基于多时相遥感数据的典型区生态格局变化分析

利用1985、2000、2013年遥感影像提取的土地覆盖数据,通过景观格局指数、动态度计算、转移矩阵等,分析1985—2013年我国典型地区各类型生态系统景观格局及其动态变化特征、生态系统相互转化时空变化特征等,揭示1985—2013年生态环境格局变化的特点和规律:一级分类生态系统综合变化率,赣江、闽江、白龙江和岷江上游流域分别为4.7%、3.9%、3.3%和1.7%,生态系统变化强度1985—2000年较缓,2000—2013年更剧烈。1985—2013年典型区生态系统的主要转化方向具有持续性和双向性特征,岷江、白龙江和赣江上游流域退耕还林还草政策效果明显,出现较高比例的耕地转为森林和草地;面积占67.4%生态系统类型变化与耕地生态系统和人工表面生态系统变化有关;生态系统变化具有明显的区域差异,生态变化主要表现为沿主要河流谷地的线状延伸,主要城镇居民点附近生态系统类型变化较为突出,人类活动是典型地区生态系统类型格局变化的主要驱动力;典型区尤其是敏感区应加大退耕还林还草政策,减少人类经济活动,降低洪水泥石流灾害发生的概率和程度。     

面向管理的海洋修复工程生态绩效考核指标体系构建研究

目前国内关于海洋修复工程的考核研究多为修复工程的生态效果考核,极少包含成本效益考核,以致无法比较和分析不同类型、不同规模的海洋修复工程的生态绩效,不利于海洋修复工程的考核和验收管理。根据当前自然资源部实施“蓝色海湾”等整治修复工程的检查验收管理的新需求,本文构建了基于海洋生态系统健康恢复、生态系统服务价值增长和单位成本生态效益等理论的海洋修复工程生态绩效考核指标体系。此外,本文以温州海域2014—2016年海洋修复的生态绩效的考核为例,结合考核结果以及直接相关性、独立性、可测量性和业务化等指标选取原则,对指标体系进行了改进。结果表明本指标体系可以在海洋修复工程的检查验收中被用于生态绩效考核,管理部门可根据海洋修复工程的类型和管理需求,有选择性地采纳部分或全部指标进行考核。     

Developing shared qualitative models for complex systems

Understanding complex systems is essential to ensure their conservation and effective management. Models commonly support understanding of complex ecological systems and, by extension, their conservation. Modeling, however, is largely a social process constrained by individuals’ mental models (i.e., a small-scale internal model of how a part of the world works based on knowledge, experience, values, beliefs, and assumptions) and system complexity. To account for both system complexity and the diversity of knowledge of complex systems, we devised a novel way to develop a shared qualitative complex system model. We disaggregated a system (carbonate coral reefs) into smaller subsystem modules that each represented a functioning unit, about which an individual is likely to have more comprehensive knowledge. This modular approach allowed us to elicit an individual mental model of a defined subsystem for which the individuals had a higher level of confidence in their knowledge of the relationships between variables. The challenge then was to bring these subsystem models together to form a complete, shared model of the entire system, which we attempted through 4 phases: develop the system framework and subsystem modules; develop the individual mental model elicitation methods; elicit the mental models; and identify and isolate differences for exploration and identify similarities to cocreate a shared qualitative model. The shared qualitative model provides opportunities to develop a quantitative model to understand and predict complex system change.     

基于地理探测器的土壤重金属影响因子分析及其污染风险评价

地理探测器能快速定量化揭示驱动重金属含量影响因素的强度,这对于重金属空间预测模型构建变量的确定和土壤污染修复措施的精准实施具有重要意义。利用地理探测器模型,对5种土壤重金属元素Cu、Zn、Pb、Cr、Ni的空间分布和11种环境因子的交互作用进行定量评估,通过单因子指数法进行重庆市土壤重金属污染风险评价。结果表明:研究区内土壤Cu、Zn、Cr和Pb的平均含量是重庆市土壤背景值的1.3—1.4倍,Ni含量低于背景值;其中Cu、Pb达到重度污染水平,其余3种重金属为中度或轻度污染水平。5种重金属元素中Cu和Pb为高度变异(变异系数为0.57、0.4),Zn、Cr和Ni为中等变异(变异系数为0.22—0.29),且各重金属元素之间呈显著正相关性,表明研究区重金属富集受人为干扰影响较大,且污染具有复合性或同源性。地理探测器的因子探测发现高程、坡度和土壤类型对5种土壤重金属含量的解释力最显著,说明地势和土壤类型是土壤重金属含量分布差异的最主要影响因素。交互作用探测发现,高程与其他因子交互作用是重金属空间分异的主导因素,气候条件和土壤类型也是重要影响因子。土壤重金属空间分布是多种因素共同作用的结果,而高程、坡度和土壤类型具有较强的解释力,这些因子可作为土壤重金属含量空间预测模型的辅助变量,也可促进重金属污染治理措施的靶向实施。     

