Determining Favourable and Unfavourable Thermal Areas in Seoul Using In-Situ Measurements: A Preliminary Step towards Developing a Smart City
Abstract
:1. Introduction
1.1. Urban Heat and Spatial Typification for Sustainability
1.2. Heat Energy and Spatial Typification
1.3. Studies Using the Existing Spatial Approaches
1.4. Typification Method
1.5. Objectives and Application
1.6. Research Process
2. Extraction of Favourable and Unfavourable Areas
2.1. Spatiotemporal Scope
2.2. Input Data
2.3. Methodology
2.4. Results of K-Means Clustering
3. Sensible Heat Flux Calculations
- : land cover ratio (unit: ratio)
- : green cover, water cover, impervious land, building cover, and road cover
- − 0.00003178
4. In-Situ Validation
- a
- BCR and net radiation time shift data
- b
- Time shifts with variations in BCR
- c
- Mixed-deployment observation of the overall effects of land coverage
- d
- Various urban climate data for calculating the sensible heat flux
4.1. In-Situ Measurement Process (Appendix B)
4.1.1. Considerations for Measurement Design
4.1.2. Observation Site Selection
4.1.3. Measurements and Data Collection
4.1.4. Context of the Study Sites
4.2. Limitations of the in-situ Measurement
5. Validation Results: Comparison of Sensible Heat Fluxes from the Six Sites
6. Discussion
6.1. Spatiotemporal Shift in Sensible Heat Flux
6.2. Development of Thermally Sustainable Smart Cities
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Pyranometer Specification | Value (unit) | Definition |
---|---|---|
Sensing sensitivity | 5–20 (µV/W/m²) | Calibration factor |
Irradiance range | 0–2000 (W/m²) | Measurement range |
Net irradiance range | −250 to +250 (W/m²) | |
Shortwave radiation spectral range | 300–2800 (nm) | |
Longwave radiation spectral range | 4500–430,000 (nm) | |
Field of view | Upper detector: 180°, lower detector: 150° | Sensor opening angle |
Nonlinearity | Less than 1 (%) | 0–1000 W/m² irradiance—Max. deviation from the responsivity at 500 W/m2 owing to change in irradiance within the indicated range. |
Uncertainty in daily total | Less than 5 (95% confidence level) | Achievable uncertainty |
Temperature dependence of sensitivity | −10 to +40 (°C) | |
Operating temperature | −40 to +80 (°C) | |
Environmental | 0–100% RH | Relative humidity |
Response time | less than 18 s | 95% response |
Directional error | less than 20 (W/m²) | Angles up to 80° with 1000 W/m² Beam radiation–combined zenith and azimuth errors of 0–80° with a 1000 W/m² beam |
Appendix B
Appendix C
Appendix D
Correlations | |||||||
---|---|---|---|---|---|---|---|
Qh | gr | bd | im | water | rd | ||
Pearson correlation | Qh | 1.000 | −0.727 | 0.593 | 0.209 | −0.003 | 0.637 |
gr | −0.727 | 1.000 | −0.799 | −0.443 | 0.048 | −0.908 | |
bd | 0.593 | −0.799 | 1.000 | 0.542 | −0.610 | 0.898 | |
im | 0.209 | −0.443 | 0.542 | 1.000 | −0.554 | 0.493 | |
water | −0.003 | 0.048 | −0.610 | −0.554 | 1.000 | −0.357 | |
rd | 0.637 | −0.908 | 0.898 | 0.493 | −0.357 | 1.000 | |
Sig. (1-tailed) | Qh | - | 0.000 | 0.000 | 0.000 | 0.439 | 0.000 |
gr | 0.000 | - | 0.000 | 0.000 | 0.003 | 0.000 | |
bd | 0.000 | 0.000 | - | 0.000 | 0.000 | 0.000 | |
im | 0.000 | 0.000 | 0.000 | - | 0.000 | 0.000 | |
water | 0.439 | 0.003 | 0.000 | 0.000 | - | 0.000 | |
rd | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | - | |
N | Qh | 3240 | 3240 | 3240 | 3240 | 3240 | 3240 |
gr | 3240 | 3240 | 3240 | 3240 | 3240 | 3240 | |
bd | 3240 | 3240 | 3240 | 3240 | 3240 | 3240 | |
im | 3240 | 3240 | 3240 | 3240 | 3240 | 3240 | |
water | 3240 | 3240 | 3240 | 3240 | 3240 | 3240 | |
