PM2.5 concentrations forecasting in Beijing through deep learning with different inputs, model structures and forecast time
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Appel, 2017, Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1, Geosci. Model Dev. (GMD), 10, 1703, 10.5194/gmd-10-1703-2017
Bai, 2016, Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions, Atmos. Pollut. Res., 7, 557, 10.1016/j.apr.2016.01.004
Biancofiore, 2017, Recursive neural network model for analysis and forecast of PM10 and PM2.5, Atmos. Pollut. Res., 652, 10.1016/j.apr.2016.12.014
Binkowski, 2003, Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description, J. Geophys. Res. Atmos., 108, 10.1029/2001JD001409
Chen, 2017, An open framework for participatory PM2.5 monitoring in smart cities, IEEE Access, 5, 14441, 10.1109/ACCESS.2017.2723919
Cheng, 2019, Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013-2017: a model-based decomposition analysis, Atmos. Chem. Phys., 19, 6125, 10.5194/acp-19-6125-2019
Cheng, 2013, Biomass burning contribution to Beijing aerosol, Atmos. Chem. Phys., 13, 7765, 10.5194/acp-13-7765-2013
Cong, 2017, The object detection based on deep learning
Cui, 2019, A framework for investigating the air quality variation characteristics based on the monitoring data: case study for Beijing during 2013–2016, J. Environ. Sci., 81, 225, 10.1016/j.jes.2019.01.009
Dixit, 2018, An overview of deep learning architectures, libraries and its applications areas, 293
Hu, 2016, One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system, Atmos. Chem. Phys., 16, 10333, 10.5194/acp-16-10333-2016
Huang, 2018, A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart cities, Sensors, 18, 2220, 10.3390/s18072220
Huang, 2017, Seasonal variation characteristics and forecasting model of PM2.5 in Changsha, central city in China, J. Environ. Anal. Toxicol., 7, 429, 10.4172/2161-0525.1000429
Huang, 2017
Jiang, 2015, Aerosol composition and sources during the Chinese Spring Festival: fireworks, secondary aerosol, and holiday effects, Atmos. Chem. Phys., 15, 20617, 10.5194/acp-15-6023-2015
Jiang, 2010, An air quality forecast model based on the BP neural network of the samples self-organization clustering, vol. 3, 1523
Kaimian, 2019, Evaluation of different machine learning approaches in forecasting PM2.5 mass concentrations, Aerosol Air Qual. Res., 19, 1400, 10.4209/aaqr.2018.12.0450
Kurt, 2010, Forecasting air pollutant indicator levels with geographic models 3days in advance using neural networks, Expert Syst. Appl., 37, 7986, 10.1016/j.eswa.2010.05.093
Li, 2020, Urban PM2.5 concentration prediction via attention-based CNN-LSTM, Appl. Sci.-Basel, 10
Li, 2020, A hybrid CNN-LSTM model for forecasting particulate matter (PM2.5), Ieee Access, 8, 26933, 10.1109/ACCESS.2020.2971348
Li, 2017, Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation, Environ. Pollut., 231, 997, 10.1016/j.envpol.2017.08.114
Liao, 2020, Deep learning for air quality forecasts: a review, Curr. Pollut. Rep., 6, 399, 10.1007/s40726-020-00159-z
Liu, 2019, Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: comparing cases for four cities in China, Atmos. Pollut. Res., 10, 1588, 10.1016/j.apr.2019.05.007
Liu, 2021, Intelligent modeling strategies for forecasting air quality time series: a review, Appl. Soft Comput., 102
Liu, 2019
Liu, 2010, Understanding of regional air pollution over China using CMAQ, part II. Process analysis and sensitivity of ozone and particulate matter to precursor emissions, Atmos. Environ., 44, 3719, 10.1016/j.atmosenv.2010.03.036
Liu, 2017, Effects of synoptic weather on ground-level PM2.5 concentrations in the United States, Atmos. Environ., 148, 297, 10.1016/j.atmosenv.2016.10.052
Liu, 2015, Seasonal and diurnal variation in particulate matter (PM10 and PM2.5) at an urban site of Beijing: analyses from a 9-year study, Environ. Sci. Pollut. Res., 22, 627, 10.1007/s11356-014-3347-0
Luo, 2015, Correlation study on PM2.5 and O3 mass concentrations in ambient air by taking urban cluster of Changsha,Zhuzhou and Xiangtan as an example, J. Saf. Environ., 15, 313
Lv, 2016, Development of nonlinear empirical models to forecast daily PM2.5 and ozone levels in three large Chinese cities, Atmos. Environ., 147, 209, 10.1016/j.atmosenv.2016.10.003
