Journal ArticleDOI
Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP
Reads0
Chats0
TLDR
In this article, a light gradient boosting machine is employed to train the estimation models to estimate the daily near surface concentration (NSC) of O3, CO, and NO2 at a high spatial resolution.Abstract:
The Near Surface Concentrations (NSC) of O3, CO, and NO2 are crucial worldwide indicators of air quality However, current frameworks devised for the estimation of the NSC of O3, CO, and NO2 have defects, such as coarse spatial resolution and large missing coverage To address this issue, this study aims to estimate the daily (~13:30 local time) full-coverage NSC of O3, CO, and NO2 at a high spatial resolution (005° for O3 and NO2; 007° for CO) over China by using datasets from S5P-TROPOMI and GEOS-FP In specific, the light gradient boosting machine is employed to train the estimation models Validation results show that the NSC of O3, CO, and NO2 are well estimated, with the R2s of 091, 071, and 083 for the sample-based cross validation, respectively Meanwhile, the proposed framework achieves a satisfactory performance in comparison to the latest related works, as reflected by the estimation accuracy and spatial resolution As for the mapping, the estimated results show coherent spatial distribution and can accurately grasp the seasonal characteristics of each air pollutant Finally, the estimated results are utilized to analyze the temporal variations of O3, CO, and NO2 during the COrona VIrus Disease 2019 (COVID-19) lockdown in China, which is an extend application for adopting the proposed framework in air quality monitoring Results show that the estimated NSC of O3, CO, and NO2 in 2020 present significant variations during different periods of the COVID-19 lockdown in China compared to last year In addition, the variations in the NSC of O3, CO, and NO2 during the COVID-19 lockdown in China possibly result from restrictions in the anthropogenic activitiesread more
Citations
More filters
Significant Increase of Summertime Ozone at Mount Tai in Central Eastern China
Lei Sun,Likun Xue,Tao Wang,J. Gao,Aijun Ding,Owen R. Cooper,Meiyun Lin,L. Yang,Jianmin Chen,Wenxing Wang +9 more
TL;DR: Wang et al. as mentioned in this paper analyzed the decadal change of Tropospheric ozone (O3) and its sources and found that ozone levels in China increased significantly due to the increased emissions of O3 precursors, in particular volatile organic compounds.
Journal ArticleDOI
Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence
Jing Wei,Song Liu,Zhanqing Li,Cheng Liu,Kai Qin,Xiong Li,Rachel T. Pinker,Russell R. Dickerson,Jintai Lin,K. Folkert Boersma,Lin Sun,Runze Li,Wenhao Xue,Yuanzhen Cui,Chengxin Zhang,Jun Wang +15 more
TL;DR: Wang et al. as discussed by the authors developed a novel artificial intelligence approach by integrating spatio-temporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface No2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations.
Journal ArticleDOI
Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia.
TL;DR: In this article, the authors developed models for estimating high spatial resolution surface concentrations of NO2 and O3 from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia.
Journal ArticleDOI
Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer
TL;DR: Zhou et al. as mentioned in this paper proposed a feature interpolation module that deeply couples optical flow and multi-scale deformable convolution to predict unknown frames to enhance the spatial and temporal resolution of satellite video.
Journal ArticleDOI
Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations
TL;DR: Wang et al. as discussed by the authors took advantage of big data and artificial-intelligence technologies to generate seamless daily maps of three major ambient pollutants (NO2, SO2, and CO) across China from 2013 to 2020 at a uniform spatial resolution of 10 km.
References
More filters
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Journal ArticleDOI
A note on a general definition of the coefficient of determination
TL;DR: In this article, a generalization of the coefficient of determination R2 to general regression models is discussed, and a modification of an earlier definition to allow for discrete models is proposed.
Proceedings Article
LightGBM: a highly efficient gradient boosting decision tree
TL;DR: It is proved that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size, and is called LightGBM.
Journal ArticleDOI
Region filling and object removal by exemplar-based image inpainting
TL;DR: The simultaneous propagation of texture and structure information is achieved by a single, efficient algorithm that combines the advantages of two approaches: exemplar-based texture synthesis and block-based sampling process.
Journal ArticleDOI
Deep learning and process understanding for data-driven Earth system science
Markus Reichstein,Gustau Camps-Valls,Bjorn Stevens,Martin Jung,Joachim Denzler,Nuno Carvalhais,Nuno Carvalhais,Prabhat +7 more
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.