Effects of Climate Change on Pedestrian Comfort: Madrid Case Study

Transcription

1 International Proceedings of Chemical, Biological and Environmental Engineering, V0l. 101 (2017) DOI: /IPCBEE V Effects of Climate Change on Pedestrian Comfort: Madrid Case Study Roberto San José 1, Juan L. Pérez 1, Libia Pérez 1 and Rosa Maria Gonzalez Barras 2 1 Environmental Software and Modelling Group, Computer Science School, Technical University of Madrid (UPM), Madrid, Spain 2 Department of Physics and Meteorology, Faculty of Physics, Complutense University of Madrid (UCM), Ciudad Universitaria, Madrid, Spain. Abstract. To study the comfort of pedestrians we have based on thermal and wind comfort, showing the impact of the global climate on the outdoor pedestrian comfort for the city of Madrid. The work proposes a dynamic downscaling methodology to study how the microclimate is affected by global climate and therefore how human comfort in urban areas will depend on the future global climate. The methodology makes use of a computational fluid dynamic model (CFD) to produce the required microclimate parameters with very high spatial resolution (50 meters). The initial and boundary conditions are supplied by a regional/ urban numerical model, called WRF/Chem. The results show the wind and thermal comfort analysis for two different climatic scenarios in the present (2011) and in the future (2030, 2050 and 2100), showing how the wind and thermal comfort will vary at the end of the century. Two climate scenarios of the IPCC, RCP 4.5 (stabilization emissions scenario) and RCP 8.5 (little effort to reduce emissions) have been simulated. We used the physiologically equivalent temperature (PET) as an index of thermal comfort. The Dutch standard NEN 8100 for wind discomfort is used to assess the comfort of the pedestrian wind, which applies a threshold of discomfort to the average wind speed per hour calculated. This work demonstrates the magnitude of spatial variability in comfort indexes. The very high spatial resolution of the results allows identifying areas of the city with uncomfortable conditions for pedestrians, so these areas have a high exposure to climate change. The information on citizen comfort allows the preparation of plans and implementation of adaptation strategies to reduce the effects of climate change on the citizen. Keywords: climate, downscaling, comfort, CFD 1. Introduction The increase in urban population and climate change are drastically affecting human well-being and the health of citizens [1]. In general, understanding how climate affects humans and the environment is a key aspect of designing strategies to improve resistance to climate change. The global climate can amplify the risk of the citizens to the exposition of the environmental problems inherent to the cities, being these spaces especially vulnerable to the climatic change. The microclimatic conditions of the cities will affect the quality of life of the citizen and that is why in recent years a great effort is being made to study the comfort of pedestrians in urban areas. Human comfort is affected by a wide range of parameters: air temperature, relative humidity, solar radiation, winds (microclimate conditions), level of clothing, people's activity and factors such as age and psychological state. Atmospheric flow and microclimate in urban areas are influenced by urban characteristics that affect atmospheric turbulence [2]. Urban geometry, vegetation, building materials and other aspects can play an important role in the microclimate of cities [3]. The global climate models (GCMs) have a coarse spatial resolution (around 1º), so we need to use higher resolution numerical models to obtain accurate data on the Corresponding author. Tel.: address: roberto@fi.upm.es. 99

