| 英文摘要： |
Urban heat islands (UHIs) have caused radical changes in urban climates. However, the extreme UHI (E-UHI) formed in factory areas deserves more attention. To mitigate the E-UHI, machine learning is used for simulating and quantifying the marginal utility of the scale, shape, type, stage, and structure of the factory on the land surface temperature (LST), factory LST (LSTf), surrounding LST (LSTs) and increase value (Delta LST) level. The results show that the scale of all types of factories affects LSTf and LSTs, and the shape of steel factories affects LSTs and Delta LST. The LST in factories that require high-temperature environments (e.g., smelters) is significantly higher than that in other factories (e.g., sales plants). The Delta LST of green space (GS), staff activity ground (SG), material transfer ground (MG), material storage area (MA), factory building (FB), smelting area (SA) and casting building (CB) are 3.95 degrees C, 4.01 degrees C, 5.08 degrees C, 5.15 degrees C, 5.24 degrees C, 5.49 degrees C and 7.32 degrees C, and their optimal ranges are 8.84%-15.09%, 16.65%-25.52%, 3.91%-35.91%, 0.00%-8.70%, 5.06%-13.60%, 23.33%-48.02%, and 0.00%- 5.73%, respectively. Appropriately standardizing the scale and shape, controlling the temperature of the hightemperature generation stage, reducing the proportion of CB, MG and MA, and increasing the proportion of GS and SG are effective ways to alleviate the E-UHI. The findings provide theoretical guidance for resource-based cities to mitigate E-UHIs. |
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