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Artículos de revistas sobre el tema "Air pollution modelling"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Manins, Peter Charles. "Regional Air Pollution Modelling for Planners." Terrestrial, Atmospheric and Oceanic Sciences 6, no. 3 (1995): 393. http://dx.doi.org/10.3319/tao.1995.6.3.393(rec).

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2

Путренко, Віктор Валентинович, and Володимир Олександрович Тихоход. "Geostatistical modelling of air pollution analysis." Eastern-European Journal of Enterprise Technologies 4, no. 10(76) (2015): 21. http://dx.doi.org/10.15587/1729-4061.2015.47889.

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3

Ebel, Adolf. "Modelling air pollution on regional scales." Journal of Aerosol Science 32 (September 2001): 597–98. http://dx.doi.org/10.1016/s0021-8502(01)00111-2.

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4

Davidson, W. "Receptor modelling in air pollution studies." TrAC Trends in Analytical Chemistry 11, no. 6 (1992): XVI. http://dx.doi.org/10.1016/0165-9936(92)80050-g.

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5

Hewitt, C. N. "Receptor modelling in air pollution studies." TrAC Trends in Analytical Chemistry 11, no. 4 (1992): xiv. http://dx.doi.org/10.1016/0165-9936(92)87082-u.

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6

Topçu, N., B. Keskinler, M. Bayramoǧlu, and M. Akçay. "Air pollution modelling in Erzurum city." Environmental Pollution 79, no. 1 (1993): 9–13. http://dx.doi.org/10.1016/0269-7491(93)90171-j.

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7

Benarie, Michael M. "The limits of air pollution modelling." Atmospheric Environment (1967) 21, no. 1 (1987): 1–5. http://dx.doi.org/10.1016/0004-6981(87)90263-0.

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8

Er., DARAPU SRIKANTH SATISH KUMAR, P.V.V. SATYANARAYANA Prof., and B. VIJAYA SARADHI Prof. "AIR POLLUTION MODELLING-SOFTWARE – A REVIEW." International Journal of Advances in Engineering & Scientific Research 1, no. 4 (2014): 70–74. https://doi.org/10.5281/zenodo.10720441.

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                     Applications of various air pollution modeling software like  MEASURE, COLE, OFFROAD, URBEMIS, MOBILE, PART5, MOBTOX, SPOT, PEMS, OEMS, EMFAC series of models have been discussed in this paper.   <strong>Keywords: </strong>Fuel Efficiency Automobile Test (FEAT), Geographic Information System (GIS), Traffic Control Management Systems (TCMS), Transportation Improvement Plans (TIPs), New Generation Models (NGMs), Remote Sensing Devices (RSDs).     

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9

Colette, A., B. Bessagnet, F. Meleux, and L. Rouïl. "Frontiers in air quality modelling." Geoscientific Model Development Discussions 6, no. 3 (2013): 4189–205. http://dx.doi.org/10.5194/gmdd-6-4189-2013.

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Abstract. The first pan-European kilometre-scale atmospheric chemistry simulation is introduced. The continental-scale air pollution episode of January 2009 is modelled with the CHIMERE offline chemistry-transport model with a massive grid of 2 million horizontal points, performed on 2000 CPU of a high performance computing system hosted by the Research and Technology Computing Center at the French Alternative Energies and Atomic Energy Commission (CCRT/CEA). Besides the technical challenge, we find that model biases are significantly reduced, especially over urban areas. The high resolution g

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10

Colette, A., B. Bessagnet, F. Meleux, E. Terrenoire, and L. Rouïl. "Frontiers in air quality modelling." Geoscientific Model Development 7, no. 1 (2014): 203–10. http://dx.doi.org/10.5194/gmd-7-203-2014.

