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Analyses et enjeux
Status and prediction of Nitrogen Dioxide as an air pollutant in Ahvaz City, Iran
Le dioxyde d’azote dans la ville d’Ahvaz en Iran
Résumé
La qualité de l’air dans la ville d’Ahvaz, située au sud de l’Iran, est présentée à travers l’analyse du dioxyde d’azote (NO_{2}), à partir de mesurages effectués dans deux sites urbains différents en 2009 et 2010, de manière à présenter des valeurs moyennes. Des relations statistiques ont été établies avec différents paramètres météorologiques à partir des valeurs quotidiennes : la mesure du vent (vitesse et direction), l’humidité relative, la température, la durée de l’insolation, l’évaporation et les précipitations considérées comme des variables indépendantes. À l’aide du logiciel de calcul SPSS, le niveau de relation entre la concentration du polluant et les paramètres météorologiques est indiqué par des régressions linéaires multiples. Le test RMSE indique que, parmi les différents modèles de prévision, le modèle pas à pas présente la meilleure option. Les concentrations moyennes de ce polluant urbain ont été calculées pour chaque jour, chaque mois et chaque saison. Les résultats indiquent que les concentrations de dioxyde d’azote les plus élevées surviennent au début de la nuit, tandis que les concentrations les plus faibles sont observées au début de la matinée. C’est en décembre qu’on observe les concentrations moyennes mensuelles les plus élevées, tandis qu’en septembre, les valeurs sont les plus basses. C’est en automne que les valeurs saisonnières sont les plus élevées.
Abstract
In the current research air quality analyses for Ahvaz, a city in the south of Iran, are conducted for Nitrogen Dioxide (NO_{2}), with the measurements taken from 2009 and 2010. Measurements were taken from two different locations in the city to make average data for the city. Some relations between the air pollutant and some meteorological parameters have been found statistically by using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine hours, evaporation and rainfall are considered as independent variables. The level relationships between concentration of pollutant and meteorological parameters are expressed by multiple linear and nonlinear regression equations for both annual and seasonal conditions using the software of SPSS. RMSE test shows that among different prediction models, stepwise model is the best option. Then the concentration averages were calculated for every 24 hours, each month and each season for the city. Results show the highest concentration of Nitrogen Dioxide occurs generally in the beginning of night time while the least concentration occurs in the beginning of morning. Monthly concentrations of the Nitrogen Dioxide show highest value in December and the least value in September. The seasonal concentrations show the highest amounts in the autumn.
Entrées d'index
Motsclés : dioxyde d’azote, modèle de régression., paramètres météorologiques, pollution de l’air
Keywords: air pollution, meteorological parameters, nitrogen dioxide, regression model.
Texte intégral
1. Introduction
Nitrogen dioxide (NO_{2}) is one of the seven Conventional (criteria) pollutants (SO_{2}, CO, particulates, hydrocarbons, nitrogen oxides, O_{3} and lead). These pollutants produce the highest volume of pollutants in the air and the most serious threat for human health and welfare. Concentration on these pollutants, especially in cities, has been regulated by Clean Air Act since 1970 (W.P. Cunningham and M.A. Cunningham, 2002).
Some properties of Nitrogen dioxide (NO_{2}) are: reddish brown gas, formed as fuel burnt in car, strong oxidizing agent and forms Nitric acid in air. Its sources are divided to two parts: 1) natural emissions including forest fires, volcanoes, bacteria in soil, lightening, etc. 2) anthropogenic activity including motor vehicle emissions and power generation. Fuel combustion increases NO_{2} production. Half of emission of HC and NOx in cities is by motor vehicles. Nitrogen oxides (NO_{x}) include different forms of oxides of nitrogen. NO_{2} generally derives from emissions of NO (in high temperature). About 95% of Nitrogen oxides are emitted as NO and 5% as NO_{2}. Other oxides are N_{2}O, N_{2}O_{3} and N_{2}O_{5} which are not so important in air pollution. Among NO_{x}, NO_{2} lead to respiratory problems and therefore NO_{2} is the most important of the oxides of nitrogen. The presence of pollutants in the atmosphere causes a lot of problems, thus the study of pollutant’s behavior is necessary. Some of main health effects of NO_{2} are such as: lung and heart problems, NO_{2 }poisoning, asthma, lowered resistance to infection. Other effects are damages on plants and material like: damages of leaves, retard photosynthesis activity, cause chlorosis, damages on various textile fibers, multiplying the photochemical smog problems and damages of acid rain.
