Association Between Socioeconomic Inequalities in Pain and All-Cause Mortality in the China Health and Retirement Longitudinal Study: Longitudinal Cohort Study.

BACKGROUND: Few studies focus on the equality of pain, and the relationship between pain and death is inconclusive. Investigating the distribution of pain and potential mortality risks is crucial for ameliorating painful conditions and devising targeted intervention measures. OBJECTIVE: Our study aimed to investigate the association between inequalities in pain and all-cause mortality in China. METHODS: Longitudinal cohort data from waves 1 and 2 of the China Health and Retirement Longitudinal Study (2011-2013) were used in this study. Pain was self-reported at baseline, and death information was obtained from the 2013 follow-up survey. The concentration index and its decomposition were used to explain the inequality of pain, and the association between pain and death was analyzed with a Cox proportional risk model. RESULTS: A total of 16,747 participants were included, with an average age of 59.57 (SD 9.82) years. The prevalence of pain was 32.54% (8196/16,747). Among participants with pain, the main pain type was moderate pain (1973/5426, 36.36%), and the common pain locations were the waist (3232/16,747, 19.3%), legs (2476/16,747, 14.78%) and head (2250/16,747, 13.44%). We found that the prevalence of pain was concentrated in participants with low economic status (concentration index -0.066, 95% CI -0.078 to -0.054). Educational level (36.49%), location (36.87%), and economic status (25.05%) contributed significantly to the inequality of pain. In addition, Cox regression showed that pain was associated with an increased risk of all-cause mortality (hazard ratio 1.30, 95% CI 1.06-1.61). CONCLUSIONS: The prevalence of pain in Chinese adults is concentrated among participants with low economic status, and pain increases the risk of all-cause death. Our results highlight the importance of socioeconomic factors in reducing deaths due to pain inequalities by implementing targeted interventions.

Relationship between denitrification and anammox rates and N2 production with substrate consumption and pH in a riparian zone.

Denitrification and anaerobic ammonium oxidation (anammox) are the key processes to quantitatively remove nitrate (NO3-) and balance the nitrogen (N) budget of the ecosystem. In this paper, a slurry-based 15N tracer approach was used to study the correlation and quantitative relation of substrate consumption and pH with rates of denitrification and anammox in a riparian zone. The results showed that the fastest rates of 0.93 µg N h-1 and 0.32 µg N h-1 for denitrification (Denitrif-N2) and anammox (Denitrif-N2), respectively. N2 produced by denitrification occupied 74.04% and produced by anammox occupied 25.96% of the total N2, proving denitrification is the dominant process to remove NO3-. The substrate content (NO3-, NH4+ and TOC) and pH varied during incubation and were significantly correlated with Dentrif-N2 and Anammox-N2. Nitrate and TOC as the substrates of denitrification demonstrated a significant correlation with Anammox-N2, which was associated with the products of denitrification involved in the anammox process. This proved a coupling of denitrification and anammox. A quantitative relationship was observed between Dentrif-N2 and Anammox-N2 in the range of 2.75-2.90 when TOC, NH4+ and NO3- consumption per unit mass or pH changed per unit. Nitrogen mass balance analysis showed that 1 mg N substrate (NO3-+NH4+) consumption in the denitrification and anammox can produce 1.05 mg N2 with a good linear relationship (r2 = 0.9334). This could be related to other processes that produced extra N2 in denitrification and anammox system.

Full-Length Model of SaCas9-sgRNA-DNA Complex in Cleavage State.

Staphylococcus aureus Cas9 (SaCas9) is a widely used genome editing tool. Understanding its molecular mechanisms of DNA cleavage could effectively guide the engineering optimization of this system. Here, we determined the first cryo-electron microscopy structure of the SaCas9-sgRNA-DNA ternary complex. This structure reveals that the HNH nuclease domain is tightly bound to the cleavage site of the target DNA strand, and is in close contact with the WED and REC domains. Moreover, it captures the complete structure of the sgRNA, including the previously unresolved stem-loop 2. Based on this structure, we build a full-length model for the ternary complex in cleavage state. This model enables identification of the residues for the interactions between the HNH domain and the WED and REC domains. Moreover, we found that the stem-loop 2 of the sgRNA tightly binds to the PI and RuvC domains and may also regulate the position shift of the RuvC domain. Further mutagenesis and molecular dynamics simulations supported the idea that the interactions of the HNH domain with the WED and REC domains play an important role in the DNA cleavage. Thus, this study provides new mechanistic insights into the DNA cleavage of SaCas9 and is also useful for guiding the future engineering of SaCas9-mediated gene editing systems.

aiWave: Volumetric Image Compression With 3-D Trained Affine Wavelet-Like Transform.

Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice. At present, the most commonly used volumetric image compression methods are based on wavelet transform, such as JP3D. However, JP3D employs an ideal, separable, global, and fixed wavelet basis to convert input images from pixel domain to frequency domain, which seriously limits its performance. In this paper, we first design a 3-D trained wavelet-like transform to enable signal-dependent and non-separable transform. Then, an affine wavelet basis is introduced to capture the various local correlations in different regions of volumetric images. Furthermore, we embed the proposed wavelet-like transform to an end-to-end compression framework called aiWave to enable an adaptive compression scheme for various datasets. Last but not least, we introduce the weight sharing strategies of the affine wavelet-like transform according to the volumetric data characteristics in the axial direction to reduce the number of parameters. The experimental results show that: 1) when cooperating our trained 3-D affine wavelet-like transform with a simple factorized entropy coding module, aiWave performs better than JP3D and is comparable in terms of encoding and decoding complexities; 2) when adding a context module to remove signal redundancy further, aiWave can achieve a much better performance than HEVC.

How working-age population education and health of older people shape the burden of population aging: A comparative study of Macau, Hong Kong, and Singapore.

Macau, Hong Kong and Singapore are all facing increasing population aging. Those aged 65 and over make up the old-age population. The working-age population refers to the population aged 15 to 64. Conventionally, the burden of population aging is measured by the Old-Age Dependency Ratio, which is the ratio of the old-age population to the working-age population. As life expectancy rises, depending exclusively on age to calculate the burden of aging hinders the development of effective anti-aging strategies. The working-age population's education and the elderly's health affect the aging burden's support and generator, respectively. Including them in the calculation gives us a fuller view of the burden of aging. Objective: To compare the population aging burden in Macau, Hong Kong, and Singapore by including working-age population education and elderly health. Methods: The overall, working-age and old-age population and proportion, as well as the Old-Age Dependency Ratio of Macau, Hong Kong, and Singapore, were collected from the World Bank database. The life expectancy at 65 was extracted from the 2022 World Population Prospect. The tertiary education rate of the working-age population and the self-rated health status of the old-age population were retrieved from governments' statistical reports. We then calculated the Education-Health Adjusted Old-Age Dependency Ratio, a set of four equations showing the support of the working-age population on the old-age population, where OADRh_t and OADRuh_t represent the burden of healthy and unhealthy old-age population on the working-age population with tertiary education; similarly, OADRh_nt and OADRuh_nt indicate the burden placed on the working-age population without tertiary education by healthy and unhealthy old-age population. Lastly, for comparison with the conventional Old-Age Dependency Ratio, we generated the Weighted Education-Health Adjusted Old-Age Dependency Ratio. Results: Hong Kong has the greatest old-age population proportion and Old-Age Dependency Ratio, yet its growth rates are moderate and stable, ranging from 0 to 4% and 0 to 6%, respectively. Macau and Singapore experienced sharper changes in old-age population proportion and the Old-Age Dependency Ratio, with Macau's Old-Age Dependency Ratio varying between -2.66 and 8.50% and Singapore's ranging from -1.53 to 9.70%. Three cities showed different patterns in four Education-Health Adjusted Old-Age Dependency Ratio indicators. In Macau, the OADRh_nt and OADRuh_nt increased by 0.4 and 6.2, while the OADRh_t and OADRuh_t decreased by 13.5 and 15.3 from 2004 to 2016. In Hong Kong, only the OADRuh_t fell by 9.4, and the other three increased from 2003 to 2015. In Singapore, the OADRh_nt and OADRh_t increased by 3.8 and 1.0, while OADRuh_nt and OADRuh_t decreased by 1.2 and 3.9 from 2007 to 2011. The Weighted Education-Health Adjusted Old-Age Dependency Ratios are all smaller than the conventional Old-Age Dependency Ratio in the three regions, particularly in Singapore. The Weighted Education-Health Adjusted Old-Age Dependency Ratio of Singapore was reduced by 9.5 to 30.5% compared with the conventional Old-Age Dependency Ratio, that of Hong Kong reduced by 6.2 to 22.5%, and that of Macau reduced by 4.4 to 16.1%. Conclusion: This is the first study to compare the aging burden in Macau, Hong Kong, and Singapore in connection to working-age population education and elderly health. With the new assessment, the burden of population aging in three regions has been reduced, showing that improving the education of the working-age population and maintaining older people's wellbeing can assist authorities to deal with population aging, especially in Macau and Hong Kong.

