Article(id=1241408722719797789, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1727107200000, receivedDateStr=2024-09-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773904503754, onlineDateStr=2026-03-19, pubDate=1745078400000, pubDateStr=2025-04-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773904503754, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773904503754, creator=13701087609, updateTime=1773904503754, updator=13701087609, issue=Issue{id=1241408710791189399, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='4', pageStart='1777', pageEnd='2368', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773904500911, creator=13701087609, updateTime=1773904624658, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241409229878259747, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241409229878259748, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241408710791189399, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2075, endPage=2085, ext={EN=ArticleExt(id=1241408723311194711, articleId=1241408722719797789, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=The spatiotemporal dynamic changes in net anthropogenic nitrogen input (NANI) in Quzhou from 2003 to 2022, columnId=1234106388083954308, journalTitle=China Environmental Science, columnName=Environmental Ecology, runingTitle=null, highlight=null, articleAbstract=

The Net Anthropogenic Nitrogen Input (NANI) model was utilized to estimate nitrogen inputs to Quzhou for the period from 2003 to 2022, and its spatial and temporal patterns, variations in components, and determining factors were examined. The results indicated that the average NANI for Quzhou over the past two decades was 12925kg/(km2·a), with a peak of 15698kg/(km2·a)in 2011. The spatial distribution was found to demonstrate an east-high and west-low pattern, which was consistent with land-use configurations. The predominant component of NANI was nitrogen fertilizer, which contributed 27% to 41%, followed by net nitrogen input from food/feed at 22% to 42%, atmospheric nitrogen deposition at 21% to 33%, and nitrogen fixation at 7% to 11%. The variations in NANI were primarily driven by nitrogen fertilizer application between 2003 and 2008, food/feed inputs between 2009 and 2014, and atmospheric deposition in recent years. Strategies were proposed to mitigate NANI, including promoting new energy vehicles, reducing nitrogen fertilizer application to align with standards of agriculturally advanced nations, and regulating livestock and poultry farming to match local demand. It was anticipated that these measures would decrease NANI by approximately 833, 893, and 896kg/(km2·a), respectively, achieving a cumulative reduction of about 2622kg/(km2·a), positioning it among the lower levels globally.

, correspAuthors=Yu-guo HAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yu-hang YANG, Yu-guo HAN, Xiao-lin ZHANG, Wei-li DUAN), CN=ArticleExt(id=1241408726104601482, articleId=1241408722719797789, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=2003~2022年衢州市人类活动净氮输入(NANI)的时空动态变化, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

为推进衢州市氮污染的治理,基于人类活动净氮输入模型(NANI)估算了衢州市2003~2022年净氮输入量,分析了NANI时空特征、组分变化原因及影响因素.结果表明:衢州市NANI 20a平均值为12925kg/(km2·a),时间变化呈现出先增加后降低的趋势,2011年达到峰值15698kg/(km2·a).各子单元空间表现出一定差异性,空间分布呈现出东高西低的分布,与土地利用状况分布接近.NANI的各组分占比表现为氮肥施用约占27%~41%,食物/饲料净氮输入约占22%~42%,大气氮沉降约占21%~33%,固氮约占7%~11%.NANI的变化在2003~2008年主要受氮肥施用的影响,在2009~2014年主要受食物饲料净氮输入的影响,近年则主要受大气氮沉降影响.减少NANI的主要策略有全面推广新能源汽车的应用;降低农田氮肥施用量逐步调整至农业发达国家平均采用的施肥标准;以满足衢州市本地人口对畜禽产品的基本需求为限,合理调控畜禽养殖规模.实施这些措施后,预计NANI可分别降低约833,893,896kg/(km2·a),总量降低约2622kg/(km2·a),达到世界上较低水平.

, correspAuthors=韩玉国, authorNote=null, correspAuthorsNote=
* 责任作者,教授,
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杨雨航(2001-),女,重庆市人,北京林业大学硕士研究生,主要研究方向为流域非点源污染治理..

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杨雨航(2001-),女,重庆市人,北京林业大学硕士研究生,主要研究方向为流域非点源污染治理..

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杨雨航(2001-),女,重庆市人,北京林业大学硕士研究生,主要研究方向为流域非点源污染治理..

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(a)为研究区行政区划.(b)为研究区2022年土地利用类型图

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Basic characteristics of research units in 2022

, figureFileSmall=null, figureFileBig=null, tableContent=
变量常山县江山市开化县柯城区龙游县衢江区
农业产值(万元)8586919300311838278179104588183582
牧业产值(万元)25316174643287251147611238791385
人口密度(人/km2)308301160732345234
耕地面积(km2)251443183245486480
林地面积(km2)783147920122635391129
), ArticleFig(id=1241408733012619927, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=CN, label=表1, caption=

2022年各研究单元基本状况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量常山县江山市开化县柯城区龙游县衢江区
农业产值(万元)8586919300311838278179104588183582
牧业产值(万元)25316174643287251147611238791385
人口密度(人/km2)308301160732345234
耕地面积(km2)251443183245486480
林地面积(km2)783147920122635391129
), ArticleFig(id=1241408733226529451, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=EN, label=Table 2, caption=

Atmospheric nitrogen deposition in nearby regions

, figureFileSmall=null, figureFileBig=null, tableContent=
地点年份模拟值[kg/(km2⋅a)]测量值[kg/(km2⋅a)]误差(%)文献来源
中国大陆20051842.971710.007.78[14]
杭州2013~20144132.524334.964.67[15]
嘉兴2013~20145699.725368.406.17[15]
温州2013~20143170.253312.004.28[16]
湖州2013~20145949.604950.7420.18[15]
), ArticleFig(id=1241408733398495932, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=CN, label=表2, caption=

