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.
Display omitted
•Machine learning methods were applied to obtain pollutant concentration in urban areas.•Population weighted exposure was estimated using dynamic mobile phone location data.•Long term NO2, PM, and O3 daily concentrations were provided for 6 urban areas.•Differences among cities were found with spatial/geographical concentration gradients.
The relationship between air pollution and respiratory morbidity has been widely addressed in urban and metropolitan areas but little is known about the effects in non-urban settings. Our aim was to ...assess the short-term effects of PM10 and PM2.5 on respiratory admissions in the whole country of Italy during 2006–2015.
We estimated daily PM concentrations at the municipality level using satellite data and spatiotemporal predictors. We collected daily counts of respiratory hospital admissions for each Italian municipality. We considered five different outcomes: all respiratory diseases, asthma, chronic obstructive pulmonary disease (COPD), lower and upper respiratory tract infections (LRTI and URTI). Meta-analysis of province-specific estimates obtained by time-series models, adjusting for temperature, humidity and other confounders, was applied to extrapolate national estimates for each outcome. At last, we tested for effect modification by sex, age, period, and urbanization score. Analyses for PM2.5 were restricted to 2013–2015 cause the goodness of fit of exposure estimation.
A total of 4,154,887 respiratory admission were registered during 2006–2015, of which 29% for LRTI, 12% for COPD, 6% for URTI, and 3% for asthma. Daily mean PM10 and PM2.5 concentrations over the study period were 23.3 and 17 μg/m3, respectively. For each 10 μg/m3 increases in PM10 and PM2.5 at lag 0–5 days, we found excess risks of total respiratory diseases equal to 1.20% (95% confidence intervals, 0.92, 1.49) and 1.22% (0.76, 1.68), respectively. The effects for the specific diseases were similar, with the strongest ones for asthma and COPD. Higher effects were found in the elderly and in less urbanized areas.
Short-term exposure to PM is harmful for the respiratory system throughout an entire country, especially in elderly patients. Strong effects can be found also in less urbanized areas.
Display omitted
•PM increases cause excess in risk of hospitalization for respiratory outcomes.•Rural areas display similar risks than urban areas.•Higher risks are found in elderly.•Almost 5000 hospitalizations could be prevented each year.
The health effects of long-term exposure to ultrafine particles (UFPs) are poorly understood. Data on spatial contrasts in ambient ultrafine particles (UFPs) concentrations are needed with fine ...resolution. This study aimed to assess the spatial variability of total particle number concentrations (PNC, a proxy for UFPs) in the city of Rome, Italy, using land use regression (LUR) models, and the correspondent exposure of population here living. PNC were measured using condensation particle counters at the building facade of 28 homes throughout the city. Three 7-day monitoring periods were carried out during cold, warm and intermediate seasons. Geographic Information System predictor variables, with buffers of varying size, were evaluated to model spatial variations of PNC. A stepwise forward selection procedure was used to develop a “base” linear regression model according to the European Study of Cohorts for Air Pollution Effects project methodology. Other variables were then included in more enhanced models and their capability of improving model performance was evaluated. Four LUR models were developed. Local variation in UFPs in the study area can be largely explained by the ratio of traffic intensity and distance to the nearest major road. The best model (adjusted R2 = 0.71; root mean square error = ±1,572 particles/cm³, leave one out cross validated R2 = 0.68) was achieved by regressing building and street configuration variables against residual from the “base” model, which added 3% more to the total variance explained. Urban green and population density in a 5,000 m buffer around each home were also relevant predictors. The spatial contrast in ambient PNC across the large conurbation of Rome, was successfully assessed. The average exposure of subjects living in the study area was 16,006 particles/cm³ (SD 2165 particles/cm³, range: 11,075–28,632 particles/cm³). A total of 203,886 subjects (16%) lives in Rome within 50 m from a high traffic road and they experience the highest exposure levels (18,229 particles/cm³). The results will be used to estimate the long-term health effects of ultrafine particle exposure of participants in Rome.
