Improved prediction of hourly PM2.5 concentrations with a long short-term memory and spatio-temporal causal convolutional network deep learning model

Author:

Chen Yinsheng,Huang Lin,Xie Xiaodong,Liu Zhenxin,Hu Jianlin

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference35 articles.

1. Hourly associations between ambient air pollution and emergency ambulance calls in one central Chinese city: implications for hourly air quality standards;Ai;Sci. Total Environ.,2019

2. A review of artificial neural network models for ambient air pollution prediction;Cabaneros;Environ. Model Softw.,2019

3. Aug. Time series based LSTM model to predict air pollutant’s concentration for prominent cities in India;Chaudhary,2018

4. Seasonal modeling of PM2.5 in California’s San Joaquin Valley;Chen;Atmos. Environ.,2014

5. Spatial and Temporal Variations of PM2. 5 and its Relation to Meteorological Factors in the Urban Area of Nanjing, China;Chen,2016

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