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Abstract. The first pan-European kilometre-scale atmospheric chemistry simulation is introduced. The continental-scale air pollution episode of January 2009 is modelled with the CHIMERE offline chemistry transport model with a massive grid of 2 million horizontal points, performed on 2000 CPU of a high-performance computing system hosted by the Research and Technology Computing Center at the French Alternative Energies and Atomic Energy Commission (CCRT/CEA). Besides the technical challenge, we find that model biases are significantly reduced, especially over urban areas. The high-resolution g

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11

Bitta, Jan, Irena Pavlíková, Vladislav Svozilík, and Petr Jančík. "Air Pollution Dispersion Modelling Using Spatial Analyses." ISPRS International Journal of Geo-Information 7, no. 12 (2018): 489. http://dx.doi.org/10.3390/ijgi7120489.

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Air pollution dispersion modelling via spatial analyses (Land Use Regression—LUR) is an alternative approach to the standard air pollution dispersion modelling techniques in air quality assessment. Its advantages are mainly a much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate more factors affecting pollutant’s concentration than standard dispersion models. The goal of the study was to model the PM10 particles dispersion via spatial analyses in the Czech–Polish border area of the Upper Silesian industrial agglomeration and compare the results

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12

Vidnerová, Petra, and Roman Neruda. "Air Pollution Modelling by Machine Learning Methods." Modelling 2, no. 4 (2021): 659–74. http://dx.doi.org/10.3390/modelling2040035.

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Precise environmental modelling of pollutants distributions represents a key factor for addresing the issue of urban air pollution. Nowadays, urban air pollution monitoring is primarily carried out by employing sparse networks of spatially distributed fixed stations. The work in this paper aims at improving the situation by utilizing machine learning models to process the outputs of multi-sensor devices that are small, cheap, albeit less reliable, thus a massive urban deployment of those devices is possible. The main contribution of the paper is the design of a mathematical model providing sen

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13

Hesek, F. "Modelling of air pollution from road traffic." International Journal of Environment and Pollution 16, no. 1/2/3/4/5/6 (2001): 366. http://dx.doi.org/10.1504/ijep.2001.000632.

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14

Panis, Luc Int, Carolien Beckx, and Geert Wets. "Modelling Gender Specific Exposure to Air Pollution." Epidemiology 20 (November 2009): S19. http://dx.doi.org/10.1097/01.ede.0000362233.79296.95.

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15

Tulet, P., V. Crassier, and R. Rosset. "Air pollution modelling at a regional scale." Environmental Modelling & Software 15, no. 6-7 (2000): 693–701. http://dx.doi.org/10.1016/s1364-8152(00)00039-6.

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16

Owczarz, Wojciech, and Zahari Zlatev. "Parallel matrix computations in air pollution modelling." Parallel Computing 28, no. 2 (2002): 355–68. http://dx.doi.org/10.1016/s0167-8191(01)00144-2.

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17

Bellasio, Roberto. "Modelling traffic air pollution in road tunnels." Atmospheric Environment 31, no. 10 (1997): 1539–51. http://dx.doi.org/10.1016/s1352-2310(96)00296-8.

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18

Valkonen, Esko, Jari Härkönen, Jaakko Kukkonen, Erkki Rantakrans, Liisa Jalkanen, and Seppo Haarala. "Modelling urban air pollution in Espoo, Finland." Science of The Total Environment 189-190 (October 1996): 205–11. http://dx.doi.org/10.1016/0048-9697(96)05211-4.

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19

Krzyzanowski, Judi. "Approaching Cumulative Effects through Air Pollution Modelling." Water, Air, & Soil Pollution 214, no. 1-4 (2010): 253–73. http://dx.doi.org/10.1007/s11270-010-0421-1.

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20

Garnett, Emma. "Breathing Spaces: Modelling Exposure in Air Pollution Science." Body & Society 26, no. 2 (2020): 55–78. http://dx.doi.org/10.1177/1357034x20902529.

