A Bio-Climatic Assessment on Urban Area at Seoul based on

Observations and Numerical Simulations

 

Sung Nam Oh

                     Applied Meteorology Research Laboratory

                      Meteorological Research Institute / KMA

             460-18 Shindaebang-dong, Dongjak-gu , Seoul 156-720, Korea

     Phone: +822-846-2850, Fax: +822-846-2851, E-mail: snoh@metri.re.kr

 

 

Abstract

 

This paper gives an overview on bio-climate analysis of urban environments at Seoul. The effects of climate elements can be assembled into a single chart for analyzing the nature of urban bio-climate condition. The chart shows the climate comfort zone. The thermal field in urban area such as the heat island is produced by the change of land use and the air pollution that provide the bio-climate change of urban eco-system. The urban wind flow is the most important bio-climate element on dispersion of air pollution and thermal effects. Numerical modeling indicates that the bio-climatic transition of wind wake in urban area and the dispersion of the air pollution by the simulations of the wind variation depend on the urban land cover change. The winds are separately simulated on small and micro-scale of Seoul with two kinds of kinetic model, WiTrak and MUKLIMO.

 

 

1.      Introduction

 

Man’s energy and health depend in large measure on the direct effects of his environments. It is also well known that in urban climatic areas, where excessive heat and pollution prevails, human being energy is diminished by the biological strain of adaptation to the environmental conditions (Stull, 1988).

The urban climate is formed by the change of land use and land cover from the urbanization or expansion of urban area where are the road area and building increase, meanwhile green area decrease with the changed urban streams. The urban climate is characterized with the heat island by the air temperature increase, and the change of wind and visibility, high smog and fog but low humidity (Simpson et al., 1994).

The climate comfort condition for human being that is introduced by Olgyay (1973) is defined with the air temperature and humidity. Finally the climate scientists suggested standard is the temperature ranges from 55.8 ° to 73.7 °F with relative humidity at noon varying from 40 % to 70 % as an ideal climate zone. The comfort zone is available as the urban bio-climate indicator, but has a different sensation with the different locations.

Heat islands, once established in a big city such as Seoul, are difficult to moderate. However, a change in impervious types of ground cover in the city, which will alter the thermal capacity of large surface area or improvements in air quality and air flow are some of the possibilities. Planting trees and shrubs to increase shade, lakes and river, water foundations with recycled water can aid in evaporative cooling, and therefore, reduce the air temperature.  Because the heat island combined with the air pollution those are input near industrial and automobile emissions at the rough topography and buildings, dispersion of air pollution entering or inside the cities is erratic and typical of unstable conditions. Use of trees in the urban center changes both the flow of air and the dispersal of pollution. The urban terrain-influenced winds are increasingly recognized as important contributors toward the local transport of biota and pollution (Kerschgens et al., 1994).

A numerical kinetic wind model to simulate near-ground air movement has been developed and verified by Deuch Weather Department (DWD) in 1999. The determined variables by the model are the strength and direction of the horizontal wind depends on the surface roughness and the wind-shadow effect of windbreaks.

In this paper, as a case study, the thermal environments in Seoul are analyzed using the local bio-climate observations, the urban effects to the wind flow are discussed on the dispersion of air pollution through the numerical model simulations. Experimental data will be analyzed to characterize canyon flow on the building street channels and surface roughness. 

 

 

2.      Methods and Data

 

Methods:

 

a. Thermal environment analysis:

   · Monthly Urban Bio-climate comfort zone

   · Seasonal Heat Island analysis between the year of 1995 and 1999

 

b. Air Pollution:

   · Atmospheric dispersion on the urban complex area

c. Local wind variation:

   Three dimensional incompressible and non-divergent wind flow simulations

   · Micro-scale wind simulation: MUKLIMO urban climate model (Sievers, 1986)

   · Small scale wind variation: WiTrak mass consistent flow model (Sievers, 1995)

  

Data:

a.       Observations: air temperature, relative humidity, pressure, winds, solar radiation, 22 of Automatic Weather Station (AWS), synoptic weather data.

 

b.       Heat island analysis: Monthly average data of 24 AWS at Seoul, and 5 of AWS at Seoul suburb as shown in Figure 1.   

 

c. Geographical Information: topographical data, land covers and buildings.

 

 

 

 

 Figure 1.  Automatic Weather Stations in Seoul

 

3.      Thermal Environment in the Urban Area

 

A. Bio-climatic Needs at Seoul

 


For the regional evaluation of a climatic situation at Seoul, the bio-climatic chart has been plotted by combination with air temperature and relative humidity as shown in Figure 2. On the regional charts, the climate situations of Seoul are plotted with hourly data of the averaged daily values of each month. It represents the climate types of Seoul with hot-humid in summer season and cold-dry in winter. Most of the month except June and September is found outside of comfort climate zone in the Seoul region. The comfort climate zone is adapted in this investigation with the zone of New York area. The appropriated comfort zone for Seoul area should be developed in the future study.

