Artificial neural network forecast application for fine particulate matter concentration using meteorological data

Memarianfard, M.; Hatami, A.M.; Memarianfard, M.

doi:10.22034/gjesm.2017.03.03.010

Document Type : SHORT COMMUNICATION

Authors

Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran

https://doi.org/10.22034/gjesm.2017.03.03.010

Abstract

Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM_2.5 dispersion in Tehran City. Factors which are influencing the predicted value consist of weather-related and air pollution-related data, i.e. wind speed, humidity, temperature, SO₂, CO, NO₂, and PM_2.5 as target values. These factors have been considered in 19 measuring stations (zones) over urban area across Tehran City during four years, from March 2011 to March 2015. The results indicate that the network with hidden layer including six neurons at training epoch 113, has the best performance with the lowest error value (MSE=0.049438) on considering PM_2.5concentrations across metropolitan areas in Tehran. Furthermore, the “R” value for regression analysis of training, validation, test, and all data are 0.65898, 0.6419, 0.54027, and 0.62331, respectively. This study also represents the artificial neural networks have satisfactory implemented for resolving complex patterns in the field of air pollution.

Graphical Abstract

Highlights

Critical importance of Tehran's air particulate matters pollution using artificial neural network
PM_2.5 temporal prediction has been estimated in Great Tehran
Using ANN approach for modeling and the data was divided into three groups of training, validating, and testing of the network
The function optimization technique used is the scaled conjugate gradient algorithm

Keywords

References

Feng, X.; Li, Q.; Zhu, Y.; Hou, J.; Jin, L.; Wang, J., (2015). Artificial neural networks forecasting of PM_2.5 pollution using air mass trajectory based geographic model and wavelet transformation, Atmos. Environ., 107: 118-128 (11 Pages)

Fierer, N.; Liu, Z.; Rodriguez-Herna´ndez, M.; Knight, R.; Henn, M.; Hernandez, M. T., (2008). Short-term temporal variability in airborne bacterial and fungal populations. Appl. Environ. Microbiol., 74(1): 200–207 (8 Pages).

Forster, P.; Ramaswamy, V.; Artaxo, P.; Berntsen, T.; Betts, R.; Fahey, D.W.; Haywood, J.; Lean, J.; Lowe, D.C.; Myhre, G.; Nganga, J.; Prinn, R.; Raga, G.; Schulz, M.; Dorland, R.V., (2007). Changes in atmospheric constituents and in radiative forcing. (Climate Change 2007: Physical Science Basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Gardner, M.W.; Dorling, S.R., (1998). Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 32: 2627–2636 (10 Pages).

Han, L.J.; Zhou, W.Q.; Li, W.F.; Li, L., (2014). Impact of urbanization level on urban air quality: a case of fine particles (PM_2.5) in Chinese cities. Environ. Pollut., 194: 163–170 (8 Pages).

Hu, J.L.; Wang, Y.G.; Ying, Q.; Zhang, H.L., (2014). Spatial and temporal variability of PM_2.5 and PM₁₀ over the North China plain and the Yangtze River Delta. China. Atmos. Environ. 95: 598–609 (12 Pages).

Huang, R.J.; Zhang, Y.; Bozzetti, C.; Ho, K.F.; Cao, J.J.; Han, Y.; Daellenbach, K.R.; Slowik, J.G.; Platt, S.M.; Canonaco, F.; Zotter, P.; Wolf, R.; Pieber, S.M.; Bruns, E.A.; Crippa, M.; Ciarelli, G.; Piazzalunga, A.; Schwikowski, M.; Abbaszade, G.; Schnelle-Kreis, J.; Zimmermann, R.; An, Z.; Szidat, S.; Baltensperger, U.; El Haddad, I.; Prevot, A.S., (2014). High secondary aerosol contribution to particulate pollution during haze events in China. Nature, 514: 218–222 (5 Pages).

Kukkonen, J.; Partanen, L.; Karppinen, A.; Ruuskanen, J.; Junninen, H.; Kolehmainen, M.; Niska, H.; Dorling, S.; Chatterton, T.; Foxall, R.; Cawley, G., (2003). Extensive evaluation of neural network models for the prediction of NO₂ and PM₁₀ concentrations, compared with a deterministic modeling system and measurements in central Helsinki. Atmos. Environ., 37: 4539–4550 (12 Pages).

Lary, D.J.; Faruque, F.S.; Malakar, N.; Moore, A.; Roscoe, B.; Adams, Z.L.; Eggelston, Y., (2014). Estimating the global abundance of ground level presence of particulate matter (PM_2.5). Geospatial Health, 8: 611–630 (20 Pages).

Li, M.; Qi, J.; Zhang, H.; Huang, S.; Li, L.; Gao, D., (2011). Concentration and size distribution in an outdoor environment in the Qingdao coastal region. Sci. Total Environ., 409: 3812–3819 (8 Pages).

Lin, J.T.; Pan, D.; Davis, S.J.; Zhang, Q.; He, K.B.; Wang, C.; Streets, D.G.;Wuebbles, D.J.; Guan, D.B., (2014). China's international trade and air pollution in the United States. Proc. Natl. Acad. Sci. U. S. A. 111: 1736–1741 (6 Pages).

