METEOROLOGICAL MODELLING INFLUENCE ON REGIONAL AND URBAN AIR POLLUTION PREDICTABILITY

ARPA Piemonte performs yearly air quality assessment running a modelling system based on a chemical transport model. The model is capable to simulate air pollutant emission, transport, diffusion and chemical transformation, to provide concentration fields of the main atmospheric pollutants (CO, NOX, SO2, PM10, PM2.5, O3, and benzene) on a hourly basis and to compute all the indicators required by EU legislation. Meteorological fields to drive air quality simulations are reconstructed assimilating ARPA Piemonte meteorological network observations within background fields obtained by ECMWF analyses. The reliability of mesoscale and urban scale meteorology is one of the key issues in determining an air quality modelling system effectiveness. Diagnostic meteorological analysis takes advantage of the wide local measurement network but cannot guarantee the dynamic and thermodynamic variables consistency provided e.g. by prognostic weather prediction models. Since July 2006 ARPA Piemonte operationally uses an air quality forecasting system driven by a numerical weather prediction model. The simultaneous availability of the two systems results provides the possibility to compare different meteorological modelling techniques effects on air pollution predictability. The two modelling systems results are compared by means of model evaluation statistical indexes showing very similar performances over a six months period. The comparison is completed by the analysis of short term critical episodes to highlight meteorological modelling effectiveness in reproducing severe air pollution episodes and short term concentrations variation. The prognostic meteorological fields showed a better capability to simulate peak episodes even if weather forecast errors can cause “false alarm” conditions due to concentration overestimation

METEOROLOGICAL MODELLING INFLUENCE ON REGIONAL AND URBAN AIR POLLUTION PREDICTABILITY

ARPA Piemonte performs yearly air quality assessment running a modelling system based on a chemical transport model. The model is capable to simulate air pollutant emission, transport, diffusion and chemical transformation, to provide concentration fields of the main atmospheric pollutants (CO, NOX, SO2, PM10, PM2.5, O3, and benzene) on a hourly basis and to compute all the indicators required by EU legislation. Meteorological fields to drive air quality simulations are reconstructed assimilating ARPA Piemonte meteorological network observations within background fields obtained by ECMWF analyses. The reliability of mesoscale and urban scale meteorology is one of the key issues in determining an air quality modelling system effectiveness. Diagnostic meteorological analysis takes advantage of the wide local measurement network but cannot guarantee the dynamic and thermodynamic variables consistency provided e.g. by prognostic weather prediction models. Since July 2006 ARPA Piemonte operationally uses an air quality forecasting system driven by a numerical weather prediction model. The simultaneous availability of the two systems results provides the possibility to compare different meteorological modelling techniques effects on air pollution predictability. The two modelling systems results are compared by means of model evaluation statistical indexes showing very similar performances over a six months period. The comparison is completed by the analysis of short term critical episodes to highlight meteorological modelling effectiveness in reproducing severe air pollution episodes and short term concentrations variation. The prognostic meteorological fields showed a better capability to simulate peak episodes even if weather forecast errors can cause “false alarm” conditions due to concentration overestimation

Impact of NOx and NH3 Emission Reduction on Particulate Matter across Po Valley: A LIFE-IP-PREPAIR Study

Air quality in Europe continues to remain poor in many areas, with regulation limits often exceeded by many countries. The EU Life-IP PREPAIR Project, involving administrations and environmental protection agencies of eight regions and three municipalities in Northern Italy and Slovenia, was designed to support the implementation of the regional air quality plans in the Po Valley, one of the most critical areas in Europe in terms of pollution levels. In this study, four air quality modelling systems, based on three chemical transport models (CHIMERE, FARM and CAMx) were applied over the Po Valley to assess the sensitivity of PM2.5 concentrations to NOx and NH3 emission reductions. These two precursors were reduced (individually and simultaneously) from 25% up to 75% for a total of 10 scenarios, aimed at identifying the most efficient emission reduction strategies and to assess the non-linear response of PM2.5 concentrations to precursor changes. The multi-model analysis shows that reductions across multiple emission sectors are necessary to achieve optimal results. In addition, the analysis of non-linearities revealed that during the cold season, the efficiency of PM2.5 abatement tends to increase by increasing the emission reductions, while during summertime, the same efficiency remains almost constant, or slightly decreases towards higher reduction strengths. Since the concentrations of PM2.5 are greater in winter than in summer, it is reasonable to infer that significant emission reductions should be planned to maximise reduction effectiveness

