Road traffic is usually the most pervasive source of noise in an urban environment. Epidemiological studies conducted at the regional or national scale have shown associations of road traffic noise with sleep disturbance, cardiovascular diseases, and mental health problems. Strategic noise mapping (European Noise Directive) only covers populations living in large urban areas. The limited coverage of harmonised noise exposure data at a pan-European scale prevents us from studying the effect of road traffic noise on health in larger populations across Europe. Therefore, this study aims to develop models capturing within-city, intra-city and national variations in road traffic noise exposures across Europe to facilitate pan-European multi-cohort health studies. To estimate noise, we used a simplified version of CNOSSOS-EU (Common NOise aSSessment MethOdS) noise modelling framework.   

The CNOSSOS-EU model requires a range of input data, including a detailed road network, traffic intensity, traffic speed, and land use data including building footprints. We used OpenStreetMap (OSM) to define the road network and buildings. Because traffic intensity is not provided in OSM, we estimated Europe-wide annual average daily traffic (AADT) counts using random forest trained by observations collected in Austria, Switzerland, Germany, France, Italy, and the United Kingdom. Three random forest models were built separately for 1) motorway and trunk roads, 2) primary roads, and 3) secondary, tertiary, residential and unclassified roads defined in OpenStreetMap (OSM). Predictor variables included road length, sizes of residential areas, and population within different circular buffer (ranging from 100m to 200km). The models were validated using 5-fold cross-validation. The 5-fold root mean square errors of AADTs were 19646, 6589, 4005, 3824 and 3210 for highway (motorway and trunk roads), primary, secondary, tertiary, and residential roads. The traffic speed was approximated by the speed limit from OSM, and the missing speed limit data was replaced by the legal country-specific speed limit separated by inside and outside built-up areas, depending on the road type. Building height was approximated by using a morphological operation on the AW3D30 digital surface model (DSM). The road traffic noise was estimated at noisiest building façades (i.e., with shortest Euclidean distance to nearby roads within 100m with the highest AADT) using CNOSSOS-EU. The modelled noise level of L_Aeq16 with these input data ranged from 52.17 dB to 72.54 dB for points in the test city of Bristol in the United Kingdom. In conclusion, we developed the input data required for noise modelling, especially traffic intensity, at a European scale. Modelled noise will be used in Europe-wide studies of health effects of noise. We will also compare our Europe-wide noise estimates with national noise model estimates in the Netherlands and Switzerland.

How to cite: Shen, Y., de Hoogh, K., Schmitz, O., Gulliver, J., Karssenberg, D., Vermeulen, R., and Hoek, G.: Europe-wide road traffic noise modelling using a harmonized methodological framework (CNOSSOS-EU), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9249, https://doi.org/10.5194/egusphere-egu23-9249, 2023.

According to the WHO, in 2019, 23% of global mortality was attributable to environmental risk factors with significant gradients related to social factors, at the origin of significant health inequalities in the cities. Air pollution is one main environmental risk in urban spaces, and must therefore also be understood by taking into account the issue of socio-spatial inequalities.

Realistic modelling of population exposure to air pollution in large cities requires taking into account air quality at the level of the individual, as well as individual spatial dynamics (mobility and realization of daily activities) that shape each person's risk of exposure. These requirements call for the development of interdisciplinary tools combining the representation of urban space, traffic simulation, emission calculation, advanced air quality models, and the consideration of behavioral and socio-economic dimensions in the modeling process.

We present here a socially and spatially differentiated modeling study of the factors and behaviors that build the exposure of individuals to air pollution in the Greater Paris. This study was carried out using an integrated urban modeling platform including the OLYMPUS emissions model and the CHIMERE chemistry-transport model. The OLYMPUS tool, developed at LISA, is an innovative emission model based on the activity of individuals, making it possible to simulate the socio-differentiated mobility of individuals, for the construction of a pollutant emission inventory adapted to a given urban area. The use of CHIMERE then makes it possible to cross air quality, individuals and mobility, and address the issue of individual exposure to air pollution in a dynamic and integrated manner.

We simulated the year 2009 in the Greater Paris region, and calculated the exposure of individuals taking into account their mobility and their social characteristics, activity and place of residence. Our results are interpreted with regard to the main scientific and societal questions that arise on the subject: Are some individuals more exposed than others? Are these inequalities in exposure linked to the places where people live? To mobility practices? Can they be dependent on socio-professional categories? Do they affect socially vulnerable populations in the same way?