rd | 3240 | 3240 | 3240 | 3240 | 3240 | 3240 |
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Classification | Input Data | Source |
---|---|---|
Meteorological data for heat flux distribution | -Air temperature, relative humidity, cloud cover, saturated water vapour pressure | -Korea Meteorological Administration (38 stations) -SKTech X (249 stations) |
Extraction of FTAs and UTAs | -Latent heat model, sensible model, storage heat model | Holtslag, 1983 Loridan, Grimmond 2011 |
Spatial attributes | -Subdivided land cover map (green spaces, wetlands, impervious surfaces) -.shp file of Seoul administrative district–building .shp file -.shp file depicting the widths of roads -.shp file depicting the width of roads | -Ministry of Environment, -Statistical Geographic Information Service (SGIS), -Seoul Information Communication Plaza |
Land Cover Coefficient | a1 (Ratio) | a2 (h) | a3 (W/m2) |
---|---|---|---|
Green | 0.34 | 0.31 | −31 |
Building | 0.07 | 0.06 | −5 |
Impervious | 0.83 | 0.4 | −54.2 |
Water | 0.5 | 0.21 | −39.1 |
Road | 0.61 | 0.41 | −27.7 |
Neighbourhood | LCZ | Anthropogenic Heat Flux (W/m2) | Site |
---|---|---|---|
Large dense, city centre | 1,2 | 100–1600 * | 4 |
Medium dense, city centre | 3 | 30–100 * | 6 |
Low dense, open, low-rise | 6 | 5–50 * | 2 |
Open high-rise | 4 | 26–80 ** | 1 |
Green (Low-planted), Water | D,G | - | 3,5 |
Type | Site 1 | Site 2 | Site 3 | Site 4 | Site 5 | Site 6 |
---|---|---|---|---|---|---|
High density | X | O | X | O | X | O |
Low density | O | X | O | X | O | X |
Site | Site 1 | Site 2 | Site 3 | Site 4 | Site 5 | Site 6 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Time | Qn | Qh | Qn | Qh | Qn | Qh | Qn | Qh | Qn | Qh | Qn | Qh |
Peak ± 0.5 (h) * | 540 | 278 | 586 | 398 | 351 | 85 | 548 | 376 | 456 | 315 | 672 | 466 |
After sunset ** | 168 | 89 | 184 | 124 | 52 | 6 | 256 | 174 | 96 | 85 | 323 | 222 |
Site 1 | Site 2 | Site 3 | Site 4 | Site 5 | Site 6 | ||
---|---|---|---|---|---|---|---|
Qh day (W/m2) | 278 | 398 | 82 | 379 | 311 | 444 | |
Qh night (W/m2) | 89 | 124 | 6 | 174 | 85 | 222 | |
Qn day (W/m2) | 540 | 586 | 351 | 548 | 456 | 673 | |
Qn night (W/m2) | 168 | 184 | 52 | 256 | 96 | 323 | |
Trend line formula: y (Qh) = ax2 + bx + c | a | −10 | −15, | −4, | −13 | −12 | −14 |
b | 252 | 358 | 100 | 308 | 293 | 353 | |
c | −1213 | −1692 | −490 | −1382 | −1368 | −1696 | |
Green | 0.23 | 0.06 | 0.96 | 0.08 | 0.17 | 0.02 | |
Building | 0.35 | 0.6 | 0.02 | 0.52 | 0 | 0.73 | |
Impervious | 0.22 | 0.14 | 0.01 | 0.15 | 0.06 | 0.1 | |
Road | 0.2 | 0.2 | 0.01 | 0.25 | 0.05 | 0.15 | |
Water | 0 | 0 | 0 | 0 | 0.72 | 0 | |
Location | Sangdo | Daehak | Montmartre | Yeoyi | Riverside | Shilim | |
Aspect (land use) | Resid. * high-rise | Resid. * low-rise | Park | Quasi_Resi. ** | Waterfront | Quasi_Resid. ** |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Kwon, Y.J.; Lee, D.K.; Lee, K. Determining Favourable and Unfavourable Thermal Areas in Seoul Using In-Situ Measurements: A Preliminary Step towards Developing a Smart City. Energies 2019, 12, 2320. https://doi.org/10.3390/en12122320
Kwon YJ, Lee DK, Lee K. Determining Favourable and Unfavourable Thermal Areas in Seoul Using In-Situ Measurements: A Preliminary Step towards Developing a Smart City. Energies. 2019; 12(12):2320. https://doi.org/10.3390/en12122320
Chicago/Turabian StyleKwon, You Jin, Dong Kun Lee, and Kiseung Lee. 2019. "Determining Favourable and Unfavourable Thermal Areas in Seoul Using In-Situ Measurements: A Preliminary Step towards Developing a Smart City" Energies 12, no. 12: 2320. https://doi.org/10.3390/en12122320