Artículos de revistas: "Air pollution modelling"

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

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 grid also allows revisiting the contribution of individual city plumes to the European burden of pollution, providing new insights for designing air pollution control strategies.

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9

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 grid also allows revisiting of the contribution of individual city plumes to the European burden of pollution, providing new insights to target the appropriate geographical level of action when designing air pollution mitigation strategies.

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10

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 with the results of the standard Gaussian dispersion model SYMOS’97. The results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover were included in the LUR model, the LUR model results improved significantly (65% determination coefficient) to a level comparable with the Gaussian model. A hybrid approach of combining the Gaussian model with the LUR gives superior quality of results (86% determination coefficient).

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11

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 sensor fusion to extract the information and transform it into the desired pollutant concentrations. Multi-sensor outputs are used as input information for a particular machine learning model trained to produce the CO, NO2, and NOx concentration estimates. Several state-of-the-art machine learning methods, including original algorithms proposed by the authors, are utilized in this study: kernel methods, regularization networks, regularization networks with composite kernels, and deep neural networks. All methods are augmented with a proper hyper-parameter search to achieve the optimal performance for each model. All the methods considered achieved vital results, deep neural networks exhibited the best generalization ability, and regularization networks with product kernels achieved the best fitting of the training set.

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12

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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13

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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14

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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15

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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16

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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17

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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18

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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19

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 exposure implies a blurring of bodies and environments which also challenges the idea of a discrete body that is distinguishable from its material context. By studying the process of modelling indoor air pollution, I highlight how air pollution, buildings and bodies are co-implicated with one another in ways that demand new ways of materialising human exposure in science.

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20

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. The area comprises of industries, residential and commercial areas, where plenty of pollution occurs due to emissions from automobiles also. The main aim of the project is to develop models using GIS for the air pollutant concentration of Coimbatore region. In order to run the model, the concentration details of PM2.5 (Particulate mass) were collected. Prediction models have been evolved for the monitoring station to predict the concentration of pollutants (PM2.5) based on the different meteorological parameters and also vice versa. The project concludes that highly polluted places are Koundampalayam and Thudiyalur compared to all other monitoring stations.

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21

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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22

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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23

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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24

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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25

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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26

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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27

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 (PALM), capitalising on local measurements. This fully three-dimensional primitive-equation hydrodynamical model resolves both structural details of the complex urban surface and turbulent eddies larger than 10 m in size. We ran a set of 27 meteorological weather scenarios in order to assess the dispersion of pollutants in Bergen, a middle-sized Norwegian city embedded in a coastal valley. This set of scenarios represents typically observed weather conditions with high air pollution from nitrogen dioxide (NO2) and particulate matter (PM2.5). The modelling methodology helped to identify pathways and patterns of air pollution caused by the three main local air pollution sources in the city. These are road vehicle traffic, domestic house heating with wood-burning fireplaces and ships docked in the harbour area next to the city centre. The study produced vulnerability maps, highlighting the most impacted districts for each weather and emission scenario. Overall, the largest contribution to air pollution over inhabited areas in Bergen was caused by road traffic emissions for NO2 and wood-burning fireplaces for PM2.5 pollution. The effect of emission from ships in the port was mostly restricted to the areas close to the harbour and moderate in comparison. However, the results have contributed to implementation of measures to reduce emissions from ships in Bergen harbour, including provision of shore power.

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28

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 conditioning system also worsens indoor air quality. It is often found that the supply of fresh air and the concentration of pollutants in the work or activity zone is unknown, even though this is a crucial matter. With the amount of time spent indoors, air quality and the distribution of pollutants in indoor air becomes very important. This research was conducted spatial modelling of air pollutant distribution using the kriging interpolation technique. The results of spatial modelling with this method produce an average of R-squared=70,98% dan RMSE = 0.03. Several factors influence the increase in pollutant concentrations that are the activity of the occupants, the number of occupants, and environmental conditions outside.

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29

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 different environmental conditions. This article presents a detailed review of the use of CFD to model air pollution dispersion in an urban environment over the last decade. The review extracts and summarises information from nearly 90 pieces of published research, categorising it according to over 190 modelling features, which are thematically systemised into 7 groups. The findings from across the field are critically compared to available urban air pollution modelling guidelines and standards. Among the various quantitative trends and statistics from the review, two key findings stand out. The first is that, despite the existence of best practice guidelines for pollution dispersion modelling, anywhere between 12% and 34% of the papers do not specify one or more aspects of the utilised models, which are required to reproduce the study. The second is that none of the articles perform verification and validation according to accepted standards. The results of this review can, therefore, be used by practitioners in the field of pollution dispersion modelling to understand the general trends in current research and to identify open problems to be addressed in the future.

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30

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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31

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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32

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 (Advanced Regional Prediction System), developed at the CAPS (Center for Analysis and Prediction of Storms) of the University of Oklahoma, enables to resolve the dynamics above a complex terrain. This model is coupled to the TAPOM 1.5.2 atmospheric chemistry (Transport and Air POllution Model) code developed at the Air and Soil Pollution Laboratory of the Ecole Polytechnique Fédérale de Lausanne. The numerical codes MM5 and CHIMERE are used to compute large scale boundary forcing. Using 300-m grid cells to calculate the dynamics and the reactive chemistry makes possible to accurately represent the dynamics in the valley (slope and valley winds) and to process chemistry at fine scale. Validation of campaign days allows to study chemistry indicators in the valley. NOy according to O3 reduction demonstrates a VOC controlled regime, different from the NOx controlled regime expected and observed in the nearby city of Grenoble.

