{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:15:06Z","timestamp":1790702106700,"version":"4.1.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2013,1,6]],"date-time":"2013-01-06T00:00:00Z","timestamp":1357430400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Environ Sci Pollut Res"],"published-print":{"date-parts":[[2013,7]]},"DOI":"10.1007\/s11356-012-1451-6","type":"journal-article","created":{"date-parts":[[2013,1,5]],"date-time":"2013-01-05T07:07:23Z","timestamp":1357369643000},"page":"4777-4789","source":"Crossref","is-referenced-by-count":111,"title":["Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations"],"prefix":"10.1007","volume":"20","author":[{"given":"Mohammad","family":"Arhami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nima","family":"Kamali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Mahdi","family":"Rajabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2013,1,6]]},"reference":[{"key":"1451_CR1","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.envint.2007.12.005","volume":"34","author":"C Borrego","year":"2008","unstructured":"Borrego C, Monteiro A, Ferreira J, Miranda AI, Costa AM, Carvalho AC, Lopes M (2008) Procedures for estimation of modeling uncertainty in air quality assessment. Environ Int 34:613\u2013620","journal-title":"Environ Int"},{"key":"1451_CR2","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/0957-1272(93)90007-S","volume":"27","author":"M Boznar","year":"1993","unstructured":"Boznar M, Lesjak M, Mlakar P (1993) A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmos Environ B 27:221\u2013230","journal-title":"Atmos Environ B"},{"key":"1451_CR3","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1016\/j.atmosenv.2006.12.013","volume":"41","author":"U Brunelli","year":"2007","unstructured":"Brunelli U, Piazza V, Pignato L, Sorbello F, Vitabile S (2007) Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy. Atmos Environ 41:2967\u20132995","journal-title":"Atmos Environ"},{"key":"1451_CR4","volume-title":"Monte Carlo methods for applied scientists","author":"IT Dimov","year":"2008","unstructured":"Dimov IT (2008) Monte Carlo methods for applied scientists. World Scientific Publishing Co. Pte. Ltd, UK"},{"issue":"2","key":"1451_CR5","first-page":"231","volume":"47","author":"J Faraway","year":"1998","unstructured":"Faraway J, Chatfield C (1998) Time series forecasting with neural networks: a comparative study using the airline data. Appl Stat 47(2):231\u2013250","journal-title":"Appl Stat"},{"key":"1451_CR6","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1007\/s11356-010-0375-2","volume":"18","author":"A Paschalidou","year":"2011","unstructured":"Paschalidou A, Karakitsios S, Kleanthous S, Kassomenos P (2011) Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management. Environ Sci Pollut Res 18:316\u2013327","journal-title":"Environ Sci Pollut Res"},{"key":"1451_CR7","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/S1352-2310(98)00230-1","volume":"33","author":"MW Gardner","year":"1999","unstructured":"Gardner MW, Dorling SR (1999) Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos Environ 33:709\u2013719","journal-title":"Atmos Environ"},{"key":"1451_CR8","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.1016\/j.atmosenv.2005.01.050","volume":"39","author":"J Hooyberghs","year":"2005","unstructured":"Hooyberghs J, Mensink C, Dumont G, Fierens F, Brasseur O (2005) A neural network forecast for daily average PM10 concentrations in Belgium. Atmos Environ 39:3279\u20133289","journal-title":"Atmos Environ"},{"key":"1451_CR9","doi-asserted-by":"crossref","first-page":"5588","DOI":"10.1016\/j.atmosenv.2009.07.048","volume":"43","author":"L Hrust","year":"2009","unstructured":"Hrust L, Bencetic Klaic Z, Krizan J, Antonic O, Hercog P (2009) Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmos Environ 43:5588\u20135596","journal-title":"Atmos Environ"},{"key":"1451_CR10","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.envsoft.2007.09.003","volume":"23","author":"G