Port cities have long played a key role in the development, discovery, and fight against diseases. They have been laboratories for policies to address public health issues. Diseases reached port cities through maritime exchanges, and the bubonic plague is a key example. Port city residents’ close contact with water further increased the chance for diseases such as cholera. Analyzing three European port cities, this article first explores the relevance of water quality for public health through the lens of the Dutch city of Rotterdam. It then examines plans and projects for London that were shaped by social Darwinism and stressed the moral failings of slum dwellers as a major cause for their misery. It finally explores the case of Hamburg as the perfect example of a city that cultivated ideals of purity and cleanliness by addressing all issues at stake in public health. This article on urban hygiene in three port cities shows how remarkably rich this field of study is; it also demonstrates that the multifaceted aspects of public health in port cities require further attention.@en

Port spaces, functions, and interests have shaped the growth and development of many cities around the world. At times, different stakeholders—private and public, local, regional, national and global—have collaborated to assure the continuity of port functions in old and new locations and, if the port relocates or if that effort fails, to redevelop former port spaces. Through the lens of port- and city-related urban developments in London, Hamburg, and Philadelphia, this article explores the multiple conditions that are part of port city resilience. It uses historical institutionalism as a theoretical framework for understanding these long-term changes, particularly in institutional and governance dynamics. It shows that the development paths of port and city spaces and the actors who shape them are not always aligned. Through the case of London, it shows a development path that is led by private investment building and relocating a world-class port and administrating it from the city center, while local and national institutions only intervene to balance spatial or social short-comings of the private actors. The case of the city-state Hamburg illustrates the development of shared port-city paths under long-term public leadership that has provided direction for the expanding port as well as for the growing city. In the case of Philadelphia, national interests, the Navy, and private investments played an important role in the creation of port infrastructure and, later, in the largely failed transformation of former port areas into public waterfronts. As shipping elites left the city and new land-based employers emerged, such as the University of Pennsylvania, the port-city path was partly discontinued. The article concludes by pointing to the expected capacity of each of these cities to address future challenges. Awareness of historical practices can help readers understand where current conditions may stand in the way of innovative solutions.@en

Resilience has become a buzzword used to describe the capacity of cities to bounce back after disasters. It carries the hope of a robust and more sustainable future. Disasters can strike any region, but port cities face complex and particular risks due to their location at the intersection of sea and land, and their role in an international maritime system. This introduction to the special section on resilience, disaster and rebuilding in modern port cities first examines resilience as a concept and course of action in a heterogenous theory landscape. It then explores different dimensions of resilience—environmental, economic, institutional, social and spatial—and their importance in port city historiography. The articles collected in this special section explore case studies from three continents. Together, they demonstrate that there is no such thing as the resilient (port) city. But, they also show that the combination of maritime and urban interests can lead to creative planning for resilience, particularly when port and city authorities pursue the same strategies. In view of contemporary challenges, this special section demonstrates the value of further research on port city resilience and vulnerability. The section raises an important question: Is it possible to balance the wide-ranging economic interests of port actors and their view of waters as sites for shipping with those of local actors concerned about water quality and ecosystems?@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