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A major challenge for the urban community is the exploitation of Earth Observation intelligence in the multidimensional nature of urban sustainability, towards enhancing urban resilience, particularly in relation to the challenges of climate change. Here, we present the ways in which the Horizon2020 funded project CURE synergistically exploited Copernicus Core Services (CCS) to support urban resilience. CURE provides spatially disaggregated environmental intelligence at a local scale, as well as a proof-of-concept that urban planning and management strategies, enhancing the resilience of cities, can be supported by CCS. CURE has developed eleven cross-cutting applications between CCS, reflecting the main urban sustainability dimensions and are relevant to user needs, which were identified based on a strong stakeholders’ engagement. CURE is built on Data and Information Access Services, as a system integrating these cross-cutting applications, capable of supporting downstream services across Europe, enabling its incorporation into operational Copernicus products portfolio in the future.

Air pollution is difficult to detect with human senses. It is to a large extent out of sight and out of sense, while causing a burden on our economy, our health and our environment. A relevant illustration of this is the exposure to air pollution during commutes. The air pollution commuters are exposed to remains to a considerable extent a hidden geography, with, for example, a lack of available reliable information regarding the on-the-road concentrations of several air pollutants. This research aims to unravel, to the best possible extent, spatio-temporal air pollution patterns (active) commuters are exposed to. Cyclists and pedestrians can be unaware that they commute in polluted air. They often travel close to motorised traffic, resulting in high exposure to several air pollutants, which have elevated levels on the road due to vehicular emissions. Significantly higher concentrations of particulate matter (

IntroductionDirect health effects of extreme temperatures are a significant environmental health problem in Lithuania, and could worsen further under climate change. This paper attempts to describe the change in environmental temperature conditions that the urban population of Vilnius could experience under climate change, and the effects such change could have on excess heat-related and cold-related mortality in two future periods within the 21st century.MethodsWe modelled the urban climate of Vilnius for the summer and winter seasons during a sample period (2009–2015) and projected summertime and wintertime daily temperatures for two prospective periods, one in the near (2030–2045) and one in the far future (2085–2100), under the Representative Concentration Pathway (RCP) 8.5. We then analysed the historical relationship between temperature and mortality for the period 2009–2015, and estimated the projected mortality in the near future and far future periods under a changing climate and population, assuming alternatively no acclimatisation and acclimatisation to heat and cold based on a constant-percentile threshold temperature.ResultsDuring the sample period 2009–2015 in summertime we observed an increase in daily mortality from a maximum daily temperature of 30 °C (the 96th percentile of the series), with an average of around 7 deaths per year. Under a no acclimatisation scenario, annual average heat-related mortality would rise to 24 deaths/year (95% CI: 8.4–38.4) in the near future and to 46 deaths/year (95% CI: 16.4–74.4) in the far future. Under a heat acclimatisation scenario, mortality would not increase significantly in the near or in the far future. Regarding wintertime cold-related mortality in the sample period 2009–2015, we observed increased mortality on days on which the minimum daily temperature fell below − 12 °C (the 7th percentile of the series), with an average of around 10 deaths a year. Keeping the threshold temperature constant, annual average cold-related mortality would decrease markedly in the near future, to 5 deaths/year (95% CI: 0.8–7.9) and even more in the far future, down to 0.44 deaths/year (95% C: 0.1–0.8). Assuming a “middle ground” between the acclimatisation and non-acclimatisation scenarios, the decrease in cold-related mortality will not compensate the increase in heat-related mortality.ConclusionThermal extremes, both heat and cold, constitute a serious public health threat in Vilnius, and in a changing climate the decrease in mortality attributable to cold will not compensate for the increase in mortality attributable to heat. Study results reinforce the notion that public health prevention against thermal extremes should be designed as a dynamic, adaptive process from the inception.

During the last 15 years, the official assessment maps in Flanders have improved from a map consisting of only measured data points to a high resolution assessment which covers the complete area and takes into account several types of sources and street canyons. In order to improve this level of detail, multiple steps were taken. First of all, a land use regression model was introduced at an hourly scale at 4 × 4 km2 resolution. Secondly, a Gaussian model was added for both point and line sources, correcting for emission double counting. Finally, a street canyon model was added to the chain, leading to improved resolution in these street canyons. In this work, we will discuss the problems encountered in these years such as how to account for double counting of emissions, how to correct the locations of the simplified road network and how to determine when street canyon calculations must be performed and how we solved them.

