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Environ Res ; : 110584, 2020 Dec 04.
Artigo em Inglês | MEDLINE | ID: mdl-33285157


Heat waves (HWs) and urban heat islands (UHIs) can potentially interact. The mechanisms behind their synergy are not fully disclosed. Starting from the localized UHI phenomenon, this study aims i) to reveal their associated impacts on human thermal comfort through three different definitions of HW events, based on air temperature (airT), wet-bulb globe temperature (WBGT) and human-perceived temperature (AppT) respectively, and ii) to understand the role of air moisture and wind. The analysis was conducted in four districts (NH, JD, MH and XJH) with different urban development patterns and geographic conditions, in the megacity of Shanghai with a subtropical humid climate. Results evidenced the localized interplay between HWs and UHIs. The results indicate that less urbanized districts were generally more sensitive to the synergies. JD district recorded the highest urban heat island intensity (UHII) amplification, regardless of the specific HW definition. Notably, during AppT-HWs, the increment was observed in terms of maximum (1.3 °C), daily average (0.8 °C), diurnal (0.4 °C) and nocturnal UHII (1.0 °C). Nevertheless, localized synergies between HWs and UHIs at different stations also exhibited some commonalities. Under airT-HW, the UHII was amplified throughout the day at all stations. Under WBGT-HW, diurnal UHII (especially at 11:00-17:00 LST) was consistently amplified at all stations. Under AppT-HW conditions, the nocturnal UHII was slightly amplified at all stations. Air moisture and wind alleviated the synergistic heat exacerbation to the benefit of thermal comfort. The extent depended on geographic condition, diurnal and nocturnal scenarios, temperature type and HW/normal conditions. Stronger HW-UHI synergies indicate the necessity to develop specific urban heat emergency response plans, able to capture and intervene on the underlying mechanisms. This study paves to way to their identification.

Sci Rep ; 10(1): 14216, 2020 08 26.
Artigo em Inglês | MEDLINE | ID: mdl-32848173


Overheated outdoor environments adversely impact urban sustainability and livability. Urban areas are particularly affected by heat waves and global climate change, which is a serious threat due to increasing heat stress and thermal risk for residents. The tropical city of Darwin, Australia, for example, is especially susceptible to urban overheating that can kill inhabitants. Here, using a modeling platform supported by detailed measurements of meteorological data, we report the first quantified analysis of the urban microclimate and evaluate the impacts of heat mitigation technologies to decrease the ambient temperature in the city of Darwin. We present a holistic study that quantifies the benefits of city-scale heat mitigation to human health, energy consumption, and peak electricity demand. The best-performing mitigation scenario, which combines cool materials, shading, and greenery, reduces the peak ambient temperature by 2.7 °C and consequently decreases the peak electricity demand and the total annual cooling load by 2% and 7.2%, respectively. Further, the proposed heat mitigation approach can save 9.66 excess deaths per year per 100,000 people within the Darwin urban health district. Our results confirm the technological possibilities for urban heat mitigation, which serves as a strategy for mitigating the severity of cumulative threats to urban sustainability.

Sci Total Environ ; 709: 136068, 2020 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-31869706


The urban heat island is a vastly documented climatological phenomenon, but when it comes to coastal cities, close to desert areas, its analysis becomes extremely challenging, given the high temporal variability and spatial heterogeneity. The strong dependency on the synoptic weather conditions, rather than on city-specific, constant features, hinders the identification of recurrent patterns, leading conventional predicting algorithms to fail. In this paper, an advanced artificial intelligence technique based on long short-term memory (LSTM) model is applied to gain insight and predict the highly fluctuating heat island intensity (UHII) in the city of Sydney, Australia, governed by the dualistic system of cool sea breeze from the ocean and hot western winds from the vast desert biome inlands. Hourly measurements of temperature, collected for a period of 18 years (1999-2017) from 8 different sites in a 50 km radius from the coastline, were used to train (80%) and test (20%) the model. Other inputs included date, time, and previously computed UHII, feedbacked to the model with an optimized time step of six hours. A second set of models integrated wind speed at the reference station to account for the sea breeze effect. The R2 ranged between 0.770 and 0.932 for the training dataset and between 0.841 and 0.924 for the testing dataset, with the best performance attained right in correspondence of the city hot spots. Unexpectedly, very little benefit (0.06-0.43%) was achieved by including the sea breeze among the input variables. Overall, this study is insightful of a rather rare climatological case at the watershed between maritime and desertic typicality. We proved that accurate UHII predictions can be achieved by learning from long-term air temperature records, provided that an appropriate predicting architecture is utilized.