科研成果

Objective: To pilot a protocol to evaluate acute cardiovascular effects in in-vehicle exposure to traffic air pollutants in people with diabetes. Methods: Twenty-one volunteers with type 2 diabetes were passengers on 90- to 110-minute car rides on a busy highway. We measured in-vehicle particle number and mass (PM(2.5)) nitrogen dioxide, and carbon monoxide and heart rate, heart rate variability (HRV), and blood pressure. Results: Compared with pre-ride measurements, we found a decrease in high frequency (HF) HRV from pre-ride to next day (ratio 0.66, 95% CI = 0.47 to 0.93) and an increase in low frequency to HF ratio at post-ride (ratio 1.92, 95% CI = 1.21 to 3.05) at post-ride. Interquartile range increases in measured pollutants were associated with next-day decreases in HR HRV. Conclusions: This protocol appears useful for assessing acute adverse cardiovascular effects of in-vehicle exposures among people who have diabetes.

Various multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA), factor analysis (FA), and principal component analysis (PCA) were used to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi. The dataset, obtained during the period 2003-2007 from the Kunming Environmental Monitoring Center, consisted of 12 variables surveyed monthly at eight sites. The CA grouped the 12 months into two groups, August-September and the remainder, and divided the lake into two regions based on their different physicochemical properties and pollution levels. The DA showed the best results for data reduction and pattern recognition in both temporal and spatial analysis. It calculated four parameters (TEMP, pH, COD(Mn), and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NHNH(4)(+)-N, and TN) to almost 71.7% correct assignment in spatial analysis of the two clusters. The FA/PCA applied to datasets of two special clusters of the lake calculated four factors for each region, capturing 72.5% and 62.5% of the total variance, respectively. Strong loadings included DO, BOD, TN, COD(Cr), COD(Mn), NH(4)(+)-N, TP, and EC. In addition, box-whisker plots and GIS further facilitated and supported the multivariate analysis results.

Recent studies of Chesapeake Bay hypoxia suggest higher susceptibility to hypoxia in years after the 1980s. We used two simple mechanistic models and Bayesian estimation of their parameters and prediction uncertainty to explore the nature of this regime shift. Model estimates show increasing nutrient conversion efficiency since the 1980s, with lower DO concentrations and large hypoxic volumes as a result. In earlier work, we suggested a 35% reduction from the average 1980-1990 total nitrogen load would restore the Bay to hypoxic volumes of the 1950s-1970s. With Bayesian inference, our model indicates that, if the physical and biogeochemical processes prior to the 1980s resume, the 35% reduction would result in hypoxic volume averaging 2.7 km(3) in a typical year, below the average hypoxic volume of 1950s-1970s. However, if the post-1980 processes persist the 35% reduction would result in much higher hypoxic volume averaging 6.0 km(3). Load reductions recommended in the 2003 agreement will likely meet dissolved oxygen attainment goals if the Bay functions as it did prior to the 1980s; however, it may not reach those goals if current processes prevail.