On the relation between urban climate and energy performance of buildings. A three-years experience in Rome, Italy

Zinzi, Michele; Carnielo, Emiliano; Mattoni, Benedetta

doi:10.1016/j.apenergy.2018.03.192

Cited by 78 publications

(31 citation statements)

References 42 publications

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“…In these regards, compact urban forms may recover definite micro-climate conditions [86][87][88]. A detailed analysis of the urban climate would help an organization of different kinds of settlement along the urban-rural gradient [33,34], e.g., a standard classification based on Local Climatic Zones [61]. Urban climatic regimes have features that explain a spatially-explicit approach for enduring monitoring and adaptation strategies.…”

Section: Discussionmentioning

confidence: 99%

“…Considering urbanization as a powerful factor of landscape transformations in Mediterranean Europe, a comparison of changes over time in local-scale climate regimes (e.g., between urban and peri-urban areas) is particularly stimulating in the light of global vs local climate change, drought and land degradation [32]. While climate change is a recognized phenomenon worldwide, an additional factor related to local changes in climate regimes is urban expansion, which generate a well-known effect known as the Urban Heat Island, hereafter UHI [33]. The UHI usually leads to local climate change, with rising temperatures and extreme rainfall events [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…While climate change is a recognized phenomenon worldwide, an additional factor related to local changes in climate regimes is urban expansion, which generate a well-known effect known as the Urban Heat Island, hereafter UHI [33]. The UHI usually leads to local climate change, with rising temperatures and extreme rainfall events [33][34][35][36]. The UHI consequences involve planning challenges that should increasingly consider the negative environmental impact of climate change and urbanization together [37], mitigating less effectively the rapid increase in temperatures within urban areas in respect with the surrounding areas [38,39].…”

Section: Introductionmentioning

confidence: 99%

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A Time-Series Analysis of Climate Variability in Urban and Agricultural Sites (Rome, Italy)

Salvati¹,

Zambon

Pignatti³

et al. 2019

Agriculture

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Identifying early signals of climate change and latent patterns of meteorological variability requires tools analyzing time series data and multidimensional measures. By focusing on air temperature and precipitation, the present study compares local-scale climate regimes at two sites in Central Italy (urban Rome and a peri-urban cropland 10 km west of Rome), using descriptive and inferential statistics on both variables and a drought index (the Standardized Precipitation Index, hereafter SPI) recorded over the last 60 years (1958–2017). The present work assumes the importance of urban-rural gradients shaping local-scale climate regimes and spatial variability, with differential impacts on individual variables depending on territorial background and intrinsic biophysical characteristics. Considering together precipitations and minimum/maximum air temperature at month and year scale, the analysis developed here illustrates two coexisting climatic trends at distinctive spatial scales: A general trend toward warming—specifically influencing temperature regimes—and a more specific pattern evidencing changes in local-scale climate regime along the urban gradient, with a more subtle impact on both precipitations and temperatures. Empirical results indicate that climate variability increased over the study period, outlining the low predictability of dry spells typical of Mediterranean climate especially in the drier season (spring/summer). On average, absolute annual differences between the two sites amounted to 70 mm (more rainfall in the peri-urban site) and 0.9 °C (higher temperature in the urban site). A similar trend toward warming was observed for air temperature in both sites. No significant trends were observed for annual and seasonal rainfalls. SPI long-term trends indicate high variability in dry spells, with more frequent (and severe) drought episodes in urban Rome. Considering together trends in temperature and precipitation, the ‘urban heat’ effect was more evident, indicating a clearer trend toward climate aridity in urban Rome. These findings support the adoption of integrated strategies for climate change adaptation and mitigation in both agricultural systems and relict natural ecosystems surrounding urban areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Time-Series Analysis of Climate Variability in Urban and Agricultural Sites (Rome, Italy)

