Optimal Strategy on Radiation Estimation for Calculating Universal Thermal Climate Index in Tourism Cities of China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Database
2.2. Calculation of UTCI
2.3. Estimation of Solar Radiation
2.3.1. Angstrom Model
2.3.2. Ogelman Model
2.3.3. Bristow Model
2.3.4. Hargreaves Model
2.3.5. BP Neural Network
2.3.6. Support Vector Machine
2.4. Statistical Analysis
3. Results
3.1. Comparison of Model Performance in Radiation Estimation and UTCI Calculation
3.2. Spatial Analysis of UTCI and Day Number within Each Category
3.3. Temporal Trend in UTCI and Day Number within Each Category
4. Discussion
4.1. Optimal Strategy on Estimating Solar Radiation for UTCI Calculation
4.2. Increase in Yearly Day Number under no Thermal Stress Accompanying with More Risks in Heat Stress in Summer in China
4.3. Certainties and Uncertainties in UTCI Estimation over China
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Cities | Latitude (N) | Longitude (E) | Altitude (m) | Climate Conditions | Population (Million) |
---|---|---|---|---|---|
Beijing (BJ) | 39.8 | 116.5 | 31.3 | temperate and sub-humid | 21.9 |
Tianjin (TJ) | 39.1 | 117.1 | 3.5 | temperate and sub-humid | 13.9 |
Dalian (DL) | 38.9 | 121.6 | 91.5 | temperate and sub-humid | 7.5 |
Qingdao (QD) | 36.1 | 120.3 | 76.0 | temperate and sub-humid | 10.0 |
Shanghai (SH) | 31.4 | 121.5 | 5.5 | subtropical humid | 24.9 |
Nanjing (NJ) | 31.9 | 118.9 | 35.2 | subtropical humid | 9.3 |
Suzhou (SZ) | 31.3 | 120.6 | 10.7 | subtropical humid | 12.7 |
Hangzhou (HZ) | 30.2 | 120.2 | 41.7 | subtropical humid | 12.2 |
Xiamen (XM) | 24.5 | 118.1 | 139.4 | tropical humid | 5.2 |
Guangzhou (GZ) | 23.2 | 113.3 | 41.0 | tropical humid | 18.7 |
Shenzhen (SE) | 22.5 | 114.0 | 63.0 | tropical humid | 17.6 |
Sanya (SY) | 18.2 | 109.6 | 419.4 | tropical humid | 1.0 |
Qinhuangdao (QH) | 39.9 | 119.5 | 2.4 | temperate and sub-humid | 3.1 |
Ningbo (NB) | 30.0 | 121.6 | 4.0 | subtropical humid | 8.5 |
Harbin (HB) | 45.8 | 126.8 | 142.3 | temperate and sub-humid | 10.0 |
Zhengzhou (ZZ) | 34.7 | 113.7 | 110.4 | temperate and sub-humid | 12.6 |
Wuhan (WH) | 30.6 | 114.1 | 23.6 | subtropical humid | 12.3 |
Zhangjiajie (ZJ) | 29.1 | 110.5 | 183.5 | subtropical humid | 1.5 |
Changsha (CS) | 28.2 | 112.9 | 68.0 | subtropical humid | 10.0 |
Huangshan (HS) | 30.1 | 118.2 | 1840.4 | subtropical humid | 1.3 |
Guilin (GL) | 25.3 | 110.3 | 164.4 | subtropical humid | 4.9 |
Changchun (CC) | 43.9 | 125.2 | 236.8 | temperate and sub-humid | 9.1 |
