[Emission of Nitrous Oxide (N2O) from Lake Taihu and the Corresponding Potential Driving Factors].

Nitrous oxide (N2O) is one of the six greenhouse gases stipulated in the Kyoto Protocol. Its greenhouse potential over the past century was 298 times that of CO2, and the concentration of atmospheric N2O has been continuously and rapidly increasing during the past hundred years. Shallow lakes are an important source of atmospheric N2O. In order to explore the temporal and spatial changes and potential driving factors of N2O emissions from eutrophic water, we conducted field observations in February (winter) and August (summer) in Lake Taihu. We used the coefficient of diffusion-headspace bottle method to trace the variability in the N2O concentration[c(N2O)] and efflux[F(N2O)] from surface water bodies and explored the potential driving factors of N2O emissions. The optical measurements of dissolved organic matter (DOM) are an effective approach for tracing the source and composition of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON). The migration and transformation processes of DOM also release a large amount of inorganic nitrogen, which changes the redox potential of the water column and thereby affects N2O emissions. Our results showed that the variability in c(N2O) and F(N2O) in the surface waters of Lake Taihu were strongly affected by water temperature and nutrient levels. The average c(N2O) of the surface waters was (19.7±2.7) nmol·L-1, corresponding to a mean F(N2O) of (41.1±1.8) µmol·(m2·d)-1, and the means of both c(N2O) and F(N2O) were higher in summer than those in winter (t-test, P<0.01). The input and accumulation of DOM could increase the production and emission potential of N2O in water bodies, as supported by both c(N2O) and F(N2O) significantly increasing with increasing level of terrestrial humic-like C1. The integration ratio of peak C to peak T IC:IT of DOM and the spectral slope S275-295 results indicated that there were high inputs of terrestrial DOM in the northwestern inflowing river mouths, concurring with the high production and emission of N2O found there. This suggested that the accumulation and degradation of terrestrial DOM potentially fueled the emission of N2O. Our results showed that water temperature, DOM composition, and nutrient level were all important factors affecting N2O emission from Lake Taihu. Long-term continuous observation can be applied to better evaluate the impact of various environmental factors on the production and emission of N2O in water bodies and to help with providing scientific emission reduction plans.

Associated and predictive factors of quality of life in patients with temporal lobe epilepsy.

OBJECTIVE: Identifying the factors that are correlated with and predictive of reduced quality of life (QOL) is essential to optimize the treatment of epilepsy and the management of comorbidities. METHODS: We analyzed the independent associations between the Quality of Life in Epilepsy-31 (QOLIE-31) inventory and the demographic, clinical, psychiatric, and cognitive variables of 47 consecutive patients with temporal lobe epilepsy (TLE). Predictors of the correlated variables were analyzed by multiple linear regression analysis. RESULTS: The QOLIE-31 total score was positively correlated with occupational status and Mini-Mental State Examination (MMSE) scores (r = 0.290 and 0.295, respectively; P < 0.05) and negatively correlated with the duration of seizures, adverse effects of antiepileptic drugs (AEDs), and the Pittsburgh Sleep Quality Inventory (PSQI), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS) scores (r = -0.357, 0.321, 0.328, -0.672, and -0.565, respectively; P < 0.05; P < 0.01 for the SAS and SDS). In the final multivariate regression model, anxiety, long durations of seizures, adverse effects of AEDs, and depression explained approximately 60.6% (adjusted R2 = 0.606, R coefficient = 0.800) of the QOLIE-31 overall score variance. CONCLUSION: Anxiety, long durations of seizures, adverse effects of AEDs, and depression were significant predictors of QOL, and these variables had relatively high prediction capacities for the overall QOLIE-31 in the regression model. Comorbid anxiety is the most powerful negative determinant of the QOLIE-31.