临近区域大气氮沉降

, figureFileSmall=null, figureFileBig=null, tableContent=
地点年份模拟值[kg/(km2⋅a)]测量值[kg/(km2⋅a)]误差(%)文献来源
中国大陆20051842.971710.007.78[14]
杭州2013~20144132.524334.964.67[15]
嘉兴2013~20145699.725368.406.17[15]
温州2013~20143170.253312.004.28[16]
湖州2013~20145949.604950.7420.18[15]
), ArticleFig(id=1241408733583045328, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=EN, label=Table 3, caption=

Parameters and sources for NANI estimation

, figureFileSmall=null, figureFileBig=null, tableContent=
计算参数参数值文献来源
人为氮消耗量[kg/(人·a)]4.39[21]
畜禽氮消耗量[kg/(头·a)]16.68[22]
54.82
0.57
0.63
6.85
主要农作物含氮量(g/kg)大豆56.00[23]
大麦16.32
稻谷13.12
柑桔1.73
梨头0.59
枇杷2.06
其他豆类34.56
蔬菜2.88
薯类4.43
桃子1.08
西瓜1.36
小麦19.04
杨梅1.45
玉米13.91
动物产品含氮量(g/kg)猪肉22.72[23]
牛肉32.00
羊肉29.60
禽肉28.64
禽蛋20.56
牛奶5.28
不同土地固氮能力[kg/(km²·a)]豆类植物种植地14000[24]
其他类型种植用地500
森林用地1000
草地1500
其他类型用地100
稻谷N肥施用量(公斤/km2)尿素10455[17]
碳铵1410
其他45
蔬菜N肥施用量(公斤/ km2)尿素16110
碳铵105
其他15
), ArticleFig(id=1241408733767594721, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=CN, label=表3, caption=

NANI估算的相关参数值及来源

, figureFileSmall=null, figureFileBig=null, tableContent=
计算参数参数值文献来源
人为氮消耗量[kg/(人·a)]4.39[21]
畜禽氮消耗量[kg/(头·a)]16.68[22]
54.82
0.57
0.63
6.85
主要农作物含氮量(g/kg)大豆56.00[23]
大麦16.32
稻谷13.12
柑桔1.73
梨头0.59
枇杷2.06
其他豆类34.56
蔬菜2.88
薯类4.43
桃子1.08
西瓜1.36
小麦19.04
杨梅1.45
玉米13.91
动物产品含氮量(g/kg)猪肉22.72[23]
牛肉32.00
羊肉29.60
禽肉28.64
禽蛋20.56
牛奶5.28
不同土地固氮能力[kg/(km²·a)]豆类植物种植地14000[24]
其他类型种植用地500
森林用地1000
草地1500
其他类型用地100
稻谷N肥施用量(公斤/km2)尿素10455[17]
碳铵1410
其他45
蔬菜N肥施用量(公斤/ km2)尿素16110
碳铵105
其他15
), ArticleFig(id=1241408734065390331, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=EN, label=Table 4, caption=

NANI comparisons with other countries and regions

, figureFileSmall=null, figureFileBig=null, tableContent=
年份地点NANI [kg/(km2·a)]
2009中国大陆平均[7]5013
2011欧洲环波罗的海区域[6]5800
2007美国东南部流域[4]4900
2011印度恒河流域[8]6955
2009日本各流域[5]9930
2008~2017中国成渝经济圈[28]15929
2006~2016中国北方农牧交错带[29]11632
2011~2019中国潮河流域[30]13063
1985~2020中国东江流域[31]3143
2014中国湖北[32]6132
2018中国三峡库区[33]20639
2013~2022中国湛江湾流域[34]8511
2003~2022本研究12925
2022本研究8828
), ArticleFig(id=1241408734258328331, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=CN, label=表4, caption=

与其他国家地区NANI对比

, figureFileSmall=null, figureFileBig=null, tableContent=
年份地点NANI [kg/(km2·a)]
2009中国大陆平均[7]5013
2011欧洲环波罗的海区域[6]5800
2007美国东南部流域[4]4900
2011印度恒河流域[8]6955
2009日本各流域[5]9930
2008~2017中国成渝经济圈[28]15929
2006~2016中国北方农牧交错带[29]11632
2011~2019中国潮河流域[30]13063
1985~2020中国东江流域[31]3143
2014中国湖北[32]6132
2018中国三峡库区[33]20639
2013~2022中国湛江湾流域[34]8511
2003~2022本研究12925
2022本研究8828
), ArticleFig(id=1241408734442877727, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=EN, label=Table 5, caption=

Factors influencing component variability

, figureFileSmall=null, figureFileBig=null, tableContent=
因素年份
20032007201220172022
耕地面积(km2)20231863197220852089
N肥施用(t/km2)2226221711
工业排放(占硝酸盐氮比例)--1039(79%)518(43%)334(30%)
汽车排放(占硝酸盐氮比例)--278(20%)676(56%)770(69%)
民用汽车拥有量(万辆)3.296.0717.7338.5357.01
粮食产量(t)655158722216796868560630555984
人口密度(人/km2)277280286291288
生猪出栏(万头)240.6341.1477.3211.7124
人消耗N[kg/(km2·a)]12151229125512801266
动物消耗N/[kg/(km2·a)]39255232781345023722
第一产业增加值(万元)391484523554760705805311931988
第二产业增加值(万元)10029722493611494198356985458741191
第三产业增加值(万元)99027717805313941710669083210361240
农业(万元)323597441264612959712086763603
牧业(万元)199525314776572062351639443932
), ArticleFig(id=1241408734606455596, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=CN, label=表5, caption=