Hohhot (HH) | 40.8 | 111.7 | 1063.0 | Inner Mongolia | 3.4 |
Jinzhong (JZ) | 37.7 | 112.8 | 831.2 | Temperate and sub-humid | 3.3 |
Nanchang (NC) | 28.6 | 115.9 | 46.9 | subtropical humid | 6.4 |
Xi’an (XA) | 34.3 | 108.9 | 397.5 | temperate and sub-humid | 12.9 |
Chongqing (CQ) | 29.5 | 106.5 | 351.1 | subtropical humid | 32.1 |
Chengdu (CD) | 30.7 | 104.0 | 507.3 | subtropical humid | 21.2 |
Kunming (KM) | 25.0 | 102.7 | 1888.1 | subtropical humid | 8.5 |
Lijiang (LJ) | 26.9 | 100.2 | 2380.9 | subtropical humid | 1.3 |
Zunyi (ZY) | 27.7 | 106.9 | 843.9 | subtropical humid | 6.6 |
Yinchuan (YC) | 38.5 | 106.2 | 1110.9 | Inner Mongolia | 2.9 |
Jiuquan (JQ) | 39.8 | 98.5 | 1477.2 | temperate and warm-temperate | 1.0 |
Xining (XN) | 36.7 | 101.8 | 2295.2 | Qinghai-Tibetan Plateau | 2.5 |
Wulumuqi (WL) | 43.8 | 87.7 | 935.0 | temperate and warm-temperate | 4.1 |
Category | UTCI Range (°C) | Stress Description |
---|---|---|
Category 1 (C1) | above +46 | extreme heat stress |
Category 2 (C2) | +38 to +46 | very strong heat stress |
Category 3 (C3) | +32 to +38 | strong heat stress |
Category 4 (C4) | +26 to +32 | moderate heat stress |
Category 5 (C5) | +9 to +26 | no thermal stress |
Category 6 (C6) | 0 to +9 | slight cold stress |
Category 7 (C7) | −13 to 0 | moderate cold stress |
Category 8 (C8) | −27 to −13 | strong cold stress |
Category 9 (C9) | −40 to −27 | very strong cold stress |
Category 10 (C10) | below −40 | extreme cold stress |
Model | City | a | b | c | NSE | MAPE | RMSE | Slope | Inter | n |
---|---|---|---|---|---|---|---|---|---|---|
Angstrom | Beijing | 0.167 | 0.518 | - | 0.930 | 11.677 | 21.408 | 0.894 | 15.637 | 7118 |
Hangzhou | 0.136 | 0.587 | - | 0.900 | 17.061 | 27.982 | 0.881 | 15.355 | 6993 | |
Guangzhou | 0.159 | 0.502 | - | 0.844 | 16.697 | 26.249 | 0.840 | 21.090 | 6665 | |
Harbin | 0.240 | 0.456 | - | 0.900 | 13.338 | 27.154 | 0.880 | 18.462 | 7006 | |
Wuhan | 0.130 | 0.537 | - | 0.881 | 17.865 | 29.602 | 0.867 | 17.049 | 7011 | |
Chongqing | 0.133 | 0.570 | - | 0.861 | 24.131 | 30.517 | 0.858 | 13.370 | 7020 | |
Average | 0.161 | 0.528 | - | 0.886 | 16.795 | 27.152 | 0.870 | 16.827 | 6969 | |
Ogelman | Beijing | 0.174 | 0.483 | 0.038 | 0.930 | 11.501 | 21.422 | 0.895 | 16.510 | 7118 |
Hangzhou | 0.122 | 0.832 | −0.310 | 0.908 | 17.158 | 26.837 | 0.890 | 14.371 | 6993 | |
Guangzhou | 0.141 | 0.743 | −0.303 | 0.858 | 16.700 | 25.032 | 0.873 | 17.165 | 6665 | |
Harbin | 0.224 | 0.585 | −0.138 | 0.902 | 13.574 | 26.910 | 0.882 | 18.270 | 7006 | |