砷尾矿污染土壤的细菌群落结构多样性及其相关环境影响因子分析

以湖南石门雄黄尾矿污染土壤为对象,研究纵向不同深度、横向不同距离土样中的重金属污染程度以及细菌群落结构变化规律,查明砷污染土壤的核心微生物组成并将其与土壤理化指标进行共存网络图分析。结果表明:该尾矿区的土壤各项重金属指标严重超标,尤以铅(626.54 mg·kg^?1,Ei=105.48)、砷(1804.75 mg·kg^?1,Ei=565.75)、镉(31.46 mg·kg^?1,Ei=7491.5)的生态危害性最强;土壤采样深度与重金属综合潜在生态风险指数(RI)呈显著正相关(r=0.79,P=0.000),而横向样品中RI与采样距离显著负相关(r=?0.85,P=0.000)。在污染土壤中,变形杆菌门(Proteobacteria,54.35%±17.16%)和放线杆菌门(Actinobacteria,22.39%±10.64%)占主导地位,属层级中假单孢杆菌属(Pseudomonas,16.47%±11.84%)、不动杆菌属(Acinetobacter,8.07%±7.11%)以及硫酸状杆菌属(Acidithiobacillus,7.53%±14.68%)相对丰度较高;而26个共享类群占据了该尾矿污染土壤中微生物群落总平均相对丰度90%以上,尽管不同属的具体相对丰度在不同样品间的分布趋势差异较大。纵剖采集的污染土样中,铁原体属(Ferroplasma)、硫酸状杆菌属(Acidithiobacillus)、硫化杆菌属(Sulfobacillus)、乳杆菌属(Lactobacillus)和硝化螺旋菌属(Nitrospira)占优势,与理化的共存网络图分析(相关系数|r|≥0.6,P<0.05)显示部分类群与亚铁、游离态砷和镉成显著正相关,而与pH成显著负相关;横向采集的土样中,以嗜酸菌属(Acidiphilium)、假单孢杆菌属(Pseudomonas)、棒状杆菌属(Corynebacterium)、硫杆菌属(Thiobacillus)等为主,部分类群与总砷、铅成显著正相关,而与结合态或包蔽型砷成显著负相关。综上,该研究不仅对目标区域尾矿不同重金属污染程度进行了分析,同时探讨了污染土壤中核心响应类群的组成多样性,为筛选潜在重金属抗性菌群或工程菌群提供理论基础。     

Five-wheel framework for system-based monitoring of heavy metal pollution in rivers

Sectorial approach for monitoring heavy metal pollution in rivers has failed to report realistic pollution status and associated ecological and human health risks. The increasing spread of heavy metals from different sources and emerging risks to human and environmental health call for reexamining heavy metal pollution monitoring frameworks. Also, the sources, spread, and load of heavy metals in the environment have changed significantly over time, requiring consequent modification in the monitoring frameworks. Therefore, studies on heavy metal monitoring in rivers conducted in the last decade were evaluated for experimental designs, research frameworks, and data presentations. Most studies (∼99%) (i) lacked inclusiveness of all environmental compartments; (ii) focused on “one pollutant – one/two compartment” or sometimes “one pollutant – one compartment – one effect” approach; and (iii) remained “data-rich but information poor.” An ecological approach with integrative system thinking is proposed to develop a holistic approach for monitoring river pollution. It is visualized that heavy metal monitoring, risk analyses, and water management must incorporate tracking pollutants in different environmental compartments of a river (water, sediment, and floodplain/bank soil) and consider correlating it with riverbank land use. The systems-based pollution monitoring and assessment studies will reveal the critical factors that drive heavy metals pollutant movement in ecosystems and associated potential risks to the environment, wildlife, and humans. Also, water quality and pollution indexing tools would help better communicate complex pollution data and associated risks among all stakeholders. Therefore, integrating systems approaches in scientific- and policy-based tools would help sustainably manage the health of rivers, wildlife, and humans.     