Finding flaws in the spatial distribution of health workforce and its influential factors: An empirical analysis based on Chinese provincial panel data, 2010-2019.

Background: The maldistributions of the health workforce showed great inconsistency when singly measured by population quantity or geographic area in China. Meanwhile, earlier studies mainly employed traditional econometric approaches to investigate determinants for the health workforce, which ignored spillover effects of influential factors on neighboring regions. Therefore, we aimed to analyze health workforce allocation in China from demographic and geographic perspectives simultaneously and then explore the spatial pattern and determinants for health workforce allocation taking account of the spillover effect. Methods: The health resource density index (HRDI) equals the geometric mean of health resources per 1,000 persons and per square kilometer. First, the HRDI of licensed physicians (HRDI_P) and registered nurses (HRDI_N) was calculated for descriptive analysis. Then, global and local Moran's I indices were employed to explore the spatial features and aggregation clusters of the health workforce. Finally, four types of independent variables were selected: supportive resources (bed density and government health expenditure), healthcare need (proportion of the elderly population), socioeconomic factors (urbanization rate and GDP per capita), and sociocultural factors (education expenditure per pupil and park green area per capita), and then the spatial panel econometric model was used to assess direct associations and intra-region spillover effects between independent variables and HRDI_P and HRDI_N. Results: Global Moran's I index of HRDI_P and HRDI_N increased from 0.2136 (P = 0.0070) to 0.2316 (P = 0.0050), and from 0.1645 (P = 0.0120) to 0.2022 (P = 0.0080), respectively. Local Moran's I suggested spatial aggregation clusters of HRDI_P and HRDI_N. For HRDI_P, bed density, government health expenditure, and GDP had significantly positive associations with local HRDI_P, while the proportion of the elderly population and education expenditure showed opposite spillover effects. More precisely, a 1% increase in the proportion of the elderly population would lead to a 0.4098% increase in HRDI_P of neighboring provinces, while a 1% increase in education expenditure leads to a 0.2688% decline in neighboring HRDI_P. For HRDI_N, the urbanization rate, bed density, and government health expenditure exerted significantly positive impacted local HRDI_N. In addition, the spillover effect was more evident in the urbanization rate, with a 1% increase in the urbanization rate relating to 0.9080% growth of HRDI_N of surrounding provinces. Negative spillover effects of education expenditure, government health expenditure, and elderly proportion were observed in neighboring HRDI_N. Conclusion: There were substantial spatial disparities in health workforce distribution in China; moreover, the health workforce showed positive spatial agglomeration with a strengthening tendency in the last decade. In addition, supportive resources, healthcare needs, and socioeconomic and sociocultural factors would affect the health labor configuration not only in a given province but also in its nearby provinces.

[Structure-based optimization and design of CRISPR protein xCas9].

Streptococcus pyogenes Cas9 (SpCas9) has become a powerful genome editing tool, but has a limited range of recognizable protospacer adjacent motifs (PAMs) and shows off-target effects. To address these issues, we present a rational approach to optimize the xCas9 mutant derived from SpCas9 by directed evolution. Firstly, energy minimization with the Rosetta program was applied to optimize the three-dimensional structure of Cas9 to obtain the lowest energy conformation. Subsequently, combinatorial mutations were designed based on the mutations sites of xCas9 acquired during the directed evolution. Finally, optimal mutants were selected from the designed mutants by free energy ranking and subjected to experimental verification. A new mutant yCas9 (262A/324R/409N/480K/543D/694L/1219T) with multiple PAM recognition ability and low off-target effects was obtained and verified by DNA cleavage experiments. This mutant recognizes the NG, GAA and GAT PAMs and shows low off-target DNA cleavage activity guided by mismatched sgRNA, thus provides a gene editing tool with potential applications in biomedical field. Furthermore, we performed molecular dynamics simulations on the structures of SpCas9, xCas9 and yCas9 to reveal the mechanisms of their PAM recognition and off-target effects. These may provide theoretical guidance for further optimization and modification of CRISPR/Cas9 proteins.

