In situ eddy covariance observations reveal unusually large nocturnal CO 2 uptake by submerged vegetation in a shallow lake.

Subtropical lakes are important source of atmospheric methane (CH4). This study aims to investigate spatial variations of CH4 flux in Lake Taihu, a large (area 2400 km2) and shallow (mean depth 1.9 m) eutrophic lake in Eastern China. The lake exhibited high spatial variations in pollution level, macrophyte vegetation abundance, and algal growth. We measured the diffusion CH4 flux via the transfer coefficient method across the whole lake. In addition, data obtained with the flux gradient and the eddy covariance methods were used in conjunction with the data on the diffusion flux to estimate the contribution by ebullition. Results from 3 years' measurements indicated high spatial variabilities in the diffusion CH4 flux. The spatial pattern of the diffusion CH4 emission was correlated with water clarity, dissolved oxygen concentration, and the spatial distributions of algal and submerged vegetation. In comparison to the transfer coefficient method, the eddy covariance and the flux gradient method observed a lake CH4 flux that was 3.39 ± 0.58 (mean ± 1 standard deviation) and 1.95 ± 0.36 times higher in an open‐water eutrophic zone and in a habitat of submerged macrophytes, respectively. The result implied an average of 71% and 49% ebullition contribution to the total CH4 flux in the two zones. The annual mean diffusion CH4 flux of the whole lake was 0.54 ± 0.30 g m−2 yr−1. Our CH4 emission data suggest that the average CH4 emission reported previously for lakes in Eastern China was overestimated.

Background Despite substantial research, uncertainty remains about the clinical and etiological heterogeneity of major depression (MD). Can meaningful and valid subtypes be identified and would they be stable cross-culturally? Method Symptoms at their lifetime worst depressive episode were assessed at structured psychiatric interview in 6008 women of Han Chinese descent, age ≥30 years, with recurrent DSM-IV MD. Latent class analysis (LCA) was performed in Mplus. Results Using the nine DSM-IV MD symptomatic A criteria, the 14 disaggregated DSM-IV criteria and all independently assessed depressive symptoms (n=27), the best LCA model identified respectively three, four and six classes. A severe and non-suicidal class was seen in all solutions, as was a mild/moderate subtype. An atypical class emerged once bidirectional neurovegetative symptoms were included. The non-suicidal class demonstrated low levels of worthlessness/guilt and hopelessness. Patterns of co-morbidity, family history, personality, environmental precipitants, recurrence and body mass index (BMI) differed meaningfully across subtypes, with the atypical class standing out as particularly distinct. Conclusions MD is a clinically complex syndrome with several detectable subtypes with distinct clinical and demographic correlates. Three subtypes were most consistently identified in our analyses: severe, atypical and non-suicidal. Severe and atypical MD have been identified in multiple prior studies in samples of European ethnicity. Our non-suicidal subtype, with low levels of guilt and hopelessness, may represent a pathoplastic variant reflecting Chinese cultural influences.

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BackgroundThe symptoms of major depression (MD) are clinically diverse. Do they form coherent factors that might clarify the underlying nature of this important psychiatric syndrome?MethodSymptoms at lifetime worst depressive episode were assessed at structured psychiatric interview in 6008 women of Han Chinese descent, age ⩾30 years with recurrent DSM-IV MD. Exploratory factor analysis (EFA) and confirmatoryfactor analysis (CFA) were performed in Mplus in random split-half samples.ResultsThe preliminary EFA results were consistently supported by the findings from CFA. Analyses of the nine DSM-IV MD symptomatic A criteria revealed two factors loading on: (i) general depressive symptoms; and (ii) guilt/suicidal ideation. Examining 14 disaggregated DSM-IV criteria revealed three factors reflecting: (i) weight/appetite disturbance; (ii) general depressive symptoms; and (iii) sleep disturbance. Using all symptoms (n = 27), we identified five factors that reflected: (i) weight/appetite symptoms; (ii) general retarded depressive symptoms; (iii) atypical vegetative symptoms; (iv) suicidality/hopelessness; and (v) symptoms of agitation and anxiety.ConclusionsMD is a clinically complex syndrome with several underlying correlated symptom dimensions. In addition to a general depressive symptom factor, a complete picture must include factors reflecting typical/atypical vegetative symptoms, cognitive symptoms (hopelessness/suicidal ideation), and an agitated symptom factor characterized by anxiety, guilt, helplessness and irritability. Prior cross-cultural studies, factor analyses of MD in Western populations and empirical findings in this sample showing risk factor profiles similar to those seen in Western populations suggest that our results are likely to be broadly representative of the human depressive syndrome.

Anthropogenic CO emissions from cities represent a major source contributing to the global atmospheric CO burden. Here, we examined the enhancement of atmospheric CO mixing ratios by anthropogenic emissions within the Yangtze River Delta (YRD), China, one of the world's most densely populated regions (population greater than 150 million). Tower measurements of CO mixing ratios were conducted from March 2013 to August 2015 and were combined with numerical source footprint modeling to help constrain the anthropogenic CO emissions. We simulated the CO enhancements (i.e., fluctuations superimposed on background values) for winter season (December, January, and February). Overall, we observed mean diurnal variation of CO enhancement of 23.5~49.7 μmol mol, 21.4~52.4 μmol mol, 28.1~55.4 μmol mol, and 29.5~42.4 μmol mol in spring, summer, autumn, and winter, respectively. These enhancements were much larger than previously reported values for other countries. The diurnal CO enhancements reported here showed strong similarity for all 3 years of the study. Results from source footprint modeling indicated that our tower observations adequately represent emissions from the broader YRD area. Here, the east of Anhui and the west of Jiangsu province contributed significantly more to the anthropogenic CO enhancement compared to the other sectors of YRD. The average anthropogenic CO emission in 2014 was 0.162 (± 0.005) mg m s and was 7 ± 3% higher than 2010 for the YRD. Overall, our emission estimates were significantly smaller (9.5%) than those estimated (0.179 mg m s) from the EDGAR emission database.

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