期刊
ATMOSPHERE
卷 13, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/atmos13101693
关键词
air quality forecasting; modeling; emissions; pollutant sources
资金
- EU H2020 RI-URBANS project [101036245]
Air pollution forecasting systems, such as SmartAQ, combine advanced meteorological and chemical models to provide detailed predictions of air pollutant concentrations. SmartAQ operates in real time and can forecast the concentrations of various pollutants, including aerosols, for the next few days.
Air pollution forecasting systems are useful tools for the reduction in human health risks and the eventual improvement of atmospheric quality on regional or urban scales. The SmartAQ (Smart Air Quality) forecasting system combines state-of-the-art meteorological and chemical transport models to provide detailed air pollutant concentration predictions at a resolution of 1 x 1 km(2) for the urban area of interest for the next few days. The Weather Research and Forecasting (WRF) mesoscale numerical weather prediction model is used to produce meteorological fields and the PMCAMx (Particulate Matter Comprehensive Air quality Model with extensions) chemical transport model for the simulation of air pollution. SmartAQ operates automatically in real time and provides, in its current configuration, a three-day forecast of the concentration of tens of gas-phase air pollutants (NOx, SO2, CO, O-3, volatile organic compounds, etc.), the complete aerosol size/composition distribution, and the source contributions for all primary and secondary pollutants. The system simulates the regional air quality in Europe at medium spatial resolution and can focus, using high resolution, on any urban area of the continent. The city of Patras in Greece is used for the first SmartAQ application, taking advantage of the available Patras' dense low-cost sensor network for PM2.5 (particles smaller than 2.5 mu m) concentration measurements. Advantages of SmartAQ include (a) a high horizontal spatial resolution of 1 x 1 km(2) for the simulated urban area; (b) advanced treatment of the organic aerosol volatility and chemistry; (c) use of an updated emission inventory that includes not only the traditional sources (industry, transport, agriculture, etc.), but also biomass burning from domestic heating and cooking; (d) forecasting of not only the pollutant concentrations, but also of the sources contributions for each one of them using the Particulate matter Source Apportionment Technology (PSAT) algorithm.
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