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In this article, I materially situate air pollution exposure as a topic of social and political inquiry by paying attention to the increasing specificity of spaces and sites of exposure in air pollution and health research. Evidence of the unevenness of exposure and differential health effects of air pollution have led to a proliferation of studies on the risks different environments pose to bodies. There are increasingly different airs in air pollution science. In this research, bodies are often relegated to passive objects, exposed according to the environments they move between. Yet exposur

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21

Wolf, Tobias, Lasse H. Pettersson, and Igor Esau. "A very high-resolution assessment and modelling of urban air quality." Atmospheric Chemistry and Physics 20, no. 2 (2020): 625–47. http://dx.doi.org/10.5194/acp-20-625-2020.

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Abstract. Urban air quality is one of the most prominent environmental concerns for modern city residents and authorities. Accurate monitoring of air quality is difficult due to intrinsic urban landscape heterogeneity and superposition of multiple polluting sources. Existing approaches often do not provide the necessary spatial details and peak concentrations of pollutants, especially at larger distances from monitoring stations. A more advanced integrated approach is needed. This study presents a very high-resolution air quality assessment with the Parallelized Large-Eddy Simulation Model (PA

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22

Eswaran, Sarojini, Bharathiraj L.T, and Jayanthi S. "Modelling of ambient air quality, Coimbatore, India." E3S Web of Conferences 117 (2019): 00002. http://dx.doi.org/10.1051/e3sconf/201911700002.

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Air pollution is dispersion of the particulates, biological molecules, or other harmful materials into the Earth’s atmosphere, possibly causing diseases. Air pollutants can be either particles, liquids or gaseous. In the recent era, air pollution has become a major environmental issue because of the enhanced anthropogenic activities such as burning fossil fuels, natural gases, coal and oil, industrial process, advanced technologies and motor vehicles. The proposed project focused on air pollution study of North Coimbatore region (11° 0’ 16.4016’’ N and 76° 57’ 41.8752’’ E), Tamilnadu, India. T

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23

Pantusheva, Mariya, Radostin Mitkov, Petar O. Hristov, and Dessislava Petrova-Antonova. "Air Pollution Dispersion Modelling in Urban Environment Using CFD: A Systematic Review." Atmosphere 13, no. 10 (2022): 1640. http://dx.doi.org/10.3390/atmos13101640.

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Air pollution is a global problem, which needs to be understood and controlled to ensure a healthy environment and inform sustainable development. Urban areas have been established as one of the main contributors to air pollution, and, as such, urban air quality is the subject of an increasing volume of research. One of the principal means of studying air pollution dispersion is to use computational fluid dynamics (CFD) models. Subject to careful verification and validation, these models allow for analysts to predict air flow and pollution concentration for various urban morphologies under dif

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24

Ostromsky, Tzvetan, Ivan Dimov, Rayna Georgieva, and Zahari Zlatev. "Air pollution modelling, sensitivity analysis and parallel implementation." International Journal of Environment and Pollution 46, no. 1/2 (2011): 83. http://dx.doi.org/10.1504/ijep.2011.042610.

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25

Ostromsky, Tzvetan, Kiril Alexiev, and Stefan Parvanov. "Air pollution modelling of accidents involving hazardous substances." Journal of Physics: Conference Series 2910, no. 1 (2024): 012002. https://doi.org/10.1088/1742-6596/2910/1/012002.

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Abstract Air pollution modeling for accidents involving hazardous substances is a critical aspect of emergency response and environmental protection. This modeling aims to predict the dispersion and impact of toxic substances released into the atmosphere during accidents, such as industrial spills, transport accidents, chemical plant incidents or acts of terrorism. Such dangers are among the challenges which the modern society is facing and has to overcome. In this study we propose a systematic approach for organizing monitoring, collecting data from mobile sensors, creating situation developm

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26

Sarah Tomlin, Alison, Saktipada Ghorai, Gordon Hart, and Martin Berzins. "3D adaptive unstructured meshes for air pollution modelling." Environmental Management and Health 10, no. 4 (1999): 267–75. http://dx.doi.org/10.1108/09566169910276238.

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27

Karppinen, A., J. Kukkonen, T. Elolähde, M. Konttinen, and T. Koskentalo. "A modelling system for predicting urban air pollution:." Atmospheric Environment 34, no. 22 (2000): 3735–43. http://dx.doi.org/10.1016/s1352-2310(00)00073-x.

