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Introduction

Air pollution in North Africa and the Middle East is receiving increase attention due to its health consequences.^1,2,3,4The Middle East is impacted by frequent dust storms in addition to regional long range transport of air pollution, carried by winds from three neighboring continents: Europe, Africa, and Asia.⁵

The Hashemite Kingdom of Jordan with a population of eleven million and a land area of 89,000 square kilometers, has undergone an unprecedented rate of growth in the last fifteen years due to regional political crises. Rapid development of the industrial sector, coupled with the lack of zoning and environmental protection legislation, has contributed to the deterioration of the Jordanian environment. For example, emissions from motor vehicles or old industrial establishments are not regulated throughout the country.⁶

Anthropogenic sources of air pollution in north Jordan include motor natural dust, vehicles, utility, smelters, cement factories, and open burning. The city of Zarqa at the middle region in Jordan where the Hashemite University Campus (HUC) is located is exhibiting rapid growing industrial activities with a population of about one million inhabitants.⁷它包含超过35%的约旦类y by number including an oil refinery, a thermal power plant, steel factories, a pipe factory, a cement factory, a fertilizers factory, a waste water treatment plant, as well as several other small industrial facilities.⁴As a result of such concentrated anthropogenic activities, air quality in Zarqa is questionable.

This paper aims at studying air quality at HUC and examined the influence of weather conditions on the concentrations of NO, NO₂, SO₂, and Ozone. More than 40,000 students and employees are spending most of their daily hours inside the campus, which is located downwind from the oil refinery and the thermal power plant.

Methodology

Sampling Location

The Hashemite University (Figure 1) is a public Jordanian university commissioned in the academic year 1995/1996. It has become one of the largest universities in Jordan. Total area of the campus is about 8500 hectares; 15% of which are designated for buildings, roads, sport facilities and other structures. The rest is meant to be planted by different tress, but in reality only one fourth of the campus is planted with olive trees. The Hashemite University has 13 Colleges including the Faculty of Natural Resources and Environment which owns and operates the air quality monitoring station that is deployed to collect required data for this study.

Figure 1: sample location at the campus of the Hashemite University, (32.099207, 36.200353) coordinates.[8] Click here to View figure

Data Collection

Instrumentation

Air quality and meteorological parameters were monitored by the air quality monitoring station in the research and teaching laboratory in the faculty of natural resources and environment inside the campus of the Hashemite University. The station is designed to provide continuous readings of criteria atmospheric pollutants including O₃, NO₂, NO, NO_xand SO₂concentrations; and meteorological parameters such as temperature, relative humidity, and wind speed. Continuous automatic measurement of above parameters were recorded every five minutes for the periods of1/1/2012 to 30/12/2013. Ozone is measured by a UV Photometric O₃Analyzer, NO_xby a Chemiluminnescence NO-NO₂-NO_xAnalyzer and SO₂by pulsed Fluorescence SO₂Analyzer (Thermo Fisher Scientific, Waltham, MA-USA). Mention reference for methodology.

Statistical Analysis

Collected air quality data was exported into SPSS software format in order to perform advanced statistical analysis including basic statistics; temporal variability and correlations multivariate.

Results and Discussion

Statistical Characterization of Climatic Variables and Air Pollutants

Data analyses were started by calculating ordinary statistics including mean, standard deviation (SD), maximum, minimum, Coefficient of Variation (CV), Skewness, and Kurtosis (Table 1).

Table 1: Descriptive statistics for Air Pollutants and climatic variables.

Parameter	Min	Max	Average	SD	CV	Skewness	Kurtosis
NOx	0	390.16	9.03	9.20	101.81	77.7	159.39
NO2	0	410.14	15.67	11.90	75.97	3.69	58.06
NO	0	228.76	1.17	2.94	250.20	16.98	744.03
O3	0	500.31	55.56	23.78	42.81	0.72	1.13
SO2	0	221.41	2.83	4.95	174.90	12.57	240.02
Wind Direction	0.76	350.04	225.46	70.10	31.09	-0.66	-0.52
Wind Speed	0.271	29.70	3.35	2.29	68.43	1.04	1.54
RH%	5.31	97.90	51.50	20.89	40.55	-0.09	-1.08
Temperature	-4.45	43.34	18.40	7.52	40.87	0.08	-0.61