Beyond access to an assessment of exposure inequalities in the current situation, this work makes it possible to support the reflection on the impact of public action on the reduction of environmental inequalities.

Acknowledgments

This research received funding from the French National Agency for Research (ANR-14-CE22-0013), the French Environment and Energy Management Agency (ADEME) and the Île-de-France region (DIM QI²). It was granted access to the HPC resources of TGCC under the allocation A0090107232 made by GENCI. We acknowledge AIRPARIF for data supply.

Références

Elessa Etuman, A., & Coll, I. (2018). OLYMPUS v1.0: Development of an integrated air pollutant and GHG urban emissions model-methodology and calibration over greater Paris. Geoscientific Model Development, 11(12), 5085–5111. https://doi.org/10.5194/gmd-11-5085-2018

Elessa Etuman A., Coll I., Makni I., Benoussaid T., Addressing the issue of exposure to primary pollution in urban areas: Application to Greater Paris, Atmospheric Environment, Volume 239, 117661, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2020.117661, 2020

How to cite: Benoussaïd, T., Coll, I., Charreire, H., and Elessa Etuman, A.: A socio-spatial analysis of air pollution exposure in the Greater Paris, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15999, https://doi.org/10.5194/egusphere-egu23-15999, 2023.

Air pollution affects the economy, the environment, and public health. This is particularly relevant in dense urban areas due to their urban built, high traffic activity, and near-the-source population exposure. In the city of Barcelona, the 40 ug/m³ nitrogen dioxide NO₂ annual limit value set up by the 2008/50/EC European Air Quality Directive (AQD) is systematically exceeded in traffic stations mainly due to the contribution of road transport. In the last Urban Mobility Plan (2019-2024), the city hall of Barcelona presented several traffic management strategies aiming to reduce on-road traffic emissions by both renewing and reducing the private motorized transport in the city. These measures include the application of tactical urban actions, green corridors and superblocks along with a Low Emission Zone, which together are expected to reduce the number of private vehicles circulating throughout the city by -25%. In parallel, the Port of Barcelona has recently announced a plan to electrify the docks and reduce emission from hotelling activities by -38%. To properly assess the impact of such measures, the AQD recommends the application of numerical models in combination with monitoring data. Following AQD recommendations, our study runs a coupled transport-emission model able to characterize traffic movement along the city and produce multiple scenarios that quantify the impact of the aforementioned measures on primary emissions. The resulting scenarios are then used to feed a multi-scale air quality modeling system to estimate NO₂ concentration values at very high resolution (20m, hourly). To reduce the uncertainty typically associated with modeling results, the estimated values are corrected with a data-fusion methodology using observations from the official monitoring network and several measurement campaigns. Our results show that the implementation of all mobility restrictions and electrification of the Port will allow Barcelona to comply with the current legislated NO₂ air quality standards at the traffic monitoring stations, with reductions up to -24.7% and -12 ug/m³. However, the resulting NO₂ levels achieved at these locations would still fail to meet the new 2021 WHO guideline (10 ug/m³) and the recent proposal for a revision of the EU AQD (20 ug/m³). Also, despite the estimated NO₂ reductions, several areas in the city would still be above the current legal limit of 40 ug/m³, including 16.7% of schools and 19.7% of hospitals and healthcare facilities. All in all, our results suggest the planned measures are steps in the right direction, yet still insufficient to ensure healthy AQ values across the entire city.

How to cite: Rodriguez-Rey, D., Guevara, M., Armengol, J. M., Criado, A., Enciso, S., Tena, C., Benavides, J., Soret, A., Jorba, O., and Pérez-Garcia Pando, C.: Challenges for achieving clean air - The case of Barcelona (Spain), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14487, https://doi.org/10.5194/egusphere-egu23-14487, 2023.

While there have been many studies on the diurnal and seasonal variations of urban heat islands (UHIs), the day-to-day variability of UHI remains largely unknown, despite being the key to explaining interactions between UHIs and extreme heat events or heatwaves. In this study, we aim to understand and quantify the persistence of urban/rural temperatures. Autocorrelation and spectral analyses are conducted on urban and rural temperatures simulated by a global land model to quantify the urban-rural difference of temperature persistence. A surface energy balance model is then derived to explain the simulation results, elucidating the key biophysical processes that contribute to the stronger persistence of urban surface temperature in certain areas.

How to cite: Li, D., Wang, L., and Sun, T.: Persistent urban heat, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1780, https://doi.org/10.5194/egusphere-egu23-1780, 2023.

