Heat risk map of Riyadh

<p>The dataset is used to assess the urban heat risk in the city of Riyadh using proxy variables to evaluate the environmental, infrastructural, and social dimensions of the city.</p><p>The environmental component was evaluated using the mean values of land surface temperature (LST), air temperature (T2m), and discomfort index (DI) across the districts of Riyadh. These factors, derived from data like MODIS LST and available WRF simulations, represented the degree of heat exposure in different regions. </p><p>The infrastructural component of heat risk was evaluated by looking at the city's infrastructure, that is the building density per district. Buildings can act as "heat traps," thus higher building density suggests increased heat risk.</p><p>The social component considered demographic factors such as the percentage of the population over 65 old (OP) and under 14 years old (YP), which can indicate sensitivity to extreme heat conditions. </p><p>To map the heat risk, these components were combined into a composite heat risk indicator. For this to be achieved, each parameter was reclassified into three categories (1-less, 2-moderate, and 3-high) using the quantile classification which is a data classification method that distributes a set of values into groups that contain an equal number of values. </p><p> </p><p>                                      LST  (°C)                   DI                     T2m  (°C)                <14 y.o. (%)             >65 y.o (%)                Buildings per sq. m.(BD)</p><p>1-Less risk                       <47.2                    <28                         <40.6                       <23                          <1                                      <66</p><p>2-Moderate risk       47.2 ≤ LST ≤ 47.9    28≤ DI ≤ 28.2     40.6 ≤ T2m ≤ 40.8       23≤ YP ≤28               1≤ OP ≤ 3                         66≤ BD ≤ 109</p><p>3-High risk                      >47.9                    >28.2                       >40.8                        >28                          >3                                     >109</p><p>LST: Land Surface Temperature; DI: Discomfort Index; T2m: Air temperature at 2m height; YP<14 y.o.: People under 14 years old; OP y.o.: Older people over 65 years old; </p><p>Since the relative importance of each parameter is unknown, we considered that all parameters contributed equally to the composite heat risk index and the arithmetic values were aggregated. The final value for each district was then reclassified into three categories using the quantile classification method resulting in the final three categories of Urban Heat Risk (Less heat risk, Moderate heat risk, High heat risk)</p&gt

Quantifying the Trends in Land Surface Temperature and Surface Urban Heat Island Intensity in Mediterranean Cities in View of Smart Urbanization

Land Surface Temperature (LST) is a key parameter for the estimation of urban fluxes as well as for the assessment of the presence and strength of the surface urban heat island (SUHI). In an urban environment, LST depends on the way the city has been planned and developed over time. To this end, the estimation of LST needs adequate spatial and temporal data at the urban scale, especially with respect to land cover/land use. The present study is divided in two parts: at first, satellite data from MODIS-Terra 8-day product (MOD11A2) were used for the analysis of an eighteen-year time series (2001–2017) of the LST spatial and temporal distribution in five major cities of the Mediterranean during the summer months. LST trends were retrieved and assessed for their statistical significance. Secondly, LST values and trends for each city were examined in relation to land cover characteristics and patterns in order to define the contribution of urban development and planning on LST; this information is important for the drafting of smart urbanization policies and measures. Results revealed (a) positive LST trends in the urban areas especially during nighttime ranging from +0.412 °K in Marseille to +0.923 °K in Cairo and (b) the SUHI has intensified during the last eighteen years especially during daytime in European Mediterranean cities, such as Rome (+0.332 °K) and Barcelona (+0.307 °K)

Recognition of Thermal Hot and Cold Spots in Urban Areas in Support of Mitigation Plans to Counteract Overheating: Application for Athens

Mitigation plans to counteract overheating in urban areas need to be based on a thorough knowledge of the state of the thermal environment, most importantly on the presence of areas which consistently demonstrate higher or lower urban land surface temperatures (hereinafter referred to as “hot spots” or “cold spots”, respectively). The main objective of this research study is to develop a methodological approach for the recognition of thermal “hot spots” and “cold spots” in urban areas during summer; this is accomplished with (a) the combined use of high and medium spatial resolution satellite data (Landsat 8 and Terra-MODIS, respectively); (b) the downscaling of the Terra-MODIS satellite data so as to acquire spatial resolution similar to the Landsat one and at the same time take advantage of the high revisit time as compared to the respective one of Landsat (16 days); and (c) the application of a statistical clustering technique to recognize “hot spots” and “cold spots”. The methodological approach was applied as a case study for the urban area of Athens, Greece for a summer period. Results demonstrated the capacity of the methodological approach to recognize “hot spots” and “cold spots”, revealed a strong relationship between land use and “hot spots” and “cold spots”, and showed that the average land surface temperature (LST) difference between the “hot spots” and “cold spots” can reach 9.1 °K

A Methodology for Bridging the Gap between Regional- and City-Scale Climate Simulations for the Urban Thermal Environment

