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Background: Despite the relevance for occupational safety policies, the health effects of temperature on occupational injuries have been scarcely investigated. A nationwide epidemiological study was carried out to estimate the risk of injuries for workers exposed to extreme temperature and identify economic sectors and jobs most at risk. Materials and methods: The daily time series of work-related injuries in the industrial and services sector from the Italian national workers' compensation authority (INAIL) were collected for each of the 8090 Italian municipalities in the period 2006–2010. Daily air temperatures with a 1 × 1 km resolution derived from satellite land surface temperature data using mixed regression models were included. Distributed lag non-linear models (DLNM) were used to estimate the association between daily mean air temperature and injuries at municipal level. A meta-analysis was then carried out to retrieve national estimates. The relative risk (RR) and attributable cases of work-related injuries for an increase in mean temperature above the 75th percentile (heat) and for a decrease below the 25th percentile (cold) were estimated. Effect modification by gender, age, firm size, economic sector and job type were also assessed. Results: The study considered 2,277,432 occupational injuries occurred in Italy in the period 2006–2010. There were significant effects for both heat and cold temperatures. The overall relative risks (RR) of occupational injury for heat and cold were 1.17 (95% CI: 1.14–1.21) and 1.23 (95% CI: 1.17–1.30), respectively. The number of occupational injuries attributable to temperatures above and below the thresholds was estimated to be 5211 per year. A higher risk of injury on hot days was found among males and young (age 15–34) workers occupied in small-medium size firms, while the opposite was observed on cold days. Construction workers showed the highest risk of injuries on hot days while fishing, transport, electricity, gas and water distribution workers did it on cold days. Conclusions: Prevention of the occupational exposure to extreme temperatures is a concern for occupational health and safety policies, and will become a critical issue in future years considering climate change. Epidemiological studies may help identify vulnerable jobs, activities and workers in order to define prevention plans and training to reduce occupational exposure to extreme temperature and the risk of work-related injuries. Keywords: Climate change, Extreme outdoor air temperature, Occupational injuries, Heat impacts, Cold impacts, Case-crossover study

Background: The majority of studies have shown higher greenness exposure associated with reduced mortality risks, but few controlled for spatially correlated air pollution and traffic noise exposures. We aim to address this research gap in the ELAPSE pooled cohort., Methods: Mean Normalized Difference Vegetation Index (NDVI) in a 300-m grid cell and 1-km radius were assigned to participants' baseline home addresses as a measure of surrounding greenness exposure. We used Cox proportional hazards models to estimate the association of NDVI exposure with natural-cause and cause-specific mortality, adjusting for a number of potential confounders including socioeconomic status and lifestyle factors at individual and area-levels. We further assessed the associations between greenness exposure and mortality after adjusting for fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ) and road traffic noise., Results: The pooled study population comprised 327,388 individuals who experienced 47,179 natural-cause deaths during 6,374,370 person-years of follow-up. The mean NDVI in the pooled cohort was 0.33 (SD 0.1) and 0.34 (SD 0.1) in the 300-m grid and 1-km buffer. In the main fully adjusted model, 0.1 unit increment of NDVI inside 300-m grid was associated with 5% lower risk of natural-cause mortality (Hazard Ratio (HR) 0.95 (95% CI: 0.94, 0.96)). The associations attenuated after adjustment for air pollution [HR (95% CI): 0.97 (0.96, 0.98) adjusted for PM 2.5 ; 0.98 (0.96, 0.99) adjusted for NO 2 ]. Additional adjustment for traffic noise hardly affected the associations. Consistent results were observed for NDVI within 1-km buffer. After adjustment for air pollution, NDVI was inversely associated with diabetes, respiratory and lung cancer mortality, yet with wider 95% confidence intervals. No association with cardiovascular mortality was found., Conclusions: We found a significant inverse association between surrounding greenness and natural-cause mortality, which remained after adjusting for spatially correlated air pollution and traffic noise., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: The association between long-term exposure to air pollution and mortality from cardiorespiratory diseases is well established, yet the evidence for other diseases remains limited., Objectives: To examine the associations of long-term exposure to air pollution with mortality from diabetes, dementia, psychiatric disorders, chronic kidney disease (CKD), asthma, acute lower respiratory infection (ALRI), as well as mortality from all-natural and cardiorespiratory causes in the Danish nationwide administrative cohort., Methods: We followed all residents aged ≥ 30 years (3,083,227) in Denmark from 1 