•PNCs were measured directly outside 28 homes for three weeks in different seasons.•LUR models were developed using standard and enhanced GIS-derived predictor variables.•Traffic intensity, population density and urban green were the main predictors of UFP.•Building and street configuration variables improved LUR model performance.•PNC exposure at a fine spatial resolution was successfully assessed.
Spatiotemporally resolved particulate matter (PM) estimates are essential for reconstructing long and short-term exposures in epidemiological research. Improved estimates of PM
and PM
concentrations ...were produced over Italy for 2013-2015 using satellite remote-sensing data and an ensemble modeling approach. The following modeling stages were used: (1) missing values of the satellite-based aerosol optical depth (AOD) product were imputed using a spatiotemporal land-use random-forest (RF) model incorporating AOD data from atmospheric ensemble models; (2) daily PM estimations were produced using four modeling approaches: linear mixed effects, RF, extreme gradient boosting, and a chemical transport model, the flexible air quality regional model. The filled-in MAIAC AOD together with additional spatial and temporal predictors were used as inputs in the three first models; (3) a geographically weighted generalized additive model (GAM) ensemble model was used to fuse the estimations from the four models by allowing the weights of each model to vary over space and time. The GAM ensemble model outperformed the four separate models, decreasing the cross-validated root mean squared error by 1-42%, depending on the model. The spatiotemporally resolved PM estimations produced by the suggested model can be applied in future epidemiological studies across Italy.
Daily air pollution has been linked with mortality from urban studies. Associations in rural areas are still unclear and there is growing interest in testing the role that air pollution has on other ...causes of death. This study aims to evaluate the association between daily air pollution and cause-specific mortality in all 8092 Italian municipalities.
Natural, cardiovascular, cardiac, ischemic, cerebrovascular, respiratory, metabolic, diabetes, nervous and psychiatric causes of death occurred in Italy were extracted during 2013–2015. Daily ambient PM10, PM2.5 and NO2 concentrations were estimated through machine learning algorithms. The associations between air pollutants and cause-specific mortality were estimated with a time-series approach using a two-stage analytic protocol where area-specific over-dispersed Poisson regression models where fit in the first stage, followed by a meta-analysis in the second. We tested for effect modification by sex, age class and the degree of urbanisation of the municipality.
We estimated a positive association between PM10 and PM2.5 and the mortality from natural, cardiovascular, cardiac, respiratory and nervous system causes, but not with metabolic or psychiatric causes of death. In particular, mortality from nervous diseases increased by 4.55% (95% CI: 2.51–6.63) and 9.64% (95% CI: 5.76–13.65) for increments of 10 μg/m3 in PM10 and PM2.5 (lag 0–5 days), respectively. NO2 was positively associated with respiratory (6.68% (95% CI: 1.04–12.62)) and metabolic (7.30% (95% CI: 1.03–13.95)) mortality for increments of 10 μg/m3 (lag 0–5). Higher associations with natural mortality were found among the elderly, while there were no differential effects between sex or between rural and urban areas.
Short-term exposure to particulate matter was associated with mortality from nervous diseases. Mortality from metabolic diseases was associated with NO2 exposure. Other associations are confirmed and updated, including the contribution of lowly urbanised areas. Health effects were also found in suburban and rural areas.
•Time-series analysis of cause-specific mortality occurred in Italy.•Machine learning estimated daily PM10, PM2.5 and NO2 concentrations.•PM10 and PM2.5 positive associated with the mortality from nervous system causes.•NO2 positively associated only with respiratory and metabolic mortality.•Associations found in highly urbanised areas but also in suburban and rural ones.
