Urban Climate最新文献

{"title":"Diurnal and seasonal variation of surface heat island of local climate zones using FengYun-4A land surface temperature data","authors":"Longteng Fu ,&nbsp;Xian-Xiang Li ,&nbsp;Rui Xin ,&nbsp;Min Min ,&nbsp;Lixin Dong","doi":"10.1016/j.uclim.2025.102317","DOIUrl":"10.1016/j.uclim.2025.102317","url":null,"abstract":"<div><div>Understanding the diurnal dynamics of the surface urban heat island (SUHI) effect in various local climate zones (LCZs) is essential for urban climate studies and planning. However, due to the limitations in spatial and temporal resolution of satellite data, current research on the continuous diurnal dynamics of LCZs at the scale of urban agglomerations remains relatively limited. This study leverages FengYun-4A (FY-4A) land surface temperature (LST) data to analyze the seasonal and diurnal characteristics of SUHI across different LCZs in the Guangdong-Hong Kong-Macao Greater Bay Area. We resampled the LCZ data and applied bilinear interpolation techniques to match the FY-4A LST data with the resampled LCZ data. We used LCZ D and LCZ A as reference to calculate the SUHI intensity (SUHII), providing a comparative baseline for assessing the SUHI effect across different urban morphologies. Additionally, we compared the LST and SUHII during heatwave periods with those during normal periods. The results indicated that all LCZ types exhibited a typical diurnal variation of SUHI, with SUHII peaking around noon and remaining stable after sunset. The LST generally increased during heat waves, with the SUHII being more pronounced in LCZ types that feature open, flat three-dimensional structures and a higher proportion of impervious surfaces. Our study demonstrates the utility of FY-4A LST data for continuous monitoring and analysis of SUHI effects within urban agglomerations, offering valuable information for urban planning and climate resilience efforts in densely populated urban agglomerations.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102317"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interaction between meteorological factors and air pollutants on the cerebrovascular diseases admissions in Lanzhou City: A time-series study","authors":"Xusong Zhang ,&nbsp;Qian Li ,&nbsp;Yongqin Cao ,&nbsp;Ke Xu ,&nbsp;Jingze Yu ,&nbsp;Miaoxin Liu ,&nbsp;Rentong Chen ,&nbsp;Tian Tian ,&nbsp;Jiyuan Dong ,&nbsp;Ye Ruan","doi":"10.1016/j.uclim.2025.102303","DOIUrl":"10.1016/j.uclim.2025.102303","url":null,"abstract":"<div><div>To explore the exposure-lag-response relationships and the interaction effects of meteorological and air pollutants on cerebrovascular disease admissions (CDA), this study used a distributed lag nonlinear model of time series. Univariate analysis showed a “U” shaped between relative humidity (RH) and CDA, daily mean temperature (DMT) and CDA showed fluctuating variations. Except O₃, the relative risk (RR) between air pollutants and CDA showed a monotonically increasing trend. The RR values were maximum at the highest air pollutant concentration, which were RR(NO₂) =2.86(95 %CI:1.79–2.58), RR(PM_2.5) = 1.82 (95 %CI:1.13–2.94), RR(PM₁₀) = 2.94(95 %CI:1.33–6.52), RR(SO₂) = 2.16(95 %CI:1.24–3.75), RR(CO) = 1.91(95 %CI: 1.19–3.06). The interaction results showed statistically significant relative excess risk of interactions (RERI) except DMT with CO and O₃, and RH with PM_2.5 and NO₂. The RERI of DMT with PM_2.5, PM₁₀, and NO₂ on CDA were all greater than 0, with RERI of 0.99 (95 %CI:0.08–1.90), 0.25 (95 %CI:0.03–0.47), and 1.51 (95 %CI:0.49–2.55), respectively, whereas the RERI of DMT with SO₂ on CDA were less than 0. The RERI of RH with PM₁₀ and O₃ on CDA were greater than 0 and were 1.08 (95 %CI:0.03–1.97), 2.32 (95 %CI:0.19–5.51), respectively. All factors except O₃ had cumulative risks on the CDA and there was an interaction between meteorological factors and air pollutants on CDA.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102303"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing air quality forecasting: Fusion of state-of-the-art deep learning models for precise classification","authors":"Umesh Kumar Lilhore ,&nbsp;Sarita Simaiya ,&nbsp;Surjeet Dalal ,&nbsp;Neetu Faujdar","doi":"10.1016/j.uclim.2025.102308","DOIUrl":"10.1016/j.uclim.2025.102308","url":null,"abstract":"<div><div>Effective management of air quality is crucial for protecting public health and enhancing environmental resilience. As urban areas and industries rapidly expand, there is a growing need for accurate systems to monitor and predict air quality. In this way, individuals can take prompt action to reduce health risks. This research introduces a novel approach for categorizing air quality using advanced deep learning techniques. It addresses common challenges in air quality datasets, such as data imbalance and multi-label classification. To improve feature extraction and classification accuracy, we propose the Fusion of Enhanced Hybrid Deep Features (FEHDF) model, which integrates the strengths of several Convolutional Neural Network (CNN) architectures, including