 


 

 


Figure 2.  The comport zone of Seoul in 1999.

B. Heat Island

 

The air temperature at the urban area of Seoul is explicit higher than the around suburb area as shown in Figure 3. The monthly averaged temperature of Seoul metropolitan area ranged from –2.5° C to 27.5° C during the last decade period. The distributions of temperature found to be the highest are within the city limits and the temperature gradient positively increase along the advance from urban center to suburb.

Most warm regions are relatively appeared at the areas of Chongyang-ri and Youndungpo and center of Gangnam as shown in Figure 3 from the average daily temperature analysis. The monthly distributions show the different understand with highest at Guro area in January and February, Dongjak in March, Seocho in May but Youngduongpo and Yangchun area in April and through the period from June to December. In the seasonal analysis, the temperature at Seoul is appeared same distribution with the monthly temperature, but the warm areas are appeared at Mapo, Guro and Seocho in winter.

Heat island is explicit at minimum temperature in urban area, therefore the seasonal temperature fields found to be the warmest at Youndungpo area in Seoul. The second warmest area is shown at Chongyangri and Gangnam area as shown in the Figure 4.

These locations correspond to the highest building density and industrialization occurring in the city. These regions are consisted with a plane surface with no green area and no hill mountain which induce the valley wind effects. The higher effects of heat island are shown at the southern area from Han rive than the northern area of Seoul because the southern region has a flat geographic characteristics. The satellite image of Landsat 7 in Figure 5 show here the additional confidence of heat island effect between two regions with the land cover distribution. The low temperature areas are appeared at the more green area of the northern part of Seoul, but higher heat island area are shown at the urban surface of the southern regions having large roads and buildings.

 

             

 

    

Spring                               Summer

 

Autumn                             Winter

 

Figure 3.  The seasonal daily mean temperature in Seoul (1999).

 

 

 

 

 

 

 

January                              April

 

July                                 November

 

Figure 4.  Monthly distribution of daily mean minimum temperature in Seoul (1999).

 

 

 

 

 

 

 

 

         Figure 5.  Land-cover distribution of Seoul area on April 19, 1999. The used Landsat TM Image distributed by the unsupervised classification using the chain method (Jensen, 1996)

 

 

   For the temperature gradient, temperatures of two locations, Youngdungpo urban area, and Sanung where located on the Seoul suburb. The temperature difference between two locations show 5.2° C (2.9°C of SD) at 0600 LST, 0.2 °C (1.0° C of SD) at 1200 LST and 4.7° C (2.8° C) at 2400 LST respectively.  The seasonal values of the temperature difference between two cities are shown the high value at January and November, but low value at July. It is understood that the winter season has less humidity and higher energy consume than the summer. The temperature differences between the center of city and the suburb area show clearly in Figure 6 which indicates the temperature shear along the geographic line.

 

 

 

 

 

 

 

Figure 6.  Series of daily temperature difference between the city center (Kangnam)

and suburb region (Sanumg) for January, April, July and November, 1999

 

4.      Wind Changes at Seoul Urban Area

 

In urbanization, Seoul metropolitan area is densely populated and concentrated with lots of buildings for residential, commercial and industries. This land use gives a lot of influence on echo-system in the thermodynamic and aerodynamic characteristics in a city. Especially, the aerodynamic change in the urban area have major effects from the surface roughness which gives a friction on the boundary layer circulation and is related to negative bio-climatic effects like heat island, smog and air pollution in a city.

The simulation of urban wind variation with respect to the change of land cover characteristics is mostly helpful to understand the urban bio-climatic change and its impacts.

 

A. Model

 

Two kinetic wind models, WiTraK and MUKLIMO are being used to simulate the winds flow into a small and micro-scale urban area where is designed with high model resolution to resolve building and urban street canyons.

WiTraK is a model that has been under development of the university of Koeln for modeling effort for the wind simulation release at the complex urban area. The experiment of the wind field in Seoul is done using WiTraK of 600m surface resolution.

MUKLIMO is adapted as a model for the simulation to find micro-scale wind variations of the urban canopy layer features. We modified the model, MUKLI MO that is developed by Deuche Weather Department (DWD) since 1995 and applied to Youido urban area under 10 m grid size.

Both models are 3-dimensional diagnostic models based on the incompressible, mass consistent and non divergent assumption. The mass consistent flow in models is determined by topography, land uses and atmospheric stability.

 

B. Inputs

 

(1) Meteorological data

· Wind speed and direction at top of boundary layer (1 km altitude) : 11 m/s at 165° (summer) and 298°(winter)

· Wind speed at surface: 1.8m/s in summer, 2.5 m/s in winter

· Air-temperature: 25.0 ° C in summer, 0.7°C in winter

 

(2) Surface roughness

 

      Table 1. The values of surface roughness for land cover

Land cover

Surface roughness (m)

Rice paddy

0.1

Forest

1.5

Residence

1.0

Urban building

2.0

Water

0.001

 

 

C. Results

 

· The small scale simulation 

The simulations of WiTraK show that both divergence and convergence zone appear in the region of windward and leeward side of Mt. Pukhan and Mt. Kwanak, respectively in fig. 7 and 8. Wind speed increase with altitude and high speed is shown on the top of the mountain area. This pattern is strongly dependent to the atmospheric stability condition. For instance, topography effect becomes more significant under the condition of surface layer inversion case.

