Modelling the Air Quality Index for Bolu, Turkey

Abstract

The monthly air quality index (AQI) derived from ground observation stations that obtained daily air pollutants information for 1990- through 2010 was analyzed in this study. AQI was evaluated using the common comparative index method presented by the U.S. Environmental Protection Agency (USEPA), and a statistically based approach was used for predicting the AQI value. With the first method, AQI was predicted using the USEPA subindex formula for different pollutants, such as particulate matter and sulfur dioxide, which contribute the most to air pollution. A combination of the principal component analysis (PCA) and multiple linear regression (MLR) methods were used with the measured values of climate variables obtained from the ground stations for the most effective contributors and a prediction was modelled. The results of these two methods were compared and evaluated for consistency. Two methods were presented for determining the AQI value. According to the findings, the common comparative index method was consistent with the statistical prediction models, and the best results were obtained using PCA models with varimax rotation.

Impact of Global Warming on Aquaculture in Norway

“…The Linear Regression Model is the most often used type of regression in applications, and it is one of statistics' oldest and most investigated areas. Regression analysis is a statistical technique for explaining quantitative relationships between one or more explanatory variables and a response variable Salihi and Üçler (2021), Zateroglu (2021b), Zateroglu (2021c), Zateroglu (2021f), Zateroglu (2022).…”

“…Furthermore, atmospheric elements that include the climate variables such as air temperature, precipitation, relative humidity, atmospheric pressure, wind speed, etc., and the air pollutants affect each other in the atmospheric periphery Zateroglu (2021a), Zateroglu (2021d). Additionally, an increment in the emissions of air pollutants originated from anthropogenic and natural sources i.e., power plants, motor vehicles, fossil fuel combustion for domestic heating and industrial usage, population growth, has an impact on the urban climate system, degrade the urban air quality, and contribute the climate change Zateroglu (2021a), Zateroglu (2021d), Zateroglu (2021e), Zateroglu (2022).…”

Estimation of Cloudiness Data Based on Multiple Linear Regression Model

“…Those have been utilized in many areas for many applications such as cloud distribution and effects on climate (Changnon, 1981;Kaiser 1998Kaiser , 2000. Cloud amount is related to sunshine duration according to some studies (Essa and Etman, 2004;Elnesr and El-Sabban, 1964;Badescu et al, 2016;Hoyt, 1977;Zateroglu 2021Zateroglu , 2022. Cloudiness and climatic variables have interactions in atmospheric environment.…”

İklimsel Değişkenlerin Partikül Madde ve Kükürt Dioksit Üzerindeki Etkisi

“…Some researchers have examined the characteristics, dispersion, and modelling of air pollutants, PM 10 and SO 2 , in recent years [19][20][21][22][23][24][25]. Climatological parameters are in interact both with air pollutants and with each other in the atmospheric environment [26][27][28][29][30]. The occurrence of physical-chemical interactions in the atmospheric environment influences air quality.…”

“…This method has been preferred and operated for many years by researchers [71][72][73][74][75][76][77][78]. Furthermore, there have been some studies about the relationship between air pollutants and climate variables [28][29][30][76][77][78][79][80][81].…”