Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models
- 1 December 2014
- journal article
- Published by Elsevier BV in Atmospheric Environment
- Vol. 98, 665-675
- https://doi.org/10.1016/j.atmosenv.2014.09.046
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (71171102)
This publication has 21 references indexed in Scilit:
- Detection and diagnosis of surface wear failure in a spur geared system using EEMD based vibration signalanalysisTribology International, 2013
- A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5Atmospheric Environment, 2011
- Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networksJournal of Hydrology, 2010
- An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrationsAtmospheric Environment, 2010
- A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurementsRemote Sensing of Environment, 2010
- A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, ChileAtmospheric Environment, 2008
- Quality and performance of a PM10 daily forecasting modelAtmospheric Environment, 2008
- An empirical relationship between PM2.5 and aerosol optical depth in Delhi MetropolitanAtmospheric Environment, 2007
- Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, ChinaEnvironment International, 2007
- An integrated neural network model for PM10 forecastingAtmospheric Environment, 2006