Wuhan | 0.116 | 0.793 | −0.316 | 0.890 | 18.152 | 28.481 | 0.874 | 16.323 | 7011 | |
Chongqing | 0.123 | 0.906 | −0.481 | 0.879 | 24.012 | 28.547 | 0.868 | 12.635 | 7020 | |
Average | 0.150 | 0.724 | −0.252 | 0.895 | 16.850 | 26.205 | 0.880 | 15.879 | 6969 | |
Bristow | Beijing | 0.602 | 0.025 | 1.840 | 0.670 | 20.070 | 46.432 | 0.711 | 47.635 | 7118 |
Hangzhou | 0.549 | 0.009 | 2.428 | 0.691 | 23.948 | 49.186 | 0.695 | 41.642 | 6993 | |
Guangzhou | 0.515 | 0.011 | 2.336 | 0.600 | 23.024 | 42.090 | 0.625 | 47.369 | 6663 | |
Harbin | 0.600 | 0.081 | 1.324 | 0.699 | 21.321 | 47.050 | 0.685 | 48.281 | 7006 | |
Wuhan | 0.496 | 0.005 | 2.938 | 0.626 | 24.209 | 52.551 | 0.607 | 53.517 | 7011 | |
Chongqing | 0.563 | 0.015 | 1.978 | 0.750 | 25.154 | 41.006 | 0.753 | 24.299 | 7020 | |
Average | 0.554 | 0.024 | 2.141 | 0.673 | 22.954 | 46.386 | 0.679 | 43.791 | 6969 | |
Hargreaves | Beijing | 0.180 | −0.098 | - | 0.649 | 21.504 | 47.874 | 0.688 | 51.044 | 7118 |
Hangzhou | 0.244 | −0.304 | - | 0.693 | 24.305 | 49.000 | 0.678 | 44.199 | 6993 | |
Guangzhou | 0.248 | −0.325 | - | 0.604 | 22.791 | 41.858 | 0.597 | 53.491 | 6663 | |
Harbin | 0.137 | 0.046 | - | 0.698 | 21.220 | 47.096 | 0.695 | 47.187 | 7006 | |
Wuhan | 0.242 | −0.296 | - | 0.625 | 23.737 | 52.674 | 0.572 | 55.445 | 7011 | |
Chongqing | 0.220 | −0.288 | - | 0.770 | 23.042 | 39.282 | 0.748 | 25.765 | 7020 | |
Average | 0.212 | −0.211 | - | 0.673 | 22.767 | 46.297 | 0.663 | 46.189 | 6969 | |
BP neural network | Beijing | - | - | - | 0.960 | 9.745 | 16.197 | 0.958 | 6.336 | 7118 |
Hangzhou | - | - | - | 0.947 | 14.718 | 20.307 | 0.947 | 7.359 | 6993 | |
Guangzhou | - | - | - | 0.925 | 13.429 | 18.252 | 0.922 | 10.088 | 6665 | |
Harbin | - | - | - | 0.925 | 11.900 | 23.519 | 0.922 | 11.988 | 7006 | |
Wuhan | - | - | - | 0.917 | 17.318 | 24.701 | 0.917 | 11.456 | 7011 | |
Chongqing | - | - | - | 0.951 | 18.147 | 18.083 | 0.951 | 4.857 | 7020 | |
Average | - | - | - | 0.938 | 14.210 | 20.177 | 0.936 | 8.681 | 6969 | |
Support vector machine | Beijing | - | - | - | 0.960 | 9.777 | 16.155 | 0.965 | 5.478 | 7118 |
Hangzhou | - | - | - | 0.947 | 14.451 | 20.369 | 0.942 | 7.554 | 6993 | |
Guangzhou | - | - | - | 0.925 | 13.183 | 18.174 | 0.927 | 10.171 | 6665 | |
Harbin | - | - | - | 0.927 | 11.256 | 23.241 | 0.915 | 11.244 | 7006 | |
Wuhan | - | - | - | 0.929 | 16.153 | 22.959 | 0.950 | 7.703 | 7011 | |
Chongqing | - | - | - | 0.953 | 17.445 | 17.786 | 0.952 | 4.932 | 7020 | |
Average | - | - | - | 0.940 | 13.711 | 19.781 | 0.942 | 7.847 | 6969 |
Model | City | NSE | MAPE | RMSE | Slope | Inter | n |
