Exposure to PM2.5 Metal Constituents and Liver Cancer Risk in REVEAL-HBV.

BACKGROUND: Ambient particulate matter is classified as a human Class 1 carcinogen, and recent studies found a positive relationship between fine particulate matter (PM2.5) and liver cancer. Nevertheless, little is known about which specific metal constituent contributes to the development of liver cancer. OBJECTIVE: To evaluate the association of long-term exposure to metal constituents in PM2.5 with the risk of liver cancer using a Taiwanese cohort study. METHODS: A total of 13,511 Taiwanese participants were recruited from the REVEAL-HBV in 1991-1992. Participants' long-term exposure to eight metal constituents (Ba, Cu, Mn, Sb, Zn, Pb, Ni, and Cd) in PM2.5 was based on ambient measurement in 2002-2006 followed by a land-use regression model for spatial interpolation. We ascertained newly developed liver cancer (ie, hepatocellular carcinoma [HCC]) through data linkage with the Taiwan Cancer Registry and national health death certification in 1991-2014. A Cox proportional hazards model was utilized to assess the association between exposure to PM2.5 metal component and HCC. RESULTS: We identified 322 newly developed HCC with a median follow-up of 23.1 years. Long-term exposure to PM2.5 Cu was positively associated with a risk of liver cancer. The adjusted hazard ratio (HR) was 1.13 (95% confidence interval [CI], 1.02-1.25; P = 0.023) with one unit increment on Cu normalized by PM2.5 mass concentration in the logarithmic scale. The PM2.5 Cu-HCC association remained statistically significant with adjustment for co-exposures to other metal constituents in PM2.5. CONCLUSION: Our findings suggest PM2.5 containing Cu may attribute to the association of PM2.5 exposure with liver cancer.

Associations of fine particulate matter and its metal constituents with blood pressure: A panel study during the seventh World Military Games.

Evidence is needed to elucidate the association of blood pressure (BP) changes with metal constituents in fine particulate matter (PM2.5). Therefore, we designed a longitudinal panel study enrolling 70 healthy students from Wuhan University in the context of the seventh World Military Games (the 7th WMG) from September 2019 to January 2020. A total of eight visits were conducted before, during, and after the 7th WMG. During every visit, each participant was asked to carry a personal PM2.5 monitor to measure hourly PM2.5 levels for three consecutive days. Questionnaire investigation and physical examination were completed on the fourth day. We analyzed ten metal constituents of ambient PM2.5 collected from the fixed station, and blood pressure was recorded during each visit. The linear mixed-effects models were performed to evaluate associations of metal constituents and blood pressure measurements. We observed a dramatic variation of PM2.5 concentration ranging from 7.38 to 132.04 µg/m3. A 10 µg/m3 increment of PM2.5 was associated with an increase of 0.64 mmHg (95% CI: 0.44, 0.84) in systolic BP (SBP), 0.40 mmHg (0.26, 0.54) in diastolic BP (DBP), 0.31 mmHg (0.15, 0.47) in pulse pressure (PP) and 0.44 mmHg (0.26, 0.62) in mean artery pressure (MAP), respectively. For metal constituents in PM2.5, robust positive associations were observed between BP and selenium, manganese, arsenic, cadmium, and thallium. For example, for an IQR (0.93 ng/m3) increment of selenium, SBP and MAP elevated by 0.98 mmHg (0.09, 1.87) and 0.71 mmHg (0.03, 1.39), respectively. Aluminum was found to be robustly associated with decreased SBP, DBP, and MAP. The study indicated that exposure to PM2.5 total mass and metal constituents including selenium, manganese, arsenic, cadmium, and thallium were associated with the elevated BP.

How long-term air pollution and its metal constituents affect type 2 diabetes mellitus prevalence? Results from Wuhan Chronic Disease Cohort.

BACKGROUND: Epidemiological evidence linking type 2 diabetes mellitus (T2DM) with air pollution is discrepant and most are restricted to the influences of air-pollutant mass concentration. This study aims to explore the effects of long-term exposure to air pollution and its metal constituents on T2DM prevalence in China. METHODS: We used data on 10,253 adult residents from the baseline survey of Wuhan Chronic Disease Cohort in 2019. Ambient PM2.5, PM10 and NO2 exposure were estimated at residences based on Chinese Air Quality Reanalysis Dataset. Concentrations of 10 metal constituents were measured by 976 PM2.5 filter samples collected from four monitoring stations. Logistic regression models were employed to examine associations of T2DM prevalence with 3-year mean concentrations of each air pollutant and PM2.5 metal constituents prior to the baseline investigation. RESULTS: A total of 673 T2DM cases (6.6%) were identified. The 3-year mean exposures to PM2.5, PM10 and NO2 were 50.89 µg/m3, 82.86 µg/m3, and 39.79 µg/m3, respectively. And interquartile range (IQR) of 10 metals in PM2.5 varied from 0.03 ng/m3 to 78.30 ng/m3. For 1 µg/m3 increment in PM2.5, PM10 and NO2, the odds of T2DM increased by 7.2% (95%CI: 1.026, 1.136), 3.1% (95%CI: 1.013, 1.050), and 2.1% (95%CI: 1.005, 1.038) after adjusting for potential confounders. Cd and Sb in PM2.5 were significant risk factors to T2DM with odds ratios of 1.350 (95%CI: 1.089, 1.673) and 1.389 (95%CI: 1.164, 1.658) for per IQR increase, respectively. Stratification analyses indicated that males and those aged ≥45 years were more susceptive to long-term air pollution. CONCLUSIONS: Long-term exposure to PM2.5, PM10 and NO2 increased T2DM prevalence in a Wuhan population, especially for men and middle-aged and elderly people. Moreover, T2DM was significantly associated with Cd and Sb in PM2.5. Further research to validate these results and to clarify the underlying mechanisms is warranted.

Hybrid land use regression modeling for estimating spatio-temporal exposures to PM2.5, BC, and metal components across a metropolitan area of complex terrain and industrial sources.

Land use regression (LUR) modeling has become a common method for predicting pollutant concentrations and assigning exposure estimates in epidemiological studies. However, few LUR models have been developed for metal constituents of fine particulate matter (PM2.5) or have incorporated source-specific dispersion covariates in locations with major point sources. We developed hybrid AERMOD LUR models for PM2.5, black carbon (BC), and steel-related PM2.5 constituents lead, manganese, iron, and zinc, using fine-scale air pollution data from 37 sites across the Pittsburgh area. These models were designed with the aim of developing exposure estimates for time periods of interest in epidemiology studies. We found that the hybrid LUR models explained greater variability in PM2.5 (R2 = 0.79) compared to BC (R2 = 0.59) and metal constituents (R2 = 0.34-0.55). Approximately 70% of variation in PM2.5 was attributable to temporal variance, compared to 36% for BC, and 17-26% for metals. An AERMOD dispersion covariate developed using PM2.5 industrial emissions data for 207 sources was significant in PM2.5 and BC models; all metals models contained a steel mill-specific PM2.5 emissions AERMOD term. Other significant covariates included industrial land use, commercial and industrial land use, percent impervious surface, and summed railroad length.