Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere

Abstract

Spatial and temporal variations in concentrations of CO, NO, NO(2), SO(2), and PM(10), measured between 1999 and 2000, at traffic-impacted and residential stations in Ankara were investigated. Air quality in residential areas was found to be influenced by traffic activities in the city. Pollutant ratios were proven to be reliable tracers to differentiate between different sources. Air pollution index (API) of the whole city was calculated to evaluate the level of air quality in Ankara. Multiple linear regression model was developed for forecasting API in Ankara. The correlation coefficients were found to be 0.79 and 0.63 for different time periods. The assimilative capacity of Ankara atmosphere was calculated in terms of ventilation coefficient (VC). The relation between API and VC was investigated and found that the air quality in Ankara was determined by meteorology rather than emissions.

Temporal and Spatial Variations in Ambient Air Quality during 1996-2009 in Bangkok, Thailand

“…It is demonstrated by both spatial and temporal variations in the PM 10 , CO 8hr , O 3-1hr , NO 2 and SO 2 models. There were marked significant effects of seasonal, temporal, and meteorological factors on air pollution concentrations, and the findings were similar to previous studies (Kim Oanh et al, 2009;Genc et al, 2010;Rose et al, 2011). Fig.…”

“…For instance, local contributions around roadside sites accounted for approximately one-third of the contributions in residential sites, most likely from high traffic and surrounding activities. The recorded daily means of air pollution concentrations were found to differ significantly between roadside and residential sites (p < 0.0001), indicating that, in addition to the negative influence of prevailing regional meteorological conditions, traffic emission patterns affected the entire area (Grivas et al, 2004;Tsai et al, 2008;Kim Oanh et al, 2009;Azmi et al, 2010;Genc et al, 2010;Vlachogianni et al, 2011). Moderate correlations were found between PM 10 in roadside sites and PM 10 and NO 2 in residential sites (r = 0.403, p < 0.0001 and r = 0.308, p < 0.0001, respectively), indicating the importance of direct road traffic emissions.…”

Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation

“…Nevertheless, the statistical approach is generally more appropriate for the discovering of underlying complex site-specific dependencies between concentrations of air pollutants and potential predictors (Hrust et al, 2009), and consequently, they often have a higher accuracy, as compared with deterministic models. The commonly-used statistical approaches include multiple linear regression (MLR) (e.g., Stadlober et al, 2008;Genc et al, 2010), ANNs (e.g., P erez and Reyes, 2006;Li and Hassan, 2010), support vector machine (SVM) (e.g., Guyon et al, 2002;Osowski and Garanty, 2007), fuzzy logic (FL) (e.g., Shad et al, 2009;Alhanafy et al, 2010), Kalman filter (KF) (e.g., Zolghadri and Cazaurang, 2006;Hoi et al, 2008) and hidden Markov model (HMM) (Sun et al, 2013). Some studies (e.g., Gardner and Dorling, 1999) have suggested that the interplay of human, climate, and air pollution is too complex to be represented in deterministic models without developing a separate statistical model.…”

A Fuzzy Expression Way for Air Quality Index with More Comprehensive Information

“…For instance, Nagendra et al [5] carried out an empirical study on the air quality evaluation not only at the residential, but also at curbside monitoring stations in some specific cities or countries. More practical applications of the AQI can be referred to [6][7][8][9][10][11].…”