Temperatures are rising and the frequency of heat waves is increasing due to anthropogenic climate change. At the same time, the population in urban areas is rapidly growing. As a result, an ever-larger part of humankind will be exposed to even greater heat stress from heat waves in urban areas in the future. In this research, we focus on studying the determinants of land surface temperature (LST) gradients in urban environments. We implement a spatial regression model that is able to predict with high accuracy (R² > 0.9 in the test phase of k-fold cross-validation) the LST of urban environments across 200 cities based on land surface properties like vegetation, built-up areas, and distance to water bodies, without any additional climate information. We show that, on average, by increasing the overall urban vegetation by 3%, it would be possible to reduce by 50% the exposure of the urban population that lives in the warmest areas of the cities for the average of the three summer months, achieving a reduction of 1 K in LST. By coupling the model information with the population layer, we show that an 11% increase in urban vegetation is necessary in order to obtain a reduction of 1 K in the most populated areas, where at least 50% of the population live. We finally discuss the challenges and the limitations of greening interventions in the context of available surfaces in urban areas.

How to cite: Massaro, E., Caporaso, L., Piccardo, M., Schifanella, R., Taubenböck, H., Cescatti, A., and Duveiller, G.: A spatial regression model to measure the urban population exposure to extreme heat, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6583, https://doi.org/10.5194/egusphere-egu23-6583, 2023.

This study shows the unintended tradeoffs of water conservation strategies in the City of Las Vegas. We used the Weather and Research Forecasting model coupled with a multilayer Urban Canopy Model, and forced with the Local Climate Zones from WUDAPTv2, to carry out cloud-resolving simulations aiming to estimate the impacts of city-wide turf removal. Results show that removing the turf, which removes most of non-functional urban irrigation needs, significantly warms up surface temperature via surface energy rebalancing by reducing latent heat and increasing sensible and ground heat fluxes. A striking result is that the increase in sensible heat also increases boundary layer instability favoring more and longer lasting clouds and invigorating afternoon storms. The enhanced afternoon storms tend to cool the surface temperature, but the turf removal net warming impact remains. We also used the model to show how climate intervention scenarios based on cool roofing and pavement strategies can ameliorate the underlying turf removal consequences.

How to cite: Mejía Valencia, J. F., Henao, J., and Saher, R.: The effect of removal of all non-functional turf in Las Vegas: tradeoffs between water conservation, excessive heat, and storminess, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17481, https://doi.org/10.5194/egusphere-egu23-17481, 2023.

In response to climate change and growing ecological threats, many cities are planning to increase the resilience of urban drainage systems, including the reduction of combined sewer overflows (CSOs) - one of the leading causes of surface water pollution. Blue green infrastructure (BGI) are growing in popularity to do so, and recent studies have made progress to evaluate the potential of BGI to eliminate CSOs. However, current research tends to consider a limited number of individual BGI elements and scenarios, often overlooking different combinations (e.g., bioretention basins combined with green roofs) and uncertainty in a future climate. The aim of this research is to evaluate the ability of a range of blue green infrastructure combinations to reduce CSOs under multiple future climate scenarios.
A hydrological simulation model, EPA SWMM, is used to simulate the performance of a 95-hectare combined sewer system near Zurich, Switzerland. Four types of BGI are evaluated, including bioretention basins, porous pavements, green roofs, and stormwater ponds. The potential surface availability for each BGI element was quantified using GIS and LiDAR data, yet scenarios include a range of different implantation rates for each type. Combinations of BGI element types are generated by combining different implementation surfaces to the share of the BGI type (e.g., 20% of the available surface with the same share of bioretention basins, porous pavements and green roofs, etc.).
Bioretention basins are assumed to be implemented on pervious surfaces (i.e., gardens, traffic islands), porous pavements on impervious surfaces (i.e., sidewalks, cycling lanes) and green roofs on flat roof buildings. Observed rainfall data (1990-2019) are used to simulated the baseline conditions, while more than five bias-corrected future rainfall timeseries (2070-2099) from EURO-CORDEX regional climate models (RCP 8.5) are used to represent a worst-case future climate. CSO Volume, duration and frequency are used to characterize system-wide CSO events across the seven outfalls.
Preliminary results show that in a current climate, bioretention basins are most effective at reducing CSO volume, followed by porous pavements and green roofs. BGI do not relevantly reduce the duration and number of CSO events. In one future scenario, future precipitation is concentrated into shorter duration events, which consistently leads to shorter, higher intensity CSO events at a frequency similar to the historical record. Overall, the only scenario that can avoid an increase in future CSO volume is an extensive implementation of bioretention basins. Porous pavement and green roofs are less effective in a future climate because they can store limited amounts of water compared to bioretention basins. As rainfall intensities increase, the ability to retain large amounts of water will be the most effective. These results point to strategies with higher storage capacities to account for high-intensity rainfall events that are expected in the future. Future work will evaluate additional BGI elements, including urban ponds, and a more comprehensive set of BGI scenarios, future climate scenarios, and case studies, enabling a definition of guidelines and BGI design requirements at an urban scale for Switzerland.