Status of pollutants concentration and effect of meteorological and atmospheric parameters on it is the base of such studies as follows: Ho and Lin (1994) studied semistatistical model for evaluating the NO_{x} concentration by considering source emissions and meteorological effects. In a study, the relationship between monitored air pollutants and meteorological factors is statistically analyzed, using SPSS. According to the results obtained through multiple linear regression analysis, for some months there is a moderate and weak relationship between the air pollutants like NO_{2} level and the meteorological factors in Trabzon city (Cuhadaroglu and Demirci, 1997).
Statistical modeling of ambient air pollutants in Delhi has been studied by Chelani et al. (2001). AbdulWahab and AlAlawi (2002) developed a neural network model to predict the tropospheric (surface or ground) ozone concentrations as a function of meteorological conditions and various air quality parameters. The observed behavior of pollution concentrations to the prevailing meteorological conditions has been studied for the Metropolitan Area of Sao Paulo (SánchezCcoyllo and Andrade, 2002). Results show low concentrations associated with intense ventilation, precipitation and high relative humidity. While high values of concentrations prevailed due to weak ventilation, absence of precipitation and low relative humidity for some pollutants.
Elminir (2005) mentioned dependence of air pollutants on meteorology over Cairo in Egypt. The results hint that, wind direction was found to have an influence not only on pollutant concentrations but also on the correlation between pollutants. At higher wind speeds, dust and sand from the surrounding desert was entrained by the wind, thus contributing to ambient particulate matter levels. It was also found that the highest average concentration for NO_{2} and O_{3} occurred at humidity ≤ 40% indicative for strong vertical mixing. In another research, data on the concentrations of seven air pollutants (CH_{4}, NMHC, CO, CO_{2}, NO, NO_{2} and SO_{2}) and meteorological variables (wind speed and direction, air temperature, relative humidity and solar radiation) were used to predict the concentration of ozone in the atmosphere using both multiple linear and principal component regression methods (AbdulWahabet al., 2005). Asrari et al. (2007) studied effect of meteorological factors for predicting co. Also variations of concentration co in different time have been shown in this study.
Li et al.(2014) presented the spatial and temporal variation of Air Pollution Index (API) and examined the relationships between API and meteorological factors during 2001–2011 in Guangzhou, China. Relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual condition. Yoo et al. (2014) mentioned that all of the pollutants show significant negative correlations between their concentrations and rain intensity due to washout or convection.
The paper presents diurnal, monthly and seasonal variations of concentration of Nitrogen Dioxide and also a statistical model that is able to predict amount of Nitrogen Dioxide. This is based on linear regression technique. Linear Regression estimates the coefficients of the linear equation, involving one or more independent variables that best predict the value of the dependent variable which is Nitrogen Dioxide amount. For this purpose a large statistical and graphical software package (SPSS, Software Package of Social Sciences, V. 20), that is one of the best known statistical packages has been used (Kinnear, 2002).
2. Material and Method
