The Fogees system for forecasting particulate matter concentrations in urban areas

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

Air quality forecasting requires appropriate models and data sources. The Fogees system presented in this paper enables mapping, evaluation and forecasting of the level of PMx pollution in the air, for urban and suburban areas. Input data were downloaded from the Meteoblue service, the GIS database and – in the case of integration with existing measurement systems – also from local air quality monitoring stations. The system uses a diagnostic model to determine the velocity field and a Lagrange model to describe pollution dispersion. The system has an autocalibration model and uses proprietary algorithms to estimate emissions from domestic heating, transport and background pollution levels. Modular design and parallel computation facilitate simultaneous calculation of forecasts for existing emission conditions and calculations for alternative emission conditions. The system permits forecasting for the next hour and for several consecutive hours. The validation results confirm that the system reliable forecasting of PM concentrations.

空气质量预测需要适当的模型和数据源。本文介绍的 Fogees 系统可以对城市和郊区空气中的 PMx 污染水平进行绘图、评估和预测。输入数据从 Meteoblue 服务、地理信息系统数据库下载,在与现有测量系统集成时,也从当地空气质量监测站下载。该系统使用诊断模型确定速度场,并使用拉格朗日模型描述污染扩散情况。该系统有一个自动校准模型,并使用专有算法来估算家庭供暖、运输和背景污染水平的排放量。模块化设计和并行计算便于同时计算现有排放条件下的预测和替代排放条件下的计算。该系统允许对下一小时和连续几个小时进行预测。验证结果证实,该系统对可吸入颗粒物浓度的预测是可靠的。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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