How to cite: Cavadini, G. B. and Cook, L.: Blue Green Infrastructure in a future climate: can we reduce combined sewer overflows?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3148, https://doi.org/10.5194/egusphere-egu23-3148, 2023.

The urban environment is made up of a complex and changing mosaic of territories. To what extent these spatial heterogeneities, between territories, determine the quality of life or social, economic or health inequalities? Within the framework of the ANR EGOUT project (egout.cnrs.fr), we assume that the spatial distribution and temporal evolution of tracers archived in the sediments of the Parisian sewerage networks can help deciphering the diversity of aboveground conditions and their temporal trajectory.

We compiled the geochemical results acquired before cleaning out operations on sediments accumulated in more than 100 silt traps (STs) that line the sewerage network of the City of Paris. These STs receive sediments that transit through the Parisian combined (wastewater and stormwater) sewer system. These analyses concern granulometry, metals, polychlorinated biphenyls (PCBs), but also 16 polycyclic aromatic hydrocarbons (PAHs). These regulatory analyses (which guide the nature of the treatment processes to be implemented) have been available since the year 2000, with cleaning out and thus measurement frequencies varying from one ST to another. They therefore allow addressing not only geochemical spatial disparities but also their temporal evolution.

In order to assign these results to the corresponding catchment areas for every ST, we first defined the catchment areas of each DT. The TIGRE 7 information system of the City of Paris was used to distinguish each sub-network draining the sediments to each ST. Two spatial scales of drainage (wet and dry weather), but also a sedimentary cascade system could be highlighted. The catchment areas of each ST were then defined by linking individual connections to individual addresses and cadastral parcels.

Here are the most striking results from the exploitation of existing data:

• The Haussmannian buildings, which are present for the most part in the city Centre, are major sources of zinc emissions (Gromaire et al., 2001). This element is found in the sediments of DT draining areas with a high density of historical buildings.
• Based on concentration ratios (Ayrault et al., 2008), PAHs mainly result from road traffic. The concentration of PAHs has been decreasing in ST sediments since 2000. This decrease could reflect the 59% decrease in the car traffic in Paris recorded between 2001 and 2018.
• PAH levels and types differ from DT to another, as already noted by Rocher et al. (2004). These differences probably indicate local specificities in PAH production of each catchment area. Cross-referencing our data with other spatialized data related to potential PAH sources (road traffic, heating, etc.) should allow us to better understanding the factors that control their presence in sewer sediments.

How to cite: Asselin, C., Jacob, J., and Moilleron, R.: Controls on the spatial distribution and temporal variation of anthropogenic tracers in the sediments of the Paris sewerage system., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13043, https://doi.org/10.5194/egusphere-egu23-13043, 2023.

Understanding urban ecohydrological interactions is crucial for the assessment of ecosystem responses to climate change and anthropogenic influences, especially in heavily urbanized environments. Urban water bodies can enhance local biodiversity, with urban blue water infrastructure providing valuable ecosystem services that contribute to healthier and more sustainable environments. Because the urban water cycle is less resilient to extreme climate events, there is a need to better understand how biological flow paths interact with climate and hydrological dynamics. To that end, synoptic sampling of environmental DNA (eDNA) was carried out on four major rivers in Berlin, Germany (Spree, Erpe, Wuhle, Panke) on a weekly basis over the course of one year. In conjunction with climate and hydrological data, the spatial and temporal variations in planktonic microbial communities were assessed in order to identify the differences in ecohydrological interactions among urban streams. Preliminary results indicate that while the rivers Wuhle and Erpe harbour similar bacterial communities, the more urbanized rivers Panke and Spree each had a different taxonomic composition. All rivers show a clear seasonal signal, although with varying intensity and directions of change. To further disaggregate the seasonal ecological changes, we determined the relative influence of climate as well as water chemistry, land use and stream flow conditions on bacterial community composition. In future, the integration of eDNA with other ecohydrological tracers such as stable water isotopes will provide even more insights into the ecological and hydrological functioning of urban environments. Such a combination of ecohydrological tracers has wider implications not only for future urban planning but for mitigating the negative effects of climate change in urban environments and assessing the resilience of urban water bodies to future extreme events.