影响组分变化的相关因素

, figureFileSmall=null, figureFileBig=null, tableContent=
因素年份
20032007201220172022
耕地面积(km2)20231863197220852089
N肥施用(t/km2)2226221711
工业排放(占硝酸盐氮比例)--1039(79%)518(43%)334(30%)
汽车排放(占硝酸盐氮比例)--278(20%)676(56%)770(69%)
民用汽车拥有量(万辆)3.296.0717.7338.5357.01
粮食产量(t)655158722216796868560630555984
人口密度(人/km2)277280286291288
生猪出栏(万头)240.6341.1477.3211.7124
人消耗N[kg/(km2·a)]12151229125512801266
动物消耗N/[kg/(km2·a)]39255232781345023722
第一产业增加值(万元)391484523554760705805311931988
第二产业增加值(万元)10029722493611494198356985458741191
第三产业增加值(万元)99027717805313941710669083210361240
农业(万元)323597441264612959712086763603
牧业(万元)199525314776572062351639443932
), ArticleFig(id=1241408734702924603, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=EN, label=Table 6, caption=

Strategies for NANI reduction and projected outcomes

, figureFileSmall=null, figureFileBig=null, tableContent=
项目2022NANI[kg/(km2·a)]减氮措施降低[kg/(km2·a)]降低百分比(%)预计降低至[kg/(km2·a)]
大气氮沉降2779减少汽车排放707301946
减少肥料挥发126
肥料氮输入2389减施氮肥893371496
食物/饲料氮输入2670降低生猪产量896341774
作物固氮990
合计88282622306206
), ArticleFig(id=1241408734979748678, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408722719797789, language=CN, label=表6, caption=

NANI削减策略及期望值

, figureFileSmall=null, figureFileBig=null, tableContent=
项目2022NANI[kg/(km2·a)]减氮措施降低[kg/(km2·a)]降低百分比(%)预计降低至[kg/(km2·a)]
大气氮沉降2779减少汽车排放707301946
减少肥料挥发126
肥料氮输入2389减施氮肥893371496
食物/饲料氮输入2670降低生猪产量896341774
作物固氮990
合计88282622306206
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2003~2022年衢州市人类活动净氮输入(NANI)的时空动态变化
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杨雨航 1 , 韩玉国 1, 2, 3, * , 张小林 4 , 段维利 1
中国环境科学 | 环境生态 2025,45(4): 2075-2085
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中国环境科学 | 环境生态 2025, 45(4): 2075-2085
2003~2022年衢州市人类活动净氮输入(NANI)的时空动态变化
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杨雨航1 , 韩玉国1, 2, 3, * , 张小林4, 段维利1
作者信息
  • 1.北京林业大学水土保持学院,北京 100083
  • 2.山西省吉县森林生态系统国家野外科学观测研究站,山西 临汾 042200
  • 3.水土保持国家林业和草原局重点实验室,北京 100083
  • 4.衢州市乌溪江饮用水源保护管理中心,浙江 衢州 324003
  • 杨雨航(2001-),女,重庆市人,北京林业大学硕士研究生,主要研究方向为流域非点源污染治理..

通讯作者:

* 责任作者,教授,
The spatiotemporal dynamic changes in net anthropogenic nitrogen input (NANI) in Quzhou from 2003 to 2022
Yu-hang YANG1 , Yu-guo HAN1, 2, 3, * , Xiao-lin ZHANG4, Wei-li DUAN1
Affiliations
  • 1.School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
  • 2.Forest Ecosystem Studies, National Observation and Research Station, Linfen 042200, China
  • 3.Key Laboratory of National Forestry and Grassland Administration on Soil and Water Conservation, Beijing 100083, China
  • 4.Quzhou Wuxi River Drinking Water Source Protection Management Center, Quzhou 324003, China
出版时间: 2025-04-20
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为推进衢州市氮污染的治理,基于人类活动净氮输入模型(NANI)估算了衢州市2003~2022年净氮输入量,分析了NANI时空特征、组分变化原因及影响因素.结果表明:衢州市NANI 20a平均值为12925kg/(km2·a),时间变化呈现出先增加后降低的趋势,2011年达到峰值15698kg/(km2·a).各子单元空间表现出一定差异性,空间分布呈现出东高西低的分布,与土地利用状况分布接近.NANI的各组分占比表现为氮肥施用约占27%~41%,食物/饲料净氮输入约占22%~42%,大气氮沉降约占21%~33%,固氮约占7%~11%.NANI的变化在2003~2008年主要受氮肥施用的影响,在2009~2014年主要受食物饲料净氮输入的影响,近年则主要受大气氮沉降影响.减少NANI的主要策略有全面推广新能源汽车的应用;降低农田氮肥施用量逐步调整至农业发达国家平均采用的施肥标准;以满足衢州市本地人口对畜禽产品的基本需求为限,合理调控畜禽养殖规模.实施这些措施后,预计NANI可分别降低约833,893,896kg/(km2·a),总量降低约2622kg/(km2·a),达到世界上较低水平.