Ibarra-Berastegi","year":"2008","unstructured":"Ibarra-Berastegi G, Elias A, Barona A, Saenz J, Ezcurra A, de Argandona JD (2008) From diagnosis to prognosis for forecasting air pollution using neural networks: air pollution monitoring in Bilbao. Environ Model Soft 23:622\u2013637","journal-title":"Environ Model Soft"},{"key":"1451_CR11","doi-asserted-by":"crossref","first-page":"7055","DOI":"10.1016\/j.atmosenv.2003.10.066","volume":"38","author":"D Jiang","year":"2004","unstructured":"Jiang D, Zhang Y, Hu X, Zeng Y, Tan J, Shao D (2004) Progress in developing an ANN model for air pollution index forecast. Atmos Environ 38:7055\u20137064","journal-title":"Atmos Environ"},{"key":"1451_CR12","doi-asserted-by":"crossref","first-page":"3723","DOI":"10.1016\/S1352-2310(00)00074-1","volume":"34","author":"A Karppinen","year":"2000","unstructured":"Karppinen A, Kukkonen J, Elolahde T, Konttinen M, Koskentalo T, Rantakrans E (2000a) A modelling system for predicting urban air pollution: model description and applications in the Helsinki metropolitan area. Atmos Environ 34:3723\u20133733","journal-title":"Atmos Environ"},{"key":"1451_CR13","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/S1352-2310(00)00073-X","volume":"34","author":"A Karppinen","year":"2000","unstructured":"Karppinen A, Kukkonen J, Elolahde T, Konttinen M, Koskentalo T (2000b) A modelling system for predicting urban air pollution: comparison of model predictions with the data of an urban measurement network in Helsinki. Atmos Environ 34:3735\u20133743","journal-title":"Atmos Environ"},{"issue":"9","key":"1451_CR14","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TNN.2011.2162110","volume":"22","author":"A Khosravi","year":"2011","unstructured":"Khosravi A, Nahavandi S, Creighton D, Atiya AF (2011) Comprehensive review of neural network-based prediction intervals and new advances. IEEE Trans Neural Netw 22(9):1341\u20131356","journal-title":"IEEE Trans Neural Netw"},{"key":"1451_CR15","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/S1352-2310(00)00385-X","volume":"35","author":"M Kolehmainen","year":"2001","unstructured":"Kolehmainen M, Martikainen H, Ruuskanen J (2001) Neural networks and periodic components used in air quality forecasting. Atmos Environ 35:815\u2013825","journal-title":"Atmos Environ"},{"key":"1451_CR16","first-page":"4549","volume":"37","author":"J Kukkonen","year":"2003","unstructured":"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 modelling system and measurements in central Helsinki. Atmos Environ 37:4549\u20134550","journal-title":"Atmos Environ"},{"key":"1451_CR17","volume-title":"Back-propagation in hydrological time series forecasting","author":"G Lachtermacher","year":"1994","unstructured":"Lachtermacher G, Fuller JD (1994) Stochastic and statistical methods in hydrology and environmental engineering. In: Hipel KW, McLeod AI, Panu US, Singh VP (eds) Back-propagation in hydrological time series forecasting. Kluwer, Dordrecht"},{"key":"1451_CR18","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S1364-8152(99)00007-9","volume":"15","author":"HR Maier","year":"2000","unstructured":"Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Soft 15:101\u2013124","journal-title":"Environ Model Soft"},{"key":"1451_CR19","first-page":"9","volume":"78","author":"AJ McMichael","year":"2000","unstructured":"McMichael AJ (2000) The urban environment and health in a world of increasing globalization: issues for developing countries. Bull of the World Health Organ 78:9","journal-title":"Bull of the World Health Organ"},{"key":"1451_CR20","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1080\/01621459.1949.10483310","volume":"44","author":"N Metropolis","year":"1949","unstructured":"Metropolis N, Ulam SM (1949) The Monte Carlo method. J Am Stat Assoc 44:335\u2013341","journal-title":"J Am Stat Assoc"},{"key":"1451_CR21","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1080\/10473289.2004.10470936","volume":"54","author":"MJ Molina","year":"2004","unstructured":"Molina MJ, Molina LT (2004) Megacities