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28

Djouad, Rafik, and Bruno Sportisse. "Solving reduced chemical models in air pollution modelling." Applied Numerical Mathematics 44, no. 1-2 (2003): 49–61. http://dx.doi.org/10.1016/s0168-9274(02)00142-3.

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29

Theurer, W. "Typical building arrangements for urban air pollution modelling." Atmospheric Environment 33, no. 24-25 (1999): 4057–66. http://dx.doi.org/10.1016/s1352-2310(99)00147-8.

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30

BRZOZOWSKI, K., and W. KOTLARZ. "Modelling of air pollution on a military airfield." Atmospheric Environment 39, no. 33 (2005): 6130–39. http://dx.doi.org/10.1016/j.atmosenv.2005.06.040.

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31

Putri, A. N. A. R., R. A. Salam, L. M. Rachmawati, et al. "Spatial Modelling of Indoor Air Pollution Distribution at Home." Journal of Physics: Conference Series 2243, no. 1 (2022): 012072. http://dx.doi.org/10.1088/1742-6596/2243/1/012072.

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Abstract In ancient times, humans were very accustomed to depending on nature, so that in the past humans held the title as an outdoor species. Over time with many technological advances, the pattern of human life has shifted to being an indoor species. Currently, almost 55% of the world’s population lives in urban areas and is projected to increase to 68% by 2050. Based on the National Human Activity Pattern Survey (NHAPS), the total time humans spend indoors is 86.9%. Research shows that air pollutants contained in indoor air are 2 to 5 times more than outdoor air. The neglect of the air con

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32

Reis, Lara Aleluia, Laurent Drouet, and Massimo Tavoni. "Internalising Health-Economic Impacts of Air Pollution into Climate Policy: A Global Modelling Study." Lancet Planetary Health 6, no. 1 (2022): e40-e48. https://doi.org/10.1016/S2542-5196(21)00259-X.

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Climate change and air pollution are two major societal problems. Their complex interplay calls for an advanced evaluation framework that can support decision making. Previous assessments have looked at the cobenefits of climate policies for air pollution, but few have optimised air pollution benefits. In our study, we lay out a modelling framework that internalises air pollution's economic impacts on human mortality, while considering climate constraints and aerosol feedback.

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33

Burov, Angel, and Danail Brezov. "Modelling Air Pollution from Transportation Along Boulevards in Sofia." Annual of Univercity of architecture, civil engineering and geodesy 57, no. 1 (2024): 243–59. https://doi.org/10.71167/uaceg.2024.570118.

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The study demonstrates a way of filling knowledge gaps about street pollution levels for particulate matter in Sofia along the linear traffic sources. The study is based on available software for street pollution modelling. The modelling of air pollution concentration from transport emissions at the street level is performed about the boulevards Aleksander Stamboliyski and Todor Aleksandrov. The methods and techniques of data collection and processing for modelling purposes include multiple attempts to overcome gaps and limitations. The results for chosen dates from January and April 2018, sho

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34

Brulfert, G., C. Chemel, E. Chaxel, and J. P. Chollet. "Modelling photochemistry in alpine valleys." Atmospheric Chemistry and Physics Discussions 5, no. 2 (2005): 1797–828. http://dx.doi.org/10.5194/acpd-5-1797-2005.

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Abstract. Road traffic is a serious problem in the Chamonix Valley, France: traffic, noise and above all air pollution worry the inhabitants. The big fire in the Mont-Blanc tunnel made it possible, in the framework of the POVA project (POllution in Alpine Valleys), to undertake measurement campaigns with and without heavy-vehicle traffic through the valley, towards Italy (before and after the tunnel re-opening). Modelling in POVA should make it possible to explain the processes leading to episodes of atmospheric pollution, both in summer and in winter. Atmospheric prediction model ARPS 4.5.2 (

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35

Dekker, C. M., and C. J. Sliggers. "Good manufacturing practice for modelling air pollution: Quality criteria for computer models to calculate air pollution." Atmospheric Environment. Part A. General Topics 26, no. 6 (1992): 1019–23. http://dx.doi.org/10.1016/0960-1686(92)90033-h.

