• Energy Research

Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To this end, in this paper we propose a conceptual list of EO-based indicators capable of supporting urban planning and management. Three cities with different typologies, namely Basel, Switzerland; Tel Aviv, Israel; and Tyumen, Russia were selected as case studies. The EO-based indicators are defined to effectively record the physical properties of the urban environment in a diverse range of environmental sectors such as energy efficiency, air pollution and public health, water, transportation and vulnerability to hazards. The results assess the potential of EO to support the development of a set of urban environmental indicators towards sustainable urban planning and management.

Comprendre les relations entre le climat et l'échange de carbone par les écosystèmes terrestres est essentiel pour prédire les niveaux futurs de dioxyde de carbone atmosphérique en raison des effets d'accélération potentiels des rétroactions positives du cycle climat-carbone. Cependant, les relations directement observées entre le climat et l'échange de CO2 terrestre avec l'atmosphère à travers les biomes et les continents font défaut. Nous présentons ici des données décrivant les relations entre l'échange net de carbone par les écosystèmes (NEE) et les facteurs climatiques tels que mesurés à l'aide de la méthode de covariance de Foucault sur 125 sites uniques dans divers écosystèmes sur six continents avec un total de 559 années de site. Nous trouvons que le NEE observé aux sites de covariance tourbillonnaire est (1) une fonction forte de la température annuelle moyenne aux latitudes moyennes et élevées, (2) une fonction forte de la sécheresse aux latitudes moyennes et basses, et (3) une fonction à la fois de la température et de la sécheresse autour de la ceinture moyenne-latitudinale (45°N). La sensibilité du NEE à la température annuelle moyenne se décompose à ~ 16 °C (une valeur seuil de la température annuelle moyenne), au-delà de laquelle aucune augmentation supplémentaire de l'absorption de CO2 avec la température n'a été observée et la sécheresse influence les règles de dépassement de l'influence de la température. Comprender las relaciones entre el clima y el intercambio de carbono por parte de los ecosistemas terrestres es fundamental para predecir los niveles futuros de dióxido de carbono en la atmósfera debido a los posibles efectos aceleradores de las retroalimentaciones positivas del ciclo clima-carbono. Sin embargo, faltan relaciones directamente observadas entre el clima y el intercambio terrestre de CO2 con la atmósfera a través de biomas y continentes. Aquí presentamos datos que describen las relaciones entre el intercambio neto de carbono (NEE) del ecosistema y los factores climáticos medidos utilizando el método de covarianza de remolinos en 125 sitios únicos en varios ecosistemas de seis continentes con un total de 559 años-sitio. Encontramos que la NEE observada en los sitios de covarianza de remolinos es (1) una fuerte función de la temperatura media anual en latitudes medias y altas, (2) una fuerte función de sequedad en latitudes medias y bajas, y (3) una función tanto de la temperatura como de la sequedad alrededor del cinturón latitudinal medio (45°N). La sensibilidad de NEE a la temperatura media anual se rompe a ~ 16 °C (un valor umbral de la temperatura media anual), por encima del cual no se observó ningún aumento adicional de la absorción de CO2 con la temperatura y la influencia de la sequedad anula la influencia de la temperatura. Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate–carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid- and high-latitudes, (2) a strong function of dryness at mid- and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45°N). The sensitivity of NEE to mean annual temperature breaks down at ~ 16 °C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence. يعد فهم العلاقات بين المناخ وتبادل الكربون بواسطة النظم الإيكولوجية الأرضية أمرًا بالغ الأهمية للتنبؤ بالمستويات المستقبلية لثاني أكسيد الكربون في الغلاف الجوي بسبب التأثيرات المتسارعة المحتملة للتغذية المرتدة الإيجابية لدورة المناخ والكربون. ومع ذلك، لا توجد علاقات ملحوظة مباشرة بين المناخ والتبادل الأرضي لثاني أكسيد الكربون مع الغلاف الجوي عبر المناطق الحيوية والقارات. نقدم هنا بيانات تصف العلاقات بين صافي تبادل النظام البيئي للكربون (NEE) والعوامل المناخية كما تم قياسها باستخدام طريقة التباين الدوامي في 125 موقعًا فريدًا في أنظمة بيئية مختلفة عبر ست قارات بإجمالي 559 سنة موقع. نجد أن NEE التي لوحظت في مواقع التباين الدوامي هي (1) وظيفة قوية لمتوسط درجة الحرارة السنوية عند خطوط العرض المتوسطة والعالية، (2) وظيفة قوية للجفاف عند خطوط العرض المتوسطة والمنخفضة، و (3) وظيفة لكل من درجة الحرارة والجفاف حول حزام العرض المتوسط (45درجةشمالاً). تنهار حساسية NEE لمتوسط درجة الحرارة السنوية عند حوالي 16 درجة مئوية (قيمة عتبة لمتوسط درجة الحرارة السنوية)، والتي لم يلاحظ فوقها أي زيادة أخرى في امتصاص ثاني أكسيد الكربون مع درجة الحرارة ويتجاوز تأثير الجفاف تأثير درجة الحرارة.

