Investigating the Relationship among Climate Factors and Air Pollution Fluctuation (Case Study of the City of Mashhad)

جغرافیا و مخاطرات محیطی

Article 1, Volume 4, Issue 2 - Serial Number 14, January 2016, Pages 77-94   

Document Type: Research Article

DOI: 10.22067/geo.v4i2.31769

Authors

Mohammad Saligheh; Hamid Kakhaki Mehneh^*

Kharazmi University

Abstract

1. Introduction
During the past decade, many efforts for investigating the relationship among air pollution and meteorological factors have been undertaken and some statistical methods have been proposed. Population increase and technological advancement were among the factors affecting creation and extension of air pollution in big cities, forcing large cities all over the world to contemplate measures to curb and prevent the spread of pollutants. Air pollution is emblematic of a wide range of impacts on biological, physical and economic systems. In recent years, air quality has been debated as a pivotal factor in materialization of the quality of life in urban areas, specifically in densely populated and industrial areas. In the present research, in order to evaluate air pollution in the city of Mashhad, eleven air-monitoring stations were used and several parameters for air pollution were studied. The main purpose of the research is to use a model which can establish a link between weather and pollutant factors as well as to identify climate factors affecting air pollution in the city of Mashhad and predict the density of pollutant gases using neural network and linear regression.

2. Study Area
Mashhad Metropolis, the capital of Khorasan Razavi province spanning over a land area of 204 square kilometers, located in northeast of Iran at a longitude of 59 degrees and 15 minutes and a latitude of 35 degrees and 43 minutes up to 38 degrees and 8 minutes in the vicinity of Abrood, Kashafrood, is flanked by Hezarmasjed and Binalood mountains. It has a cold and arid climate and has its own specific climate and regional specifications. The preponderance of the city has a cold and air climate, with some cold and semi-arid regions and a small cold and wet section situated in the loftiest heights of Hezarmasjed and Binalood mountains. Overall, the city of Mashhad has a changing climate, inclining towards cold and arid.

3. Material and Methods
Data and information regarding pollutants in the city of Mashhad for a one-year period was obtained from Khorasan Razavi’s environment organization. The data is collected from eleven air-monitoring stations on an hourly basis. The average for each day has been used as the data for that day. Then meteorological data, including wind speed; wind direction; rain; air pressure; relative humidity; average temperature and maximum and minimum temperature were obtained from meteorological organization. After collecting the required data and information, first the average for the data concerning air pollutants, gathered on an hourly basis, was calculated. Then using ArcGis, climate parameters and pollution information were merged, using merge button, to form a coherent daily output. The next step involved using inverse distance weighting interpolation, which is one of the methods used in meteorological and geographical studies. In this method, for the purpose of prediction in locations where data is not collected, the data gathered in the vicinity of the intended location is used. In the next stage, the correlation among meteorological data and air pollutants was calculated. Finally, Neuorosolutions was used in order to predict non-linear factors and teach the neural network.

4. Conclusion
The findings of the study reveal that the potential neural network, compared to regression and multilayer perceptron, was able to establish a link between meteorological parameters and the density of pollutants (NO-O3-CO-SO2) in the city of Mashhad. The aim of the present study is to use a model which can establish a link between climate factors and air pollutants and identify the climate factors which affect air pollution. The result of sensitivity analysis graph indicates that among the climate factors affecting the pollutant of CO, relative humidity at the hour (12:30) and the direction of wind are the most influential. It was also revealed that relative humidity at the hour (6:30) and maximum absolute temperature are the climate factors affecting the density of sulphur dioxide. As for the graphs depicted in the study, it was revealed that the potential neural network, compared to the other two models, was able to establish a more logical relation among air pollutants and climate factors. In the potential neural network for carbon monoxide, the Mean Square Error (MSE) was calculated to be 66.61 and in regression model it was 76.92. Similarly, the Mean Absolute Error (MAE) was 3.005 and 3.12 respectively. In the neural network model for sulphur dioxide, MSE was calculated to be 14.12 and MAE was 2.15; while in the regression model these measurements were 14.003, -0.18 and 2.13 respectively.

Keywords

Pollution; Pollutant; Influential climate factors; Potential neural network; Multilayer perceptron; Regression

References

انتظاری، علی‌رضا (1384)؛ مطالعه آماری وسینوپتیکی آلودگی هوای تهران؛ استاد راهنما، بهلول علیجانی‌. پایان نامه دکتری آب وهواشناسی؛ دانشگاه تربیت معلم.

اصغری مقدم، اصغر; نورانی، وحید; ندیری، عطاالله.(1387). مدل سازی بارش دشت تبریز با استفاده از شبکه‌های عصبی مصنوعی، مجله دانش کشاورزی، شماره 1، صص1-15.

