Air quality has become one of the main issues in public health and urban planning management, due to the proven adverse effects of high pollutant concentrations. Considering the mitigation measures that cities all over the world are taking in order to face frequent low air quality episodes, the capability of foreseeing future pollutant concentrations is of great importance. Through this paper, we present SOCAIRE, an operational tool based on a Bayesian and spatiotemporal ensemble of neural and statistical nested models. SOCAIRE integrates endogenous and exogenous information in order to predict and monitor future distributions of the concentration for several pollutants in the city of Madrid. It focuses on modeling each and every available component which might play a role in air quality: past concentrations of pollutants, human activity, numerical pollution estimation, and numerical weather predictions. This tool is currently in operation in Madrid, producing daily air quality predictions for the next 48 hours and anticipating the probability of the activation of the measures included in the city's official air quality \no protocols through probabilistic inferences about compound events.
翻译:空气质量已成为公共卫生和城市规划管理的主要问题之一,因为已证实污染浓度高,因此空气质量已成为公共卫生和城市规划管理的主要问题之一。考虑到全世界各城市正在采取减缓措施,以应对空气质量经常低的情况,预测未来污染物浓度的能力非常重要。我们通过本文介绍SOCAIRE,这是一个基于贝耶斯和西班牙神经和统计巢式模型组合的操作工具。SOCAIRE整合了内生和外生信息,以便预测和监测马德里市若干污染物浓度的未来分布情况。它侧重于模拟在空气质量中可能发挥作用的每一种现有成分:过去污染物浓度、人类活动、数量污染估计和数字天气预测。这一工具目前正在马德里运行,为今后48小时的空气质量作出每日预测,并通过对复合事件进行概率性判断,预测该市官方空气质量协议中包含的措施的启动概率。