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36

Bekesiene, Svajone, and Ieva Meidute-Kavaliauskiene. "Artificial Neural Networks for Modelling and Predicting Urban Air Pollutants: Case of Lithuania." Sustainability 14, no. 4 (2022): 2470. http://dx.doi.org/10.3390/su14042470.

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This study focuses on the Vilnius (capital of Lithuania) agglomeration, which is facing the issue of air pollution resulting from the city’s physical expansion. The increased number of industries and vehicles caused an increase in the rate of fuel consumption and pollution in Vilnius, which has rendered air pollution control policies and air pollution management more significant. In this study, the differences in the pollutants’ means were tested using two-sided t-tests. Additionally, a 2-layer artificial neural network and a pollution data were both used as tools for predicting and warning ai

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37

Batista, Nathale, Noela Pina, and Oxana Tchepel. "Modelling of Glass Soiling Due to Air Pollution Exposure at Urban and National Scales: Coimbra (Portugal) Case Study." Environments 11, no. 10 (2024): 215. http://dx.doi.org/10.3390/environments11100215.

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Impacts of air pollution are not limited to human health and ecosystems, but are also important for building materials. The main objective of this study is the quantification and mapping of air pollution effects on the materials, namely the soiling effect of modern glass. An integrated modelling approach was implemented to quantify and analyze the spatial distribution of glass soiling due to exposure to air pollution. The methodology is based on an integrated modelling approach (transportation-emissions-dispersion modelling) applied with high spatial resolution for Coimbra (Portugal) urban are

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38

Brulfert, G., C. Chemel, E. Chaxel, and J. P. Chollet. "Modelling photochemistry in alpine valleys." Atmospheric Chemistry and Physics 5, no. 9 (2005): 2341–55. http://dx.doi.org/10.5194/acp-5-2341-2005.

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Abstract. Road traffic is a serious problem in the Chamonix Valley, France: traffic, noise and above all air pollution worry the inhabitants. The big fire in the Mont-Blanc tunnel made it possible, in the framework of the POVA project (POllution in Alpine Valleys), to undertake measurement campaigns with and without heavy-vehicle traffic through the Chamonix and Maurienne valleys, towards Italy (before and after the tunnel re-opening). Modelling is one of the aspects of POVA and should make it possible to explain the processes leading to episodes of atmospheric pollution, both in summer and in

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39

Ridzuan, N., U. Ujang, S. Azri, and T. L. Choon. "3D AIR POLLUTION COMPUTATIONAL FLUID MODELLING DATA ANALYSIS USING TERRESTRIAL LASER SCANNING (TLS) AND UNMANNED AERIAL VEHICLE (UAV) APPROACH." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 451–56. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-451-2021.

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Abstract. Air pollution is a global event that can harm the environment and people. It is recommended that effective management be implemented to allow for the sustainable development of a specific area. The 3D building model is employed in the study to support air pollution modelling for this purpose. A proper mode of data acquisition is required to produce the building model. Many data acquisition (Terrestrial Laser Scanning and Unmanned Aerial Vehicle) approaches can be utilized, but the most appropriate one for the use in outdoor air pollution is needed. This is because it can assist in pr

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40

Richards, M., M. Ghanem, M. Osmond, Y. Guo, and J. Hassard. "Grid-based analysis of air pollution data." Ecological Modelling 194, no. 1-3 (2006): 274–86. http://dx.doi.org/10.1016/j.ecolmodel.2005.10.042.

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41

Baklanov, A., O. Hänninen, L. H. Slørdal, et al. "Integrated systems for forecasting urban meteorology, air pollution and population exposure." Atmospheric Chemistry and Physics Discussions 6, no. 2 (2006): 1867–913. http://dx.doi.org/10.5194/acpd-6-1867-2006.