تقوی، هدی. دانش، شهناز. مساعدی ابولفضل;(1391); مقایسه کارایی مدل های شبکه عصبی مصنوعی ورگرسیون خطی در پیش‌بینی غلظت روزانه منواکسید کربن بر اساس پارامتر های هواشناسی. همایش ملی جریان و آلودگی هوا. صص1-6.

خزاعی، الهه.آل شیخ ، علی اصغر.کریمی، محمد.وحیدنیا، محمدحسن(1391);مقایسه دو روش مدل سازی با استفاده از شبکه عصبی-فازی در پیش‌بینی غلظت آلاینده منواکسید کربن.مجله محیط شناسی، شماره4 .صص 29-44.

علیجانی؛ بهلول،. نجفی نیک؛ زهرا؛ (1388). بررسی الگوهای سینوپتیکی اینورژن در مشهد با استفاده از تحلیل عاملی. جغرافیا وتوسعه ناحیه‌ای؛ شماره12، صص1- 11.

حبیب‌پور، کرم; صفری، رضا. (1390) راهنمای جامع کاربرد SPSS در تحقیقات پیمایشی. چاپ چهارم.انتشارات لویه. صص444-447.

حقیقت ضیابری،سیده مرضیه;حقیقت ضیابری،سیده مریم;دوستان،رضا;حسین زاده،سیدرضا.(1392).بررسی الگوهای همدید موثر بر آلودگی هوا در کلان شهر مشهد. ،مجله جغرافیا وتوسعه ناحیه ای ;صص 82-100

گلریزضیایی، زهرا.صادقی حصار، حمید.معتمدی، محمد.ژاله رجبی، میترا.پیش‌بینی، ارزش گذاری و ترسیم نقشه آلودگی هوای شهر مشهد: مطالعه موردی گاز منواکسید کربن(1389)، صص1-11.

میرستار، صدرموسی. رحیمی؛ اکبر. (1388). مقایسه نتایج شبکه های عصبی پرسپترون چندلایه با رگرسیون چندگانه در پیش بینی غلظت ازن در شهر تبریز. پژوهش‌های جغرافیای طبیعی؛ شماره71؛ صص 65-72.

موسوی، محمد. معروف؛ محمدصادق. نائینی؛ امین. (1381). بررسی پتانسیل تشکیل مه‌دود فتوشیمیایی ناشی از صنایع در مشهد؛ نشریه‌ نیوار. صص 44و45.

یاوری، حسین. سلیقه؛ محمد.(1390). سطوح وارونگی در آلودگی‌های شهرتهران. تحقیقات کاربردی علوم جغرافیایی؛ شماره20، صص 89- 105.

Croxford, B., Penn, A., Hillier, B. (1996). Spatial distribution of urbanollution: civilizing urban traffic, Science of the Total Environment, 190, 3–9.

Comrie،AndrewC.(1997).Comparing eural networks and regression models for ozone forecasting, Journal of Air & Waste Management, 47(6), 653- 663.

Conrads، P.A., Roehle, E. A. (1999). Comparing Physics- Based and Neural Network Mo

Simulating Salinity، Temperature and Dissolved in a Complex، Tidally Affected River Basin،Proceeding of the South Carolina Environmental Conference, 1-15.

Deleaw, S., Kusznir, J., Lamb, B., Cook, D. (2010)، Predicting air quality

in smart environments, Journal of Ambient Intelligence and Smart Environments,2,145- 154.

Fabio Tramuto. Cusimano, R., Cerame, G., Vultaggio, M., Calamusa, G., Maida,

G. M., Vitale, F. (2011). Urban air pollution and emergency room admissions for

respiratory symptoms: a casecrossover study in palermo، italy. Environmental

Health, 10(31), 1-11.

Fulop, I. A., Jozsa، J., Karamer. T. (1998). A neural network application in estimating wind

induced shallow lake motion. Hydro informatics, 98, 753-757.

Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., Brasseur, O. ( 2005). A neural network forecastfor daily average PM10 concentrations in Belgium. Atmospheric Environment, 39, 3279–3289.

Piringer, M., Kukkonen, J. (2002). Mixing height and inversions in urban

areas. In: Piringer, M., Kukkonen, J. (Eds. ). Proceedings of workshop, 3–4October 2001، Toulouse, France. COST Action 715، EUR 20451,EuropeanCommission، Brussels.،113.

Kaan Yetilmezsoy. Erturk،F.. (2010). A neural network- based approach for the

prediction of urban SO2 concentrations in the Istanbul metropolitan area. Int. J. Environment and Pollution, 40 (4),301- 3 19.

Ranjithan, S., Eheart, J. W., & Garrett, J. H. (1995). Application of neural network in groundwater remediation under conditions of uncertainty. In Z. W. Kundzewicz (Ed.), New uncertainty concepts in hydrology and water resources,133-140. Cambridge University Press.

Vedat, U., Ozkan, O., Koyuncu, I., Deniz Demirba, ،K. (2012). Modeling Air Pollutantsin Turkey with Artificial Neural Network and Multiple LineerRegression. Iccit, 79- 83.