2020, Pandolfi, Marco, Mooibroek, Dennis, Hopke, Philip, van Pinxteren, Dominik, Querol, Xavier, Herrmann, Hartmut, Alastuey, Andrés, Favez, Olivier, Hüglin, Christoph, Perdrix, Esperanza, Riffault, Véronique, Sauvage, Stéphane, van der Swaluw, Eric, Tarasova, Oksana, Colette, Augustin

Here we report results of a detailed analysis of the urban and non-urban contributions to particulate matter (PM) concentrations and source contributions in five European cities, namely Schiedam (the Netherlands, NL), Lens (France, FR), Leipzig (Germany, DE), Zurich (Switzerland, CH) and Barcelona (Spain, ES). PM chemically speciated data from 12 European paired monitoring sites (one traffic, five urban, five regional and one continental background) were analysed by positive matrix factorisation (PMF) and Lenschow's approach to assign measured PM and source contributions to the different spatial levels. Five common sources were obtained at the 12 sites: sulfate-rich (SSA) and nitrate-rich (NSA) aerosols, road traffic (RT), mineral matter (MM), and aged sea salt (SS). These sources explained from 55 % to 88 % of PM mass at urban low-traffic-impact sites (UB) depending on the country. Three additional common sources were identified at a subset of sites/countries, namely biomass burning (BB) (FR, CH and DE), explaining an additional 9 %-13 % of PM mass, and residual oil combustion (V-Ni) and primary industrial (IND) (NL and ES), together explaining an additional 11 %-15 % of PM mass. In all countries, the majority of PM measured at UB sites was of a regional+continental (R+C) nature (64 %-74 %). The R+C PM increments due to anthropogenic emissions in DE, NL, CH, ES and FR represented around 66 %, 62 %, 52 %, 32 % and 23 %, respectively, of UB PM mass. Overall, the R+C PM increments due to natural and anthropogenic sources showed opposite seasonal profiles with the former increasing in summer and the latter increasing in winter, even if exceptions were observed. In ES, the anthropogenic R+C PM increment was higher in summer due to high contributions from regional SSA and V-Ni sources, both being mostly related to maritime shipping emissions at the Spanish sites. Conversely, in the other countries, higher anthropogenic R+C PM increments in winter were mostly due to high contributions from NSA and BB regional sources during the cold season. On annual average, the sources showing higher R+C increments were SSA (77 %-91 % of SSA source contribution at the urban level), NSA (51 %-94 %), MM (58 %-80 %), BB (42 %-78 %) and IND (91 % in NL). Other sources showing high R+C increments were photochemistry and coal combustion (97 %-99 %; identified only in DE). The highest regional SSA increment was observed in ES, especially in summer, and was related to ship emissions, enhanced photochemistry and peculiar meteorological patterns of the Western Mediterranean. The highest R+C and urban NSA increments were observed in NL and associated with high availability of precursors such as NOx and NH3. Conversely, on average, the sources showing higher local increments were RT (62 %-90 % at all sites) and V-Ni (65 %-80 % in ES and NL). The relationship between SSA and V-Ni indicated that the contribution of ship emissions to the local sulfate concentrations in NL has strongly decreased since 2007 thanks to the shift from high-sulfur-to low-sulfur-content fuel used by ships. An improvement of air quality in the five cities included here could be achieved by further reducing local (urban) emissions of PM, NOx and NH3 (from both traffic and non-traffic sources) but also SO2 and PM (from maritime ships and ports) and giving high relevance to non-urban contributions by further reducing emissions of SO2 (maritime shipping) and NH3 (agriculture) and those from industry, regional BB sources and coal combustion. © 2020 Copernicus GmbH. All rights reserved.

2015, Matczak, P., Lewandowski, J., Choryński, A., Szwed, M., Kundzewicz, Z.W.

2020, Milojevic-DupontI, Nikola, Hans, Nicolai, Kaack, Lynn H., Zumwald, Marius, Andrieux, François, de Barros Soares, Daniel, Lohrey, Steffen, PichlerI, Peter-Paul, Creutzig, Felix

Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.