The research area, Ahvaz, capital of Khuzestan Province, is the biggest city in the southwestern part of Iran (figure 1) which is located around 31° 19' N and 48° 40' E and the elevation is about 20 m above the mean sea level. Annual precipitation of Ahvaz is about 213.4 mm that almost 45% of total precipitation occurs in December and January. It has arid climate and residential population in 2006 was 1,425,000. Ahvaz is consistently one of the hottest cities on the planet during the summer, with summer temperatures regularly at least 45 degrees Celsius, sometimes exceeding 50 degrees Celsius (the highest monthly maximum average of temperature is 46.3ºC in July) with many sandstorms and dust storms common during the summer period while in winters the monthly minimum average of temperature could fall 7.2 degrees Celsius in January. Also annual average of wind speed measured by the synoptic station (located at 31° 20' N and 48º 40' E) is 5 knot which is higher in warm months compared to cold months (maximum and minimum of average of wind speed are observed in June and December).
Ahvaz is built on the banks of the Karun River and is situated in the middle of Khuzestan Province. Iraq attempted to annex Khuzestan and Ahvaz in 1980, resulting in the Iran–Iraq War (1980–1988). Ahvaz was close to the front lines and suffered badly during the war. There are a lot of cars in city and many factories and industrial places around the city. Because of these problems Ahvaz is one of the most polluted cities in Iran. Hence a need was felt to carry out an ambient air quality analysis in this city.
Figure 1. Location of Ahvaz city in Iran
La situation de la ville d’Ahvaz en Iran
Currently Ahvaz is introduced as the worst polluted city of the world according to a survey by the World Health Organization in 2011 because of high concentration of dust during year (Guinness World Records, 2013). Increasing of dust (figure 2) causes different impacts and problems like occurring and increasing of related illness to this pollution such as cancer and lung damages during recent years which have been recorded by Health offices of region. High amounts of PM_{10} are observed more during recent years in western and southern parts of Iran. The main source of this pollution is arid lands of western neighbors especially in Iraq. Especially after wars of USA with this country the number of critical zones for detachment of soil particles in wind erosion process have been increased because of mismanagements and forgetfulness of doing remedial measures and conservation against wind erosion. Especially in the end of spring and summer that precipitation is very low and wind speed and evaporation are high, therefore soil is very dry allowing for wind erosion and carrying soil suspended particles to long distances.
Figure 2. Two photos from a location in Ahvaz city showing bad situation of dust pollution during recent years
Deux photos indiquant l’importance des poussières au cours de ces dernières années
Two available sampling stations of city namely, Administration and Naderi, belong to Environmental Organization of Iran were selected to represent different traffic volumes and activities (figure 3).
La circulation automobile à proximité d’une station de mesure
The sampling was done every 30 minutes everyday for each pollutant in 2009 and 2010. Among measured data in two stations Nitrogen Dioxide was chosen. Then the averages were calculated for every hour, each month and each season for both stations in Excel. Finally averages of data of two stations were used to show air pollution situation as diurnal, monthly and seasonal graphs of concentration of Nitrogen Dioxide in the city.
NO_{X} is monitored using a chemiluminescent gas analyzer. Sample air is drawn through a molycon converter that converts NO_{2} to NO. The sample air is then mixed with a defined concentration of ozone that is provided by an internal ozone generator. The chemiluminescent reaction between the NO and the ozone is measured to obtain the NO concentration. The process is repeated without the sample air passing through the molycon converter. The difference in the measured concentrations can be calculated to determine the NO_{2} concentration.Two models of devices namely, Ecotec and EnviroTech, have been used for measuring of air pollution in the stations.
studying correlation of Nitrogen Dioxide and metrological parameters of synoptic station of city was next step. The metrological parameters studied include: temperature (min & max), humidity (min & max), precipitation, sunshine, wind direction, wind speed and evaporation.