Wind speed over the Han-river and suburb is larger than that in the center of Seoul and forest. The land cover effect is important in the surface layer, while wind flow is dominated by the characteristics of Ekman layer. And it is also found that the flow patterns control the distribution of pollutant transport inside the city in fig. 7 and 8.

 

· The micro scale simulation

The micro scale climate change influenced by the establishment of Youido park is investigated by the numerical experiments of wind and diffusion field in fig. 9 and 10.

The results show the features of the wind field of neutral stability condition in the urban canopy layer with a high resolution near the ground. And the wind speed is weaken at the lower level by the Youido park establishment. This reduction was proportional to the initial wind velocity. Its amount is 47% at the 6m level over the ground, compared with the results in the absence of the park. And we also found that the pollutants transport field is influenced by the land-use change fig. 9 and 10.

The study results will be used to find optimum climate condition for development of air quality in a city. The numerical models are also found to be a useful tool for evaluating bio-climate change effect. The wind simulations in urban area based on the land cover give a guideline information for urban planning under the bio-climatic and eco-urban environmental conservation.

 

 

(a)                                (b)

 

(c)                                (d)

 

 

Figure 7.  Horizontal wind field and non-reactive pollutant diffusion field at 5 m level over the ground at the time of a) 15, b) 30, c) 45 and d) 60 minutes after emission, respectively on January 1999 in Seoul.

 

 

 

 

(a)                                (b)

 

(c)                                (d)

 

 

Figure 8.  Horizontal wind field and non-reactive pollutant diffusion field at 5 m level over the ground at the time of a) 30, b) 60, c) 90 and d) 120 minutes after emission, respectively on July 1999 in Seoul.

 

 

 

 

 

 

Figure 9.  Simulated horizontal wind velocity and diffusion field of air pollutant

in Youido region.

 

 

 

 

 

           (a)

Youido park
 

          (b)

 

Figure 10.  Simulated horizontal wind velocity at 2m level over the ground (a) in Youido region including park (forest). The (b) is a same as (a), but for in the absence of Park (asphalt).

5.      Conclusion and Summary

 

As a new technique for the bio-climatic analysis of Seoul urban area, the climate comfort chart, heat island and aerosol induced by solar radiation amount are analyzed. The comfort climatic zone of Seoul only involves in November that is explicitly similar with New York. The wind value doesn’t include for the comfort zone analysis. The heat islands are mostly shown at the Southern part of Seoul where is more flat plan than the northern part where has mountains.

Small-scale wind simulations using WiTrak non-hydrostatic kinetic model gives the information on wind variations depended on urban topographic terrain conditions. We have demonstrated analysis and visualizations for use the data provided by the automatic wind system (AWS) network in the urban mesoscale. We have simulated the air pollution dispersion over the urban complexes.

The urban street canyon flow has been examined by the micro-scale incompressible kinetic model, MUKLIMO that show the variations of the urban street canyon flow and the building effects of wind breaks. We found the updraft and downdraft motion of the wind are dependent on the building size but the vortex and building aspects didn’t include completely in the simulations.

This study introduces that the bio-climatic analysis and wind simulations on the urban area are useful for the urban planing related to climatic conditions and echo-environments.

 

 

Acknowledgement

 

This study is supported by the research project of METRI / National Research Laboratory (NRL) which is concerned with the research for “Development of Monitoring Technology for Background Atmosphere and Climate Change over Korean Peninsula.”

 

 

References

 

Kerschgens, M., W. Brueher, F. Steffany, 1994: WiTraK, Windfeld-, Transport- und Klimatologie Programm, Institut fuer Geophysik und Meteorologie der Universitaet zu Koeln.

Olgyay, V., 1973: Design with Climate, Bioclimatic approach to architecture regionalism. Priceton University Press, 14-112.

Sievers, U., 1995: Verallgemeinerung der Stromfunktionsmerhode, Meteorol. Zeitscbrift. N.F. 4, 3-15.

Sievers, U., and W. G. Zdunkowski, 1986: A micro-scale urban climate model, Beitr. Phys. Atmosph., Vol. 69, No 1. 13-40.Simpson, J. R., D. G. Levitt, C. S. B. Grimmond, E. G. McPherson, and R. A. Rowntree, 1994: Effects of vegetative cover on climate, local scale evaporation and air Conditioning energy use in urban southern California. 11th conference on biometeorology and aerobiology, San Diego, California, March 7-11, 1994, 345-348.

Stull, R. B., 1988: An introduction to boundary layer meteorology, Kluwer Academic Publishers, 666pp.