Abstract
Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The resulting model is an interpretable, useful and easily implementable model for forecasting ozone maxima. Moreover, it proved to be consistent with the general dynamics of ozone formation and provides a suitable platform for forecasting, showing similar or better performance compared to models in other existing studies.
Highlights
Forecasting is an integral and useful task for managing urban air quality
The Seasonal Autoregressive Moving Average (SARMA) models in the present study show Mean Absolute Percentage Error (MAPE) ranging between 19.50 and 25.65 and Mean percentage error (MPE) ranging between −3.09 and −14.03, depending on the site
A univariate multiple regression model with meteorological predictors and SARMA errors has proven to be useful for representing the dynamics of O3 maxima at four times of the day in five sites of the Monterrey Metropolitan Area (MMA) monitoring network in Mexico
Summary
Forecasting is an integral and useful task for managing urban air quality. Since the 1970s, forecasting techniques and tools have been developed in response to the severe pollution episodes that occurred between 1930 and 1960 in diverse parts of the world, in Europe and the United States of America. Afterwards, between 1970 and 1990, 3D air quality models were developed and applied on urban, regional and global scales [1]. Comprehensive 3D photochemical models solve the mathematical equations that describe the chemical and physical dynamics of pollutants in the atmosphere [2]. 3D air quality models require a large amount of reliable meteorological, geographical and emissions data. In order to be implemented, they require high computational capacity as well as specialized knowledge about atmospheric chemical reactions and physical processes. These factors make the setup, execution and operation of these comprehensive models for forecasting pollutant concentrations technically complicated in some urban
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