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 PM10 (PM < 10 μm), fine (PM < 2.5 μm, PM2.5) and coarse particles (PM between 2.5 and 10 μm, PM2.5–10) at 1-km2 grid for 2013–2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5–10 concentrations in monitors where only PM10 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-km2 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) R2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5–10, 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.
•Estimates of fine and coarse particles at fine spatiotemporal scale are lacking in Italy•We applied a multistage random forest model combining PM data with satellite, land-use and meteorology•We imputed missing satellite AOD data using ensemble atmospheric models•We estimated daily PM10, PM2.5 and PM2.5-10 at a 1-km2 grid over Italy for the years 2013-2015•Our model displayed good CV fitting (R2=0.75 for PM10, R2=0.80 for PM2.5, R2=0.64 for PM2.5-10) and negligible bias
Few European studies have investigated the effects of long-term exposure to both fine particulate matter (≤ 2.5 µm; PM2.5) and nitrogen dioxide (NO2) on mortality.
We studied the association of ...exposure to NO2, PM2.5, and traffic indicators on cause-specific mortality to evaluate the form of the concentration-response relationship.
We analyzed a population-based cohort enrolled at the 2001 Italian census with 9 years of follow-up. We selected all 1,265,058 subjects ≥ 30 years of age who had been living in Rome for at least 5 years at baseline. Residential exposures included annual NO2 (from a land use regression model) and annual PM2.5 (from a Eulerian dispersion model), as well as distance to roads with > 10,000 vehicles/day and traffic intensity. We used Cox regression models to estimate associations with cause-specific mortality adjusted for individual (sex, age, place of birth, residential history, marital status, education, occupation) and area (socioeconomic status, clustering) characteristics.
Long-term exposures to both NO2 and PM2.5 were associated with an increase in nonaccidental mortality hazard ratio (HR) = 1.03 (95% CI: 1.02, 1.03) per 10-µg/m3 NO2; HR = 1.04 (95% CI: 1.03, 1.05) per 10-µg/m3 PM2.5. The strongest association was found for ischemic heart diseases (IHD) HR = 1.10 (95% CI: 1.06, 1.13) per 10-µg/m3 PM2.5, followed by cardiovascular diseases and lung cancer. The only association showing some deviation from linearity was that between NO2 and IHD. In a bi-pollutant model, the estimated effect of NO2 on mortality was independent of PM2.5.
This large study strongly supports an effect of long-term exposure to NO2 and PM2.5 on mortality, especially from cardiovascular causes. The results are relevant for the next European policy decisions regarding air quality.
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.
The causal association between mesothelioma and asbestos exposure is conclusive, and many studies have proved that the trend in asbestos use is a strong predictor of the pattern in mesothelioma cases ...with an adequate latency time (generally around 30-40 years or more). Recently, a novel approach for predicting malignant pleural mesothelioma, based on asbestos consumption trend and using distributed non-linear models, has been applied.
The purpose of this study is to analyse trends in asbestos consumption and malignant mesothelioma mortality in the major asbestos-user countries. Furthermore, we applied distributed non-linear models to estimate and compare epidemiological relationships between asbestos consumption and mesothelioma mortality across these countries.
The study involves major asbestos-user countries in which historical asbestos consumption and mesothelioma mortality data are available. Data on asbestos consumption were derived from worldwide asbestos supply and mesothelioma mortality data from World Health Organization (WHO) mortality archives. A quasi-Poisson generalized linear model was used to model past asbestos exposure and male mesothelioma mortality rates in each country. Exposure-response associations have been modelled using distributed lag non-linear models.
According to the criteria defined above, we selected 18 countries with raw asbestos cumulative consumptions higher than two million tons in the period 1933-2012. Overall, a clear linear relationship can be observed between total consumption and total deaths for mesothelioma. Country-specific exposure, lag and age-response relationships were identified and common functions extracted by a meta-analysis procedure. Non-linear models appear suitable and flexible tools for investigating the association between mesothelioma mortality and asbestos consumption. There is a need to improve the global epidemiological surveillance of asbestos-related diseases, particularly mesothelioma mortality, and the absence of reliable data for some major asbestos-user countries is a real concern. A reliable assessment of mesothelioma mortality is a fundamental step towards increasing the awareness of related risks and the need of an international ban on asbestos.
Few studies have explored the role of air pollution in neurodegenerative processes, especially various types of dementia. Our aim was to evaluate the association between long-term exposure to air ...pollution and first hospitalization for dementia subtypes in a large administrative cohort.
We selected 350,844 subjects (free of dementia) aged 65-100 years at inclusion (21/10/2001) and followed them until 31/12/2013. We selected all subjects hospitalized for the first time with primary or secondary diagnoses of various forms of dementia. We estimated the exposure at residence using land use regression models for nitrogen oxides (NOx, NO
) and particulate matter (PM) and a chemical transport model for ozone (O
). We used Cox models to estimate the association between exposure and first hospitalization for dementia and its subtypes: vascular dementia (Vd), Alzheimer's disease (Ad) and senile dementia (Sd).
We selected 21,548 first hospitalizations for dementia (7497 for Vd, 7669 for Ad and 7833 for Sd). Overall, we observed a negative association between exposure to NO
(10 μg/m
) and dementia hospitalizations (HR = 0.97; 95% CI: 0.96-0.99) and a positive association between exposure to O
, NOx and dementia hospitalizations, (O
: HR = 1.06; 95% CI: 1.04-1.09 per 10 μg/m
; NOx: HR = 1.01; 95% CI: 1.00-1.02 per 20 μg/m
).H. Exposure to NOx, NO
, PM
, and PM
was positively associated with Vd and negatively associated with Ad. Hospitalization for Sd was positively associated with exposure to O
(HR = 1.20; 95% CI: 1.15-1.24 per 10 μg/m
).
