Fine‐resolution conservation planning with limited climate‐change information

Climate‐change induced uncertainties in future spatial patterns of conservation‐related outcomes make it difficult to implement standard conservation‐planning paradigms. A recent study translates Markowitz's risk‐diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate‐change scenarios for carrying out fine‐resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk‐return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate‐change information and full climate‐change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate‐change forecasts such that the best possible risk‐return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate‐change information could be reduced by 17% relative to other iterative approaches.     

An investigation on the fuel savings potential of hybrid hydraulic refuse collection vehicles

Refuse trucks play an important role in the waste collection process. Due to their typical driving cycle, these vehicles are characterized by large fuel consumption, which strongly affects the overall waste disposal costs. Hybrid hydraulic refuse vehicles offer an interesting alternative to conventional diesel trucks, because they are able to recuperate, store and reuse braking energy. However, the expected fuel savings can vary strongly depending on the driving cycle and the operational mode. Therefore, in order to assess the possible fuel savings, a typical driving cycle was measured in a conventional vehicle run by the waste authority of the City of Stuttgart, and a dynamical model of the considered vehicle was built up. Based on the measured driving cycle and the vehicle model including the hybrid powertrain components, simulations for both the conventional and the hybrid vehicle were performed. Fuel consumption results that indicate savings of about 20% are presented and analyzed in order to evaluate the benefit of hybrid hydraulic vehicles used for refuse collection.     

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box–Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand–metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model–genetic algorithm (RSM–GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47 μg/g) is facilitated at 30.22 mg C/L of EtOH with initial As(III) concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM.     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.     

基于ES-VSP分布的交通状态对公交车动态排放的影响

为评估不同交通状态下公交车运行特征和排放水平的差异,现场采集广州市B9、226线路公交车的逐秒GPS数据,以ES-VSP（发动机负荷-机动车比功率）分布表征畅通、轻度拥堵和中度拥堵下的公交车运行特征,结合IVE（international vehicle emission）模型求得公交车平均排放因子并分析其差异.结果表明:①所测公交车的发动机低负荷区中bin11（-1.6 < ES ≤ 3.1,-2.9 kW/t ≤ VSP < 1.2 kW/t）频率范围为50.55%~83.39%,中度拥堵时bin 11频率是畅通时的1.1~1.3倍;② 3种交通状态下公交车的CO、VOC（运行产生的挥发性有机物）、VOC_evap（蒸发产生的挥发性有机物）、NO_x（氮氧化物）和PM（颗粒物）平均排放因子范围分别为7.63~11.40、0.26~0.46、0.68~1.56、0.32~0.51和0.72×10-2~1.28×10-2 g/km;③同种交通状态下,主干路公交车专用道和BRT车道的公交车的大部分污染物平均排放因子低于次干路混行车道、主干路混行车道,中度拥堵时主干路BRT车道的CO、VOC、VOC_evap、NO_x和PM平均排放因子相对其他道路最低,分别为7.66、0.27、0.87、0.32和0.75×10-2 g/km;④次干路混行车道、主干路混行车道的公交车污染物平均排放因子随交通状态愈加拥堵而增大,但畅通时主干路BRT车道的公交车行驶速度、加速度较高,导致CO平均排放因子较高,对应3种交通状态其比例为1.0:0.9:0.8.研究显示,交通状态对公交车运行和排放具有显著影响.      

军绿有机涂层和金属漆涂层全浸泡下的腐蚀行为对比研究

目的针对车辆装备长期在高盐雾、高湿热、高日照等恶劣的气候环境下使用,会受到腐蚀影响的现状,探究现役军用有机涂层防护性能。方法采用电化学阻抗谱技术研究军绿有机涂层和金属漆涂层在3.5%NaCl溶液中的腐蚀电化学行为,分析这两种涂层在浸泡期间的电化学阻抗谱特征,通过拟合等效电路得到涂层电阻R_c和涂层电容C_c,并用这两个电化学参数评价军绿有机涂层和金属漆涂层的耐蚀性能。结果军绿有机涂层抗腐蚀介质渗透能力很强,而金属漆涂层抗腐蚀介质渗透能力稍弱。结论两种涂层都表现出很好的防护性能,且金属漆涂层在腐蚀后期的防护性能要优于军绿有机涂层。     

城区机动车尾气排放量与雾霾关系研究

近年来雾霾严重影响到了人们正常的生活和身体健康,许多报道都将机动车尾气看做是雾霾的"元凶".G20期间,在柯桥区及周边区域几乎全部企业都处于停产状态下,根据柯桥区域机动车限行前后的数量与空气报告得出的数据显示:PM2.5浓度没有随着机动车减少而降低,汽车的尾气对PM2.5浓度的影响有限,机动车尾气是雾霾的"元凶"的说法有些言过其实.     

军绿有机涂层破损腐蚀行为研究

目的研究不同破损程度下军绿有机涂层的腐蚀行为,确定破损率对其防护性能变化的影响。方法通过人工制作不同的破损率,并利用电化学阻抗谱技术,研究不同破损程度下军绿有机涂层的电化学特征。结果根据不同破损程度下的军绿有机涂层的电化学特征,确定了涂层从完好到失效的四个阶段,初始完好阶段(破损率K≤0.0004%)、破损增大阶段(0.0016%K≤0.04%)、破损过大阶段(0.16%K≤1%)和失效阶段(4%K≤16%)。结论破损率K为0.04%的点是该涂层防护性能急转直下的转折点,因此破损率K大于0.04%时,应及时对车辆表面涂层进行修补和防护。     

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。     

绿色会计视角下能源企业的成本核算优化研究

针对绿色会计视角下能源企业的成本核算优化,介绍了绿色会计视角下能源企业的成本核算,由于能源企业的发展,对环境产生了巨大危害,也导致能源企业由于本身成本过高,这才提出绿色会计,这样才能优化能源企业的成本,可以保证能源企业的可持续发展;同时,也保障了能源企业生态的稳定建设.基于绿色会计视角,探讨了能源企业的绿色成本优化研究,主要包括能源企业坚持绿色会计,确定绿色成本原则,能源企业绿色成本优化条件和能源企业加强绿色会计实践.