Urban residents are exposed to higher levels of heat stress in comparison to the rural population. For the city of Brussels, we explore the influence of urban planning and global greenhouse gas emissions (GHG) for the near (2031-2050) and far (2081-2100) future. We implemented two urban planning expansion scenarios (translated into Local Climate Zones, LCZ) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). The projections show that the influence of GHG emissions trumps urban planning measures in each of the two periods. In the near future, no large differences are noted between the RCP scenarios. In the far future on the contrary, both heat stress and risk values are twice as large for RCP 8.5 compared to RCP 4.5. Depending on the GHG scenario and the LCZ, heat stress is projected to increase with a factor of 10 by 2090 compared to the present-day climate and urban planning conditions. The imprint of vulnerability and exposure is clearly visible in the heat risk assessment, leading to very high levels of heat risk most notable for the northwestern part of the Brussels Capital Region (BCR). The results demonstrate the need for mitigation and adaptation plans at different policy levels that strive for lower GHG emissions and the development of sustainable urban areas safeguarding livability in future cities.

This paper assesses the seasonality of the urban heat island (UHI) effect in the Greater London area (United Kingdom). Combining satellite-based observations and urban boundary layer climate modeling with the UrbClim model, the authors are able to address the seasonality of UHI intensity, on the basis of both land surface temperature (LST) and 2-m air temperature, for four individual times of the day (0130, 1030, 1330, and 2230 local time) and the daily means derived from them. An objective of this paper is to investigate whether the UHI intensities that are based on both quantities exhibit a similar hysteresis-like trajectory that is observed for LST when plotting the UHI intensity against the background temperature. The results show that the UrbClim model can satisfactorily reproduce both the observed urban–rural LSTs and 2-m air temperatures as well as their differences and the hysteresis in the surface UHI. The hysteresis-like seasonality is largely absent in both the observed and modeled 2-m air temperatures, however. A sensitivity simulation of the UHI intensity to incoming solar radiation suggests that the hysteresis of the LST can mainly be attributed to the seasonal variation in incoming solar radiation.

This study examines the urban heat island (UHI) of Brussels, for both current (2000–2009) and projected future (2060–2069) climate conditions, by employing very high resolution (250 m) modelling experiments, using the urban boundary layer climate model UrbClim. Meteorological parameters that are related to the intensity of the UHI are identified and it is investigated how these parameters and the magnitude of the UHI evolve for two plausible trajectories for future climate conditions. UHI intensity is found to be strongly correlated to the inversion strength in the lowest 100 m of the atmosphere. The results for the future scenarios indicate that the magnitude of the UHI is expected to decrease slightly due to global warming. This can be attributed to the increased incoming longwave radiation, caused by higher air temperature and humidity values. The presence of the UHI also has a significant impact on the frequency of extreme temperature events in the city area, both in present and future climates, and exacerbates the impact of climate change on the urban population as the amount of heat wave days in the city increases twice as fast as in the rural surroundings.

A new dynamical downscaling methodology to analyze the impact of global climate change on the local climate of cities worldwide is presented. The urban boundary layer climate model UrbClim is coupled to 11 global climate models contained in the Coupled Model Intercomparison Project 5 archive, conducting 20-year simulations for present (1986–2005) and future (2081–2100) climate conditions, considering the Representative Concentration Pathway 8.5 climate scenario. The evolution of the urban heat island of eight different cities, located on three continents, is quantified and assessed, with an unprecedented horizontal resolution of a few hundred meters. For all cities, urban and rural air temperatures are found to increase strongly, up to 7 °C. However, the urban heat island intensity in most cases increases only slightly, often even below the range of uncertainty. A potential explanation, focusing on the role of increased incoming longwave radiation, is put forth. Finally, an alternative method for generating urban climate projections is proposed, combining the ensemble temperature change statistics and the results of the present-day urban climate.

The effects of four regional emission scenarios on the concentrations of PM10, PM2.5, NO2 and EC (elementary carbon) in the North-West European (NWE) Hot Spot region have been studied. The emission estimates were provided by TNO, regional calculations were carried out for the years 2009 and 2020 using the BelEUROS model (Deutsch et al. 2009) on a 15 × 15 km2 grid. The effect of a highway speed limit reduction to 90 km/h on all highways in the NWE region (Belgium, France, Germany, Luxemburg, the Netherlands and the UK) showed up to 4.4% lower concentrations of EC and up to 3.5% reduction of NO2 concentrations (mean over Belgium). The introduction of low emission zones (LEZ) in all cities in the NWE region with more than 50.000 inhabitants and more than 700 inhabitants/km2 resulted in a reduction of 1.8% of the EC-concentrations (mean over Belgium). However, in the areas that actually make part of a LEZ the EC concentration is reduced by 19% and hence this scenario could be more effective in terms of population exposure. In the healthy diet scenario, 75% less meat production in Europe was assumed, leading basically to lower ammonia emissions (reduction of approximately 30%). This scenario results in significant reductions (4.2% as a mean over Belgium) in particulate matter (PM2.5) concentrations due to a reduction of secondary aerosol formation. EC-concentrations are not affected by this scenario. Finally, a pellet stoves scenario was calculated in which 20% of the non-wood energy consumption (gas, oil) in the residential combustion sector had been converted to pellet stoves. This scenario resulted in considerable increases in emissions and in an increase of EC-concentrations by up to 21% as a mean value over Belgium. PM2.5-concentrations increase by up to 4%. Results for all scenarios are available for the whole NWE region.

