Spatiotemporally resolved particulate matter (PM) estimates are essential for reconstructing long and short-term exposures in epidemiological research. Improved estimates of PM2.5 and PM10 ...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.
We developed an integrated approach coupling a chemical transport model (CTM) with machine learning (ML) techniques to produce high spatial resolution NO
2
and O
3
daily concentration fields over ...Italy. Three years (2013–2015) simulations, at a spatial resolution of 5 km, performed by the Flexible Air quality Regional Model (FARM) were used as predictors, together with other spatial-temporal data, such as population, land-use, surface greenness and road networks, by a ML Random Forest (ML-RF) algorithm to produce daily concentrations at higher resolution (1 km) over the national territory. The evaluation of the adopted integrated approach was based on NO
2
and O
3
observations available from 530 and 293 monitoring stations across Italy, respectively. A good performance for NO
2
and excellent results for O
3
were obtained from the application of the CTM; as for NO
2
, the levels at urban traffic stations were not captured by the simulations due to the adopted horizontal resolution and related emissions uncertainties. Performance improvements were achieved with ML-RF predictions, reducing NO
2
underestimation (near zero fractional bias results) and better capturing spatial contrasts. The results obtained in this work were used to support the national exposure assessment and environmental epidemiology studies planned in the BEEP (Big data in Environmental and occupational Epidemiology) project and confirm the potential of machine learning methods to adequately predict air pollutant levels at high spatial and temporal resolutions.
Graphical 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.
•Chemical Transport, Random Forest and Lagrangian particle models used for exposure.•Building effects and city structure considered at different buffer sizes.•NO2 and PM10 long-term health effects evaluated by different models and resolution.•Significant associations with natural and cardiovascular mortality shown by all models.•Hazard Ratios for increments of interquartile ranges are not statistically different.
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.
Nature-based solutions and green urban infrastructures are becoming common measures in local air quality and climate strategies. However, there is a lack of analytical frameworks to anticipate the ...effect of such interventions on urban meteorology and air quality at a city scale. We present a modelling methodology that relies on the weather research and forecasting model (WRF) with the building effect parameterization (BEP) and the community multiscale air quality (CMAQ) model and apply it to assess envisaged plans involving vegetation in the Madrid (Spain) region. The study, developed within the VEGGAP Life project, includes the development of two detailed vegetation scenarios making use of Madrid’s municipality tree inventory (current situation) and future vegetation-related interventions. An annual simulation was performed for both scenarios (considering constant anthropogenic emissions) to identify (i) variations in surface temperature and the reasons for such changes, and (ii) implications on air-quality standards according to EU legislation for the main pollutants (PM10, PM2.5, NO2 and O3). Our results suggest that vegetation may have significant effects on urban meteorology due to changes induced in relevant surface properties such as albedo, roughness length or emissivity. We found a net-heating effect of around +0.18 °C when trees are introduced in dry, scarcely vegetated surfaces in the city outskirts. In turn, this enhances the planetary boundary layer height (PBLH), which brings about reductions in ambient concentrations of relevant pollutants such as NO2 (in the range of 0.5–0.8 µg m−3 for the annual mean, and 2–4 µg m−3 for the 19th highest 1 h value). Conversely, planting new trees in consolidated urban areas causes a cooling effect (up to −0.15 °C as an annual mean) that may slightly increase concentration levels due to less-effective vertical mixing and wind-speed reduction caused by increased roughness. This highlights the need to combine nature-based solutions with emission-reduction measures in Madrid.
This is the first study that quantifies explicitly the impact of present vegetation on concentrations and depositions, considering simultaneously its effects on meteorology, biogenic emissions, ...dispersion, and dry deposition in three European cities: Bologna, Milan, and Madrid. The behaviour of three pollutants (O3, NO2, and PM10) was investigated considering two different scenarios, with the actual vegetation (VEG) and without it (NOVEG) for two months, representative of summer and winter seasons: July and January. The evaluation is based on simulations performed with two state-of-the-art atmospheric modelling systems (AMS) that use similar but not identical descriptions of physical and chemical atmospheric processes: AMS-MINNI for the two Italian cities and WRF-CMAQ for the Spanish city. The choice of using two AMS and applying one of them in two cities has been made to ensure the robustness of the results needed for their further generalization. The analysis of the spatial distribution of the vegetation effects on air concentrations and depositions shows that they are highly variable from one grid cell to another in the city area, with positive/negative effects or high/low effects in adjacent cells being observed for the three pollutants investigated in all cities. According to the pollutant, on a monthly basis, the highest differences in concentrations (VEG-NOVEG) produced by vegetation were estimated in July for O3 (−7.40 μg/m3 in Madrid and +2.67 μg/m3 in Milan) and NO2 (−3.01 μg/m3 in Milan and +7.17 μg/m3 in Madrid) and in January for PM10 (−3.14 μg/m3 in Milan +2.01 μg/m3 in Madrid). Thus, in some parts of the cities, the presence of vegetation had produced an increase in pollutant concentrations despite its efficient removal action that ranges from ca. 17% for O3 in Bologna (January) to ca. 77% for NO2 in Madrid (July).
