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THE EFFECTS OF INDUSTRY INCREASE AND URBANIZATION ON AIR POLLUTANTS IN TURKEY: A NONLINEAR AIR QUALITY MODEL

Daily concentrations of air pollutants greatly affect air quality as an increase in industry and urbanization deteriorate the environment. For forty cities in Turkey, eleven variables are recorded to investigate the determinants of air pollution presumably by regressing the air pollutants PM10, NOX, and NO2. The temperature, wind, human factors such as population, vehicles, manufacturer, and suchlike are used to create a nonlinear air quality model in Turkey due to the multivariate nature of the data. A comparison of the nonparametric models of the concentration of these pollutants, using multivariate adaptive regression splines (MARS), was obtained to estimate the dependence between air pollutants and various factors. Finally, a model for PM10 concentration shows that climate effects are the most significant variables, whereas the predicted models for NOX and NO2 indicate that human factors, such as the number of manufacturers and the number of vehicles, are significant variables. In conclusion, the predicted models are easy to interpret and have advantages of capacity to produce the contributions of the factors for each pollutant model. It is advisable for researchers to examine and determine the suitability of their data sets using nonlinear models when atypical observations and high correlations exist in the data.


Introduction
The exposure of ambient air pollutants in industrial or metropolitan cities adversely affect humans, animals, plants, food crops or other living creatures (Lutgens and Tarbuck, 2001; Fussell, 2015, 2017). The measurements of meteorological pollution, such as NO2 (nitrogen dioxide), and PM10 (particulate matter) have become more important due to their harmful effects on human health (Akkoyunlu, 2003;Elbir et al., 2000;Garcia, 2001;Godish, 2004;Garcia, 2006;Lutgens and Tarbuck, 2001). For example, the adverse effects on human health may include coughing, asthma, chronic bronchitis, and cardio vascular morbidity (Lutgens and Tarbuck According to WHO statements, 1.4 out of 2.4 million deaths each year are caused by indoor air pollution while the remaining deaths are due directly to attributable air pollution (Lutgens and Tarbuck, 2001;Wark et al., 1997;Wang et al., 2004). The WHO Air Quality Guidelines provide a Global Update of 205 for threshold values of air pollution. In addition, it reports that 91% of the world population live in places where WHO air quality levels were not met. The WHO guidelines are currently under revision with an expected new publication in 2020. The latest Global conference on air pollution was organized in Geneva on 20 November 2018. Almost 900 participants registered for the conference, including 70 commitments from countries, cities and organizers discussing how to tackle climate change and related health considerations.
Furthermore, publications from the European Union (EU) regarding emissions of the main air pollutants monitored at more 2500 stations throughout Europe present updated data and its impact. Polices and air quality regimes are aimed at providing the lowest target concentrations of possible pollutants (Holnicki et al., 2017;Isikli et al., 2015). The guidelines apply worldwide and are based on expert evaluations for PM10 and NO2 (see Table 1). PM10, whichis often a proxy indicator, for air quality refers to fine particles with a diameter of 10 microns or less, and are tiny solid particles and liquid suspended in a gas. It affects living organisms on a daily basis or over time. As the sources of PM10 can be humans, fossil fuel combustion (primarily from road transport), factories or natural, the increase of PM10 levels can be a cause of mortality or morbidity (Kelly and Fussell, 2015).
The potential risk of PM10 air pollution is critical in certain cities in Turkey, considering that the measured daily PM10 concentrations should be in the range of 50 μg/m 3 from January 1, 2019, due to EU Commission regulations (Istanbul Metropolitan Municipality).
Serious risks are posed to the habitat of humans, animals and plants, not only from exposure of PM10, but also from NOX (Shi et al., 2014). This is a general term that is used to describe certain gases, namely NO2 and NO. NO2 is especially emitted from high temperature combustion processes (power generation, engines in vehicles, heating, and suchlike) and are also produced naturally during thunderstorms by electric discharge. NO2, a reddish-brown toxic gas, has a sharp and biting odour. The initial product formed is NO (nitric oxide). When NO oxidizes further in the atmosphere, nitrogen dioxide (NO2) forms. Similarly, studies on NO2 show that it is mainly due to the eutrophication of lakes and marine environments and acidification of terrestrial ecosystems (Greenfelt et al., 1994).
The concentration levels of NO2 can also play a significant role in asthma and lung functions. Shao et al. (2006) show that urbanization and industrial developments cause deterioting air quality. The results of their study are that emissions of NO2 are from industrial and domestic energy production and transportation. When NO2 is combined with water, the main component of acid rain, which is one cause for deforestation, is observed (WHO).
With the negative effects of growing population trends and urbanization, the role of such atmospheric pollutants has received increasing attention. Many scientists study on global and regional estimations for such emissions. It is often assumed that multivariate data should follow a known specific statistical distribution. However, the complex structure of the data and the relationship among various factors may be too complicated to explain general patterns or to model dependency between variables. When examining ecological data, the interaction of several environmental factors have to be recognized carefully and properly analysed.
The aim of this study is to investigate the significant variables by using daily patterns of PM10, NOX and NO2 and to provide meaningful future informative models for air quality control and regimes in Turkey. This study is organized as follows. Firstly, the current state of knowledge based on air pollutants in the literature is explained. Secondly, the motivations and data collection concerning our study are described. Next, a novel exploratory modelling technique, multivariate adaptive regression splines (MARS), is introduced by Friedman (1991). The results and comparisons from MARS models for each pollutant follow in the next section. Finally, the highlights of the paper are summarized in the conclusion.

