李承霖(1994—),男,辽宁葫芦岛人,博士研究生,主要从事农田氮素循环过程及其环境效应的研究。E-mail:
反硝化是生态系统氮循环的最后一环同时也是活性氮转化为惰性氮(N2)的最主要过程。由于空气中背景N2浓度高达78%,在如此高的背景浓度N2环境中直接和准确测定反硝化过程产生的微量N2,一直是个巨大的挑战。Robot系统(Robotized incubation and analyzing system)是基于无N2背景(氦环境)的用以研究纯菌或土壤体系N2排放速率的方法,该系统平台搭建简单且测定效率高,目前应用比较广泛。但该系统在运行过程中需要频繁利用微量注射器进行取样和测定,极易造成外界N2的渗漏。为解决这一问题,通过使用预先置于氦环境的橡胶隔垫、采用充氦后的蒸馏水配制溶液及实施破坏性取样的处理,对Robot系统测定旱地N2排放速率的方法进行优化,同时与乙炔抑制法和RoFlow系统(Robotized continuous flow incubation system)的测定结果进行对比。研究结果表明,通过方法优化,可以大幅降低Robot系统的N2渗漏率,方法优化后系统的渗漏率在0~0.78 μL·L–1·h–1之间。优化后的Robot系统对碳源和氮源添加后N2排放速率差异的响应较好,并且对旱地土壤N2排放速率的测定误差最小(0.003~0.045 mg·kg–1·d–1),显著优于乙炔抑制法(0.34~3.29 mg·kg–1·d–1)和RoFlow系统(0.41~1.02 mg·kg–1·d–1)。综上,优化后的Robot系统在测定旱地N2排放速率时具有N2渗漏率低,对外源底物添加响应好及测定结果精确度高的特点,未来在研究旱地土壤背景N2排放及相关机理方面有较好的应用前景。
The massive application of nitrogen fertilizer to agricultural soils plays an important role in ensuring the world's food supply. However, it also leads to a large amount of reactive nitrogen(N) entering the environment, which strongly interferes with the biogeochemical cycle of N and causes a series of ecological and environmental problems. As the last step of N cycling, denitrification is the predominant pathway, converting reactive N into inert N(i.e., N2). However, measuring soil N2 production from denitrification is a major challenge in terrestrial ecosystems because of the high atmospheric background N2 concentration. Recently, direct methods for measuring N2 emission rates have been developed. Among them, robotized incubation and analyzing system(Robot system) which is based on N2 free headspace(i.e., helium environment) have been widely used for measuring N2 emission in pure denitrifying culture or soil, due to its advantage on platform construction and high throughput for N2 determination. Nevertheless, frequent sampling with the small-diameter steel needle is required during the operation and determination of the Robot system, which inevitably causes leakage of N2. This seriously interferes with the determination of low N2 emission rates(i.e., background N2 flux in upland soil). Therefore, to enable the Robot system to measure background N2 emission rate in upland soil without exogenous substrate, the leakage rate of the system must be further reduced.
In this study, helium-washed rubber septa, solutions prepared by helium-washed distilled water and destructive sampling treatments were explored to optimize the Robot system aiming at reducing the N2 leakage therein. Additionally, results of soil N2 emission determined by the optimized Robot system were compared with those of acetylene inhibition technique(AIT) and Robotized continuous flow incubation system(RoFlow system).
Our results showed that the N2 leakage rate of the Robot system could be remarkably reduced by optimizing with helium-washed septa, solutions prepared by helium-washed distilled water and destructive sampling treatments. The N2 leakage rate was reduced to 0 ~ 0.78 μL·L–1·h–1 by the aforementioned treatments. Under similar treatments, the N2 emission rate measured by the acetylene inhibition method was highest, followed by the RoFlow system, and the Robot system had the lowest results. Furthermore, the optimized Robot system was capable of determining upland soil N2 emissions in response to carbon and N addition, which also had the smallest standard error(0.003 ~ 0.045 mg·kg–1·d–1) compared with the AIT method(0.34 ~ 3.29 mg·kg–1·d–1) and RoFlow system(0.41 ~ 1.02 mg·kg–1·d–1).
