Spatio – Temporal Estimation of Carbon Monoxide and Nitrogen Dioxide based on Remote Sensing Data and Ancillary Data in Tehran

Document Type : Research Paper

Authors

1 Department of Remote Sensing and Geographic Information System, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran

2 Department of Remote Sensing and Geographic Information System, Faculty of Humanities, Tarbiat Modares University, Tehran, IranIran

Abstract

Biodiversity is one of the most important indicators of ecosystem diversity and dynamism. Birds, as a clinker indicator of ecosystem biodiversity, are considered as the habitat suitability and other necessary living conditions for any species. Therefore, the study of birds, especially migratory birds, is of particular importance as a clinker indicator. Due to the need for studies in this field, the present study was conducted to investigate the desirability of the habitat and identify the most important environmental variables affecting the distribution of the Anser anser species as a migratory and index species in Iran. Using 23 environmental variables and the nine models in the BIOMOD software package under R software, the Anser anser species is distributed in three types of habitats including winter-passing, summer-passing and breeding and stopeover modeling. The findings from species modeling showed that the models, used in species distribution modeling, have high accuracy in studying species distribution. In general, temperature and precipitation variables are the most important, while the variables such as vegetation and distance to roads are less important in the distribution of Anser anser species in Iran.  According to the results, 15.91% of the surface of Iran was identified as a desirable habitat for Anser anser species, which overlaps with 15.95% of the protected areas. Therefore, the used method in this study identifies the desired habitats of the species correctly. Besides, it can be applied as a suitable method to model the habitat suitability of similar species, which is essential from the perspective of conservation providing comprehensive and practical wildlife management programs.
Extended Abstract
1-Introduction
Bird watching or the scientific study of birds is one of the oldest environmental sciences. The migration of birds, especially aquatic species, has long been of interest to many researchers. Birds, as a clinker indicator of ecosystem biodiversity, are considered as the habitat suitability and other necessary living conditions for any species. Therefore, the study of birds, especially migratory waterfowl, is of particular importance. Due to the need for studies in this field, the present study aims to investigate the desirability of the habitat and identify the most important environmental variables affecting the distribution of Anser anser species as a migratory and index aquatic species in Iran.
2-Materials and Methods
In this study, 10 presence spots for the summer-passing and breeding population, 116 presence spots for overwintering population and 25 presence spots for the passing population of Anser anser have been applied in order to model the distribution of Anser anser species in three habitat types including winter-passing, summer-passing and hatchery, as well as stopping places in Iran. These spots were obtained from the bird census reports of the Environmental Protection Agency. Three groups of environmental variables including topography, climate and land use / land cover were used to investigate the factors affecting the distribution of Anser anser species. Finally, 23 environmental variables were called for modeling in the biomod software package.
3-Results and Discussion
The values ​​of accuracy evaluation indices reveal that models such as FDA and RF have high accuracy in the modeling process out of the nine models implemented in modeling Anser anser stopover. Moreover, variables such as seasonal rainfall, distance to rural areas and the amount of rainfall in the least rainy season play an important role in the selection of stopover by the Anser anser. According to the results of the overlap analysis of the total area of ​​Iran, only 20.99% is known as desirable habitats of this species, which includes 18.69% of the total desirable habitats in protected areas.
Based on from modeling the habitat of summer Anser anser, all the models used in this study have high accuracy in studying the species distribution. On the other hand, the distribution of this species in Iran depends on factors such as distance to wetlands, distance to forest and distance to streams. According to the findings, 3.22% of the total area of ​​Iran is known as a suitable habitat summer Anser anser and overlaps with the presence of species, which is 4.09% of the total summer habitats. Optimal laying and hatching of this species is covered by protected areas.
The results of modeling the Anser anser species in the surface of wintering habitats indicate that all models have high accuracy. The distribution of this species in the surface of wintering habitats is affected by factors such as rainfall, distance to the city and the warmest rainfall of the year. Optimal habitats of this species cover 23.52% of Iran, which is 25.09% of the total desirable habitats of the studied species overlap with protected areas.
Examining the distribution of species at the level of ecological nests and understanding the relationship between environmental variables and the distribution of species using a biomod software package show how species respond to environmental changes at the present time. The findings from evaluating the algorithms used in modeling the habitat types of the Anser anser species reveal that the biomod software package has a high ability to predict the optimal habitats of this species. Thus, it has identified desirable species habitats at the present time, habitats that can be used in the future and have favorable conditions for species introduction, as well as habitats that had ideal conditions in the past. In this regard, the species of Anser anser Choose to spend winters in areas such as Urmia Lake, Fars province, Sistan and Baluchestan, Azerbaijan, Kurdistan and the southern parts of the Caspian Sea and in some eastern and northern areas of the country that have ideal biological conditions. According to the frindings from modeling, the Anser anser is mainly present in summers in Azerbaijan province, especially Urmia Lake and the surrounding wetlands. These areas are more preferred than other parts of the country due to favorable weather conditions, adequate security and availability of food resources. Locations of Anser anser in the country include areas such as the southern shores of the Caspian Sea, parts of the west and northwest, as well as northeastern areas. Proper identification of stops and providing applications to protect these areas is important due to the functional role of stopping places in meeting the needs of migratory species. In general, in the present study, it is found that 15.95% of the total habitats of the Anser anser species are located in protected areas. This indicates the existence of a large part of the desirable habitats of this species outside the protected areas. Therefore, the results of the present study show the need to reconsider the demarcation of protected areas in the future more than in the past in Iran.
4-Conclusion
The current study aims to investigate the distribution of Anser anser species and the effect of environmental variables on the distribution of this species in Iran using a biomod software package.  Based on the results, the method used in this study correctly identifies the desired habitats of the species and can be used as a suitable method to model the habitat suitability of similar species and also to study the biodiversity of habitats. This is essential from the point of view of conservation and the presentation of comprehensive and practical wildlife management programs.
 