---|---|---|---|---|---|---|---|
Angstrom | Beijing | 0.866 | 14.284 | 31.979 | 0.823 | 13.784 | 3157 |
Hangzhou | 0.839 | 21.244 | 39.101 | 0.794 | 14.421 | 3181 | |
Guangzhou | 0.848 | 18.853 | 28.226 | 0.849 | 8.514 | 3048 | |
Harbin | 0.758 | 17.935 | 42.942 | 0.745 | 30.079 | 2982 | |
Wuhan | 0.838 | 21.756 | 35.622 | 0.838 | 6.737 | 3090 | |
Chongqing | 0.865 | 25.230 | 34.305 | 0.833 | 11.752 | 2666 | |
Average | 0.836 | 19.884 | 35.363 | 0.814 | 14.215 | 3021 | |
Ogelman | Beijing | 0.871 | 13.925 | 31.430 | 0.824 | 14.753 | 3157 |
Hangzhou | 0.850 | 20.544 | 37.641 | 0.805 | 13.005 | 3181 | |
Guangzhou | 0.859 | 18.600 | 27.114 | 0.870 | 5.298 | 3048 | |
Harbin | 0.766 | 17.675 | 42.292 | 0.758 | 28.327 | 2982 | |
Wuhan | 0.851 | 20.876 | 34.192 | 0.851 | 5.718 | 3090 | |
Chongqing | 0.869 | 25.018 | 33.765 | 0.812 | 13.284 | 2666 | |
Average | 0.844 | 19.440 | 34.406 | 0.820 | 13.398 | 3021 | |
Bristow | Beijing | 0.676 | 20.839 | 49.781 | 0.663 | 47.803 | 3156 |
Hangzhou | 0.688 | 27.359 | 54.323 | 0.665 | 36.553 | 3180 | |
Guangzhou | 0.673 | 22.910 | 41.338 | 0.670 | 39.519 | 3047 | |
Harbin | 0.616 | 23.049 | 54.105 | 0.602 | 55.703 | 2981 | |
Wuhan | 0.631 | 25.879 | 53.798 | 0.612 | 58.487 | 3088 | |
Chongqing | 0.728 | 29.767 | 48.626 | 0.698 | 21.339 | 2665 | |
Average | 0.669 | 24.967 | 50.329 | 0.652 | 43.234 | 3020 | |
Hargreaves | Beijing | 0.659 | 21.534 | 51.117 | 0.650 | 50.072 | 3156 |
Hangzhou | 0.693 | 25.461 | 53.913 | 0.665 | 36.779 | 3180 | |
Guangzhou | 0.683 | 22.481 | 40.661 | 0.673 | 42.975 | 3047 | |
Harbin | 0.618 | 22.622 | 54.003 | 0.607 | 55.754 | 2981 | |
Wuhan | 0.659 | 23.908 | 51.761 | 0.686 | 52.406 | 3088 | |
Chongqing | 0.744 | 26.345 | 47.135 | 0.696 | 22.075 | 2665 | |
Average | 0.676 | 23.725 | 49.765 | 0.663 | 43.344 | 3020 | |
BP neural network | Beijing | 0.902 | 13.060 | 27.385 | 0.864 | 7.698 | 3158 |
Hangzhou | 0.894 | 18.316 | 31.648 | 0.854 | 7.498 | 3182 | |
Guangzhou | 0.878 | 16.842 | 25.254 | 0.910 | −4.069 | 3049 | |
Harbin | 0.792 | 17.363 | 39.825 | 0.796 | 20.184 | 2983 | |
Wuhan | 0.891 | 20.557 | 29.313 | 0.899 | −0.245 | 3091 | |
Chongqing | 0.912 | 20.084 | 27.717 | 0.862 | 8.983 | 2667 | |
Average | 0.878 | 17.704 | 30.190 | 0.864 | 6.675 | 3022 | |
Support vector machine | Beijing | 0.899 | 13.192 | 27.851 | 0.861 | 7.882 | 3158 |
Hangzhou | 0.890 | 18.298 | 32.325 | 0.845 | 8.477 | 3182 | |
Guangzhou | 0.881 | 17.139 | 24.889 | 0.919 | −5.099 | 3049 | |
Harbin | 0.791 | 17.470 | 39.939 | 0.787 | 20.197 | 2983 | |