How to cite: Warter, M., Monaghan, M. T., Ring, A.-M., Christopher, J., Kissener, H. L., Funke, E., Soulsby, C., and Tetzlaff, D.: Spatio-temporal variations in environmental DNA within heavily urbanized streams in Berlin, Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-107, https://doi.org/10.5194/egusphere-egu23-107, 2023.

The urban transport network is threatened by urbanisation and climate change-enhanced urban flood, leading to substantial impacts on economic activities, social well-being and the environment. By taking the flood propagation process into consideration, we developed a flood-impact-assessment method to comprehensively assessed the economic impacts of traffic disruption in terms of time delay, fuel consumption and pollutant emission. The flood is simulated with CADDIES-2D flood model and the traffic flow is simulated with a microscopic model (SUMO). We applied this method to Beijing and quantified the economic damage of various flood scenarios. Comparing the baseline traffic scenario with those of three flooded scenarios yields the impacts of floods on traffic. The study revealed three key findings: (a) a rain occurring at 7 a.m. induces four times more cost than the baseline scenario, while rain of the same intensity and duration occurring at 8 a.m. or 9 a.m. lead to a traffic cost increase for 37.33% and 13.21% respectively. (b) The central and southern parts of Beijing suffer more from flooding and should be given priority for adaptation planning. (c) There is no significant spatial correlation between flood depth and traffic cost increase on a census block level. The proposed framework has the potential to assist decision-makers in prioritizing flood mitigation investments and therefore increase the resilience of transport networks to flooding impacts.  

How to cite: Liu, Y., Yang, S., Dawson, R., Ford, A., and Tang, J.: Flood Impact to Urban Transport Networks Considering the Flooding Propagation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6611, https://doi.org/10.5194/egusphere-egu23-6611, 2023.

While road transport is a cornerstone of modern civilization bringing profound positive impacts to the economy and human well-being, it is also associated with several undesirable and unsustainable outcomes including urban air pollution, climate change, noise and congestion. Hence, sustainable road transport has been given significant attention in the past two decades. Digital twins of urban transport are promising digital assets to evaluate and improve the sustainability level of the transport systems.  Digital twins provides a testbed whereby the impacts of the current and future policies and strategies can be modelled and analysed in a digital environment, helping ensure that tax money spent delivers the expected results. However, the desperate shortage of spatiotemporal road data is the major challenge in establishing data flow between the digital and physical twins of road transport. Vehicle telematics data, typically collected from GPS-connected, can provide an excellent source of intormation with which to address the spatial and temporal aspects of transport data. This presentation will highlight how telematics data can be used within road transport digital twins.

In this study, we develop digital twins of road transport for Tyseley Environmental Enterprise District (TEED) a small area of east Birmingham, UK, using the newly-developed approach of GeoSpatial and Temporal Mapping of Urban Mobility (GeoSTMUM). GeoSTMUM uses vehicle telematics (location and time) data to estimate several road transport characteristics such as the average speed of traffic flow, travel time, etc., with high spatial and temporal resolutions of 15m and 2h, respectively. It also allows for evaluation of the average vehicle dynamic status as the speed-time-acceleration profile of the roads. Vehicle telematics data for this study were collected for the years 2016 and 2018 through the WM-AIR projct (www.wm-air.org.uk). We then use real-world fleet composition and exhaustive emission measurements to translate the vehicle dynamics status into the real-urban fuel consumption and CO₂ and NO_x emission factors. Results highlight the importance on fleet renovation, in terms of vehicle propulsion systems (EURO class, fuel type, etc.) upon real-urban emissions and fuel consumption. The presentation will end with example future use cases of telematics data within digital twins.            

How to cite: Ghaffarpasand, O. and Pope, F.: Using vehicle telematics data within a digital twin of urban transport systems; a case study in the West Midlands, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5315, https://doi.org/10.5194/egusphere-egu23-5315, 2023.