2 urban microclimate [4]. Recent advances in computer and atmospheric sciences, particularly in the use of dynamic downscaling techniques, provide an opportunity to investigate the effects of climate on the comfort of pedestrians [5]. Computational Fluid Dynamics (CFD) is a useful simulation tool for the prediction of climatic parameters. A CFD allows the investigator to analyse large areas of the city, provide a complete picture of the problem and present the results in an easy-to-understand graphical form. We have chosen a dynamic downscaling process using high resolution climate and air quality models at the regional and urban levels and including a computational fluid dynamics (CFD) model to take into account the effects of buildings on urban ventilation. CFD simulations are computationally demanding, but are based on physical laws and produce a complete set of climate output variables. Climate change is often discussed in terms of changes in isolated indicators; however, to evaluate its impact on the citizen, it is necessary to analyse the combined effect of different meteorological variables. Comfort expresses the level of human satisfactions in a given environment. Comfort is closely related to human health and, therefore, is a key component in our daily routine, since depending on comfort we will be more or less time outdoor environments [6]. For the analysis of comfort we have used several meteorological variables: wind speed, wind direction, air temperature, air humidity and radiation. Unfortunately, data from these parameters with very high spatial resolution are not available in the climate community, so the major studies on climate and comfort use coarse resolutions. Studies on the impact of urban climate on human comfort are very few. Earlier studies examining the consequences of climate change for pedestrian comfort typically quantify impacts on relatively coarse spatial resolution. However, mean responses have little value [7]; therefore, it should be done on a fine scale, taking into account the 3D shape of buildings, local urban conditions as well as the city's boundary conditions. 2. Methodology The starting point is the results of the global climate model CESM which was forces by the IPCC scenario. Our methodology consists to apply a dynamical downscaling process to get data with 50 meters of resolution from 1ºglobal climate model data. The downscaling is made nesting regional and urban domains to end with a CFD simulation implemented with the MICROSYS model. MICROSYS is a three-dimensional microclimate model than simulates the interactions between surface, air and urban buildings, with a spatial resolution of meters. MICROSYS combines a CFD model and urban canopy model and micro-radiation model to simulate the interactions between air, surfaces, soil and energy fluxes. Turbulent air flow is modelled in three dimensions using the non-hydrostatic incompressible Navier Stokes equations with density removed using the Boussinesq approximation. Lateral and outflow boundary conditions come from a three-dimensional regional/urban model WRF/Chem. The description of the dynamical downscaling method was published already, for detailed information; refer to publication [8]. The simulation results provide the data required for a robust assessment of the human comfort based on requirements established by the specific wind comfort and thermal comfort indexes. The outputs of MICROSYS include the main parameters that affect the human comfort: air temperature, mean radiant temperature, winds and humidity. The study area corresponding with area of 12 km by 12 km of Madrid, although results of a specific areas will be show to get more detailed information. The 3D computational tool has been applied for two different IPCC climate scenarios RCP 4.5 and RCP 8,5 for actual year (2011) and future ( 2030, 2050 and 2100.). Comparing each future simulation to the actual simulation (2011) assuming current urban structures, we can show the impact of the global climate on the pedestrian comfort. Future climatic conditions have been identified in two climate scenarios developed by the Intergovernmental Panel on Climate Change (IPCC) [9] in the Fifth Assessment Report (AR5). The two selected global climate scenarios, RCP 4.5 [10] and RCP 8.5 [11] are the most used by the scientific community because they represent relatively low and high greenhouse gas projections/radiative forcing respectively. Also, the choice of the worst-case scenario (RCP 8.5) and the best-realistic-case scenario (RCP 4.5) was motivated by the goal of displaying extreme changes that can be forecasted at city scale to allow implementing mitigation and adaptation strategies Wind Comfort 100

