Abstract
The monthly maximum of the 24-h average time-series data of ambient air quality—sulphur dioxide (SO2), nitrogen dioxide (NO2) and suspended particulate matter (SPM) concentration monitored at the six National Ambient Air Quality Monitoring (NAAQM) stations in Delhi, was analysed using Box–Jenkins modelling approach (Box et al. 1994). Univariate linear stochastic models were developed to examine the degree of prediction possible for situations where only the past record of pollutant data are available. In all, 18 models were developed, three for each station for each of the respective pollutant. The model evaluation statistics suggest that considerably satisfactory real-time forecasts of pollution concentrations can be generated using the Box–Jenkins approach. The developed models can be used to provide short-term, real-time forecasts of extreme air pollution concentrations for the Air Quality Control Region (AQCR) of Delhi City, India.
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Sharma, P., Chandra, A. & Kaushik, S.C. Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City. Environ Monit Assess 157, 105–112 (2009). https://doi.org/10.1007/s10661-008-0520-2
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DOI: https://doi.org/10.1007/s10661-008-0520-2