Sustainable Cities and Society 70 (2021) 102882 Available online 24 March 2021 2210-6707/© 2021 Elsevier Ltd. All rights reserved. Atmospheric dispersion and urban planning: An interdisciplinary approach to city modeling Fabiana Trindade da Silva a, *, Neyval Costa Reis Jr. a , Jane Meri Santos a , Elisa Valentim Goulart a , Felipe Sim˜ oes Maciel a , Luís Bragança c , Cristina Engel de Alvarez b a Department of Environmental Engineering, Universidade Federal do Espírito Santo, Av. Fernando Ferrari 514, 29.075-910, Vitoria, ES, Brazil b Department of Architecture and Urban Planning, Universidade Federal do Espírito Santo, Brazil. Av. Fernando Ferrari, 514, 29.075-910, Vit´ oria, ES, Brazil c Department of Civil Engineering, University of Minho Portugal, Campus de Azur´ em, 4800-058, Guimar˜ aes, Portugal A R T I C L E INFO Keywords: Pollutant dispersion Block typology Urban metrics Parameterization Urban planning ABSTRACT Geometry modeling is a common approach in pollutant dispersion studies. Block typology is a key element for representing geometries closer to real city environments. However, urban pollutant modeling studies and urban planning processes have different approaches regarding block typology and applied metrics. Therefore, the objective of this work is to compare urban block typologies and urban metrics used in literature studies with those found in real cities. The methodology combined a literature review with an empirical analysis of sample areas in selected cities. The results showed that more than 50 % of the studies applied idealized building arrays. Nonetheless, the idealized array tends to underestimate real densities, often misrepresenting urban planning indices. On the other hand, derived geometry reduces modeling complexity and increases the applicability of studies in urban planning. Based on our fndings, we suggest an urban block parameterization derived from real urban areas (representative of the densest cities in Asia, Europe, and America). This study selects fve block typologies derived from actual cities (single block, detached buildings, courtyard, inner courtyards, and row buildings) with estimated values of the foor area ratio (FAR) and surface coverage (SC) that, when combined, provide a more precise representation of density. 1. Introduction In 2018, 55 % (approximately 4.2 billion) of the world population was living in cities, and this number is estimated to reach 68 % (approximately 6.7 billion) by 2050 (United Nations Department of Economic and Social Affairs, 2018). This growth can increase building density in urban areas (Tang and Wang, 2007). As building density in- creases, the airfow pattern can trap pollutants, resulting in their accu- mulation within the urban canopy. In general, compact urban areas frequently lead to higher pollutant concentrations, for both high (An et al., 2019; Yuan et al., 2019) and medium building densities (Bucco- lieri et al., 2015; Hang et al., 2015). In this context, urban planning can regulate city confgurations and contribute to establishing a healthy urban environment. However, a gap remains between urban air quality studies and their application in urban planning (Badach et al., 2020; C´ ardenas Rodríguez et al., 2016). It is possible to identify two key aspects restricting the use of air quality studies in urban planning: (i) the application of urban geometries that are often overly idealized or overly specifc and (ii) air pollution dispersion studies adopt different metrics than those used in the urban planning process. Air quality studies often model urban geometries using computa- tional fuid dynamics (CFD). This technique offers advantageous fea- tures, such as its affordability, accuracy, reasonable response time, and comprehensive visualization (An et al., 2019; Blocken and Gualtieri, 2012; Buccolieri et al., 2015; Nebenzal et al., 2020). The quality of the results depends not only on using the appropriate equations to represent the phenomenon and employing suitable numerical strategies but also on the correct description of the urban geometry (Carpentieri and Robins, 2015a; Guo et al., 2017a; Peng et al., 2019; You et al., 2017). Therefore, the urban geometry must be carefully taken into consider- ation for the model to provide a realistic representation of the environment. * Corresponding author. E-mail addresses: fabianatrindade.silva@gmail.com (F. Trindade da Silva), neyval@pq.cnpq.br (N. Costa Reis Jr.), jane.santos@pq.cnpq.br (J.M. Santos), elisa. goulart@ufes.br (E. Valentim Goulart), felipesimoesmaciel@gmail.com (F. Sim˜ oes Maciel), braganca@civil.uminho.pt (L. Bragança), cristina.engel@ufes.br (C. Engel de Alvarez). Contents lists available at ScienceDirect Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs https://doi.org/10.1016/j.scs.2021.102882 Received 13 August 2020; Received in revised form 20 March 2021; Accepted 22 March 2021