3 Strong winds are usually accelerated at the pedestrian level within the urban environment, say around tall buildings, due to particular aerodynamic configurations generally associated with tall buildings. The effect of wind around buildings depends not only on topography and roughness of the surface, but also on the building s geometry, the layout of surrounding buildings and the wind direction Several criteria have been developed in the wind engineering community for evaluating only the wind-induced mechanical forces on the human body and the resulting pedestrian comfort and safety. We propose to use the Dutch wind nuisance standard (NEN 8100) applies a discomfort threshold for the hourly mean wind speed (UTHR) of 5 m/s for all types of activities [12]. Depending on the exceedance probability P of the threshold wind speed, the code defines five quality classes of wind comfort 0 4 These quality classes define a good, moderate or poor wind climate for the activities traversing, strolling and sitting [13] Thermal Comfort Pedestrian comfort level with the thermal environment is one of the important subjective indications that determine the amount of time to spend in outdoor public spaces. However, it is difficult to judge one ś satisfaction level with the thermal environment as it can vary from a person to another. Researches have attempted to design models that can estimate the comfort conditions people find acceptable outdoors. To understand thermal comfort and its effects on the human being, air temperature is not an adequate indicator. Outdoor thermal comfort is governed by winds conditions, both direct and diffuse solar irradiation, the exchange of long-wave radiation between a person and the environment. We assess thermal comfort based on physiological equivalent temperature (PET), which is one of the most commonly used indices for outdoor thermal comfort [14]. PET is defined as the physiological equivalent temperature at any given place and is equivalent to the air temperature at which, in a typical indoor setting, the heat balance of the human body (work metabolism 80 W of light activity, added to basic metabolism; heat resistance of clothing 0.9 clo) is maintained with core and skin temperatures equal to those under the conditions being assessed. We have calculated the PET using the following input data: human activity level, clothing, insulation ratio, air temperature, mean radiant temperature, relative humidity and wind speed. The microclimatic parameters were got from the dynamical downscaling tool. 3. Results The air temperature and wind velocity are compared between the simulated outputs and measures values for real (no climatic scenario) 2011 year. Madrid meteorological stations were used to evaluate the accuracy of the modelling system outputs (Table 1). The monitoring stations have been identified with theirs typical identifier names. AVG Stations means the average of the values where stations are located. The following statistical metrics have been used in this study to verify the performance of the modelling system when compared with the meteorological observations of the Madrid.. Bias or mean error (BIAS) is defined as the mean of the differences between the simulated outputs and observations. Root Mean Square Error (RMSE) is a frequently used measure of the difference between values predicted by a model and the values actually observed. It measures the average magnitude of the error and it is defined as the measure of the combined systematic error (bias) and random error (standard deviation).therefore, the RMSE will only be small when both the variance and the bias of an estimator are small. Pearson correlation coefficient (R2) is defined as the measure of the linear dependence between the simulated results and the observational data, giving a value between +1 and 1 inclusive. It thus indicates the strength and direction of a linear relationship between these two variables. A value of 1 implies that a linear equation describes the relationship between models and the observations perfectly, with all data points lying on a line for which the model values increase as the data values increase. The correlation is 1 in case of a decreasing linear relationship and the values in between indicates the degree of linear relationship between the model and the observations. According to Table 1, there is an acceptable agreement between the measured and simulated vales for the air temperature and wind in the majority of the locations (R2>0.5), this means than the average simulated levels are within the inter-annual variability. The lowest level of R2 is achieved in wind speed. The statistical evaluation shows significant evidence that high resolution downscaling procedure could achieve reasonably 101

4 good performance, particularly for BIAS and R2 statistics. In case of the temperature are really good results, the prediction is within 5% error. Table 1. Madrid results of the evaluation of the results of the modelling system AVG STATIONS Fuencarral Moratalaz Villaverde China Acustica Hortaleza AVG STATIONS Fuencarral San Blas Villaverde China Calidad aire Hortaleza PARAMETER NMB(%) 33, ,2 23,6 57,2 45,8 36,6 1,02 3,65 2,83 1,48-3,29 4,77 0,26 RMSE 2,42 2,24 1,7 2 2,97 2,61 2,44 1,37 1,58 1,53 1,43 1,75 2,24 1,47 R2 0,51 0,37 0,42 0,52 0,55 0,41 0,55 0,96 The model simulations of the two climate scenarios for the future (2030, 2050 and 2100) were then compared with the present (2011). The differences in each 50 meters grid cell give us the impact of the global climate. A comparison of current and future conditions, projected by the scenarios, shows remarkable changes which are showed in the next figures corresponding to maps of the spatial distribution of the main results. Fig. 1 shows the spatial distributions of the differences of the average annual PET over area of 2 km. by 2 km of Madrid between and Fig. 1: Differences of annual Physiological Equivalent Temperature (ºC), Madrid 50 m spatial resolution, (top), (bottom), RCP 4.5 (left) and RCP 8.5 (right). 102

