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Influence of Meteorological Parameters on Air Quality at Hashemite University, Jordan

Sana A Odat1*, Mahmoud Abu-Allaban2and Khitam Odibat2

1Department of Water Management & Environment, The Hashemite University, Faculty of Natural Resources and Environment, Zarqa, Jordan

Corresponding author Email:sanaa.owdat@yu.edu.jo

DOI:http://dx.doi.org/10.12944/CWE.12.2.04

Four threshold air pollutants (SO2, NO, NO2, and O3) in addition to meteorological parameters were monitored at the Campus of the Hashemite University (HU) for two years (1/1/2012 through 30/12/013). Correlations between air pollution and meteorological parameters were derived. The results showed that O3has a positive correlation with air temperature, wind speed and wind direction, but has a negative correlation with the relative humidity (RH). SO2was found to have a negative correlation with the RH and wind speed, but positive correlation with air temperature. NO has negative correlation with air temperature, RH, and wind speed. And finally, NO2has a negative correlation with RH and wind speed, but it has positive correlation with air temperature. Justify the reasons in brief with recommendations to improve the air quality


Air Pollutants; Sulfur Dioxide; Nitrogen Oxides; Ozone; Meteorological Parameters

Copy the following to cite this article:

Odat S, Abu-Allaban M, Odibat K. Influence of Meteorological Parameters on Air Quality at Hashemite University, Jordan. Curr World Environ 2017;12(2). DOI:http://dx.doi.org/10.12944/CWE.12.2.04

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Odat S, Abu-Allaban M, Odibat K. Influence of Meteorological Parameters on Air Quality at Hashemite University, Jordan. Curr World Environ 2017;12(2). Available from://www.a-i-l-s-a.com/?p=17515


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Received: 2017-07-12
Accepted: 2017-08-13

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]



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, NOxand 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, NOxby a Chemiluminnescence NO-NO2-NOxAnalyzer 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,NOxand 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, NOx, 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, NOx, 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): NOx relationship with temperature and their correlation



Figure 2:NOxrelationship with temperature and their correlation
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Figure (3): NOx relationship with Relative Humidity and their correlation



Figure 3:NOxrelationship with Relative Humidity and their correlation
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Figure (4): NOx relationship with wind speed and their correlation



Figure 4:NOxrelationship with wind speed and their correlation
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Figure (5): NO relationship with temperature and their correlation



Figure 5:NO relationship with temperature and their correlation
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Figure (6): NO relationship with relative humidity and their correlation



Figure 6:NO relationship with relative humidity and their correlation
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Figure (7): NO relationship with wind speed and their correlation



Figure 7:NO relationship with wind speed and their correlation
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Figure (8): NO2 relationship with temperature and their correlation



Figure 8:NO2relationship with temperature and their correlation
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Figure (9): NO2 relationship with relative humidity and their correlation



Figure 9:NO2relationship with relative humidity and their correlation
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Figure (10): NO2 relationship with wind speed and their correlation



Figure 10:NO2relationship with wind speed and their correlation
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Figure (11): SO2 relationship with temperature and their correlation



Figure 11:SO2relationship with temperature and their correlation
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Figure (12): SO2 relationship with relative humidity and their correlation



Figure 12:SO2relationship with relative humidity and their correlation
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Figure (13): SO2 relationship with wind speed and their correlation



Figure 13:SO2relationship with wind speed and their correlation
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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 NOxand 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 NOx.

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 NOxand NO.

O3has weak negative relationship with SO2, NO, NO2, moderate negative relationship with RH%, NOx, 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 NOx.

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 NOx.

NOxhas 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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