Based on the understanding of the built environment as result of competing claims on space that must be resolved via recognition, fair distribution of burdens and benefits of our human association, respect and care for the planet and just procedures to decide on those claims, Spatial Planning and Strategy is a chair in the Department of Urbanism within the Faculty of Architecture and the Built Environment of the Delft University of Technology, committed to helping create sustainability, resilience and spatial justice through the implementation of the New Urban Agenda, the Paris Climate Agreement and the European New Deal, among other frameworks. This commitment is reflected in activities, events, and courses. We are concerned with knowledge about the formulation, implementation, and evaluation of strategic and urban planning tools – visions, strategies, plans and programmes.@en

Long-term exposure to ambient air pollution is one of the main public health concerns worldwide. Exposure to air pollution is highly related to a range of diseases including respiratory and cardiovascular diseases, such as lung cancers, asthma, diabetes, irregular heartbeat, stroke and obesity [1-3]. The outbreak of the pathogenic agent of coronavirus disease 19 (Covid-19) has led to a large number of deaths worldwide, and previous studies have pointed out how the long-term exposure to air pollution may have an impact on its high death rate [4]. Moreover, the hospitalization rate and infected population numbers are central indicators for lock-down policy-making, indicating whether the local medical system is able to handle the increasing infected population number through its available intensive care facilities. In fact, predicting hospitalization is vital for authorities and policymakers. We hereby hypothesize that high air pollutants concentration leads to a rise in the hospitalization rate under the influence of Covid-19 outbreaks. We attempt to predict such hospitalization numbers for past data by means of a task-specific optimized machine learning model, after we integrate social, economic, cultural, and other environmental features in future with an ongoing project we are conducting. While such a prediction model cannot directly be used for predicting the future development of the pandemic, analysing it still gives valuable insights on the influence of various environmental features had on it in the past.Air pollution is a mixture of a large number of chemical compounds such as CO2, CO, NOx, SO2, O3, heavy metals, and respirable particulate matter (PM2.5 and PM10); the main sources of such pollutants are identified as vehicle traffic, heating systems, and industrial plants [5]. Previous studies focused on the relationships between the variables of pandemic with the air pollutants information. Among all the air pollutants, NO2 and respirable articulate matter are highly related to the pandemic variables [6-8]. In our research, we extract the air pollutants information (CO, NO2, CH4, SO2) from the Sentinel-5P TROPOMI sensor, and integrate it with open-access data on Covid-19 features (mortality, infection rate, intensive care rate, etc). The air pollutant data is processed from the Sentinel-5P data catalog provided in Google Earth Engine. We therefore aim to ascertain the relationships between hospitalization and air pollutants concentration with the incidence of Covid-19. In particular, our ultimate research purpose is to develop a machine learning model to uncover the relationships between a mixture of features derived from air pollutants and Covid-19 related information, at municipality scales in Germany and the Netherlands. The relationships provide important clues on understanding how air pollution may affect on hospitalization rate and other features of Covid-19, through the evidence of potential low hospitalization or low mortality with better air quality. The output will deliver key information regarding public health effects and control of emission in Germany and the Netherlands. Specifically, on a temporal scale, we aggregated daily Covid-19 data and four air pollutant measures into weekly measures. On a spatial scale, the air pollutants were aggregated based on each municipality in Germany and the Netherlands to match the Covid-19 features. A choice of machine learning models were trained and evaluated on historical data (from March of 2020 to Oct of 2021), using features comprising weekly hospitalizations, death rate, and infected rate, tropospheric NO2 concentration, CO, SO2, CH4 concentrations. In addition, a post-processing analysis using machine-learning explainability methodologies was carried out to mine potential relationships between hospitalization attributes and specific air pollution concentration features. By processing municipalities as separate spatial entities, the results are intended to highlight hospitalization disparities and pollutants’ effect diversities among different geographic areas. By highlighting the relationships between air pollutant concentrations and incidence of Covid-19 with the hospitalization rate, and illustrating the hospitalization disparities among municipalities, our results provide key information regarding policymaking on urban emission control and public health at municipality level. When integrating other Covid-related features, our models could offer support to policymakers on effective lock-down decisions and health system management. Keywords: Air pollutant, Covid-19, supervised machine learning models, Google Earth Engine. Reference 1. Bernstein, J.A., et al., Health effects of air pollution. Journal of allergy and clinical immunology, 2004. 114(5): p. 1116-1123. 2. Brunekreef, B. and S.T. Holgate, Air pollution and health. The lancet, 2002. 360(9341): p. 1233-1242. 3. Strak, M., et al., Long-term exposure to particulate matter, NO2 and the oxidative potential of particulates and diabetes prevalence in a large national health survey. Environment international, 2017. 108: p. 228-236. 4. Ogen, Y., Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality. Science of The Total Environment, 2020. 726: p. 138605. 5. Vineis, P., et al., Air pollution and risk of lung cancer in a prospective study in Europe. International Journal of Cancer, 2006. 119(1): p. 169-174. 6. Gautam, S., COVID-19: air pollution remains low as people stay at home. Air Quality, Atmosphere & Health, 2020. 13: p. 853-857. 7. Vîrghileanu, M., et al., Nitrogen Dioxide (NO2) Pollution monitoring with Sentinel-5P satellite imagery over Europe during the coronavirus pandemic outbreak. Remote Sensing, 2020. 12(21): p. 3575. 8. Omrani, H., et al., Spatio-temporal data on the air pollutant nitrogen dioxide derived from Sentinel satellite for France. Data in Brief, 2020. 28: p. 105089.@en

The great number of actors in port city regions, such as port authorities, municipalities, national governments, private companies, societal groups, and flora and fauna, need to develop shared visions. Collaborative approaches that focus on combined values can help achieve long-term resilience and enable a sustainable and just coexistence of port and city actors within the same territory. However, the sheer focus on economic profit generated by port activities overshadows and ignores equally essential cultural, societal, and environmental values and needs. The lack of pluralities in planning and decision-making processes creates challenges for the cohabitation of the many actors and their interests within port-city regions. On the one hand, contemporary spaces in port cities cannot be classified and defined by traditional dichotomies anymore. On the other hand, the perception of spatial and institutional boundaries between port and city leads to a positivistic-driven definition of a rigid and inflexible, line-like interface physically and mentally separating the port from the urban activities and stakeholders, neglecting the inseparable character of many parts of our society. By investigating and re-imagining the future port-development plans within the historic mining town of Kirkenes, located around 400 km above the Arctic Circle in Northern Norway, the aim of this article is to explore and combine the concepts of negative and positive porosity and liminality and arrive at a renewed perception of the port cityscape, which can function as dynamic thresholds inbetween the multiple dualities and realities of various port and city actors. The article bridges the theoretical/conceptual sphere of urban porosity and the practical approaches of liminal design. By using Design Fiction as a tool for creating new, innovative, and pluralistic port city narratives, the article contributes to contemporary research that aims for imaginary, value-based, and history-informed approaches to designing future-proof, resilient, just, and sustainable port cities.@en