Big Data Processing Strategies for Low-cost Monitoring of Airborne Particulate Matter in Mine Sites
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Monitoring particulate matter (PM) in mine sites is challenging owing to the intermittent PM emission and dispersion over widespread areas at these locations. In these circumstances, multiple monitoring stations, including PM sensors, should be installed to monitor PM distributions throughout the site. However, this may result in high operation costs. Monitoring based on low-cost PM sensors is advantageous with respect to cost, though its poor data quality is problematic. Yet, big data processing techniques can be utilized to remedy this drawback. This study proposed a strategic solution to enhance the data reliability of low-cost PM monitoring through big data processing. The significance of an effective calibration was emphasized to overcome the poor quality of low-cost PM sensor data. In particular, this study focused on the usefulness of blind calibration for adjustment of background PM levels and the traditional linear regression-based calibration through direct comparison between low-cost and reference sensor data. The applicability of an ANN (Artificial Neural Network) algorithm was also considered for automatic decision making and implementation of the whole calibration, as well as for global optimization in nonlinear regression coefficients with a high complexity.
Abstract
Monitoring particulate matter (PM) in mine sites is challenging owing to the intermittent PM emission and dispersion over widespread areas at these locations. In these circumstances, multiple monitoring stations, including PM sensors, should be installed to monitor PM distributions throughout the site. However, this may result in high operation costs. Monitoring based on low-cost PM sensors is advantageous with respect to cost, though its poor data quality is problematic. Yet, big data processing techniques can be utilized to remedy this drawback. This study proposed a strategic solution to enhance the data reliability of low-cost PM monitoring through big data processing. The significance of an effective calibration was emphasized to overcome the poor quality of low-cost PM sensor data. In particular, this study focused on the usefulness of blind calibration for adjustment of background PM levels and the traditional linear regression-based calibration through direct comparison between low-cost and reference sensor data. The applicability of an ANN (Artificial Neural Network) algorithm was also considered for automatic decision making and implementation of the whole calibration, as well as for global optimization in nonlinear regression coefficients with a high complexity.
참고문헌 (20)
- Sairanen M, "A review of dust emission dispersions in rock aggregate and natural stone quarries", Int J Min Reclam, 32, pp.196~220 (2018)
- 이상훈, "광산배출 미세먼지의 특성 및 모니터링 방안", 한국자원공학회지, 57(2), pp.234~242 (2020), 한국자원공학회
- 이상훈, "Strategy for Modeling Airborne Particulate Matter from Mines", Quantitative Bio-Science, 39(1), pp.41~46 (2020), 자연과학연구소[DOI]
- Petavratzi E, "Assessment of the dustiness and the dust liberation mechanisms of limestone quarry operations", Chem Eng Process, 46, pp.1412~1423 (2007)
- 윤진호, "국내 노천 석회석 광산먼지 확산 모니터링 방안", 자원환경지질, 54(4), pp.475~482 (2021), 대한자원환경지질학회
- Liang L, "Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges", Environ Res, 197, pp.1~13 (2021)
- Badura M, "Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements", SN Appl Sci, 1, pp.1~11 (2019)
- Maag B, "A survey on sensor calibration in air pollution monitoring deployments", IEEE Internet Things J, 5, pp.4857~4870 (2018)
- Topalovic DB, "In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants : Comparison of linear, multilinear and artificial neural network approaches", Atmos Environ, 213, pp.640~658 (2019)
- "1912", ~
- Williams DE, "Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network", Meas Sci Technol, 24, pp.1~13 (2013)
- Feinberg SN, "Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a net"work of low-cost air pollution sensors", Atmos Environ, 213, pp.579~584 (2019)
- Liu HY, "Performance assessment of a low-cost PM2.5 sensor for a near four-month period in Oslo, Norway", Atmosphere, 10, pp.1~18 (2019)
- Castell N, "Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates", Environ Int, 99, pp.293~302 (2017)
- De Vito S, "Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices", Pattern Recogn Lett, 136, pp.264~271 (2020)
- Kohonen T, "An introduction to neural computing", Neural Network, 1, pp.3~16 (1988)
- Gevrey M, "Review and comparison of methods to study the contribution of variables in artificial neural network models", Ecol Model, 160, pp.249~264 (2003)
- Nwamaka U. Okafor, "Improving Data Quality of Low-cost IoT Sensors in Environmental Monitoring Networks Using Data Fusion and Machine Learning Approach", ICT Express, 6(3), pp.220~228 (2020), 한국통신학회
- Yamamoto K, "Machine learning-based calibration of low-cost air temperature sensors using environmental data", Sensors, 17, pp.1~16 (2017)
- Mahajan S, "Evaluation of low-cost sensors for quan"titative personal exposure monitoring", Sustain Cities Soc, 57, pp.1~13 (2020)