5 In Fig. 1 we can see that by the year 2030 no major changes are expected in the PET compared to 2011, slight decrease with the climatic scenario RCP 4.5 and small increments with the scenario RCP 8.5. The situation is different in the year 2100, where large decrements of PET (between 2 and 3 C) can be observed if the climatic scenario follows RCP 4.5 or large increases of PET (between 1.4 and 2.1 C) in the climatic scenario RCP 8.5. The most affected areas would be the wide streets because in these there are fewer shadows of buildings and therefore the solar radiation that would arrive would be greater. Now we show in Fig. 2 the differences of the annual exceedance probabilities (P > 5 m/s) for wind nuisance for future (2050) respect to the present (2011) and in the Fig. 3 the accompanying quality classes according the Dutch Standard NEN 8100 over a zoom area of 2 km. by 2 km of Madrid. Fig. 2: Differences of annual probability than wind speed is higher than 5 m/s over Madrid, , 50 m spatial resolution, RCP 4.5 (right) and RCP 8.5 (left). In Fig. 2 we can see how small increments (less than 1%) are expected for the year 2050 in the probability that the wind speed will exceed 5 m/s in some streets for the RCP 4.5 scenario combining with descents in other streets. For the scenario RCP 8.5 the areas with decrements are very few and highlight some streets and squares with increases in the probability near 2%. Fig. 3: Quality class NEN 8100 over Madrid, 2050, 50 m spatial resolution, RCP 4.5 (right) and RCP 8.5 (left). Fig. 3 shows that majority of the streets present good quality of wind comfort, except a big avenue (Castellana) with poor quality zones. In Fig. 3 we have indicated with a black box a square in which it is shown as a citizen located in the square in the years 2050 can have a quality of good comfort (level 0) if the 103

6 climatic scenario is obtained RCP 4.5 or by the otherwise have a moderation wind comfort (level 1) if the global climatic scenario becomes RCP 8.5. This is a clear example of how the global climate can affect citizens in a square, and the only way to know it is to apply dynamical downscaling techniques such as this one. 4. Conclusions This works present a pedestrian comfort study, showing the thermal and wind comfort for actual (2011) and future (2030, 2050 and 2100) for Madrid city. In this study climate projections were based on two IPCC scenarios: RCP 8.5 and RCP 4.5 that were dynamical downscaled from a global scale to a street level scale where a CFD model has been applied. Dynamical downscaling tools which include CFD simulations are useful tools for the prediction and assessment of citizen comfort in outdoor urban microclimate because they simulate microclimatic parameters (air temperature, wind speed, solar radiation, relative humidity, etc). The RCP 8.5 will produce climate conditions that are more stressful to people and affect theirs health and will being. RCP 8.5 is characterized by temperature increments. The reduction in the effects of the wind speeds of the cities can be explained by change in surface roughness as well as enhanced temperatures that reduce pressure over the city forming a center of low level convergence. Densely constructed buildings may serve as obstacles to wind. On the other hand, open areas can enhance wind tunnelling effect, which is associated with strong wind that cause discomfort in the outdoor. The outcomes of this study will assist policy makers in developing the policies which can effectively increase the pedestrian comfort in the future. These kinds of studies are needed to know local effects of the global climate to design comfortable and safe urban plan development and buildings. 5. Acknowledgements The UPM authors acknowledge the computer resources and technical assistance provided by the Centro de Supercomputación y Visualización de Madrid (CeSViMa). The UPM authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación.). We acknowledge the DECUMANUS EU project from EU Space Call FP7-SPACE at SPA References [1] A. Haines, R. S. Kovats, D. Campbell-Lendrum, C. Corvalan: Climate change and human health: Impacts, vulnerability and public health, Public Health, , [2] M. Piringer, E. Petz, I. Groehn, and G. Schauberger, A sensitivity study of separation distances calculated with the Austrian Odour Dispersion Model (AODM), Atmospheric Environment, pp.41, , [3] H. R. Silva, P. E. Phelan and J. S. Golden, Modeling effects of urban heat island mitigation strategies on heatrelated morbidity: a case study for Phoenix, Arizona, USA, International Journal of Biometeorology, 2010, 54: [4] J. H. Christensen and O.B. Christensen, A summary of the PRUDENCE model projections of changes in European climate by the end of this century, Climatic Change, 2007, 81: [5] C. Rosenzweig, W. Solecki, S. A. Hammer and S. Mehrotra, Cities lead the way in climate-change action, Nature 467: , [6] G. W. Evans, Environmental Stress. Cambridge University Press. [7] T. J. Wilbanks and R.W. Kates, Global change in local places: How scale matters Climatic Change 43, 1999, [8] R. José, J. Pérez, L. Pérez, R. González, J. Pecci, A. Garzón and M. Palacios, Impacts on the Urban Air Quality and Health of Global Climate Scenarios Using Different Dynamical Downscaling Approaches, Journal of Geoscience and Environment Protection, 4, , [9] IPCC. Climate Change, The Physical Science Basis, Cambridge University Press: Cambridge, UK; New York, NY, USA,

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