人类活动净氮输入(NANI)  /  衢江流域  /  氮沉降  /  削减策略

The Net Anthropogenic Nitrogen Input (NANI) model was utilized to estimate nitrogen inputs to Quzhou for the period from 2003 to 2022, and its spatial and temporal patterns, variations in components, and determining factors were examined. The results indicated that the average NANI for Quzhou over the past two decades was 12925kg/(km2·a), with a peak of 15698kg/(km2·a)in 2011. The spatial distribution was found to demonstrate an east-high and west-low pattern, which was consistent with land-use configurations. The predominant component of NANI was nitrogen fertilizer, which contributed 27% to 41%, followed by net nitrogen input from food/feed at 22% to 42%, atmospheric nitrogen deposition at 21% to 33%, and nitrogen fixation at 7% to 11%. The variations in NANI were primarily driven by nitrogen fertilizer application between 2003 and 2008, food/feed inputs between 2009 and 2014, and atmospheric deposition in recent years. Strategies were proposed to mitigate NANI, including promoting new energy vehicles, reducing nitrogen fertilizer application to align with standards of agriculturally advanced nations, and regulating livestock and poultry farming to match local demand. It was anticipated that these measures would decrease NANI by approximately 833, 893, and 896kg/(km2·a), respectively, achieving a cumulative reduction of about 2622kg/(km2·a), positioning it among the lower levels globally.

