K. Kaur, A. E. Butterfield, S. Collingwood, Y. Zhang, P. E. Gaillardon, and K. E. Kelly. 2019. Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors. Environ. Pollut. 255:113131. doi: 10.1016/j.envpol.2019.113131.
  • Sharma, S. R., B. Singh, and M. Kaur. 2022. A novel approach of ensemble methods using the stacked generalization for high-dimensional datasets. IETE J. Res. :1–16. doi: 10.1080/03772063.2022.2028582.
  • Si, M., Y. Xiong, S. Du, and K. Du. 2020. Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods. Atmos. Meas. Tech. 13 (4):1693–707. doi: 10.5194/amt-13-1693-2020.
  • Volkwein, J. C., R. P. Vinson, S. J. Page, L. J. McWilliams, G. J. Joy, S. E. Mischler, and D. P. Tuchman. 2006. Laboratory and field performance of a continuously measuring personal respirable dust monitor.
  • Wang, S.-Q., J. Yang, and K.-C. Chou. 2006. Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition. J. Theor. Biol. 242 (4):941–6. doi: 10.1016/j.jtbi.2006.05.006.
  • Wang, Y., Y. Du, J. Wang, and T. Li. 2019. Calibration of a low-cost PM2.5 monitor using a random forest model. Environ. Int. 133 (Pt A):105161. doi: 10.1016/j.envint.2019.105161.
  • Wang, Y., J. Li, H. Jing, Q. Zhang, J. Jiang, and P. Biswas. 2015. Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement. Aerosol Sci. Technol. 49 (11):1063–77. doi: 10.1080/02786826.2015.1100710.
  • Wu, Q., L. Han, M. Xu, H. Zhang, B. Ding, and B. Zhu. 2019. Effects of occupational exposure to dust on chest radiograph, pulmonary function, blood pressure and electrocardiogram among coal miners in an eastern province, China. BMC Public Health 19 (1):1229. doi: 10.1186/s12889-019-7568-5.
  • Xie, R., C.-M. Vong, C. L. P. Chen, and S. Wang. 2022. Dynamic network structure: doubly stacking broad learning systems with residuals and simpler linear model transmission. IEEE Trans. Emerg. Top. Comput. Intell. 6 (6):1378–95. doi: 10.1109/TETCI.2022.3146983.
  • Zhao, G., Z. Shen, C. Miao, and R. Gay. 2008. Enhanced extreme learning machine with stacked generalization. Paper presented at the International Joint Conference on Neural Networks, Hong Kong, Peoples Republic of China.
  • Zheng, T., M. H. Bergin, K. K. Johnson, S. N. Tripathi, S. Shirodkar, M. S. Landis, R. Sutaria, and D. E. Carlson. 2018. Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments. Atmos. Meas. Tech. 11 (8):4823–46. doi: 10.5194/amt-11-4823-2018.

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