In the next step, average of data of two stations in 2010 for everyday for which represents the air pollution of city has been considered as dependent variable for statistical analysis while daily data of meteorological parameters during this year have been selected as independent variables. Software of SPSS has been used for this purpose and the linear regression equation shows that the concentration of Nitrogen Dioxide depends on which meteorological parameters and also gives an idea about the levels of this relation. The relationship between the dependent variable and each independent variable should be linear. The significant values in output are based on fitting a single model. Also linear regression equation has been made for different seasons maybe show those relationships which are not observed using annual data.
Some options are available in this software; these options apply when the ‘enter’, ‘forward’, ‘backward’, or ‘stepwise’ variable selection method has been specified. Method selection allows you to specify how independent variables are entered into the analysis. Using different methods, you can construct a variety of regression models from the same set of variables. The model for predicting Nitrogen Dioxide was determined by using two multiple regression modeling procedures of ‘enter method’ and ‘stepwise method’. In ‘enter method’ all independent variables selected are added to a single regression model. In ‘stepwise’ which is better, all variables can be entered or removed from the model depending on the significance. Therefore only those variables which have more influence on dependent variable are observed in a regression model.
3. Results and Discussion
In figures 4, 5 and 6, the diurnal, monthly and seasonal variations of concentration of Nitrogen Dioxide have been presented. As can be seen in figure 4 the high concentration of Nitrogen Dioxide occurs in the beginning of night time while the least concentration occurs in the beginning of morning. High traffic during these times may be responsible for this high concentration. Monthly concentration of the NO_{x} shows the highest values in December and the least amounts in September (figure 5). Seasonal concentration of the NO_{x} shows the highest values in fall and the least amounts in summer (figure 6). Unfortunately, it is derived that the concentration levels of the Nitrogen Dioxide during critical times like the beginning of night and in the cold months are upper than Primary Standards of Nitrogen dioxide recommended by National Ambient Air Quality Standards (NAAQS) of Iran (0.021 ppm), protecting human health. These results are almost in good agreement with other results regarding Nitrogen Dioxide assessment in other Iranian cities like Shiraz (Ordibeheshti and Rajai poor, 2014), Esfahan (Gerami, 2014) and Tehran (Behzadi and Sakhaei, 2014).
Variations de la concentration diurne de dioxyde d’azote à Ahvaz (20092010)
Variations de la concentration mensuelle de dioxyde d’azote à Ahvaz (20092010)
Variations de la concentration saisonnière de dioxyde d’azote à Ahvaz (20092010)
Table 1 shows the relationships between Nitrogen Dioxide and other air pollutants. For example the concentration of Nitrogen Dioxide shows negative correlation with PM_{10}, while it shows positive correlation with NOx, O_{3 }and SO_{2}. NO_{2 }like most of pollutants is increased when traffic and industrial activity are increased while PM_{10} in this city, its main source is detached soils from western neighbors like Iraq. Correlation coefficients significant at the 0.05 level are identified with a single asterisk (significant), and those significant at 0.01 level are identified with two asterisks (highly significant).
Analysis of variance (a)
Model 
CO 
NO_{x} 
O_{3} 
PM_{10} 
SO_{2} 
Pearson Correlation 
.070 
.888^{**} 
.335^{**} 
.155^{**} 
.443^{**} 
Sig. (2tailed) 
.240 
.000 
.000 
.009 
.000 
N 
286 
286 
286 
286 
286 
Table 1. Correlation between air pollutants and Nitrogen Dioxide.
Coefficients de correlation entre les polluants atmosphériques et le dioxide d’azote.
Table of analysis of variance (table 2) show both regressions of ‘enter’ and ‘stepwise’ methods for annual condition are highly significant indicating a significant relation between the different variables.
Analysis of variance (a)
Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 
Regression 
11943.684 
10 
1194.368 
26.053^{**} 
.000 
Residual 
15678.872 
342 
45.845 