Air pollution effects on cardiovascular hospitalizations in small urban/suburban areas have been scantly investigated. Such effects were assessed among the participants in the analytical ...epidemiological survey carried out in Pisa and Cascina, Tuscany, Italy (2009-2011). Cardiovascular hospitalizations from 1585 subjects were followed up (2011-2015). Daily mean pollutant concentrations were estimated through random forests at 1 km (particulate matter: PM
, 2011-2015; PM
, 2013-2015) and 200 m (PM
, PM
, NO
, O
, 2013-2015) resolutions. Exposure effects were estimated using the case-crossover design and conditional logistic regression (odds ratio-OR-and 95% confidence interval-CI-for 10 μg/m
increase; lag 0-6). During the period 2011-2015 (137 hospitalizations), a significant effect at lag 0 was observed for PM
(OR = 1.137, CI: 1.023-1.264) at 1 km resolution. During the period 2013-2015 (69 hospitalizations), significant effects at lag 0 were observed for PM
(OR = 1.268, CI: 1.085-1.483) and PM
(OR = 1.273, CI: 1.053-1.540) at 1 km resolution, as well as for PM
(OR = 1.365, CI: 1.103-1.690), PM
(OR = 1.264, CI: 1.006-1.589) and NO
(OR = 1.477, CI: 1.058-2.061) at 200 m resolution; significant effects were observed up to lag 2. Larger ORs were observed in males and in subjects reporting pre-existent cardiovascular/respiratory diseases. Combining analytical and routine epidemiological data with high-resolution pollutant estimates provides new insights on acute cardiovascular effects in the general population and in potentially susceptible subgroups living in small urban/suburban areas.
In the framework of the UNECE Task Force on Measurement and Modelling (TFMM) under the Convention on Long-range Transboundary Air Pollution (LRTAP), the EURODELTAIII project is evaluating how well ...air quality models are able to reproduce observed pollutant air concentrations and deposition fluxes in Europe. In this paper the sulphur and nitrogen deposition estimates of six state-of-the-art regional models (CAMx, CHIMERE, EMEP MSC-W, LOTOS-EUROS, MINNI and CMAQ) are evaluated and compared for four intensive EMEP measurement periods (25 Feb–26 Mar 2009; 17 Sep–15 Oct 2008; 8 Jan–4 Feb 2007 and 1–30 Jun 2006).
For sulphur, this study shows the importance of including sea salt sulphate emissions for obtaining better model results; CMAQ, the only model considering these emissions in its formulation, was the only model able to reproduce the high measured values of wet deposition of sulphur at coastal sites. MINNI and LOTOS-EUROS underestimate sulphate wet deposition for all periods and have low wet deposition efficiency for sulphur.
For reduced nitrogen, all the models underestimate both wet deposition and total air concentrations (ammonia plus ammonium) in the summer campaign, highlighting a potential lack of emissions (or incoming fluxes) in this period. In the rest of campaigns there is a general underestimation of wet deposition by all models (MINNI and CMAQ with the highest negative bias), with the exception of EMEP, which underestimates the least and even overestimates deposition in two campaigns. This model has higher scavenging deposition efficiency for the aerosol component, which seems to partly explain the different behaviour of the models.
For oxidized nitrogen, CMAQ, CAMx and MINNI predict the lowest wet deposition and the highest total air concentrations (nitric acid plus nitrates). Comparison with observations indicates a general underestimation of wet oxidized nitrogen deposition by these models, as well as an overestimation of total air concentration for all the campaigns, except for the 2006 campaign. This points to a low efficiency in the wet deposition of oxidized nitrogen for these models, especially with regards to the scavenging of nitric acid, which is the main driver of oxidized N deposition for all the models. CHIMERE, LOTOS-EUROS and EMEP agree better with the observations for both wet deposition and air concentration of oxidized nitrogen, although CHIMERE seems to overestimate wet deposition in the summer period. This requires further investigation, as the gas-particle equilibrium seems to be biased towards the gas phase (nitric acid) for this model.
In the case of MINNI, the frequent underestimation of wet deposition combined with an overestimation of atmospheric concentrations for the three pollutants indicates a low efficiency of the wet deposition processes. This can be due to several reasons, such as an underestimation of scavenging ratios, large vertical concentration gradients (resulting in small concentrations at cloud height) or a poor parameterization of clouds.
Large differences between models were also found for the estimates of dry deposition. However, the lack of suitable measurements makes it impossible to assess model performance for this process. These uncertainties should be addressed in future research, since dry deposition contributes significantly to the total deposition for the three deposited species, with values in the same range as wet deposition for most of the models, and with even higher values for some of them, especially for reduced nitrogen.