Recent years have shown significant decrease in concentrations levels of particulate matter (PM10) and nitrogen dioxide (NO2) in Belgium. For ozone (O3), no such trend is found. Recent years, however, did not feature many periods with unfavourable meteorological dispersion conditions, casting some ambiguity on the underlying reasons for the decrease. This study tries to separate the impact of weather effects from emission reductions in the long-term trend. We build a statistical model explaining the daily averaged concentrations based on 32 meteorological parameters, the day of the week, the month of the year and the year itself, for the period 2004–2014. The 32 meteorological parameters are those considered to train the neural network prediction model OVL. Many of these meteo variables have only a small predictability and are intercorrelated with each other. Therefore, only those meteo parameters are used that have a significant impact on concentration levels. This procedure is applied for the complete time series and for each air quality monitoring location separately. In order to avoid overfitting, the same analysis is done, restricted to the data of even-numbered years, and the regression is then applied to the odd-numbered years. It is shown that the statistical parameters remain reasonably constant, which proves that the amount of overfitting is not significant. The results show, on average over all measurement locations, a range of yearly meteorological effects of 1.9 µg/m3 for NO2, 3.1 µg/m3 for PM10 and 2.7 µg/m3 for O3. Meteorology combined with the residuals of the statistical fit show a range of 1.2 µg/m3 for NO2, 2.9 µg/m3 for PM10 and 4.4 µg/m3 for O3. Finally, the long-term trend shows a range of 5.3 µg/m3 for NO2, 11.1 µg/m3 for PM10 and 2.3 µg/m3 for O3, with clearly decreasing trends for NO2 and PM10, and an oscillating trend for O3. Differences between rural, urban background, urban and industrial stations exist but are rather small. We can conclude that the major trend in air pollution (Belgium) is a long-term trend, linked to emission changes, and it can be expected that the concentration decreases of the last years will not suddenly disappear in the near future given unchanged policy. Furthermore, it can be concluded that emission reductions at the local, regional, European and worldwide scale are the dominant factors explaining the improvement of air quality.

Urban residents are exposed to higher levels of heat stress in comparison to the rural population. As this phenomenon could be enhanced by both global greenhouse gas emissions (GHG) and urban expansion, urban planners and policymakers should integrate both in their assessment. One way to consider these two concepts is by using urban climate models at a high resolution. In this study, the influence of urban expansion and GHG emission scenarios is evaluated at 100 m spatial resolution for the city of Brussels (Belgium) in the near (2031-2050) and far (2081-2100) future. Two possible urban planning scenarios (translated into local climate zones, LCZs) in combination with two representative concentration pathways (RCPs 4.5 and 8.5) have been implemented in the urban climate model UrbClim. The projections show that the influence of GHG emissions trumps urban planning measures in each period. In the near future, no large differences are seen between the RCP scenarios; in the far future, both heat stress and risk values are twice as large for RCP 8.5 compared to RCP 4.5. Depending on the GHG scenario and the LCZ type, heat stress is projected to increase by a factor of 10 by 2090 compared to the present-day climate and urban planning conditions. The imprint of vulnerability and exposure is clearly visible in the heat risk assessment, leading to very high levels of heat risk, most notably for the North Western part of the Brussels Capital Region. The results demonstrate the need for mitigation and adaptation plans at different policy levels that strive for lower GHG emissions and the development of sustainable urban areas safeguarding livability in cities.

