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This document is the deliverable D3.4 of the CURE (Copernicus for Urban Resilience in Europe) Project. The goal of this deliverable is to fully describe the development process of the 11 CURE Cross-Cutting Applications, including the list of Copernicus and non-Copernicus data used as input, the algorithms and methods adapted for the needs of CURE, example of resulting products for the CURE front-runner and follower cities and the methods evaluation and the dockerization process.

This document constitutes a digital collection of all sample products from the developed cross-cutting applications. The complete Urban Cross-cutting Applications Sample Dataset can be found in https://doi.org/10.5281/zenodo.5812049. The purpose of this document is to describe the database of sample products from the cross-cutting applications developed in CURE and to define the data specifications.

This document aims to describe the final updated methodology of the CURE Cross-Cutting Applications (after their first description in Deliverable D3.1) and to describe and explain in more detail the sample results for the front-runner and follower cities that are submitted in Deliverable D3.3.

This document aims to describe in more detail the CURE cross-cutting applications and to summarize the preparatory activities for the individual applications development, following the first description in Methodology Review and Selection Deliverable (D1.3). The purpose of each CURE application, the data and methodology used, as well as the output features and characteristics are presented. Preliminary results of some applications for the front-runner cities are demonstrated, where possible, and feedback is provided on the stakeholder needs, as identified in Summary of User Requirements Deliverable (D1.1).

Air pollution remains a key environmental problem in an increasingly urbanized world. To quantify health impacts and support informed policies, the population exposure needs to be accurately monitored. However, the inherent spatial variability of air quality poses a tenacious challenge to this. While concentrations of traffic-related pollutants like nitrogen dioxide (NO2) are known to vary over short distances, official monitoring networks remain inherently sparse, as reference stations are costly to construct and operate. Here we show that citizen science provides an cost-effective way to collect large, spatially distributed datasets that critically complement official monitoring.

We are presenting an evaluation of the outcomes of the FAIRMODE & AQUILA intercomparison exercise (IE) on spatial representativeness (SR). The assessment of the spatial representativeness (SR) of air quality monitoring stations is an important subject that is linked to several highly topical areas, including risk assessment and population exposure, the design of monitoring networks, model development, model evaluation and data assimilation. Nevertheless, European regulations lack a clear definition and provisions to determine the SR of the stations. Also in the scientific literature, there is no unified agreement to address this complex problem. In order to further explore this topic and to make progress in the harmonization of the related assessment procedures, the FAIRMODE (Forum for Air Quality Modelling in Europe) Cross-Cutting Activity group on SR organized a comprehensive intercomparison exercise (IE). The main objective of this IE was to evaluate the scattering of spatial representativeness results obtained by applying the range of different contemporary approaches to a jointly used example case study. In order to ensure a broad participation in this exercise, a collaborative effort has been established between FAIRMODE and AQUILA (the European Network of Air Quality Reference Laboratories). As a working basis, a shared dataset has been collected among a set of monitoring, emission and modelling data from the city of Antwerp. Within this IE, 11 different teams from 9 different countries provided their SR estimates for PM10 and NO2 at one traffic site, and for PM10, NO2 and O3 at two urban background sites. In order to narrow down the range of conceivable SR approaches and definitions, it was beforehand suggested to use the area of SR of the monitoring sites as a general concept to work with. During the course of the exercise, this concept of the SR area in fact turned out to be a useful indicator, and 10 of 11 teams were able to define shapes surrounding the stations under investigation, whereas one team rather worked towards a classification of the stations, as this was more common practice for SR evaluation in their member state. The resulting SR areas nevertheless revealed a considerable range of dissimilarity between the different teams - not only in terms of the extent and position of the SR perimeters, but also in the technical procedures and the extent of input data effectively used. These differences required detailed evaluations in order to identify the major factors triggering and controlling this spread, which can be found amongst (1) the basic principles of the methods, (2) the parameterization of the similarity criteria and thresholds, (3) the effective use of input data, and (4) the detailed conceptualization and definitions of SR. These outcomes do underline the need for (i) a more harmonized definition of the concept of “the area of representativeness” and (ii) consistent and transparent criteria used for its quantification. A comprehensive concluding section (chapter 10) is highlighting the challenges that the expert community working on spatial representativeness is currently facing. Recommendations are given for the directions to be focused on SR in the near to mid-term future. In this regards, we are outlining a roadmap towards a modular approach for better SR characterization. It is stressed that that for the aim of harmonization the concept of spatial representativeness will probably require a paradigm shift in its definition. Beyond the questions of harmonization, it should not be disregarded that alternative interpretations for the strong variability of the SR results might exist. The observed divergences in SR results could for example point us to some more fundamental discrepancies related to the evaluation of the air quality data. It is advised to take care, that in the endeavour for methodological harmonization such alternative explanations are not overlooked. An example could be a potential inconsistency within the input data coming from emission, monitoring and modelled data. Furthermore, the findings of this study are not only relevant with regard to the SR of a single monitoring station. It also gives evidence that questions need to be raised about what is the real representativeness of network monitoring data in general since it seems that there is no current consensus on its evaluation. Example given: Is there a need for the European Commission to check the criteria for the number and the siting of Air Quality Monitoring Stations set in the Air Quality Directive when a consensus on SR is reached?, JRC.C.5-Air and Climate

This report contains the results of the advanced numerical models and on-site measurements on the impacts of adaptation on urban climate through meso and microscale analyses conducted by RAMSES researchers on the case study cities of Antwerp, Paris and Delhi at the mesoscale, and Antwerp and Bilbao at the micro-scale. The outcomes are design and policy guidelines for new and consolidated urban areas.

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 (250m) 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 100m 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.