Temporal Variation of Oxides

The results of linear regression analyses for SO₂, NO, NO₂, NOx and O₃in time, daily, monthly, and yearly basis, using ANOVA tests, are presented in Tables 2-5, respectively. The results indicate that the changes are not exactly linear, however the attenuations could be attributed to many factors such as human induced activities (e.g. daily factories working hours, vacations, diversity of burning fuel type, factory malfunctions, etc) or could be attributed to climatic variables as temperature, wind speed and relative humidity. The severity of the change in pollutant concentration with time can be estimated by the magnitude of the slope.

Table 2: Changes of air pollutants on Time basis

Pollutants	Linear Equation	F-test	R2	RMSE
SO2	SO2= 36.16 - 9.6879e-9*Time	<.0001*	0.001271	54.9
NO	NO = 5.32 - 1.2099e-9*Time	0.0002*	6.426e-5	52.7
NO2	NO2= 515.51 - 1.4533e-7*Time	0.0000*	0.049919	511.5
NOx	NOx= -74.96 + 2.44e-8*Time	<.0001*	0.002371	9.12
O3	O3= -1071.84 + 3.28e-7*Time	<.0001*	0.061755	23.27

Table 3: Changes of air pollutants over Day Time

Pollutants	Linear Equation	F-test	R2	RMSE
SO2	SO2= 2.90 - 0.00459*Day	0.0002*	6.64e-5	4.95
NO	NO = 1.04 + 0.01*Day	<.0001*	0.00056	2.75
NO2	NO2= 14.69 + 0.054*Day	<.0001*	0.001599	11.84
NOx	NOx= 8.87 + 0.01*Day	0.0010*	5.111e-5	9.13
O3	O3= 55.1 + 0.055*Day	<.0001*	0.000404	24.02

Table 4: Changes of air pollutants over Month Time

Pollutants	Linear Equation	F-test	R2	RMSE
SO2	SO2= 3.053 - 0.034*Month	<.0001*	0.000553	4.95
NO	NO = 0.66 + 0.08*Month	0.0000*	0.009068	2.74
NO2	NO2= 6.49 + 1.38*Month	0.0000*	0.159523	10.86
NOx	NOx= 7.49 + 0.23*Month	0.0000*	0.007333	9.099
O3	O3= 36.28 + 3.002*Month	0.0000*	0.183735	21.71

Table 5: Changes of air pollutants over years:

Pollutants	Linear Equation	F-test	R2	RMSE
SO2	SO2= 549.84 - 0.27*Year	<.0001*	0.000754	4.95
NO	NO = 721.74 - 0.36*Year	<.0001*	0.004239	2.74
NO2	NO2= 23338.92 - 11.59*Year	0.0000*	0.239152	10.33
NOx	NOx= -230.15 + 0.12*Year	0.0027*	4.231e-5	9.13
O3	O3= -3877.79 + 1.95*Year	<.0001*	0.001654	24.01

Comparison between Mean Values using Tukey-Kramer HSD

Monthly Variation

A comparison between mean values calculated using the Tukey-Kramer HSD method is presented in Table (6) for monthly variation of climatic variables and air pollutants. Concentrations are classified into classes where class A denotes highest concentration, while class L represents the lowest concentration or pollution level. For example higher ozone values are recorded in September and August, whereas January and February experienced the lowest O₃concentration. Temperature exhibits a similar trend.

Table 6: Monthly variation and means of Climatic Variables and Air Pollutants through months.