Keywords


آل­شیخ، علی­اصغر؛ قراگوزلو، علیرضا؛ سجادیان، مهیار (1391). بهره‌گیری از شبکة عصبی به­منظور استفاده در فرایند مدیریت ریسک زیست‌محیطی ناشی از آلودگی هوای منتج از ترافیک در کلان‌شهر تهران.جغرافیایی چشم‌انداز زاگرس، 4 (14)، 25-38.
اجتهادی، مرجان (1386). بررسی آلودگی هوای شهری ناشی از سامانة حمل‌ونقل با تأکید بر ذرّات معلّق و ارائة راهکارهای مدیریتی (مطالعة موردی، تهران). دهمین همایش ملّی بهداشت محیط، همدان: دانشگاه علوم پزشکی همدان.
حسینی شفیع، روجا؛ علیمحمدی، عباس؛ قاسمیان یزدی، محمدحسن (1395). ارزیابی آثار موجک پایه و تعداد سطوح تجزیه جهت تخمین نقشة تغییرات با استفاده از الگوریتم موجک. سنجش‌ازدور و GIS ایران، 8 (2)، 17-34.
خبری، زهرا؛ موسویان ندوشن، نرجس السادات؛ نژاد کورکی، فرهاد؛ منصوری، نبی­ا... (1392).  تأثیر مدل رقومی ارتفاعی در مدل‌سازی آلودگی هوا با استفاده از ائرمود (AERMOD).سنجش‌ازدور و سامانة اطّلاعات جغرافیایی در منابع طبیعی، 4 (4)، 25-33.
رفیع­­پور، مهرداد؛ آل­شیخ، علی­اصغر؛ علیمحمدی سراب، عباس؛ صادقی نیارکی، ابوالقاسم (1392). مدل‌سازی مکانی غلظت منواکسید کربن در تهران با استفاده از رگرسیون چندمتغیّره و شبکه­های عصبی. همایش ملّی ژئوماتیک، 20. تهران: دانشگاه آزاد اسلامی.
شرعی­پور، زهرا (1388). بررسی تغییرات فصلی و روزانة آلاینده­های هوا و ارتباط آن با پارامترهای هواشناسی. مجلّة فیزیک زمین و فضا، 35 (2)، 119-137.
شرکت کنترل کیفیت هوای تهران (1395). گزارش سالانة کیفیت هوای تهران در سال 1394. تهران: شهرداری تهران.
عبودی، محمدرضا؛ کرمی، جلال؛ شمس‌الدینی، علی (1394). مدل‌سازی خطّی و غیر خطّی آلاینده­های هوای شهر تهران با استفاده از خصیصه­های محیطی و ترافیک.  اوّلین کنفرانس ملّی مهندسی فنّاوری اطّلاعات مکانی. تهران: دانشگاه صنعتی خواجه نصیر طوسی.
متکان، علی‌اکبر؛ شکیبا، علیرضا؛ پورعلی، سید حسین؛ بهارلو، ایمان (1388). تعیین تغییرات مکانی و زمانی آلودگی­های منواکسید کربن و ذرّات معلّق با استفاده از تکنیک­های GIS در شهر تهران.سنجش‌ازدور و GIS ایران، 1(1)، 57-72.
References
Aboodi, M., Karami, J. & Shamsoddini, A. (2015). Linear and nonlinear modeling of air pollutants in Tehran using environmental and traffic characteristics. In: First National Conference on Spatial Information Technology Engineering. Tehran: K. N. Toosi University of Technology. (In Persian)
Agirre-Basurko, E., Ibarra-Berastegi, G. & Madariaga, I. (2006). Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environmental Modelling & Software, 21 (4), 430-446.
Alesheikh, A., Qara Gozlu, A. & Sajjadian, M. (2012). Use of neural network for use in environmental risk management process due to air pollution caused by traffic in Tehran metropolis. Zagros Landscape Geography and Planning, 4 (14), 25-38.(In Persian)
Alimissis, A., Philippopoulos, K., Tzanis, C. G. & Deligiorgi, D. (2018). Spatial estimation of urban air pollution with the use of artificial neural network models. Atmospheric environment, 191, 205-213.
Artis, D. A. & Carnahan, W. H. (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, 12 (4), 313-329.
Chen, G., Knibbs, L. D., Zhang, W., Li, S., Cao, W., Guo, J., ... & Hamm, N. A. S. (2018). Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information. Environmental pollution, 233, 1086-1094.
Chelani, A. B., Rao, C. C., Phadke, K. M. & Hasan, M. Z. (2002). Prediction of sulphur dioxide concentration using artificial neural networks. Environmental Modelling & Software, 17 (2), 159-166.
