Analysis of the lockdown effects due to the COVID-19 on air pollution in Brescia (Lombardy).

SARS-CoV-2 virus (COVID-19) pandemic has impacted several countries, with also some differences at local levels. When lockdown restrictions were imposed, the concentrations of some air pollutants were reduced, as reported in some other cities in the world. This was often considered a positive by-product of the pandemic. However, often literature reporting the connection of air quality (AQ) and lockdown, suffers of limited and incomplete data analysis, not considering, for example, some confounding factors. This work presents a methodology, and the results of its application, to assess the impact of pandemic restrictions on AQ (in particular nitrogen oxides, NO2 and particulate matter, PM10) in spring 2020 in Brescia, located in one of the most affected areas in terms of virus diffusion and in one of the most polluted areas in Europe (Po Valley, Italy). In particular, the proposed methodology integrates data and AQ modelling simulations to distinguish between the changes in the PM10 and NO2 pollutants concentration that occurred due to the restriction measures and due to other factors, like spatial-temporal characteristics (for example the seasonality), meteorological factors, and governmental actions that were introduced in the past to improve the air quality. Results show that NO2 is strongly dependent to traffic emission. On the contrary, although the expected decrease in PM10 concentrations, the results highlight that the reduction of transport emission would not help to avoid severe air pollution, due to the other pollution sources that contribute to its origin. The results presented for the first time in this work are of particular interest because they may be used as a basis to investigate in more details the sources that can impact on the air quality in Brescia, with the aim to propose effective measures able to reduce it.

COVID-19 incidence and mortality in Lombardy, Italy: An ecological study on the role of air pollution, meteorological factors, demographic and socioeconomic variables.

Lombardy, the most populated and industrialized Italian region, was the epicentre of the first wave (March and April 2020) of COVID-19 in Italy and it is among the most air polluted areas of Europe. We carried out an ecological study to assess the association between long-term exposure to particulate matter (PM) and nitrogen dioxide (NO2) on COVID-19 incidence and all-cause mortality after accounting for demographic, socioeconomic and meteorological variables. The study was based on publicly available data. Multivariable negative binomial mixed regression models were fitted, and results were reported in terms of incidence rate ratios (IRRs) and standardized mortality ratios (SMR). The effect of winter temperature and humidity was modelled through restricted cubic spline. Data from 1439 municipalities out of 1507 (95%) were included in the analyses, leading to a total of 61,377 COVID-19 cases and 40,401 deaths from all-causes collected from February 20th to April 16th and from March 1st to April 30th, 2020, respectively. Several demographic and socioeconomic variables resulted significantly associated with COVID-19 incidence and all-cause mortality in a multivariable fashion. An increase in average winter temperature was associated with a nonlinear decrease in COVID-19 incidence and all-cause mortality, while an opposite trend emerged for the absolute humidity. An increase of 10 µg/m3 in the mean annual concentrations of PM2.5 and PM10 over the previous years was associated with a 58% and 34% increase in COVID-19 incidence rate, respectively. Similarly, a 10 µg/m3 increase of annual mean PM2.5 concentration was associated with a 23% increase in all-cause mortality. An inverse association was found between NO2 levels and COVID-19 incidence and all-cause mortality. Our ecological study showed that exposure to PM was significantly associated with the COVID-19 incidence and excess mortality during the first wave of the outbreak in Lombardy, Italy.

The greatest air quality experiment ever: Policy suggestions from the COVID-19 lockdown in twelve European cities.

COVID-19 (Coronavirus disease 2019) hit Europe in January 2020. By March, Europe was the active centre of the pandemic. As a result, widespread "lockdown" measures were enforced across the various European countries, even if to a different extent. Such actions caused a dramatic reduction, especially in road traffic. This event can be considered the most significant experiment ever conducted in Europe to assess the impact of a massive switch-off of atmospheric pollutant sources. In this study, we focus on in situ concentration data of the main atmospheric pollutants measured in twelve European cities, characterized by different climatology, emission sources, and strengths. We propose a methodology for the fair comparison of the impact of lockdown measures considering the non-stationarity of meteorological conditions and emissions, which are progressively declining due to the adoption of stricter air quality measures. The analysis of these unmatched circumstances allowed us to estimate the impact of a nearly zero-emission urban transport scenario on air quality in 12 European cities. The clearest result, common to all the cities, is that a dramatic traffic reduction effectively reduces NO2 concentrations. In contrast, each city's PM and ozone concentrations can respond differently to the same type of emission reduction measure. From the policy point of view, these findings suggest that measures targeting urban traffic alone may not be the only effective option for improving air quality in cities.