Month	Climatic Variables				Air Pollutants
	Temperature	RH%	Wind Speed	Wind Direction	SO2	NOx	NO	NO2	O3
1	11.17J	62.05A	3.05G	194.83JK	4.01A	9.14CD	1.20D	9.53I	32.49L
2	11.90I	58.89B	3.35E	203.27I	2.94D	9.38C	1.08E	9.77HI	37.75K
3	15.35G	55.23D	3.29EF	216.37G	2.59E	9.38C	0.92F	10.05H	40.28J
4	18.44F	44.03H	3.23F	232.51F	2.41F	8.88D	0.81GH	9.66I	45.75I
5	21.87E	41.04I	3.54D	243.52E	2.72E	8.40E	0.84FG	11.94G	51.57H
6	23.54B	46.04G	4.18A	247.57D	3.46C	7.84F	1.06E	18.26C	60.23D
7	23.38B	53.09E	3.70C	257.64A	3.30C	6.61G	0.93F	17.20D	59.31E
8	24.69A	48.70F	4.02B	254.29B	1.99H	5.46I	0.76GH	14.84F	82.17A
9	22.50C	52.67E	3.65C	251.00C	1.33I	5.83H	0.74H	16.64E	78.62B
10	22.18D	37.31J	2.65I	209.22H	2.19G	11.06B	1.32C	21.55B	66.25C
11	13.32H	56.99C	2.57J	192.58K	3.37C	13.18A	2.06B	23.34A	57.01F
12	11.11J	62.48A	2.80H	196.11J	3.78B	13.25A	2.20A	23.19A	54.35G

The concentrations of NO, NO₂,NO_xand SO₂are high in winter months (January, February, November and December), and low in Spring and Summer months ( April, May, June, July, August and September). It seems that in June month there is relatively high concentration of these pollutants and the reason for this also, is due to low abnormal temperature or heavily working hours at that time. Whereas the concentration of O₃高夏季(6月、7月、8月和September) and low in winter and Autumn months ( January, February, November and December) and this because Ozone is a greenhouse gas depends on temperature in its formation which they have a strong positive relationship between them and have the same tend.

All of air pollutants including (NO, NO₂, NO_x, SO₂) have shown a negative relationship with ozone concentration. The levels of various air pollutants are closely correlated with the level of heavily working hours. Therefore heavily working hours affects the concentration of these pollutants.

Annual Variation

Table (7) indicates that there is little deferent between annual means of air pollutants and meteorological parameters as calculated using the nonlinear regression analysis of the Tukey-Kramer HSD. Temperature, SO₂, NO and NO₂are higher in the year of 2012 which take A class, but considered to be class B in 2013. However, O₃, NO_x, RH%, and wind speed are higher in 2013.

Table 7: Annual variation and meansof meteorological parameters and air pollutants

Year	Climatic Variables				Air Pollutants
	Temperature	RH%	Wind Speed	Wind Direction	SO2	O3	NO	NO2	NOx
2012	18.9A	50.6B	3.33B	225.13A	2.96A	54.96B	1.34A	21.44A	8.92B
2013	17.9B	52.3A	3.35A	225.65A	2.69B	56.91A	.098B	9.85B	9.04A

Stepwise Regression Analysis (Fit Model)

According to stepwise regression analyses using Fit Model algorithm within JMP software, indicated that all climatic variables are highly effective on pollutant concentrations (P<0.0001) and thus cannot be removed from the full model. According to Table (8) the concentration of pollutants varies in its relation to climatic variables. The rate of each climatic variable on the final prediction of the air contaminant can be distinguished by the associated slope that has a scientific meaning Figure (2) to Figure (13).

表8:Step wise regression analyses for the measured air pollutants as affected by all Climatic variables.

Parameter	SWR Equation	F-test	R2	RMSE
SO2	SO2= 5.5120626-0.008771*Temperature-0.035511* RH%-0.207024*Wind Speed	0.0000*	0.02342	4.89
NO	NO=2.1239881-0.017782*Temperature-0.007371* RH%-0.077986*Wind Speed	<.0001*	0.00671	42.7
NO2	NO2=13.049102+0.2281184*Temperature+0.0308087*RH%-0.987845*Wind Speed	0.0000*	0.03323	511.6
NOx	NOx=16.603619-0.138474*Temperature-0.04792* RH%-0.779246*Wind Speed	0.0000*	0.05318	98.8
O3	O3=5.90+1.92*Temperature+0.159*RH%+1.97*Wind Speed	0.0000*	0.37529	18.99

Figure 2:NOxrelationship with temperature and their correlation Click here to View figure

Figure 3:NOxrelationship with Relative Humidity and their correlation Click here to View figure

Figure 4:NOxrelationship with wind speed and their correlation Click here to View figure

Figure 5:NO relationship with temperature and their correlation Click here to View figure

