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 (PM 10 ) with nitrogen dioxide (NO 2 ) and ozone (O 3 ) 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 PM 10 , NO 2 and O 3 in 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 PM 10 was 1.6 times higher and NO 2 2.6 times higher than WHO limits, with wide gradients between urban and non-urban areas. PM 10 and NO 2 were positively associated with SARS-CoV-2 infectivity and mortality, and PM 10 with hospitalizations in urban areas. Interaction analyses estimated that the effect of PM 10 (per 3.5 µg/m 3 ) 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 NO 2 co-exposure (HR=1.15, 1.08-1.22). At higher levels of PM 10 co-exposure the protective association of ozone reversed (HR=1.32, 1.17-1.49), yielding to 278 additional cases per µg/m 3 increase in O 3 . 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.

Association between short-term exposure to air pollutants and cause-specific daily mortality in Italy. A nationwide analysis.

BACKGROUND/AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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

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.

Estimating Daily PM2.5 and PM10 over Italy Using an Ensemble Model.

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.

Effects of traffic-related air and noise pollution exposure on allergic diseases in the elderly: an observational study.

INTRODUCTION: Traffic-related air and noise pollution are important public health issues. The aim of this study was to estimate their effects on allergic/respiratory outcomes in adult and elderly subjects. MATERIALS AND METHODS: Six hundred and forty-five subjects living in Pisa (Tuscany, Italy) were investigated through a questionnaire on allergic/respiratory symptoms and diseases. Traffic-related air pollution and noise exposures were assessed at residential address by questionnaire, modelled annual mean NO2 concentrations (1 km and 200 m resolution), and noise level over a 24-h period (Lden). Exposure effects were assessed through logistic regression models stratified by age group (18-64 years, ≥65 years), and adjusted for sex, educational level, occupational exposure, and smoking habits. RESULTS: 63.6% of the subjects reported traffic exposure near home. Mean exposure levels were: 28.24 (±3.26 SD) and 27.23 (±3.16 SD) µg/m3 for NO2 at 200 m and 1 km of resolution, respectively; 57.79 dB(A) (±6.12 SD) for Lden. Exposure to vehicular traffic (by questionnaire) and to high noise levels [Lden ≥ 60 dB(A)] were significantly associated with higher odds of allergic rhinitis (OR 2.01, 95%CI 1.09-3.70, and OR 1.99, 95%CI 1.18-3.36, respectively) and borderline with rhino-conjunctivitis (OR 2.20, 95%CI 0.95-5.10, and OR 1.76, 95%CI 0.91-3.42, respectively) only in the elderly. No significant result emerged for NO2. CONCLUSIONS: Our findings highlighted the need to better assess the effect of traffic-related exposure in the elderly, considering the increasing trend in the future global population's ageing.

Global population is ageing.Allergic diseases are globally widespread even on adult population.The susceptibility due to ageing may increase the impact of air pollution on the elderly.Traffic-related air and noise pollution affects allergic status of the elderly.

Long-term residential exposure to air pollution and risk of chronic respiratory diseases in Italy: The BIGEPI study.

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.

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.

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

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.