Active-Site Models of Streptococcus pyogenes Cas9 in DNA Cleavage State.

CRISPR-Cas9 is a powerful tool for target genome editing in living cells. Significant advances have been made to understand how this system cleaves target DNA. HNH is a nuclease domain, which shares structural similarity with the HNH endonuclease characterzied by a beta-beta-alpha-metal fold. Therefore, based on one- and two-metal-ion mechanisms, homology modeling and molecular dynamics (MD) simulation are suitable tools for building an atomic model of Cas9 in the DNA cleavage state. Here, by modeling and MD, we presented an atomic model of SpCas9-sgRNA-DNA complex with the cleavage state. This model shows that the HNH and RuvC conformations resemble their DNA cleavage state where the active-sites in the complex coordinate with DNA, Mg2+ ions, and water. Among them, residues D10, E762, H983, and D986 locate at the first shell of the RuvC active-site and interact with the ions directly, residues H982 or/and H985 are general (Lewis) bases, and the coordinated water is located at the positions for nucleophilic attack of the scissile phosphate. Meanwhile, this catalytic model led us to engineer a new SpCas9 variant (SpCas9-H982A + H983D) with reduced off-target effects. Thus, our study provided new mechanistic insights into the CRISPR-Cas9 system in the DNA cleavage state and offered useful guidance for engineering new CRISPR-Cas9 editing systems with improved specificity.

Molecular ecological network analysis of the response of soil microbial communities to depth gradients in farmland soils.

Soil microorganisms are considered to be important indicators of soil fertility and soil quality. Most previous studies have focused solely on surface soil, but there were numerous active cells in deeper soil layers. However, studies regarding microbial communities in deeper soil layers were not comprehensive and sufficient. In this study, phylogenetic molecular ecological networks (pMENs) based on the 16S rRNA Miseq sequencing technique were applied to study the response of soil microbial communities to depth gradients and the changes of key genera along 3 meter depth gradients (0-0.2 m, 0.2-0.4 m 0.4-0.6 m, 0.6-0.8 m, 0.8-1.0 m, 1.0-1.3 m, 1.3-1.6 m, 1.6-2.0 m, 2.0-2.5 m, and 2.5-3.0 m). The results showed that the modularity of microbial communities was consistently high in all soil layers and each layer was similar, which indicated that microbial communities were more resistant to depth changes. The pMENs further demonstrated that microbial community interactions were stable as the depth increased and they cooperated well to adapt to changes in different soil gradients. This was evidenced by similar positive links, average degree, and average clustering coefficient. In addition, key genera were obtained by analyzing module hubs in the pMENs. There may be at least one dominant genus in each layer that adapted to and resisted changes in the soil environment. It seems microbial communities demonstrate a stable and strong adaptability to depth gradients in farmland soils.

Long-term effects of nitrogen fertilization on aggregation and localization of carbon, nitrogen and microbial activities in soil.

Long-term nitrogen (N) fertilization affects soil aggregation and localizations of soil organic carbon (SOC), N and microbial parameters within aggregates. The mechanisms of these N effects are poorly understood. We studied these processes in a loamy soil from a 23-year repeated N addition field experiment under a rice-barley rotation. Nitrogen fertilization increased plant productivity and the portion of large macroaggregates (>2mm). However, SOC contents in macro- and micro-aggregates remained constant despite an N-induced increase of 27% in root C input into soil. Therefore, N fertilization accelerated SOC turnover. Nitrogen addition increased total N (TN) content in bulk soil and two macroaggregates (>2, and 1-2mm), but not in microaggregates (<0.25mm). Also, N fertilization increased the phospholipid fatty acids (PLFAs) contents of fungi in the large macroaggregates, but not in the microaggregates. In contrast, the effect of N addition on contents of bacterial and total microbial PLFAs was not apparent. Nitrogen fertilization increased N-acetyl-ß-D-glucosaminidase (NAG) activities in the two larger macroaggregate size classes (>2, and 1-2mm), but not in the aggregates (<1mm). In both control and N fertilization, the large macroaggregates localized more TN, microbial PLFAs, and NAG activities than the microaggregates. In conclusion, long-term N fertilization not only directly promotes soil N resource but also indirectly improves soil structure by forming large macroaggregates, accelerates SOC turnover, and shiftes localization of microorganisms to the macroaggregates.