Figure 6:NO relationship with relative humidity and their correlation Click here to View figure

Figure 7:NO relationship with wind speed and their correlation Click here to View figure

Figure 8:NO2relationship with temperature and their correlation Click here to View figure

Figure 9:NO2relationship with relative humidity and their correlation Click here to View figure

Figure 10:NO2relationship with wind speed and their correlation Click here to View figure

Figure 11:SO2relationship with temperature and their correlation Click here to View figure

Figure 12:SO2relationship with relative humidity and their correlation Click here to View figure

Figure 13:SO2relationship with wind speed and their correlation Click here to View figure

Multivariance correlation between climatic variables and air pollutants

The Multivariance method is deployed to retrieve correlations between air pollutants and basic meteorological parameters. Results are summarized in Table (9):

Table 9: Correlation between air pollutants and meteorological

	Temperature	RH%	Wind Speed	SO2	NOx	NO	NO2	O3
Temperature	1.0000	-0.69	0.38	0.05	-0.114	-0.035	0.035	0.58
RH%	-0.69	1.000	-0.23	-0.12	0.014	-0.008	-0.002	-0.32
Wind Speed	0.38	-0.23	1.0000	-0.07	-0.21	-0.071	-0.14	0.38
Wind Direction	0.31	-0.02	0.33	-0.1	-0.04	-0.03	0.007	0.31
SO2	0.05	-0.12	-0.07	1.0000	0.25	0.24	0.14	-0.07
NOx	-0.114	0.014	-0.21	0.25	1.0000	0.62	0.65	-0.25
NO	-0.04	-0.008	-0.07	0.24	0.62	1.0000	0.39	-0.12
NO2	0.035	-0.002	-0.15	0.14	0.65	0.39	1.0000	-0.02
O3	0.58	-0.32	0.38	-0.07	-0.25	-0.12	-0.02	1.000

Based on the findings presented in Table (9), the following remarks are derived:

Temperature has strong negative relationship with RH%, weak negative relationship with NO_xand NO, moderate positive relationship with Wind Speed, Wind Direction, week positive relationship with NO₂and SO₂, and strong positive relationship with O₃.

RH %和建立具有较强的负相关关系ure, moderate negative relationship with wind speed, weak negative relationship with O₃, SO₂, NO, and NO₂. However, it has weak positive relationship with Wind Speed and NO_x.

Wind Speed has weak negative relationship with SO₂, NO, NO₂, moderate negative relationship with RH%, NOx, and moderate positive relationship with Temperature and Wind Direction and O₃.

SO₂has weak negative relationship with Wind Speed, Wind Direction, RH%, and O₃, and weak positive relationship with Temperature, NO₂, moderate positive relationship with NO_xand NO.

O₃has weak negative relationship with SO₂, NO, NO₂, moderate negative relationship with RH%, NO_x, moderate positive relationship with Wind Speed, and Wind Direction, and strong positive relationship with temperature.

NO has weak negative relationship with temperature, wind speed and wind direction, RH% and O₃, moderate positive relationship with SO₂, NO₂, strong positive relationship with NO_x.

NO₂has weak negative relationship with wind direction, RH%, O₃, weak positive relationship with temperature, SO₂and wind Speed, moderate positive relationship with NO, and strong positive relationship with NO_x.

NO_xhas weak negative relationship with temperature, wind direction, and moderate negative relationship with Wind speed, O₃and weak to moderate positive relationship with SO₂, RH% and strong positive relationship with NO, NO₂.

Conclusion

The findings of this study show that variation trends of SO₂, NO, NO₂, NOx and O₃correlate well with meteorological conditions parameters. O₃demonstrates a positive correlation with air temperature, wind speed and wind direction and a negative correlation with the relative humidity. SO₂has a negative correlation with the relative humidity and wind speed and a positive correlation with air temperature. NO shows a negative correlation with air temperature, relative humidity and wind speed. And NO₂demonstrates a negative correlation with relative humidity and wind speed and a positive correlation with air temperature.

Acknowledgment

我们感谢所有员工在空气质量监视器ring station in the research and teaching laboratory in the faculty of natural resources and environment inside the campus of the Hashemite University for the logistic support to conduct our current research work.

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