Abstract Hydrothermal H 2 S is an important energy source for hydrothermal ecosystems. However, it is difficult to obtain accurate hydrogen sulfide concentrations in high‐temperature hydrothermal fluids because they are highly susceptible to oxidation and compositional variability with mixing. In this study, a new in situ approach for measuring H 2 S, HS − and pH in hydrothermal fluids was developed and applied to the detections of Okinawa Trough hydrothermal activities. The in situ total H 2 S concentrations in the Jade and Biwako fluids were determined to be 31.4 and 76.7 mmol/kg, respectively. The in situ measured pH of the Jade fluids was determined to be 6.3, which has exceeded that of a neutral fluid at a specific temperature and pressure, indicating that the pH of Jade fluids is weakly alkaline. The pH transition of hydrothermal fluids from alkaline to acidic may be attributed to the thermal decomposition of organic matter and sulfide precipitation.
Long-term exposure to low polycyclic aromatic hydrocarbon (PAH) concentration may ave detrimental effects, including changing platelet indices. Effects of chronic exposure to low PAH concentrations have been evaluated in cross-sectional, but not in longitudinal studies, to date. We aimed to assess the effects of long-term exposure to the low-concentration PAHs on alterations in platelet indices in the Chinese population. During 2014–2017, we enrolled 222 participants who had lived in a village in northern China, 1–2 km downwind from a coal plant, for more than 25 years, but who were not employed by the plant or related businesses. During three follow-ups, annually in June, demographic information and urine and blood samples were collected. Eight PAHs were tested: namely 2-hydroxynaphthalene, 1-hydroxynaphthalene, 2-hydroxyfluorene, 9-hydroxyfluorene (9-OHFlu), 2-hydroxyphenanthrene (2-OHPh), 1-hydroxyphenanthrene (1-OHPh), 1-hydroxypyrene (1-OHP), and 3-hydroxybenzo [a] pyrene. Five platelet indices were measured: platelet count (PLT), platelet distribution width (PDW), mean platelet volume (MPV), platelet crit, and the platelet-large cell ratio. Generalized mixed and generalized linear mixed models were used to estimate correlations between eight urinary PAH metabolites and platelet indices. Model 1 assessed whether these correlations varied over time. Models 2 and 3 adjusted for additional personal information and personal habits. We found the following significant correlations: 2-OHPh (Model1 β 1 = 18.06, Model2 β 2 = 18.54, Model β 3 = 18.54), 1-OHPh (β 1 = 16.43, β 2 = 17.42, β 3 = 17.42), 1-OHP(β 1 = 13.93, β 2 = 14.03, β 3 = 14.03) with PLT, as well as 9-OHFlu with PDW and MPV (odds ratio or Model3 OR PDW [95%CI] = 1.64[1.3–2.06], OR MPV [95%CI] = 1.33[1.19–1.48]). Long-term exposure to low concentrations of PAHs, indicated by2-OHPh, 1-OHPh, 1-OHP, and 9-OHFlu, as urinary biomarkers, affects PLT, PDW, and MPV. 9-OHFlu increased both PDW and MPV after elimination of the effects of other PAH exposure modes.
Abstract Agriculture emerges as a prominent contributor to CH4 and N2O emissions in China. However, estimates of these two non-CO2 greenhouse gases (GHGs) remain poorly constrained, hindering a precise understanding of their spatiotemporal dynamics and the development of effective mitigation strategies. Here, we established a consistent estimation framework that integrates emission factor methods, data-driven models and process-based biogeochemical models, to identify the magnitudes, spatial variations, and long-term trends of agricultural non-CO2 GHG emissions in mainland China from 1980 to 2023. Over the study period, the average total agricultural non-CO2 GHG emissions amounted to 722.5 ± 102.3 Tg CO2-eq yr−1, with livestock CH4, cropland CH4, cropland N2O and livestock N2O contributing 41% (297.4 ± 64.3 Tg CO2-eq yr−1), 31% (225.0 ± 69.6 Tg CO2-eq yr−1), 18% (130.6 ± 9.4 Tg CO2-eq yr−1) and 10% (69.4 ± 20.2 Tg CO2-eq yr−1), respectively. Approximately 70% of these emissions were concentrated in the eastern region beyond the Hu Line, with emission hotspots identified in South-central China, East China, and the Sichuan Basin. Our analysis revealed three distinct temporal stages of total emissions during the study period: rapid growth (1980–late 1990s), slow growth (late 1990s–middle 2010s), and a stabilization stage (since the middle 2010s). These stages reflect the evolving trajectory of agriculture in China, from the expansion of agricultural yields, to the transformation of agricultural practices, and ultimately the pursuit of sustainable development. However, the temporal trajectory of emissions varied significantly across different regions, highlighting divergent levels of agricultural development. This study presents a comprehensive, gridded, and consistent estimate of agricultural non-CO2 GHG emissions in China, offering valuable insights for policymakers to develop tailored strategies that adapt to local conditions, enabling effective emission reduction measures.
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