The motivation
One major environmental risk is the concentration of air pollutants due to their harmful influence on health of humans, animals and plants or other living organisms. Low or middle income countries are exposed to pollutants in and around homes from the heating, lighting, traditional stoves used for cooking, polluting fuels on open fires and so on (WHO, 2018). The European Environmental Agency (EEA) indicate that there is an increasing trend of pollutants in urban regions. The WHO Air Quality 2005 document provides a guideline for an assessment of the health effects of air pollutants and guideline values for health risk pollution levels. The EU establishes the standards and objectives for a number of air pollutants in the air to be applied over different periods of time. Although Turkey and Switzerland are not the members of the EU, Turkey at least aims to adapt to EU standards by reaching the air quality limits. Table 1 shows the limits aimed for in order to achieve the lowest possible concentrations of PM10, NOX, and NO2.
There are a number of examples that can be used as sources of air pollution including industrial activity, energy (heating, cooking, lighting, electricity), transportation (vehicles and fuels), waste reduction of forest pollution, increasing numbers of inhabitants, and climate. Addressing the risk factors for air pollution, countries could reduce levels of air pollution to safeguard the public health.
The Ministry of the Environment and Urbanisation is the main institution of the Turkish government that is responsible for transboundary atmospheric pollution control (Yurtseven et al., 2018). The measurements of PM10, NOx and NO2 in forty cities organized from the Air Quality Monitoring System are shown in Figure 1. These are used to evaluate the priority of the variables that may be sources of the corresponding pollutants. Next, predictions from MARS models are depicted correctly as well as those cities with high levels of pollutants.  Figure 1 shows concentrations of PM10, NOX, and NO2 observed for the forty cities between 1 November, 2017, and 1 November, 2018. It is clear that the emissions peaks of PM10, NOX and NO2 occur in some of the cities. Although the trend of the PM10, NOX, NO2 is a quasi-sinoidal curve with peaks and valleys, these gases reach the greatest concentrations in Izmir and Konya, respectively. Additionally, certain relationships among variables are given in Figure 2.

Model construction
The measurement of climatic variables is organized by the Ministry of the Environment and Urbanization Measurements and Monitoring Office, Turkey. For forty cities, eleven variables are recorded to investigate the determinants of air pollution presumably by regressing PM10, NOX, NO2 on those related to human ecology and climate. The measurements are collected at monitoring stations located within an industrial area close to the city or within the city limits. The results relating to climate (temperature, wind speed, precipitation, and number of days with precipitation) are average daily measurements (Air Quality Monitoring System).

Figure 2. Correlations among variables
Addition, the effects of car pollution are widespread and is one of major cause of global warming. The increasing number of people which negatively effects the atmosphere and motorized vehicles can be geo-demographic predictors of the air pollutions and may be useful include in the model. The measurements for Radiation and Pressure are arranged from Turkish State Meteorological Service. The complete list of the data set was created using publicly online related web sources and the names of the variables are given in Table 2.

Multivariate adaptive regression splines
MARS is a nonparametric approximation of the relationship between a response and a set of independent variables in reflected pairs of simple linear splines (Friedman, 1991). The reflected pairs take the form given in Equation 1: where the subscript '+' means the argument is a truncated power function.
MARS models do not assume any underlying relationship between the dependent variable and the predictors. This relationship is constructed by an adaptive fitting procedure where piecewise polynomials of degree q (splines) are driven from regressing the dependent variable onto predictors. The MARS model has the following form given The MARS algorithm adaptively selects the basis function set by two iterative approaches; forward and backward selection. It uses the residual squared error in iterations to compare the partition points. The criterion used to set the final model is a modified generalized cross validation (GCV) of the one first proposed by Craven and Wahba in 1979. The difference between the two criteria comes from a penalty term that reflects the complexity of the model in MARS.
The MARS approach allows a nonlinear relationship over different intervals of the vector of the explanatory variables for modeling Y (Sephton, 2001). The relationship between the explanatory variables and the response is fitted by basis functions that are basically splines. The main idea behind MARS is to explore the relationship by splitting the explanatory variables over its region into several intervals and to transform the original input variables over the intervals. MARS fits a spline based model in each interval. Basis Functions (Bi) include knot locations relating to the explanatory variables. Bi can be a single or multivariable interaction term. The final model is a combination of Bi. Model selection is accomplished using the GCV criterion given in where is the complexity parameter to the corresponding model, and N represents the total number of observations. The final MARS model is the one with the smallest GCV and the largest .

