000			01499nam a22001577a 4500
999			_c518211 _d518211
008			210830b ||||| |||| 00| 0 eng d
100			_aGupta, Naina et al _928696
245			_aStatistical approach for modelling exposure to background air pollution
260			_aUrban India: A Journal of the National Institute of Urban Affairs
300			_a40(3), Jul-Dec, 2020: p.51-60
520			_aBackground air pollution is a long term resident and low-level concentration pollution difficult to quantify and to which the population chronically exposed. In this study, hourly time series data of air pollutants CO, NO2 , PM10 were analyzed using the four statistical methods k-means clustering, Finite mixture model, Hidden Markov model and Hierarchical clustering to estimate the exposure to background pollution in Lucknow in two monitoring sites Lalbagh and central school from 2009 to 2019 using 47 data sets. The data sets represents the lowest cluster corresponds to background concentration and hmm clustering technique outperforms compared to the remaining showing estimates of exposure related to background pollution in the form of its percentage cluster size, mean and standard deviation in the ambient air for all the air pollutants and sites studied. – Reproduced
650			_aBackground pollution, Finite mixture model, Hidden markov model, K-mean _927119
773			_aUrban India: A Journal of the National Institute of Urban Affairs
906			_aPOLLUTION
942			_cAR