中亚热带韶山森林的大气沉降特征及对酸沉降的生态响应研究
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摘要
近几十年来,随着经济迅猛发展导致的燃料的大量消耗,使我国的酸沉降问题愈演愈烈,我国南方已成为继欧美之后的第三大酸沉降区,而且呈现不断发展的趋势,而地处中南的湖南更是受到酸沉降的严重污染,酸雨频率长期居高不下。森林生态系统为酸沉降提供了巨大的接受面,过量的酸沉降可能会对森林生态系统产生严重的影响,导致土壤酸化,甚至使森林衰退。那么,研究森林生态系统对酸沉降的生态响应过程并及时提出酸沉降的控制措施,对防止森林衰退显得至关重要。因此,本论文选择具有典型亚热带特征的湖南韶山森林为研究对象,研究了韶山的大气沉降特征和酸沉降下韶山森林生态系统的生态响应过程,可为我国乃至世界酸沉降下的森林生态响应研究积累宝贵资料,同时为韶山的酸沉降控制和森林资源管理提供决策参考,具有一定的实践意义。
研究课题组2001年~2004年在韶山针阔叶混交林设置了4个标准样方进行气象、气体和气溶胶、降雨、林内穿透水、凋落物和地表径流的监测,研究了韶山大气干湿沉降的典型特征,采用正定矩阵方法(PMF)对韶山降雨和气溶胶的离子来源进行了解析,深入分析了韶山森林生态系统中盐基离子、营养氮及溶解性有机碳的时空动态变化规律,探讨了韶山森林冠层、亚冠层和凋落物层对酸沉降的酸化缓冲影响作用机制,并采用收支平衡理论评估了韶山森林生态系统的氮饱和状况,采用稳态模型计算了韶山森林的氮沉降临界负荷,最后尝试采用BP神经网络对韶山降水和地表水酸度进行了预测,研究取得了如下一些成果:(1)韶山降水酸性较强,年均pH值均低于5.0,酸雨频率非常高,普遍达到90%以上,大气降水中离子基本平衡,SO42-/NO3-比值在1.72-5.65之间,韶山降水仍属于硫酸型降水,该比值呈逐渐降低的趋势,表明氮氧化物的贡献在增强。采用沉降速率法和净穿透通量法估算韶山大气中硫的干沉降通量月均值分别为188 mg m~(-2) month~(-1)和105.25 mg m~(-2) month~(-1)。采用正定矩阵方法(PMF)对韶山降雨和气溶胶的离子来源解析结果表明土壤尘是韶山气溶胶和降水化学成分最主要的来源,其次是农业源,另外,交通排放源和高空远距离输送对降水成分来源也有一定的贡献。(2)韶山森林冠层及亚冠层对酸沉降均表现出了良好的缓冲作用,植被对酸性降水比较敏感,冠层的缓冲作用较亚冠层强烈。韶山森林冠层和亚冠层盐基淋溶总通量分别为259.84 meq m~(-2) a~(-1)和63.3 meq m~(-2) a~(-1),其对H~+的吸收通量分别为165.9 meq m~(-2) a~(-1)和40.9 meq m~(-2) a~(-1),冠层和亚冠层盐基淋溶总通量要高于其对H~+的吸收通量,这主要是与韶山森林的弱酸滤出消耗盐基阳离子有关。韶山森林凋落物淋滤液pH值均高于表层土壤pH值均值,但由于凋落物中盐基阳离子不足以抵消凋落物分解产生的有机酸类物质的酸化作用,在春、夏、秋季并不能降低雨水酸度,而是增加其酸度,但在冬季是一个较强的酸缓冲系统。(3)韶山森林冠层、亚冠层和凋落物层DOC浓度对降雨酸度并不敏感,亚冠层对DOC表现出一定的吸附或吸收作用,韶山森林DOC的积累主要出现在凋落物层,年总净增量达到11.08 g m~(-2) a~(-1)。地表径流中DOC浓度与其pH值呈正的相关关系,但通过地表径流输出的DOC浓度很低,输出通量亦较少。(4)韶山森林无机氮输入以NH_4~+-N为主,年均总输入量达到3.136 g m~(-2) a~(-1),但尚未超过氮沉降临界负荷估算值,地表径流中无机氮输出以NO3--N为主,输出通量平均值为0.22 g m~(-2) a~(-1),仅占输入总无机氮的7%,即有超过90%以上的无机氮被截留,尽管韶山森林有高氮的输入,但是其生态系统还没有达到氮饱和状态。(5)采用BP神经网络模型,对降水和地表径流的酸度进行了模拟预测,选择SO_4~(2-)、NO_3~-、Ca~(2+)、NH_4~+的浓度与组合因子(Ca~(2+)+NH_4~+)/(SO_4~(2-)+NO_3~-)作为输入,以降雨pH值作为输出,预测值的绝对误差均在±0.5个pH值单位之内,而选择降雨酸度和前一次地表径流酸度作为输入,以地表径流的pH值作为输出,预测值的绝对误差均在±0.15个pH值单位之内,表明BP神经网络模型精度较高,可以用来粗略预测韶山降雨和地表径流的酸度。
In recent decades, the problem of acid deposition is intensified in China due to the consumption of large quantities of fuel as a result of rapid economic development, and the South China has become the third largest acid deposition zone after Europe and America, also showed the trend of continuous development, and Hunan province located at Central-south China is even more serious, acid rain frequency is long-term high. Forest ecosystems provides a great receptor surface to acid deposition, excess acid deposition may be affected it seriously, leading to soil acidification, forest decline and even so. So, study of acid deposition on forest ecosystems, the ecological response to acid deposition process and propose the controlling method to prevent forest decline is crucial. Therefore, this paper select the typical characteristics of Shaoshan subtropical forest as research object, studying the characteristics of atmospheric deposition, the effect to Shaoshan forest ecosystems by acid deposition and feedback mechanism, can accumulate a useful data of forest ecological responses under acid deposition for China even for the world, and at the same time, can provided a practical application consultation for acid deposition controlling and forest resource management in Shaoshan.
