|关键词||渤海及沿岸河流 氮 源解析 稳定同位素 Bayesian混合模型 Bohai Sea And Surrounding Rivers Nitrogen Source Apportionment Stable Isotopes Bayesian Mixing Model|
入海河流悬浮颗粒物浓度6月最高（43.76±53.06 mgL-1），12月最低（30.04±53.38 mgL-1）。氮和碳的同位素组成大小为6月>9月＞12月。混合模型的定量结果显示，6月颗粒态有机质（POM）来源以污水（29.3%）、陆生植物（23.0%）和浮游藻类（20.8%）为主，9月主要来源为浮游藻类（44.2%）和陆生植物（22.7%），12月为浮游藻类（45.5%）、陆生植物（24.7%）和土壤（18.2%）。空间上，黄河POM的最大来源是土壤，其他水系主要来源为内源浮游藻类和陆生植物碎屑。河流DIN以NO3--N和NH4+-N为主且冬季（12月）浓度较高，水体δ15N-NO3-值无明显季节差异，但δ18O-NO3-值差异明显，6月最高（15.00±7.00‰），12月最低（5.99±3.87‰）。NO3-的主要来源为生活污水和畜禽粪便；大气沉降的贡献从6月到12月呈递减态势。6月NO3-的主要来源为畜禽粪便（27.1%）、大气沉降（23.4%）和生活污水（15.0%）；9月畜禽粪便（23.2%）、生活污水（20.7%）、土壤氮（16.8%）和硝态氮肥（15.7%）是主要来源；12月以生活污水（23.4%）、畜禽粪便（20.6%）和土壤氮（19.6%）来源为主。空间上，NO3-来源的贡献差异与土地利用类型和悬浮颗粒含量有关：黄河流域污水和土壤氮为主要来源；滦河水系和海河水系硝态氮肥对NO3-的贡献较高；辽河水系和莱州湾诸河中土壤氮的贡献升高。河流NH4+浓度6月和9月受大气沉降影响较大，12月份主要以人畜粪肥和燃料燃烧为主。
|其他摘要||The Bohai Sea is a typical semi-closed shallow shelf, with dozens of rivers including the Yellow River flowing into it. With the rapid development of industry and agriculture, in conjunction with the rapid process of urbanization, excessive nitrogen in the Bohai Sea and surrounding rivers from human activities would increase the nitrogen load. As a result, Bohai Sea becomes a vital pool of terrigenous nitrogen. To study the distribution characteristics and source apportionment of various nitrogen species in Bohai Sea and surrounding rivers on spatio-temporal scale can offer comprehensive knowledge of geochemical characteristics and the nitrogen source-sink relationship in coastal zone. In this research, multi-medium samples, including surface sediment in Bohai Sea, riverine water and total suspended particulate matter in 30 rivers, were collected to analyze the spatial distribution characteristics of nitrogen in surface sediment of Bohai Sea, also to realize the spatio-temporal variation features and influence factors of the dissolved inorganic nitrogen (DIN) and particulate nitrogen in surrounding rivers. The relationship among various geochemical parameters were discussed in this study. The source contributions of nitrogen in surface sediment and source proportions of various nitrogen forms in water and suspended particulate matter on temporal and spatial scales were estimated by the combination of stable isotopes and Bayesian model, which can illuminate the source apportionment of nitrate-nitrogen (NO3--N), ammonium-nitrogen (NH4+-N) and organic nitrogen in different sink of coastal zone. The main formations are as follows:|
The total organic carbon (TOC) contents in surface sediment of the whole Bohai Sea showed significant positive correlation with total nitrogen (TN) contents, whose highest values were in the mud zone of the Bohai Central. The qualitative analysis of the values of δ13C-TOC (-23.59‰ to -19.54‰) and δ15N-TN (+2.80‰ to +8.07‰) indicated three dominant sources including marine phytoplankton, riverine source, and atmospheric deposition contributed to surface sediments organic matter (OM). The results based on combination of δ13C-TOC, δ15N-TN and the MixSIR model revealed the dominant OM source was marine phytoplankton, with an approximate contribution of 69.0%; atmospheric deposition as another main source contributed around 21.4% and riverine input contributed only 9.6%. On the spatial scale, the contributions of marine phytoplankton, atmospheric deposition and riverine input were different in each area of Bohai Sea. Higher proportion contribution of atmospheric deposition was observed in Bohai Bay (24.9%) than other areas, which demonstrated the heavy air pollution in North China Plain. Furthermore, the MixSIR results revealed that atmospheric deposition contributed more OM than riverine input in Bohai Sea, and became the major pathway of transporting terrestrial OM to the sediment. This conclusion was confirmed by calculation of OM flux from atmospheric deposition (180809 ton) and flux from river input (33905.4 ton). The former was estimated by OC in total suspended particulates of atmosphere around coastal cities and the latter was calculated by average POC concentration of four larger rivers.