OpenStreetMap (OSM) is the largest crowd-sourced mapping effort to date, with an infrastructure network that is considered near-complete. The mapping activities started as any crowd-sourced information platform: the community expanded OSM anywhere there was a collective interest. Initial efforts were found around universities, hometowns of mappers and areas designated by organizations like the Humanitarian OSM Team (HOT). This resulted in a map that is of non-uniform completeness, with some areas having all building footprints in, while other areas remain incomplete or even untouched. Currently, with 530 million footprints, OSM identifies between a quarter and half of the total building footprints in the world, if we estimate that there are around 1-2 billion buildings in the world.

A global view on the completeness of buildings existing in OSM did not yet exist. Unlike other efforts, that only look at a subset of OSM building data (Biljecki & Ang 2020; Orden et al., 2020; Zhou et al., 2020), we have used the Global Human Settlement Layer (GHSL) to estimate completeness of the entire dataset. The remote sensing dataset is distributed onto a grid and in each tile of the grid, the built area of GHSL is compared to the total area of OSM building footprints. The computed ratio is measured against a completeness threshold that is calibrated using areas that were manually assessed.

Using information derived from remote sensing datasets can be problematic: GHSL does not only measure building footprints: it includes any human-built structures, including infrastructure and industrial areas. Next to that, due to circumstances like imperfect input data or failing algorithms, the dataset is not of the same quality as the crowd-sourced data in OSM in areas that are complete. False positives (i.e. rocky coasts) and false negatives (i.e. buildings missing in mountainous areas) exist in automatically generated data.

Even with these limitations, a comprehensive global completeness assessment is created. The assessment should not be used as ground truth, but rather as reflection on the OSM building dataset as is and as a guideline for priorities for the future. Statistics on regional completeness can be created and the quality of GHSL could be assessed on countries that are considered to be complete, such as France or the Netherlands.

How to cite: Oostwegel, L. J. N., Garcia Ospina, N., Evaz Zadeh, T., Shinde, S., and Schorlemmer, D.: Automatic global building completeness assessment of OpenStreetMap using remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13160, https://doi.org/10.5194/egusphere-egu23-13160, 2023.

Buildings are common components in the urban environment whose 3D information is fundamental for urban hydrometeorological modeling and planning applications. In order to monitor building footprint and height across large areas on a regular basis, recent earth observation research has witnessed promising progress in mapping such information from publicly available satellite imagery by statistical methods using regression between multi-source remotely sensed data and target variables. However, most of them often involve tedious feature preprocessing, which constrains their capability to establish a comprehensive representation of an ever-changing and multi-scale urban system efficiently.

Considering this bottleneck, this work develops a deep-learning-based (DL) Python package-SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel Imagery) to estimate 3D building information at various scales. SHAFTS provides Convolutional Neural Networks (CNN) with the Multi-Branch Multi-Head (MBMH) structure to automatically learn representative features shared by building height and footprint mapping tasks from multi-modal Sentinel imagery and additional background DEM information. Besides, to leverage the power of big data infrastructures, SHAFTS offers essential functionality including automatically collecting potential reference datasets by web scraping and filtering appropriate input imagery from Google Earth Engine, which can effectively ease model upgrading and deployment for large-scale mapping.

To evaluate the patch-level prediction skills and city-level spatial transferability of developed models, this work performs diagnostic performance comparisons in 46 cities worldwide by using conventional machine-learning-based (ML) models and CNN with the Multi-Branch Single-Head (MBSH) structure as benchmarks. Patch-level results show that DL models successfully produce more discriminative feature representation and improve the coefficient of determination of building height and footprint prediction over ML models by 0.27-0.63, 0.11-0.49, respectively. Moreover, stratified error assessment reveals that DL models effectively mitigate severe systematic underestimation of ML models in the high-value domain. Additionally, within the DL family, comparison in spatial transferability demonstrates that the MBMH structure improves the accuracy of CNN and reduces the uncertainty of building height predictions in the high-value domain at the refined scale. Therefore, multi-task learning can be considered as a possible solution for improving the generalization ability of models for 3D building information mapping.

How to cite: Li, R., Sun, T., Tian, F., and Ni, G.: SHAFTS – A deep-learning-based Python package for Simultaneous extraction of building Height And Footprint from Sentinel Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4192, https://doi.org/10.5194/egusphere-egu23-4192, 2023.