Total 
27622.556 
352 
Dependent Variable: NO_{2}
Predictors: (Constant), Rain, Wind direction (max), Wind speed (max), Temperature (max), Temperature (min), Sunshine Hours, Ratio of Humidity (min), Ratio of Humidity (max) (max), Ratio Humidity (avg), Evaporation.
Analysis of variance (b)
Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 
Regression 
11890.517 
3 
3963.506 
87.927^{**} 
.000 
Residual 
15732.039 
349 
45.077 

Total 
27622.556 
352 
Dependent Variable: NO2
Predictors: (Constant), Wind speed (max), Temperature (min), Evaporation.
Table 2. Tables of analysis of variance for both regressions of ‘enter’ (a) and ‘stepwise’ (b) methods for annual condition.
Table de l’analyse de la variance et des méthodes de regressions “directe” et “pas à pas” au pas de temps annuel.
In table 3 the coefficients of Nitrogen Dioxide pollution model and regression lines for both enter and stepwise methods for annual condition are presented. Regression coefficients, standard errors, standardized coefficient beta, t values, and twotailed significance level of t have been shown in the Tables.
Coefficients (a)
Model 

t 
Sig. 

(Constant) 
15.308 
5.481 
2.793 
.006 

Temperature (max) 
.036 
.182 
.045 
.198 
.843 

Temperature (min) 
.236 
.207 
.227 
1.140 
.255 

Ratio of Humidity (max) 
.377 
.770 
1.187 
.490 
.625 

Ratio of Humidity (min) 
.267 
.765 
.574 
.349 
.727 

Ratio of Humidity (mean) 
.641 
1.538 
1.633 
.417 
.677 

Rain 
.048 
.194 
.012 
.246 
.805 

Sunshine Hours 
.068 
.150 
.030 
.455 
.650 

Evaporation 
.547 
.167 
.390 
3.272** 
.001 

Wind direction (max) 
.002 
.006 
.019 
.344 
.731 

Wind speed (max) 
.161 
.128 
.061 
1.256 
.210 
Dependent Variable: Nitrogen Dioxide
Coefficients (b)
Model 

t 
Sig. 