•The estimates of N and S deposition by six regional models are evaluated.•The inclusion of sea salt sulfate emissions was found to be important.•Formation of NH3+NH4+ is generally underestimated in summer.•There is a general underestimation of wet deposition of reduced N by most models.•Different performance was found for the different models and pollutants.
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.
•Hybrid modelling approach to model air pollutant dispersion in an urban environment.•Long-term simulations over extended urban areas at building-resolving scale.•Combining micro-scale Lagrangian Particle Dispersion Model and Chemical Transport Model.•Innovative time-saving kernel method to compute concentrations at micro-scale.•Supporting advanced high-quality exposure assessment epidemiology studies.
Polycyclic Aromatic Hydrocarbons (PAHs) are considered among the most dangerous air pollutants due to their carcinogenic and mutagenic characteristics. Populations living in urban area are exposed to ...these pollutants because of their proximity to the emission sources. However, the spatial and temporal characteristics of PAHs concentrations in such areas are not well known. An integrated modeling approach is here presented to estimate exposure to PAHs content in PM2.5 of children and elderly people living in the city of Rome, Italy. It is based on a microenvironment approach in which exposure is estimated by accounting for PAHs concentrations experienced by the target population in the most visited living environments. The model uses data provided by the EU LIFE + EXPAH project: indoor/outdoor PAHs concentrations collected in homes, schools, cars, buses and offices to derive PAHs infiltration factors for the specific environments; time activity to identify daytime profiles of the target population and information on the prevailing living environments; ambient PAHs concentration fields. The latter have been obtained by integrating Chemical Transport Model (CTM) results with measurements collected by the EXPAH project. Uncertainties in the estimation of PAHs exposure has been evaluated by applying a Monte Carlo statistical approach using probability density function based on observed exposure parameters. Results were calculated for one year (June 2011–May 2012). The downtown area was found to be the most contaminated one with concentrations up to 2 ± 1 and 0.6 ± 0.2 ng/m3, on an annual basis, respectively for ∑4PAHs (e.g. BaP, BbF, BkF and indeno(1,2,3-cd)pyrene) and BaP. Results do not exhibit significant differences on ∑4PAHs exposure between children and elderly people, mainly due to the prevalence of indoor microenvironments in the time activity data, and to the little difference in the indoor/outdoor infiltration. Seasonality was identified as an important factor contributing to the overall exposure. The higher PAHs emissions during the heating period determine a greater exposure during winter. Homes have been identified as the microenvironments that most contribute to PAHs exposure followed by schools.
Display omitted
•PAHs exposures during heating season are higher than in non-heating season.•Indoor environments play a dominant role in the daily PAHs exposure.•Infiltration reduces indoors PAHs exposure levels by 10–40% depending on the living environment.•PAHs exposures between elderly and children did not vary substantially.•Based on PAHs modeling results the highest exposure occur in the city center.
Heavy metals (HMs) are pollutants that have both anthropogenic and natural sources. In the last decade, the European Commission (EC) has imposed limit and target values for some of them that are ...toxic both for humans and for the environment. This study aims to assess the HM concentrations over Italy by means of the atmospheric modelling system (AMS) of the MINNI project. The AMS is based on the chemical transport model (CTM) FARM. The sensitivity of model simulations to horizontal grid resolution, lateral boundary conditions and the contribution of emissions from neighbouring countries has been also evaluated. The simulations have been carried out for the year 2005 considering a spatial resolution of 20 km over the Italian domain and 4 km over northern Italy (Po Valley). The CTM has been extended to take into account the HMs considered by EC directives such as arsenic (As), cadmium (Cd), nickel (Ni) and lead (Pb). Both anthropogenic and natural emissions have been considered in this study. Model results have been compared with available observations. Results show realistic concentration of HMs and suggest the importance of using boundary conditions, while foreign emissions have less impact. In addition, the present work highlights the necessity of more observations in space and time for a comprehensive validation of the model. High-resolution simulation gives more realistic pattern with respect to the low-resolution one for the highly polluted areas. However, future improvements require a better knowledge of the space/time distribution of the emissions, currently not available.
PDF