Building energy use is one of the largest global demands,  accounting for 36% of final energy use and 39% of energy and process-related carbon dioxide (CO2) emissions. Green plans are the recommended planning technique for reducing the energy demand of buildings without changing the current built environment. This study investigates the effect of neighbourhood features on the energy performance of buildings. On the basis of building age and tree density, four typical Dublin city centre neighbourhoods are chosen to generate simulations. Surface Urban Energy and Water Balance Scheme (SUEWS) was used to generate the forcing climate data surrounding the neighbourhood, which was then fed into Integrated Environmental Solutions Virtual Environment (IES VE) as the meteorological data for conducting building energy simulations. The results showed that the fraction of trees plays an important role in wind speed in neighbourhoods. Incorporating the missing neighbourhood signature into the forcing data for building energy modelling improves the simulation's efficiency and precision. This study illustrates the importance of considering the local climate while simulating building energy efficiency.

How to cite: Ren, Z., Mills, G., and Pilla, F.: Urban meteorological forcing data for building energy simulations at a neighbourhood scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15145, https://doi.org/10.5194/egusphere-egu23-15145, 2023.

Building-integrated photovoltaic technology (BIPV) has been proven as an effective way to increase renewable energy in the urban environment. Without occupying any land resources, this technology has great potentials for achieving low carbon in the economically developed cities. Due to the lack of modelling tools, the impact of BIPV window in the street canyon is not well understood. To fill the gap, we developed a new parameterization scheme for BIPV window, and incorporated it into building energy simulations coupled with a single-layer urban canyon model. Model evaluation suggests that the coupled model is able to reasonably capture the diurnal profiles of BIPV window temperature and power generation, building cooling load, and outdoor microclimate. Canyon aspect ratio, window coverage, façade orientation, and power generation efficiency are found to be the most critical factors in maximizing the power generation of BIPV windows. Simulation results of an office floor in three Chinese cities under different climate backgrounds show that Beijing has the greatest solar potential in south orientation for power generation, which is 1.5 times the power generation in Shenzhen and Nanjing. Compared to clear window, BIPV window has positive benefits when window coverage is greater than 60% in open canyon. With lighting energy saving and power generation, BIPV window consistently has positive benefits than wall materials. The benefit of BIPV windows is larger in Beijing, followed by Shenzhen and Nanjing. Under future climate forcing of year 2050, the net electricity benefit of BIPV window will be larger than 15%. Findings in this study provide guidance for BIPV application in the built environment, and cast light on the construction of sustainable and low-carbon neighborhoods.

How to cite: yang, J., Chen, L., and Zheng, X.: Quantifying the BIPV window benefit in urban environment under climate change: a comparison of three Chinese cities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-970, https://doi.org/10.5194/egusphere-egu23-970, 2023.

Heating and cooling of buildings is one of the largest final energy uses and largest sources of greenhouse gas emissions. To reduce the impact of heating and cooling on our climate, more efficient strategies are needed. Coupling and centralizing the production of heat and cold in combination with underground seasonal thermal storage (UTES) can significantly reduce CO₂ emissions and costs. To plan and implement such strategies for heating and cooling, information on sources and sinks of heat and cold is essential for local authorities. However, spatial information on the cooling sector is rare and difficult to obtain. Often, the theoretical cooling demand of specific buildings and building types is modeled, but not met by air-conditioning equipment in reality. On the other hand, large-scale cooling demand models, which focus on entire countries, may use data from different countries as proxy or are not applicable below kilometer-scale.

In this study, we present a method to identify air-conditioning equipment on the rooftops of buildings and quantify their cooling capacity. Thus, air-cooled and hybrid evaporative condensers, cooling towers and packaged rooftop units are detected on aerial images. Using manufacturer data, regression analyses are created to estimate the cooling capacity based on the size of the units and the number of condenser fans. The unit locations and all required parameters are obtained by convolutional neural network-based pixel classification models, which are easily executable within a geographical information system (GIS) framework. The approach is successfully evaluated by testing the capability of the detection models and comparing our estimated cooling capacities to the actual installed cooling capacities of air-conditioners for different locations. The detection performance strongly depends on the resolution of the used aerial images. At a resolution of 8 cm/pixel, the model detects 93% of the units and the pixel classification overestimates the relevant parameters for the regression by 0.7%. Using the regression analyses, the overall capacity in the evaluated areas is overestimated by 7-21%. To demonstrate the capability of our approach, we map the cooling capacity of air-conditioners in parts of Manhattan. In the Manhattan financial district alone, a cooling capacity of over 2 GW is estimated, which is equivalent to 1.3% of the summer peak load demand of the energy grid of the entire state of New York.