January 2000 until 31 December 2017. Annual mean concentrations of fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), black carbon (BC), and ozone (warm season) were estimated using European-wide hybrid land-use regression models (100 m × 100 m) and assigned to baseline residential addresses. We used Cox proportional hazard models to evaluate the association between air pollution and mortality, accounting for demographic and socioeconomic factors. We additionally applied indirect adjustment for smoking and body mass index (BMI)., Results: During 47,023,454 person-years of follow-up, 803,881 people died from natural causes. Long-term exposure to PM 2.5 (mean: 12.4 µg/m 3 ), NO 2 (20.3 µg/m 3 ), and/or BC (1.0 × 10 -5 /m) was statistically significantly associated with all studied mortality outcomes except CKD. A 5 µg/m 3 increase in PM 2.5 was associated with higher mortality from all-natural causes (hazard ratio 1.11; 95% confidence interval 1.09-1.13), cardiovascular disease (1.09; 1.07-1.12), respiratory disease (1.11; 1.07-1.15), lung cancer (1.19; 1.15-1.24), diabetes (1.10; 1.04-1.16), dementia (1.05; 1.00-1.10), psychiatric disorders (1.38; 1.27-1.50), asthma (1.13; 0.94-1.36), and ALRI (1.14; 1.09-1.20). Associations with long-term exposure to ozone (mean: 80.2 µg/m 3 ) were generally negative but became significantly positive for several endpoints in two-pollutant models. Generally, associations were attenuated but remained significant after indirect adjustment for smoking and BMI., Conclusion: Long-term exposure to PM 2.5 , NO 2 , and/or BC in Denmark were associated with mortality beyond cardiorespiratory diseases, including diabetes, dementia, psychiatric disorders, asthma, and ALRI., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Desert dust storms pose real threats to air quality and health of millions of people in source regions, with associated impacts extending to downwind areas. Europe (EU) is frequently affected by atmospheric transport of desert dust from the Northern Africa and Middle East drylands. This investigation aims at quantifying the role of desert dust transport events on air quality (AQ) over Italy, which is among the EU countries most impacted by this phenomenon. We focus on the particulate matter (PM) metrics regulated by the EU AQ Directive. In particular, we use multiannual (2006-2012) PM10 records collected in hundreds monitoring sites within the national AQ network to quantify daily and annual contributions of dust during transport episodes. The methodology followed was built on specific European Commission guidelines released to evaluate the natural contributions to the measured PM-levels, and was partially modified, tested and adapted to the Italian case in a previous study. Overall, we show that impact of dust on the yearly average PM10 has a clear latitudinal gradient (from less than 1 to greater than 10 µg/m 3 going from north to south Italy), this feature being mainly driven by an increased number of dust episodes per year with decreasing latitude. Conversely, the daily-average dust-PM10 (≅12 µg/m 3 ) is more homogenous over the country and shown to be mainly influenced by the site type, with enhanced values in more urbanized locations. This study also combines the PM10 measurements-approach with geostatistical modelling. In particular, exploiting the dust-PM10 dataset obtained at site- and daily-resolution over Italy, a geostatistical, random-forest model was set up to derive a daily, spatially-continuous field of desert-dust PM10 at high (1-km) resolution. This finely resolved information represent the basis for a follow up investigation of both acute and chronic health effects of desert dust over Italy, stemming from daily and annual exposures, respectively., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: We evaluated methods for the analysis of multi-level survival data using a pooled dataset of 14 cohorts participating in the ELAPSE project investigating associations between residential exposure to low levels of air pollution (PM 2.5 and NO 2 ) and health (natural-cause mortality and cerebrovascular, coronary and lung cancer incidence)., Methods: We applied five approaches in a multivariable Cox model to account for the first level of clustering corresponding to cohort specification: (1) not accounting for the cohort or using (2) indicator variables, (3) strata, (4) a frailty term in frailty Cox models, (5) a random intercept under a mixed Cox, for cohort identification. We accounted for the second level of clustering due to common characteristics in the residential area by (1) a random intercept per small area or (2) applying variance correction. We assessed the stratified, frailty and mixed Cox approach through simulations under different scenarios for heterogeneity in the underlying hazards and the air pollution effects., Results: Effect estimates were stable under approaches used to adjust for cohort but substantially differed when no adjustment was applied. Further adjustment for the small area grouping increased the effect estimates' standard errors. Simulations confirmed identical results between the stratified and frailty models. In ELAPSE we selected a stratified multivariable Cox model to account for between-cohort heterogeneity without adjustment for small area level, due to the small number of subjects and events in the latter., Conclusions: Our study supports the need to account for between-cohort heterogeneity in multi-center collaborations using pooled individual level data., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background/aim: Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence., Methods: The "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO 2 ), fine particulate matter (PM 2.5 ), black carbon (BC), and ozone (O 3 ) to assign exposure to cohort participants' residential addresses in 100 m by 100 m grids. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines., Results: The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO 2 , PM 2.5 , BC and O 3 (warm season) were 24.2 µg/m 3 (19.5, 29.7), 15.4 µg/m 3 (12.8, 17.3), 1.6 10 -5 m -1 (1.3, 1.8), and 86.6 µg/m 3 (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM 2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m 3 ). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exposure below the EU limit value of 25 µg/m 3 . We did not observe associations between NO 2 , BC or O 3 and lung cancer incidence., Conclusions: Long-term ambient PM 2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM 10 (PM < 10 μm), fine (PM < 2.5 μm, PM 2.5 ) and coarse particles (PM between 2.5 and 10 μm, PM 2.5-10 ) at 1-km 2 grid for 2013-2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM 2.5 and PM 2.5-10 concentrations in monitors where only PM 10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km 2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R 2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM 10 and PM 2.5 , respectively. Model fitting was less optimal for PM 2.5-10 , in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Increased PM 10 concentrations are commonly observed during Saharan dust advections. Limited epidemiological evidence suggests that PM 10 from anthropogenic and desert sources increase mortality. We aimed to evaluate the association between source-specific PM 10 (non-desert and desert) and cause-specific mortality in Sicily during 2006-2012 period., Methods: Daily PM 10 concentrations at 1-km 2 were estimated in Sicily using satellite-based data, fixed monitors and land use variables. We identified Saharan dust episodes using meteorological models, back-trajectories, aerosol maps, and satellite images. For each dust day, we estimated desert and non-desert PM 10 concentrations. We applied a time-series approach on 390 municipalities of Sicily to estimate the association between PM 10 (non-desert and desert) and daily cause-specific mortality., Results: 33% of all days were affected by Saharan dust advections. PM 10 concentrations were 8 μg/m 3 higher during dust days compared to other days. We found positive associations of both non-desert and desert PM 10 with cause-specific mortality. We estimated percent increases of risk (IR%) of non-accidental mortality equal to 2.3% (95% Confidence Interval [CI]: 1.4, 3.1) and 3.8% (3.2, 4.4), per 10 μg/m 3 increases in non-desert and desert PM 10 at lag 0-5, respectively. We also observed significant associations with cardiovascular (2.4% [1.3, 3.4] and 4.5% [3.8, 5.3]) and respiratory mortality (8.1% [6.8, 9.5], and 6.3% [5.4, 7.2]). We estimated higher effects during April-September, with IR% = 4.4% (3.2, 5.7) and 6.3% (5.4, 7.2) for non-desert and desert PM 10 , respectively., Conclusions: Our results confirm previous evidence of harmful effects of desert PM 10 on non-accidental and cardio-respiratory mortality, especially during the warm season., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The few cohort studies that have investigated the association between exposure to air pollution and occurrence of diabetes have reported conflicting results. We aimed to evaluate the association of long-term exposure to particulate matter (PM), nitrogen oxides (NO x ) and ozone (O 3 ), with baseline prevalence and incidence of type 2 diabetes in a large administrative cohort in Rome, Italy. A total of 1,425,580 subjects aged 35+years (January 1st, 2008) were assessed and followed for six years. We estimated PM 10 , PM 2.5-10 , PM 2.5 , NO 2 , and NO x exposures at residence using land use regression models, and summer O 3 exposure using dispersion modeling. To estimate the association between air pollutant exposures and prevalence and incidence of diabetes, we used logistic and Cox regression models, considering individual, environmental (noise and green areas), and contextual characteristics. We identified 106,387 prevalent cases at baseline and 65,955 incident cases during the follow-up period. We found positive associations between nitrogen oxides exposures and prevalence of diabetes with odds ratios (ORs) up to 1.010 (95% CI: 1.002, 1.017) and 1.015 (1.009, 1.021) for NO 2 and NOx, respectively, per fixed increases (per 10μg/m 3 and 20μg/m 3 , respectively). We also found some evidence of an association between NOx and O 3 and incidence of diabetes, with hazard ratios (HRs) of 1.011 (95%CI: 1.003-1.019) and 1.015 (1.002-1.027) per 20 and 10μg/m 3 increases, respectively. The association with O 3 with incident diabetes was stronger in women than in men and among those aged <50years. In sum, long-term exposure to nitrogen oxides was associated with prevalent diabetes while NOx and O 3 exposures were associated with incident diabetes., (Copyright © 2017 Elsevier Ltd. All rights reserved.)