The main objective of this study is to bridge the gap between regional- and city-scale climate simulations, with the focus given to the thermal environment. A dynamic-statistical downscaling methodology for defining daily maximum (Tmax) and minimum (Tmin) temperatures is developed based on artificial neural networks (ANNs) and multiple linear regression models (MLRs). The approach involves the use of simulations from two EURO-CORDEX regional climate models (RCMs) (at approximately 12 km × 12 km) that are further downscaled to a finer resolution (1 km × 1 km). A feature selection methodology is applied to select the optimum subset of parameters for training the machine learning models. The downscaling methodology is initially applied to two RCMs, driven by the ERA-Interim reanalysis (2008–2011) and high-resolution urban climate model simulations (UrbClims). The performance of the relationships is validated and found to successfully simulate the spatiotemporal distribution of Tmax and Tmin over Athens. Finally, the relationships that were extracted by the models are further used to quantify changes for Tmax and Tmin in high resolution, between the historical period (1971–2000) and mid-century (2041–2071) climate projections for two different representative concentration pathways (RCP4.5 and RCP8.5). Based on the results, both mean Tmax and Tmin are estimated to increase by 1.7 °C and 1.5 °C for RCP4.5 and 2.3 °C and 2.1 °C for RCP8.5, respectively, with distinct spatiotemporal patterns over the study area

A Methodology for Bridging the Gap between Regional- and City-Scale Climate Simulations for the Urban Thermal Environment

The main objective of this study is to bridge the gap between regional- and city-scale climate simulations, with the focus given to the thermal environment. A dynamic-statistical downscaling methodology for defining daily maximum (Tmax) and minimum (Tmin) temperatures is developed based on artificial neural networks (ANNs) and multiple linear regression models (MLRs). The approach involves the use of simulations from two EURO-CORDEX regional climate models (RCMs) (at approximately 12 km × 12 km) that are further downscaled to a finer resolution (1 km × 1 km). A feature selection methodology is applied to select the optimum subset of parameters for training the machine learning models. The downscaling methodology is initially applied to two RCMs, driven by the ERA-Interim reanalysis (2008–2011) and high-resolution urban climate model simulations (UrbClims). The performance of the relationships is validated and found to successfully simulate the spatiotemporal distribution of Tmax and Tmin over Athens. Finally, the relationships that were extracted by the models are further used to quantify changes for Tmax and Tmin in high resolution, between the historical period (1971–2000) and mid-century (2041–2071) climate projections for two different representative concentration pathways (RCP4.5 and RCP8.5). Based on the results, both mean Tmax and Tmin are estimated to increase by 1.7 °C and 1.5 °C for RCP4.5 and 2.3 °C and 2.1 °C for RCP8.5, respectively, with distinct spatiotemporal patterns over the study area

State-of-the-art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database

Purpose We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT). Methods and results We included adult HT recipients from the United Network for Organ Sharing (UNOS) database between 2010 and 2018 using solely pre-transplant variables. The study cohort comprised 18 625 patients (53 +/- 13 years, 73% males) and was randomly split into a derivation and a validation cohort with a 3:1 ratio. At 1-year after HT, there were 2334 (12.5%) deaths. Out of a total of 134 pre-transplant variables, 39 were selected as highly predictive of 1-year mortality via feature selection algorithm and were used to train five ML models. AUC for the prediction of 1-year survival was .689, .642, .649, .637, .526 for the Adaboost, Logistic Regression, Decision Tree, Support Vector Machine, and K-nearest neighbor models, respectively, whereas the Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score had an AUC of .569. Local interpretable model-agnostic explanations (LIME) analysis was used in the best performing model to identify the relative impact of key predictors. ML models for 3- and 5-year survival as well as acute rejection were also developed in a secondary analysis and yielded AUCs of .629, .609, and .610 using 27, 31, and 91 selected variables respectively. Conclusion Machine learning models showed good predictive accuracy of outcomes after heart transplantation

Energy simulation outputs for different mitigation scenarios

<p><span>Advanced urban heat mitigation technologies that involve the use of super cool materials combined with properly designed green infrastructure, lower the urban ambient and land surface temperatures and reduce the cooling consumption at the city scale. We present the </span>results of the <span>world's largest heat mitigation project in Riyadh, KSA. Daytime radiative coolers as well as cool materials combined with irrigated or non-irrigated greenery, have been used to design eight holistic heat mitigation scenarios. We assessed the climatic impact of the scenarios as well as the corresponding energy benefits </span>of <span>3,323 urban buildings. An impressive decrease of the peak ambient temperature, up to 4.5°C, is calculated, consisting of the highest reported urban ambient temperature reduction, while the annual sum of the differences of the ambient temperature against a standard temperature base, (cooling degree hours), in the city decrease by up to 26%. We found that innovative urban heat mitigation strategies contribute to </span>remarkable cooling energy conservation by up to 16%, while the combined implementation of heat mitigation and energy adaptation technologies decreases the cooling demand by up to 35%. <span>It is the first article investigating the large-scale energy benefits of modern heat mitigation technologies when they are implemented in cities. </span></p><p>Funding provided by: Royal Commission of Riyadh*<br>Crossref Funder Registry ID: <br>Award Number: </p><p>Data was generated by CityBES for each building. </p&gt