The presented approach is a fast and easy to conduct method that requires little input data. It can detect individual air conditioners over large areas. The obtained information can support the creation of cooling cadastres and can serve as supplement or validation for other cooling demand models, such as building stock models, or example to include additional building types, such as industrial buildings.

How to cite: Barth, F., Schüppler, S., Menberg, K., and Blum, P.: Estimating installed cooling capacities on city scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1969, https://doi.org/10.5194/egusphere-egu23-1969, 2023.

Knowledge creation is essential to encourage regional development especially for areas in regional transition. The positive impact of university-enterprise cooperation on the process has been extensively discussed in previous studies, but a comprehensive picture of the mechanism has not been fully described, such as how academic researches promote practical projects. Modeling this complex nature has been dealt and required by both academia and planner. Toward this target, we propose an empirically founded agent-based model to demonstrate the collaborative network. It uses the literature and patents created in each project as a basis for measuring regional knowledge production and innovation. Based on the analysis of the observed data, a conceptual model of network-based university-enterprise cooperation was constructed with the help of NetLogo. It will simulate the whole process from academic research to industrial practice to explore the driving mechanism of universities for regional knowledge production and innovation. The collaborative network of practice projects is constructed from the partnership data of every project recorded within the REVIERa platform, where each node in the network was classified into fields of research. Knowledge was quantified by metrics such as the quantity and quality of literature. Based on the various characteristics of nodes, networks and their path dependencies, the birth of innovative projects will be simulated and the impact of interdisciplinary on regional transformation will be quantified in the model. The model is based on the real world data and corroborated with it to capture the mechanism and characteristics of this complex process, showing its value to boost the scientific regional planning in the future.

How to cite: Feng, W. and Li, B.: A Network-Based Model for Simulating Regional Transformation Driven by University-Enterprise Collaboration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1091, https://doi.org/10.5194/egusphere-egu23-1091, 2023.

It is predicted that over 60% of the urban population in sub-Saharan Africa (SSA) lives in slums, and this number is increasing in the coming years. However, issues on urban poverty and slum persistence in SSA cities are ignored and suffered from a paucity of robust evidence for a longtime. As reliable data on where are locations of urban deprived areas and slums, and on how these areas have evolved remain scarce, the scale of urban deprivation and challenges related to slums in SSA cities are underestimated.

This study explores to which extent urban morphology and accessibility of social services could explain urban poverty and slum locations, by using geospatial and socio-economic data, as well as machine learning techniques. Taking four African countries including Nigeria, Kenya, Ghana, and Malawi as examples, we mapped slum locations and demonstrate that urban building morphological variables only can explain up to over 78% of slum locations. Our results further showed a declining trend in slum growth in old towns that are compacted in space. However, slums are not representing the most deprived urban area, while outskirts of megacities, middle-sized and small cities showed the least economic well-being, demonstrated by lower GDP and wealth index value; poor road and water access services. Our proposed slum and urban poverty mapping methods and results will be accessible and instrumental for scientists, local communities, policy-makers, and city planners, which will accelerate the process of finding solutions for tackling poverty, better managing public health and infrastructure in developing countries.

How to cite: Li, C., Yu, L., and Hong, J.: Monitoring slum and urban deprived area in sub-Saharan Africa using geospatial and socio-economic data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10872, https://doi.org/10.5194/egusphere-egu23-10872, 2023.

The potential to deliver better, more efficient and sustainable cities has motivated recent research on Digital Twins (DTs) that seek to support planning decisions by displaying analytical evidence to inform collaborative design actions. Prior research identifies different types of DT tools and gives recommendations for their use, but it has not been grounded in engaged research that co-designs DTs with planning users from the context description and problem formulation stage. We report on a research project to co-create DTs with local government planners to visualize interactions between water resources and housing development. We describe co-creating two different DTs starting from the context and problem for water management and assess the steps against these three categories. While engaging with the field we built on prior studies to identify a set of categories relevant to co-creating and assessing DTs: context, governance, spatial and technical definition.  By recording the steps of the DT design process, and contrasting the results with the theoretical proposals, we develop a three-step framework for the co-creation of DTs. This step-by-step framework, illustrated by examples, provides a contribution to the literature on the co-design of DT, and we conclude by discussing implications for practitioners and areas for further research.  

How to cite: Rico Carranza, E., Mijic, A., and Whyte, J.: Co-Creating Digital Twins for Planning of Water Resources and Housing Development, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17338, https://doi.org/10.5194/egusphere-egu23-17338, 2023.