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33

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 years of daily data on pollutant concentrations from two stations (Klang and Shah Alam) obtained from the Department of Environment Malaysia (DOE) from 2009 until 2018. Six wrapper methods (forward selection, backward elimination, stepwise, brute-force, weight-guided and genetic algorithm evolution and the predictive analytics multiple linear regression (MLR) and artificial neural network (ANN)) were implemented in this study. This study found that brute-force is the dominant wrapper method in most of the best models in selecting important features for MLR. Moreover, compared to MLR, ANN provides more advantages regarding model accuracy and permits feature selection in predicting PM10. The overall results revealed that the RMSE value for next day prediction in Klang is 20.728, while the AE value is 15.69. Furthermore, the RMSE value for next day prediction in Shah Alam is 10.004, while the AE value is 7.982. Finally, all of the predicted models in Klang and Shah Alam can be used to predict the PM10 concentrations. This proposed model can be used as a tool for an early warning system in giving air quality information to local authorities in order to formulate air-quality-improvement strategies.

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34

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 the Copenhagen experiment are presented and prove the good performance of the model.

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35

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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36

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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37

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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38

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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39

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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40

Juda, Katarzyna. "Modelling of the air pollution in the cracow area." Atmospheric Environment (1967) 20, no. 12 (1986): 2449–58. http://dx.doi.org/10.1016/0004-6981(86)90074-0.

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41

SCHMITZ, R. "Modelling of air pollution dispersion in Santiago de Chile." Atmospheric Environment 39, no. 11 (2005): 2035–47. http://dx.doi.org/10.1016/j.atmosenv.2004.12.033.

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42

Sánchez-Balseca, Joseph, and Agustí Pérez-Foguet. "Spatio-temporal air pollution modelling using a compositional approach." Heliyon 6, no. 9 (2020): e04794. http://dx.doi.org/10.1016/j.heliyon.2020.e04794.

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43

Romanowicz, Renata, Helen Higson, and Ian Teasdale. "Bayesian uncertainty estimation methodology applied to air pollution modelling." Environmetrics 11, no. 3 (2000): 351–71. http://dx.doi.org/10.1002/(sici)1099-095x(200005/06)11:3<351::aid-env424>3.0.co;2-z.

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44

Kalenderski, S., and D. G. Steyn. "Mixed deterministic statistical modelling of regional ozone air pollution." Environmetrics 22, no. 4 (2011): 572–86. http://dx.doi.org/10.1002/env.1088.

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45

Koleva, S. K., S. G. Gocheva-Ilieva, and H. N. Kulina. "Stochastic modelling of daily air pollution in Burgas, Bulgaria." Journal of Physics: Conference Series 2675, no. 1 (2023): 012003. http://dx.doi.org/10.1088/1742-6596/2675/1/012003.

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Abstract Exceeding the norms and limits of atmospheric air pollution causes enormous damage to the population’s health and the environment. Determining the factors affecting air quality is a current task in a local, regional, and global scale. In this study, we use daily time series data for the main air pollutants in Burgas, Bulgaria – O3, NO, NO2, CO, SO2, and PM10, to analyze, model, and forecast these levels depending on meteorological factors. For this purpose, the stochastic ARIMA method and ARIMA with transfer functions are applied. Results are obtained for univariate and multivariate time series. Particular attention is paid to the concentrations of the secondary pollutant ground-level ozone (O3), which are modelled as a function of all variables considered. Results were evaluated using root mean square error, mean absolute percentage errors, and the coefficient of determination. Short-term forecasts have been obtained for seven days ahead. Model accuracy up to 84% has been established.

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46

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 winter. Atmospheric prediction model ARPS 4.5.2 (Advanced Regional Prediction System), developed at the CAPS (Center for Analysis and Prediction of Storms) of the University of Oklahoma, enables to resolve the dynamics above a complex terrain. This model is coupled to the TAPOM 1.5.2 atmospheric chemistry (Transport and Air POllution Model) code developed at the Air and Soil Pollution Laboratory of the Ecole Polytechnique Fédérale de Lausanne. The numerical codes MM5 and CHIMERE are used to compute large scale boundary forcing. This paper focuses on modelling Chamonix valley using 300-m grid cells to calculate the dynamics and the reactive chemistry which makes possible to accurately represent the dynamics in the Chamonix valley (slope and valley winds) and to process chemistry at fine scale. The summer 2003 intensive campaign was used to validate the model and to study chemistry. NOy according to O3 reduction demonstrates a VOC controlled regime, different from the NOx controlled regime expected and observed in the nearby city of Grenoble.

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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 air pollution after loop traffic has taken effect in Vilnius Old Town from July of 2020. Highly accurate data analysis methods provide reliable data for predicting air pollution. According to the validation, the multilayer perceptron network (MLPN1), with a hyperbolic tangent activation function with a 4-4-2 partition, produced valuable results and identified the main pollutants affecting and predicting air quality in the Old Town: maximum concentration of sulphur dioxide per 1 hour (SO2_1 h, normalized importance = 100%); carbon monoxide (CO) was the second pollutant with the highest indication of normalized importance, equalling 59.0%.

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48

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 providing precise data for the modelling of a 3D building while maintaining the shape and geometry of the real-world structure. The accurate data can support modelling of surrounding air pollution concerning wind data and surrounding conditions, where different generated structures can influence the flow of the pollutants. The suitable model can be determined by using suitability analysis and with the implementation of Computational Fluid Dynamics (CFD) simulation. However, from these, no specific technique is chosen because the generated models presented incomplete model. Hence, it is suggested to combine both techniques to acquire building data as the missing surfaces from each technique can be completed by another technique. Thus, this study provides a good reference for responsible agencies or researchers in selecting the best technique for modelling the building model in air pollution-related studies.

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49

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 of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

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50

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 of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

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