Particulate matter (PM) in outdoor air pollution was recently designated a Group I carcinogen by the International Agency for Research on Cancer (IARC). This determination was based on the evidence ...regarding the relationship of PM2.5 and PM10 to lung cancer risk; however, the IARC evaluation did not include a quantitative summary of the evidence.
Our goal was to provide a systematic review and quantitative summary of the evidence regarding the relationship between PM and lung cancer.
We conducted meta-analyses of studies examining the relationship of exposure to PM2.5 and PM10 with lung cancer incidence and mortality. In total, 18 studies met our inclusion criteria and provided the information necessary to estimate the change in lung cancer risk per 10-μg/m3 increase in exposure to PM. We used random-effects analyses to allow between-study variability to contribute to meta-estimates.
The meta-relative risk for lung cancer associated with PM2.5 was 1.09 (95% CI: 1.04, 1.14). The meta-relative risk of lung cancer associated with PM10 was similar, but less precise: 1.08 (95% CI: 1.00, 1.17). Estimates were robust to restriction to studies that considered potential confounders, as well as subanalyses by exposure assessment method. Analyses by smoking status showed that lung cancer risk associated with PM2.5 was greatest for former smokers 1.44 (95% CI: 1.04, 1.22), followed by never-smokers 1.18 (95% CI: 1.00, 1.39), and then current smokers 1.06 (95% CI: 0.97, 1.15). In addition, meta-estimates for adenocarcinoma associated with PM2.5 and PM10 were 1.40 (95% CI: 1.07, 1.83) and 1.29 (95% CI: 1.02, 1.63), respectively.
The results of these analyses, and the decision of the IARC Working Group to classify PM and outdoor air pollution as carcinogenic (Group 1), further justify efforts to reduce exposures to air pollutants that can arise from many sources.
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.
Background: Particulate matter (PM) in outdoor air pollution was recently designated a Group I carcinogen by the International Agency for Research on Cancer (IARC). This determination was based on ...the evidence regarding the relationship of PM2.5 and PM10 to lung cancer risk; however, the IARC evaluation did not include a quantitative summary of the evidence. Objective: Our goal was to provide a systematic review and quantitative summary of the evidence regarding the relationship between PM and lung cancer. Methods: We conducted meta-analyses of studies examining the relationship of exposure to PM2.5 and PM10 with lung cancer incidence and mortality. In total, 18 studies met our inclusion criteria and provided the information necessary to estimate the change in lung cancer risk per 10- mu g/m3 increase in exposure to PM. We used random-effects analyses to allow between-study variability to contribute to meta-estimates. Results: The meta-relative risk for lung cancer associated with PM2.5 was 1.09 (95% CI: 1.04, 1.14). The meta-relative risk of lung cancer associated with PM10 was similar, but less precise: 1.08 (95% CI: 1.00, 1.17). Estimates were robust to restriction to studies that considered potential confounders, as well as subanalyses by exposure assessment method. Analyses by smoking status showed that lung cancer risk associated with PM2.5 was greatest for former smokers 1.44 (95% CI: 1.04, 1.22), followed by never-smokers 1.18 (95% CI: 1.00, 1.39), and then current smokers 1.06 (95% CI: 0.97, 1.15). In addition, meta-estimates for adenocarcinoma associated with PM2.5 and PM10 were 1.40 (95% CI: 1.07, 1.83) and 1.29 (95% CI: 1.02, 1.63), respectively. Conclusion: The results of these analyses, and the decision of the IARC Working Group to classify PM and outdoor air pollution as carcinogenic (Group 1), further justify efforts to reduce exposures to air pollutants that can arise from many sources. Citation: Hamra GB, Guha N, Cohen A, Laden F, Raaschou-Nielsen O, Samet JM, Vineis P, Forastiere F, Saldiva P, Yorifuji T, Loomis D. 2014. Outdoor particulate matter exposure and lung cancer: a systematic review and meta-analysis. Environ Health Perspect 122:906-911; http://dx.doi.org/10.1289/ehp.1408092
BACKGROUNDRecent epidemiological evidence suggests associations between air pollution exposure and major depressive disorders, but the literature is inconsistent for other mental illnesses. We ...investigated the associations of several air pollutants and road traffic noise with the incidence of different categories of mental disorders in a large population-based cohort.METHODSWe enrolled 1,739,277 individuals 30 + years from the 2011 census in Rome, Italy, and followed them up until 2019. In detail, we analyzed 1,733,331 participants (mean age 56.43 +/- 15.85 years; 54.96 % female) with complete information on covariates of interest. We excluded subjects with prevalent mental disorders at baseline to evaluate the incidence (first hospitalization or co-pay exemption) of schizophrenia spectrum disorders, bipolar, anxiety, personality, or substance use disorders. In addition, we studied subjects with first prescriptions of antipsychotics, antidepressants, and