Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters
<p dir="ltr">In this paper, we propose a machine learning system for the estimation of atmospheric particulate matter (PM) concentration, specifically, particles with a maximum diameter of 2.5μm . These very fine particles, also known as PM<sub>2.5</sub> particles, are ve...
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2022
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| author | Maen Takruri (10873368) |
| author2 | Abubakar Abubakar (18278998) Abdul-Halim Jallad (19521913) Basel Altawil (19521916) Prashanth R. Marpu (19521919) Amine Bermak (1895947) |
| author2_role | author author author author author |
| author_facet | Maen Takruri (10873368) Abubakar Abubakar (18278998) Abdul-Halim Jallad (19521913) Basel Altawil (19521916) Prashanth R. Marpu (19521919) Amine Bermak (1895947) |
| author_role | author |
| dc.creator.none.fl_str_mv | Maen Takruri (10873368) Abubakar Abubakar (18278998) Abdul-Halim Jallad (19521913) Basel Altawil (19521916) Prashanth R. Marpu (19521919) Amine Bermak (1895947) |
| dc.date.none.fl_str_mv | 2022-02-15T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3151632 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_Learning-Based_Estimation_of_PM_sub_2_5_sub_Concentration_Using_Ground_Surface_DoFP_Polarimeters/26893720 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Environmental engineering Information and computing sciences Machine learning Division of focal plane environmental monitoring machine learning polarization image Aerosols Machine learning Wavelength measurement Atmospheric measurements Particle measurements Optical variables measurement Polarimetry |
| dc.title.none.fl_str_mv | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In this paper, we propose a machine learning system for the estimation of atmospheric particulate matter (PM) concentration, specifically, particles with a maximum diameter of 2.5μm . These very fine particles, also known as PM<sub>2.5</sub> particles, are very dangerous to the human body as they are small enough to penetrate deep areas of the vital organs. The proposed system uses a combination of features from both polarimetric and spectral imaging modalities in training and developing a machine learning model that provides high accuracy PM<sub>2.5</sub> estimates. Furthermore, acquisition of the polarimetric images is done near the ground surface with a horizontal field of view aiming at standard targets which enables higher accuracy at the surface level. The accuracy of the approach was verified through a study conducted during the summer months of the United Arab Emirates (UAE). The proposed system employs different machine learning techniques such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Bagging Ensemble Trees (BET), to provide high accuracy PM<sub>2.5</sub> estimates. Our proposed system achieves the best performance within the red wavelength with accuracy up to 93.8627% and an R<sup>2</sup> score up to 0.9420.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3151632" target="_blank">https://dx.doi.org/10.1109/access.2022.3151632</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a0563823532c9ebd1adb9efa318d229e |
| identifier_str_mv | 10.1109/access.2022.3151632 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26893720 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP PolarimetersMaen Takruri (10873368)Abubakar Abubakar (18278998)Abdul-Halim Jallad (19521913)Basel Altawil (19521916)Prashanth R. Marpu (19521919)Amine Bermak (1895947)EngineeringEnvironmental engineeringInformation and computing sciencesMachine learningDivision of focal planeenvironmental monitoringmachine learningpolarization imageAerosolsMachine learningWavelength measurementAtmospheric measurementsParticle measurementsOptical variables measurementPolarimetry<p dir="ltr">In this paper, we propose a machine learning system for the estimation of atmospheric particulate matter (PM) concentration, specifically, particles with a maximum diameter of 2.5μm . These very fine particles, also known as PM<sub>2.5</sub> particles, are very dangerous to the human body as they are small enough to penetrate deep areas of the vital organs. The proposed system uses a combination of features from both polarimetric and spectral imaging modalities in training and developing a machine learning model that provides high accuracy PM<sub>2.5</sub> estimates. Furthermore, acquisition of the polarimetric images is done near the ground surface with a horizontal field of view aiming at standard targets which enables higher accuracy at the surface level. The accuracy of the approach was verified through a study conducted during the summer months of the United Arab Emirates (UAE). The proposed system employs different machine learning techniques such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Bagging Ensemble Trees (BET), to provide high accuracy PM<sub>2.5</sub> estimates. Our proposed system achieves the best performance within the red wavelength with accuracy up to 93.8627% and an R<sup>2</sup> score up to 0.9420.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3151632" target="_blank">https://dx.doi.org/10.1109/access.2022.3151632</a></p>2022-02-15T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3151632https://figshare.com/articles/journal_contribution/Machine_Learning-Based_Estimation_of_PM_sub_2_5_sub_Concentration_Using_Ground_Surface_DoFP_Polarimeters/26893720CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268937202022-02-15T12:00:00Z |
| spellingShingle | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters Maen Takruri (10873368) Engineering Environmental engineering Information and computing sciences Machine learning Division of focal plane environmental monitoring machine learning polarization image Aerosols Machine learning Wavelength measurement Atmospheric measurements Particle measurements Optical variables measurement Polarimetry |
| status_str | publishedVersion |
| title | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters |
| title_full | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters |
| title_fullStr | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters |
| title_full_unstemmed | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters |
| title_short | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters |
| title_sort | Machine Learning-Based Estimation of PM<sub>2.5</sub> Concentration Using Ground Surface DoFP Polarimeters |
| topic | Engineering Environmental engineering Information and computing sciences Machine learning Division of focal plane environmental monitoring machine learning polarization image Aerosols Machine learning Wavelength measurement Atmospheric measurements Particle measurements Optical variables measurement Polarimetry |