net anthropogenic nitrogen input (NANI)  /  Qujiang River Basin  /  nitrogen deposition  /  mitigation strategy
杨雨航, 韩玉国, 张小林, 段维利. 2003~2022年衢州市人类活动净氮输入(NANI)的时空动态变化. 中国环境科学, 2025 , 45 (4) : 2075 -2085 .
Yu-hang YANG, Yu-guo HAN, Xiao-lin ZHANG, Wei-li DUAN. The spatiotemporal dynamic changes in net anthropogenic nitrogen input (NANI) in Quzhou from 2003 to 2022[J]. China Environmental Science, 2025 , 45 (4) : 2075 -2085 .
衢江流域作为浙江省水资源战略安全重要水源地之一,是衢州乃至钱塘江流域下游重要用水来源.因此保护衢江流域水源,不仅是保护当地居民饮用水安全,也是维护衢州及钱塘江流域生态环境安全的重要部分.人类活动强度的加剧,对衢州的氮循环产生显著影响[1],导致衢州偶有氮污染事件发生,并且由于畜牧业、水产养殖及种植业三方面发展,导致局部区域藻类、水华爆发[2],给该地区带来了一定的富营养化压力.因此有效控制氮污染是维护流域生态健康和控制河流湖泊富营养化的重要策略,了解流域氮输入的近期趋势对流域内制定减氮措施具有重要意义.
定量识别人类活动氮输出通量演化趋势及来源组成是实现氮污染控制的关键[3].目前国内外研究面源氮污染负荷的计算主要采用模型计算方法,对关键源区的识别通常采用模型定量评估法.Howarth等[4]首次提出了人类活动净氮输入(NANI)模型,该模型在欧洲、美国和亚洲等多个国家的地区和流域得到应用[5-8],在国内韩玉国等[9]首次应用于北京地区的氮积累研究;Grizzetti B等[10]利用SWAT模型对芬兰的营养物质进行模拟;吴哲等[11]应用InVEST模型中水质净化模块计算海南岛的氮磷污染物负荷,并以此结果对当地污染风险情况进行评价及关键源区识别.各模型具有不同特征所得到的结果也有一定差异,其中NANI模型主要基于经济社会统计数据,模型基本覆盖了人类活动造成的主要净氮输入的类型,所得到的结果可以表现该区域内人类活动对区域氮循环的影响,及各组成成分变化特征;并且可以识别出影响该区域氮循环的重要组成成分,是识别氮污染源及制定环境治理措施的重要方法.
本研究基于衢州2003~2022年各氮源输入数据,计算了2003~2022年NANI及其组分的年际动态变化,分析了组分变化原因以及影响因素,识别了不同氮源对NANI的影响,并讨论削减NANI的具体措施.以上分析对衢州市识别氮污染来源、人为氮输入宏观调控和提出氮污染治理措施提供关键科学依据.
浙江省衢州市位于浙江省西部118°01′~119°20′E,28°14′~29°30′N,衢州市域属亚热带季风气候区,平均气温为16.3~17.4℃,总面积约为8844km²,衢州市行政区划图如图1(a)所示,辖柯城、衢江2个区,龙游、常山、开化3个县和江山市,以行政边界线为界共6个研究单元.衢州市全境横跨东北—西南向的江山—绍兴深断裂,分属扬子准地台和华南褶皱系两个一级大地构造单元,地质环境复杂,构造形态多样,地层及岩浆岩发育良好.截至2022年末,市内常住人口229万人,农业人口93.25万人,占比40.7%;衢州市土地利用如图1(b)所示,森林面积约6205km2,森林覆盖率达70.16%;农业用地面积约2089km2.
6个研究单元2022年的经济及人口状况如表1所示,可以看出柯城区人口密度远高于其他研究单元;牧业产值状况为江山市高于龙游县高于衢江区,其余3个研究单元牧业产值较小;农业产值前三分别为江山市、衢江区和龙游县;耕地面积前三分别为龙游县、衢江区、江山市;林地面积前三为开化县、江山市、衢江区.
本研究以衢州市5个区县和1个地级市共6个单元作为研究对象,计算得到衢州2003~2022年间衢州市人类活动净氮输入量,本文采用的NANI模型各输入项如式(1)所示.
式中:Ndep代表大气氮沉降量,kg/(km2·a) ;Nfer表示氮肥施用量,kg/(km2·a); Nim表示人类食物动物饲料净氮输入量,kg/(km2·a);Ncro表示作物固氮量,kg/(km2·a).
由于该研究区大气氮沉降数难以获得,并且此前该地区无相关研究,因此本研究采取对比邻近区域测量值与可获得数据集的模拟值来验证可使用的数据集,选取了如表2的地区进行分析,用贾彦龙等[12-13]大气氮沉降栅格数据进行计算,用ArcGIS分别进行区域的提取氮的干沉降和湿沉降并相加得到大气氮沉降的总量,作为该区域的大气氮沉降.并且通过线性插值方法得到其他年份数据,与其他文献里得到的测量值进行对比,两者误差除湖州误差都在8%以内,该数据完整并且具有系统性,能很好表现该地区大气氮沉降状况.
本研究中直接采用统计年鉴中的氮肥折纯量和复合肥含氮量计算,其中根据当地农业局经常采购的两种复合肥种类的氮占比为18%和17%,由于具体复合肥比例难以获得,此处两种复合肥氮含量比例相差较小,假设以两种复合肥极值18%和17%计算,所造成的肥料氮输入误差仅为0.5%,该误差可忽略不计,因此使用平均得到氮含量为17.5%进行计算.该输入项计算方法如下:氮肥消费量=100%纯氮肥施用量+17.5%复合肥施用量.
这部分氮肥施用的计算包含了挥发进入大气的部分,因此需要减去进入大气氮沉降的部分氮.本研究参考全国稻谷及蔬菜平均施用氮肥种类及用量标准[17],具体为尿素、碳铵及其他混合肥量,用以进行计算衢州主要农作物稻谷及蔬菜的每种含氮肥料施肥量,具体施用量如表3所示.并且根据Battye等[18]的研究计算氮的挥发,具体为:尿素挥发占15%、碳铵及其他混合肥占2%.
本研究采用Jordan等[19]的方法进行计算,由区域内人类和畜禽的氮消费量与氮素产品含量的差值获得,该值的计算公式[20]如式(2)所示.
式中:Nhc代表人类食物氮输入量,kg/(km2·a);Nlc代表畜牧饲料氮输入量,kg/(km2·a);Nlp代表畜产品氮产量,kg/(km2·a);Ncp代表作物氮产量,kg/(km2·a).
(1)人类食物和动物饲料氮消费量
