Gema Fernández-Avilés , Raffaele Mattera , Germana Scepi
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引用次数: 0
Abstract
Air pollution poses a significant threat to public health and the environment in urban areas worldwide. In the context of urban air quality, nitrogen oxides (NOx), comprising nitrogen dioxide (NO2) and nitric oxide (NO), stand out as key pollutants with well-documented adverse effects. The city of Madrid, as the capital and largest urban center of Spain and the third largest of Europe, is no exception to the challenges posed by NOx pollution. Most of the recent literature on forecasting air pollution, and specifically on NOx, is based on the use of Neural Networks (NN). Little is known about the forecasting ability of factor models in this context. The main aim of this paper is to use Factor-Augmented Autoregressive Neural Networks (FA-ARNN-X) to predict future patterns of NOx pollutants in the territorial monitoring stations of Madrid, using lagged NOx values, meteorological variables and latent factors. The main results indicate that the proposed forecasting model provides statistically more accurate predictions of air pollution than its competing benchmarks and should be used by policymakers for more accurate air pollution monitoring.
期刊介绍:
Studies directed toward the more effective utilization of existing resources, e.g. mathematical programming models of health care delivery systems with relevance to more effective program design; systems analysis of fire outbreaks and its relevance to the location of fire stations; statistical analysis of the efficiency of a developing country economy or industry.
Studies relating to the interaction of various segments of society and technology, e.g. the effects of government health policies on the utilization and design of hospital facilities; the relationship between housing density and the demands on public transportation or other service facilities: patterns and implications of urban development and air or water pollution.
Studies devoted to the anticipations of and response to future needs for social, health and other human services, e.g. the relationship between industrial growth and the development of educational resources in affected areas; investigation of future demands for material and child health resources in a developing country; design of effective recycling in an urban setting.