Long-term exposure to air pollution has adverse respiratory health effects. We investigated the cross-sectional relationship between residential exposure to air pollutants and the risk of suffering ...from chronic respiratory diseases in some Italian cities.
In the BIGEPI project, we harmonised questionnaire data from two population-based studies conducted in 2007–2014. By combining self-reported diagnoses, symptoms and medication use, we identified cases of rhinitis (n = 965), asthma (n = 328), chronic bronchitis/chronic obstructive pulmonary disease (CB/COPD, n = 469), and controls (n = 2380) belonging to 13 cohorts from 8 Italian cities (Pavia, Turin, Verona, Terni, Pisa, Ancona, Palermo, Sassari). We derived mean residential concentrations of fine particulate matter (PM10, PM2.5), nitrogen dioxide (NO2), and summer ozone (O3) for the period 2013–2015 using spatiotemporal models at a 1 km resolution. We fitted logistic regression models with controls as reference category, a random-intercept for cohort, and adjusting for sex, age, education, BMI, smoking, and climate.
Mean ± SD exposures were 28.7 ± 6.0 μg/m3 (PM10), 20.1 ± 5.6 μg/m3 (PM2.5), 27.2 ± 9.7 μg/m3 (NO2), and 70.8 ± 4.2 μg/m3 (summer O3). The concentrations of PM10, PM2.5, and NO2 were higher in Northern Italian cities. We found associations between PM exposure and rhinitis (PM10: OR 1.62, 95%CI: 1.19–2.20 and PM2.5: OR 1.80, 95%CI: 1.16–2.81, per 10 μg/m3) and between NO2 exposure and CB/COPD (OR 1.22, 95%CI: 1.07–1.38 per 10 μg/m3), whereas asthma was not related to environmental exposures. Results remained consistent using different adjustment sets, including bi-pollutant models, and after excluding subjects who had changed residential address in the last 5 years.
We found novel evidence of association between long-term PM exposure and increased risk of rhinitis, the chronic respiratory disease with the highest prevalence in the general population. Exposure to NO2, a pollutant characterised by strong oxidative properties, seems to affect mainly CB/COPD.
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•Long-term exposure to air pollution has detrimental respiratory health effects.•We conducted a cross-sectional study on adults from 8 Italian centres.•PM10, PM2.5 and NO2 levels were above the WHO 2021 air quality guideline levels.•PM10 and PM2.5 exposures were mainly associated with the risk of having rhinitis.•Exposure to NO2 was associated with the risk of having chronic bronchitis/COPD.
The EURODELTA III exercise has facilitated a comprehensive intercomparison and evaluation of chemistry transport model performances. Participating models performed calculations for four 1-month ...periods in different seasons in the years 2006 to 2009, allowing the influence of different meteorological conditions on model performances to be evaluated. The exercise was performed with strict requirements for the input data, with few exceptions. As a consequence, most of differences in the outputs will be attributed to the differences in model formulations of chemical and physical processes. The models were evaluated mainly for background rural stations in Europe. The performance was assessed in terms of bias, root mean square error and correlation with respect to the concentrations of air pollutants (NO2, O3, SO2, PM10 and PM2.5), as well as key meteorological variables. Though most of meteorological parameters were prescribed, some variables like the planetary boundary layer (PBL) height and the vertical diffusion coefficient were derived in the model preprocessors and can partly explain the spread in model results. In general, the daytime PBL height is underestimated by all models. The largest variability of predicted PBL is observed over the ocean and seas. For ozone, this study shows the importance of proper boundary conditions for accurate model calculations and then on the regime of the gas and particle chemistry. The models show similar and quite good performance for nitrogen dioxide, whereas they struggle to accurately reproduce measured sulfur dioxide concentrations (for which the agreement with observations is the poorest). In general, the models provide a close-to-observations map of particulate matter (PM2.5 and PM10) concentrations over Europe rather with correlations in the range 0.4–0.7 and a systematic underestimation reaching −10 µg m−3 for PM10. The highest concentrations are much more underestimated, particularly in wintertime. Further evaluation of the mean diurnal cycles of PM reveals a general model tendency to overestimate the effect of the PBL height rise on PM levels in the morning, while the intensity of afternoon chemistry leads formation of secondary species to be underestimated. This results in larger modelled PM diurnal variations than the observations for all seasons. The models tend to be too sensitive to the daily variation of the PBL. All in all, in most cases model performances are more influenced by the model setup than the season. The good representation of temporal evolution of wind speed is the most responsible for models' skillfulness in reproducing the daily variability of pollutant concentrations (e.g. the development of peak episodes), while the reconstruction of the PBL diurnal cycle seems to play a larger role in driving the corresponding pollutant diurnal cycle and hence determines the presence of systematic positive and negative biases detectable on daily basis.
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