Liu, Z.; Guan, D.B.; Crawford-Brown, D.; Zhang, Q.; He, K.B.; Liu, J.G., (2013). A low-carbon road map for China. Nature, 500: 143–145 (3 Pages).

Lotfabadi, P., (2014). High-rise buildings and environmental factors. Renewable Sustainable Energy Rev., 38: 285–295 (11 Pages).

Lu, W.Z.; Wang, W.J.; Wang, X.K.; Xu, Z.B.; Leung, Y.T., (2003). Using improved neural network model to analyze RSP, NO_x and NO₂ levels in urban air in Mong Kok, Hong Kong. Environ. Monit. Assess., 87: 235–254 (20 Pages).

Mohammadizadeh, M.J.; Karbassi, A.R.; Nabi Bidhendi, Gh.R.; Abbaspour, M., (2016). Integrated environmental management model of air pollution control by hybrid model of DPSIR and FAHP. Global J. Environ. Sci. Manage., 2(4): 381-388 (8 pages).

Molina, M.J.; Molina, L.T., (2004). Megacities and atmospheric pollution. J. Air Waste Manag. Assoc. 54: 644-680 (37 Pages).

Oberdorster, G.; Oberdorster, E.; Oberdorster, J., (2005). Nano toxicology: an emerging discipline evolving from studies of ultrafine particles. Environ Health Perspectives 113: 823–839 (17 Pages).

Oliveira, M.; Ribeirio, H.; Delgado, J.L.; Abreu, I., (2009). The effects of meteorological factors on airborne fungal spore concentration in two areas differing in urbanization level. Int. J. Biometeorol., 53, 61–73 (13 Pages).

Ordieres, J.B.; Vergara, E.P.; Capuz, R.S.; Salazar, R.E., (2005). Neural network prediction model for fine particulate matter (PM_2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Jua´ rez (Chihuahua). Environ. Model. Software, 20: 547–559 (13 Pages).

Perez, P.; Reyes, J., (2002). Prediction of maximum of 24-h average of PM₁₀ concentrations 30 h in advance in Santiago, Chile. Atmos. Environ. 36: 4555–4561 (7 Pages).

Pope, C.A.; Burnett, R.T.; Thun, M.J.; Calle, E.E.; Krewski, D.; Ito, K.; Thurston, G.D., (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. Am. Med. Assoc. 287: 1132-1141 (10 Pages).

Pyrri, I.; Kapsanaki-Gotsi, E., (2011). Diversity and annual fluctuations of cultivable airborne fungi in Athens, Greece: A 4-year study. Aerobiologia. 10: 2-15 (14 Pages).

Qin, S.; Liu, F.; Wang, J.; Sun, B., (2014). Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models. Atmos. Environ., 98: 665–675 (11 Pages).

Raisi, L.; Aleksandropoulou V.; Lazaridis M.; Katsivela E., (2013). Size distribution of viable, cultivable, airborne microbes and their relationship to particulate matter concentrations and meteorological conditions in a Mediterranean site, Aerobiologia, 29: 233–248 (16 Pages).

Song, C.; Pei, T.; Yao, L., (2015). Analysis of the characteristics and evolution modes of PM_2.5 pollution episodes in Beijing, China during 2013. Int. J. Environ. Res. Public Health. 12: 1099–1111 (13 Pages).

Trizio, L.; Angiuli, L.; Menegotto, M.; Fedele, F.; Giua, R.; Mazzone, F.; Carducci, A.G.C.; Bellotti, R.; Assennato, G., (2016). Effect of the Apulia air quality plan on PM₁₀ and benzo(a)pyrene exceedances. Global J. Environ. Sci. Manage., 2(2): 95-104 (10 pages).

Wang, H.L.; Qiao, L.P.; Lou, S.R.; Zhou, M.; Ding, A.J.; Huang, H.Y.; Chen, J.M.; Wang, Q.; Tao, S.K.; Chen, C.H.; Li, L.; Huang, C., (2016). Chemical composition of PM_2.5 and meteorological impact among three years in urban Shanghai, China, J. Cleaner Product., 112: 1302-1311 (10 Pages).

WHO, (2014). WHO's ambient air pollution database: Update 2014. http://www.who.int/phe/health_topics/outdoorair/databases/AAP_database_methods_2014.pdf?

WHO, (2015). Ambient outdoor air pollution in cities database 2014. http://www.who.int/phe/health_topics/outdoorair/databases/cities/en/.

Zhang, Q.; He, K.B.; Huo, H., (2012). Cleaning China's air. Nature 484: 161–162 (2 Pages).

Zhou, Q.; Jiang, H.; Wang, J., Zhou, J., (2014). A hybrid model for PM_2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci.Total Environ., 496: 264-274 (11 Pages).

Letters to Editor

GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Global Journal of Environmental Science and Management

Artificial neural network forecast application for fine particulate matter concentration using meteorological data

References

References

Letters to Editor

Send comment about this article

Volume 3, Issue 3 - Serial Number 11
September 2017
Pages 333-340

Artificial neural network forecast application for fine particulate matter concentration using meteorological data

References

References

Letters to Editor

Send comment about this article

Volume 3, Issue 3 - Serial Number 11September 2017Pages 333-340

Volume 3, Issue 3 - Serial Number 11
September 2017
Pages 333-340