Overall, the optimized Robot system is characterized by low N2 leakage, effective response to substrate addition and good consistency in determining soil N2 emission. In the future, it will have a favorable application in investigating background N2 emissions and the associated mechanism of upland soil.
农业氮肥的大量施用导致大量活性氮进入环境[
空气中极高的背景N2浓度(78%)导致反硝化过程终产物N2的直接测定一直是个世界性难题[
近年来,有学者在前人旱地N2直接测定系统之上进行改进,研制了更适用于旱地N2排放速率测定的氦(He)环境-密闭培养及He环境-气体同步测定系统[
针对上述问题,本研究通过使用预先置于He环境的橡胶隔垫、采用充He后的蒸馏水配制相关溶液并结合实施破坏性取样技术对Robot系统测定旱地N2排放速率的方法进行优化,利用优化后的Robot系统测定旱地土壤背景N2排放速率及添加碳源和氮源后的N2排放速率,并与乙炔抑制法和RoFlow系统测定结果进行对比,以期验证方法优化后的Robot系统在测定旱地土壤背景N2排放速率上的优势。
供试土壤采自中国科学院常熟生态实验站附近试验地(31°33′16″ N,120°43′17″ E),试验地土壤为由湖泊沉积物发育而成的水耕人为土。该区域属于亚热带季风气候,年平均降水量990 mm,约60%~70%降水发生在6至9月间,年平均气温16.1 ℃[
培养实验采用同一份风干土样,共设置5个不同碳氮底物浓度的添加处理,分别为:(1)仅添加蒸馏水的对照;(2)添加60 mg·kg–1(以N计,下同)硝酸钾(KNO3)溶液;(3)添加150 mg·kg–1 KNO3溶液;(4)添加60 mg·kg–1(以C计,下同)葡萄糖(C6H12O6)溶液;(5)添加60 mg·kg–1 C6H12O6和60 mg·kg–1 KNO3混合溶液。每个处理设置3个重复,在室温(25℃)和无氧条件下,分别采用三种不同方法/系统(Robot系统、乙炔抑制法和RoFlow系统)测定土壤N2排放速率。
Robot系统主要包括三个组成模块:气体监测模块、自动进样模块和恒温水浴模块[
前期预实验表明,空气与橡胶隔垫之间的气体渗漏、吸附在橡胶隔垫中气体的扩散、溶解在底物溶液中的气体扩散和吸附在土壤颗粒上的气体扩散作用均会向血清瓶中带入N2(将这几个过程统称为气体渗漏),并对实验过程中N2测定产生影响。因此需要对以上可能的气体渗漏环节进行优化以提高实验的精度。
针对空气与橡胶隔垫之间的气体渗漏采用如下方式控制:(1)由于隔垫是阻拦空气向血清瓶中扩散的唯一介质,而多次对其穿刺取样将产生较大的创口使渗漏量不断增加,采用破坏性取样的方式进行取样即将每组处理设置更多的平行,所有血清瓶只取样测定一次,可降低该环节导致的气体泄漏。(2)在样品培养期间,由于空气与橡胶隔垫的扩散主要是由外界空气引起,因此在培养期间将血清瓶放入厌氧箱中(厌氧箱使用He作为气源,在箱体内部营造低N2环境)降低外界N2的扩散作用,并在橡胶隔垫外涂上一层硅橡胶可进一步阻隔外界空气的渗漏。
针对吸附在橡胶隔垫中气体的扩散采用如下方式控制:考虑到气体是吸附在橡胶隔垫中因此采用He置换的方式处理橡胶隔垫,使橡胶隔垫预先置于He环境,将吸附在橡胶隔垫中的气体置换出来。具体步骤为:向体积为120 mL的柱形瓶(svg100,Nichiden-rika,Japan)中放入20个橡胶隔垫并密封,接着对柱形瓶进行He置换(先抽真空处理300 s后用He充满,反复进行该过程6次),放置24 h后再进行一次He置换后备用。在对空气与橡胶隔垫之间的气体渗漏采取控制措施后,为验证这两种措施的组合处理效果,设计如下试验:向空血清瓶上安放预先置于He环境的橡胶隔垫以及未预先置于He环境的橡胶隔垫,随后对这两种处理的空血清瓶进行He置换,并采用破坏性取样方式分别在试验开始的36、48、60 h对血清瓶内顶空N2进行测定。通过比较上述两种处理之间的差异,确定破坏性取样和使用预先置于He环境橡胶隔垫处理的组合效果。预实验结果表明,使用未预先置于He环境的橡胶隔垫,血清瓶中顶空N2会在36 h后趋于稳定,因此将实验测定的初始时间设定为36 h。