Four criteria plots were set in Shaoshan mixed forest, where acid deposition、rainfall below canopy and stream water were monitored from January 2001 to December 2004. We studied the characteristics of atmospheric dry and wet deposition of Shaoshan, analyzed the ion source of bulk precipitation and aerosol by employing PMF method, also analyzed the temporal and spatial dynamic change characteristics of base cations, nutrient nitrogen and dissolved organic carbon in forest ecosystems, then we discussed the acid buffering effect to acid deposition from canopy, sub-canopy and litterfall layer. We evaluated the nitrogen saturation condition by using in-out budget theory and calculated the critical loads of nitrogen by simple mass balance (SMB) method, and used BP neural network to forecast the acid of precipitation and stream water. The research results showed that: (1) The precipitation acidity in Shaoshan is strong, the annual average pH values is below 5.0, acid rain frequency is very high, generally above 90%, the basic ions in precipitation is poised, the ratio of SO42-/NO3- is between 1.72 to 5.65, so precipitation type is still a sulfuric acid precipitation, but the ratio is gradually decreased, indicating that the contribution of nitrogen oxides is increased. The mean dry deposition flux of sulfur is 188 mg m~(-2) month~(-1) and 105.25 mg m~(-2) month~(-1) estimating by sedimentation rate method and the net flux method separately. The PMF analytical method of rainfall and aerosol in Shaoshan show that the most important source is soil dust, followed by agricultural sources, and some contribution is just come from traffic sources and long-range transport. (2) The canopy and sub-canopy in Shaoshan forest have shown a good buffering effect, vegetation is sensitive to acid rain, the buffering effect of the canopy is stronger than sub-canopy. The mean net leaching fluxes of base cations is 259.84 meq m~(-2) a~(-1) and 63.3 meq m~(-2) a~(-1) from canopy and sub-canopy respectively, which is higher than the H~+ absorbing fluxes, with 165.9 meq m~(-2) a~(-1) and 40.9 meq m~(-2) a~(-1), because of weak acid leaching. The value of pH in litterfall leaching is higher than the average pH of surface soil layer, but because the base cations leaching from litterfall are not enough to counteract the organic acid produced by litterfall decomposition in spring, summer and autumn, so the pH of litterfall leaching is going down in these seasons except in winter, and litterfall in Shaoshan forest is a fine acid-buffering system in winter. (3) There is no evident influence between DOC concentration and bulk precipitation acidity, whether in canopy or in litterfall leaching. Some absorbing phenomenon of DOC has been found in sub-canopy, and accumulation of DOC mainly occurred in litterfall layer in Shaoshan forest, with net DOC fluxes of 11.08 g m~(-2) a~(-1). A posistive relationship between DOC concentration and pH in stream water is also found, but the output DOC concentration and flux by stream water is very low. (4)The input of inorganic nitrogen in Shaoshan is major in NH_4~+-N, mean fluxes of inorganic nitrogen input is 3.136 g m~(-2) a~(-1), but luckily is that it is lower than critical loads of nitrogen. The output of inorganic nitrogen in Shaoshan is major in NO3--N, mean fluxes of inorganic nitrogen output is 0.22 g m~(-2) a~(-1), just as 7% of inorganic nitrogen input, that to say, more than 90% of inorganic nitrogen is reserved in forest. Though high inorganic nitrogen emission and deposition have been found, Shaoshan forest is nitrogen-limited and far from nitrogen saturation. (5) We simulated the rainfall and stream water acidity using BP neural network model, found that when we selected the concentrations of SO42-, NO3-, Ca2+, NH_4~+ and combination of factors (Ca2++NH_4~+)/(SO42-+NO3-) as input, and pH value of precipitation as output, the absolute forecast errors are within±0.4 pH units. We selected precipitation acidity and the acidity of the previous stream water as input, and selected the pH value of stream water as output, the predicted absolute errors are within±0.1 pH units, suggesting that the BP neural network model has high precision and can be used to predict acidity of rainfall and stream water in Shaoshan.
研究课题组2001年~2004年在韶山针阔叶混交林设置了4个标准样方进行气象、气体和气溶胶、降雨、林内穿透水、凋落物和地表径流的监测,研究了韶山大气干湿沉降的典型特征,采用正定矩阵方法(PMF)对韶山降雨和气溶胶的离子来源进行了解析,深入分析了韶山森林生态系统中盐基离子、营养氮及溶解性有机碳的时空动态变化规律,探讨了韶山森林冠层、亚冠层和凋落物层对酸沉降的酸化缓冲影响作用机制,并采用收支平衡理论评估了韶山森林生态系统的氮饱和状况,采用稳态模型计算了韶山森林的氮沉降临界负荷,最后尝试采用BP神经网络对韶山降水和地表水酸度进行了预测,研究取得了如下一些成果:(1)韶山降水酸性较强,年均pH值均低于5.0,酸雨频率非常高,普遍达到90%以上,大气降水中离子基本平衡,SO42-/NO3-比值在1.72-5.65之间,韶山降水仍属于硫酸型降水,该比值呈逐渐降低的趋势,表明氮氧化物的贡献在增强。采用沉降速率法和净穿透通量法估算韶山大气中硫的干沉降通量月均值分别为188 mg m~(-2) month~(-1)和105.25 mg m~(-2) month~(-1)。采用正定矩阵方法(PMF)对韶山降雨和气溶胶的离子来源解析结果表明土壤尘是韶山气溶胶和降水化学成分最主要的来源,其次是农业源,另外,交通排放源和高空远距离输送对降水成分来源也有一定的贡献。(2)韶山森林冠层及亚冠层对酸沉降均表现出了良好的缓冲作用,植被对酸性降水比较敏感,冠层的缓冲作用较亚冠层强烈。韶山森林冠层和亚冠层盐基淋溶总通量分别为259.84 meq m~(-2) a~(-1)和63.3 meq m~(-2) a~(-1),其对H~+的吸收通量分别为165.9 meq m~(-2) a~(-1)和40.9 meq m~(-2) a~(-1),冠层和亚冠层盐基淋溶总通量要高于其对H~+的吸收通量,这主要是与韶山森林的弱酸滤出消耗盐基阳离子有关。韶山森林凋落物淋滤液pH值均高于表层土壤pH值均值,但由于凋落物中盐基阳离子不足以抵消凋落物分解产生的有机酸类物质的酸化作用,在春、夏、秋季并不能降低雨水酸度,而是增加其酸度,但在冬季是一个较强的酸缓冲系统。(3)韶山森林冠层、亚冠层和凋落物层DOC浓度对降雨酸度并不敏感,亚冠层对DOC表现出一定的吸附或吸收作用,韶山森林DOC的积累主要出现在凋落物层,年总净增量达到11.08 g m~(-2) a~(-1)。地表径流中DOC浓度与其pH值呈正的相关关系,但通过地表径流输出的DOC浓度很低,输出通量亦较少。(4)韶山森林无机氮输入以NH_4~+-N为主,年均总输入量达到3.136 g m~(-2) a~(-1),但尚未超过氮沉降临界负荷估算值,地表径流中无机氮输出以NO3--N为主,输出通量平均值为0.22 g m~(-2) a~(-1),仅占输入总无机氮的7%,即有超过90%以上的无机氮被截留,尽管韶山森林有高氮的输入,但是其生态系统还没有达到氮饱和状态。(5)采用BP神经网络模型,对降水和地表径流的酸度进行了模拟预测,选择SO_4~(2-)、NO_3~-、Ca~(2+)、NH_4~+的浓度与组合因子(Ca~(2+)+NH_4~+)/(SO_4~(2-)+NO_3~-)作为输入,以降雨pH值作为输出,预测值的绝对误差均在±0.5个pH值单位之内,而选择降雨酸度和前一次地表径流酸度作为输入,以地表径流的pH值作为输出,预测值的绝对误差均在±0.15个pH值单位之内,表明BP神经网络模型精度较高,可以用来粗略预测韶山降雨和地表径流的酸度。
In recent decades, the problem of acid deposition is intensified in China due to the consumption of large quantities of fuel as a result of rapid economic development, and the South China has become the third largest acid deposition zone after Europe and America, also showed the trend of continuous development, and Hunan province located at Central-south China is even more serious, acid rain frequency is long-term high. Forest ecosystems provides a great receptor surface to acid deposition, excess acid deposition may be affected it seriously, leading to soil acidification, forest decline and even so. So, study of acid deposition on forest ecosystems, the ecological response to acid deposition process and propose the controlling method to prevent forest decline is crucial. Therefore, this paper select the typical characteristics of Shaoshan subtropical forest as research object, studying the characteristics of atmospheric deposition, the effect to Shaoshan forest ecosystems by acid deposition and feedback mechanism, can accumulate a useful data of forest ecological responses under acid deposition for China even for the world, and at the same time, can provided a practical application consultation for acid deposition controlling and forest resource management in Shaoshan.
Four criteria plots were set in Shaoshan mixed forest, where acid deposition、rainfall below canopy and stream water were monitored from January 2001 to December 2004. We studied the characteristics of atmospheric dry and wet deposition of Shaoshan, analyzed the ion source of bulk precipitation and aerosol by employing PMF method, also analyzed the temporal and spatial dynamic change characteristics of base cations, nutrient nitrogen and dissolved organic carbon in forest ecosystems, then we discussed the acid buffering effect to acid deposition from canopy, sub-canopy and litterfall layer. We evaluated the nitrogen saturation condition by using in-out budget theory and calculated the critical loads of nitrogen by simple mass balance (SMB) method, and used BP neural network to forecast the acid of precipitation and stream water. The research results showed that: (1) The precipitation acidity in Shaoshan is strong, the annual average pH values is below 5.0, acid rain frequency is very high, generally above 90%, the basic ions in precipitation is poised, the ratio of SO42-/NO3- is between 1.72 to 5.65, so precipitation type is still a sulfuric acid precipitation, but the ratio is gradually decreased, indicating that the contribution of nitrogen oxides is increased. The mean dry deposition flux of sulfur is 188 mg m~(-2) month~(-1) and 105.25 mg m~(-2) month~(-1) estimating by sedimentation rate method and the net flux method separately. The PMF analytical method of rainfall and aerosol in Shaoshan show that the most important source is soil dust, followed by agricultural sources, and some contribution is just come from traffic sources and long-range transport. (2) The canopy and sub-canopy in Shaoshan forest have shown a good buffering effect, vegetation is sensitive to acid rain, the buffering effect of the canopy is stronger than sub-canopy. The mean net leaching fluxes of base cations is 259.84 meq m~(-2) a~(-1) and 63.3 meq m~(-2) a~(-1) from canopy and sub-canopy respectively, which is higher than the H~+ absorbing fluxes, with 165.9 meq m~(-2) a~(-1) and 40.9 meq m~(-2) a~(-1), because of weak acid leaching. The value of pH in litterfall leaching is higher than the average pH of surface soil layer, but because the base cations leaching from litterfall are not enough to counteract the organic acid produced by litterfall decomposition in spring, summer and autumn, so the pH of litterfall leaching is going down in these seasons except in winter, and litterfall in Shaoshan forest is a fine acid-buffering system in winter. (3) There is no evident influence between DOC concentration and bulk precipitation acidity, whether in canopy or in litterfall leaching. Some absorbing phenomenon of DOC has been found in sub-canopy, and accumulation of DOC mainly occurred in litterfall layer in Shaoshan forest, with net DOC fluxes of 11.08 g m~(-2) a~(-1). A posistive relationship between DOC concentration and pH in stream water is also found, but the output DOC concentration and flux by stream water is very low. (4)The input of inorganic nitrogen in Shaoshan is major in NH_4~+-N, mean fluxes of inorganic nitrogen input is 3.136 g m~(-2) a~(-1), but luckily is that it is lower than critical loads of nitrogen. The output of inorganic nitrogen in Shaoshan is major in NO3--N, mean fluxes of inorganic nitrogen output is 0.22 g m~(-2) a~(-1), just as 7% of inorganic nitrogen input, that to say, more than 90% of inorganic nitrogen is reserved in forest. Though high inorganic nitrogen emission and deposition have been found, Shaoshan forest is nitrogen-limited and far from nitrogen saturation. (5) We simulated the rainfall and stream water acidity using BP neural network model, found that when we selected the concentrations of SO42-, NO3-, Ca2+, NH_4~+ and combination of factors (Ca2++NH_4~+)/(SO42-+NO3-) as input, and pH value of precipitation as output, the absolute forecast errors are within±0.4 pH units. We selected precipitation acidity and the acidity of the previous stream water as input, and selected the pH value of stream water as output, the predicted absolute errors are within±0.1 pH units, suggesting that the BP neural network model has high precision and can be used to predict acidity of rainfall and stream water in Shaoshan.
引文
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