Spatiotemporal evolution and prediction of habitat quality in Hohhot City of China based on the InVEST and CA-Markov models

With the acceleration of urbanization,changes in the urban ecological environment and landscape pattern have led to a series of prominent ecological environmental problems.In order to better coordinate the balanced relationship between city and ecological environment,we selected land use change data to evaluate the habitat quality in Hohhot City of China,which is of great practical significance for regional urban and economic development.Thus,the integrated valuation of ecosystem services and tradeoffs(InVEST)and Cellular Automata-Markov(CA-Markov)models were used to analyze,predict,and explore the Spatiotemporal evolution path and characteristics of urban land use,and forecast the typical evolution pattern of land use in 2030.The results showed that the land use types in Hohhot City changed significantly from 2000 to 2020,and the biggest change took place in cultivated land,grassland,shrub,and artificial surface.The decrease of cultivated land area and the increase of artificial surface area were the main impact trend of land use change.The average value of habitat quality had been decreasing continuously from 2000 to 2020,and the values of habitat degradation were 0.2605,0.2494,and 0.2934 in 2000,2010,and 2020,respectively,showing a decreasing trend.The decrease of habitat quality was caused by the needs of economic development and urban construction,as well as the impact of land occupation.During this evolution,many cultivated land and urban grassland had been converted into construction land.The simulated land use changes in 2030 are basically the same as those during 2000-2020,and the habitat quality will still be declining.The regional changes are influenced by the urban rapid development and industrial layout.These results can provide decision-making reference for regional urban planning and management as well as habitat quality evaluation.

不确定两阶段机会约束低碳电力规划模型

为探索碳交易市场中区域电力系统供应的协调有序问题,对电力系统规划进行研究,以总利润函数为目标函数,建立了两阶段随机机会约束电力规划模型。模型将电力需求、碳配额的分配政策、超发风险等不确定因素纳入规划,分析了2种碳配额分配情形,用以优化电力行业生产资源的配置,实现碳排放总量控制。模型应用于仿真案例,用来分析碳交易政策对电力成本、利润以及CO2排放量的影响。结果表明,所提出的模型能够在区域电力系统规划和运行阶段选择更为经济环保的策略,从而为电力生产决策提供支持。

介入栓塞术对aSAH患者HMGB1、Copeptin和MMP-9的影响

目的探讨介入栓塞术对动脉瘤性蛛网膜下腔出血(aSAH)患者高迁移率族蛋白1(HMGB1)、和肽素(Copeptin)和基质金属蛋白酶-9(MMP-9)的影响。方法将114例aSAH患者根据不同手术方式均分为介入栓塞组和开颅夹闭组。比较两组手术疗效、手术情况、HMGB1、Copeptin、MMP-9及并发症情况。结果介入栓塞组手术时间、术中出血量及住院时间均低于开颅夹闭组(P<0.05)。两组术后HMGB1、Copeptin及MMP-9水平均低于术前,且介入栓塞组HMGB1、Copeptin、MMP-9水平及并发症发生率均低于开颅夹闭组(P<0.05)。结论介入栓塞术可改善患者血清中HMGB1、Copeptin及MMP-9水平,且并发症少。

多重不确定条件下的乌鲁木齐分布式绿色电力规划研究

考虑季节和系统风险,设计3个情景,将两阶段随机规划、模糊随机规划以及随机鲁棒优化方法,分季节和系统风险多情景设置发电目标,以系统成本为模型目标,以电力供需平衡。电网交易、区域限制、污染物排放为约束条件,建立绿色电力规划模型进行研究,得到乌鲁木齐区域的分布式发电的优化配置,可为在乌鲁木齐推进绿色能源及能源发展提供决策支持。

海南岛土地利用变化对生态环境质量的影响

评估海南岛土地利用变化对生态环境质量的影响,为海南岛的经济与生态可持续发展提供实践指导.利用2000年、2010年和2020年的土地利用数据,采用ArcGIS10.3软件与InVEST模型分析海南岛的生态环境质量演变特征.结果表明:2000年-2020年人造地表面积的增加来源于耕地、草地与海域面积的减少;草地转出、耕地转出、人造地表转入和森林转入这4种地类的转移最为显著;2000年、2010年和2020年海南岛的平均生态环境质量总得分为0.409,0.431,0.404,表现为中等的生态环境质量水平且呈逐渐下降的态势;由于城市建设与经济发展的需求,土地利用结构在不同发展阶段有差异性.海南岛的土地利用对生态环境质量的影响具有区域差异性,未来应以土地低碳利用为目标,推动土地减排与生态协调发展.

某燃煤超低排放机组非常规污染物脱除

燃煤电厂非常规污染物的排放尚未引起足够的重视。为全面表征燃煤电厂非常规污染物脱除性能,针对某1000 MW燃煤超低排放机组,分别采用FPM和CPM一体化采样系统、安大略法(OHM)、控制冷凝法、HJ 646-2013规定的有机物测试方法,系统研究了CPM、Hg、SO3、PAHs等非常规污染物的梯级脱除特性。结果表明:100%、75%负荷时低-低温电除尘系统对CPM脱除率分别为87.15%、92.20%,湿法脱硫分别为49.65%、45.55%,不同负荷下FPM分别为3.6、4.4 mg·m^−3,但CPM却分别达14.2、15.3 mg·m^−3,CPM的浓度远超FPM;低-低温电除尘系统脱Hg效率为64.81%,整个系统的脱Hg效率为75.5%,Hgp全部被脱除,剩余的是难以脱除的Hg0、Hg^2+,脱除率分别为为63.01%、64.29%,Hg0排放浓度为5.4μg·m^−3,Hg^2+排放浓度为0.5μg·m−3;SCR脱硝催化剂将SO2氧化成SO3的转化率约为0.7%,低-低温电除尘系统可脱除88.7%的SO3,湿法脱硫对SO3的脱除率为29.63%,最终SO3排放浓度为1.9 mg·m^−3;全系统对16种PAHs脱除率达94.25%,其中,气相、固相脱除率分别为91.61%、99.27%,最终气相、固相PAHs排放浓度分别为2.39μg·m−3和0.11μg·m^−3。现有超低排放设备对非常规污染物均有不同程度的协同脱除效果,满负荷条件下该机组CPM、Hg、SO3、PAHs排放浓度分别为14.2 mg·m^−3、5.9μg·m^−3、1.9 mg·m^−3、2.5μg·m^−3,Hg的排放浓度满足火电厂大气污染物排放标准(GB 13223-2011)中30μg·m^−3的要求,CPM、SO3、PAHs尚无国家强制排放标准。本研究结果可为燃煤电厂后续非常规污染物的控制提供参考。

Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model

By predicting influent quantity,a wastewater treatment plant(WWTP)can be well controlled.The non-linear dynamic characteristic of WWTP influent quantity time series was analyzed,with the assumption that the series was predictable.Based on this,a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction.Reasonable forecasting results were achieved using this method.