假设氮在人体内不积累(人体内只存在氮的新旧更替),也就是人类摄入的氮完全被排泄出来.根据武淑霞[21]等研究,我国人均年消耗食物的含氮量为4.39kg,本研究即采用该区域内常住人口数量与人均消耗食物含氮量得出结果.畜禽饲料氮消费量是其在饲养过程中摄入的氮含量,是其个体排泄物和本身质量增加两项含氮量之和,相关计算参数来源于文献[22],本研究根据统计年鉴中主要饲养动物统计了5种动物的氮消耗量如表3.
(2)作物氮产量
本研究中根据衢州实际情况采用了14种农作物作为作物氮来源的计算依据,如表3所示,这14种农作物基本覆盖衢州的所有农作物,能够很好代表当地农作物种植情况.不同作物的含氮量与作物产量相乘所得结果就是作物氮产量,各作物类型含氮量如表3所示.
(3)动物产品氮产量
动物产品一般包括肉类,蛋类,奶类,水产品等(表3),氮素产量采用不同动物产品含氮量与其产量的乘积计算获得,本研究共计算了6种动物产品.研究表明,在食物加工、处理、运输等过程会造成一定的浪费[22],这部分按10%进行计算.
研究揭示了不同植物体和土地利用类型的固氮能力也不相同[25].从Burns等[24]研究结果中得到不同土地利用类型的固氮量,如表3所示.本研究以各类土地利用类型种植面积与各类型土地固氮量相乘得到结果.其中土地利用面积由武汉大学杨杰和黄昕教授团队[26]得到的结果提取得到,其中除了农业用地、林地、草地都归为其他用地,非豆类种植面积由农业用地减去豆类种植面积,豆类种植面积由统计年鉴得到.
本文所使用的统计数据来源于衢州市统计局统计年鉴[27]2003~2022年共20年的数据,土地利用数据来源于武汉大学杨杰和黄昕教授团队[26]的2002~2022年的数据,大气氮沉降数据来源于贾彦龙等[12-13].
利用EXCEL2019、SPSS、ArcGIS10.6以及Origin2021进行数据分析处理、计算及制图.
图2所示为整个衢州市区域及各个区县2003~2022年间人类活动氮输入(NANI)的年际变化,整体上,衢州市的NANI呈现出先上升后下降的趋势,2011年达到峰值为15698.15kg/(km2·a),较2003年增长19%,多年平均值为12924.91kg/(km2·a),2011年后呈现出降低趋势,2022年相对于2011年降低44%.
各区县NANI的年际变化存在差异.龙游县、柯城区、衢江区、江山市的NANI变化较大,均呈现先增后减的趋势,而常山县和开化县呈现出逐年降低的趋势,具体而言,柯城区NANI最大值最小值相差20438.85kg/(km2·a),衢江区12421.67kg/(km2·a),龙游县10212.84kg/(km2·a),江山市为6670.28kg/(km2·a),均高于或接近全市平均值.常山县和开化县的NANI变化则均在全市平均水平之下,分别为5167.81和2940.48kg/(km2·a),特别是柯城区,作为市中心其NANI减少最为显著.
图3显示了各研究单元2003~2022年衢州NANI值的空间分布状况.可以发现氮输入严重的区域逐渐从衢州的中心柯城区向东部的龙游县转移.2007年以前柯城区为NANI值最高的区域,2012年后龙游县NANI值为最高的区域.以2012年为界限,此前NANI较大值分布区域较广且NANI值更大,而此后相对缩小且数值减小.龙游县虽逐年有减少的趋势但在2012年后NANI值仍最大,并且2022年除了龙游县,其他5个区域的氮输入量都在11000kg/(km2·a)以下,相对此前NANI值减少了很多,从NANI的平均分布来看龙游县和衢江区和柯城区NANI的值较大,明显呈现出由西向东逐渐增大,东部低、西部高的特征.通过对比NANI平均空间分布与土地利用图,可以发现西部主要为森林覆盖,尤其是开化县NANI的值相对较小,向东部变化农业用地逐渐增多,在人口密度更大和畜牧业更发达以及农业氮肥的应用更多的东部地区其NANI的值也相对较大.前人研究成果均表明[36],区域地形和农业的分布对NANI空间分布的影响显著,由此衢州西部多丘陵低山植被覆盖度较高,其交通相对不便,农业工业产业规模较小,因此氮输入量较小.中部河谷平原、丘陵低山交错分布,东部以河谷平原为主,地势平缓,其农业工业产业较密集,并且人口密度相对更大,因此净氮输入量大.
通过文献整理,对世界上其他国家和中国其他地区的NANI值进行了统计,进行分析比对.如表4所示,美国、欧洲、印度和日本等国家和地区的NANI值在4000~10000kg/(km2·a),国内的NANI值在3000~21000kg/(km2·a),本研究区域衢州在2003~2022年的平均值为12925kg/(km2·a),2022年NANI为8828kg/(km2·a)对比发达国家高出了1倍左右,在国内高于大陆平均值,但在我国发达地区对比可知衢州NANI处于中间水平,可以看出该区域NANI还有一定的降低空间.
本研究对衢州整体及各区县的NANI组分进行了深入分析,结合图4所示,2003~2022年间衢州NANI各输入项的贡献比例随时间发生变化.其中,氮肥施用和人类食物/动物饲料净氮输入的贡献比例最大,分别约为27.06%~40.87%和22.14%~41.70%,年均贡献值分别为4398.98和4200.91kg/(km2·a),分别约占有衢州20a平均NANI值的34.03%和32.50%.其次为大气氮沉降和作物固氮,并且两者的贡献比例较为稳定,分别为21.13%~32.68%和6.90%~11.36%,年均贡献值分别为3282.03和1043.00kg/(km2·a),分别占有衢州平均NANI值的25.39%和8.07%.由图4中可以看出影响该区域NANI值的主要因素是食物/饲料净氮输入和肥料氮的输入,两者对NANI的变化趋势起着关键性作用.可以看出前期(2003~2008)氮肥施用对NANI的影响最大,中期(2009~2014)食物饲料净氮输入的影响最大,近几年(2015~2022)氮肥施用和食物/饲料净氮输入的影响逐渐减小,大气氮沉降所占比例呈现增加趋势.