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Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction

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42

Baklanov, A., O. Hänninen, L. H. Slørdal, et al. "Integrated systems for forecasting urban meteorology, air pollution and population exposure." Atmospheric Chemistry and Physics 7, no. 3 (2007): 855–74. http://dx.doi.org/10.5194/acp-7-855-2007.

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Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction

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43

Kalniņš, Viesturs. "Cumulative Impact Evaluation in Central Part of Liepaja with Comulative Pollution Index Method and Air Pollution Dispersion Modelling." Proceedings of the Latvia University of Agriculture 33, no. 1 (2015): 2–7. http://dx.doi.org/10.1515/plua-2015-0001.

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Abstract Cumulative impact evaluation is one of the most actual problems in air quality monitoring. At the same time, it is also the most problematic factor to evaluate due to lack of appropriate methodology. The aim of this study was to assess the opportunity to use a new method – Cumulative Pollution Index (CPI) in cumulative impact calculation from two different sets of data – bioindication survey with Index of Atmospheric Purity method and air pollution dispersion modelling. Results show that the usage of modelling data, instead of measurements, in cumulative impact evaluation can be quite

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44

Ul-Saufie, Ahmad Zia, Nurul Haziqah Hamzan, Zulaika Zahari, et al. "Improving Air Pollution Prediction Modelling Using Wrapper Feature Selection." Sustainability 14, no. 18 (2022): 11403. http://dx.doi.org/10.3390/su141811403.

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Feature selection is considered as one of the essential steps in data pre-processing. However, all of the previous studies on predicting PM10 concentration in Malaysia have been limited to statistical method feature selection, and none of these studies used machine-learning approaches. Therefore, the objective of this research is to investigate the influence variables of the PM10 prediction model by using wrapper feature selection to compare the prediction model performance of different wrapper feature selection and to predict the concentration of PM10 for the next day. This research uses 10 y

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45

Quadros, Régis S., Glênio A. Gonçalves, Daniela Buske, and Guilherme J. Weymar. "An Analytical Methodology to Air Pollution Modelling in Atmosphere." Defect and Diffusion Forum 396 (August 2019): 91–98. http://dx.doi.org/10.4028/www.scientific.net/ddf.396.91.

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This work presents an analytical solution for the transient three-dimensional advection-diffusion equation to simulate the dispersion of pollutants in the atmosphere. The solution of the advection-diffusion equation is obtained analytically using a combination of the methods of separation of variables and GILTT. The main advantage is that the presented solution avoids a numerical inversion carried out in previous works of the literature, being by this way a totally analytical solution, less than a summation truncation. Initial numerical simulations and statistical comparisons using data from t

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46

Durcanska, Daniela, and Ferdinand Hesek. "Mathematical Modelling of the Highway Influence to Air Pollution." Communications - Scientific letters of the University of Zilina 2, no. 4 (2000): 59–68. http://dx.doi.org/10.26552/com.c.2000.4.59-68.

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47

Brandt, J., J. H. Christensen, L. M. Frohn, and Z. Zlatev. "Operational air pollution forecast modelling using the THOR system." Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 26, no. 2 (2001): 117–22. http://dx.doi.org/10.1016/s1464-1909(00)00227-6.

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48

Freijer, J. I., H. J. Th Bloemen, S. de Loos, et al. "Modelling exposure of the Dutch population to air pollution." Journal of Hazardous Materials 61, no. 1-3 (1998): 107–14. http://dx.doi.org/10.1016/s0304-3894(98)00114-9.

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49

Chiquetto, Sergio, and Roger Mackett. "Modelling the effects of transport policies on air pollution." Science of The Total Environment 169, no. 1-3 (1995): 265–71. http://dx.doi.org/10.1016/0048-9697(95)04657-m.

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50

Bartoletti, Silvia, and Nicola Loperfido. "Modelling air pollution data by the skew-normal distribution." Stochastic Environmental Research and Risk Assessment 24, no. 4 (2009): 513–17. http://dx.doi.org/10.1007/s00477-009-0341-z.

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