Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms

The vertical distribution of aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter <2.5 µm</span&...

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Autores principales:	Y. Ma, Y. Zhu, B. Liu, H. Li, S. Jin, Y. Zhang, R. Fan, W. Gong
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a0df4129578d4bec8fe1a53740265387
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spelling	oai:doaj.org-article:a0df4129578d4bec8fe1a537402653872021-11-24T07:37:10ZEstimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms10.5194/acp-21-17003-20211680-73161680-7324https://doaj.org/article/a0df4129578d4bec8fe1a537402653872021-11-01T00:00:00Zhttps://acp.copernicus.org/articles/21/17003/2021/acp-21-17003-2021.pdfhttps://doaj.org/toc/1680-7316https://doaj.org/toc/1680-7324<p>The vertical distribution of aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter <span class="inline-formula"><2.5</span> <span class="inline-formula">µm</span> (PM<span class="inline-formula"><sub>2.5</sub></span>). However, the traditional linear model (LM) cannot consider the influence of multiple meteorological variables sufficiently and then induce the low inversion accuracy. Generally, the machine learning (ML) algorithms can input multiple features which may provide us with a new way to solve this constraint. In this study, the surface aerosol EC and meteorological data from January 2014 to December 2017 were used to explore the conversion of aerosol EC to PM<span class="inline-formula"><sub>2.5</sub></span> concentrations. Four ML algorithms were used to train the PM<span class="inline-formula"><sub>2.5</sub></span> prediction models: random forest (RF), <span class="inline-formula"><i>K</i></span>-nearest neighbor (KNN), support vector machine (SVM) and extreme gradient boosting decision tree (XGB). The mean absolute error (root mean square error) of LM, RF, KNN, SVM and XGB models were 11.66 (15.68), 5.35 (7.96), 7.95 (11.54), 6.96 (11.18) and 5.62 (8.27) <span class="inline-formula">µg</span>/m<span class="inline-formula"><sup>3</sup></span>, respectively. This result shows that the RF model is the most suitable model for PM<span class="inline-formula"><sub>2.5</sub></span> inversions from EC and meteorological data. Moreover, the sensitivity analysis of model input parameters was also conducted. All these results further indicated that it is necessary to consider the effect of meteorological variables when using EC to retrieve PM<span class="inline-formula"><sub>2.5</sub></span> concentrations. Finally, the diurnal and seasonal variations of transport flux (TF) and PM<span class="inline-formula"><sub>2.5</sub></span> profiles were analyzed based on the lidar data. The large PM<span class="inline-formula"><sub>2.5</sub></span> concentration occurred at approximately 13:00–17:00 local time (LT) in 0.2–0.8 km. The diurnal variations of the TF show a clear conveyor belt at approximately 12:00–18:00 LT in 0.5–0.8 km. The results indicated that air pollutant transport over Wuhan mainly occurs at approximately 12:00–18:00 LT in 0.5–0.8 km. The TF near the ground usually has the highest value in winter (0.26 mg/m<span class="inline-formula"><sup>2</sup></span> s), followed by the autumn and summer (0.2 and 0.19 mg/m<span class="inline-formula"><sup>2</sup></span> s, respectively), and the lowest value in spring (0.14 mg/m<span class="inline-formula"><sup>2</sup></span> s). These findings give us important information on the atmospheric profile and provide us sufficient confidence to apply lidar in the study of air quality monitoring.</p>Y. MaY. ZhuB. LiuH. LiS. JinY. ZhangR. FanW. GongCopernicus PublicationsarticlePhysicsQC1-999ChemistryQD1-999ENAtmospheric Chemistry and Physics, Vol 21, Pp 17003-17016 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Chemistry QD1-999
spellingShingle	Physics QC1-999 Chemistry QD1-999 Y. Ma Y. Zhu B. Liu H. Li S. Jin Y. Zhang R. Fan W. Gong Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms
description	<p>The vertical distribution of aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter <span class="inline-formula"><2.5</span> <span class="inline-formula">µm</span> (PM<span class="inline-formula"><sub>2.5</sub></span>). However, the traditional linear model (LM) cannot consider the influence of multiple meteorological variables sufficiently and then induce the low inversion accuracy. Generally, the machine learning (ML) algorithms can input multiple features which may provide us with a new way to solve this constraint. In this study, the surface aerosol EC and meteorological data from January 2014 to December 2017 were used to explore the conversion of aerosol EC to PM<span class="inline-formula"><sub>2.5</sub></span> concentrations. Four ML algorithms were used to train the PM<span class="inline-formula"><sub>2.5</sub></span> prediction models: random forest (RF), <span class="inline-formula"><i>K</i></span>-nearest neighbor (KNN), support vector machine (SVM) and extreme gradient boosting decision tree (XGB). The mean absolute error (root mean square error) of LM, RF, KNN, SVM and XGB models were 11.66 (15.68), 5.35 (7.96), 7.95 (11.54), 6.96 (11.18) and 5.62 (8.27) <span class="inline-formula">µg</span>/m<span class="inline-formula"><sup>3</sup></span>, respectively. This result shows that the RF model is the most suitable model for PM<span class="inline-formula"><sub>2.5</sub></span> inversions from EC and meteorological data. Moreover, the sensitivity analysis of model input parameters was also conducted. All these results further indicated that it is necessary to consider the effect of meteorological variables when using EC to retrieve PM<span class="inline-formula"><sub>2.5</sub></span> concentrations. Finally, the diurnal and seasonal variations of transport flux (TF) and PM<span class="inline-formula"><sub>2.5</sub></span> profiles were analyzed based on the lidar data. The large PM<span class="inline-formula"><sub>2.5</sub></span> concentration occurred at approximately 13:00–17:00 local time (LT) in 0.2–0.8 km. The diurnal variations of the TF show a clear conveyor belt at approximately 12:00–18:00 LT in 0.5–0.8 km. The results indicated that air pollutant transport over Wuhan mainly occurs at approximately 12:00–18:00 LT in 0.5–0.8 km. The TF near the ground usually has the highest value in winter (0.26 mg/m<span class="inline-formula"><sup>2</sup></span> s), followed by the autumn and summer (0.2 and 0.19 mg/m<span class="inline-formula"><sup>2</sup></span> s, respectively), and the lowest value in spring (0.14 mg/m<span class="inline-formula"><sup>2</sup></span> s). These findings give us important information on the atmospheric profile and provide us sufficient confidence to apply lidar in the study of air quality monitoring.</p>
format	article
author	Y. Ma Y. Zhu B. Liu H. Li S. Jin Y. Zhang R. Fan W. Gong
author_facet	Y. Ma Y. Zhu B. Liu H. Li S. Jin Y. Zhang R. Fan W. Gong
author_sort	Y. Ma
title	Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms
title_short	Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms
title_full	Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms
title_fullStr	Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms
title_full_unstemmed	Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms
title_sort	estimation of the vertical distribution of particle matter (pm<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/a0df4129578d4bec8fe1a53740265387
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Estimation of the vertical distribution of particle matter (PM<sub>2.5</sub>) concentration and its transport flux from lidar measurements based on machine learning algorithms

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