Ricerc@Sapienza

This research analyzes the role that air pollution plays, controlling for other socio-economic factors, in the movement of the population among Italian provinces. In spite of the increased prominence of environmental degradation in the public policy debate, empirical evidence on the effects of air quality on the attraction or retention of people is still limited in Italy. This study aims at partly filling this gap. Specific attention is paid to the environmental risks presented in provinces contaminated by air pollution.

The aim of this observational study was to assess the relationship between environmental risk factors and some aspects of social economic status (SES) of the population in different Italian municipalities. Nitrogen dioxide (NO2) and particulate matter (PM10) annual means were extracted from ISPRA-BRACE (environmental information system of 483 Italian municipalities, 6% of the total amount of administrative units) from 2002 to 2012.

Particulate Matter (PM) is a well-known health risk factor and pollutes both outdoor and indoor air. Using PM as an air pollution indicator, the aims were to assess outdoor and indoor air pollution due to combustion and/or non-combustion products used outdoors and to compare the PM levels emitted by different products.

Editorial

Rationale: Studies suggest a relation between exposure to air particulate matter (PM)2.5 pollution and greater cardiovascular morbidity, as well as increased risk for obesity and diabetes. We aimed to identify association(s) between nutritional and metabolic status and exposure to environmental pollution in a cohort of policemen exposed to high levels of air pollution. Methods: We considered adult municipal policemen, working in an urban area at high-traffic density with documented high levels of air PM2.5 (exposed group) compared to non-exposed policemen.

The recent epidemic of the new SARS-CoV-2 in the northern regions of Italy is putting the organization of the Italian health system under serious attack. The current emergency requires all possible efforts to stem the spread of the virus. In this context, it is clear that we have the urgent need to rely upon etiopathogenetic data, in order to do all possible efforts to block the epidemic. However, observing the trend of the infections in China and the geographic areas of the main outbreaks, it could be hypothesized that air pollution plays a role.

In this paper, we propose a quaternion widely linear approach for the forecasting of environmental data, in order to predict the air quality. Specifically, the proposed approach is based on a fusion of heterogeneous data via vector spaces. A quaternion data vector has been constructed by concatenating a set of four different measurements related to the air quality (such as CO, NO:2, SO:2, PM:10, an similar ones), then a Quaternion LMS (QLMS) algorithm is applied to predict next values from the previously ones.