Wuhan | 0.894 | 20.770 | 28.865 | 0.933 | −5.885 | 3091 | |
Chongqing | 0.909 | 20.048 | 28.068 | 0.852 | 10.173 | 2667 | |
Average | 0.877 | 17.820 | 30.323 | 0.866 | 5.958 | 3022 |
Model | City | NSE | MAPE | RMSE | Slope | Inter | n |
---|---|---|---|---|---|---|---|
Angstrom | Beijing | 0.993 | 7.683 | 1.121 | 0.994 | −0.375 | 3157 |
Hangzhou | 0.990 | 7.210 | 1.137 | 0.992 | −0.275 | 3181 | |
Guangzhou | 0.989 | 3.788 | 0.907 | 1.001 | −0.404 | 3048 | |
Harbin | 0.987 | 10.162 | 2.013 | 1.017 | −0.601 | 2982 | |
Wuhan | 0.989 | 7.093 | 1.238 | 0.992 | −0.309 | 3090 | |
Chongqing | 0.989 | 4.505 | 0.998 | 0.983 | 0.175 | 2666 | |
Average | 0.990 | 6.740 | 1.236 | 0.997 | −0.298 | 3021 | |
Ogelman | Beijing | 0.993 | 7.617 | 1.112 | 0.994 | −0.350 | 3157 |
Hangzhou | 0.991 | 6.453 | 1.044 | 0.993 | −0.245 | 3181 | |
Guangzhou | 0.990 | 3.584 | 0.843 | 1.003 | −0.425 | 3048 | |
Harbin | 0.987 | 10.034 | 1.994 | 1.018 | −0.582 | 2982 | |
Wuhan | 0.990 | 6.449 | 1.146 | 0.990 | −0.213 | 3090 | |
Chongqing | 0.990 | 4.331 | 0.967 | 0.975 | 0.308 | 2666 | |
Average | 0.990 | 6.411 | 1.184 | 0.996 | −0.251 | 3021 | |
Bristow | Beijing | 0.982 | 11.797 | 1.778 | 0.996 | −0.134 | 3156 |
Hangzhou | 0.974 | 8.787 | 1.790 | 0.986 | −0.144 | 3180 | |
Guangzhou | 0.979 | 4.816 | 1.242 | 0.975 | 0.400 | 3047 | |
Harbin | 0.981 | 12.933 | 2.366 | 1.006 | −0.211 | 2981 | |
Wuhan | 0.976 | 9.028 | 1.779 | 0.940 | 1.347 | 3088 | |
Chongqing | 0.980 | 5.246 | 1.368 | 0.972 | 0.079 | 2665 | |
Average | 0.979 | 8.768 | 1.721 | 0.979 | 0.223 | 3020 | |
Hargreaves | Beijing | 0.980 | 12.380 | 1.858 | 0.994 | −0.178 | 3156 |
Hangzhou | 0.975 | 8.810 | 1.774 | 0.988 | −0.219 | 3180 | |
Guangzhou | 0.978 | 5.079 | 1.280 | 0.976 | 0.393 | 3047 | |
Harbin | 0.983 | 12.607 | 2.297 | 1.005 | −0.218 | 2981 | |
Wuhan | 0.980 | 8.134 | 1.635 | 0.951 | 1.197 | 3088 | |
Chongqing | 0.982 | 4.970 | 1.277 | 0.965 | 0.269 | 2665 | |
Average | 0.980 | 8.663 | 1.687 | 0.980 | 0.207 | 3020 | |
BP neural network | Beijing | 0.994 | 7.024 | 1.036 | 0.992 | −0.322 | 3158 |
Hangzhou | 0.993 | 5.496 | 0.944 | 0.996 | −0.274 | 3182 | |
Guangzhou | 0.992 | 3.008 | 0.774 | 0.999 | −0.369 | 3049 | |
Harbin | 0.988 | 9.361 | 1.878 | 1.011 | −0.573 | 2983 | |
Wuhan | 0.991 | 6.008 | 1.106 | 0.996 | −0.326 | 3091 | |
Chongqing | 0.992 | 3.206 | 0.874 | 0.983 | 0.137 | 2667 | |
Average | 0.992 | 5.684 | 1.102 | 0.996 | −0.288 | 3022 | |
Support vector machine | Beijing | 0.994 | 7.201 | 1.056 | 0.992 | −0.343 | 3158 |
Hangzhou | 0.993 | 5.533 | 0.950 | 0.996 | −0.284 | 3182 | |
