差分光学吸收光谱法测量大气污染气体的研究

介绍了差分光学吸收光谱(DOAS)法测量大气污染气体的基本原理,描述了收发一体的DOAS系统的设计以及在同一波段对多种被测气体浓度同时进行反演的数据处理方法,并且对周围环境中的SO₂,O₃和NO₂进行了长期监测,得到的结果与实际情况相符合.      

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

Implementing SDG 15: Can large‐scale public programs help deliver biodiversity conservation,restoration and management,while assisting human development?

Among the Sustainable Development Goals (SDGs), the proposed SDG 15 promotes activities that, inter alia, “Protect, restore and promote sustainable use of terrestrial ecosystems”. An important potential contribution in achieving SDG 15 is through public programmes designed to jointly promote human development through poverty alleviation and improvement of human livelihoods and biodiversity conservation/management/restoration. An analysis of twenty public programmes with such joint objectives yielded twelve lessons learned. In addition to financial commitments, government and intergovernmental agency input for such public programmes includes ensuring political will and appropriate legal frameworks. Local communities and civil society provide input through traditional and indigenous ecological knowledge and stewardship. Appropriate shared inputs in development and the implementation of such public programmes, with communication between local community, broader civil society, the scientific community and governments will result in: better use and management of biodiversity; alleviation of poverty; security of livelihoods and better governance systems. The Ecosystem Approach of the Convention on Biological Diversity provides an ideal framework when planning and implementing new programmes. Application of the lessons learned to new public programmes will ensure that the answer to the question posed in the title is an emphatic “Yes”, and assist with the achievement of SDG 15.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Reservoir‐host amplification of disease impact in an endangered amphibian

Emerging wildlife pathogens are an increasing threat to biodiversity. One of the most serious wildlife diseases is chytridiomycosis, caused by the fungal pathogen, Batrachochytrium dendrobatidis (Bd), which has been documented in over 500 amphibian species. Amphibians vary greatly in their susceptibility to Bd; some species tolerate infection, whereas others experience rapid mortality. Reservoir hosts—species that carry infection while maintaining high abundance but are rarely killed by disease—can increase extinction risk in highly susceptible, sympatric species. However, whether reservoir hosts amplify Bd in declining amphibian species has not been examined. We investigated the role of reservoir hosts in the decline of the threatened northern corroboree frog (Pseudophryne pengilleyi) in an amphibian community in southeastern Australia. In the laboratory, we characterized the response of a potential reservoir host, the (nondeclining) common eastern froglet (Crinia signifera), to Bd infection. In the field, we conducted frog abundance surveys and Bd sampling for both P. pengilleyi and C. signifera. We built multinomial logistic regression models to test whether Crinia signifera and environmental factors were associated with P. pengilleyi decline. C. signifera was a reservoir host for Bd. In the laboratory, many individuals maintained intense infections (>1000 zoospore equivalents) over 12 weeks without mortality, and 79% of individuals sampled in the wild also carried infections. The presence of C. signifera at a site was strongly associated with increased Bd prevalence in sympatric P. pengilleyi. Consistent with disease amplification by a reservoir host, P. pengilleyi declined at sites with high C. signifera abundance. Our results suggest that when reservoir hosts are present, population declines of susceptible species may continue long after the initial emergence of Bd, highlighting an urgent need to assess extinction risk in remnant populations of other declined amphibian species.     

基于机器视觉的鱼类行为特征提取与分析

近年来,水污染问题备受关注。生物式水质监测成为目前国家环境保护工作的重要任务之一。为准确监测水质污染情况,本文以青鳉鱼(Oryzias latipes)为研究对象,采用非接触式的机器视觉监测技术,提取青鳉鱼的生理特征(呼吸频率)和运动特征(胸鳍和尾鳍的摆动频率),并分析这些特征与水质之间的关系。本文采用支持向量机(Support Vector Machine,SVM)准确提取鱼鳃,并根据鱼鳃呼吸面积大小变化计算出鱼的呼吸频率。基于形态学细化算法提取青鳉鱼骨架,求出胸鳍和尾鳍的摆动频率。结果显示:不同浓度铜离子暴露实验测得的青鳉鱼生理特征和运动特征与实际情况一致;通过对不同铜离子浓度下的毒性实验数据对比,发现了青鳉鱼的生理特征和运动特征会随不同的铜离子浓度发生相应变化,可以作为水质监测的评价标准。     

