Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors
- PMID: 38894441
- PMCID: PMC11175279
- DOI: 10.3390/s24113650
Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors
Abstract
The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.
Keywords: air quality; calibrations; low-cost sensors; water quality.
Conflict of interest statement
The authors declare no conflict of interest.
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References
-
- Lehtonen A., Salonen A.O., Cantell H. Sustainability, Human Well-Being, and the Future of Education. Springer; Cham, Switzerland: 2018. Climate change education: A new approach for a world of wicked problems. - DOI
-
- Tran H.M., Tsai F.J., Lee Y.L., Chang J.H., Chang L.T., Chang T.Y., Chung K.F., Kuo H.P., Lee K.Y., Chuang K.J., et al. The impact of air pollution on respiratory diseases in an era of climate change: A review of the current evidence. Sci. Total Environ. 2023;898:166340. doi: 10.1016/j.scitotenv.2023.166340. - DOI - PubMed
-
- Ameen R.F.M., Mourshed M. Urban environmental challenges in developing countries—A stakeholder perspective. Habitat Int. 2017;64:1–10. doi: 10.1016/j.habitatint.2017.04.002. - DOI
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