在对空气与橡胶隔垫之间的气体渗漏及吸附在橡胶隔垫中气体的扩散采取控制措施后,针对溶解在蒸馏水与底物溶液中的气体扩散,采用如下方式控制:对实验用的蒸馏水进行充He处理3 h,并利用充He后的蒸馏水配制相关溶液,设计如下两种实验处理以验证溶解性气体扩散的平衡时间:分别向血清瓶中放入8 mL充He后的蒸馏水(充He水)和8 mL未充He的蒸馏水(非充He水),随后在36、48、60 h使用Robot系统对两种处理的瓶内顶空气体进行监测。而针对吸附在土壤颗粒上的气体扩散,由于实验中会向土壤中加入蒸馏水或底物溶液进行培养,因此对于该扩散作用仅考虑血清瓶中土壤加入蒸馏水或底物溶液后的N2扩散平衡时间,使用该平衡时间作为正式实验的测定起始点,用以消除溶解在底物溶液中和吸附在土壤颗粒上的气体扩散作用。设计如下两种处理:血清瓶中加入30 g风干后的灭菌土(高压蒸汽灭菌,121℃,2.50 h)并加入8 mL充He水和血清瓶中加入30 g风干后的灭菌土并加入8 mL非充He水处理,分别在36、48、60 h使用Robot系统对血清瓶的顶空气体进行监测,以确定吸附与溶解性N2的平衡时间。
乙炔抑制法测定N2排放速率的具体操作如下[
实验中N2O的排放速率由不加乙炔的处理确定,而N2的排放速率由加乙炔与不加乙炔处理之间排放速率的差值计算而来:
式中,
本研究所用的RoFlow系统主要由密封罐、中央控制器以及气相色谱仪(7890B,Agilent,USA)三部分组成[
式中,
采用SPSS 18.0对数据进行统计分析,数据以平均值□±□标准差(
对比预先置于He环境橡胶隔垫与未预先置于He环境橡胶隔垫处理组N2排放的差异(
使用预先置于He环境或未预先置于He环境橡胶隔垫血清瓶中N2含量变化
Concentration of N2 in serum bottles using He-washed rubber septa or normal rubber septa
进一步对比装有充He水和非充He水血清瓶中的N2含量变化差异(
装有充He水或非充He水的血清瓶中N2含量变化
Concentration of N2 in serum bottles with He-washed water or normal water
监测装有30 g灭菌风干土并加入8 mL充He水和非充He水血清瓶中N2含量的变化,结果见
装有灭菌风干土并加入充He水或非充He水的血清瓶中N2含量变化
Concentration of N2 in serum bottles with soil and He-washed water or with soil and normal water
整体而言,旱地土壤N2排放速率以乙炔抑制法测定的结果最高,RoFlow系统次之,而Robot系统测定结果最低(
不同处理及测定方法下N2排放速率
N2 emission rates under different treatments as determined by three methods
而对于添加了C6H12O6的处理,无论使用何种测定方法测定,N2排放速率均比仅添加蒸馏水的处理有所升高。相对于仅添加60 mg·kg–1 C6H12O6的处理而言,同时加入60 mg·kg–1 C6H12O6和KNO3的处理中N2排放速率低于前者(
与N2排放速率类似,同一种处理使用不同测定方法测得的旱地土壤N2O排放速率也并不一致(
不同处理及测定方法下N2O排放速率
N2O emission rates under different treatments as determined by three methods
对不同处理中三种测定方法的测定结果进行相关性分析(
三种N2排放速率测定方法测定结果的相关性分析
Correlation analysis among the results from the three methods
在利用Robot系统测定血清瓶中N2排放速率时,Robot系统的研发者Molstad等[
