Benzo[a]pyrene modelling over Italy: comparison with experimental data and source apportionment

Abstract This work describes the extension of the Flexible Air quality Regional Model (FARM) to polycyclic aromatic hydrocarbons (PAHs). Modules accounting for the partitioning of these species between gaseous and particulate phases were inserted in a simplified version of the model and in a more state–of–the–art configuration implementing the SAPRC99 gas–phase chemical mechanism coupled with the aero3 aerosol module. Both versions of FARM were applied over Italy for the year 2005. The analysis of model results was focused on benzo[a]pyrene (B[a]P), which is considered a marker substance for the carcinogenic risk of PAHs. Simulated B[a]P concentrations were compared with observed data, collected at background sites mainly located in Po Valley, and with concentrations produced at continental scale by EMEP/MSC–E model. Higher B[a]P yearly average concentrations were simulated by the national modelling system as a result of different factors: the higher resolution adopted by the national modelling system, the greater Italian emissions estimated by the national inventory and the effects induced by the use of a high resolution topography on meteorological fields and thus on the dispersion of pollutants. The comparison between observed and predicted monthly averaged concentrations evidenced the capability of the two versions of FARM model to capture the seasonal behaviour of B[a]P, characterised by higher values during the winter season due to the large use of wood for residential heating, enhanced by lower dispersion atmospheric conditions. The statistical analysis evidenced, for both versions of the model, a good performance and better indicators than those associated to EMEP/MSC–E simulations. A source apportionment was then carried out using the simplified version of the model, which proved to perform similarly to the full chemistry version but with the advantage to be computationally less expensive. The analysis revealed a significant influence of national sources on B[a]P concentrations, with non–industrial combustion employing wood burning devices being the most important sector. The contribution of the industrial sectors is relevant around major industrial facilities, with the largest absolute contribution in Taranto (above 1 ng m −3 ), where steel industries are the largest individual source of PAHs in the country

Characterization of urban pollution in two cities of the Puglia region in Southern Italy using field measurements and air quality (AQ) model approach

Abstract Passive air sampling (PAS) consisting of polyurethane foam (PUF) disks were deployed simultaneously over four periods of 2–5 months at four locations in urban and sub–urban sites of Bari and San Vito Taranto in Southern Italy. The purpose of the study was to characterize the urban pollution for two groups of semi volatile organic compounds (SVOCs), polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs), by using two different approaches consisting of PAS–PUF and air quality models (Flexible Air quality Regional Model, FARM). The concentrations in the air ranged from 20 to 200 pg m−3 for PCBs and from 5 to 48 ng m−3 for PAHs with the highest concentrations being detected at Bari center. PCB composition was dominated by the 3–Cl congeners (periods 1 and 2) and by 5–Cl (periods 3 and 4). PCB–28 and –37 were the most abundant congeners during the four periods. The PAHs profile was dominated by the 3–ring (70±6)%, with phenanthrene alone accounting for (49±2)%. On a seasonal basis opposite patterns were observed for PCBs and PAHs showing high PCB concentrations during the warm periods, period 3: summer and 2: spring, while PAHs were found during cool periods, period 4: autumn, and 1: winter. The results obtained from the application of the FARM model, during 2010, and limited to period 4 in this study, showed similar estimated levels for PCBs indicating a good performance for PCB modeled concentrations whilst for benzo[b]fluoranthene (B[b]F) the results showed a less better agreement. This study represents one of the few efforts at characterizing PCBs and PAHs compositions in ambient air in southern Italy and also represents one of the preliminary attempts at using PAS–PUF to give more insight into a modeling prediction in Italy. These results also provide useful information for the future development of the FARM model

A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data

Abstract Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO2, O3, PM2.5 and PM10 concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013–2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R2 of 0.59, 0.72, 0.76 and 0.75 for NO2, PM10, PM2.5 and O3, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO2. 50% of the population was estimated to be exposed to NO2 concentrations between 12 and 38 μg/m3 and PM10 between 20 and 35 μg/m3. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions

DUST GENERATION AND DISPERSION (PM10 AND PM2.5) IN THE AOSTA VALLEY: ANALYSIS WITH THE FARM MODEL

The aim of this work is to analyze the origin and the dispersion of the particulate matter (PM10 and PM2.5) in a mountainous region: the Aosta Valley. To meet this goal, different simulations were performed, using the flexible air quality regional model (FARM), to study two scenarios: winter and summer situations. To evaluate the performance of the FARM model in order to simulate the air quality situation of the selected periods, a comparison of modelled results against observed air quality data was carried out for both primary pollutants and particulate matter next to the measurement stations . Farm performed well in simulating especially ozone (O3) and nitrogen dioxide (NO2) concentrations, showing a good reproduction of both daily peaks and their daytime variations. PM model results revealed the tendency to under-predict the observed values, so we tried to use a different emission factor for the road traffic (Lohmeyer factor). The new results were good for the urban and suburban areas, but they give over-predictions close to highways. The PM characterisation provided by the model gives good results: in some different points of the analysis domain (mountain, plain and urban points) we found PM profiles wich reproduce expected values

Impact of different exposure models and spatial resolution on the long-term effects of air pollution.

Abstract Long-term exposure to air pollution has been related to mortality in several epidemiological studies. The investigations have assessed exposure using various methods achieving different accuracy in predicting air pollutants concentrations. The comparison of the health effects estimates are therefore challenging. This paper aims to compare the effect estimates of the long-term effects of air pollutants (particulate matter with aerodynamic diameter less than 10 μm, PM10, and nitrogen dioxide, NO2) on cause-specific mortality in the Rome Longitudinal Study, using exposure estimates obtained with different models and spatial resolutions. Annual averages of NO2 and PM10 were estimated for the year 2015 in a large portion of the Rome urban area (12 × 12 km2) applying three modelling techniques available at increasing spatial resolution: 1) a chemical transport model (CTM) at 1km resolution; 2) a land-use random forest (LURF) approach at 200m resolution; 3) a micro-scale Lagrangian particle dispersion model (PMSS) taking into account the effect of buildings structure at 4 m resolution with results post processed at different buffer sizes (12, 24, 52, 100 and 200 m). All the exposures were assigned at the residential addresses of 482,259 citizens of Rome 30+ years of age who were enrolled on 2001 and followed-up till 2015. The association between annual exposures and natural-cause, cardiovascular (CVD) and respiratory (RESP) mortality were estimated using Cox proportional hazards models adjusted for individual and area-level confounders. We found different distributions of both NO2 and PM10 concentrations, across models and spatial resolutions. Natural cause and CVD mortality outcomes were all positively associated with NO2 and PM10 regardless of the model and spatial resolution when using a relative scale of the exposure such as the interquartile range (IQR): adjusted Hazard Ratios (HR), and 95% confidence intervals (CI), of natural cause mortality, per IQR increments in the two pollutants, ranged between 1.012 (1.004, 1.021) and 1.018 (1.007, 1.028) for the different NO2 estimates, and between 1.010 (1.000, 1.020) and 1.020 (1.008, 1.031) for PM10, with a tendency of larger effect for lower resolution exposures. The latter was even stronger when a fixed value of 10 μg/m3 is used to calculate HRs. Long-term effects of air pollution on mortality in Rome were consistent across different models for exposure assessment, and different spatial resolutions

A microscale hybrid modelling system to assess the air quality over a large portion of a large European city