Our results showed a positive association between exposure to NOx and O
and hospitalization for dementia and a negative association between NO
exposure and hospitalization for dementia. In the analysis by subtype, exposure to each pollutants (except O
) demonstrated a positive association with vascular dementia, while O
exposure was associated with senile dementia. The results regarding vascular dementia are a clear indication that the brain effects of air pollution are linked with vascular damage.
•Road accidents analysed for their association with extreme temperatures.•Temperatures were provided by a meteorological model.•Case-crossover and time series study designs were applied for risk ...analysis.•Road accidents show a positive association with hot temperatures.•Effect modifications estimated for general indistinct and work-related accidents.
Despite the relevance of road crashes and their impact on social and health care costs, the effects of extreme temperatures on road crashes risk have been scarcely investigated, particularly for those occurring in occupational activities. A nationwide epidemiological study was carried out to estimate the risk of general indistinct and work-related road crashes related with extreme temperatures and to identify crash and occupation parameters mostly involved. Data about road crashes, resulting in death or injury, occurring during years 2013–2015 in Italy, were collected from the National Institute of Statistics, for general indistinct road crashes, and from the compensation claim applications registered by the national workers' compensation authority, for work-related ones. Time series of hourly temperature were derived from the results provided by the meteorological model WRF applied at a national domain with 5 km resolution. To consider the different spatial-temporal characteristics of the two road crashes archives, the association with extreme temperatures was estimated by means of a case-crossover time-stratified approach using conditional logistic regression analysis, and a time-series analysis, using over-dispersed Poisson generalized linear regression model, for general indistinct and work-related datasets respectively. The analyses were controlled for other covariates and confounding variables (including precipitation). Non-linearity and lag effects were considered by using a distributed lag non-linear model. Relative risks were calculated for increment from 75th to 99th percentiles (hot) and from 25 to first percentile (cold) of temperature. Results for general indistinct crashes show a positive association with hot temperature (RR = 1.12, 95 % CI: 1.09–1.16) and a negative one for cold (RR = 0.93, 95 % CI: 0.91−0.96), while for work-related crashes a positive association was found for both hot and cold (RR = 1.06 (95 % CI: 1.01–1.11) and RR = 1.10 (95 % CI: 1.05–1.16). The use of motorcycles, the location of accident (urban vs out of town), presence of crossroads, as well as occupational factors like the use of a vehicle on duty were all found to produce higher risks of road crashes during extreme temperatures. Mitigation and prevention measures are needed to limit social and health care costs.
Mobility of population is one of major problems in large metropolitan areas. It affects different social, economic and environmental aspects of a city’s life. Urban mobility is characterised by ...highly dynamic spatial-temporal variability not detected by census or surveys. Mobile phones allow tracking of the population, giving information about their time–space location. A multi-city population mobility study was carried out in seven main Italian large metropolitan areas for 2 months (1st March to 30th April, 2015), starting from high-resolution gridded population data derived from mobile phone traffic data. Mean daily–nightly variations of population density were calculated during weekdays with increments of population of about 25% on average and peaks up by 200% in hotspot areas. Strong spatial gradients were identified with both accumulation and depletion of population in urban zones of geographical dimensions of 55 and 44%, respectively, of the municipality areas on average. Larger metropolitan areas, such as Rome, Milan, and Turin, show the highest increments of population, while medium-sized cities (e.g., Bari and Palermo) exhibit lower values and weaker gradients of variation of population. By means of a developed mobility factor, three main mobility phenomena (morning and evening commuting, diffuse mobility) occurring during weekdays were identified and characterized.
•185,000 Nationwide occupational injuries in construction sector were analyzed;•A significant association of occupational injuries with high temperatures was found;•Occupation injuries among ...construction workers increased during heat waves;•Workers operating with hand-held tools, machine and handling of objects were at risk;•Construction, quarry and industrial sites were the work environments most at risk.
Extreme temperatures have impact on the health and occupational injuries. The construction sector is particularly exposed. This study aims to investigate the association between extreme temperatures and occupation injuries in this sector, getting an insight in the main accidents-related parameters.
Occupational injuries in the construction sector, with characteristic of accidents, were retrieved from Italian compensation data during years 2014–2019. Air temperatures were derived from ERA5-land Copernicus dataset. A region based time-series analysis, in which an over-dispersed Poisson generalized linear regression model, accounting for potential non-linearity of the exposure- response curve and delayed effect, was applied, and followed by a meta-analysis of region-specific estimates to obtain a national estimate. The relative risk (RR) and attributable cases of work-related injuries for an increase in mean temperature above the 75th percentile (hot) and for a decrease below the 25th percentile (cold) were estimated, with effect modifications by different accidents-related parameters.
The study identified 184,936 construction occupational injuries. There was an overall significant effect for high temperatures (relative risk (RR) 1.216 (95% CI: (1.095–1.350))) and a protective one for low temperatures (RR 0.901 (95% CI: 0.843–0.963)). For high temperatures we estimated 3,142 (95% CI: 1,772–4,482) attributable cases during the studied period. RRs from 1.11 to 1.30 were found during heat waves days. Unqualified workers, as well as masons and plumbers, were found to be at risk at high temperatures. Construction, quarry and industrial sites were the risky working environments, as well as specific physical activities like working with hand-held tools, operating with machine and handling of objects. Contact with sharp, pointed, rough, coarse ‘Material Agent’ were the more risky mode of injury in hot conditions.
Prevention policies are needed to reduce the exposure to high temperatures of construction workers. Such policies will become a critical issue considering climate change.
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.
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