此外,由于室温下水中溶解有一定浓度的N2,使得培养实验中在向血清瓶中加入蒸馏水以调节土壤含水量时,会向培养体系中引入外界N2。特别是当对血清瓶进行He置换处理后,由于瓶中顶空气体N2含量极低,此时溶解于水中的N2就会逐步扩散出来。本研究发现,装有充He水的血清瓶中顶空气体在整个培养期间内均保持在一个相对稳定的N2浓度,可见血清瓶中加入经过充He的水后,水中溶解的N2可以更快与瓶中顶空气体达到交换平衡且具有更好的平行性,可有效降低水中溶解性N2的渗漏(
最后,从装有灭菌风干土并加入充He水与非充He水的血清瓶中N2含量变化中可以发现,无论使用充He水还是非充He水,在经过36 h培养后瓶内N2含量均始终保持稳定(
综合以上对Robot系统可能存在N2渗漏环节的优化,通过向装有土样的血清瓶中加入充He水配置的底物溶液,使用预先置于He环境的橡胶隔垫和在培养周期内采用破坏性取样的方式进行测定,并在整个培养周期内将样品瓶置于以He作为气源的厌氧培养箱中,可大幅降低Robot系统的N2渗漏率。Qin等[
在三种测定方法测得的所有处理N2排放速率中,乙炔抑制法的测定结果最高,主要原因是乙炔抑制法在测定过程中将水土比设定为了1:1并在摇床中震荡培养,该过程可以使反硝化微生物跟反应底物在整个培养体系内充分接触,有利于反硝化微生物对反应底物的高效利用,所以相对于RoFlow和Robot系统,乙炔抑制法测定的N2排放速率更高。对RoFlow系统而言,由于测定周期更长(20 d)且体系内土柱能更好地反映野外情况下土壤的N2动态排放过程,也能够更好地捕获N2排放的峰值,因此相比于Robot系统,其测定的N2排放速率更高;相对于RoFlow系统,Robot系统测定周期短、体系内土壤质量小,只能反应短时间内土壤反硝化过程产生N2的累积排放量[
虽然乙炔抑制法采用的土水比(1:1)与Robot系统测定所采用的近似田间原位含水量有所差别,但乙炔抑制法与Robot系统均是在含30 g土壤的120 mL血清瓶体系中完成测定,无论是从反应体系还是测定周期上二者都相近,因此两种方法之间的测定结果有显著的正相关关系(
尽管Robot系统无法良好地反映田间真实的N2排放情况,但是优化后的Robot系统相比于乙炔抑制法和RoFlow系统具有一定优势。相对于乙炔抑制法,Robot系统既可以实现体系内N2的直接测定又能够还原一定的田间条件;相对于RoFlow系统,Robot系统对于外源底物的添加有更好的响应且测定效率高,虽然Robot系统测定结果低于RoFlow系统测定结果3倍~5倍,但二者还处于同一数量级上,因此Robot系统在机理的研究上会有更好的应用效果。
综合评估以上三种旱地N2排放速率测定方法,我们认为优化后的Robot系统测定结果合理,测定效率高,对外源底物添加的响应较好,未来在研究旱地土壤背景N2排放及相关机理方面有较好的应用前景。但是需要特别指出的是,在使用优化后的Robot系统进行反硝化速率研究时,为了更好地反映田间实际情况,最好采用原状土模拟田间水分情况下进行测定。
通过使用预先置于He环境的橡胶隔垫、采用充He后的蒸馏水配制溶液和实施破坏性取样的处理,可以大幅降低Robot系统的N2渗漏率,优化后的系统渗漏率在0~0.78 μL·L–1·h–1之间。旱地N2排放速率测定中,不同实验处理之间使用乙炔抑制法测定的N2排放速率最高,RoFlow系统次之,Robot系统最低。三种测定方法之间误差最小的为Robot系统,不同实验处理之间误差范围在0.003~0.045 mg·kg–1·d–1。整体上,相对于乙炔抑制法,优化后的Robot系统优势体现在可以实现N2的直接定量并能还原一定的田间条件。其次,相对于RoFlow系统而言,优化后的Robot系统对外源底物添加的响应较好,具有更高的精确度并且测定周期短。综上,经过优化的Robot系统在测定旱地N2排放速率时具有N2渗漏率低,测定结果精确度高及对外源底物添加响应较好的特点,未来在研究旱地土壤背景N2排放及相关机理的方面有较好的应用前景。
Castaldelli G, Colombani N, Soana E, et al. Reactive nitrogen losses via denitrification assessed in saturated agricultural soils[J]. Geoderma, 2019, 337: 91-98.
