我国地下水硝酸盐污染及源解析研究进展

我国地下水中硝酸盐污染问题严峻,尤其农业产区地下水硝酸盐污染日益突出,严重影响了地下饮用水安全,急需引起重视.通过综述发现,我国地下水中硝酸盐的主要来源为大气沉降、土壤氮、农业施肥和粪便污水等,其中粪便污水和农业施肥是地下水硝酸盐超标的主要原因.总结了水化学分析法、多元统计分析法、稳定同位素示踪法和微生物源追踪等技术在地下水硝酸盐溯源中的应用.各种溯源方法均有一定的局限性,建议采用多种方法联合识别地下水中硝酸盐来源,并通过多元统计分析和同位素定量解析模型计算不同污染来源贡献率.硝酸盐污染溯源经历了从定性到定量的研究过程,目前基于δ¹⁵N-NO₃ ^-和δ¹⁸O-NO₃ ^-解析硝酸盐来源的SIAR和MixSIAR模型已经相当成熟,但由于不同输入端元同位素特征值范围相互重叠,不同时空变化等条件下δ¹⁵N-NO₃ ^-和δ¹⁸O-NO₃ ^-值具有一定差异,以及氮迁移转化过程中的同位素分馏等的影响,导致模型计算得出的结果还存在不确定性,需要进一步优化模型的解析方法,以更精准地获取硝酸盐污染来源及其贡献率,服务于地下水资源的科学管理.     

Application of Sample Selection Model in Estimating Response Functions for Nitrate Percolation

The Seymour aquifer region of Texas has been identified as containing elevated levels of nitrate in ground water. Various state and federal agencies are currently studying policy options for the region by gathering more site-specific information. However, because of lack of sufficient information, cause and effect relationships between water quality and agricultural practices have not been well established for the region. Some recently available biophysical simulation models have impressive capabilities in generating large amounts of data on environmental pollution resulting from agricultural production practices. In this study, the data generated by a biophysical simulation model were used to estimate the nitrate percolation response functions for the Seymour aquifer region. Interestingly, nitrate percolation values obtained from simulation models often comprise acensoredsample because the non-zero percolation values are only observed under certain climatic events and input levels. It has been shown in the econometric literature that the use of Ordinary Least Squares (OLS) on censored sample data produces biased and inconsistent parameter estimates. Thus, a sample selection model was used in this study to estimate the response functions for nitrate percolation. The study provides some insight into the relationship between nitrate percolation and agricultural production practices. In particular, the study demonstrates the potential of selected design standards in minimizing agricultural nonpoint-source (NPS) pollution for the study area.     

化学氮肥对蔬菜硝酸盐污染影响的研究

蔬菜是一种容易富集硝酸盐的作物,菜农为了获得高产,而超量施用化学氮肥,致使蔬菜硝酸盐累积量剧增,品质恶化。研究表明：筛选出氯化铵和硫酸铵两种氮肥;施氮剂量每hm²以200kg为临界值;施氮安全始期,以追氮后8天上市为宜;化学氮肥与厩肥、土杂肥配用,能有效控制和降低蔬菜硝酸盐的累积分别达67.4％和45.7％。硝酸盐是亚硝胺的前体物,应减轻其对人体健康的潜在危害,并提高蔬菜品质。     

北京市燕山地区大气颗粒物中NO3－和SO42－浓度变化规律及其与O3

用ＫＢ－１２０型大气颗粒采样器采样,离子色谱法分析其中水溶性无机阴离子组分。结果表明,北京市燕山石油化工区大气颗粒物中和浓度随季节变化显著,夏季和平均浓度分别为９．４０μｇ／ｍ３和２８．２２μｇ／ｍ３;秋季分别为４．１２μｇ／ｍ３和３．８０μｇ／ｍ３。     

甲醇燃料发动机排气中的亚硝酸甲酯和硝酸甲酯的分析

为了测定使用甲醇燃料的发动机排气里的亚硝酸甲酯(MeONO)和硝酸甲酯(MeONO_2)含量,建立了色谱分析方法。本方法的最小检测限MeONO为20ppb,MeONO_2为50ppb。对自制的MeONO和MeONO_2进行了质谱鉴定,并对所制备的一定浓度的样品进行了稳定性考察。应用本方法测定了甲醇机排气中的MeONO含量为5ppm—250ppm,MeONO_2含量为<50ppb。     

土壤中硒酸盐还原作用与硝酸盐脱氮化作用的相关关系

在查明土壤中硒酸盐还原作用与硝酸盐脱氮化作用的相关关系,在不同类型土－水系统中,加入一定量的ＫＮＯ３和ＮａＳｅＯ４溶液,观察ＮＯ＾－３和ＳｅＯ＾２－４的化学形态转变过程。实验结果表明,硒酸盐的还原作用和硝酸盐的脱氮化作用在时空变化上具有一致性。     

Water quality indices appraisal and health risk assessment of nitrate,mercury and lead in water distribution network: A case study of Robat Karim in Tehran,Iran

