N. A. Amin, M. B. Adam, and A. Z. Aris, Bayesian Extreme for Modeling High 685 PM10 Concentration in Johor, vol.30, pp.309-314, 2015.

M. Beauchamp, C. De-fouquet, and L. Malherbe, Dealing with non-stationarity 689 through explanatory variables in kriging-based air quality maps, Spatial Statistics, vol.22, pp.690-708, 2017.

M. Beauchamp, L. Malherbe, C. De-fouquet, and L. Létinois, A necessary 693 distinction between spatial representativeness of an air quality monitoring station and 694 the delimitation of exceedance areas, Environmental Monitoring and Assessment, vol.695, issue.7, p.441, 2018.

A. Beloconi, N. Chrysoulakis, A. Lyapustin, J. Utzinger, and P. Vounatsou, , 2018.

, Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in 700

, Europe using high-resolution satellite-derived products, Environment International, vol.701, issue.121, pp.57-70

J. J. Boreux, E. Parent, and J. Bernier, Pratique du calcul bayésien, Statistique et 704 probabilités appliquées, 2010.
DOI : 10.1007/978-2-287-99667-2

M. K. Cowles and D. L. Zimmerman, A Bayesian space time analysis of acid 707 deposition data combined from two monitoring networks, Journal of Geophysical 708 Research: Atmospheres, vol.108, 2003.

H. Elbern and H. Schmidt, Ozone episode analysis by four-dimensional 711 variational chemistry data assimilation, J. Geophys. Res, vol.106, issue.D4, pp.3569-3590, 2001.
DOI : 10.1029/2000jd900448

URL : https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2000JD900448

M. Fuentes and A. E. Raftery, Model Evaluation and Spatial Interpolation by 715, 2005.

, Bayesian Combination of Observations with Outputs from Numerical Models

, Biometrics, vol.61, pp.36-45

R. G. Hanea, G. J. Velders, and A. Heemink, Data assimilation of ground-level 719 ozone in Europe with a Kalman filter and chemistry transport model, J. Geophys. Res, vol.720, issue.109, 2004.

S. Hanna, J. White, J. Trolier, R. Vernot, M. Brown et al., , p.723

Y. Alexander, J. Moussafir, Y. Wang, C. Williamson, J. Hannan et al., Comparisons of JU2003 observations with four diagnostic urban wind flow and 725 Lagrangian particle dispersion models, Atmospheric Environment, vol.45, pp.4073-4081, 2011.

R. M. Harrison, Urban atmospheric chemistry: a very special case for study. 729 npj Climate and Atmospheric Science 1, 2018.
DOI : 10.1038/s41612-017-0010-8

URL : http://pure-oai.bham.ac.uk/ws/files/46383033/doi_10.1038_s41612_017_0010_8_Urban_Atmospheric_Chemistry.pdf

S. Janssen, G. Dumont, F. Fierens, F. Deutsch, B. Maiheu et al., , p.733

E. Trimpeneers and C. Mensink, Land use to characterize spatial 734 representativeness of air quality monitoring stations and its relevance for model 735 validation, Atmospheric Environment, vol.59, pp.492-500, 2012.


S. Mailler, L. Menut, D. Khvorostyanov, M. Valari, F. Couvidat et al., , p.739

S. Turquety, R. Briant, P. Tuccella, B. Bessagnet, A. Colette et al., , p.740

K. Markakis and F. Meleux, CHIMERE-2017: From urban to hemispheric 741 chemistry-transport modeling. Geoscientific Model Development, 2017.

F. Martín, I. Palomino, and M. G. Vivanco, Combination of measured and 745 modelling data in air quality assessment in Spain, Int. J. Environment and Pollution, p.746, 2012.

, , vol.49, pp.36-44

N. J. Mcmillan, D. M. Holland, M. Morara, and J. Feng, Combining numerical 749 model output and particulate data using Bayesian space-time modeling, 2010.

, Environmetrics, vol.21, pp.48-65

J. Moussafir, C. Olry, M. Nibart, A. Albergel, P. Armand et al., , 2014.

, American Society of Mechanical 755 Engineers, Fluids Engineering Division (Publication) FEDSM

A. O'hagan, The Bayesian approach to statistics, Rudas, T. Handbook of 759 probability: Theory and applications, pp.85-100, 2008.

A. Pasquier and M. Ré, Considering criteria related to spatial variabilities for 763 the assessment of air pollution from traffic, Transportation Research Procedia, pp.765-3354, 2016.

M. Pirani, J. Gulliver, G. W. Fuller, and M. Blangiardo, Bayesian spatiotemporal 768 modelling for the assessment of short-term exposure to particle pollution in urban 769 areas, J Expo Sci Environ Epidemiol, vol.24, pp.319-327, 2014.


D. Rodriguez, M. Valari, S. Payan, and L. Eymard, On the spatial 773 representativeness of NO X and PM 10 monitoring-sites in, 2019.

S. K. Sahu and K. V. Mardia, A Bayesian Kriged Kalman Model for Short-777

, Term Forecasting of Air Pollution Levels, Journal of the Royal Statistical Society. 778 Series C (Applied Statistics), vol.54, issue.1, pp.223-267, 2005.

Y. Son, Á. R. Osornio-vargas, M. S. O'neill, P. Hystad, and J. L. Texcalac-sangrador, , p.782

P. Ohman-strickland, Q. Meng, and S. Schwander, Land use regression models 783 to assess air pollution exposure in Mexico City using finer spatial and temporal input 784 parameters, Science of The Total Environment, vol.639, pp.40-48, 2018.


S. Trini-castelli, P. Armand, G. Tinarelli, C. Duchenne, and M. Nibart, Validation 788 of a Lagrangian particle dispersion model with wind tunnel and field experiments in 789 urban environment, Atmospheric Environment, vol.193, pp.273-289, 2018.


C. Wu, Y. Zeng, and S. C. Lung, A hybrid kriging/land-use regression 793 model to assess PM2.5 spatial-temporal variability. Science of The Total 794 Environment 645, pp.1456-1464, 2018.