Reis S, Bekunda M, Howard C M, et al. Synthesis and review: Tackling the nitrogen management challenge: From global to local scales[J]. Environmental Research Letters, 2016, 11(12): 120205.
Malique F, Ke P P, Boettcher J, et al. Plant and soil effects on denitrification potential in agricultural soils[J]. Plant and Soil, 2019, 439(1/2): 459-474.
Harter J, Weigold P, El-Hadidi M, et al. Soil biochar amendment shapes the composition of N2O-reducing microbial communities[J]. Science of the Total Environment, 2016, 562: 379-390.
Song X T, Liu M, Ju X T, et al. Nitrous oxide emissions increase exponentially when optimum nitrogen fertilizer rates are exceeded in the North China plain[J]. Environmental Science & Technology, 2018, 52(21): 12504-12513.
Hu H W, Chen D L, He J Z. Microbial regulation of terrestrial nitrous oxide formation: Understanding the biological pathways for prediction of emission rates[J]. FEMS Microbiology Reviews, 2015, 39(5): 729-749.
Zhang Z J, Qin S P, Yuan H J, et al. Advance in soil dinitrogen emission[J]. Chinese Journal of Eco-Agriculture, 2018, 26(2): 182-189.
张志君, 秦树平, 袁海静, 等. 土壤氮气排放研究进展[J]. 中国生态农业学报, 2018, 26(2): 182-189.
Kunu T M, Sullivan M B, Cornwell J C, et al. Denitrification in estuarine sediments determined by membrane inlet mass spectrometry[J]. Limnology and Oceanography, 1998, 43(2): 334-339.
Li J F, Chai Y C, Chen S T, et al. Measurement of denitrification, Anammox, DNRA rates, and net N2 flux in paddy soil using a membrane inlet mass spectrometer[J]. Journal of Agro-Environment Science, 2019, 38(7): 1541-1549.
李进芳, 柴延超, 陈顺涛, 等. 利用膜进样质谱仪测定水稻土几种厌氧氮转化速率[J]. 农业环境科学学报, 2019, 38(7): 1541-1549.
Li X B, Xia Y Q, Lang M, et al. N2: Ar technique for direct determination of denitrification rate of aquatic ecosystems using membrane inlet mass spectrometry[J]. Journal of Agro-Environment Science, 2013, 32(6): 1284-1288.
李晓波, 夏永秋, 郎漫, 等. N2: Ar法直接测定淹水环境反硝化产物N2的产生速率[J]. 农业环境科学学报, 2013, 32(6): 1284-1288.
Butterbach-Bahl K, Baggs E M, Dannenmann M, et al. Nitrous oxide emissions from soils: How well do we understand the processes and their controls?[J]. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 2013, 368(1621): 20130122.
Groffman P M, Altabet M A, Böhlke J K, et al. Methods for measuring denitrification: Diverse approaches to a difficult problem[J]. Ecological Applications, 2006, 16(6): 2091-2122.
Yan X Y, Zhou W. Groundwater nitrate removal through denitrification under farmland in Yangtze River Delta[J]. Acta Pedologica Sinica, 2019, 56(2): 350-362.
颜晓元, 周伟. 长江三角洲农田地下水反硝化对硝酸盐的去除作用[J]. 土壤学报, 2019, 56(2): 350-362.