Guangzhou | 0.992 | 3.103 | 0.770 | 1.001 | −0.391 | 3049 | |
Harbin | 0.988 | 9.638 | 1.878 | 1.010 | −0.597 | 2983 | |
Wuhan | 0.992 | 5.576 | 1.038 | 0.993 | −0.258 | 3091 | |
Chongqing | 0.992 | 3.176 | 0.847 | 0.981 | 0.204 | 2667 | |
Average | 0.992 | 5.705 | 1.090 | 0.996 | −0.278 | 3022 |
Cities | UTCI (°C) | C3 (Day) | C4 (Day) | C5 (Day) | C6 (Day) | C7 (Day) | C8 (Day) | C9 (Day) |
---|---|---|---|---|---|---|---|---|
BJ | 11.6 ± 1.3 [8.7,13.9] | 7.2 ± 5.0 [0,22] | 60.2 ± 8.7 [40,81] | 146.8 ± 10.6 [122,171] | 61.1 ± 11.1 [39,84] | 74.2 ± 9.8 [53,95] | 14.6 ± 8.8 [1,36] | 1.2 ± 2.0 [0,8] |
TJ | 11.7 ± 1.3 [8.8,14.1] | 8.6 ± 5.5 [0,24] | 61.4 ± 10.1 [38,82] | 142.9 ± 10.0 [113,165] | 62.4 ± 10.2 [36,91] | 74.6 ± 11.5 [53,102] | 14.8 ± 8.3 [1,41] | 0.9 ± 1.2 [0,5] |
DL | 5.5 ± 2.8 [0.3,10.8] | 0.5 ± 1.8 [0,13] | 24.2 ± 12.6 [3,53] | 150.3 ± 11.8 [126,179] | 56.2 ± 9.3 [35,85] | 81.5 ± 10.5 [60,105] | 40.0 ± 13.5 [9,66] | 10.8 ± 7.3 [0,28] |
QD | 7.3 ± 2.3 [1.8,11.7] | 0.8 ± 1.7 [0,9] | 27.3 ± 13.0 [6,55] | 154.2 ± 10.8 [135,178] | 67.7 ± 10.7 [49,97] | 77.9 ± 13.3 [46,102] | 31.6 ± 10.7 [10,61] | 5.3 ± 4.6 [0,19] |
SH | 14.2 ± 1.9 [10.2,17.8] | 17.8 ± 10.2 [1,42] | 54.1 ± 8.2 [37,66] | 166.8 ± 17.1 [133,208] | 72.6 ± 9.7 [50,93] | 48.5 ± 15.7 [14,79] | 5.3 ± 6.1 [0,28] | 0.1 ± 0.3 [0,1] |
NJ | 15.4 ± 1.2 [12.8,18.3] | 23.6 ± 9.8 [6,52] | 57.7 ± 9.5 [35,80] | 168.2 ± 13.4 [138,207] | 70.4 ± 9.2 [53,91] | 41.5 ± 12.2 [12,71] | 3.2 ± 3.4 [0,15] | 0.0 ± 0.0 [0,0] |
SZ | 14.8 ± 2.2 [10.1,18.5] | 22.1 ± 11.4 [4,45] | 54.3 ± 9.4 [33,79] | 167.6 ± 16.1 [129,201] | 72.6 ± 9.6 [56,91] | 44.0 ± 19.5 [8,89] | 4.1 ± 5.4 [0,22] | 0.1 ± 0.3 [0,1] |
HZ | 16.8 ± 1.1 [14.5,18.8] | 32.6 ± 12.1 [14,61] | 56.3 ± 9.7 [25,82] | 176.8 ± 13.5 [145,205] | 68.0 ± 9.1 [48,95] | 29.7 ± 10.6 [8,58] | 1.5 ± 2.0 [0,10] | 0.0 ± 0.0 [0,0] |
XM | 20.5 ± 1.3 [17.4,22.4] | 28.5 ± 14.7 [3,66] | 93.1 ± 13.8 [59,122] | 198.6 ± 13.9 [169,232] | 39.2 ± 13.0 [13,63] | 5.8 ± 5.6 [0,24] | 0.0 ± 0.0 [0,0] | 0.0 ± 0.0 [0,0] |
GZ | 23.4 ± 0.9 [21.2,25.0] | 48.7 ± 11.1 [21,71] | 120.0 ± 13.0 [95,156] | 171.8 ± 11.9 [149,200] | 20.4 ± 7.1 [5,39] | 4.3 ± 4.9 [0,18] | 0.1 ± 0.2 [0,1] | 0.0 ± 0.0 [0,0] |
SE | 23.5 ± 1.0 [21.5,26.0] | 47.2 ± 12.4 [25,86] | 122.5 ± 13.8 [100,160] | 173.8 ± 15.2 [143,201] | 18.0 ± 7.3 [3,34] | 3.7 ± 3.6 [0,16] | 0.0 ± 0.2 [0,1] | 0.0 ± 0.0 [0,0] |
SY | 26.7 ± 3.0 [19.4,30.4] | 66.7 ± 45.6 [0,153] | 160.3 ± 33.2 [76,204] | 134.2 ± 63.0 [46,272] | 4.0 ± 8.1 [0,39] | 0.2 ± 0.7 [0,4] | 0.0 ± 0.0 [0,0] | 0.0 ± 0.0 [0,0] |