An integrated air quality forecast system for a metropolitan area.

Air quality forecasting is an important issue in environmental research, due to the effects that air pollutants have on population health. To deal with this topic, in this work an integrated modelling system has been developed to forecast daily maximum eight hours ozone concentrations and daily mean PM10 concentrations, up to two days in advance, over an urban area. The presented approach involves two steps. In the first step, artificial neural networks are identified and applied to get point-wise forecasting. In the second step, the forecasts obtained at the monitoring station locations are spatially interpolated all over the domain using the cokriging technique, which allows to improve the spatial interpolation in the absence of densely sampled data. The integrated modelling system has been then applied to a case study over Northern Italy, performing a validation over space and time for the year 2004 and analyzing if the limit values for the protection of human health set by the European Commission are respected. The presented approach represents a fast and reliable way to provide decision makers and the general public with air quality forecasting, and to support prevention and precautionary measures.

Minimizing external indirect health costs due to aerosol population exposure: a case study from Northern Italy.

Environmental Agencies require Decision Support Systems, in order to plan Air Quality Policies considering the cost of emission reduction measures and the human health effects (with related social costs). The use of Decision Support Systems is also useful to spread information to general public, explaining the effectiveness of proposed air quality plans. In this paper, a multi-objective approach to control PM10 concentration at a regional level is presented. The problem considers both the internal costs (due to the implementation of emission reduction measures) and the external costs (due to population exposure to high PM10 concentrations). To model PM10 concentrations, a single surrogate model is used for the entire domain, allowing the implementation of a very efficient optimization procedure. The surrogate model is derived through a set of 10 simulations, performed using a Chemistry Transport Model fed with different emission reduction scenarios. The methodology is applied to Northern Italy, a region affected by very high PM10 concentrations that exceed the limit values specified by the EU legislation.

Environmental exposure and health effects in a highly polluted area of Northern Italy: a narrative review.

Human health and well-being are strongly linked to the state of the environment. The high industrial pressure present in the Province of Brescia, located in Northern Italy, produced strong environmental and health concerns. This narrative review of the literature aims at identifying the studies focused on the association between exposure to environmental pollutants and health effects in the population living in this area. Thirteen papers fitted the inclusion criteria: five were focused on the connection among pollutants present in air matrix and health effects, seven on both air and soil, and one on soil. No study investigated the relationship with water pollution. The great variability in the analyzed end-points made it difficult to draw precise conclusions, but the fact that, in almost all the studies, the investigated health effects have a positive association with the exposure to different kinds of pollutants, allows us to hypothesize that the considered population is living in an area where the "environmental pressure" could produce significant health effects in the future.

Design and validation of a multiphase 3D model to simulate tropospheric pollution.

This work presents the Transport and Chemical Aerosol Model (TCAM) formulation and its validation in the frame of CityDelta-CAFE project. TCAM is a 3D eulerian multiphase model simulating tropospheric secondary pollution at mesoscale. It is included in the GAMES (Gas Aerosol Modelling Evaluation System) modelling system, designed to support the analysis of secondary pollution dynamics and to assess the impact of emission control strategies. The presented validation assessment has been performed in the frame of the CityDelta II project over the Milan domain and concerns both gas and aerosol 1999 simulations. Ozone, nitrogen oxides and aerosol computed and observed patterns have been compared and analysed by means of statistical indicators showing high model performances for both winter and summer pollution regimes.

A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions.

