湿地甲烷排放估算模型的研究进展

湿地是大气甲烷（CH₄）的主要排放源,大气甲烷排放量的日益增加,给全球气候变化带来很大影响,准确估算湿地甲烷的排放量意义重大。目前湿地甲烷排放量估算模型主要有经验模型、机理过程模型和遥感参数模型,通过对三种模型的发展过程、应用状况及优缺点的对比分析和总结,分析了湿地甲烷排放、监测过程及模型估算方面的不确定性,指出了湿地甲烷排放估算的发展趋势,提出遥感参数模型将是湿地甲烷排放估算模型今后的发展方向。     

Using empirical models of species colonization under multiple threatening processes to identify complementary threat‐mitigation strategies

Approaches to prioritize conservation actions are gaining popularity. However, limited empirical evidence exists on which species might benefit most from threat mitigation and on what combination of threats, if mitigated simultaneously, would result in the best outcomes for biodiversity. We devised a way to prioritize threat mitigation at a regional scale with empirical evidence based on predicted changes to population dynamics—information that is lacking in most threat‐management prioritization frameworks that rely on expert elicitation. We used dynamic occupancy models to investigate the effects of multiple threats (tree cover, grazing, and presence of an hyperaggressive competitor, the Noisy Miner (Manorina melanocephala) on bird‐population dynamics in an endangered woodland community in southeastern Australia. The 3 threatening processes had different effects on different species. We used predicted patch‐colonization probabilities to estimate the benefit to each species of removing one or more threats. We then determined the complementary set of threat‐mitigation strategies that maximized colonization of all species while ensuring that redundant actions with little benefit were avoided. The single action that resulted in the highest colonization was increasing tree cover, which increased patch colonization by 5% and 11% on average across all species and for declining species, respectively. Combining Noisy Miner control with increasing tree cover increased species colonization by 10% and 19% on average for all species and for declining species respectively, and was a higher priority than changing grazing regimes. Guidance for prioritizing threat mitigation is critical in the face of cumulative threatening processes. By incorporating population dynamics in prioritization of threat management, our approach helps ensure funding is not wasted on ineffective management programs that target the wrong threats or species.     

The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple‐use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders’ willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders’ decisions to engage in management. To test the utility of our best‐supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost‐effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost‐effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost‐efficient and effective manner.     

我国城镇化进程中碳排放影响因素的实证研究

利用1980—2013年间的城镇人口与碳排放数据,选取城镇化进程中影响碳排放的城镇化水平、城镇建设用地面积、第三产业增加值、人均可支配收入、城镇人均绿化面积等因素,实证分析了城镇化对我国碳排放的影响程度,并利用格兰杰夫因果关系与误差修正模型分析了二者的因果关系与时间效应。最后,从降低工业碳排放、加快低碳城镇化试点建设、推进低碳社区发展,促进新能源发展等方面提出了相关建议。     

兰州市废旧手机产生量预测分析研究

手机普及率日益提高,随之产生的废弃量也日益增多,分别采用时间梯度模型、市场供给A模型、斯坦福(stanford)模型对兰州市2013年-2022年废旧手机的产生量进行了预测.结果显示,不同模型预测结果之间存在较大差异,市场供给A模型和斯坦福(Stanford)模型的预测结果比较接近,均大于时间梯度模型的预测结果,三种模型预测的兰州市2022年废旧手机的产生量分别为40.40万、46.67万和21.04万部.总体上看,兰州市废旧手机的产生量增长趋势较快.     

ABC算法优化SVR的磨损故障预测模型

目的为了提高故障预测的精度,针对支持向量回归SVR(Support vector machine for regression,SVR)参数选择困难的问题,提出一种采用人工蜂群(artificial bee colony,ABC)算法优化支持向量回归(SVR)的故障预测模型(ABC-SVR)。方法该模型先对样本数据进行重构,然后将故障预测误差(适应度)作为优化目标,通过ABC算法寻优找到最优的SVR参数,建立故障预测模型。最后通过实例仿真验证模型的优越性。结果采用ABC算法优化的SVR故障预测模型进行时间序列预测,能够较好地跟踪发动机滑油金属元素浓度的变化过程,并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题,能够更加准确地表现故障变化规律,提高了故障预测精度。     