Assessing the Effects of Acidification on N Dynamics in Jiyun River System of Tianjin, China.

Alterations in pH have significant effects on nitrification and denitrification processes in aquatic systems. The Jiyun River in northern China experiences significant acid precipitation, and as such was selected to investigate the effects of decreasing pH (river pH at 8.2, pH at 6 and 5) on N dynamics via incubation experiments (35 and 10°C). Statistical results indicated that the NO3 (-) concentrations of the control (pH at 8.2) were either significantly lower (at 35°C) or significantly higher (at 10°C) than the treatments of pH at 6 and 5 at the alpha level of 0.05 in the incubation. However, the NO3 (-) concentrations of the two pH treatments showed significant difference during part of the reaction stage at 35°C and no difference at 10°C. Analysis indicated that nitrification and coupled nitrification-aerobic denitrification occurred for all treatments, which resulted in NO3 (-) being either accumulated or removed at the end of the incubation.

Decision-tree-model identification of nitrate pollution activities in groundwater: A combination of a dual isotope approach and chemical ions.

To develop management practices for agricultural crops to protect against NO3(-) contamination in groundwater, dominant pollution activities require reliable classification. In this study, we (1) classified potential NO3(-) pollution activities via an unsupervised learning algorithm based on δ(15)N- and δ(18)O-NO3(-) and physico-chemical properties of groundwater at 55 sampling locations; and (2) determined which water quality parameters could be used to identify the sources of NO3(-) contamination via a decision tree model. When a combination of δ(15)N-, δ(18)O-NO3(-) and physico-chemical properties of groundwater was used as an input for the k-means clustering algorithm, it allowed for a reliable clustering of the 55 sampling locations into 4 corresponding agricultural activities: well irrigated agriculture (28 sampling locations), sewage irrigated agriculture (16 sampling locations), a combination of sewage irrigated agriculture, farm and industry (5 sampling locations) and a combination of well irrigated agriculture and farm (6 sampling locations). A decision tree model with 97.5% classification success was developed based on SO4(2-) and Cl(-) variables. The NO3(-) and the δ(15)N- and δ(18)O-NO3(-) variables demonstrated limitation in developing a decision tree model as multiple N sources and fractionation processes both resulted in difficulties of discriminating NO3(-) concentrations and isotopic values. Although only the SO4(2-) and Cl(-) were selected as important discriminating variables, concentration data alone could not identify the specific NO3(-) sources responsible for groundwater contamination. This is a result of comprehensive analysis. To further reduce NO3(-) contamination, an integrated approach should be set-up by combining N and O isotopes of NO3(-) with land-uses and physico-chemical properties, especially in areas with complex agricultural activities.

The effects of salinity on coupled nitrification and aerobic denitrification in an estuarine system.

Salinity has significant effects on nitrification and denitrification processes, particularly in estuarine systems. A dissolved oxygen-enriched river and its estuary in northern China were selected to investigate the impact of salinity gradients (0.6, 4, 7.6, 11.4 and 14.7‰) obtained from the mixing of river samples and estuarine samples with different proportions on coupled nitrification and aerobic denitrification via incubation experiments (35 and 10 °C). Results indicated that: (a) nitrification and coupled nitrification-aerobic denitrification occurred for all treatments, which resulted in NO3- being either accumulated or removed at the end of the incubation; (b) a suitable range of salinity is 4.0-11.4‰ for nitrification and 4.0-7.6‰ for coupled nitrification-aerobic denitrification; and (c) the relatively higher temperature (35 °C) can effectively stimulate N transformation processes compared to the lover temperature (10 °C) in the incubation experiment.

Classification of Nitrate Polluting Activities through Clustering of Isotope Mixing Model Outputs.