This paper introduces the MAQ (Multi-dimensional Air Quality) model aimed at defining cost-effective air quality plans at different scales (urban to national) and assessing the co-benefits for GHG emissions. The model implements and solves a non-linear multi-objective, multi-pollutant decision problem where the decision variables are the application levels of emission abatement measures allowing the reduction of energy consumption, end-of pipe technologies and fuel switch options. The objectives of the decision problem are the minimization of tropospheric secondary pollution exposure and of internal costs. The model assesses CO2 equivalent emissions in order to support decision makers in the selection of win-win policies. The methodology is tested on Lombardy region, a heavily polluted area in northern Italy.

Optimal air quality policies and health: a multi-objective nonlinear approach.

The use of modelling tools to support decision-makers to plan air quality policies is now quite widespread in Europe. In this paper, the Regional Integrated Assessment Tool (RIAT+), which was designed to support policy-maker decision on optimal emission reduction measures to improve air quality at minimum costs, is applied to the Porto Urban Area (Portugal). In addition to technological measures, some local measures were included in the optimization process. Case study results are presented for a multi-objective approach focused on both NO2 and PM10 control measures, assuming equivalent importance in the optimization process. The optimal set of air quality measures is capable to reduce simultaneously the annual average concentrations values of PM10 and NO2 in 1.7 and 1.0 µg/m3, respectively. This paper illustrates how the tool could be used to prioritize policy objectives and help making informed decisions about reducing air pollution and improving public health.

Exploring trade-offs between air pollutants through an Integrated Assessment Model.

When designing air pollution reduction policies, regional decision makers face a limited budget to choose the most efficient measures which will have impacts on several pollutants in different ways. RIAT+ is a regional integrated assessment tool that supports the policy maker in this selection of the optimal emission reduction technologies, to improve air quality at minimum costs. In this paper, this tool is formalized and applied to the specific case of a French region (Alsace), to illustrate how focusing on one single pollutant may exacerbate problems related to other pollutants, on top of conflicts related to budget allocation. In our case, results are shown for possible trade-offs between NO2 and O3 control policies. The paper suggests an approach to prioritize policy maker objectives when planning air pollution policies at regional scale.

A comparison of reanalysis techniques: applying optimal interpolation and Ensemble Kalman Filtering to improve air quality monitoring at mesoscale.

To fulfill the requirements of the 2008/50 Directive, which allows member states and regional authorities to use a combination of measurement and modeling to monitor air pollution concentration, a key approach to be properly developed and tested is the data assimilation one. In this paper, with a focus on regional domains, a comparison between optimal interpolation and Ensemble Kalman Filter is shown, to stress pros and drawbacks of the two techniques. These approaches can be used to implement a more accurate monitoring of the long-term pollution trends on a geographical domain, through an optimal combination of all the available sources of data. The two approaches are formalized and applied for a regional domain located in Northern Italy, where the PM10 level which is often higher than EU standard limits is measured.

A non-linear analysis to detect the origin of PM10 concentrations in Northern Italy.

This work presents the formalization and the application of the factor separation technique in order to investigate the impact of precursor emission and their nonlinear interaction (in particulate matter accumulation processes). By processing the simulations of a 3D multiphase modeling system, the factor separation methodology can support the Environmental Authority in quantifying the impact of precursor emissions on PM10 production and consequently in assessing the feasible efficiency of different emission control strategies over a considered domain. The case study proposed by this paper focuses on the Po Valley region (Northern Italy), characterized by critical PM10 levels claiming for sound emission reduction policies. The results show the heavy nonlinearities and the strong seasonal dependence in the formation of PM10, over the study domain. Furthermore the results highlight that peak PM10 concentrations are mainly related to primary PM emissions in urban areas, and gas emissions (mainly NOx and NH3) in rural areas.

Multi-objective analysis of ground-level ozone concentration control.

To develop sound air quality plans, regional authorities should have instruments that link the complex behaviour of pollutants both in time and space with costs of emission reduction. The problem is particularly important for ground level ozone which forms kilometres away, hours later from the emission of its precursors. To approach this problem, a method (1) to identify local pollutant-precursor models on the basis of results from a large photochemical model (CALGRID), (2) to integrate them in a multi-objective mathematical program, together with an estimate of the emission reduction costs, is suggested. The method has been used to assess action priorities in Lombardy (Northern Italy). This area, characterised by a complex terrain, high urban and industrial emissions and a dense road network is often affected by severe photochemical pollution episodes during summer.