(Constant) 
21.186 
1.298 
16.318 
.000 

Evaporation 
.689 
.134 
.451 
5.156** 
.000 

Wind speed (max) 
.315 
.126 
.109 
2.507* 
.013 

Temperature (min) 
.230 
.101 
.202 
2.289* 
.023 
Dependent Variable: Nitrogen Dioxide
Table 3. Coefficients of Nitrogen Dioxide pollution model and regression lines for both enter (a) and stepwise (b) methods for annual condition.
Coefficients des modèles de pollution au dioxyde d'azote et méthodes de regression linéaire “directe” (a) et “pas à pas” (b) au pas de temps annuel.
The linear regression equations show that the Nitrogen Dioxide pollution depends on the meteorological parameters and also give an idea about the levels of relations. The linear model equations after using ‘enter method’ and ‘stepwise method’ for annual condition are:
Nitrogen Dioxide amount (ppb) using ‘enter method’ for annual condition = 15.308+
(0.236) Temperature_{(min)}+ (0.036) Temperature_{(max) }+ (0.267) Ratio of Humidity _{(min)}+ (0.377) Ratio of Humidity_{(max) }+ (0.641) Ratio of Humidity_{(avg) }+ 0.048)) Rain + (0.068) Sunshine Hours + 0.002)( Wind direction_{(max) }+ (0.161) Wind speed_{(max) }+ )0.547) Evaporation R= 0.652 (significant at 0.01)
Nitrogen Dioxide amount (ppb) using ‘stepwise method’ for annual condition = 21.186 + (0.689) Evaporation + (0.315) Wind speed_{(max) }+ (0.230) Temperature(min)_{ }_{ }R= 0.678 (significant at 0.05)
Results of linear regression model show Wind speed_{(max)}, Evaporation and Temperature_{(min)} have reverse effect on concentration of NO_{2}, when these parameters increase, the concentration of NO_{2} decreases. Other meteorological parameters show different effects on NO_{2} amounts although they are not significant results. For example Sunshine Hours has positive effect on concentration of NO_{2} (Table 3a). These results are almost in good agreement with other results regarding NO_{2} assessment in other Iranian cities like Tehran (Behzadi and Sakhaei, 2014), Esfahan (Gerami, 2014) and Shiraz (Ordibeheshti and Rajai poor, 2014) and other regions (Elminir, 2005; Li et al., 2014). Actually some of these events happen in real condition. Increasing in rainfall, wind speed and temperature (inversion happens in low temperatures) usually decrease most of air pollutants (Asrari et al., 2007).
The values and significance of R (multiple correlation coefficient) in both equations show capability of them in predicting Nitrogen Dioxide amount. The amount of Adjusted R^{2} in enter model is 0.405 and in stepwise model is 0.455 showing different parameters used can calculate almost 4045% variability of Nitrogen Dioxide. This result indicates for predicting most of air pollutants like Nitrogen Dioxide, we should take into consideration consumption of fossil fuel especially motor vehicles. Half of emission of (VOC) Hydrocarbons and NOx in cities is by motor vehicles. The automobile exhaust produces 75% of total air pollution. Release poisonous gases of CO (77%), NOx (8%) and Hydrocarbons (14%) (Sharma, 2001). On the other hand, R in enter method (0.65) is equaled to stepwise method (0.68), showing no difference. Therefore, second equation based on stepwise method can be used to predict Nitrogen Dioxide in the city instead of using first equation which needs more data. On the other hand, no difference between the two R values indicates that the excluded variables in second equation have less effect on measuring of Nitrogen Dioxide in the city.
Beta in Table 3 shows those independent variables (meteorological parameters) which have more effect on dependent variable (Nitrogen Dioxide). The beta in the both Table 3 shows a highly significant effect of some variables like Evaporation and Temperature compared to other meteorological parameters for measuring the Nitrogen Dioxidewhich is close to the results of Tehran (Behzadi and Sakhaei, 2014), Esfahan (Gerami, 2014) and Shiraz (Ordibeheshti and Rajai poor, 2014). Parameter Sig (Pvalue) from Table 3 shows amount of relation between Nitrogen Dioxide and meteorological parameters. For example, Table 3a shows wind speed has higher effect than wind direction on Nitrogen Dioxide.
On the other hand, in Table 4 the linear regression equations of Nitrogen Dioxide amount for both enter and stepwise methods for different seasonal condition are presented. Almost all of the models except summer model of enter and spring model of stepwise methods are significant which is close to the results of Masoudi et al. (2014). Stepwise methods show those meteorological parameters which are most important during these seasons for estimating the pollution. Again those parameters showing important in annual model like temperature and wind speed are observed as the most important among the others. Among the models, autumn and spring models have the higher R while the R of summer models shows the least which these results for different seasonal condition differ a little from the results of Tehran (Behzadi and Sakhaei, 2014), Esfahan (Gerami, 2014) and Shiraz (Ordibeheshti and Rajai poor, 2014).