图5展示了衢州各区县2003~2022年各组分变化状况,可以看出开化县和常山县各组分没有特别剧烈的变化,肥料氮部分逐年减少.其中开化县占比最大为大气氮沉降,由于其森林覆盖面积大,产业不发达,所以影响NANI的最大因素是大气氮沉降.柯城区和衢江区两个区属于市区,这两个地区人口呈稳定增长,近两年有人口流失的趋势,总体增长速率比较小,分别为2.99%和6.31%,这两个地区作为市区的农业结构调整较大,前文分析柯城区2007年前为NANI值最大的区域,根据组分分析可知主要原因是由于柯城区氮肥施用量远高于其他研究单元,2007~ 2022年柯城区NANI值降低明显主要由于氮肥施用量降低较快,由7899kg/(km2·a)降低至1407kg/ (km2·a).并且柯城区的食物/饲料氮输入中人消耗氮量较高主要由于柯城区人口密度较大.2022年为732人/km2,远大于其他研究单元.其中衢江区食物/饲料净氮输入变化明显,在食物和饲料输入中影响最大的不是人口数量的变动,而是动物消耗的氮量,其动物养殖数量在2012年后减少迅速,因此食物/饲料氮输入部分先呈增加趋势后减少.江山市的各组分成分相对稳定,其人口数量稳定增长,动物消耗呈先增加后减少的趋势,相对来说稳定.最后是龙游县,龙游县的食物/饲料氮输入所占比例呈现出稳步增加的趋势,主要是因为动物饲养增多.结合前面的分析可能是柯城区和衢江区作为市区发展重点不在农业,逐年将农业发展转移到地形条件更好的龙游县,并且由于龙游县肥料施用量逐渐降低,所以其肥料氮输入所占比例逐渐降低,龙游县2012~2022年人类活动动物饲料氮输入部分由12197kg/(km2·a)降低至8851kg/(km2·a),肥料氮输入由9570kg/(km2·a)降低至3294kg/(km2·a),龙游县的畜牧业在2012年后虽然有降低但一直保持较高的产量,因此其人类食物动物饲料氮输入在2012~2022年呈现出远超其他研究单元的水平,龙游县人类食物动物饲料氮输入较大是导致NANI值在2012~2022年呈现为最大值的原因,但其中龙游县氮肥施用降低较大,但作为主要影响因素的食物与饲料氮输入多,占比大且降低速率较慢.
从NANI的四个组成部分可知,大气氮沉降约占21%~33%,主要受汽车氮排放、肥料挥发及工业氮排放的影响.大气氮沉降主要包括硝酸盐氮和铵态氮,根据贾彦龙等[12-13]的数据可知衢州硝酸盐氮约占40%,铵态氮约占60%,大气氮沉降根据线性插值的结果呈现出下降趋势,铵态氮的来源主要是农业氮肥挥发,如表5所示氮肥施用水平从2003年22t/km2降低至2022年11t/km2,计算得出氮肥挥发减少约为370kg/(km2·a).硝酸盐氮来源主要为工业排放和汽车排放,根据《中国环境统计年鉴》[35]可知浙江省工业和汽车硝酸盐氮排放比例如表5所示,工业氮排放逐年减少,而汽车氮排放随着民用汽车拥有量增加而逐年增加,并且工业硝酸盐氮排放减少约为700kg/(km2·a),汽车硝酸盐氮排放增加约为500kg/(km2·a),总体硝酸盐氮减少约为200kg/(km2·a),综合看出硝酸盐氮和铵态氮都呈现为减少趋势,符合大气氮沉降减少规律.因此大气氮沉降的变化原因主要由氮肥挥发,工业排放及汽车排放变化引起.
氮肥施用约占27%~41%,在2009年以前氮肥施用占比较大,众多学者的研究也表明氮肥输入对区域氮素收支的贡献率较高[38],观察发现氮肥的施用量呈现逐渐降低的趋势,由表5可知,在衢州市耕地面积在20年间基本不变的前提下,氮肥施用量降低了近50%,粮食产量减产了15%,这表明肥料的利用率增加,植物对肥料中氮的吸收率增加,科学施肥生态农业的观念深入人心[37],也可以看出氮肥施用是影响NANI的主要因素之一.
食物/饲料氮输入部分约占22%~42%,2009~2014年间是影响NANI值的主要因素,由表5可知,衢州市人口密度变化不大,动物饲养量变化较大,且动物消耗氮量约为人消耗氮量3~6倍,因此该区域人口数量并非影响NANI的最主要因素,动物消耗才是最主要因素.2012年的生猪出栏数量较2003年增加了98.4%,此期间衢州畜牧业尤其是生猪养殖产业作为支柱产业之一,生猪养殖量成为影响NANI的主要因素.在2012年后生猪出栏量呈现较大的降低趋势,2022年相比2012年降低了74.0%,生猪产量呈现减小显著的趋势是因为衢州的生态保护政策实施,在这之前饮用水源地附近主要增收产业,如生猪养殖、网箱渔业养殖等在多轮的环境综合整治中都已被关停或禁止.因此,在近年来,肥料氮和食物/饲料氮输入的氮输入量都显著减少,导致NANI在近几年呈现出较低值,氮固定占比约在7%~11%,并且衢州的土地利用类型没有较大的变化,因此氮固定对NANI的影响较小.综上分析,NANI的最主要影响因素是汽车氮排放,肥料施用和畜牧业的发展.
综合看衢州产业结构变化,由衢州统计年鉴可知衢州的三次产业结构比例从2003年的16:42:42,调整为2012年的8:51:41,再到2022年的5:43:52.,可以看出第一产业所占比例(第一产业是指农、林、牧、渔业)从2003~2022年逐年呈下降趋势,从2003年的16.4%下降到2022年的4.7%,但第一产业生产总值从2003年的391484万元逐步增加到2022年的931988万元,说明衢州的第一产业的重要性相对第二/三产业的重要性降低,虽然生产总值逐年增加,但衢州的农业发展转向更绿色更生态的方式.也间接表明氮肥施用逐年降低的原因,同时也可以看出在2012年以前,第二产业逐渐增加占主导地位,2012年后第二产业比重也逐渐降低,衢州的中心更加倾向于第三产业.
基于NANI组成变化的原因分析,本研究针对各组分分别采取了不同的削减措施.这些措施对降低NANI值的效果如表6所示,对未来衢州地区NANI的削减工作具有指导意义.
近年来肥料氮输入和人类食物动物饲料逐年降低,大气氮沉降所占比例逐年增加.为减少NANI的输入,可采取减少大气氮排放的措施,其中减少肥料挥发和汽车氮排放具有可行性.2022年衢州的施肥水平为11.0t/(km2·a),按照Ludemann等[39]统计的粮农组织全球各国的氮肥施用量,选择美国、澳大利亚、以色列、荷兰、加拿大、俄罗斯、德国、法国以及阿根廷九个农业发达国家的氮肥施用水平的平均值7.2t/(km2·a)作为目标值,若衢州将氮肥施用水平降低至此目标值,氮肥挥发部分相对2022年将减少126kg/(km2·a).此外,鉴于工业排放逐年降低且所占比例减小,汽车氮排放呈现增加趋势,将汽油驱动汽车更换为新能源汽车可有效降低大气氮沉降量,衢州2022年民用汽车拥有量为57万辆[27],一辆汽油驱动的汽车年均氮氧化合物排放量为11.75kg[40],由公安部可知2022年浙江新能源汽车保有量为134.82万辆,民用汽车拥有量为2049.15万辆,可知浙江新能源汽车保有量占比为6.6%.以浙江新能源汽车比例计算衢州新能源汽车保有量,若将其余汽车全部更换为新能源汽车,大气氮沉降将降低707kg/(km2·a),因此改变民用汽车的氮排放具有显著效果,衢州应大力推广新能源汽车,降低汽油的使用,减少汽车尾气排放,综上,将氮肥施用水平降低至7.2t/(km2·a),并将所有民用汽车更换为新能源汽车,预计大气氮沉降将降低30%,达到1946kg/(km2·a)的水平,如表6所示.
