Field Calibration of Low-Cost Particulate Matter Sensors Using Artificial Neural Networks and Affine Response Correction - Publikacja - MOST Wiedzy

Twoje konto

zaloguj

Wyszukiwarka

Field Calibration of Low-Cost Particulate Matter Sensors Using Artificial Neural Networks and Affine Response Correction

Abstrakt

Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM1 and PM2.5, and 0.76 PM10 (particles categorized as having diameter equal to or less than 1m, 2.5m, and 10m, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 µg/m³.

Cytowania

  • 2

    CrossRef

  • 0

    Web of Science

  • 3

    Scopus

Autorzy (4)

Cytuj jako

Pełna treść

pobierz publikację
pobrano 40 razy
Wersja publikacji
Accepted albo Published Version
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.measurement.2024.114529
Licencja
Creative Commons: CC-BY-NC otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
MEASUREMENT nr 230, strony 1 - 16,
ISSN: 0263-2241
Język:
angielski
Rok wydania:
2024
Opis bibliograficzny:
Kozieł S., Pietrenko-Dąbrowska A., Wójcikowski M., Pankiewicz B.: Field Calibration of Low-Cost Particulate Matter Sensors Using Artificial Neural Networks and Affine Response Correction// MEASUREMENT -Vol. 230, (2024), s.1-16
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.measurement.2024.114529
Źródła finansowania:
  • Publikacja bezkosztowa
Weryfikacja:
Politechnika Gdańska

wyświetlono 64 razy

Publikacje, które mogą cię zainteresować

Accurate Lightweight Calibration Methods for Mobile Low-Cost Particulate Matter Sensors

2023

Statistical Data Pre-Processing and Time Series Incorporation for High-Efficacy Calibration of Low-Cost NO2 Sensor Using Machine Learning

2024

Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment

2024

Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing

2024
Meta Tagi