The role of atmospheric dispersion models is becoming increasingly relevant to assess air pollution urban population exposure for epidemiological studies. Estimating urban air quality is challenging, because of the intrinsic characteristics of cities atmospheric structure, such as high density of primary emissions and presence of local dispersion processes, that produce strong concentration gradients. Therefore, very high spatial resolution simulations may often be required to improve the accuracy of estimations. The objective of this study is developing a microscale hybrid modelling system (HMS) to carry out, in a reasonable computational time, long-term simulations providing hourly concentration fields at building-resolving scale in extended urban areas in order to calculate annual indicators to evaluate exposure. The proposed system couples two atmospheric dispersion models suited for different scales: a Eulerian chemical transport model, FARM (Flexible Air quality Regional Model), accounting for dispersion phenomena due to regional and local emission sources, and a Lagrangian particle micro-scale dispersion model, PMSS (Parallel Micro Swift Spray), used to compute concentrations induced by vehicular traffic inside the city. The HMS has been applied on 12 × 12 km2 domain in Rome with a horizontal resolution of 4 m for calculating NO2 and PM10 concentrations for all year 2015. This study has been carried out in the frame of project BEEP (Big data in Environmental and occupational Epidemiology), that is an Italian research project in epidemiological field. Results show that the combined use of the two models reproduces the spatial and temporal variability of the observed atmospheric pollutants with a good agreement. The statistical analysis performed on daily average concentrations proves that the HMS suits the standard acceptance criteria for urban dispersion model evaluation, with a FAC2 of 0.92 and 0.80 and a Fractional Bias of −0.03 and −0.2 for NO2 and PM10 respectively. Furthermore, the implementation of an innovative kernel method to calculate concentrations in PMSS has made possible to reduce the computational time by 80%, leading to an average computational time of 3 h per simulated day on an HPC (High Performance Computing) system with 180 cores

The Effect of Non-Compliance of Diesel Vehicle Emissions with Euro Limits on Mortality in the City of Milan

Diesel exhaust is hazardous to human health. In time, this has led the EU to impose on manufacturers lower and lower emission standards. These limits are very challenging in particular for nitrogen oxides (NOx) emitted by diesel-fueled vehicles. For the town of Milan (Italy), we used a complex modeling system that takes into account the NOx emissions from vehicular traffic and other urban sources, as well as their dispersion and chemical transformations in the atmosphere related to meteorological parameters. The traffic emissions in the Milan urban area were estimated using the geometric and structural characteristics of the road network, whereas the traffic flows were provided by the Environment and Territory Mobility Agency. Car emissions were estimated by the official European method COPERT 5. The nitrogen dioxide (NO2) concentrations were estimated under two scenarios: the actual scenario with real emissions and the Diesel Emission Standards Compliance (DESC) scenario. Using a recent meta-analysis, limited to European studies, we evaluated the relationship between NO2 concentrations and natural mortality. For the actual scenario, the NO2 annual concentration mean was 44.3 µg/m3, whereas under the DESC hypothetical scenario, this would have been of 37.7 µg/m3. This “extra” exposure of 6.6 µg/m3 of NO2 leads to a yearly excess of 574 “natural” deaths. Diesel emissions are very difficult to limit and are harmful for exposed people. This suggests that specific policies, including traffic limitations, need to be developed and enforced in urban environments

The Effect of Non-Compliance of Diesel Vehicle Emissions with Euro Limits on Mortality in the City of Milan

Diesel exhaust is hazardous to human health. In time, this has led the EU to impose on manufacturers lower and lower emission standards. These limits are very challenging in particular for nitrogen oxides (NOx) emitted by diesel-fueled vehicles. For the town of Milan (Italy), we used a complex modeling system that takes into account the NOx emissions from vehicular traffic and other urban sources, as well as their dispersion and chemical transformations in the atmosphere related to meteorological parameters. The traffic emissions in the Milan urban area were estimated using the geometric and structural characteristics of the road network, whereas the traffic flows were provided by the Environment and Territory Mobility Agency. Car emissions were estimated by the official European method COPERT 5. The nitrogen dioxide (NO2) concentrations were estimated under two scenarios: the actual scenario with real emissions and the Diesel Emission Standards Compliance (DESC) scenario. Using a recent meta-analysis, limited to European studies, we evaluated the relationship between NO2 concentrations and natural mortality. For the actual scenario, the NO2 annual concentration mean was 44.3 µg/m3, whereas under the DESC hypothetical scenario, this would have been of 37.7 µg/m3. This “extra” exposure of 6.6 µg/m3 of NO2 leads to a yearly excess of 574 “natural” deaths. Diesel emissions are very difficult to limit and are harmful for exposed people. This suggests that specific policies, including traffic limitations, need to be developed and enforced in urban environments