Nature-based solutions for urban heat island effect mitigation: the case study of Isla, Malta

Global population growth reflects how humans increasingly exploited Earth's resources. Urbanization develops along with anthropization. It is estimated that nearly 60% of the world's population lives in urban areas, which symbolize the denaturalized dimension of current modernity. Cities are artificial ecosystems that suffer most from environmental issues and climate change. The Urban Heat Island (UHI) effect is a common microclimatic phenomenon affecting cities, which causes considerable differences between urban and rural areas temperatures. Among the driving factors, the lack of vegetation in urban settlements can damage both humans and the environment (health diseases, heat waves caused deaths, biodiversity loss, and so on). As the world continues to urbanize, sustainable development increasingly depends on successful management of urban areas. To enhance cities’ resilience, Nature-based Solutions (NbSs), are defined as an umbrella concept that encompasses a wide range of ecosystem-based approaches and actions to climate change adaptation (CCA) and disaster risk reduction (DRR). This paper analyzes a 15-days study on air temperature trends carried out in Isla, a small locality in the Maltese archipelago, and proposes Nature-based Solutions-characterized scenarios to mitigate the Urban Heat Island effect the Mediterranean city is affected by. The results demonstrates how in some areas where vegetation is present, lower temperatures are recorded than in areas where vegetation is absent or scarce. It also appeared that in one location, the specific type of vegetation does not contribute to high temperature mitigation, whereas in another one, different environmental parameters can influence the measurements. Among the case-specific Nature-based Solutions proposed there are vertical greening (green wall, façades, ground based greening, etc.), tree lines, green canopy, and green roofs.

Spatial-temporal variability of the benthic communities in Ravenna harbour using Autonomous Reef Monitoring Structures (ARMS) with focus on Non-Indigenous Species (NIS)

A Non-Indigenous Species (NIS) is defined as an organism, introduced outside its natural past or present range of distribution by humans, that successfully survives, reproduces, and establish in the new environment. Harbors and tourist marinas are considered NIS hotspots, as they are departure and arrival points for numerous vessels and because of the presence of free artificial substrates, which facilitate colonization by NIS. To early detect the arrival of new NIS, monitoring benthic communities in ports is essential. Autonomous Reef Monitoring Structures (ARMS) are standardized passive collectors that are used to assess marine benthic communities. Here we use an integrative approach based on multiple 3-month ARMS deployment (from April 2021 to October 2022) to characterize the benthic communities (with a focus on NIS) of two sites: a commercial port (Harbor) and a touristic Marina (Marina) of Ravenna. The colonizing sessile communities were assessed using percentage coverage of the taxa trough image analyses and vagile fauna (> 2 mm) was identified morphologically using a stereomicroscope and light microscope. Overall, 97 taxa were identified and 19 of them were NIS. All NIS were already observed in port environments in the Mediterranean Sea, but for the first time the presence of the polychaete Schistomeringos cf. japonica (Annenkova, 1937) was observed; however molecular analysis is needed to confirm its identity. Harbor and Marina host significantly different benthic communities, with significantly different abundance depending on the sampling period. While the differences between sites are related to their different environmental characteristic and their anthropogenic pressures, differences among times seems related to the different life cycle of the main abundant species. This thesis evidenced that ARMS, together with integrative taxonomic approaches, represent useful tools to early detect NIS and could be used for a long-term monitoring of their presence.

Correlation between urban form and PM2.5 concentration — Evidence from the New York metropolitan Area and Shanghai city

With the development of the economy and society, air pollution has posed a huge threat to public health around the world, especially to people who live in urban areas. Typically, urban development patterns can be roughly divided into compact cities and urban sprawl. In recent years, the relationship between urban form and air quality (especially PM2.5) is gaining more and more attention from urban planners, environmentalists, and governments. This study is focusing on The New York metropolitan area and Shanghai city, which are both megacities but with different urban spatial forms. For both study areas,there are five main variables to measure the urban form metrics, naming Population Density, Artificial Land Area Per Ten Thousand People, Road Density, Green Land Area Ratio and Artificial Land Area Ratio. In addition, considering the impact of economic activities and public transportation, GDP per capita, Number of bus stop and Number of subway station are used as control variables. Based on the results of regression, a megacity like the New York metropolitan area with urban sprawl shows a low spatial correlation on PM2.5 concentration. Meanwhile, almost all the spatial form indicators effect on PM2.5 concentration is not significant. However, a compact megacity like Shanghai shows a diametrically opposite result. Urban form, especially population density, has a strong relationship with PM2.5 concentration. It can be predicted that a reduction in population density would lead to significant improvements on decrease the PM2.5 concentration in Shanghai. Meanwhile, increasing the ratio of green land and construction area per capita will get a positive influence on reducing PM2.5 concentration as well. Road density is not a significant factor for a megacity in both two urban forms. The way and type of energy used by vehicles on megacities maybe more critical.

Private sensors and crowdsourced rainfall data: accuracy and effectiveness for monitoring severe events and mapping pluvial flooding in urban areas

The increasing number of extreme rainfall events, combined with the high population density and the imperviousness of the land surface, makes urban areas particularly vulnerable to pluvial flooding. In order to design and manage cities to be able to deal with this issue, the reconstruction of weather phenomena is essential. Among the most interesting data sources which show great potential are the observational networks of private sensors managed by citizens (crowdsourcing). The number of these personal weather stations is consistently increasing, and the spatial distribution roughly follows population density. Precisely for this reason, they perfectly suit this detailed study on the modelling of pluvial flood in urban environments. The uncertainty associated with these measurements of precipitation is still a matter of research. In order to characterise the accuracy and precision of the crowdsourced data, we carried out exploratory data analyses. A comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national weather services is presented. The crowdsourced stations have very good skills in rain detection but tend to underestimate the reference value. In detail, the accuracy and precision of crowd- sourced data change as precipitation increases, improving the spread going to the extreme values. Then, the ability of this kind of observation to improve the prediction of pluvial flooding is tested. To this aim, the simplified raster-based inundation model incorporated in the Saferplaces web platform is used for simulating pluvial flooding. Different precipitation fields have been produced and tested as input in the model. Two different case studies are analysed over the most densely populated Norwegian city: Oslo. The crowdsourced weather station observations, bias-corrected (i.e. increased by 25%), showed very good skills in detecting flooded areas.