Achieving climate neutrality by 2050 requires ground-breaking technological and methodological advancements in climate change mitigation planning and actions from local to regional scales. Monitoring the cities' CO2 emissions with sufficient detail and accuracy is crucial for guiding sustainable urban transformation. Current methodologies for CO2 emission inventories rely on bottom-up (BU) approaches which do not usually offer information on the spatial or temporal variability of the emissions and present substantial uncertainties. This study develops a novel approach which assimilates direct CO2 flux observations from urban eddy covariance (EC) towers with very high spatiotemporal resolution information from an advanced urban BU surface flux model (Part 1 of this study, Stagakis et al., 2023) within a Bayesian inversion framework. The methodology is applied to the city centre of Basel, Switzerland (3 × 3 km domain), taking advantage of two long-term urban EC sites located 1.6 km apart. The data assimilation provides optimised gridded CO2 flux information individually for each urban surface flux component (i.e. building heating emissions, commercial/industrial emissions, traffic emissions, human respiration emissions, biogenic net exchange) at 20 m resolution and weekly time-step. The results demonstrate that urban EC observations can be consistently used to improve high-resolution BU surface CO2 flux model estimations, providing realistic seasonal variabilities of each flux component. Traffic emissions are determined with the greatest confidence among the five flux components during the inversions. The optimised annual anthropogenic emissions are 14.7 % lower than the prior estimate, the human respiration emissions have decreased by 12.1 %, while the biogenic components transformed from a weak sink to a weak source. The root-mean-square errors (RMSEs) of the weekly comparisons between EC observations and model outputs are consistently reduced. However, a slight underestimation of the total flux, especially in locations with complex CO2 source/sink mixture, is still evident in the optimised fluxes.

Monitoring carbon dioxide (CO2) emissions of urban areas is increasingly important to assess the progress towards the Paris Agreement goals for climate neutrality. Cities are currently voluntarily developing their local inventories, however, the approaches used across different cities are not systematically assessed, present consistency issues, neglect the biogenic fluxes and have restricted spatial and temporal resolution. In order to assess the accuracy of the urban emission inventories and provide information which is useful for planning local climate change mitigation actions, high resolution modelling approaches combined or evaluated with atmospheric observations are needed. This study presents a new high-resolution bottom-up (BU) model which provides hourly maps of all major components contributing to the local urban surface CO2 flux (i.e. building emissions, traffic emissions, human respiration, soil respiration, plant respiration, plant photosynthetic uptake) and can therefore be used for direct comparison with in-situ atmospheric observations and development of local scale atmospheric inversion methodologies. The model design aims to be simple and flexible using inputs that are available in most cities, facilitating transferability to different locations. The inputs are primarily based on open geospatial datasets, census information, road traffic monitoring and basic meteorological parameters. The model is applied on the city centre of Basel, Switzerland, for the year 2018 and the results are compared to a local inventory. It is demonstrated that the model captures the highly dynamic spatiotemporal variability of the urban CO2 fluxes according to main environmental drivers, population activity dynamics and geospatial information proxies. The annual modelled emissions from buildings and traffic are estimated 14.8 % and 9 % lower than the respective information derived by the local inventory. The differences are mainly attributed to the emissions from the industrial areas and the highways which are beyond the geographical coverage of the model.