Open AccessIntervention analysis with autoregressive integrated moving average models for time series of urban air pollutants
Author(s) -
L A Manco-Perdomo,
L A Pérez-Padilla,
Carlos Alfonso Zafra-Mejía
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2139/1/012002
Subject(s) - megacity , pollutant , environmental science , autoregressive model , air pollution , air quality index , air pollutants , meteorology , psychological intervention , econometrics , geography , mathematics , economics , psychology , chemistry , economy , organic chemistry , psychiatry
The objective of this paper is to show an intervention analysis with autoregressive integrated moving average models for time series of air pollutants in a Latin American megacity. The interventions considered in this study correspond to public regulations for the control of urban air quality. The study period comprised 10 years. Information from 10 monitoring stations distributed throughout the megacity was used. Modelling showed that setting maximum emission limits for different pollution sources and improving fuel were the most appropriate regulatory interventions to reduce air pollutant concentrations. Modelling results also suggested that these interventions began to be effective between the first 4 days-15 days after their publication. The models developed on a monthly timescale had a short autoregressive memory. The air pollutant concentrations at a given time were influenced by the concentrations of up to three months immediately preceding. Moving average term of the models showed fluctuations in time of the air pollutant concentrations (3 months - 14 months). Within the framework of the applications of physics for the air pollution control, this study is relevant for the following findings: the usefulness of autoregressive integrated moving average models to temporal simulate air pollutants, and for its suitable performance to detect and quantify regulatory interventions.