VGG16, VGG19, ResNet50, DenseNet121, InceptionV3, and EfficientNet. To validate this methodology, extensive tests were conducted on multiple datasets, including records from the US Environmental Protection Agency (EPA), air quality data from Beijing, and the UCI air quality dataset. The experimental results demonstrate that the proposed FEHDF model achieves accuracy rates of 98.42 %, 98.75 %, and 98.63 %, respectively, for the EPA dataset, the Beijing air quality data, and the UCI air quality dataset. It outperforms standalone CNN models. These results highlight that FEHDF overcomes the limitations of traditional models, positioning it as a crucial tool for improving air quality predictions. This research marks a significant advancement in the application of deep learning in environmental science, providing a foundation for better public health and regulatory strategies.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102308"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of urban lake breeze circulation: Implications for the mitigation of urban overheating","authors":"Qilong Zhong ,&nbsp;Jiyun Song ,&nbsp;Xiaoxue Wang ,&nbsp;Huanhuan Wang ,&nbsp;Dachuan Shi ,&nbsp;Yuguo Li","doi":"10.1016/j.uclim.2025.102295","DOIUrl":"10.1016/j.uclim.2025.102295","url":null,"abstract":"<div><div>The urban overheating phenomenon, induced by both global warming and the urban heat island (UHI) effect, has been exacerbated by worsened urban wind environments. Urban lakes may aid in mitigating urban overheating through the process of lake breeze circulation (LBC). In this study, we developed a multi-scale water-energy coupled CFD model to simulate the transport of heat and moisture between lake surfaces and built-up areas and to resolve dynamics of atmospheric temperature, humidity, and wind at both street canyon scale (1m) and city scale (50km) with relatively low computational costs. Based on this model, we conducted sensitivity analyses to study the impact of urban, lake, and atmospheric parameters on thermal comfort conditions in the city. Our results show that the cooling capacity of LBC is more evident in hotter and denser cities. Furthermore, in the process of urban expansion, horizontal sprawl (i.e., increasing city size) is more advantageous than vertical growth (i.e., increasing building height) and infilling growth (i.e., increasing building density), considering the cooling potential of LBC. Our results can provide significant references for urban planning and city design for the sake of the mitigation of urban overheating via lake breezes.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102295"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature-related mortality and future health risks from climate change in a middle eastern Metropolis","authors":"Peran Riahi,&nbsp;Babak Khorsandi","doi":"10.1016/j.uclim.2025.102327","DOIUrl":"10.1016/j.uclim.2025.102327","url":null,"abstract":"<div><div>This study investigates the effect of past and future temperatures on five distinct causes of mortality, including non-accidental causes, as well as diseases related to the cardiovascular, respiratory, nervous systems, and diabetes in Tehran, Iran.</div><div>Data on meteorological conditions and mortality were obtained for 7 years. To create a model for the association between temperature and death from the diseases, a time-series quasi-Poisson regression in combination with a distributed lag non-linear model was used. Extreme cold and hot temperatures were assessed to determine relative risks (RRs) at the 2.5th and 97.5th percentiles of the temperature distribution. To quantify the future risks, projections of average temperature for 2090–2099 from General Circulation Models under Representative Concentration Pathways scenarios downscaled by LARS-WG were used.</div><div>Our results revealed that cold temperatures are more harmful to mortality than hot temperatures. The RRs for non-accidental deaths at extreme cold for the group of elderlies (aged≥65) is significant (1.20, %95 CI: 1.02–1.41, <em>p</em>-value&lt;0.05). Elderlies are also at a higher risk of dying from respiratory ailments. The RRs for nervous system diseases and diabetes are significant (<em>p</em>-value&lt;0.05) for extreme cold temperatures. The effects of projected temperature changes on causes of mortality except non-accidental causes and cardiovascular diseases are not statistically significant. However, the RRs of respiratory mortality rises with colder temperatures.</div><div>The findings of this research can lead to the identification of sensitive populations exposed to non-optimal temperatures. Lastly, this research provides decision-makers with important information for planning preventive measures to reduce temperature-related mortality.