When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for...

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Autores principales:	Yingying Mei, Jiayi Li, Deping Xiang, Jingxiong Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ground-level ozone national scale China spatiotemporal distribution hybrid model Science Q
Acceso en línea:	https://doaj.org/article/bf944db98e42405aa6cc051b8ce50af4
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id	oai:doaj.org-article:bf944db98e42405aa6cc051b8ce50af4
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spelling	oai:doaj.org-article:bf944db98e42405aa6cc051b8ce50af42021-11-11T18:53:54ZWhen a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method10.3390/rs132143242072-4292https://doaj.org/article/bf944db98e42405aa6cc051b8ce50af42021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4324https://doaj.org/toc/2072-4292In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (<i>R</i><sup>2</sup>) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.Yingying MeiJiayi LiDeping XiangJingxiong ZhangMDPI AGarticleground-level ozonenational scaleChinaspatiotemporal distributionhybrid modelScienceQENRemote Sensing, Vol 13, Iss 4324, p 4324 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ground-level ozone national scale China spatiotemporal distribution hybrid model Science Q
spellingShingle	ground-level ozone national scale China spatiotemporal distribution hybrid model Science Q Yingying Mei Jiayi Li Deping Xiang Jingxiong Zhang When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method
description	In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (<i>R</i><sup>2</sup>) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.
format	article
author	Yingying Mei Jiayi Li Deping Xiang Jingxiong Zhang
author_facet	Yingying Mei Jiayi Li Deping Xiang Jingxiong Zhang
author_sort	Yingying Mei
title	When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method
title_short	When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method
title_full	When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method
title_fullStr	When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method
title_full_unstemmed	When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method
title_sort	when a generalized linear model meets bayesian maximum entropy: a novel spatiotemporal ground-level ozone concentration retrieval method
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bf944db98e42405aa6cc051b8ce50af4
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When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method

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