Interactive effects of long-Term exposure to air pollutants on SARS-CoV-2 infection and severity: A northern Italian population-based cohort study

Background We examined interactions, to our knowledge not yet explored, between long-Term exposures to particulate matter (PM10) with nitrogen dioxide (NO2) and ozone (O3) on SARS-CoV-2 infectivity and severity. Methods We followed 709,864 adult residents of Varese Province from 1 February 2020 until the first positive test, COVID-19 hospitalization, or death, up to 31 December 2020. We estimated residential annual means of PM10, NO2and O3in 2019 from chemical-Transport and random-forest models. We estimated interactive effects of pollutants with urbanicity on SARS-CoV-2 infectivity, hospitalization, and mortality endpoints using Cox regression models adjusted for socio-demographic factors and comorbidities, and additional cases due to interactions using Poisson models. Results 41,065 individuals were infected, 5,203 were hospitalized and 1,543 died from COVID-19 during follow-up. Mean PM10was 1.6 times higher and NO22.6 times higher than WHO limits, with wide gradients between urban and non-urban areas. PM10and NO2were positively associated with SARS-CoV-2 infectivity and mortality, and PM10with hospitalizations in urban areas. Interaction analyses estimated that the effect of PM10(per 3.5 μg/m3) on infectivity was strongest in urban areas (HR=1.12, 95%CI:1.09-1.16), corresponding to 854 additional cases per 100,000 person-years, and in areas at high NO2co-exposure (HR=1.15, 1.08-1.22). At higher levels of PM10co-exposure the protective association of ozone reversed (HR=1.32, 1.17-1.49), yielding to 278 additional cases per μg/m3increase in O3. We estimated similar interactive effects for severity endpoints. Conclusions We estimate that interactive effects between pollutants exacerbated the burden of SARS-CoV-2 pandemic in urban areas

Interactive effects of long-term exposure to air pollutants on SARS-CoV-2 infection and severity: a northern Italian population-based cohort study

Background We examined interactions, to our knowledge not yet explored, between long-Term exposures to particulate matter (PM10) with nitrogen dioxide (NO2) and ozone (O3) on SARS-CoV-2 infectivity and severity. Methods We followed 709,864 adult residents of Varese Province from 1 February 2020 until the first positive test, COVID-19 hospitalization, or death, up to 31 December 2020. We estimated residential annual means of PM10, NO2and O3in 2019 from chemical-Transport and random-forest models. We estimated interactive effects of pollutants with urbanicity on SARS-CoV-2 infectivity, hospitalization, and mortality endpoints using Cox regression models adjusted for socio-demographic factors and comorbidities, and additional cases due to interactions using Poisson models. Results 41,065 individuals were infected, 5,203 were hospitalized and 1,543 died from COVID-19 during follow-up. Mean PM10was 1.6 times higher and NO22.6 times higher than WHO limits, with wide gradients between urban and non-urban areas. PM10and NO2were positively associated with SARS-CoV-2 infectivity and mortality, and PM10with hospitalizations in urban areas. Interaction analyses estimated that the effect of PM10(per 3.5 μg/m3) on infectivity was strongest in urban areas (HR=1.12, 95%CI:1.09-1.16), corresponding to 854 additional cases per 100,000 person-years, and in areas at high NO2co-exposure (HR=1.15, 1.08-1.22). At higher levels of PM10co-exposure the protective association of ozone reversed (HR=1.32, 1.17-1.49), yielding to 278 additional cases per μg/m3increase in O3. We estimated similar interactive effects for severity endpoints. Conclusions We estimate that interactive effects between pollutants exacerbated the burden of SARS-CoV-2 pandemic in urban areas

Past exposure to PM10 and lung cancer risk in the EAGLE case–control study

Within the EAGLE population-based case-control study, the present study aims to integrate previous analyses which suggested an increased lung cancer risk associated with particulate matter ≤ 10 μm (PM10) exposure estimated 2–5 years before diagnosis (year 2000), by considering pollutant levels estimated 12–15 years before diagnosis (year 1990), i.e., in a potentially more relevant time window. Odds ratios (OR) and 95% confidence intervals (CI) were calculated through multivariate unconditional logistic regression. Mean PM10 levels were higher in 1990 than in 2000 (68 vs. 46.6 μg/m3). Contrary to what we previously observed, among 1,665 cases and 1,808 controls we found no association between 1990 PM10 concentrations and lung cancer risk (OR: 0.96, 95%CI: 0.75–1.24). We further confirmed this difference also considering exposure from the two years combined, mutually adjusting for one another (0.89, 0.87–1.00 in 1990 and 1.49, 1.06–2.08 in 2000). The observed differences might be related to several factors: distinct methodologies used to estimate exposure, coarser granularity of 1990 exposure estimates, dissimilar levels in pollutant concentrations. However, our results might also suggest a greater relevance of more recent exposures in the carcinogenic process, thus contributing to the intriguing hypothesis that air pollution might act as a promoter of cancer development

Vegetation Effects on Air Pollution: A Comprehensive Assessment for Two Italian Cities