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102327"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of ozone pollution causes under influence of extreme high temperatures and power rationing measures: Chengdu, China","authors":"Lijuan Lei ,&nbsp;Miao Feng ,&nbsp;Yi Zhang ,&nbsp;Wei Li ,&nbsp;Shishi Yang ,&nbsp;Danlin Song ,&nbsp;Yang Chen ,&nbsp;Junjie Wang","doi":"10.1016/j.uclim.2024.102239","DOIUrl":"10.1016/j.uclim.2024.102239","url":null,"abstract":"<div><div>Ozone(O₃) pollution has become increasingly severe and persistent in recent years. A persistent O₃ pollution event occurred in Chengdu in 2022 during a period of extreme high-temperature weather. In response, the government implemented power rationing measures to restrict industrial electricity consumption. Based on the observational data, an observation based model (OBM) and probability mass function (PMF) are used to study the effect of power rationing on the O₃ formation mechanism. After power rationing, the temperature increased by 1.7 °C,despite the conditions for O₃ generation were more favorable, the O₃ concentration was reduced. This is mainly because the power rationing measures reduced non-methane hydrocarbons (NMHCs) by about 22.71 % and reduced the generation of organic peroxide radicals (RO₂), thereby suppressing O₃ generation. The reduced NMHCs primarily came from solvent use sources. In recent years, extreme high-temperature days have occurred frequently, and the O₃ pollution situation has become grim. This study seeks to understand the influence of human activities on O₃ in Chengdu under extreme high-temperature conditions, emphasizes that O₃ pollution prevention and control measures in Chengdu should consider addressing solvent use sources, reducing the NMHC, and the adopting of regional joint preventive and control measures, and provides references for other regions.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102239"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between climate variability and injury-causing road traffic accidents in Singapore – A time-stratified case-crossover study","authors":"Rae Chua ,&nbsp;Yih Yng Ng ,&nbsp;Andrew F.W. Ho ,&nbsp;Joel Aik","doi":"10.1016/j.uclim.2024.102257","DOIUrl":"10.1016/j.uclim.2024.102257","url":null,"abstract":"<div><h3>Introduction</h3><div>Studies examining the association between weather exposures and the likelihood of road traffic accidents (RTAs) have widely been conducted in temperate settings. However, evidence on such associations in tropical urban settings where the climate differs is limited.</div></div><div><h3>Methods</h3><div>National level data on road locations with injury-causing RTAs in Singapore was obtained. We linked the accident data to weather exposure measures from the nearest weather station. We used a time-stratified, case-crossover analytical study design, the Distributed Lag Non-Linear Modelling framework and conditional logistic regression to assess the association between weather exposures and the odds of injury-causing traffic accidents on roads.</div></div><div><h3>Results</h3><div>There were 42,989 injury-causing RTAs from January-2016 to November-2021. Cool temperatures (adjusted OR₂₅°_C: 1.087, 95 % CI: 1.023 to 1.156) and higher rainfall (adjusted OR_38mm: 1.168, 95 % CI: 1.129 to 1.208) were positively associated with increased odds of injury-causing traffic accidents. These effects were greater on expressways compared to normal roads, suggesting potential effect modification by road type.</div></div><div><h3>Conclusion</h3><div>Lower ambient temperature and higher rainfall were positively associated with injury-causing RTAs in Singapore. Prevention measures ought to be timed with anticipated cooler and wetter days to maximise their uptake and effectiveness among motorists, especially those who use expressways frequently.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102257"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping flood risk using a workflow including deep learning and MCDM– Application to southern Iran","authors":"Hamid Gholami ,&nbsp;Aliakbar Mohammadifar ,&nbsp;Shahram Golzari ,&nbsp;Reza Torkamandi ,&nbsp;Elahe Moayedi ,&nbsp;Maryam Zare Reshkooeiyeh ,&nbsp;Yougui Song ,&nbsp;Christian Zeeden","doi":"10.1016/j.uclim.2024.102272","DOIUrl":"10.1016/j.uclim.2024.102272","url":null,"abstract":"<div><div>Floods - an important risk that threatens many people worldwide – affect both the environment and human-made structures, and can cause loss of agricultural activities and life, economic challenges such as the destruction of infrastructure. Therefore, spatial maps of flooding probability can be useful to identify regions with high risk, these can be used to mitigate its negative consequences. Here, we developed a methodology to map flood risk in a catchment in southern Iran by combining a hazard map produced by a bidirectional long short-term memory (bLSTM) deep learning (DL) model, and a flood vulnerability map produced by a complex proportional assessment (COPRAS) model as a multi-criteria decision making (MCDM) model. Different environmental variables as lithology, vegetation cover, land use were mapped spatially, and a GrootCV was employed to identifying the most important variables controlling flood risk. Among various variables explored as controls flood risk, the