AIR QUALITY FORECASTING FOR ALL SEASONS IN LARGE GEOGRAPHICAL AREAS
Today, air quality monitoring plays a vital role due to increasing number of pollutants that threaten human health. Importance of providing accurate information on air quality for forthcoming times is therefore very high. For this purpose, many studies have been carried out to develop air quality forecasting models. However, most of these studies focus on a particular season and relatively small geographical areas. In this paper, unlike the previous ones, an air quality forecasting model is proposed for all seasons in large geographical areas. Turkiye that is a pretty large country where there are seven distinct regions with different geographical and meteorological characteristics is selected to apply the forecasting model. The proposed model categorizes the upcoming 6-hour air quality level as “healthy”, “moderate” and “unhealthy”. The model utilizes low and high order statistical features extracted from the measurements of air quality monitoring stations covering most parts of the geographical regions of Turkiye. The features are then fed into both linear and non-linear classifiers including artificial neural networks, Fisher’s linear discriminant analysis, nearest neighbor and Bayes classifier. Results of the experimental study indicate that the proposed forecasting model is a promising candidate to predict air quality through all seasons at relatively large geographical areas with varying characteristics.
Keywords:
Air pollution, Air quality index Forecasting model,
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- Chaudhuri S, Chowdhury AR. Air quality index assessment prelude to mitigate environmental hazards. Natural Hazards 2018; 91(1): 1–17.
- Yang Z, Wang J. A new air quality monitoring and early warning system: air quality assessment and air pollutant concentration prediction. Environmental Research 2017; 158: 105-117.
- Wang J, Zhang X, Guo Z, Lu H. Developing an early-warning system for air quality prediction and assessment of cities in China. Expert Systems with Applications 2017; 84:102-116.
- Motesaddi S, Nowrouz P, Alizadeh B, Khalili F, Nemati R. Sulfur dioxide AQI modeling by artificial neural network in Tehran between 2007 and 2013. Environmental Health Engineering and Management Journal 2015; 2(4): 173–178.
- Carbajal-Hernández JJ, Sánchez-Fernández LP, Carrasco-Ochoa JA, Martínez-Trinidad JF. Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmospheric Environment 2012; 60: 37–50.
- Sahu SK, Yip S, Holland DM. Improved space–time forecasting of next day ozone concentrations in the eastern US. Atmospheric Environment 2009; 43: 494–501.
- Cai C, Hogrefe C, Katsafados P, Kallos G, Beauharnois M, Schwab JJ, Ren X, Brune WH, Zhou X, He Y et al. Performance evaluation of an air quality forecast modeling system for a summer and winter season – photochemical oxidants and their precursors. Atmospheric Environment 2008; 42: 8585–99.
- Perez P, Salini G. PM2.5 forecasting in a large city: comparison of three methods. Atmospheric Environment 2008; 42: 8219–24.
- Perez P, Reyes J. An integrated neural network model for PM10 forecasting. Atmospheric Environment 2006; 40: 2845–51.
- Ordieres JB, Vergara EP, Capuz RS, Salazar RE. Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juarez (Chihuahua). Environmental Modelling & Software 2005; 20(5): 547–59.
- Duda RO, Hart PE, Stork DG. Pattern Classification: John Wiley & Sons, Inc.; 2001.
- De Blij HJ, Downs R. College Atlas of the World: Wiley/National Geographic; 2007.
- Ansari K, Corumluoglu O, Sharma S. Numerical simulation of crustal strain in Turkey from continuous GNSS measurements in the interval 2009–2017. Journal of Geodetic Science 2017; 7(1): 113–129.
- ISSN: 2667-4211
- Yayın Aralığı: Yılda 4 Sayı
- Başlangıç: 2000
- Yayıncı: Eskişehir Teknik Üniversitesi