season 
enter method 
R 
stepwise method 
R 
Spring 
= 2.946 + (0.126) Temperature_{(min)}+ (0.111) Temperature_{(avg) }+ (0.010) Ratio of Humidity _{(min) }+ (0.002) Ratio of Humidity_{(avg) }+ (0.046) Rain + (0.16) Sunshine Hours + (0.000) Wind direction_{(max) }+ (0.044) Wind speed_{(max) }+ (0.041) Evaporation 
0. 570 (significant at 0.01) 
= 2.056 +(0.041) Wind speed(max) + (0.141) Temperature_{(min)}+ (0.121) Temperature_{(avg)} 
0.510 (not significant) 
Summer 
= 4.362 + (0.089) Temperature_{(min)}+ (0.018) Temperature_{(max) }+ (0.091) Ratio of Humidity _{(min) }+ (0.007) Ratio of Humidity_{(avg) }+ (0.101) Sunshine Hours + (0.001) Wind direction_{(max) }+ (0.029) Wind speed_{(max) }+ (0.100) Evaporation 
0.324 (not significant) 
= 4.190 +(0.129) Sunshine Hours 
0.214 (significant at 0.05) 
Autumn 
= 67.658 + (0.102) Temperature_{(min)}+ (0.929) Temperature_{(max) }+ (0.235) Ratio of Humidity _{(min) }+ (0.108) Ratio of Humidity_{(avg) }+ (0.072) Rain + (0.292) Sunshine Hours + (0.001) Wind direction_{(max) }+ (0.077) Wind speed_{(max) }+ (1.049) Evaporation 
0.562 (significant at 0.01) 
= 49.841 + (1.206)Temperature_{(avg)}+ (0.177) Ratio of Humidity _{(min)} 
0.516 (significant at 0.05) 
Winter 
= 16.614 + (0.180) Temperature_{(min)}+ (0.034) Temperature_{(max) }+ (0.145) Ratio of Humidity _{(min) }+ (0.102) Ratio of Humidity_{(avg) }+ (0. 438) Rain + (0.079) Sunshine Hours + (0.006) Wind direction_{(max) }+ (1.450) Wind speed_{(max) }+ (0.852) Evaporation 
0.535 (significant at 0.01) 
= 20.155 + (1.597) Wind speed_{(max)} 
0.438 (significant at 0.01) 
Table 4. Nitrogen Dioxide amount (ppb) using two methods of enter and stepwise for different seasonal condition.
Les niveaux de dioxide d’azote évalués selon deux méthodes de regression pour différentes conditions saisonnières.
Also the nonlinear multiple regression equation of Nitrogen Dioxide amount using parameters of linear stepwise method for annual condition is calculated which is significant:
Nitrogen Dioxide amount (ppb) using nonlinear regression for annual condition = 58902.611 Evaporation ^{(7.988E005)} + 2574.623 e ^{(153.362)}^{Temperature(min) }+  58875.773+ (3.384)Wind speed+ (0.272) Wind speed ^{2} + (0.004) Wind speed ^{3} R2= 0. 469 (significant at 0.01)
To test which annual model is better to use, RMSE (Root Mean Square of Error) is calculated for different linear models of enter and stepwise and nonlinear model. Predicted amounts using the different annual models for 30 days during 2009 are calculated and compared with observed data during those days using RMSE equation:
O_{obs}: observed NO_{2} value O_{Pre}: predicted NO_{2} value using model
The values of RMSE in both linear models of enter (32.61) and stepwise (5.17) show capability of them in predicting Nitrogen Dioxide amount compared to nonlinear model value (74.18). This result which is the same as the results of Masoudi et al. (2014) indicates for predicting most of air pollutants like Nitrogen Dioxide, we may take into consideration only linear models of stepwise which need less data compared to enter model and also its calculation is easier than nonlinear model.
4. Conclusions
In the current research air quality analyses for Ahvaz, a city in the south of Iran, were conducted for Nitrogen Dioxide (NO_{2}). Ahvaz is one of the most polluted cities in Iran and also world. Hence a need was felt to carry out an ambient air quality analysis in this city. Results showed there were significant relationships between NO_{2} and some meteorological parameters. Based on these relations, different multiple linear and nonlinear regression equations for NO_{2} for annual and seasonal conditions were prepared. Results showed among different prediction models, stepwise model was the best option. Also different variations in concentration during day, months and seasons were observed. Unfortunately, it is derived that the concentration levels of the NO_{2} during critical times like the beginning of night and in the cold months are upper than primary standards of NO_{2} showing unhealthy condition.
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Référence électronique : Masoud Masoudi et Elmira Asadifard « Status and prediction of Nitrogen Dioxide as an air pollutant in Ahvaz City, Iran », Pollution atmosphérique [En ligne], N° 225, mis à jour le : 23/05/2017, URL : http://lodel.irevues.inist.fr/pollutionatmospherique/index.php?id=4827, https://doi.org/10.4267/pollutionatmospherique.4827