Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/17250
Title: Improvement of chemical initialization in the air quality forecast system in North Macedonia, based on WRF-Chem model
Authors: Anchev, Nenad
Velinov, Goran 
Jakimovski, Boro 
Spiridonov, Vlado 
Keywords: WRF-Chem . Air quality modeling . Extreme pollution episode . PM10 . Urban area
Issue Date: 16-Sep-2020
Publisher: Springer Netherlands
Journal: Air Quality, Atmosphere & Health
Abstract: Urban air quality is determined by a complex interaction of factors associated with anthropogenic emissions, atmospheric circulation, and geographic factors. Most of the urban-present pollution aerosols and trace gases are toxic to human health and responsible for damage of flora, fauna, and materials. The air quality prediction system based on state-of-the-art Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) has been configured and designed for North Macedonia. An extensive set of experiments have been performed with different model settings to forecast simultaneously the weather and air quality over the country. The initial results and the finding from other similar studies suggest that chemical initialization plays a significant role in a more accurate, both qualitative and quantitative forecast and assessment of urban air pollution. The main objective of the present research is to develop and test for a novel chemical initialization input in the air quality forecast system in North Macedonia. It is performed using ensemble technique in respect to treatment of the mobile emissions data using scaling factors. The WRF-Chem prediction has shown a high sensitivity to different scaling methods. While scaling of the overall mobile annual emissions tends to produce some discrepancies regarding the PM10 concentration level (overestimation during summer and underestimation during winter), an improved approach that utilizes scaling, in a wider range, only the mobile emissions originated from household heating offers the possibility of more detailed parameter fitting. The verification results indicate that the best accuracy across all scores for the winter months was achieved when scaling up the baseline pollutant input using a higher factor, while in the other seasons, the best results were achieved when scaling down the baseline pollutant emissions by a significant factor. Taking all into account, we can conclude that the seasonal variation in the pollutant input to the atmosphere is a significant factor in simulating the pollution in this region. Therefore, these seasonal variations must be taken into account when fitting the pollutant emission input to any model.
URI: http://hdl.handle.net/20.500.12188/17250
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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