Apportionment of nitrate (NO) sources in surface water and classification of monitoring locations according to NO polluting activities may help implementation of water quality control measures. In this study, we (i) evaluated a Bayesian isotopic mixing model (stable isotope analysis in R [SIAR]) for NO source apportionment using 2 yr of δN-NO and δO-NO data from 29 locations within river basins in Flanders (Belgium) and five expert-defined NO polluting activities, (ii) used the NO source contributions as input to an unsupervised learning algorithm (k-means clustering) to reclassify sampling locations into NO polluting activities, and (iii) assessed if a decision tree model of physicochemical data could retrieve the isotope-based and expert-defined classifications. Based on the SIAR and δB results, manure/sewage was identified as a major NO source, whereas soil N, fertilizer NO, and NH in fertilizer and rain were intermediate sources and NO in precipitation was a minor source. The k-means clustering algorithm allowed classification of NO polluting activities that corresponded well to the expert-defined classifications. A decision tree model of physicochemical parameters allowed us to correctly classify 50 to 100% of the sampling locations as compared with the k-means clustering approach. We suggest that NO polluting activities can be identified via clustering of NO source contributions from samples representing an entire river basin. Classification of future monitoring locations into these classes could use decision tree models based on physicochemical data. The latter approach holds a substantial degree of uncertainty but provides more inherent information for dedicated abatement strategies than monitoring of NO concentrations alone.

Use of a Bayesian isotope mixing model to estimate proportional contributions of multiple nitrate sources in surface water.

To identify different NO(3)(-) sources in surface water and to estimate their proportional contribution to the nitrate mixture in surface water, a dual isotope and a Bayesian isotope mixing model have been applied for six different surface waters affected by agriculture, greenhouses in an agricultural area, and households. Annual mean δ(15)N-NO(3)(-) were between 8.0 and 19.4‰, while annual mean δ(18)O-NO(3)(-) were given by 4.5-30.7‰. SIAR was used to estimate the proportional contribution of five potential NO(3)(-) sources (NO(3)(-) in precipitation, NO(3)(-) fertilizer, NH(4)(+) in fertilizer and rain, soil N, and manure and sewage). SIAR showed that "manure and sewage" contributed highest, "soil N", "NO(3)(-) fertilizer" and "NH(4)(+) in fertilizer and rain" contributed middle, and "NO(3)(-) in precipitation" contributed least. The SIAR output can be considered as a "fingerprint" for the NO(3)(-) source contributions. However, the wide range of isotope values observed in surface water and of the NO(3)(-) sources limit its applicability.

Isotopes for improved management of nitrate pollution in aqueous resources: review of surface water field studies.

BACKGROUND: Environmental agencies have to take measures to either reduce discharges and emissions of nitrate or to remediate nitrate-polluted water bodies where the nitrate concentrations exceed threshold values. Isotope data can support the identification of nitrate pollution sources and natural attenuation processes of nitrate. REVIEW: This review article gives an overview of the information available to date regarding nitrate source apportionment in surface waters with the ambition to help improving future studies. Different isotope approaches in combination with physicochemical and hydrological data can successfully be used in source apportionment studies. A sampling strategy needs to be developed based on possible nitrate sources, hydrology and land use. Transformations, transport and mixing processes should also be considered as they can change the isotope composition of the original nitrate source. CONCLUSION: Nitrate isotope data interpreted in combination with hydrological and chemical data provide valuable information on the nitrate pollution sources and on the processes nitrate has undergone during its retention and transport in the watershed. This information is useful for the development of an appropriate water management policy.

Error assessment of nitrogen and oxygen isotope ratios of nitrate as determined via the bacterial denitrification method.

Currently, bacterial denitrification is becoming the accepted method for delta(15)N- and delta(18)O-NO(3)(-) determination. However, proper correction methods with international references (USGS32, USGS34 and USGS35) are needed. As a consequence, it is important to realize that the corrected isotope values are derived from a combination of several other measurements with associated uncertainties. Therefore, it is necessary to consider the propagated uncertainty on the final isotope value. This study demonstrates how to correctly estimate the uncertainty on corrected delta(15)N- and delta(18)O-NO(3)(-) values using a first-order Taylor series approximation. The bacterial denitrification method errors from 33 batches of 561 surface water samples varied from 0.2 to 2.1 per thousand for delta(15)N-NO(3)(-) and from 0.7 to 2.3 per thousand for delta(18)O-NO(3)(-), which is slightly wider than the machine error, which varied from 0.2 to 0.6 per thousand for delta(15)N-N(2)O and from 0.4 to 1.0 per thousand for delta(18)O-N(2)O. The overall uncertainties, which are composed of the machine error and the method error, for the 33 batches ranged from 0.3 to 2.2 per thousand for delta(15)N-NO(3)(-) and from 0.8 to 2.5 per thousand for delta(18)O-NO(3)(-). In addition, the mean corrected delta(15)N and delta(18)O values of 132 KNO(3)-IWS (internal working standard) measurements were computed as 8.4 +/- 1.0 per thousand and 25.1 +/- 2.0 per thousand, which is a slight underestimation for delta(15)N and overestimation for delta(18)O compared with the accepted values (delta(15)N = 9.9 +/- 0.3 per thousand and delta(18)O = 24.0 +/- 0.3 per thousand). The overall uncertainty of the bacterial denitrification method allows the use of this method for source identification of NO(3)(-).