The role of urban vegetation in urban air quality is usually assessed by considering only the pollutant removal capacity of the plants. This study aims to show, for the first time, the effects of vegetation on air pollutant concentrations through its effects on meteorology, separately from its biogenic emissions. It also investigates how air quality changes when only biogenic emissions are altered by using plants with different emission factors, as well as the potential effects of introducing new vegetation into urban areas. These assessments were conducted using atmospheric modelling systems currently employed for air quality forecasting and planning, configured specifically for the cities of Bologna and Milan. Simulations were performed for two representative months, July and January, to capture summer and winter conditions, respectively. The variability in air concentrations of ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10) within the municipal boundaries was assessed monthly. When evaluating the impact of future vegetation, changes in temperature, wind speed, and relative humidity were also considered. The results indicate that vegetation influences air quality more significantly through changes in meteorological conditions than through biogenic emissions. Changes in biogenic emissions result in similar behaviours in O3 and PM10 concentrations, with the latter being affected by the changes in the concentrations of secondary biogenic aerosols formed in the atmosphere. Changes in NO2 concentrations are controlled by the changes in O3 concentrations, increasing where O3 concentrations decrease, and vice versa, as expected in highly polluted areas. Meteorologically induced vegetation effects also play a predominant role in depositions, accounting for most of the changes; however, the concentrations remain high despite increased deposition rates. Therefore, understanding only the removal characteristics of vegetation is insufficient to quantify its effects on urban air pollution

Short-term effects of air pollution on cardiovascular hospitalizations in the pisan longitudinal study

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: PM10, 2011-2015; PM2.5, 2013-2015) and 200 m (PM10, PM2.5, NO2, O3, 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/m3 increase; lag 0-6). During the period 2011-2015 (137 hospitalizations), a significant effect at lag 0 was observed for PM10 (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 PM10 (OR = 1.268, CI: 1.085-1.483) and PM2.5 (OR = 1.273, CI: 1.053-1.540) at 1 km resolution, as well as for PM10 (OR = 1.365, CI: 1.103-1.690), PM2.5 (OR = 1.264, CI: 1.006-1.589) and NO2 (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

Assessment of Air Quality and Meteorological Changes Induced by Future Vegetation in Madrid

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

The impact of the spatial resolution of vegetation cover on the prediction of airborne pollen concentrations over northern Italy

Accurate pollen forecasting models can help the self-management of allergic respiratory diseases. Our study introduces and validates, for the first time, a pollen modelling system covering the Veneto Region (Italy) at the 3 km spatial resolution for 2019. The model simulated the pollen dispersion, diffusion and deposition processes, using vegetation cover (VC) maps, phenological pollen emission algorithms, and meteorological forecasting. We have specifically analysed the influence of the spatial resolution of VC maps on predicted airborne pollen concentrations for alder, birch, olive, grass, and ragweed. Two VC datasets were used: CAMS VC: the European CAMS dataset at ca. 10 km horizontal resolution; detailed VC: high-resolution datasets (from 250 m to 1 km spatial resolution). Predicted daily averaged concentrations obtained with CAMS and detailed VC were compared to the observations collected at 15 monitoring stations using model performance indicators and pollen seasonal-derived parameters. A stratified analysis assessed performance variations in lowland versus mountain environments. The results showed a reduction of the root mean square error (RMSE) for alder and birch pollen using the detailed VC (detailed VC vs. CAMS VC: 15.7 vs. 133.6; 17.8 vs. 52.5 p/m3, respectively), while higher RMSE resulted for grass (24.5 vs. 20.7 p/m3). Similar RMSEs were obtained for olive and ragweed pollen (3.8 vs. 4.0; 3.9 vs. 3.9 p/m3, respectively). Results from the differences in Seasonal Pollen Integrals (SPIn) were consistent with the RMSE patterns. The onset of pollen seasons was more accurately predicted than their end. The general improvement of pollen predictions obtained with the detailed VC was particularly evident in the mountains. Incorporating data from detailed vegetation maps into atmospheric dispersion models has significantly improved predictions for arboreal pollen (alder, birch, olive), especially in complex surfaces where high-resolution input data is crucial

Calibrated prediction regions for Gaussian random fields

This paper proposes a method to construct well-calibrated frequentist prediction regions, with particular regard to the highest prediction density regions, which may be useful for multivariate spatial prediction. We consider, in particular, Gaussian random fields, and using a calibrating procedure we effectively improve the estimative prediction regions, because the coverage probability turns out to be closer to the target nominal value. Whenever a closed-form expression for the well-calibrated prediction region is not available, we may specify a simple bootstrap-based estimator. Particular attention is dedicated to the associated, improved predictive distribution function, which can be usefully considered for identifying spatial locations with extreme or unusual observations. A simulation study is proposed in order to compare empirically the calibrated predictive regions with the estimative ones. The proposed method is then applied to the global model assessment of a deterministic model for the prediction of PM10 levels using data from a network of air quality monitoring stations

FUTURE EMISSION SCENARIO ANALYSIS OVER ROME URBAN AREA USING COUPLED TRAFFIC ASSIGNMENT AND CHEMICAL TRANSPORT MODELS

The city of Rome is characterized by high ozone, NO2 and PM10 levels claiming for the implementation of emission control strategies to improve the air quality and to decrease the risks of health effects on inhabitants. In this perspective an atmospheric modelling system based on the chemical transport model FARM has been applied for the year 2005 over a nested domain including the metropolitan area. To improve the description of local scale atmospheric circulation characteristics, observational meteorological data are analysed using the Isentropic Analysis package (ISAN). Since urban traffic emissions represent a relevant source of pollutants, hourly emissions coming from this sector have been estimated by means of a traffic assignment model, based on a source-destination approach, coupled with an emission model based on COPERT-3 methodology. The emissions from the other sectors have been derived from the national inventory and then disaggregated at the municipal level. The analysis of model results for the year 2005 against experimental data reveals a good agreement suggesting the use of the modelling system to study the impact on the air quality of different emission control strategies at both regional and urban scales. The 2010 has been considered as the future year base case scenario and the traffic limitation within the Rome urban core has been considered as an emission control action. The impact of this emission scenario has been then analysed by means of a semi-empiric approach: a significant decrease of PM10 and NO2 yearly average concentrations is expected to occur at urban traffic stations while the minimum reduction is expected at urban background and rural stations