QD | 9.9 ± 1.6 [5.7,12.7] | 2.3 ± 3.2 [0,14] | 44.6 ± 9.6 [16,66] | 152.8 ± 9.9 [128,170] | 59.5 ± 8.4 [45,81] | 88.7 ± 9.3 [53,109] | 16.5 ± 11.2 [1,45] | 0.8 ± 1.6 [0,9] |
NB | 16.8 ± 1.5 [14.0,20.3] | 31.0 ± 13.2 [6,60] | 58.9 ± 9.1 [32,78] | 177.8 ± 14.8 [146,209] | 68.2 ± 10.1 [46,92] | 27.6 ± 12.1 [4,50] | 1.6 ± 2.2 [0,10] | 0.0 ± 0.0 [0,0] |
HB | 0.5 ± 3.0 [−5.1,5.9] | 0.3 ± 0.7 [0,3] | 16.9 ± 10.0 [1,46] | 129.3 ± 13.6 [101,163] | 46.7 ± 8.1 [31,72] | 62.4 ± 8.5 [47,90] | 85.3 ± 11.2 [52,110] | 23.3 ± 19.4 [1,72] |
ZZ | 13.9 ± 1.6 [9.7,17.4] | 14.6 ± 7.7 [4,42] | 64.3 ± 9.9 [40,86] | 153.7 ± 11.0 [127,179] | 69.9 ± 9.4 [45,91] | 55.1 ± 12.8 [24,79] | 7.1 ± 6.7 [0,30] | 0.4 ± 1.1 [0,7] |
WH | 17.4 ± 1.6 [14.1,20.3] | 36.8 ± 10.8 [14,60] | 65.9 ± 10.4 [46,89] | 164.5 ± 15.8 [126,209] | 66.6 ± 9.6 [40,95] | 27.4 ± 14.0 [2,61] | 2.3 ± 3.2 [0,14] | 0.1 ± 0.3 [0,2] |
ZJ | 18.3 ± 0.9 [16.5,19.8] | 32.7 ± 9.9 [13,55] | 68.9 ± 9.7 [51,90] | 183.4 ± 14.0 [156,220] | 65.3 ± 10.0 [44,95] | 14.4 ± 9.6 [0,40] | 0.5 ± 1.2 [0,7] | 0.0 ± 0.0 [0,0] |
CS | 16.7 ± 0.9 [14.8,19.4] | 37.6 ± 10.5 [18,73] | 64.0 ± 8.0 [49,86] | 161.7 ± 13.2 [129,195] | 61.3 ± 9.6 [33,84] | 36.7 ± 9.5 [16,60] | 3.5 ± 3.0 [0,12] | 0.1 ± 0.3 [0,2] |
HS | −1.7 ± 1.7 [−4.8,1.3] | 0. ± 0.1 [0,1] | 0.1 ± 0.3 [0,2] | 91.6 ± 17.3 [61,125] | 92.9 ± 11.1 [62,121] | 101.6 ± 8.9 [80,120] | 62.7 ± 10.5 [43,85] | 14.8 ± 6.1 [1,30] |
GL | 18.2 ± 1.2 [15.5,20.2] | 36.0 ± 9.3 [13,59] | 89.0 ± 9.7 [65,117] | 156.7 ± 12.6 [122,190] | 48.9 ± 7.6 [30,67] | 30.8 ± 10.2 [6,51] | 3.9 ± 4.3 [0,16] | 0.1 ± 0.2 [0,1] |
CC | 1.3 ± 2.5 [−4.5,6.4] | 0.4 ± 0.8 [0,4] | 17.8 ± 10.0 [3,44] | 131.9 ± 10.7 [106,158] | 46.3 ± 8.3 [27,66] | 68.1 ± 12.1 [41,91] | 79.9 ± 11.7 [52,107] | 20.3 ± 15.5 [0,65] |
HH | 6.9 ± 1.8 [2.4,9.2] | 0.2 ± 0.6 [0,3] | 18.4 ± 9.6 [0,41] | 162.9 ± 12.0 [134,186] | 59.3 ± 8.7 [34,81] | 87.7 ± 11.5 [65,109] | 33.4 ± 15.8 [9,74] | 3.4 ± 3.2 [0,16] |
JZ | 8.9 ± 1.3 [5.7,11.5] | 0.2 ± 0.6 [0,3] | 26.5 ± 8.7 [7,48] | 169.2 ± 10.4 [152,199] | 60.4 ± 9.6 [40,80] | 89.0 ± 9.7 [64,110] | 18.8 ± 9.1 [2,41] | 1.1 ± 1.3 [0,5] |
NC | 17.7 ± 1.7 [14.5,20.4] | 45.8 ± 10.3 [22,74] | 64.0 ± 11.4 [38,87] | 162.3 ± 14.8 [128,198] | 57.0 ± 8.5 [36,75] | 29.8 ± 12.9 [7,59] | 4.6 ± 6.0 [0,24] | 0.3 ± 0.7 [0,4] |
XA | 14.5 ± 1.1 [12.6,18.0] | 11.7 ± 6.9 [0,29] | 57.2 ± 9.0 [37,78] | 172.0 ± 13.3 [150,197] | 80.5 ± 10.6 [57,103] | 41.9 ± 12.2 [18,73] | 2.0 ± 2.5 [0,13] | 0.0 ± 0.0 [0,0] |
