The impact of the urban canyon geometry in the nocturnal heat island intensity: analysis by a simplified model adapted to a GIS

A geometria urbana é um dos fatores de maior influência na intensidade da ilha de calor urbana. Seu estudo requer a caracterização de cânions urbanos, geralmente medidos pela relação entre a altura dos edifícios e a largura da rua (H/W), conceito aplicado no modelo numérico de Oke em 1981. O objetivo deste artigo é verificar o impacto da geometria do cânion urbano na intensidade de ilhas de calor noturna. Para isso, foram realizados levantamento de dados climáticos e de geometria urbana em duas cidades brasileiras. Os valores de intensidade de ilha de calor foram confrontados com os simulados pelo modelo original de Oke (1981), o qual foi calibrado e adaptado à plataforma SIG, de forma a possibilitar a incorporação de outro parâmetro de geometria, além da relação H/W: o comprimento de rugosidade. Esse processo gerou uma nova ferramenta de cálculo, que é denominda THIS (Tool for Heat Island Simulation). Aplicou-se o novo modelo para simular alguns cenários urbanos hipotéticos, que representam vários tipos de cânions urbanos. Os resultados demonstraram que cânions urbanos de maior rugosidade amenizam as intensidades de ilha de calor noturna em relação a um cânion de mesmo valor de relação H/W e menor rugosidade.Urban geometry is one of the main factors influencing the development of urban heat islands. The study of urban geometry requires a characterization of urban canyons, which can be usually measured by the H/W ratio (a relationship between the height and the width of a street), a concept applied in a numerical model by Oke in 1981. The aim of this paper is to verify the impact of the canyon geometry on the intensity of the nocturnal urban heat islands. For this purpose, measurements of climate data and urban geometry were conducted in two Brazilian cities. The values of heat island intensity were cross-examined to those generated with the application of the original Oke's model. Therefore, this latter was calibrated and adapted to run in a GIS platform, allowing the incorporation of a geometric parameter other than the H/W ratio - the roughness length. Then, this process produced a new calculation tool, which is called THIS (Tool for Heat Island Simulation). The new model was applied to simulate some hypothetical urban scenarios representing several urban canyons types. The results showed that the urban canyons with the largest roughness reduce the nocturnal heat island intensities in relation to an urban canyon of the same H/W value, but presenting lower roughness rates instead.Fundação de Amparo à Pesquisa do Estado de São Paulo (Fapesp)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

The Treatment of Uncertainties in Reactive Pollution Dispersion Models at Urban Scales

The ability to predict NO2 concentrations ([NO¬2]) within urban street networks is important for the evaluation of strategies to reduce exposure to NO2. However, models aiming to make such predictions involve the coupling of several complex processes: traffic emissions under different levels of congestion; dispersion via turbulent mixing; chemical processes of relevance at the street-scale. Parameterisations of these processes are challenging to quantify with precision. Predictions are therefore subject to uncertainties which should be taken into account when using models within decision making. This paper presents an analysis of mean [NO¬2] predictions from such a complex modelling system applied to a street canyon within the city of York, UK including the treatment of model uncertainties and their causes. The model system consists of a micro-scale traffic simulation and emissions model, a Reynolds Averaged turbulent flow model coupled to a reactive Lagrangian particle dispersion model. The analysis focuses on the sensitivity of predicted in-street increments of [NO¬2] at different locations in the street to uncertainties in the model inputs. These include physical characteristics such as background wind direction, temperature and background ozone concentrations; traffic parameters such as overall demand and primary NO2 fraction; as well as model parameterisations such as roughness lengths, turbulent time- and length-scales and chemical reaction rate coefficients. Predicted [NO¬2] is shown to be relatively robust with respect to model parameterisations, although there are significant sensitivities to the activation energy for the reaction NO+O3 as well as the canyon wall roughness length. Under off-peak traffic conditions, demand is the key traffic parameter. Under peak conditions where the network saturates, road-side [NO¬2] is relatively insensitive to changes in demand and more sensitive to the primary NO2 fraction. The most important physical parameter was found to be the background wind direction. The study highlights the key parameters required for reliable [NO¬2] estimations suggesting that accurate reference measurements for wind direction should be a critical part of air quality assessments for in-street locations. It also highlights the importance of street scale chemical processes in forming road-side [NO¬2], particularly for regions of high NOx emissions such as close to traffic queues

Evaluation of urban local-scale aerodynamic parameters: implications for the vertical profile of wind speed and for source areas

Nine methods to determine local-scale aerodynamic roughness length (z0) and zero-plane displacement (zd) are compared at three sites (within 60 m of each other) in London, UK. Methods include three anemometric (single-level high frequency observations), six morphometric (surface geometry) and one reference-based approach (look-up tables). A footprint model is used with the morphometric methods in an iterative procedure. The results are insensitive to the initial zd and z0 estimates. Across the three sites, zd varies between 5 – 45 m depending upon the method used. Morphometric methods that incorporate roughness-element height variability agree better with anemometric methods, indicating zd is consistently greater than the local mean building height. Depending upon method and wind direction, z0 varies between 0.1 and 5 m with morphometric z0 consistently being 2 – 3 m larger than the anemometric z0. No morphometric method consistently resembles the anemometric methods. Wind-speed profiles observed with Doppler lidar provide additional data with which to assess the methods. Locally determined roughness parameters are used to extrapolate wind-speed profiles to a height roughly 200 m above the canopy. Wind-speed profiles extrapolated based on morphometric methods that account for roughness-element height variability are most similar to observations. The extent of the modelled source area for measurements varies by up to a factor of three, depending upon the morphometric method used to determine zd and z0

Characterization of Geographical and Meteorological Parameters

[EN]This chapter is devoted to the introduction of some geographical and meteorological information involved in the numerical modeling of wind fields and solar radiation. First, a brief description of the topographical data given by a Digital Elevation Model and Land Cover databases is provided. In particular, the Information System of Land Cover of Spain (SIOSE) is considered. The study is focused on the roughness length and the displacement height parameters that appear in the logarithmic wind profile, as well as in the albedo related to solar radiation computation. An extended literature review and characterization of both parameters are reported. Next, the concept of atmospheric stability is introduced from the Monin–Obukhov similarity theory to the recent revision of Zilitinkevich of the Neutral and Stable Boundary Layers (SBL). The latter considers the effect of the free-flow static stability and baroclinicity on the turbulent transport of momentum and of the Convective Boundary Layers (CBL), more precisely, the scalars in the boundary layer, as well as the model of turbulent entrainment

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome