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To predict the long-term response of soil and surface water chemistry after the reduction in acid deposition, a dynamic MAGIC model combined with long-term monitoring data was conducted on a subtropical forest in Tieshanping, Chongqing, Southwest China. Under the “actual emission reduction” scenario based on China's “14th Five-Year Plan”(where the sulfur dioxide(SO2)emissions remained at the 2020 level, and the nitrogen oxides(NOx)and ammonia(NH3)emissions were reduced by over 10%and 8%, respectively, by 2025), the simulation results indicated that sulfate(SO42−)concentrations in soil water(S1and S2)and surface water(SW)initially increased, and stabilized after 2028 until 2050. The average SO42− concentrations in S1, S2 and SW water from 2021 to 2050 were 1426, 1414, and 938µeq/L, respectively, which were still above the 1980levels. The decline of SO42− concentrations in surface water was delayed by approximately 23 years. Soil water nitrate(NO3)concentrations showed a declining trend by 2050, but it remained above the threshold(443µeq/L), whereas surface water NO3 concentrations had decreased below its threshold(411µeq/L). The decline of NO3 concentrations in surface water was lagged approximately 13 years, compared to it in throughfall. Additionally, the concentrations of base cation(calcium, Ca2+)in both soil and surface water increased. The pH and Acid Neutralizing Capacity(ANC)in soil and surface water remained below their acidification thresholds. The acidification recovery showed a lag effect. The strong acidic anions in soil and surface water will decrease below their thresholds, pH will increase, and ANC will increase above 0µeq/L, when the stricter emission control policies were implemented, for example the SO2 emissions decrease to 80% of 2021l evels by 2030 and 70% of 2021 levels by 2050, and the NH3 emissions, NOx emissions, and Ca2+ deposition decrease to 60% of 2021 levels by 2030 and 40% of 2021 levels by 2050. Moreover, further global temperature increases showed insignificant impact on the major strong acidic anions and acidification indicators in the highly acidic soils and surface waters of the subtropical forest.

, correspAuthors=Dan-ni XIE, 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=Jian-xing ZHOU, Bing-zhen LI, Yi-meng HAN, Fan XIA, Dan-ni XIE), CN=ArticleExt(id=1241057224634716254, articleId=1241057221191193407, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=MAGIC动态模型模拟预测我国亚热带森林土壤和地表水化学对氮硫沉降降低的响应, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

以我国西南部重庆铁山坪亚热带森林为研究对象,运用动态模型MAGIC,结合长期观测数据,预测酸沉降减少后土壤和地表水化学的长期响应.模拟结果表明基于“十四五”的排放控制政策(到2025年SO2排放总量保持与2020年相同,NOx和氨气(NH3)排放总量比2020年分别下降10%以上和8%)设置的“实际减排”情景,到2050年亚热带森林土壤水(S1和S2)和地表水(SW)硫酸根(SO42−)浓度先升高,到2028年之后保持平稳,2021~2050年其平均值分别为1426,1414和938µeq/L,尚未恢复到1980年水平,恢复滞后23年左右;土壤水硝酸根(NO3)到2050年虽呈下降趋势但仍高于其阈值(443µeq/L),地表水NO3浓度已经低于其阈值(411µeq/L),地表水NO3浓度得降低相对于穿透水滞后13年左右;盐基阳离子(钙,Ca2+)在土壤和地表水中浓度升高;土壤和地表水pH值和ANC均低于酸化指标阈值,酸化恢复存在滞后效应.当实施更加严格的排放控制政策,即SO2至2030年降到2021年的80%,至2050年降到2021年的70%,NH3、NOx和Ca2+至2030年降到2021年的60%,至2050年降到2021年的40%,才可以使强酸性阴离子恢复到阈值以内,pH值持续上升,土壤和地表水ANC恢复到0µeq/L以上.此外,未来全球温度上升对土壤酸化严重的亚热带森林土壤和地表水中主要强酸性阴离子和酸化指标的影响不显著.

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* 责任作者,副教授,
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周剑兴(2001−),男,河南南阳人,长安大学硕士研究生,主要从事氮的生物地球化学循环研究.发表论文2篇..

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周剑兴(2001−),男,河南南阳人,长安大学硕士研究生,主要从事氮的生物地球化学循环研究.发表论文2篇..

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周剑兴(2001−),男,河南南阳人,长安大学硕士研究生,主要从事氮的生物地球化学循环研究.发表论文2篇..

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Environmental Pollution2019244:980-994., articleTitle=Responses of forest ecosystems in Europe to decreasing nitrogen deposition, refAbstract=null)], funds=[Fund(id=1241057233224651589, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, awardId=42303061, language=CN, fundingSource=国家自然科学基金项目(42303061), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241057225993670796, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, xref=null, ext=[AuthorCompanyExt(id=1241057226002059406, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, companyId=1241057225993670796, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Land Engineering, Chang’an University, Xi’an 710054, China), AuthorCompanyExt(id=1241057226014642321, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, companyId=1241057225993670796, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=长安大学土地工程学院,陕西 西安 710054)])], figs=[ArticleFig(id=1241057231161053769, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=EN, label=Fig.1, caption=Monitoring point location, figureFileSmall=ZTYSCnJz56fjp6Tr+Psirg==, figureFileBig=o0sjU1Dc7W4yLfNXFdx7tQ==, tableContent=null), ArticleFig(id=1241057231307854428, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=CN, label=图1, caption=监测点位置, figureFileSmall=ZTYSCnJz56fjp6Tr+Psirg==, figureFileBig=o0sjU1Dc7W4yLfNXFdx7tQ==, tableContent=null), ArticleFig(id=1241057231563707006, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=EN, label=Fig.2, caption=Comparison of the simulation results of relevant ions from the MAGIC model with observation data, figureFileSmall=mBP1eUuk/PLk7CTXGzYzQQ==, figureFileBig=WCPV+mdD+/MUOP8W4y6FGw==, tableContent=null), ArticleFig(id=1241057231689536137, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=CN, label=图2, caption=MAGIC模型相关离子的模拟结果与观测结果比较, figureFileSmall=mBP1eUuk/PLk7CTXGzYzQQ==, figureFileBig=WCPV+mdD+/MUOP8W4y6FGw==, tableContent=null), ArticleFig(id=1241057231811170971, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=EN, label=Fig.3, caption=Long-term variations in ion concentrations and acidification indicators in soil surface, subsoil, and surface water under different sedimentation scenarios, figureFileSmall=isR+ek4hAK7RuK0LXvdnjw==, figureFileBig=HfTak1vuWi+/28Rg1lZ6Tw==, tableContent=null), ArticleFig(id=1241057231920222888, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=CN, label=图3, caption=不同沉降情景下土壤表层、土壤下层和地表水中离子浓度和酸化指标的长期变化, figureFileSmall=isR+ek4hAK7RuK0LXvdnjw==, figureFileBig=HfTak1vuWi+/28Rg1lZ6Tw==, tableContent=null), ArticleFig(id=1241057232029274809, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=EN, label=Fig.4, caption=Delayed recovery of acidification in subtropical forest soils and surface water[19,84], figureFileSmall=AHGbKANa7XCSmZgZvE8u4A==, figureFileBig=LaX0DSxVdKIdenoybg5ayQ==, tableContent=null), ArticleFig(id=1241057232113160899, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=CN, label=图4, caption=亚热带森林土壤和地表水酸化恢复的滞后现象[19,84], figureFileSmall=AHGbKANa7XCSmZgZvE8u4A==, figureFileBig=LaX0DSxVdKIdenoybg5ayQ==, tableContent=null), ArticleFig(id=1241057232251572946, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=EN, label=Table 1, caption=

MAGIC input parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
参数类型参数名称单位土壤表层土壤下层湿地
土壤参数深度m0.150.302.00
孔隙度0.470.460.50
容积密度kg/m3136014501500
阳离子交换容量(CEC)meq/m3806035
温度16.716.717
溶解有机碳(DOC)mmol/m3502010
土壤pCO2%331530
水流阈值cm/a303030
水流量cm/a37.937.9100
固定参数Al(OH)3溶解度log7.07.87.0
Al(OH)3H量2.603.152.50
有机酸常数pK1DOClog2.102.102.64
有机酸常数pK2DOClog5.605.605.66
有机酸常数pK3DOClog7.007.005.94
Al-Ca选择性系数log0.080.98−0.12
Al-Mg选择性系数log0.701.24−1.22
Al-Na选择性系数log−1.87−2.62−1.68
Al-K选择性系数log−0.59−1.22−1.13
硫动态参数硫还原通量mmol/(m2·a)1020100
硫氧化通量mmol/(m2·a)000
硫还原初始库存mol/m2000
S还原和氧化发生条件一直还原;水流量小于阈值时氧化
SO42−吸附半饱和浓度meq/m3100010001000
SO42−最大吸附量meq/kg1410
氮动态参数NH4+和NO3保留 C/N10~2010~1810~20
初始氮库mol/m22216260
初始碳库mol/m24401904550
植物和土壤氮利用条件植物不利用有机氮,土壤会在植物之前利用无机氮
植物利用NH4+通量mmol/(m2·a)000
植物利用NO3通量mmol/(m2·a)39.860.20
硝化%−100−100−30
反硝化%−13.9−39.2−36
枯落物有机碳mmol/(m2·a)400000
枯落物 C/N2500
分解有机碳mmol/(m2·a)400038000
分解 C/N20120
总氮保留%100100100
), ArticleFig(id=1241057232431928043, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=CN, label=表1, caption=

MAGIC模型输入参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数类型参数名称单位土壤表层土壤下层湿地
土壤参数深度m0.150.302.00
孔隙度0.470.460.50
容积密度kg/m3136014501500
阳离子交换容量(CEC)meq/m3806035
温度16.716.717
溶解有机碳(DOC)mmol/m3502010
土壤pCO2%331530
水流阈值cm/a303030
水流量cm/a37.937.9100
固定参数Al(OH)3溶解度log7.07.87.0
Al(OH)3H量2.603.152.50
有机酸常数pK1DOClog2.102.102.64
有机酸常数pK2DOClog5.605.605.66
有机酸常数pK3DOClog7.007.005.94
Al-Ca选择性系数log0.080.98−0.12
Al-Mg选择性系数log0.701.24−1.22
Al-Na选择性系数log−1.87−2.62−1.68
Al-K选择性系数log−0.59−1.22−1.13
硫动态参数硫还原通量mmol/(m2·a)1020100
硫氧化通量mmol/(m2·a)000
硫还原初始库存mol/m2000
S还原和氧化发生条件一直还原;水流量小于阈值时氧化
SO42−吸附半饱和浓度meq/m3100010001000
SO42−最大吸附量meq/kg1410
氮动态参数NH4+和NO3保留 C/N10~2010~1810~20
初始氮库mol/m22216260
初始碳库mol/m24401904550
植物和土壤氮利用条件植物不利用有机氮,土壤会在植物之前利用无机氮
植物利用NH4+通量mmol/(m2·a)000
植物利用NO3通量mmol/(m2·a)39.860.20
硝化%−100−100−30
反硝化%−13.9−39.2−36
枯落物有机碳mmol/(m2·a)400000
枯落物 C/N2500
分解有机碳mmol/(m2·a)400038000
分解 C/N20120
总氮保留%100100100
), ArticleFig(id=1241057232599700220, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=EN, label=Table 2, caption=

Deposition coefficients for future scenarios

, figureFileSmall=null, figureFileBig=null, tableContent=
参数年份“保持不变”情景“实际减排”情景“严格减排”情景
O-1O-2S-1S-2Y-1Y-2
SO42−沉降系数2021111111
203011110.80.8
205011110.70.7
207011110.70.7
NH4+沉降系数2021111111
2030110.80.80.60.6
2050110.70.70.40.4
2070110.70.70.40.4
NO3沉降系数2021111111
2030110.80.80.60.6
2050110.70.70.40.4
2070110.70.70.40.4
Ca2+沉降系数2021111111
2030110.80.80.60.6
2050110.70.70.40.4
2070110.70.70.40.4
气候变化(℃)202116.716.716.716.716.716.7
203016.716.916.716.916.716.9
205016.717.316.717.316.717.3
207016.717.316.717.316.717.3
), ArticleFig(id=1241057232746500871, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=CN, label=表2, caption=

未来情景的沉降系数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数年份“保持不变”情景“实际减排”情景“严格减排”情景
O-1O-2S-1S-2Y-1Y-2
SO42−沉降系数2021111111
203011110.80.8
205011110.70.7
207011110.70.7
NH4+沉降系数2021111111
2030110.80.80.60.6
2050110.70.70.40.4
2070110.70.70.40.4
NO3沉降系数2021111111
2030110.80.80.60.6
2050110.70.70.40.4
2070110.70.70.40.4
Ca2+沉降系数2021111111
2030110.80.80.60.6
2050110.70.70.40.4
2070110.70.70.40.4
气候变化(℃)202116.716.716.716.716.716.7
203016.716.916.716.916.716.9
205016.717.316.717.316.717.3
207016.717.316.717.316.717.3
), ArticleFig(id=1241057232922661664, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=EN, label=Table 3, caption=

MAGIC calibration parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
参数单位土壤表层土壤下层湿地
Ca2+汇 源 风化当汇的数值为负数时表示汇占输入总量的百分比(%);源和风化单位为meq/(m2·a)−32 0 0−15 0 0−70 0 0
Mg2+汇 源 风化−37 0 00 0 0−60 0 0
Na+汇 源 风化0 0 00 0 00 0 8
K+汇 源 风化−72 0 0−27 0 0−88 0 0
NH4+汇 源 风化0 0 00 0 00 0 0
SO42−汇 源 风化 Cl汇 源 风化0 0 00 0 00 0 0
−60 0 00 0 0−65 0 0
NO3汇 源 风化0 0 00 0 00 0 0
F汇 源 风化−38 0 00 0 0−28 0 0
初始阳基交换 ECa%19415.8
初始阳基交换 EMg%2.37.73.8
初始阳基交换 ENa%0.150.60.39
初始阳基交换 EK%0.20.530.1
), ArticleFig(id=1241057233040102186, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241057221191193407, language=CN, label=表3, caption=

MAGIC模型校准参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数单位土壤表层土壤下层湿地
Ca2+汇 源 风化当汇的数值为负数时表示汇占输入总量的百分比(%);源和风化单位为meq/(m2·a)−32 0 0−15 0 0−70 0 0
Mg2+汇 源 风化−37 0 00 0 0−60 0 0
Na+汇 源 风化0 0 00 0 00 0 8
K+汇 源 风化−72 0 0−27 0 0−88 0 0
NH4+汇 源 风化0 0 00 0 00 0 0
SO42−汇 源 风化 Cl汇 源 风化0 0 00 0 00 0 0
−60 0 00 0 0−65 0 0
NO3汇 源 风化0 0 00 0 00 0 0
F汇 源 风化−38 0 00 0 0−28 0 0
初始阳基交换 ECa%19415.8
初始阳基交换 EMg%2.37.73.8
初始阳基交换 ENa%0.150.60.39
初始阳基交换 EK%0.20.530.1
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MAGIC动态模型模拟预测我国亚热带森林土壤和地表水化学对氮硫沉降降低的响应
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周剑兴 , 李秉珍 , 韩怡蒙 , 夏凡 , 谢丹妮 *
中国环境科学 | 环境生态 2025,45(5): 2700-2712
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中国环境科学 | 环境生态 2025, 45(5): 2700-2712
MAGIC动态模型模拟预测我国亚热带森林土壤和地表水化学对氮硫沉降降低的响应
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周剑兴 , 李秉珍, 韩怡蒙, 夏凡, 谢丹妮*
作者信息
  • 长安大学土地工程学院,陕西 西安 710054
  • 周剑兴(2001−),男,河南南阳人,长安大学硕士研究生,主要从事氮的生物地球化学循环研究.发表论文2篇..

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* 责任作者,副教授,
Simulation and prediction of subtropical forest soil and surface water chemistry responses to reduced nitrogen and sulfur deposition in China using the MAGIC dynamic model
Jian-xing ZHOU , Bing-zhen LI, Yi-meng HAN, Fan XIA, Dan-ni XIE*
Affiliations
  • School of Land Engineering, Chang’an University, Xi’an 710054, China
出版时间: 2025-05-20
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以我国西南部重庆铁山坪亚热带森林为研究对象,运用动态模型MAGIC,结合长期观测数据,预测酸沉降减少后土壤和地表水化学的长期响应.模拟结果表明基于“十四五”的排放控制政策(到2025年SO2排放总量保持与2020年相同,NOx和氨气(NH3)排放总量比2020年分别下降10%以上和8%)设置的“实际减排”情景,到2050年亚热带森林土壤水(S1和S2)和地表水(SW)硫酸根(SO42−)浓度先升高,到2028年之后保持平稳,2021~2050年其平均值分别为1426,1414和938µeq/L,尚未恢复到1980年水平,恢复滞后23年左右;土壤水硝酸根(NO3)到2050年虽呈下降趋势但仍高于其阈值(443µeq/L),地表水NO3浓度已经低于其阈值(411µeq/L),地表水NO3浓度得降低相对于穿透水滞后13年左右;盐基阳离子(钙,Ca2+)在土壤和地表水中浓度升高;土壤和地表水pH值和ANC均低于酸化指标阈值,酸化恢复存在滞后效应.当实施更加严格的排放控制政策,即SO2至2030年降到2021年的80%,至2050年降到2021年的70%,NH3、NOx和Ca2+至2030年降到2021年的60%,至2050年降到2021年的40%,才可以使强酸性阴离子恢复到阈值以内,pH值持续上升,土壤和地表水ANC恢复到0µeq/L以上.此外,未来全球温度上升对土壤酸化严重的亚热带森林土壤和地表水中主要强酸性阴离子和酸化指标的影响不显著.

酸性气体减排  /  酸沉降  /  亚热带森林  /  土壤和地表水化学  /  MAGIC

To predict the long-term response of soil and surface water chemistry after the reduction in acid deposition, a dynamic MAGIC model combined with long-term monitoring data was conducted on a subtropical forest in Tieshanping, Chongqing, Southwest China. Under the “actual emission reduction” scenario based on China's “14th Five-Year Plan”(where the sulfur dioxide(SO2)emissions remained at the 2020 level, and the nitrogen oxides(NOx)and ammonia(NH3)emissions were reduced by over 10%and 8%, respectively, by 2025), the simulation results indicated that sulfate(SO42−)concentrations in soil water(S1and S2)and surface water(SW)initially increased, and stabilized after 2028 until 2050. The average SO42− concentrations in S1, S2 and SW water from 2021 to 2050 were 1426, 1414, and 938µeq/L, respectively, which were still above the 1980levels. The decline of SO42− concentrations in surface water was delayed by approximately 23 years. Soil water nitrate(NO3)concentrations showed a declining trend by 2050, but it remained above the threshold(443µeq/L), whereas surface water NO3 concentrations had decreased below its threshold(411µeq/L). The decline of NO3 concentrations in surface water was lagged approximately 13 years, compared to it in throughfall. Additionally, the concentrations of base cation(calcium, Ca2+)in both soil and surface water increased. The pH and Acid Neutralizing Capacity(ANC)in soil and surface water remained below their acidification thresholds. The acidification recovery showed a lag effect. The strong acidic anions in soil and surface water will decrease below their thresholds, pH will increase, and ANC will increase above 0µeq/L, when the stricter emission control policies were implemented, for example the SO2 emissions decrease to 80% of 2021l evels by 2030 and 70% of 2021 levels by 2050, and the NH3 emissions, NOx emissions, and Ca2+ deposition decrease to 60% of 2021 levels by 2030 and 40% of 2021 levels by 2050. Moreover, further global temperature increases showed insignificant impact on the major strong acidic anions and acidification indicators in the highly acidic soils and surface waters of the subtropical forest.

acidic gas emission reduction  /  acid deposition  /  subtropical forest  /  soil and surface water chemistry  /  MAGIC model
周剑兴, 李秉珍, 韩怡蒙, 夏凡, 谢丹妮. MAGIC动态模型模拟预测我国亚热带森林土壤和地表水化学对氮硫沉降降低的响应. 中国环境科学, 2025 , 45 (5) : 2700 -2712 .
Jian-xing ZHOU, Bing-zhen LI, Yi-meng HAN, Fan XIA, Dan-ni XIE. Simulation and prediction of subtropical forest soil and surface water chemistry responses to reduced nitrogen and sulfur deposition in China using the MAGIC dynamic model[J]. China Environmental Science, 2025 , 45 (5) : 2700 -2712 .
人为二氧化硫(SO2)、氮氧化物(NOx)和氨气(NH3)排放增加引起的高酸沉降曾是中国的主要环境问题之一[1].长期的高酸沉降,特别是我国南方地区氮沉降峰值曾达到60kg/(hm2·a),硫沉降峰值曾达到160kg/(hm2·a),已经造成了严重的土壤酸化,同时水体也出现了酸化趋势[2-4].我国酸性气体排放量从1980年开始增加,SO2排放量在2005年达到峰值(30.81Tg/a),NOx排放量在2011年达到峰值(28.69Tg/a),NH3排放量在2014年达到峰值(11.43Tg/a).之后SO2、NOx和NH3排放量分别开始下降,到2021年分别降至5.55,15.79和9.73Tg/a[5-8],从而带来了我国酸沉降的显著下降[9].但是南方地区的氮(N)和硫(S)沉降仍然高于亚热带森林氮沉降临界负荷(26~36kg/(hm2·a))和硫沉降临界负荷(8~32kg/(hm2·a))[3,10-11].
随着20世纪80年代欧洲和北美开始控制酸性气体(SO2、NOx和NH3)排放,其酸沉降大幅下降[12],许多酸化的森林小流域已经开始恢复[13-15].然而由于历史高硫沉降导致土壤吸附了大量的硫酸根(SO42−)或将其转化为有机硫,所以当硫沉降减少时,这些被储存的硫化物被重新释放,因此在这些地区观察到地表水中SO42−浓度的变化相比于当地硫沉降的下降存在显著的滞后现象[13,16].同时在氮沉降减少后,土壤和地表水中硝酸根(NO3)浓度的变化呈现上升、下降或不变的趋势[17-18].此外,我国典型亚热带森林观测站点铁山坪的观测结果表明,氮硫沉降下降后土壤和地表水中SO42−和NO3浓度虽然呈现下降趋势,但是相比于氮硫沉降的下降存在大约5~10年的滞后[19],因此尚不清楚森林土壤和地表水酸化恢复到阈值以下的具体时间和过程.
森林流域中土壤SO42−和无机氮的保留和释放等过程对气候变量敏感[20-21],比如升高的土壤温度会增加土壤厌氧状态[22-23],加快土壤的硝化和反硝化速率[24-25]等.美国新罕布什尔州森林流域年径流SO42−输出控制因素已由大气硫沉降转向气候因子[26],由此可见森林酸化恢复过程会受到气候变化的影响[20,27-29].因此在预测未来长期酸化恢复结果时考虑气候变化的影响能够更好地指导我国氮减排政策.
动态模型是对现场观测氮沉降长期影响的重要补充,能够预测土壤元素化学对大气沉降的未来响应,国内外大量研究使用流域地下水酸化模型(MAGIC)预测不断变化的酸沉降对土壤和地表水酸化状态的影响[30-35].MAGIC模型是一个集总参数、中等复杂度的动态模型,通过该模型可以在流域尺度上重建和预测土壤和水化学的长期趋势[36-37].MAGIC模型曾预测欧洲氮沉降下降时期10个不同地区共202个样地在2010年的地表水酸化恢复情况,并且在2014年对比了预测恢复情况与实际观测数据,大部分样地中SO42−浓度和酸中和容量(ANC)的模拟值和实测值之间非常接近[31].在北美安大略省中南部的25个温带森林小流域中,依据北美2003年制定的减排计划,结合MAGIC模型,预测其中16个流域到2050年地表水中SO42−浓度将降低至50~70μeq/L,ANC将大于50μeq/L,pH值恢复到5.97以上,但是该地区的土壤将继续酸化,只有当风化作用超过大气沉降作用与自然酸化作用时土壤酸化才能开始恢复,表明当地需要更加严格的控制措施以促进酸化恢复[38].国内现已有少量研究运用MAGIC模型预测我国酸沉降对亚热带森林的影响[30,39],但是由于监测数据不足,无法准确预测我国亚热带森林恢复到阈值以下的氮硫沉降水平以及氮硫沉降长期变化(特别是氮硫沉降减少阶段)对土壤和地表水的影响.
我国南方的亚热带森林占全球亚热带森林面积的45%,占我国森林总面积的70%[9],是全球硫和氮生物地球化学循环最活跃的地区[19,40-41].亚热带森林的土壤普遍呈酸性[42-43],比欧洲和北美的温带森林具有更高的降水量和温度[44],以及更高的土壤氮、硫含量[45-46],其可能具有独特的氮硫循环特征.因此,本研究以已进行25年连续观测的铁山坪亚热带森林站点为研究对象,(1)利用铁山坪亚热带森林长期自然观测数据结合MAGIC模型,根据氮硫沉降到2050年的预定变化趋势,预测氮硫沉降持续下降阶段,亚热带森林土壤和地表水化学的响应,确定酸化恢复到阈值以下的具体时间;(2)结合全球气候变化和大气沉降减少的情景,探讨我国氮硫沉降降低到何种程度才能使我国亚热带森林酸化恢复到阈值以下.
研究区域位于我国重庆市市中心东北约25km处的亚热带森林站点铁山坪(图1),流域总面积为16hm2,铁山坪流域监测项目属于中国陆地系统酸化综合监测计划(IMPACTS)项目[1].土壤为黄壤(Haplic Acrisols,铁铝土),植被主要为马尾松(Pinus massoniana Lamb),林下植被有狗脊蕨(Woodwardia japonica L. f. Sm.)、黑足鳞毛蕨(Dryopteris fuscipes C. Chr.)、芒萁(Dicranopteris dichotoma Thunb.Berhn.)和檵木(Loropetalum chinense R. Br. Oliver)等.2001~2021年间,铁山坪土壤和地表水pH值为4.4~5.6,高硫氮沉降[39,47]导致了站点土壤的严重酸化.
在铁山坪流域中,96%为山坡,4%为山坡与溪流之间进行水文连通的地下水排泄区.地下水排泄区地势较低且平缓,植被为草本植物和灌木.在土壤深约2m处有一层致密的基岩,阻碍土壤水与深层地下水的交汇,因此该流域没有地下水渗漏.地下水排泄区的末端为地表水出流口,安装一量水堰用以测量地表水的流量.该小集水区包含4个观测样地(图2),其中2块样地,分别为K和L,从2001年起对这2个30m×30m的自然氮沉降样地进行长期野外观测[39].另外2块样地分别为P01和P02,从2004年10月起对这2个14m×14m的自然氮沉降样地进行长期野外观测[9].为了避免边缘效应,仅在每个样地中心10m×10m内进行样品采集.
在每个样地中心10m×10m区域各随机放置5个穿透水收集器,同时在样地中心15cm和30cm深度各安装1套陶瓷吸力蒸渗仪,用于收集土壤水分.在30cm深度(低于根系主分布深度)测量的土壤水浓度代表了土壤层的浸出浓度(考虑了植物吸收和微生物同化).所有采集的样品在4℃下冷藏,将每4周采集得到的全部穿透水、全部上层土壤水和全部下层土壤水分别进行混合,得到每月的穿透水和土壤水水样,并记录下总的穿透水体积.所有水样都经过过滤(用子样进行pH值测量后,用孔径为0.45μm的水相针状过滤器(SCAA−201,美国)过滤),并用离子色谱法(ICS:DX−1000;ICS:DX−2000,美国)分析主要阳离子(钙,Ca2+、镁,Mg2+、钠,Na+、钾,K+、铝,Al3+和铵根,NH4+)和阴离子(SO42−、NO3、氯,Cl和氟,F).
由于干旱或土壤水采样器损坏等原因,在某些月份没有采集到可用的土壤水样品.同时每个季节都随机采集4份枯落物层、表层(0~15cm层,S1)和下层(15~30cm层,S2)土壤样品,且随机采集植物样品,包括马尾松针叶、树干和地表植物(狗脊蕨、黑足鳞毛蕨、芒萁和檵木等).4个样地均处于铁山坪小流域,由于自然条件相似,因此取4个样地平均值作为本研究实验数据.
本研究使用的MAGIC模型为Version7.77版本.相对于早期MAGIC版本[36-37],新版本在考虑集水区土壤中阴离子的滞留(SO42−吸附和各阴离子的汇等)、土壤中阳离子交换、碳酸分解产生碱度、土壤中铝矿物的溶解和土壤矿物的风化等重要的控制土壤和地表水化学性质过程的基础上,改进有机酸缓冲[48-49]和铝溶解度[50],将集水区氮的保持和损失的动态与土壤氮和碳库联系起来[51],增加更为精准的氮动力学模型[52],从而能够更好地对土壤和地表水的化学性质进行模拟.
根据铁山坪站点的地形特征,本研究采用垂直双箱和湿地3个单元的集水区构型.根据土壤水采样器的安置位置,设置0~15cm土层(土壤表层,S1)和15~30cm土层(土壤下层,S2)两个山坡土壤单元;根据地下水排泄区面积占比设置了占流域总面积4%的湿地单元,湿地土壤深度为200cm;此外,由于在出流口采集地表水,集水区不设置地表水单元.模型运行以年为单位的时间分辨率,对土壤和地表水化学进行长期的模拟.
MAGIC模型所需的输入数据包括径流分配、基准年沉降、两层土壤和湿地土壤的化学和物理指标、地表水参数、氮动态参数、硫还原参数和长期历史变化序列(包括主要离子沉降、源汇、气候相关的参数、硫循环参数和氮动态参数的历史变化序列).基准年径流量设置为0.379m/a,径流分配设置为全部经过表层和下层土壤,然后进入地下水排泄区.
两层土壤的化学和物理指标如表1所示,包括直接测定的参数、间接计算的硫和氮动态参数.本研究中MAGIC模型输入数据中土壤参数和固定参数参考之前对铁山坪的观测工作[39,53-54],硫动态参数参考余倩等人的工作[54],氮动态参数参考刘文静等人的工作[55].SO42−吸附参数等没有测量数据,但是可以通过调整这些参数来校准模型,直到模拟的土壤和地表水数据与观测数据相匹配[51-52].在本研究中,观测到的土壤和地表水化学数据没有作为输入数据,而是用作模型校准的目标.
以2014年为基准年,对110年的历史时期(从1904年开始)进行了模型校准.根据各离子相关前体的排放趋势,确定了2001年之前所有离子的历史沉降序列(从监测数据可得之时起).由于缺少对历史沉降的观测数据,1904~2000年SO42−、NO3和NH4+沉降根据中国SO2(1904~1985年:Xie等[56];1986~2000年:Duan等[39])、NO2(1904~1980年:Xie等[56];1981~2000年:田贺忠等[57])和NH3(1951~1980年:王文兴等[58];1981~2000年:王书伟等[59])的排放总量进行重建.2001~2021年期间的沉降数据来源于铁山坪亚热带森林站点长期的穿透水观测数据.
我国的盐基阳离子(BC,包括Ca2+、Mg2+、Na+和K+)的沉降量很高,它们对土壤和地表水的酸化起缓冲作用[60-61],因此需要对盐基阳离子的历史沉降序列进行合理设置.1990~2000年间Ca2+沉降依据中国Ca2+排放清单进行重建(1990~2000年:Lei等[62]),2001~2021年的沉降数据来源于该站点穿透水的直接观测结果[19,54].由于1990年之前缺乏Ca2+排放数据,1910~1990年Ca2+沉降被假定为25%为自然源和75%的人为源[39].Mg2+、Na+、K+由于缺乏排放和观测数据,假定Mg2+、Na+、K+在1910~2000年的沉降趋势与Ca2+一致.Cl和F认为有60%来源于自然源,1950年之后有部分人为源排放,按相应比例进行重建.2001~2021年Mg2+、Na+、K+、Cl和F的沉降依据铁山坪站点穿透水的沉降数据[19,54]获得.
随着人口增加和工业化发展,1910年之后各种离子的沉降都呈现上升趋势.但是在“十一五”(2006~2010年)、“十二五”(2011~2015年)和“十三五”(2016~2020年)期间,我国相继实施了《大气污染物排放总量控制》和《大气污染防治行动计划》,SO2、NOx和NH3排放量持续下降,SO2排放量从2006年到2020年下降了77%,NOx排放量从2012年到2020年下降了32%,NH3排放量从2015年到2020年下降了13%[63].在“十四五”(2021~2025)期间,我国政府制定了强制性目标,即到2025年NOx和NH3排放总量比2020年分别下降10%以上和8%,据此设置了SO42−、NH4+和NO3减少情景.2000~2017年,全国盐基阳离子湿沉降平均为84.8kg/(hm2·a),与全国湿沉降中SO42−和NO3之和相当[3,64-65].但是土壤沙尘、水泥生产以及煤炭燃烧等活动排放颗粒物带来盐基阳离子(主要成分为CaCO3[66-67]的同时,也会排放大量的有害颗粒物,降低区域空气质量,给社会生产生活和生态环境造成损害,因此我国对颗粒物的排放同样进行了限制[68].据此同步设置了Ca2+减少情景,模拟盐基阳离子下降对酸化恢复的影响.
本研究设置了3种未来情景:“保持不变”情景O-1,到2050年SO2、NH3、NOx排放总量与2021年保持不变,即SO2、NH3、NOx和Ca2+在2021年以后保持与基准年一致;“实际减排”情景S-1,根据“十四五”规划,设置SO2在2021年以后与基准年保持一致,NH3、NOx和Ca2+至2030年降到2021年的80%,至2050年降到70%;以及“严格减排”情景Y-1,SO2至2030年降到2021年的80%,至2050年降到70%,NH3、NOx和Ca2+至2030年降到2021年的60%,至2050年降到40%,具体各离子沉降系数(沉降系数是指该年的沉降量与基准年沉降量的比值)见表2.在所有未来情景设置中,只考虑酸性离子沉降变化,其他主要离子的沉降系数自2021年后均设为1,即与基准年沉降保持一致.
联合国政府间气候变化专门委员会(IPCC)2018年发布的一项专题报告:Global warming of 1.5℃指出,到2017年全球人为增温比工业革命前高1℃,由于过去和持续的排放,预计人为全球变暖平均每十年增加0.2℃[69].基于此在上述三种情景的基础上设定O-2、S-2和Y-2气候情景,沉降情景不变,温度至2050年比2021年升高0.6℃.
对于各离子的源汇以及硫和氮的动态等相关数据的长期变化进行设置.Ca2+、Mg2+、Na+、K+、Cl和F的源、汇和风化的长期序列都设为1,即与基准年一致;SO42−的还原通量的长期序列与SO42−的沉降序列设置为一致;硝化和反硝化等过程的参数都为输入的百分比,所以长期序列都设为1,即与输入的相对比例不变,但其绝对通量会随着输入的变化而变化;植物吸收和枯落物的长期序列设为1,即假设植物对氮的利用通量不变;有机氮的分解的长期序列设置时与总氮(NO3+NH4+)沉降的变化设为一致.
在MAGIC模型中通过调整酸化强相关性离子(SO42−、NO3和NH4+)以及酸化弱相关性离子(Cl、Na+和K+)等离子的源汇和风化速率以及初始盐基阳离子交换速率校准模型,校准后的各离子相关参数见表3.
SO42−、NO3和NH4+的沉降都能够导致土壤和地表水的酸化,主要的酸化途径为:SO42−和NO3的沉降伴随着H+的直接输入;NH4+的硝化和利用过程中会产生H+;以及淋溶过程中会伴随着盐基阳离子或Al3+的流失等.因此,SO42−、NO3和NH4+是主要的致酸离子.而Ca2+和Mg2+等较高浓度的阳离子可以提供碱度,对酸沉降起缓冲作用.表层土壤水(S1)、下层土壤水(S2)和地表水(SW)中SO42−、NO3、NH4+、Ca2+和Mg2+的模型模拟结果与实际观测结果如图2所示.
总体来说模型模拟结果与实际观测结果呈现一致的变化趋势.2001~2021年SO42−的模拟结果与实际观测结果的相对偏差在S1、S2和SW中平均为10%,表明模型参数能够较好地模拟实际土壤水和溪流的SO42−浓度动态变化.本研究相对于以往亚热带酸化模拟的研究[39],新增了硫酸根还原过程的模拟.由于模型模拟的是年际变化,不考虑季节波动带来的氧化还原波动,因此将硫氧化通量设置为0,硫的初始还原库存也设为0,而还原和氧化的触发条件设置为:一直还原以及水流量小于阈值时氧化,从而使得设置的硫还原通量为净还原量,不考虑土壤中发生的硫氧化过程.同时调整SO42−吸附容量以平衡SO42−输入和输出浓度,本研究使用的调整值(1~10meq/kg)符合欧洲和北美森林使用的范围(0.1~10meq/kg)[70-71].
本研究相对于以往的模型模拟[39]更新了氮动态模块,采用2014年的氮循环速率(硝化速率、反硝化速率、氮固持速率和氮矿化速率等)[72].根据亚热带森林氮饱和的C/N阈值(表层土壤和枯落物层为20)[73],设置表层NO3和NH4+保留的C/N范围为10~20,植物对氮的利用类型设为不吸收有机氮,同时根据马尾松对氮的吸收通量[74]和马尾松根系分布特征,设置在上层和下层土壤中植物对NO3的吸收分别为39.8和60.2mmol/(m2·a).2001~2021年NO3的模拟结果与实际观测结果见图2,除了未能反映出个别年份(2007年和2008年)较高的NO3离子浓度外,NO3模拟结果与观测结果的相对偏差均在5%以内.个别年份地表水中较高的NO3浓度可能由于暴雨冲刷导致的淋溶增多,而模型不能很好地模拟这种突变情况.
Ca2+和Mg2+的模拟结果与实测结果也较为相符(图2).Ca2+和Mg2+主要通过调整源汇、风化速率和初始盐基阳离子交换速率校验参数.从校验结果(表3)可以发现土壤对Ca2+和Mg2+具有很大的汇,如Ca2+在表层土壤、下层土壤和地下水排泄区的汇分别占总沉降量的32%、15%和70%,Mg2+在表层和地下水排泄区分别具有37%和60%的汇.Ca2+和Mg2+的汇一部分可能来源于植物吸收,另一部分可能被固定到土壤矿物[47],故风化作用表现为0.同时土壤中的阳离子交换库由于盐基阳离子的沉降被一定的补充,从而可能缓冲酸沉降带来的酸化效应.
由于沉降量小,在土壤水和溪流水中浓度较低,Cl、Na+和K+对土壤和地表水的酸碱性贡献很小.
在以往研究中经常用Cl来指示土壤溶液中的水量动态和其他离子的源汇,但铁山坪站点前期观测表明,铁山坪森林站点的Cl并不平衡,在土壤表层存在相当大量的未知的汇[53].在本研究的模型参数校验过程中,表层土壤的Cl的汇高达输入的60%,地表水排泄区也存在大量的汇.由于Na+在铁山坪站点的土壤中表现出相对稳定的通量,因此在铁山坪的土壤水水量动态衡算中往往依据Na+的平衡[53].因此在模型中,两层土壤中Na+的源汇都设为0.K+在各单元都存在一定的汇,主要是由于植物的吸收作用.
图2中可以看出,在表层土壤水(S1)、下层土壤水(S2)和溪流水(SW)中,K+浓度的模型模拟结果与实际观测结果符合较好,85%以上的年份模拟结果与观测结果的相对偏差小于5%,表明表1表3中各参数适用于模拟这些离子的水化学趋势.
pH值和ANC是有效的酸化指标,其模型模拟结果与实测结果的比较如图2所示,除少部分年份有较大偏离以外,大部分模型模拟结果与实测结果的相对偏差在10%以内.
酸化强相关、酸化弱相关和酸化指标的模型模拟与实际观测的结果十分接近,说明本套参数能够较好地模拟铁山坪土壤和地表水化学过程,从而可以根据设置的历史和未来沉降及气候变化的序列对流域长期的土壤和地表水化学的影响进行预测.
本研究的MAGIC模型模拟硫酸根还原过程时设置硫还原通量为净还原量,不考虑硫氧化通量.同时对于氮动态模块,采用2014年的氮循环速率(硝化速率、反硝化速率、氮固持速率和氮矿化速率等)[72].设置表层NO3和NH4+保留的C/N范围为10~20,植物对氮的利用类型设为不吸收有机氮,上层和下层土壤中植物对NO3的吸收分别为39.8和60.2mmol/(m2·a).
“保持不变”情景O-1即为SO2、NOx和NH3的排放总量到2050年和2021年保持相同.模拟结果(图3)显示到2050年,SO42−当量浓度在土壤水(S1和S2)和地表水(SW)中均呈现上升趋势,平均值分别为1446,1423和940μeq/L,远高于1980年水平(1980年S1、S2和SW分别为1295,1357和711μeq/L).2021~2050年土壤水NO3当量浓度持续升高,到2050年分别达到了710和614μeq/L,远高于亚热带森林土壤水阈值(阈值为411μeq/L)[75-76].土壤水和地表水中Ca2+当量浓度不断增加,2021~2050年S1、S2和SW平均值达到1291,969和476μeq/L.土壤水和地表水中pH值和ANC在2050年之前呈现下降趋势,地表水pH值和ANC到2050年分别下降了0.05和58μeq/L,2021~2050年在地表水中二者平均值分别为4.43和−148μeq/L,远低于酸化指标临界值.
依据现行“十四五”规划中控制排放的政策(2021~2025年,到2025年SO2排放总量保持与2020年相同,NOx和NH3排放总量比2020年分别下降10%以上和8%),在“实际减排”S-1情景中设置SO2排放量到2050年与2021年保持一致,且NH3、NOx和Ca2+至2030年降至2021年的80%,至2050年降至2021年的70%,模拟结果见图3.
在2021~2028年,SO42−当量浓度在土壤水(S1和S2)和地表水(SW)中均呈现上升趋势,2029年后其保持稳定.2021~2050年S1(上层土壤水)、S2(下层土壤水)和SW中SO42−当量浓度平均值分别为1426,1414和938μeq/L,高于1980年水平.
地表水NO3当量浓度在2024年之前增加,后开始降低,到2030年降低至168μeq/L,已经低于亚热带森林地表水阈值(阈值为443μeq/L)[75-76].土壤水NO3当量浓度呈现持续下降趋势,至2050年S1和S2相对于2021年分别下降了18.5%和9.88%,2021~2050年S1和S2中NO3当量浓度平均值分别为573和545μeq/L,仍高于亚热带森林土壤水阈值(阈值为411μeq/L)[76].
2021~2028年期间Ca2+当量浓度在S1、S2和SW均呈现上升趋势,在2029年之后土壤水中Ca2+当量浓度保持稳定,2021~2050年在S1、S2和SW中平均值为1056,856和435μeq/L.2021~2024年间S2和SW中pH值呈下降趋势,2025~2050年呈上升趋势,分别上升了0.02和0.01.土壤水ANC(盐基阳离子当量浓度之和与强酸阴离子当量浓度之和的差,大于0μeq/L表明土壤不再继续酸化)在2021~2023年下降,之后开始上升,地表水ANC在2021~2026年下降,2027~2050年上升,2021~2050年S1、S2和SW中ANC平均值分别为−91.7,−315和−37.8μeq/L.
按照现行政策进行排放控制(“实际减排”S-1情景),直到2050年,SO42−当量浓度未恢复到1980年水平,土壤水NO3当量浓度仍低于阈值,pH值和ANC均低于酸化指标临界值,这表明S-1排放控制的情景并不能促进土壤酸化严重的亚热带森林恢复至安全水平.因此加强对SO2、NH3、NOx和Ca2+的排放控制,即在“严格减排”情景Y-1中设置SO2至2030年降到2021年的80%,至2050年降到70%,NH3、NOx和Ca2+沉降系数至2030年降到2021年的60%,至2050年降到40%,以降低土壤和地表水SO42−和NO3浓度,模拟结果见图3.
在Y-1情景中,2021~2024年间SO42−当量浓度在S1、S2和SW中均呈现上升趋势,2025年之后开始下降,到2030年分别下降至1156,1182和646μeq/L,已经恢复到了我国亚热带森林1980年的水平.
2021~2023年地表水NO3当量浓度呈现升高趋势,2024~2030年期间持续降低,至2030年降低至143μeq/L,已经低于亚热带森林地表水阈值(阈值为443μeq/L).土壤水NO3当量浓度呈现持续下降趋势,到2030年S1和S2中分别达到505和511μeq/L,到2050年分别达到447和482μeq/L,接近亚热带森林土壤水阈值(阈值为411μeq/L).
在2021~2025年,Ca2+当量浓度在S1、S2和SW中均呈现上升趋势,2026~2050年呈下降趋势,到2050年分别下降至563,568和278μeq/L.在2021~2024年,S2和SW中pH值呈下降趋势,2025~2050年呈上升趋势,分别上升了0.03和0.07.2021~2023年间土壤水ANC下降,之后开始上升,地表水ANC在2021~2025年下降,2026~2050年上升,到2050年SW中ANC达到60.3μeq/L,高于0μeq/L.
气候变暖可能会导致微生物和分子交换活动更剧烈,因此可能会影响亚热带森林土壤和地表水酸化的恢复.为阐明气候变暖对酸化恢复的影响,基于保持不变情景O-1、“实际减排”情景S-1和“严格减排”情景Y-1,设置了2021~2050年期间温度升高0.6℃的气候变暖情景O-2、S-2和Y-2,观察全球温度上升对酸化恢复的响应(图3).模拟结果表明各离子浓度和酸化指标均接近温度未上升之前的水平,变化幅度为0~8.28μeq/L,表明至2050年温度相对于2021年上升0.6℃对土壤和地表水化学的酸化恢复仅有微弱的正效益.
在“实际减排”情景S-1模拟结果中,地表水SO42−当量浓度直到2028年才开始下降(图3),但是铁山坪亚热带森林站点在2005年就观察到穿透水中SO42−当量浓度下降[76],这表明在S-1减排情景下(SO2排放量到2050年与2021年保持一致),铁山坪地表水中SO42−当量浓度的下降相对于氮硫沉降的下降滞后了23年左右.这种滞后现象与捷克共和国克鲁姆洛夫森林流域的研究结果一致,该地通过实测数据发现即使当地SO42−沉降已经下降了30年,但土壤仍是储存硫的净来源,人为酸沉降对酸化恢复的影响至少持续了30年[77].土壤硫源的释放被认为是森林集水区从以前大气高硫沉降中的滞后响应(图4),在我国亚热带森林以及欧洲和北美的温带森林中都发现类似的SO42−滞后现象[19-20,26,54,78-83].
地表水NO3当量浓度在2024年开始下降(“实际减排”情景S-1),相对于在2011年观察到的穿透水NO3当量浓度下降滞后了13年左右.地表水NO3的响应滞后归因于土壤NO3的淋溶较高.在高大气氮沉降背景下,微生物氮矿化与氮固定保持平衡[85].2010年铁山坪土壤固持速率和矿化速率分别为5.9和1.6g/(m2·a),随着大气中氮沉降的减少,到2014年,土壤固持速率和矿化速率分别为2.8和1.7g/(m2·a),氮固定迅速减少,同时升高的氮矿化会持续进行[9]图4).这就导致土壤NO3淋溶持续升高,从而使地表水NO3当量浓度的下降相对于氮沉降下降的滞后13年左右[76].
在“严格减排”情景Y-1的模拟结果中,到2024年地表水中的SO42−当量浓度开始出现下降,相对于穿透水中SO42−当量浓度的下降滞后了19年左右.地表水NO3当量浓度在2023年开始下降,相对于穿透水滞后了12年左右.与“实际减排”情景S-1相比,地表水SO42−当量浓度和NO3当量浓度分别提前4年和1年左右开始恢复,这表明控制力度会影响恢复滞后时间,力度越大,酸化恢复的滞后时间越短.
在S-1情景中,地表水pH值和ANC也出现了滞后恢复现象,二者分别在2024年和2027年左右开始缓慢增长,到2050年,土壤水和地表水pH和ANC仍低于酸化指标阈值,土壤酸化尚未恢复.当实施更严格的“严格减排”情景Y-1时,pH值持续上升,到2050年土壤水和地表水ANC已经恢复到0μeq/L以上,可以更快恢复亚热带森林严重酸化土壤.
我国高Ca2+沉降对土壤和地表水酸化具有重要缓冲作用,在北美和欧洲的温带森林流域中Ca2+沉降普遍为2~10kg/(hm2·a)[86],而我国Ca2+沉降较高,南方亚热带森林Ca2+沉降达到16~114kg/(hm2·a)[47],这是因为我国土壤沙尘、水泥生产和人为活动等颗粒物排放带来较高的盐基阳离子沉降,且Ca2+作为其中主要的盐基阳离子[87-89].2001~2021年铁山坪Ca2+在S1、S2和SW中分别为935,679和647μeq/L,尽管在S1、S2和SW中SO42−平均当量浓度分别高达992,1228和673μeq/L,NO3平均当量浓度分别高达716,691和163μeq/L,但是pH值和ANC却没有明显低于欧美国家[1,60,62,89].
在欧美温带森林的相关研究中发现气候变化是长期影响土壤氮循环的主要因素,综合分析欧洲180余项研究表明气候变化和森林管理策略将会主导未来森林氮过程的变化[90],但MAGIC模型结果表明气候变化对铁山坪亚热带森林影响很小,可能是由于欧美所研究的是温带森林,氮沉降水平低,土壤酸化不严重,但是铁山坪站点位于亚热带森林且土壤严重酸化,氮沉降量长期高于氮沉降临界负荷,表明在土壤严重酸化的亚热带森林中,氮沉降仍是土壤酸化的主要影响因素.
5.1 模拟结果说明了我国多污染物控制的难度和复杂性.我国亚热带森林地表水中SO42−和NO3恢复均存在10~25年的滞后响应,从而影响pH值和ANC,延迟酸化的恢复,因此在制定排放控制措施时,必须要考虑地表水的滞后响应对酸化恢复的影响.
5.2 MAGIC模拟结果表明按照现行“十四五”排放控制政策(2021~2025年,SO2排放量保持不变,NOx和NH3排放量比2020年分别下降10%以上和8%)进行减排,不足以使我国亚热带森林严重酸化土壤在2050年之前恢复.实施更加严格的“严格减排”情景(SO42−到2030年降至2021年的80%,到2050年降至2021年的70%,NH4+和NO3到2030年降至2021年的60%,到2050年降至2021年的40%),在考虑可能实施的大幅减少颗粒物排放政策(Ca2+到2030年降至2021年的60%,到2050年降至2021年的40%)以及地表水酸化响应滞后的情况下,可以在2050年之前使主要强酸性阴离子的浓度恢复到阈值以内,pH值持续上升,土壤和地表水ANC恢复到0μeq/L以上.
5.3 全球变暖对土壤酸化严重的亚热带森林的酸化恢复影响不显著.
  • 国家自然科学基金项目(42303061)
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2025年第45卷第5期
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  • 接收时间:2024-09-30
  • 首发时间:2026-03-18
  • 出版时间:2025-05-20
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  • 收稿日期:2024-09-30
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国家自然科学基金项目(42303061)
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    长安大学土地工程学院,陕西 西安 710054

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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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