近年来,衢州市在农业氮肥管理方面取得了一定成效,可知衢州氮肥施用量逐年减少,肥料氮输入比例相对下降,但仍具下降潜力.2022年,衢州的施肥水平为11.0t/(km2·a),相较于九个农业发达国家[39](美国、澳大利亚、以色列、荷兰、加拿大、俄罗斯、德国、法国、阿根廷)的平均氮肥施用水平7.2t/(km2·a),衢州肥料氮输入将减少893kg/(km2·a),表明减少氮肥施用是直接有效的措施,因此,建议衢州在未来维持正常生产水平的同时,将氮肥施用量减少至7.2t/(km2·a),预计肥料氮输入将降低37%,降至1496kg/(km2·a)(表6),但作物产量会因此降低,参考2016年氮肥施用量16.9t/(km2·a)降低至2020年13.1t/(km2·a)降低了3.8t/(km2·a),粮食产量由71.0万t降低至55.1万t.如果将氮肥施用量由11.0t/(km2·a)降低为7.2t/(km2·a),同样降低了3.8t/(km2·a),如果忽略氮肥利用率改变及作物种植管理改进,粮食产量将会降低22%,由2022年的55.6万t下降至约为43.4万t.但由于作物管理方式的改善等实际粮食产量下降值应比该预测值小.因此未来要注重在降低氮肥施用量的基础上提升化肥利用率,例如完善氮肥施用管理方式,注重氮肥与其余营养肥料的互相利用达到较高的肥料利用率,使作物产量尽量减产较少.
对于人类食物动物饲料氮输入部分,人类食物消耗氮量相对较小,且人口密度变化不大,疏解人口难度大,因此调控此部分可行性较小.根据前文分析,动物消耗氮量是最主要因素,故调控畜牧业更具可行性,过去20年间生猪出栏量是影响该组分的主要因素,由秦越[41]可知中国人均每年猪肉消耗量为19kg,若仅满足衢州内所有人口的猪肉需求量,衢州市生猪养殖出栏量可降低至55万头,生猪产量在原有基础上可降低56%,在此措施调整下,人类食物和动物饲料氮输入这部分将降低34%.考虑经济影响,该区域的原有猪产业值为255740万元,牧业产值为443932万元,若只满足区域内人口猪肉量需求,那么猪产业值将会下降至113806万元,牧业产值下降至330125万元.此措施虽然可减少氮输入,但会牺牲部分经济发展.因此,建议在满足衢州区域内猪肉需求的前提下,减少部分出口,并在降低生猪产量的同时,合理调节农业畜牧业结构,推动畜牧业向高质量发展.
作物固氮所占比例较小,重要性较低,对NANI的影响相对较小,且土地利用类型面积没有明显变化,故减少氮固定部分意义较小.因此,有效降低衢州NANI的主要措施应着重于减少大气氮沉降、肥料施用以及人类食物和动物饲料氮输入部分.如表6所示,在采取所有减氮措施的情况下,衢州未来的NANI将会下降约30%,达到约为6206kg/(km2·a),与全球其他国家NANI 4000~10000kg/(km2·a)相比可以看出也处于较低水平,因此采取上述措施具有可行性.
NANI计算数据均由前人研究及统计年鉴等,这些数据的误差对NANI结果有着一定影响,其中该区域肥料氮输入中复合肥计算方式所造成的肥料氮输入误差仅为0.5%.大气氮沉降获得结果可能会与实际大气氮沉降产生一定误差,但前文分析可知误差范围在8%以内,因此这些不确定性因素会对实验结果产生一定误差,但对整个区域来说这些误差较小,对该区域影响研究的分析产生影响较小.
4.1 衢州市NANI估量存在明显的时空差异.时间分布上,NANI值从2003年的13152kg/(km2·a)上升至2011年的15698kg/(km2·a),随后持续降低至2022年为8828kg/(km2·a),与发达国家相比高出1倍,在国内处于中等水平.空间分布上,衢州市NANI分布特征为西部低、东部高,并且与土地利用分布密切相关,各区县地级市的峰值普遍出现在2010~2012年之间.
4.2 影响衢州NANI总值的主要组成部分包括:氮肥施用量占比约27%~41%,食物/饲料净氮输入占比约22%~42%,大气氮沉降占比约21%~33%,作物固氮占比约7%~11%.影响衢州NANI的主要因素是氮肥施用以及食物/饲料氮输入及大气氮沉降,三个因素在不同时期均占据过主要因素.
4.3 减少NANI的主要策略包括:辅以政策激励与市场引导,大力倡导新能源汽车的应用;降低农田氮肥施用量,逐步调整至与农业发达国家广泛采用的施肥标准相接轨的水平;以满足衢州市本地人口对畜禽产品的基本需求为限,合理调控畜禽养殖规模,实施这些措施后,预计NANI可分别降低约833,893,896kg/(km2·a),总量降低约2622kg/(km2·a),达到世界上较低水平.
  • 乌溪江库区社会发展对生态环境影响调查研究(ZJJCCG2023-43)
  • 国家重点研发计划项目(2022YFF1303004-4)
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  • 接收时间:2024-09-24
  • 首发时间:2026-03-19
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  • 收稿日期:2024-09-24
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乌溪江库区社会发展对生态环境影响调查研究(ZJJCCG2023-43)
国家重点研发计划项目(2022YFF1303004-4)
作者信息
    1.北京林业大学水土保持学院,北京 100083
    2.山西省吉县森林生态系统国家野外科学观测研究站,山西 临汾 042200
    3.水土保持国家林业和草原局重点实验室,北京 100083
    4.衢州市乌溪江饮用水源保护管理中心,浙江 衢州 324003

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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