Comparison of the silver nitrate and bacterial denitrification methods for the determination of nitrogen and oxygen isotope ratios of nitrate in surface water.

Nitrogen (N) and oxygen (O) isotope ratios of NO(3) (-) are often used to trace dominant NO(3) (-) pollution sources in water. Both the silver nitrate (AgNO(3)) method and the bacterial denitrification method are frequently used analytical techniques to determine delta(15)N- and delta(18)O-NO(3) (-) in aqueous samples. The AgNO(3) method is applicable for freshwater and requires a concentration of 100-200 micromol of NO(3) (-) for isotope determination. The bacterial denitrification method is applicable for seawater and freshwater and for KCl extracts of soils with a NO(3) (-) concentration as low as 1 micromol. We have carried out a thorough method comparison using 42 real surface water samples having a wide range of delta(15)N- and delta(18)O-NO(3) (-) values and NO(3) (-) concentrations. Various correction pairs using three international references and blanks were used to correct raw delta(15)N- and delta(18)O-NO(3) (-) values. No significant difference between the corrected data was observed when using various correction pairs for each analytical method. Both methods also showed excellent repeatability with high intraclass correlation coefficients (ICC). The ICC of the AgNO(3) method was 0.992 for delta(15)N and 0.970 for delta(18)O. The ICC of the bacterial denitrification method was 0.995 for delta(15)N and 0.954 for delta(18)O. Moreover, a positive linear relationship with a high correlation coefficient (r >or= 0.88) between the two methods was found for delta(15)N- and delta(18)O-NO(3) (-). The comparability of the methods was assessed by the Bland-Altman technique using 95% limits of agreement. The average difference between results obtained by the bacterial denitrification and the AgNO(3) method for delta(15)N was -1.5 per thousand with 95% limits of agreement -3.6 and +0.5 per thousand. For delta(18)O this was +2.0 per thousand, with 95% limits of agreement -3.3 and +7.3 per thousand. We found that for delta(15)N and for delta(18)O, 97% of the differences fell within these 95% limits of agreement. In conclusion, the AgNO(3) and the bacterial denitrification methods are highly correlated and statistically interchangeable.

Present limitations and future prospects of stable isotope methods for nitrate source identification in surface- and groundwater.

Nitrate (NO3(-)) contamination of surface- and groundwater is an environmental problem in many regions of the world with intensive agriculture and high population densities. Knowledge of the sources of NO3(-) contamination in water is important for better management of water quality. Stable nitrogen (delta15N) and oxygen (delta18O) isotope data of NO3(-) have been frequently used to identify NO3(-) sources in water. This review summarizes typical delta15N- and delta18O-NO3(-) ranges of known NO3(-) sources, interprets constraints and future outlooks to quantify NO3(-) sources, and describes three analytical techniques ("ion-exchange method", "bacterial denitrification method", and "cadmium reduction method") for delta15N- and delta18)O-NO3(-) determination. Isotopic data can provide evidence for the presence of dominant NO3(-) sources. However, quantification, including uncertainty assessment, is lacking when multiple NO3(-) sources are present. Moreover, fractionation processes are often ignored, but may largely constrain the accuracy of NO3(-) source identification. These problems can be overcome if (1) NO3(-) isotopic data are combined with co-migrating discriminators of NO3(-) sources (e.g. (11)B), which are not affected by transformation processes, (2) contributions of different NO3(-) sources can be quantified via linear mixing models (e.g. SIAR), and (3) precise, accurate and high throughput isotope analytical techniques become available.