CQ | 19.0 ± 0.8 [17.1,20.5] | 33.5 ± 10.5 [13,58] | 63.2 ± 11.6 [41,94] | 203.1 ± 12.4 [179,234] | 62.7 ± 12.6 [37,97] | 2.0 ± 2.6 [0,14] | 0.0 ± 0.0 [0,0] | 0.0 ± 0.0 [0,0] |
CD | 17.5 ± 0.7 [16.4,20.1] | 6.6 ± 5.4 [0,21] | 65.7 ± 8.8 [50,101] | 216.3 ± 15.0 [176,243] | 72.2 ± 12.2 [45,105] | 4.4 ± 3.9 [0,22] | 0.0 ± 0.1 [0,1] | 0.0 ± 0.0 [0,0] |
KM | 15.8 ± 1.3 [13.4,18.5] | 0. ± 0. [0,0] | 1.7 ± 2.3 [0,10] | 319.2 ± 18.4 [274,350] | 39.0 ± 17.4 [11,78] | 5.3 ± 3.3 [0,13] | 0.1 ± 0.2 [0,1] | 0.0 ± 0.1 [0,1] |
LJ | 11.3 ± 1.6 [8.6,14.0] | 0. ± 0. [0,0] | 0.1 ± 0.4 [0,2] | 226.2 ± 27.7 [185,279] | 113.1 ± 14.3 [83,140] | 25.7 ± 19.9 [1,75] | 0.1 ± 0.3 [0,2] | 0.0 ± 0.0 [0,0] |
ZY | 16.5 ± 0.8 [15.1,18.4] | 3.4 ± 4.6 [0,18] | 64.6 ± 12.9 [39,93] | 204.9 ± 17.8 [175,245] | 78.2 ± 11.5 [45,111] | 14.2 ± 8.2 [1,35] | 0.0 ± 0.2 [0,1] | 0.0 ± 0.0 [0,0] |
YC | 10.1 ± 1.1 [7.5,12.5] | 0.7 ± 1.3 [0,7] | 32.3 ± 8.9 [9,51] | 171.0 ± 10.5 [151,199] | 64.8 ± 9.4 [40,89] | 84.4 ± 11.4 [55,113] | 11.9 ± 6.3 [1,28] | 0.2 ± 0.4 [0,1] |
JQ | 7.4 ± 1.3 [4.6,10.1] | 0. ± 0.1 [0,1] | 13.1 ± 7.0 [0,32] | 168.2 ± 7.7 [155,186] | 64.6 ± 9.0 [46,84] | 97.9 ± 10.8 [68,122] | 21.1 ± 10.7 [5,45] | 0.3 ± 0.5 [0,2] |
XN | 7.7 ± 1.6 [3.8,9.9] | 0. ± 0. [0,0] | 0.9 ± 1.9 [0,8] | 182.9 ± 15.5 [136,208] | 86.0 ± 11.2 [54,117] | 86.5 ± 13.4 [59,116] | 8.9 ± 8.3 [0,29] | 0.1 ± 0.4 [0,2] |
WL | 6.5 ± 1.5 [1.6,9.0] | 0.5 ± 1.3 [0,7] | 21.8 ± 9.9 [3,42] | 160.2 ± 11.5 [139,198] | 50.5 ± 8.6 [36,71] | 85.9 ± 14.0 [57,118] | 45.1 ± 14.0 [13,75] | 0.1 ± 2.0 [0,9] |
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Liu, J.; Zhou, G.; Linderholm, H.W.; Song, Y.; Liu, D.-L.; Shen, Y.; Liu, Y.; Du, J. Optimal Strategy on Radiation Estimation for Calculating Universal Thermal Climate Index in Tourism Cities of China. Int. J. Environ. Res. Public Health 2022, 19, 8111. https://doi.org/10.3390/ijerph19138111
Liu J, Zhou G, Linderholm HW, Song Y, Liu D-L, Shen Y, Liu Y, Du J. Optimal Strategy on Radiation Estimation for Calculating Universal Thermal Climate Index in Tourism Cities of China. International Journal of Environmental Research and Public Health. 2022; 19(13):8111. https://doi.org/10.3390/ijerph19138111
Chicago/Turabian StyleLiu, Jiandong, Guangsheng Zhou, Hans W. Linderholm, Yanling Song, De-Li Liu, Yanbo Shen, Yanxiang Liu, and Jun Du. 2022. "Optimal Strategy on Radiation Estimation for Calculating Universal Thermal Climate Index in Tourism Cities of China" International Journal of Environmental Research and Public Health 19, no. 13: 8111. https://doi.org/10.3390/ijerph19138111