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Air pollution is a condition where there are substances or other energy that enters the air resulting in decreased air quality. One of the most dangerous substances in the air is PM10. Indeks Standar Pencemaran Udara (ISPU) with PM10 parameters measured every day is used to indicate the level of air quality dangerous or not so that the Surabaya City air quality data seen from the ISPU level PM10 parameters are binary time series. Binary data analysis is usually done using logistic regression. However, logistic regression has not been able to accommodate the time factor so a special approach is needed. This research will explore models for binary time series data, namely Generalized Linear Autoregressive Moving Average (GLARMA) and Generalized Autoregressive Moving Average (GARMA) to analyze air pollution data. The results show that the GARMA model chosen to be the best model with the smallest AIC value. Futhermore, there is a significant influence between the meteorological variables and the time series dependence on the response variable.

Air pollution; binary time series; GARMA; GLARMA