Groundwater is a basic source of drinking water supply for urban and rural areas. This is especially the case for communities located in arid and semi-arid regions that rely on groundwater for drinking purposes. The present study set out to assess the potential health impacts of water impurities and to investigate the qualitative status of drinking water in Robat Karim rural areas, located in southwest Tehran, Iran. A total of 66 samples were collected from the water distribution network of 11 villages (33 sampling points, on two occasions) during September 2020 and were tested in terms of the most common quality parameters such as pH, mercury (Hg), lead (Pb), copper (Cu), zinc (Zn), chloride (Cl^–), chlorate (ClO₃^–), nitrite (NO₂^–), nitrate (NO₃^–), and flouride (F^–). Multiple methods and indexes including water quality index (WQI), hazard quotient (HQ), and hazard index (HI), were worked out to assess the quality of water and health risk assessment of NO₃^– Pb²⁺ and Hg²⁺. The results revealed that 33% and 90% of sampling sites have significantly high nitrate and total hardness (TH) concentrations, exceeding the maximum permissible limits set by World Health Organization (WHO; 50 and 200 mg/L, respectively). Furthermore, five sampling points exhibited poor WQIs mainly related to NO₃^– and TH. HQ values higher than 1 for nitrate were noticed in most sampling locations. Except for one sampling point, the HQ obtained for Pb²⁺ and Hg²⁺ were below 1 indicating no obvious health hazard. This study represents that children and infants are at higher risk of chronic toxicity by excess NO₃^– intake. The health hazard that is yet imposed on the community by NO₃^– necessitates regular monitoring of drinking water, the use of advanced technologies to purify water or otherwise alternative resources should be proposed.     

A Fuzzy Logic Approach to Assess Groundwater Pollution Levels Below Agricultural Fields

A fuzzy logic approach has been developed to assess the groundwater pollution levels below agricultural fields. The data collected for Kumluca Plain of Turkey have been utilized to develop the approach. The plain is known with its intensive agricultural activities, which imply excessive application of fertilizers. The characteristics of the soils and underlying groundwater for this plain were monitored during the years 1999 and 2000. Additionally, an extensive field survey related to the types and yields of crops, fertilizer application and irrigation water was carried out. Both the soil and groundwater have exhibited high levels of nitrogen, phosphorus and salinity with considerable spatial and temporal variations. The pollution level of groundwater at several established stations within the plain were assessed using Fuzzy Logic. Water Pollution Index (WPI) values are calculated by Fuzzy Logic utilizing the most significant groundwater pollutants in the area namely nitrite, nitrate and orthophosphate together with the groundwater vulnerability to pollution. The results of the calculated WPI and the monitoring study have yielded good agreement. WPI indicated high to moderate water pollution levels at Kumluca plain depending on factors such as agricultural age, depth to groundwater, soil characteristics and vulnerability of groundwater to pollution. Fuzzy Logic approach has shown to be a practical, simple and useful tool to assess groundwater pollution levels.     

Spatial Variability of Nitrate Concentrations Under Diverse Conditions in Tributaries to a Lake Watershed1

Abstract: Nitrate‐nitrogen (NO₃‐N) concentrations in stream water often respond uniquely to changes in inter‐annual conditions (e.g., biological N uptake and precipitation) in individual catchments. In this paper, we assess (1) how the spatial distribution of NO₃‐N concentrations varies across a dense network of nonnested catchments and (2) how relationships between multiple landscape factors [within whole catchments and hydrologically sensitive areas (HSAs) of the catchments] and stream NO₃‐N are expressed under a variety of annual conditions. Stream NO₃‐N data were collected during two synoptic sampling events across >55 tributaries and two synoptic sampling periods with >11 tributaries during summer low flow periods. Sample tributaries drain mixed land cover watersheds ranging in size from 0.150 to 312 km² and outlet directly to Cayuga Lake, New York. Changes in NO₃‐N concentration ratios between each sampling event suggest a high degree of spatial heterogeneity in catchment response across the Cayuga Lake Watershed, ranging from 0.230 to 61.4. Variations in NO₃‐N concentrations within each of the large synoptic sampling events were also high, ranging from 0.040 to 8.7 mg NO₃‐N/l (March) and 0.090 to 15.5 mg NO₃‐N/l (October). Although Pearson correlation coefficients suggest that this variability is related to multiple landscape factors during all four sampling events, partial correlations suggest percentage of row crops in the catchments as the only similar factor in March and October and catchment area as the only factor during summer low flows. Further, the strength of the relationships is typically lower in the HSAs of catchment. Advancing current understanding of such variations and relationships to landscape factors across multiple catchments – and under a variety of biogeochemical and hydrological conditions – is important, as (1) nitrate continues to be employed as an indicator of regional aquatic ecosystem health and services and (2) a unified framework approach for understanding individual catchment processes is a rapidly evolving focus for catchment‐based science and management.     

Impact of an Invasive Exotic Species on Stream Nitrogen Levels in Southern Illinois1

Abstract: Autumn‐olive (Elaeagnus umbellata Thunb.) is an invasive, exotic shrub that has become naturalized in the eastern United States. Autumn‐olive fixes nitrogen (N) via a symbiotic relationship with the actinomycete Frankia. At the plot scale, the presence of autumn‐olive has been related to elevated soil water nitrate‐N (NO₃^?‐N) concentrations. This study examined the relationship between autumn‐olive cover in a watershed and stream water quality. Stream water nitrate‐N (NO₃^?‐N) and ammonium‐N (NH₄⁺‐N) concentrations were measured in 12 first order ephemeral streams draining watersheds with mixed forest cover and a range of 0‐35% autumn‐olive cover. Percent autumn‐olive cover was positively correlated with mean stream NO₃^?‐N concentrations, but was not correlated with mean stream NH₄⁺‐N concentrations. While other studies have demonstrated a significant relationship between native N‐fixers and stream NO₃^?‐N, this is the first study to document a relationship for an invasive, exotic N‐fixing species. Results suggest that this exotic species can be an additional source of NO₃^? in local and regional water bodies and demonstrates an additional negative ecosystem consequence of invasion beyond losses in biodiversity.