利用集合均方根卡尔曼滤波反演重庆地区SO2源排放

传统的"自下而上"清单方法估算的排放清单,其数据的准确性和时效性存在较大局限.基于集合均方根卡尔曼滤波的源清单反演方法,结合WRF-CMAQ（天气研究和预报模式-公共多尺度空气质量模型）被用于对以清华大学编制的2010年MEIC（中国多尺度排放清单模型）排放清单为基础制作的重庆地区SO₂排放源进行反演试验以解决准确性和时效性问题,试验时间段为2014年10月15-31日,重庆主城17个环境空气质量国控监测点ρ（SO₂）小时观测资料用于反演及检验.结果表明:该方法能够反演重庆地区SO₂源排放量,随着反演次数增加,基于反演排放源预报的ρ（SO₂）预报误差持续减小,反演4次后预报误差达到比较低的稳定的水平,其均方根误差均低于20 μg/m3. 5次反演后SO₂源排放量用于2014年10月24-29日每天起始预报,其预报的站点、时间平均的均方根误差从100~400 μg/m3降至30 μg/m3以下.反演中应用局地化尺度减少集合取样误差影响,54与81 km两个局地化尺度反演结果对预报改善效果相当,表明主要影响重庆主城ρ（SO₂）的源排放位于主城及周边地区,也说明内源排放对重庆主城ρ（SO₂）起主要影响.反演后面源排放量主城区降幅约为30 kg/（d·km2）,周边地区减少10~20 kg/（d·km2）,主城区部分SO₂点源排放量降幅约为25 kg/（d·km2）,说明2010年MEIC排放清单高估了试验时段重庆地区的SO₂排放.      

不同SAPO分子筛负载MnOx催化剂的低温NH3-SCR性能研究

以磷酸硅铝分子筛SAPO-5、SAPO~(-1)1和SAPO-34为载体,采用乙醇分散法制备了用于低温氨选择性还原(NH_3-SCR)NO_x的分子筛负载MnO_x催化剂.活性测试结果显示,3种分子筛催化剂均展现出优良的NH_3-SCR活性,但三者在低温区间的SCR活性存在较明显差异,其SCR活性顺序如下:MnO_x/SAPO-34MnO_x/SAPO-5MnO_x/SAPO~(-1)1.借助XRD、N2吸附-脱附、XPS、H2-TPR、NH_3-TPD、NH_3FT-IR等技术对催化剂的表面活性物种及表面酸性等进行表征分析,结果表明,MnO_x主要以无定型状态分散于载体上,负载后载体的比表面积和孔体积均有所下降.XPS和H2-TPR分析证实,不同分子筛载体上MnO_x的表面浓度与氧化态明显不同.NH_3-TPD和NH_3FT-IR分析揭示了催化剂表面均存在Bronsted酸位和Lewis酸位,其中,Lewis酸性位对低温SCR反应起着关键作用.研究表明,催化剂的催化性能会因载体不同而存在差异,高Mn4+表面浓度和丰富的Lewis酸性位对催化剂在低温区间实现优良的催化活性尤为重要.     

碳中和趋势下数学模拟在污水处理系统中的发展与综合应用

数学模拟技术在污水处理方面被广泛应用,为了系统总结相关技术,本文回顾了污水处理系统中数学模拟技术的发展历程;综述了活性污泥模型(ASM)与机器学习(ML)在水质预测及参数工况优化领域中的应用;重点探究了污水处理系统中温室气体排放模型,以及多目标优化模型在污水处理系统中温室气体排放(GHG)、出水质量(EQI)和运行成本(OCI)的权衡问题;归纳了数学模拟技术在实现污水厂能量自给与资源回收的应用发展.研究结果表明数学模拟技术能准确预测出水水质、快速优化工艺参数、权衡温室气体排放、出水水质与运行成本之间的关系、以及提高资源回收效率等.因此,数值模拟技术可有效指导污水处理工艺的运行优化以及管理,为污水处理行业减污降碳协同增效提供技术支撑.     

基于无人机影像的城市植被精细分类

针对传统植被资源调查方法工作量大、成本高、效率低的问题,利用高分辨率无人机遥感影像,联合地物光谱-纹理-空间信息,构建了一种适用于描述城市不同植被种类的多维特征空间,在此基础上对三种应用广泛的分类算法(基于像素的、面向对象的支持向量机及深度学习Mobile-Unet语义分割模型)开展了对比分析研究.结果表明:本文提出的联合地物光谱-纹理-空间信息的特征空间构建方法能够有效地描述城市不同类型植被的特征差异,提升影像分割、植被分类的精度;在分类精度上,基于像素和面向对象的支持向量机分类结果的总体精度均超过90%,深度学习方法的总体分类精度为84%;在算法效率上,传统机器学习方法也优于深度学习方法.因此,得出结论针对城市小区域、小样本的植被精细分类,传统机器学习分类方法比深度学习方法效果更好.