Wang J Y, Yan X Y. Denitrification in upland of China: Magnitude and influencing factors[J]. Journal of Geophysical Research: Biogeosciences, 2016, 121(12): 3060-3071.
Qin S P, Yuan H J, Dong W X, et al. Relationship between soil properties and the bias of N2O reduction by acetylene inhibition technique for analyzing soil denitrification potential[J]. Soil Biology & Biochemistry, 2013, 66: 182-187.
Bowen H, Maul J E, Cavigelli M A, et al. Denitrifier abundance and community composition linked to denitrification activity in an agricultural and wetland soil[J]. Applied Soil Ecology, 2020, 151: 103521.
Yuan H J, Qin S P, Dong W X, et al. Denitrification rate and controlling factors for accumulated nitrate in the deep subsoil of intensive farmlands: A case study in the North China Plain[J]. Pedosphere, 2019, 29(4): 516-526.
Qin S P, Hu C S, Oenema O. Quantifying the underestimation of soil denitrification potential as determined by the acetylene inhibition method[J]. Soil Biology & Biochemistry, 2012, 47: 14-17.
Hao Y X. Effects of long-term fertilization on N2O emissions and denitrification potential in agricultural soils in Guanzhong Plain[D]. Yangling, Shaanxi: Northwest A & F University, 2017.
郝耀旭. 关中平原长期定位施肥农田土壤N2O排放和反硝化潜势的观测研究[D]. 陕西杨凌: 西北农林科技大学, 2017.
Malghani S, Kim J, Lee S H, et al. Application of two contrasting rice-residue-based biochars triggered gaseous loss of nitrogen under denitrification-favoring conditions: A short-term study based on acetylene inhibition technique[J]. Applied Soil Ecology, 2018, 127: 112-119.
Wang R. Measurement of N2, N2O, NO and CO2 emissions from soil with the gas-flow-soil-core technique[D]. Beijing: University of Chinese Academy of Sciences, 2012.
王睿. 直接测定N2法与土壤N2、N2O、NO和CO2排放研究[D]. 北京: 中国科学院大学, 2012.
Molstad L, Dörsch P, Bakken L R. Robotized incubation system for monitoring gases(O2, NO, N2O N2) in denitrifying cultures[J]. Journal of Microbiological Methods, 2007, 71(3): 202-211.
Senbayram M, Well R, Bol R, et al. Interaction of straw amendment and soil NO3− content controls fungal denitrification and denitrification product stoichiometry in a sandy soil[J]. Soil Biology & Biochemistry, 2018, 126: 204-212.
Gao Y, Mania D, Mousavi S A, et al. Competition for electrons favours N2O reduction in denitrifying
Wu D, Wei Z J, Well R, et al. Straw amendment with nitrate-N decreased N2O/(N2O+N2) ratio but increased soil N2O emission: A case study of direct soil-born N2 measurements[J]. Soil Biology & Biochemistry, 2018, 127: 301-304.
Qin S P, Pang Y X, Clough T, et al. N2 production via aerobic pathways may play a significant role in nitrogen cycling in upland soils[J]. Soil Biology & Biochemistry, 2017, 108: 36-40.
Yuan H J, Zhang Z J, Li M Y, et al. Biochar's role as an electron shuttle for mediating soil N2O emissions[J]. Soil Biology & Biochemistry, 2019, 133: 94-96.
Wei Z J, Shan J, Chai Y C, et al. Regulation of the product stoichiometry of denitrification in intensively managed soils[J]. Food and Energy Security, 2020, 9(4): e251.
Smith M S, Tiedje J M. Phases of denitrification following oxygen depletion in soil[J]. Soil Biology & Biochemistry, 1979, 11(3): 261-267.
Senbayram M, Budai A, Bol R, et al. Soil NO3− level and O2 availability are key factors in controlling N2O reduction to N2 following long-term liming of an acidic sandy soil[J]. Soil Biology & Biochemistry, 2019, 132: 165-173.
Giles M, Morley N, Baggs E M, et al. Soil nitrate reducing processes - drivers, mechanisms for spatial variation, and significance for nitrous oxide production[J]. Frontiers in Microbiology, 2012, 3: 407.