Coulibaly, P., Dibike, Y. B. & Anctil, F. (2005). Downscaling precipitation and temperature with temporal neural networks. Journal of Hydrometeorology, 6 (4), 483-496.
Ejtehadi, M. (2007). Investigation of urban air pollution caused by transportation system with emphasis on suspended particles and presentation of management solutions (Case study, Tehran). In: 10th National Conference on Environmental Health. Hamadan: Hamadan University of Medical Sciences. (In Persian)
Elangasinghe, M. A., Singhal, N., Dirks, K. N. & Salmond, J. A. (2014). Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmospheric Pollution Research, 5 (4), 696-708.
Gardner, M. W. & Dorling, S. R. (1999). Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmospheric Environment, 33 (5), 709-719.
Grgurić, S., Krizan, J., Gasparac, G., Antonic, O., Spiric, Z.; Mamouri, R. E., Christodoulou, A.; Nisantzi, A., Agapiou, A., Themistocleous, K., Fedra, K., Panayiotou, C. & Hadjimitsis, D. (2014). Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia. Central European Journal of Geosciences. 6 (1), 2-16.
Hewitt, C. N. (1991). Spatial variations in nitrogen dioxide concentrations in an urban area. Atmospheric Environment. Part B. Urban Atmosphere, 25 (3), 429-434.
Hosseini Shafi, R., Ali Mohammadi, A. & Qasemian Yazdi, M. (2016). Evaluation of base wavelet effects and number of decomposition levels to estimate change map using wavelet algorithm. Iranian Remote sensing & GIS, 8 (2), 17-34. (In Persian)
Hrust, L., Klaić, Z. B., Križan, J., Antonić, O. & Hercog, P. (2009). Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmospheric Environment, 43 (35), 5588-5596.
Karatzas, K. D. & Kaltsatos, S. (2007). Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simulation Modelling Practice and Theory, 15 (10), 1310-1319.
Khabari, Z., Mousavian Nodoshan, N., Nejad Korki, F. &  Mansoori, N. (2013). The effect of digital elevation model on air pollution modeling using AERMOD. Remote sensing and GIS in natural resources, 4 (4), 25-33.(In Persian)
Kharytonov, M. M., Khlopova, V. M., Stankevich, S. A. & Titarenko, O. V. (2013). Remote and ground-based sensing of air polluted by nitrogen dioxide in the Dnepropetrovsk region (Ukraine). In Disposal of Dangerous Chemicals in Urban Areas and Mega Cities (pp. 291-298). Springer, Dordrecht.
Knelson, J. H. & Lee, R. E. (1977). Oxides of nitrogen in the atmosphere: origin, fate and public health implications. Ambio, 6 (2/3), 126-130.
Koller, D. & Sahami, M. (1996). Toward optimal feature selection. Stanford InfoLab.
Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., ... & Cawley, G. (2003). Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmospheric Environment, 37 (32), 4539-4550.
Mao, L., Qiu, Y., Kusano, C. & Xu, X. (2012). Predicting regional space–time variation of PM 2.5 with land-use regression model and MODIS data. Environmental Science and Pollution Research, 19 (1), 128-138.
Martin, R. V. (2008). Satellite remote sensing of surface air quality. Atmospheric environment, 42 (34), 7823-7843.
Matkan, A., Shakiba, A., Pour Ali, S. & Baharlou, I. (2009). Determination of spatial and temporal variations of carbon monoxide and particulate pollutants using GIS techniques in Tehran. Iranian Remote sensing & GIS, 1 (1), 57-72. (In Persian)
