We present new advances in monitoring particulate matter (PM) in urban areas within a participatory vehicle sensor network (VSN) that exploits the use of multiple mobile low-cost IoT devices. These devices send geolocated PM measurements to an IT infrastructure and enabled us to reconstruct, in real time, the spatial and temporal distribution of pollutants in the study area in a web-based environment. The newly acquired data were integrated with independent reference measurements available from governmental environmental agencies. We deployed the infrastructure in the city of Trieste (Italy), since the beginning of 2021, with the help of several volunteers and the local transportation authority (Trieste Trasporti). By analysing the data, we delineate areas with lower air quality and identify the possible causes of these anomalies. We were able to define a belt outside the urban center where an enhanced concentration of pollutants occurs due to a higher flux of vehicular traffic that tends to jam there. Overall, our results demonstrate that this approach can be helpful in supporting urban planning and can also stimulate the community to reflect on how they can improve air quality in the area they live by reducing the use of private cars in favour of more widespread public transportation usage.

Marine life can be severely affected by anthropogenic underwater noise. This latter increased proportionally to the rise of human activities such as maritime traffic, marine civil engineering works, oil- and gas-related activities or offshore wind farms; so much so that, currently, it can be considered a threat to the environment. Assessing underwater noise requires quite some investments both in personnel and instrumentation. If this is affordable by several governmental and scientific organizations, this cannot be extended straightforwardly to all research initiatives or to developing countries. In addition, time and geographic coverage of monitoring can also be significantly limited by the costs of multiple installations. We explore the possibility to use a solution based on off-the-shelf and low-cost technologies combined with a scalable infrastructure developed with open-source tools only. The perspective to avoid proprietary solutions allows great flexibility in extending the current paradigm toward real-time transmission, processing, and web-based data access. Our solution has been deployed at sea in November 2020 and is providing data continuously ever since. First results from the analysis of these data allowed us to highlight several interesting abiotic and anthropogenic temporal patterns.

This work aims at understanding whether a citizen science-based monitoring system could be adequate to detect the effects, in terms of air quality, of solid and liquid fuel combustion for household heating. Citizen science is known to be able to improve the coverage and resolution of measurements at a very low cost. On the other hand, it also has severe limitations. Since low-cost sensors are to be used, measurements are problematic in terms of precision and accuracy. In order to test these aspects, we developed a system named COCAL that supports all the phases of air quality monitoring, from data acquisition, georeferencing, transmission, and processing up to web mapping. In this work, we focus on particulate matter. To address the limitations of the citizen science approach, we carefully tested all the parts of the system and, in particular, the performances of the low-cost sensors. We highlighted that their precision is acceptable, while their accuracy is insufficient. Measurements taken within such a paradigm cannot be used, therefore, as reference values. They can be used, instead, as relative values, in order to identify and to map trends, anomalies and hotspots. We used COCAL extensively in the city of Trieste and were able to identify different behaviors in different areas of the city. In the city center, PM values increase constantly during the day. In the rural suburbs of the city, we observed that PM values are low during the day but increase very rapidly after 5 p.m. It is important to note that, in the city center, household heating is based almost completely on natural gas. In the rural areas, household heating is generally based on wood burning stoves or liquid and solid fuel. A possible explanation of the different behavior between the two areas can then be related to commuters living in the rural areas but working in the city center. When they return home in the evening, they switch on the heating systems triggering the release of large quantities of particulate matter. We were able to map peaks of particulate matter values and highlight that they are initially located within the village centers to later propagate to the areas around them. The possibility of mapping air quality with the coverage and resolution we were able to obtain within a citizen science approach is very encouraging. This can be very helpful in understanding the impact that liquid and solid fuel combustion can have on the environment and human health. In addition, we think that this opportunity can be very important considering the current geopolitical situation where a (hopefully only temporary) shift toward pollutant fuels is expected in the near future.

This work reports on the development of a real-time vehicle sensor network (VSN) system and infrastructure devised to monitor particulate matter (PM) in urban areas within a participatory paradigm. The approach is based on the use of multiple vehicles where sensors, acquisition and transmission devices are installed. PM values are measured and transmitted using standard mobile phone networks. Given the large number of acquisition platforms needed in crowdsensing, sensors need to be low-cost (LCS). This sets limitations in the precision and accuracy of measurements that can be mitigated using statistical methods on redundant data. Once data are received, they are automatically quality controlled, processed and mapped geographically to produce easy-to-understand visualizations that are made available in almost real time through a dedicated web portal. There, end users can access current and historic data and data products. The system has been operational since 2021 and has collected over 50 billion measurements, highlighting several hotspots and trends of air pollution in the city of Trieste (north-east Italy). The study concludes that (i) this perspective allows for drastically reduced costs and considerably improves the coverage of measurements; (ii) for an urban area of approximately 100,000 square meters and 200,000 inhabitants, a large quantity of measurements can be obtained with a relatively low number (5) of public buses; (iii) a small number of private cars, although less easy to organize, can be very important to provide infills in areas where buses are not available; (iv) appropriate corrections for LCS limitations in accuracy can be calculated and applied using reference measurements taken with high-quality standardized devices and methods; and that (v) analyzing the dispersion of measurements in the designated area, it is possible to highlight trends of air pollution and possibly associate them with traffic directions. Crowdsensing and open access to air quality data can provide very useful data to the scientific community but also have great potential in fostering environmental awareness and the adoption of correct practices by the general public.

Marine research is as important as very demanding since it requires expensive infrastructures and resources. Scientific institutions, on the contrary, have very limited funding so that the seas remain, still, mostly unexplored. Another serious concern is that society at large often resonates with fake news, while scientists sometimes tend to bias research with their