Reconstructing global PM2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM

Reconstructing global PM2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM

Fine particulate matter (PM _2.5 ) is widely concerned for its harmful impacts on global environment and human health, making air pollution monitoring so crucial and indispensable. As the world’s first open, real-time, and historical air quality platform, OpenAQ collects and provides government meas...

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Bibliographic Details
Main Authors: Siyu Tan, Yuan Wang, Qiangqiang Yuan, Li Zheng, Tongwen Li, Huanfeng Shen, LiangPei Zhang
Format: Article
Language:English
Published: IOP Publishing 2022-01-01
Series:Environmental Research Letters
Subjects:
global PM2.5
OpenAQ
single exponential smoothing
inverse distance weighted
long short-term memory
Online Access:https://doi.org/10.1088/1748-9326/ac52c9

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