Difference in the net value of ecological services between natural and artificial forests in China

Land degradation is a global problem that seriously threatens human society. However, in China and elsewhere, ecological restoration still largely relies on a traditional approach that focuses only on ecological factors and ignores socioeconomic factors. To improve the effectiveness of ecological restoration and maximize its economic and ecological benefits, a more efficient approach is needed that provides support for policy development and land management and thereby promotes environmental conservation. We devised a framework for assessing the value of ecosystem services that remain after subtracting costs, such as the opportunity costs, costs of forest protection, and costs for the people who are affected by the program; that is, the net value of ecosystem services (NVES). To understand the difference between the value of a resource and the net value of the ecosystem service it provides, we used data on VES, timber sales, and afforestation costs from China's massive national afforestation programs to calculate the net value of forest ecosystem services in China. Accounting for the abovementioned costs revealed an NVES of ¥6.1 × 10¹² for forests in 2014, which was 35.9% less than the value calculated without accounting for costs. As a result, the NVES associated with afforestation was 55.9% less than the NVES of natural forests. In some regions, NVES was negative because of the huge costs of human-made plantations, high evapotranspiration rates (thus, high water opportunity costs), and low forest survival rates. To maximize the ecological benefits of conservation, it is necessary to account for as many costs as possible so that management decisions can be based on NVES, thereby helping managers choose projects that maximize both economic and ecological benefits.     

南方典型水源地及水产养殖区抗生素的复合污染特征及生态风险

抗生素作为生长促进剂和疾病预防控制药物在水产养殖领域得到广泛应用,目前在许多环境水体中检测到不同类型的抗生素。环境中抗生素的残留问题也是目前环境研究的热点问题之一。本研究选择南方某市8个水源地和5个典型水产养殖区作为研究对象,采用固相萃取、高效液相色谱串联三重四级杆质谱联用仪方法,调查了32种常用抗生素在水体中的含量水平和空间分布特征,揭示了抗生素的来源,并对其生态风险进行了评价。水源地共检出12种抗生素,浓度范围为0.12～44.6 ng·L~(-1),以磺胺甲噁唑含量最高;水产养殖区检出14种抗生素,浓度范围为0.95～716 ng·L~(-1),以氯四环素检出浓度最高。整体上水产养殖区抗生素的浓度高于水源地。抗生素浓度与环境因子的冗余分析表明,水产养殖和生活污水排放是水体中抗生素的主要来源。对检出的13种抗生素进行生态风险评价,单一抗生素而言,环丙沙星、氧氟沙星、磺胺嘧啶、氯四环素和脱水红霉素的风险商值大于0.01而小于0.1,表现为低风险。总抗生素风险商值加和在大部分水源地大于0.01而小于0.1,表现为低风险;总抗生素风险商值加和在2个水产养殖区大于0.1,表现为中等风险,水产养殖区抗生素的长期生态风险应该引起关注。     

大数据解析技术在大气环境监测中的应用研究

针对近年来新兴的大数据及挖掘、分析技术,对大数据解析技术在环境科学研究中的应用进行了分析和探析。以城市局部地区大气细颗粒污染物PM2.5浓度计算为例,筛选了PM2.5浓度历史数据、气象条件、交通状况、人群活动情况、网格道路状况等数据为影响特征量,分析了用神经网络法进行大数据解析和推演的原理和数学过程,是用数学、计算机、统计等方法研究解决多元、非线性复杂环境问题的一种新的探索。     

快速溶剂萃取-分子筛脱水净化-气质联用法测土壤有机氯农药

用丙酮和正己烷(体积比为1∶1)作溶剂,在100℃和1 500 psi的压力下,用快速溶剂萃取仪从土壤中萃取出有机氯农药,用分子筛进行脱水和净化,GC-MS进行分析,对土壤中有机氯农药进行测定。方法的检出限为0.000 4~0.000 7 mg/kg,曲线的相关系数在0.998以上。对进口标准土进行测定,回收率为77.1%~105.2%,相对误差为-22.9%~5.2%,测定结果在证书允许误差范围内。用硅藻土制成高、中、低3个浓度级别的加标回收和精密度实验,加标回收率为84.3%~101.5%,相对标准偏差为0.6%~11.4%,表明方法准确可靠,方法用于实际土壤测定时发现土壤有不同程度的有机氯农药污染。