and atmospheric pollution. AirWaste Manag Assoc 54:644\u2013680","journal-title":"AirWaste Manag Assoc"},{"key":"1451_CR22","volume-title":"Classical and modern regression with applications","author":"RH Myers","year":"1990","unstructured":"Myers RH (1990) Classical and modern regression with applications, 2nd edn. PWS-Kent, Boston","edition":"2"},{"key":"1451_CR23","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1016\/j.atmosenv.2006.01.010","volume":"40","author":"P Perez","year":"2006","unstructured":"Perez P, Reyes J (2006) An integrated neural network model for PM10 forecasting. Atmos Environ 40:2845\u20132851","journal-title":"Atmos Environ"},{"key":"1451_CR24","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/S1352-2310(99)00316-7","volume":"34","author":"P Perez","year":"2000","unstructured":"Perez P, Trier A, Reyes J (2000) Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmos Environ 34:1189\u20131196","journal-title":"Atmos Environ"},{"key":"1451_CR25","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S1352-2310(01)00446-0","volume":"36","author":"D Podnar","year":"2002","unstructured":"Podnar D, Koracin D, Panorska A (2002) Application of artificial neural networks to modeling the transport and dispersion of tracers in complex terrain. Atmos Environ 36:561\u2013570","journal-title":"Atmos Environ"},{"key":"1451_CR26","doi-asserted-by":"crossref","first-page":"3045","DOI":"10.1016\/S1352-2310(98)00418-X","volume":"33","author":"SL Reich","year":"1999","unstructured":"Reich SL, Gomez DR, Dawidowski LE (1999) Artificial neural network for the identification of unknown air pollution sources. Atmos Environ 33:3045\u20133052","journal-title":"Atmos Environ"},{"key":"1451_CR27","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316726","volume-title":"Stochastic simulation","author":"BD Ripley","year":"1987","unstructured":"Ripley BD (1987) Stochastic simulation. Wiley, New York"},{"key":"1451_CR28","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316511","volume-title":"Simulation and the Monte Carlo method","author":"BY Rubinstein","year":"1981","unstructured":"Rubinstein BY (1981) Simulation and the Monte Carlo method. Wiley, New York"},{"key":"1451_CR29","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1016\/j.envsoft.2007.11.009","volume":"23","author":"E Salazar-Ruiz","year":"2008","unstructured":"Salazar-Ruiz E, Ordieres JB, Vergara EP, Capuz-Rizo SF (2008) Development and comparative analysis of tropospheric O3 prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US). Environ Model Soft 23:1056\u20131069","journal-title":"Environ Model Soft"},{"key":"1451_CR30","volume-title":"Atmospheric chemistry and physics: from air pollution to climate change","author":"JH Seinfeld","year":"2006","unstructured":"Seinfeld JH, Pandis SN (2006) Atmospheric chemistry and physics: from air pollution to climate change. Wiley, New York"},{"key":"1451_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/eej.20689","volume":"8","author":"MA Shahin","year":"2008","unstructured":"Shahin MA, Jaksa MB, Maier HR (2008) State of the art of artificial neural networks in geotechnical engineering. Electro J Geotech Eng 8:1\u201326","journal-title":"Electro J Geotech Eng"}],"container-title":["Environmental Science and Pollution Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-012-1451-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11356-012-1451-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-012-1451-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T13:59:35Z","timestamp":1559397575000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11356-012-1451-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,1,6]]},"references-count":31,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2013,7]]}},"alternative-id":["1451"],"URL":"https:\/\/doi.org\/10.1007\/s11356-012-1451-6","relation":{},"ISSN":["0944-1344","1614-7499"],"issn-type":[{"value":"0944-1344","type":"print"},{"value":"1614-7499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,1,6]]}}}