In surrounding rivers, total suspended particulate matter content was highest in June (3.76±53.06 mgL-1) and lowest in December (30.04±53.38 mgL-1). The trend of carbon and nitrogen isotopes was June > September > December. Based on Bayesian mixing model results, particulate organic matter (POM) was derived from sewage (29.3%), terrestrial plants (23.0%), planktonic algae (20.8%) in June. Plandtonic algae (44.2%) and terrestrial plants (22.7%) were two main sources of POM in September. The dominant sources of POM in December were algae (45.5%), terrestrial plants (24.7%) and soil OM (18.2%). Especially, soil OM was primary source in the Yellow River, while autochtohnous POM and land plants are main sources in other rivers. NO3--N and NH4+-N were dominant DIN species, showing highest concentrations in winter (December). The monthly variation of δ15N-NO3- was not obvious, but δ18O-NO3- values was showed significant variation by month, with highest values in June (15.00±7.00‰) and lowest values in December (5.99±3.87‰). The results of Bayesian mixing model suggested that domestic sewage and manure were dominant NO3- sources during the whole year, atmospheric deposition contributed most in June, then decreased to December gradually. Further, main sources of NO3- in June were manure (27.1%), atmospheric deposition (23.4%) and domestic sewage (15.0%); Manure (23.2%), domestic sewage (20.7%), soil (16.8%) and nitrate-N fertilizer (15.7%) were primary sources in September; NO3- were mainly derived from domestic sewage (23.4%), manure (20.6%) and soil (19.6%) in December. The spatial variations of source contribution were related to the land use type and river characteristics: NO3- in Yellow River was derived from domestic sewage and soil. Nitrate-N fertilizer contributed more NO3- in Luanhe River system and Haihe River system. The proportion of soil increased in Liaohe River system and rivers around Laizhou Bay. Moreover, contents of NH4+ in rivers were affected by atmospheric deposition in June and September, while dominant sources were livestock manure and fuel combustion in December.
In order to analyze nitrogen source characters, Yellow River was chosen to study the various nitrogen source during a whole year. The results are as follows: NO3--N was dominant DIN species at downstream of the Yellow River. The concentration of NO3--N in summer (1.31±0.14 mgL-1) was lower than that in other seasons (2.68±0.52mgL-1). The contents of NH4+-N and NO2--N in spring and winter were higher than that in summer and autumn. The δ15N-NO3- values were correlated with δ18O-NO3- values, which were lower during high flow season. The MixSIAR analysis indicated that sources of NO3- in whole year showed domestic sewage (24.7%) > soil (24.4%) > ammonium-N fertilizer (19.7%) > manure (14.8%) > nitrate-N fertilizer (10.9%) > atmospheric deposition (5.5%). Moreover, soil (28.2%) was main source during high flow season, while domestic sewage (24.7%) was primary source during other flow conditions. There were significant correlation between suspended particulate matter and water discharge, showing high values of PN, δ15N-PN and δ13C-POC in summer than autumn and winter. In the whole year, POM at downstream were derived from soil and river autochthonous OM. The contributions of each sources were followed by soil (35.7%), macrophytes (23.0%), planktonic algae (21.5%), terrestrial plants (14.5%) and sewage (5.3%). POM mainly derived from soil OM (29.0%) during high flow season, while planktonic algae (35.8%) was main source during other period. In general, compared with the various nitrogen species source apportionment in the whole year, soil was the primary source contributed both NO3--N and PN at downstream of the Yellow River. However, domestic sewage contributed more nitrogen to NO3--N rather than PN. MixSIR and MixSIAR models based on Bayesian algorithm can be used for source apportionment at various sink and provide a numerical simulation technology for study latent multiple sources of nitrogen species in coastal areas.
|于靖. 基于同位素混合模型方法的渤海及沿岸河流氮源汇关系研究[D]. 北京. 中国科学院大学,2017.|