Prediction of ambient PM10 and toxic metals using artificial neural networks.
In this study, an artificial neural network is employed to predict the concentration of ambient respirable particulate matter (PM10) and toxic metals observed in the city of Jaipur, India. A feed-forward network with a back-propagation learning algorithm is used to train the neural network the behavior of the data patterns. The meteorological variables of wind speed, wind direction, relative humidity, temperature, and time are taken as input to the network. The results indicate that the network is able to predict concentrations of PM10 and toxic metals quite accurately.
Air Pollution Control Division, National Environmental Engineering Research Institute, Nagpur, India.
Asha B
AB
Chelani
D G
DG
Gajghate
M Z
MZ
Hasan
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000517:prevention & control; D001673:Biodegradation, Environmental; D002947:Cities; D004777:Environment; D005544:Forecasting; D019216:Metals, Heavy / Q000032:analysis*; D008685:Meteorological Concepts; D016571:Neural Networks, Computer; D010316:Particle Size; D012016:Reference Values
D000393:Air Pollutants; D019216:Metals, Heavy