McKendry, I. G. (2002). Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2. 5) forecasting. Journal of the Air & Waste Management Association, 52 (9), 1096-1101.
Montero-Lorenzo, J. M., Fernández-Avilés, G., Mondéjar-Jiménez, J. & Vargas-Vargas, M. (2013). A spatio-temporal geostatistical approach to predicting pollution levels: The case of mono-nitrogen oxides in Madrid. Computers, Environment and Urban Systems, 37, 95-106.
Nisantzi, A., Hadjimitsis, D. G., Akylas, E., Agapiou, A., Panayiotou, M., Michaelides, S., & Paronis, D. (2013). Study of air pollution with the use of modis data, lidar and sun photometers in Cyprus. In Advances in Meteorology, Climatology and Atmospheric Physics (pp. 1133-1139). Springer, Berlin, Heidelberg.
Perez, P. & Trier, A. (2001). Prediction of NO and NO2 concentrations near a street with heavy traffic in Santiago, Chile. Atmospheric Environment, 35 (10), 1783-1789.
Pfeiffer, H., Baumbach, G., Sarachaga-Ruiz, L., Kleanthous, S., Poulida, O. & Beyaz, E. (2009). Neural modelling of the spatial distribution of air pollutants. Atmospheric Environment, 43 (20), 3289-3297.
Rafiepour, M., Alesheikh, A., Ali Mohammadi Sarab, A. & Sadeghi Niaraki, A. (2013). Spatial modeling of carbon monoxide concentration in Tehran using multivariate regression and neural networks. In: National Geomatics Conference. Tehran: Islamic Azad University. (In Persian)
Shamsoddini, A., Aboodi, M. R. & Karami, J. (2017). Tehran air pollutants prediction based on random forest feature selection method. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 483-488.
Shamsoddini, A., Raval, S. & Taplin, R. (2014). Spectroscopic analysis of soil metal contamination around a derelict mine site in the Blue Mountains, Australia. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 75-79.
Sharie pour, Z. (2009). Investigation of seasonal and daily changes of air pollutants and its relationship with meteorological parameters. Journal of Earth and Space Physics, 35 (2), 119-137. (In Persian)
Shi, J. P. & Harrison, R. M. (1997). Regression modelling of hourly NOx and NO2 concentrations in urban air in London. Atmospheric Environment, 31 (24), 4081-4094.
Snyder, W. C., Wan, Z., Zhang, Y. & Feng, Y. Z. (1998). Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19 (14), 2753-2774.
Siwek, K. & Osowski, S. (2012). Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Engineering Applications of Artificial Intelligence, 25 (6), 1246-1258.
Tehran Air Quality Control Company (2016). Annual Report of Tehran Air Quality In 1394. Tehran: Municipality of Tehran. (In Persian)
Tomczak, M. (1998). Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW)-cross-validation/jackknife approach. Journal of Geographic Information and Decision Analysis, 2 (2), 18-30.
Zhang, H., Wu, W. & Yao, M. (2012 a). Boundedness and convergence of batch back-propagation algorithm with penalty for feedforward neural networks. Neurocomputing, 89, 141-146.
Zhang, Y., Yiyun, C., Qing, D. & Jiang, P. (2012 b). Study on urban heat island effect based on Normalized Difference Vegetated Index: A case study of Wuhan City. Procedia environmental sciences, 13, 574-581.