variables extracted from a digital elevation model (DEM) (e.g., topographic wetness index (TWI), river density, topographic position index (TPI), stream power index (SPI), slope, elevation and distance to river) were recognized as the most effective features controlling the flood risk. Finally, a bLSTM model was employed to map the flood hazard. Its performance was assessed by the cumulative gain and Kolmogorov Smirnov (KS) tests. To map flood vulnerability, seven socio-economic variables were mapped as key controls, and then, analytical hierarchy process (AHP) and COPRAS models were employed to determine the weights of variables to map flood vulnerability. Finally, a flood risk model was generated by integration of the bLSTM and COPRAS. The results revealed that 23.2 %, 27.7 %, 18.7 %, 15.8 % and 14.6 % of the total study area are classified as very low to very high risk classes, respectively. Overall, our methodology based on DL and MCDM can employ to map flood risk and another disasters (e.g., landslide, land subsidence, soil erosion, etc.) in different climatic regions worldwide.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102272"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disparities in the impact of urban heat island effect on particulate pollutants at different pollution stages - A case study of the “2 + 36” cities","authors":"Chang Yinghui ,&nbsp;Guo Xiaomin","doi":"10.1016/j.uclim.2024.102273","DOIUrl":"10.1016/j.uclim.2024.102273","url":null,"abstract":"<div><div>Urban heat island (UHI) and atmospheric pollution are critical ecological challenges in urban areas. China's air quality presents a stage-specific “polluting first and cleaning up later” model. However, the differential impact of UHI on particulate matter (PM) pollution across various stages remains unclear. This study utilizes land surface temperature (LST) data between 2000 and 2021 to calculate the surface urban heat island intensity (SUHII) across the “2 + 36” cities, a key pollution prevention and control area. The inflection points of PM pollution in each city were identified through piecewise linear regression, dividing the study period into distinct pollution stage. The geographical and temporal weighted regression (GTWR) model was employed to analyze the varying impacts of SUHII on PM pollution across different stages. The result indicate that UHI persisted significantly during the study period. The impact of SUHII on PM pollution exhibits pronounced temporal and regional characteristics, with an increasing influence of Nighttine SUHII (N_SUHII) on pollution. This finding highlights a crucial entry point for future pollution control measures. Moreover, under the influence of SUHII, ecological factors such as enhanced vegetation index and precipitation may paradoxically exacerbate PM pollution at certain times. These findings reveal the multifaceted causes and dynamics of urban PM pollution in developing countries, validate the necessity of implementing targeted management measures in the context of future climate change, and are important for the formulation of strategies to promote sustainable urban development.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102273"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial bias in placement of citizen and conventional weather stations and their impact on urban climate research: A case study of the Urban Heat Island effect in Canada","authors":"Priyadharshini Sakthivel ,&nbsp;Raja Sengupta","doi":"10.1016/j.uclim.2024.102280","DOIUrl":"10.1016/j.uclim.2024.102280","url":null,"abstract":"<div><div>Citizen Weather Stations (CWS) are a source of Crowdsourced Geographic Information for urban climate research, which can provide extensive datasets in areas where data are scarce or unavailable. In this article, we explore the efficacy of using meteorological data from CWS in studying the Urban Heat Island (UHI) effect across Canada during late spring and summer of 2022. In particular, we evaluate the distribution of CWS before relying on them for UHI intensity estimates, since potential spatial biases in placement of these sensors can greatly affect canopy-level measurements. We compared the spatial distribution of Netatmo CWS with conventional weather stations from Environment and Climate Change Canada (ECCC), and found that ECCC sensors are more numerous in rural areas, while Netatmo sensors are present in greater numbers in urban areas. We then computed UHI intensity using urban temperature from Netatmo sensors and peri-urban temperature from ECCC sensors. The resulting intensity values were higher than those estimated using either the Netatmo or the ECCC sensors individually, thus highlighting the influence of sensor distribution in estimating UHI magnitude. Overall, our research explores the distribution of both ECCC and CWS sensors, and highlights their potential complementarity in urban climate research.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"59 ","pages":"Article 102280"},"PeriodicalIF":6.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}