Analysis and results
The conventional methods, multiple linear regression (MLR) and principal component analysis are used to address the main determinants of pollutants of PM10, NOx, NO2, respectively. Multiple linear regression is used by regressing several predictors listed in Table 2  In this work, a new approach for modelling PM10, NOx, and NO2 concentrations are developed. MARS models have been used (Milborrow, 2011;Hastie et al., 2003), the basis functions of the models, consisting of linear and second-order splines, are shown in Tables 3, 4, and 5. There are three dependent variables used to build the MARS models; particulate matter less than 10 μg/m3 (PM10), nitrogen dioxide (NO2), and nitric oxide (NOX). The results of MARS computed using all the available observations show a list of 4, 9, and 6 basis functions (Bi) for each of the three MARS models and their coefficients (Ci), respectively.
It should be noted that, MARS constructs nonparametric regression models as an extension of the linear models, and automatically determines the linearities and interactions and models a weighted sum of basis functions (hinge functions). The hinge function h() is x if the first quadrant is greater than 0, and is 0 if x is less than or equal to 0.
The regression equation of PM10, which includes linear form of its term, such as Precipitation and two second order basis functions, can be easily generated using Table 3 and Equation 5 as follows: (Eq.5) In Equation 5, there are two interaction terms, B3 and B4, where certain relationships between the variables Pressure and Forest, and the variables Electricity and Pressure found to be important, respectively. Using Equation 5, PM10 predictions can be calculated easily. The variation between the predictions and the actual data is presented in Figure 4. Similarly, the regression equations for NOX and NO2 can be constructed easily from Tables 4 and 5. It is worth to notice that the MARS model for NOX allows us to assess many interactions between different hinge functions. This can be easily seen in Table 4. The NOX model includes three interaction terms between hinge functions and linear terms.  According to the results of NO2, the model is constructed by linear and second-order basis functions. There are two linear basis functions and three second-order basis functions where certain relationships between Prep and the variables Manuf, Vehicle, and Pressure are found to be statistically important. The most significant variables in the prediction of PM10, NOX and NO2 can be obtained from each MARS model as well. The list of each model is shown in Tables 6,  7, and 8. These tables list the values of three criteria for estimating variable importance. The nSubset criterion indicates the counts of those models that include the variable. More subsets are considered of greater importance. The residual sum of the squares (RSS) criterion calculates the decrease in the RSS for each subset. The variables which cause a large decrease in RSS are considered more important. The last criterion GCV is similar. The most important variables in the PM10 prediction given in Table 6 are obtained from electricity, pressure, precipitation, and forest in order of priority. The results in Table 7 indicate that the most significant variables are Manufacturer, Radiation, Temperature, Vehicle and Pressure in the prediction of NOX.
In Table 8, the most significant variables in the NO2 prediction are Precipitation, Manufacturer, Vehicle, Pressure and Radiation.   In Table 9, an evaluation of the GCV and R2 (coefficient of determination) of the models is summarized. A coefficient of determination value indicates how well a model explains and predicts the future. A value of 1.0 is assessed as a perfect fit. For the PM10, NOX and NO2 models, the coefficients of determination are 92.5%, 96.2%, and 94.2%, respectively.

Discussion and conclusion
In this research work, three models based on the multivariate adaptive regression splines for the study of considered pollutants PM10, NOX, and NO2 are proposed. The main purpose of this study is to increase accuracy in the prediction of three air pollutants when human factors and climate effects are present and physical-chemical parameters are combined. The innovative methodology MARS is applied to a real data set and is able to predict successfully.
One conclusion of this study is to set the order of the most significant variables in the prediction of PM10, NOX, and NO2. The priority of the predictor variables involved in the estimation of PM10 are actually related to climate parameters. On the other hand, human factors such as Manufacturer and Vehicles, included in the prediction of NOX and NO2, are the most influential parameters followed by Precipitation and Forest, respectively. Furthermore, this paper presents simple statistical models for the