厌氧-准好氧联合型生物反应器填埋场产气规律的研究

通过将厌氧型生物反应器填埋场（ANBL）和准好氧矿化垃圾生物反应床（SAARB）串联,组成新型的厌氧-准好氧联合型生物反应器填埋场（AN-SABL）,研究其产气速率、产气量以及产气组分的变化规律,以期为填埋气体的收集、利用和处理提供理论依据.实验表明,AN-SABL中的厌氧填埋单元的产气受到了抑制,其中ANBL2号单元和ANBL3号单元的产气率分别为49 L.kg-1和39 L.kg-1,仅占ANBL1号的94.2%和75.0%,但提高回灌频率,能促进厌氧填埋单元的产气,其甲烷含量最大值可达到62.67%;ANBL夏季产气速率和产气量明显高于冬季,并以12 h为周期交替出现产气高峰;此外,AN-SABL能够促进其厌氧单元的硝化和反硝化作用,N2O的含量受季节和填埋场类型影响显著,其变化范围在0.001 7%～4.017 9%之间.ANBL的累积产气量在初始调整阶段呈对数增长,过渡酸化阶段呈线性增长,酸化产甲烷阶段呈指数增长.     

HSPF水文水质模型应用研究综述

HSPF(Hydrological Simulation Program-Fortran)模型采用FORTRAN语言编写,以Stanford水文模型为基础,能够综合模拟径流、土壤流失、污染物传输、河道水力等过程,并大量应用于气候变化与土地利用变化的流域水环境效应情景模拟.该模型是半分布式水文水质模型的优秀代表,在国外得到广泛的应用.HSPF模型包括PERLND、IMPLND与RCHRES等3个主要模块,分别实现对透水地段、不透水地段与地表水体的水文水质模拟.总体来看,HSPF模型在国外水文、水质过程模拟,以及涉及气候变化和土地利用影响的情景分析中发挥重要作用,但是国内该模型的应用非常有限.HSPF模型存在的主要问题包括:①模型中某些方案和算法还有改进和完善的空间;②模型对数据输入要求较高,模拟的精度受到空间和属性等数据的限制;③模型只限于均匀混合的河流、水库和一维水体模拟,对于复杂流域或水体的模拟研究,需要与其它模型整合以解决更加综合的问题.目前,针对发展与完善HSPF模型的研究仍在继续,包括模型平台开发、模型功能扩展、模型校正方法研究、参数敏感性研究等方面.随着我国基础数据的积累及共享程度的提高,HSPF模型在我国的应用也将更加广泛.     

模型模拟再生水灌溉对土壤水盐运动的影响

随着水资源供需矛盾日益尖锐,污水再生利用成为了缓解北京水资源紧缺的重要举措.为推动北京再生水安全灌溉,本研究运用ENVIRO-GRO模型模拟缺水城市北京再生水灌溉土壤水盐运动规律,并对土壤盐分累积进行预测,探讨了不同灌溉措施对土壤水盐运动规律和盐分累积的影响.结果表明,高水量、常规、低水量灌溉,平衡时土壤含盐量（ECe）年均值分别增加了29.5%、97.2%、197.8%;灌溉频率对土壤水盐运动规律影响不大,但低频灌溉下土壤中盐分的累积有减少的趋势;灌溉水含盐量（ECw）为0.6、1.2、2.4 dS.m-1灌溉条件下,平衡时ECe的年均值分别增加了23.7%、97.2%、208.5%,自来水灌溉下土壤盐分虽有增加,但土壤未出现盐渍化.总的来说,目前北京常规再生水灌溉,平衡时土壤盐分累积不会影响早熟禾的生长,但土壤会出现轻度盐渍化.     

ASM1耦合曝气模型对污水处理厂的模拟研究

为了更好地模拟污水处理厂实际的曝气方式及溶解氧浓度,采用更加精确的曝气模型对某污水处理厂进行了模拟研究.该模型考虑了污泥浓度、堵塞及温度等因素对氧传质的影响,并将该曝气模型与ASM1相耦合.首先,通过灵敏度分析确定了对该污水处理厂出水指标影响较大的参数,分别为异养菌产率系数YH、异养菌最大比增长速率μH、自养菌最大比增长速率μA及自养菌氧利用半饱和系数KOA.然后,测定部分灵敏度较高的参数并使用测量结果对模型进行校正,校正结果使用希尔系数表征.最后,使用另一批数据对校正参数进行验证.模拟的COD、氨氮、总氮和好氧池末端溶解氧的希尔不等系数分别是0.13、0.23、0.20和0.15,而通过控制溶解氧浓度或理论氧转移效率模型模拟则出现氨氮、溶解氧、总氮及COD的希尔不等系数不同程度上升的结果.校验后的ASM1/曝气耦合模型参数可以很好地应用于污水处理厂的模拟当中,对于优化污水厂曝气控制具有重要意义.