mood stabilizers. Annual average concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO₂), Black Carbon (BC), ultrafine particles (UFP), and road traffic noise were assigned to baseline residential addresses. We applied Cox regression models adjusted for individual and area-level covariates.RESULTSEach interquartile range (1.13 µg/m3) increase in PM2.5 was associated with a hazard ratio (HR) of 1.070 (95 % confidence interval CI: 1.017, 1.127) for schizophrenia spectrum disorder, 1.135 (CI: 1.086, 1.186) for depression, 1.097 (CI: 1.030, 1.168) for anxiety disorders. Positive associations were also detected for BC and UFP, and with the three categories of drug prescriptions. Bipolar, personality, and substance use disorders did not show clear associations. The effects were highest in the age group 30-64 years, except for depression.CONCLUSIONSLong-term exposure to ambient air pollution, especially fine and ultrafine particles, was associated with increased risks of schizophrenia spectrum disorder, depression, and anxiety disorders. The association of the pollutants with the prescriptions of specific drugs increases the credibility of the results.
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 American Thoracic Society has previously published statements on what constitutes an adverse effect on health of air pollution in 1985 and 2000. We set out to update and broaden these past ...statements that focused primarily on effects on the respiratory system. Since then, many studies have documented effects of air pollution on other organ systems, such as on the cardiovascular and central nervous systems. In addition, many new biomarkers of effects have been developed and applied in air pollution studies.This current report seeks to integrate the latest science into a general framework for interpreting the adversity of the human health effects of air pollution. Rather than trying to provide a catalogue of what is and what is not an adverse effect of air pollution, we propose a set of considerations that can be applied in forming judgments of the adversity of not only currently documented, but also emerging and future effects of air pollution on human health. These considerations are illustrated by the inclusion of examples for different types of health effects of air pollution.
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.
Summary Background Ambient air pollution is suspected to cause lung cancer. We aimed to assess the association between long-term exposure to ambient air pollution and lung cancer incidence in ...European populations. Methods This prospective analysis of data obtained by the European Study of Cohorts for Air Pollution Effects used data from 17 cohort studies based in nine European countries. Baseline addresses were geocoded and we assessed air pollution by land-use regression models for particulate matter (PM) with diameter of less than 10 μm (PM10 ), less than 2·5 μm (PM2·5 ), and between 2·5 and 10 μm (PMcoarse ), soot (PM2·5absorbance ), nitrogen oxides, and two traffic indicators. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effects models for meta-analyses. Findings The 312 944 cohort members contributed 4 013 131 person-years at risk. During follow-up (mean 12·8 years), 2095 incident lung cancer cases were diagnosed. The meta-analyses showed a statistically significant association between risk for lung cancer and PM10 (hazard ratio HR 1·22 95% CI 1·03–1·45 per 10 μg/m3 ). For PM2·5 the HR was 1·18 (0·96–1·46) per 5 μg/m3 . The same increments of PM10 and PM2·5 were associated with HRs for adenocarcinomas of the lung of 1·51 (1·10–2·08) and 1·55 (1·05–2·29), respectively. An increase in road traffic of 4000 vehicle-km per day within 100 m of the residence was associated with an HR for lung cancer of 1·09 (0·99–1·21). The results showed no association between lung cancer and nitrogen oxides concentration (HR 1·01 0·95–1·07 per 20 μg/m3 ) or traffic intensity on the nearest street (HR 1·00 0·97–1·04 per 5000 vehicles per day). Interpretation Particulate matter air pollution contributes to lung cancer incidence in Europe. Funding European Community's Seventh Framework Programme.
Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term ...assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM10 measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM10 concentrations at 1-km2 grid over Italy, for the years 2006–2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM10=0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM10 levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM10 concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data.
•Estimates of air pollution levels at fine spatiotemporal scale are lacking in Italy•We combined satellite data with land use variables and ground-level PM10 measurements•We estimated daily PM10 concentrations at a 1-km2 grid over Italy, 2006-2012•Our model displayed good cross-validation fitting (R2=0.65) and negligible bias•Spatiotemporal predictions will allow estimation of short and long term health effects of PM10
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