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Rapid urbanization and population growth resulted in severe deterioration of air quality in most of the major cities in India. Therefore, it is essential to ascertain the contribution of various sources of air pollution to enable us to determine effective control policies. The present work focuses on the holistic approach of combining factor analysis (FA), positive matrix factorization (PMF), and chemical mass balance (CMB) for receptor modeling in order to identify the sources and their contributions in air quality studies. Insight from the emission inventory was used to remove subjectivity in source identification. Each approach has its own limitations. Factor analysis can identify qualitatively a minimal set of important factors which can account for the variations in the measured data. This step uses information from emission inventory to qualitatively match source profiles with factor loadings. This signifies the identification of dominant sources through factors. PMF gives source profiles and source contributions from the entire receptor data matrix. The data from FA is applied for rank reduction in PMF. Whenever multiple solutions exist, emission inventory identifies source profiles uniquely, so that they have a physical relevance. CMB identifies the source contributions obtained from FA and PMF. The novel approach proposed here overcomes the limitations of the individual methods in a synergistic way. The adopted methodology is found valid for a synthetic data and also the data of field study.

Highlights Air Quality Index (AQI) of Thiruvananthapuram city has been calculated  AQI forecasting using ARIMA and SARIMA model were introduced  Error between actual and predicted AQI has been reduced using optimization technique ABSTRACTDeterioration of air quality is an important issue faced by many cities in India. The increase in the number of vehicles, unrestrained burning of plastics, unacceptable construction and demolition activities and industrial activities are the main reasons for this deterioration. So it is necessary to assess the effectiveness of air quality monitoring programs for planning air pollution control actions by analyzing the trends in air quality regularly. In this study, the varying trends of ambient air quality were analyzed and forecasted in terms of Air Quality Index (AQI) based on the database monitored at different monitoring stations in Thiruvananthapuram District, Kerala, India. The air quality data from the Kerala State Pollution Control Board (KSPCB) shows that the responsible pollutant for AQI in all these stations were Respirable Suspended Particulate Matter (RSPM) due to its abundance in the atmosphere. By forecasting, we can predict the future air quality in terms of AQI or individual pollutants in order to reduce the pollutant concentration and exposure to air pollutants. For air quality forecasting, Auto Regressive Integrated Moving Average (ARIMA) and Seasonal Auto Regressive Integrated Moving Average (SARIMA) method was used. ARIMA models gave satisfactory results than the SARIMA models and these results can be combined with other models to create more accurate results.