An elementary Ising spin model is proposed for demonstrating cascading failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in realistic networks for distribution and delivery by suppliers to consumers. A ferromagnetic Hamiltonian with quenched random fields results from policies that maximize the gap between demand and delivery. Such policies can arise in a competitive market where firms artificially create new demand, or in a solidary environment where too high a demand cannot reasonably be met. Network failure in the context of a policy of solidarity is possible when an initially active state becomes metastable and decays to a stable inactive state. We explore the characteristics of the demand and delivery, as well as the topological properties, which make the distribution network susceptible of failure. An effective temperature is defined, which governs the strength of the activity fluctuations which can induce a collapse. Numerical results, obtained by Monte Carlo simulations of the model on (mainly) scale-free networks, are supplemented with analytic mean-field approximations to the geometrical random field fluctuations and the thermal spin fluctuations. The role of hubs versus poorly connected nodes in initiating the breakdown of network activity is illustrated and related to model parameters.

The urban heat island effect, in which air temperatures tend to be higher in urban environments than in rural areas, is known to exacerbate the heat impact on population health. We introduce a new urban climate model, further referred to as UrbClim, designed to study the urban heat island effect at a spatial resolution of a few hundred metres. Despite its simplicity, UrbClim is found to be of the same level of accuracy as more sophisticated models, while also being much faster than high-resolution mesoscale climate models. Because of that, the model is well suited for long time integrations, in particular for applications in urban climate projections. In this contribution, we present temperature maps for London, including an assessment of the present-day climate, and projections for the future (2081–2100).

Recent studies introduced biased (degree-dependent) edge percolation as a model for failures in real-life systems. In this work, such process is applied to networks consisting of two types of nodes with edges running only between nodes of unlike type. Such bipartite graphs appear in many social networks, for instance in affiliation networks and in sexual contact networks in which both types of nodes show the scale-free characteristic for the degree distribution. During the depreciation process, an edge between nodes with degrees k and q is retained with probability proportional to (kq)^(-alpha), where alpha is positive so that links between hubs are more prone to failure. The removal process is studied analytically by introducing a generating functions theory. We deduce exact self-consistent equations describing the system at a macroscopic level and discuss the percolation transition. Critical exponents are obtained by exploiting the Fortuin-Kasteleyn construction which provides a link between our model and a limit of the Potts model., Comment: 12 pages, 1 figure. To appear in Physica A: Special Issue in honour of Prof. A. N. Berker (2009)

Biased (degree-dependent) percolation was recently shown to provide new strategies for turning robust networks fragile and vice versa. Here we present more detailed results for biased edge percolation on scale-free networks. We assume a network in which the probability for an edge between nodes $i$ and $j$ to be retained is proportional to $(k_ik_j)^{-\alpha}$ with $k_i$ and $k_j$ the degrees of the nodes. We discuss two methods of network reconstruction, sequential and simultaneous, and investigate their properties by analytical and numerical means. The system is examined away from the percolation transition, where the size of the giant cluster is obtained, and close to the transition, where nonuniversal critical exponents are extracted using the generating functions method. The theory is found to agree quite well with simulations. By introducing an extension of the Fortuin-Kasteleyn construction, we find that biased percolation is well described by the $q\to 1$ limit of the $q$-state Potts model with inhomogeneous couplings., Comment: 17 pages, 8 figures

As most of the population lives in urban environments, the simulation of the urban climate has become a key problem in the framework of the climate change impact assessment. However, the high computational power required by high-resolution (sub-kilometre) fully coupled land–atmosphere simulations using urban canopy parameterisations is a severe limitation. Here we present a study on the performance of UrbClim, an urban boundary layer model designed to be several orders of magnitude faster than a full-fledged mesoscale model. The simulations are evaluated with station data and land surface temperature observations from satellites, focusing on the urban heat island (UHI). To explore the advantages of using a simple model like UrbClim, the results are compared with a simulation carried out with a state-of-the-art mesoscale model, the Weather Research and Forecasting Model, which includes an urban canopy model. This comparison is performed with driving data from ERA-Interim reanalysis (70 km). In addition, the effect of using driving data from a higher-resolution forecast model (15 km) is explored in the case of UrbClim. The results show that the performance of reproducing the average UHI in the simple model is generally comparable to the one in the mesoscale model when driven with reanalysis data (70 km). However, the simple model needs higher-resolution data from the forecast model (15 km) to correctly reproduce the variability of the UHI at a daily scale, which is related to the wind speed. This lack of accuracy in reproducing the wind speed, especially the sea-breeze daily cycle, which is strong in Barcelona, also causes a warm bias in the reanalysis driven UrbClim run. We conclude that medium-complexity models as UrbClim are a suitable tool to simulate the urban climate, but that they are sensitive to the ability of the input data to represent the local wind regime. UrbClim is a well suited model for impact and adaptation studies at city scale without high computing requirements, but does not replace the need for mesoscale atmospheric models when the focus is on the two-way interactions between the city and the atmosphere.