A real opportunity to modify cardiovascular risk through primary care and prevention: A pilot study

Cardiovascular diseases (CVDs) represent a major threat to health and primary prevention outstands as the most effective instrument to face this issue, addressing multiple risk factors at a time and influencing behavioral patterns. Community nurses have been involved in many interdisciplinary prevention activities, resulting in effective control of CV risk factors. We conducted a pilot study aiming at describing the impact on the CV risk profile of an 18-month interdisciplinary intervention on lifestyle habits. From September 2018 to May 2020, four general practitioners (GPs) working in the Roman neighborhood of Torresina recruited patients having a cardiovascular risk score (CRS) equal to or higher than 3% and lower than 20%; those patients were included in a nutritional, physical, and psychological counseling program. Assessments of patients' health status were led at baseline, 6, 12, and 18 months by a nutritionist, a physiotherapist, a psychologist, their GPs, and a community nurse. The CRS was estimated at every examination, based on the Italian Progetto Cuore algorithm. A total of 76 patients were included (mean age of 54.6 years; 33 men and 43 women). Mean CRS showed a significant reduction between baseline and 12 months (from 4.9 to 3.8); both total cholesterol and systolic blood pressure (SBP) significantly decreased at 6 months of follow-up (respectively, from 211.1 to 192 and from 133.1 to 123.1). Nonetheless, the reduction was later maintained only for SBP. However, during the last 6 months of the intervention, the COVID-19 pandemic broke out, thus, it is not possible to know how much the results achieved at 18 months were influenced by the restrictive measures introduced by the Italian government. When stratifying according to the presence of hypertension/diabetes and physical activity, no differences in the CRS could be highlighted between the two groups. Our pilot study proved that an interdisciplinary counseling intervention program can improve CV risk profile and could be further spread to people that, according to their CRS, would benefit more from changes in lifestyles

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%.JRC.C.5-Air and Climat

Functional zoning for air quality

This paper presents a land classification in zones featured by different criticality levels of atmospheric pollution, considering pollutant time series as functional data: we call this proposal “Functional Zoning”. We aim to meet a request of European laws that impose to dis- tinguish zones needing further actions from those needing only maintenance according to air quality status. To carry out zoning for Piemonte (northern Italy), we consider the hourly concentration fields of the main pollutants produced by a deterministic air quality model, and we preprocess them by assimilating observations gathered by monitoring networks. In order to consider administrative units which policy makers refer to, we present three different alternatives to upscale data to municipality scale. Then, to aggregate by pollutant, we evaluate two strategies to summarize time series: air quality index assessment, and use of the Multivariate Functional Principal Component Analysis (MFPCA), respectively. Therefore, we partition municipalities clustering air quality time series and MPFCA scores, and finally we illustrate a comparison study of the different strategies’ results

Comparing spatio-temporal models for particulate matter in Piemonte

In the last two decades, increasing attention has been given to air pollution around the world, mainly because of its impact on human health and on the environment. In the Po valley (northern Italy), one of the most troublesome pollutant is PM10 (particulate matter with an aerodynamic diameter of less than 10 micron). In order to assess PM10 concentration over an entire region, environmental agencies need models to predict PM10 at unmonitored sites. To choose among possible predictive models and then meet the agencies’ request, we focus on the class of Bayesian hierarchical models as they provide a flexible framework for incorporating relevant covariates as well as spatio-temporal interactions. We consider six alter- native models for PM10 concentration in Piemonte region (north-western Po Valley), during the winter season October 2005–March 2006. Our aim is to choose a model that is satisfactory in terms of goodness of fit, interpretability, parsimony, prediction capability and computational costs. In order to support this choice, we propose a comparison approach based on a set of criteria summarized in a table that can be easily communicated to non-statisticians. The comparison findings allow to provide Piemonte environmental agencies with an effective statistical model for building reliable PM10 concentration maps, equipped with the corresponding uncertainty measure