基于双稳定同位素和MixSIAR模型的冬小麦根系吸水来源研究

灌溉和施肥措施对农田水文循环具有重要影响,根系吸水是联系植物蒸腾和土壤水分运动的关键水文过程,定量识别灌溉施肥影响下作物根系吸水来源对农业用水优化管理具有重要意义。氘氧稳定同位素(D和18O)是追溯农田水分运移过程的理想天然示踪剂。基于2013—2015年北京市典型农田不同灌溉施肥处理冬小麦水分运移试验,利用D和18O双稳定同位素和MixSIAR贝叶斯混合模型,量化冬小麦主要根系吸水深度及其贡献比例,阐明作物水分来源的季节变化及不同处理间的差异,分析根系吸水与土壤水分分布变化的相互关系。研究结果表明:两季冬小麦返青-拔节、拔节-抽穗、抽穗-灌浆和灌浆-收获期主要根系吸水深度均值分别为0—20 cm(67.0%)、20—70 cm(42.0%)、0—20 cm(38.7%)和20—70 cm(34.9%),但季节变化差异显著,2014季主要吸水深度随作物的生长发育而逐渐增加,2015季则主要集中于浅层土壤(0—70 cm)。返青-抽穗期仅灌水20 mm或施肥105 kg/hm2N促使拔节-抽穗期深层(70—200 cm)土壤水分利用率平均增加29%,而前期充分灌水且大量施肥(≥当地施肥量210 kg hm-2N)时拔节-抽穗期根系吸水深度为土壤表层0—20 cm。在干旱少雨的冬小麦生长季内作物吸水来源与土壤水分消耗变化基本一致。     

Assessing the sources and magnitude of diurnal nitrate variability in the San Joaquin River (California) with an in situ optical nitrate sensor and dual nitrate isotopes

1. We investigated diurnal nitrate (NO₃⁻) concentration variability in the San Joaquin River using an in situ optical NO₃⁻ sensor and discrete sampling during a 5‐day summer period characterized by high algal productivity. Dual NO₃⁻ isotopes (δ¹⁵N_NO3 and δ¹⁸O_NO3) and dissolved oxygen isotopes (δ¹⁸O_DO) were measured over 2 days to assess NO₃⁻ sources and biogeochemical controls over diurnal time‐scales. 2. Concerted temporal patterns of dissolved oxygen (DO) concentrations and δ¹⁸O_DO were consistent with photosynthesis, respiration and atmospheric O₂ exchange, providing evidence of diurnal biological processes independent of river discharge. 3. Surface water NO₃⁻ concentrations varied by up to 22% over a single diurnal cycle and up to 31% over the 5‐day study, but did not reveal concerted diurnal patterns at a frequency comparable to DO concentrations. The decoupling of δ¹⁵N_NO3 and δ¹⁸O_NO3 isotopes suggests that algal assimilation and denitrification are not major processes controlling diurnal NO₃⁻ variability in the San Joaquin River during the study. The lack of a clear explanation for NO₃⁻ variability likely reflects a combination of riverine biological processes and time‐varying physical transport of NO₃⁻ from upstream agricultural drains to the mainstem San Joaquin River. 4. The application of an in situ optical NO₃⁻ sensor along with discrete samples provides a view into the fine temporal structure of hydrochemical data and may allow for greater accuracy in pollution assessment.     

Human health risk assessment and sources analysis of nitrate in shallow groundwater of the Liujiang basin,China

High levels of nitrates in groundwater pose a risk to human health. Hence, groundwater-human health risk assessment and sources analysis are essential. The article aims to assess risk level and identify sources of nitrate in shallow groundwater of the Liujiang basin by using a human health risk assessment (HHRA) model, Factor analysis (FA) and GIS spatial analysis. The results indicated that the most serious pollution was distributed in southern region of the basin; about 60% of the samples exceeded the recommended limit of nitrate as per the World Health Organization limit. Moreover, ingants' health risk were greater than those of adults and children, and about 56% of the groundwater samples will put the infants at risk of health. FA was used to identify various underlying natural and anthropogenic processes that created these distinct risk levels. The FA results can be categorized by two major factors: (1) Organic fertilizers and sewage discharge contamination in central region. (2) Blocking effect of granite and redox conditions in southern parts. This study demonstrates that the great variation of nitrate risk levels in the basin should be attributed to both natural and anthropogenic processes.     

Predicting leaf wax n‐alkane 2H/1H ratios: controlled water source and humidity experiments with hydroponically grown trees confirm predictions of Craig–Gordon model

The extent to which both water source and atmospheric humidity affect δ²H values of terrestrial plant leaf waxes will affect the interpretations of δ²H variation of leaf waxes as a proxy for hydrological conditions. To elucidate the effects of these parameters, we conducted a long‐term experiment in which we grew two tree species, Populus fremontii and Betula occidentalis, hydroponically under combinations of six isotopically distinct waters and two different atmospheric humidities. We observed that leaf n‐alkane δ²H values of both species were linearly related to source water δ²H values, but with slope differences associated with differing humidities. When a modified version of the Craig–Gordon model incorporating plant factors was used to predict the δ²H values of leaf water, all modelled leaf water values fit the same linear relationship with n‐alkane δ²H values. These observations suggested a relatively constant biosynthetic fractionation factor between leaf water and n‐alkanes. However, our calculations indicated a small difference in the biosynthetic fractionation factor between the two species, consistent with small differences calculated for species in other studies. At present, it remains unclear if these apparent interspecies differences in biosynthetic fractionation reflect species‐specific biochemistry or a common biosynthetic fractionation factor with insufficient model parameterization.     

Source apportionment of pollution in groundwater source area using factor analysis and positive matrix factorization methods

By means of factor analysis (FA) and positive matrix factorization (PMF) methods, groundwater pollution sources were identified in the Jinji groundwater source area, which is beside the Yellow River and is the only urban water supply source for the city of Wuzhong in Northwestern China. The sources of groundwater were quantified based on 16 samples of shallow groundwater from the source area. The source apportionment with the PMF model identified three dominant groundwater pollution sources. These were anthropogenic activities of agricultural and industrial pollution with a pollution contribution of 53.0%, water–rock interaction of 24.6%, and evaporation and concentration of 22.4%. The source apportionment with the FA model identified four sources which were evaporation and concentration, with the largest contribution (42.6%), followed by anthropogenic activities (29.2%), mineral dissolution and industrial pollution (22.4%), and natural effects (5.8%). Specific attention should be paid to these natural (fluoride, TH, etc.) and anthropogenic sources (NH₄⁺, NO₂^?, turbidity, total bacterial count, etc.), and pertinent measures should be taken to control local groundwater pollution. The most significant trait of the PMF is its scientific interpretation and physical explanation of the results, depending on non-negative restriction of the pollution source profiles and contributions.