Salvati¹,

Zambon

Pignatti³

et al. 2019

Agriculture

View full text Add to dashboard Cite

show abstract

“…One of the most obvious urban climate features is the urban heat island effect [3]. is effect can directly and indirectly affect regional climate [4], energy use [5], air quality [6], urban hydrology [7], soil physicochemical properties [8], creature distribution and activities [9], and human health, comfort, and quality of life [10]. e approaches used for studying urban heat island (UHI) include weather station observations [11], fixed-point field measurements [12], mobile belt transect surveys [13], numerical modelling [14], and remote sensing monitoring, which have been widely accepted and adopted [15,16].…”

Section: Introductionmentioning

confidence: 99%

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

Wang

Liu

et al. 2019

Advances in Meteorology

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The thermal environment is closely related to human well-being. Determinants of surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, some research fields remain blank or have conflicting findings, which need to be further addressed. Particularly, few studies focus on drivers of SUHIs in massive cities with different sizes under various contexts at large scales. Using multisource data, we explored 11 determinants of surface urban heat island intensity (SUHII) for 1449 cities in different ecological contexts throughout China in 2010, adopting the Spearman and partial correlation analysis and machine learning method. The main results were as follows: (1) Significant positive partial correlations existed between daytime SUHII and the differences in nighttime light intensity and built-up intensity between cities and their corresponding villages except in arid or semiarid western China. The differences in the enhanced vegetation index were generally partially negatively correlated with daytime and nighttime SUHII. The differences in white sky albedo were usually partially negatively correlated with nighttime SUHII. The mean air temperature was partially positively correlated with nighttime SUHII in 40% of cases. Only a few significant partial relationships existed between SUHII and urban area, total population, and differences in aerosol optical depth. The explanation rates during daytime were larger than during nighttime in 72% of cases. The largest and smallest rates occurred during summer days in humid cold northeastern China (63.84%) and in southern China (10.44%), respectively. (2) Both the daytime and nighttime SUHII could be well determined by drivers using the machine learning method. The RMSE ranged from 0.49°C to 1.54°C at a national scale. The simulation SUHII values were always significantly correlated with the actual SUHII values. The simulation accuracies were always higher during nighttime than daytime. The highest accuracies occurred in central-northern China and were lowest in western China during both daytime and nighttime.

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“…Office buildings (including 23% of nonresidential buildings) are responsible for more than 48% of the annual energy demand (heating and cooling) in urban areas [5,6]. It is commonly accepted that urban 2 of 19 microclimate conditions have a significant impact on urban climates [7,8], urban comfort [9,10], and the energy performance of buildings [11,12]. At the urban microscale level, the average wind speed is lower, with more complex flow patterns as compared to rural areas [13].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Microclimate Conditions on the Energy Performance of Buildings in Urban Areas

Javanroodi

Nik

2019

Buildings

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Urbanization trends have changed the morphology of cities in the past decades. Complex urban areas with wide variations in built density, layout typology, and architectural form have resulted in more complicated microclimate conditions. Microclimate conditions affect the energy performance of buildings and bioclimatic design strategies as well as a high number of engineering applications. However, commercial energy simulation engines that utilize widely-available mesoscale weather data tend to underestimate these impacts. These weather files, which represent typical weather conditions at a location, are mostly based on long-term metrological observations and fail to consider extreme conditions in their calculation. This paper aims to evaluate the impacts of hourly microclimate data in typical and extreme climate conditions on the energy performance of an office building in two different urban areas. Results showed that the urban morphology can reduce the wind speed by 27% and amplify air temperature by more than 14%. Using microclimate data, the calculated outside surface temperature, operating temperature and total energy demand of buildings were notably different to those obtained using typical regional climate model (RCM)–climate data or available weather files (Typical Meteorological Year or TMY), i.e., by 61%, 7%, and 21%, respectively. The difference in the hourly peak demand during extreme weather conditions was around 13%. The impact of urban density and the final height of buildings on the results are discussed at the end of the paper.

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On the relation between urban climate and energy performance of buildings. A three-years experience in Rome, Italy

Cited by 78 publications

References 42 publications

A Time-Series Analysis of Climate Variability in Urban and Agricultural Sites (Rome, Italy)

A Time-Series Analysis of Climate Variability in Urban and Agricultural Sites (Rome, Italy)

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

Impacts of Microclimate Conditions on the Energy Performance of Buildings in Urban Areas

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