Memarianfard, 2017, Artificial neural network forecast application for fine particulate matter concentration using meteorological data, Glob. J. Environ. Sci. Manag., 3, 333
Ong, 2016, Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5, Neural Comput. Appl., 27, 1553, 10.1007/s00521-015-1955-3
Oprea, 2017, Computational intelligence-based PM2.5 air pollution forecasting, Int. J. Comput. Commun. Contr., 12, 365, 10.15837/ijccc.2017.3.2907
Pak, 2018, A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction, Air Qual. Atmos. Health, 11, 883, 10.1007/s11869-018-0585-1
Ren, 2021, Combining machine learning models through multiple data division methods for PM2.5 forecasting in Northern Xinjiang, China, Environ. Monit. Assess., 193, 10.1007/s10661-021-09233-5
Sainath, 2013, Deep Convolutional Neural Networks for LVCSR, 8614
Sak, 2016
Sharma, 2020, Deep air quality forecasts: suspended particulate matter modeling with convolutional neural and long short-term memory networks, Ieee Access, 8, 209503, 10.1109/ACCESS.2020.3039002
Tang, 2015, Diurnal, weekly and monthly spatial variations of air pollutants and air quality of Beijing, Atmos. Environ., 119, 21, 10.1016/j.atmosenv.2015.08.040
Tian, 2019, Temporal and spatial trends in air quality in Beijing, Landsc. Urban Plann., 185, 35, 10.1016/j.landurbplan.2019.01.006
Wang, 2016, Analysis of spatial-temporal distribution characteristics and main cause of air pollution in Beijing-Tianjin-Hebei region in 2014, Meteorol. Environ. Sci., 39, 34
Wang, 2012, Urban air quality and regional haze weather forecast for Yangtze River Delta region, Atmos. Environ., 58, 70, 10.1016/j.atmosenv.2012.01.014
2003, 13
Wu, 2018, PM2.5 concentration prediction using convolutional neural networks, Sci. Surv. Mapp., 43, 68
Xu, 2017, Spatiotemporal characteristics of PM2.5 and PM10 at urban and corresponding background sites in 23 cities in China, Sci. Total Environ., 599–600, 2074, 10.1016/j.scitotenv.2017.05.048
Xu, 2017, Study on the spatial distribution characteristics and the drivers of AQI in North China, Huanjing Kexue Xuebao/Acta Scientiae Circumstantiae, 37, 3085
Yan, 2021, Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering, Expert Syst. Appl., 169
Yang, 2002, Variation characteristics of PM2.5 concentration and its relationship with PM10 and TSP in Beijing, China Environ. Sci., 22, 27
Yang, 2020, A hybrid deep learning model to forecast particulate matter concentration levels in Seoul, South Korea, Atmosphere, 11, 10.3390/atmos11040348
Yu, 2004, Characteristics of mass concentration variations of PM10 and PM2.5 in Beijing area, Res. Environ. Sci., 45
Yuan, 2019, A novel multi-factor & multi-scale method for PM2.5 concentration forecasting, Environ. Pollut., 255, 10.1016/j.envpol.2019.113187
Zhang, 2004, Progress of weather Research and forecast (WRF) model and application in the United States, Meteorol. Mon., 30, 27
Zhang, 2014, Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States, Sci. Total Environ., 473, 275
Zhang, 2016, Response of aerosol composition to different emission scenarios in Beijing, China, Sci. Total Environ., 571, 902, 10.1016/j.scitotenv.2016.07.073
Zhang, 2015, Fine particulate matter (PM2.5) in China at a city level, Sci. Rep., 5, 14884, 10.1038/srep14884
Zhang, 2017, The contribution of residential coal combustion to PM2.5 pollution over China's Beijing-Tianjin-Hebei region in winter, Atmos. Environ., 159, 147, 10.1016/j.atmosenv.2017.03.054
Zhang, 2015, Evolution of surface O3 and PM2.5 concentrations and their relationships with meteorological conditions over the last decade in Beijing, Atmos. Environ., 108, 67, 10.1016/j.atmosenv.2015.02.071
Zhao, 2019, Long short-term memory - fully connected (LSTM-FC) neural network for PM2.5 concentration prediction, Chemosphere, 220, 486, 10.1016/j.chemosphere.2018.12.128
Zhao, 2009, Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing, Atmos. Environ., 43, 2893, 10.1016/j.atmosenv.2009.03.009
Zhou, 2017, Numerical air quality forecasting over eastern China: an operational application of WRF-Chem, Atmos. Environ., 153, 94, 10.1016/j.atmosenv.2017.01.020
Zhu, 2021, Attention-based parallel networks (APNet) for PM2.5 spatiotemporal prediction, Sci. Total Environ., 769