中国水禽产业时空演进及影响因素——基于全要素生产率增长视角

水禽产业快速发展离不开技术进步的贡献。准确审视水禽产业全要素生产率的时空演进规律,探讨不同阶段水禽全要素生产率（TFP）的提升策略十分重要。基于中国水禽体系产业经济团队的调研数据和固定观测点数据,结合有关年鉴整理出水禽的投入和产出数据,运用GIS技术分析后得出结论：2010—2018年水禽产业时空演进特征为“东退西进,北向南移”。结合永续盘存法和索洛余值,运用数据包络分析法（DEA-Malmquist）估算全国29个省份水禽TFP指数,比较各地区的TFP增长率和贡献率,进而分析此期间的时空演进规律,结果显示：2014年TFP增长率大幅下跌,2015年又逐渐上升,2016年以后才趋于平稳增长,概括水禽TFP增长率的时空变化规律为先降后升的“U”型走势。运用Tobit模型分析其变动的影响因素,结果表明：受H7N9突发事件影响,水禽产业劳动力投入和资本投入发生了改变,主要影响因素为水电及燃料动力投入、基础设施维护及新增投入、疫苗防疫及医疗投入等,这些因素对水禽TFP增长的时空变动影响显著,进一步说明水禽技术效率有待提升,风险规避机制有待完善。     

中国交通碳排放及影响因素时空异质性

选取30个省级行政单位作为空间单元,采用探索性空间数据分析（ESDA）方法对交通碳排放时空分布格局进行研究,同时考虑空间单元的差异性,构建地理加权回归（GWR）模型分析交通碳排放影响因素的时空异质性.研究发现：2000~2015年交通碳排放量呈现显著的空间聚类特征,聚类趋势逐年加强.双变量空间自相关指数为0.165~0.274,显著性水平介于0.016~0.045,表明交通碳排放同机动车保有量、GDP、货运周转量及客运周转量之间存在显著的空间正相关关系.GWR模型的R²在0.783~0.865之间,而OLS模型的R²在0.675~0.844之间,且GWR模型的AICc值均低于OLS模型的,说明GWR模型的拟合结果明显优于OLS模型,可以更好地解释交通碳排放的影响机制.GWR的回归结果表明碳排放的影响因素存在明显的时空异质性特征,其中GDP是主要的推动因素,部分地区回归系数高达0.91,2000年影响程度由东向西递减,而2005、2010和2015年由北向南递减.客运周转量起到关键的抑制作用,影响程度由东北向西南递减.因此建议应当充分考虑碳排放影响因素的时空异质性特征,制定差异化的碳减排政策.     

A simple semi-empirical model for predicting missing carbon monoxide concentrations

Carbon monoxide monitoring using continuous samplers is carried out in most major urban centres in the world and generally forms the basis for air quality assessments. Such assessments become less reliable as the proportion of data missing due to equipment failure and periods of calibration increases. This paper presents a semi-empirical model for the prediction of atmospheric carbon monoxide concentrations near roads for the purpose of interpolating missing data without the need for any traffic or emissions information. The model produces reliable predictions while remaining computationally simple by being site-specifically optimized. The model was developed for, and evaluated at, both a suburban site and an inner city site in Hamilton, New Zealand. Model performance statistics were found to be significantly better than other simple methods of interpolation with little additional computational complexity.     

基于多源数据融合的煤矿工作面瓦斯浓度预测*

为了解决瓦斯浓度预测使用的单一数据在预测中影响还不够深入的问题,提出基于LSTM神经网络的多源数据融合瓦斯浓度预测模型。模型将上隅角瓦斯浓度、采煤机速度、工作面吨煤瓦斯涌出量等不同数据融合作为输入层参数,使用Adam优化算法更新LSTM网络层参数,利用Attention机制突出关键影响瓦斯浓度的因素,开展多源数据融合的瓦斯浓度预测,结合某矿1008工作面的实际数据,分析不同数据在瓦斯浓度预测中的作用。研究结果表明:单变量下的Attention-aLSTM预测效果相比LSTM提升14.2%;多源数据融合下的Attention-aLSTM相比自身提升了5%。     

土地资源的多级网格数据结构建立与应用研究

传统的基于行政区的土地统计数据不能完全表现区域内部土地利用的空间分异特征,以武汉市为实验区,对基于网格的统计信息算法STING(Statistical Information Grid-based method)进行扩展,以景观多样性指数为定量化指标对实验区进行四叉树划分生成不均匀多级网格,建立一种拟合了行政区划界线的不均匀的多级网格结构来存储、管理和分析土地数据。并以此多级网格数据结构为平台计算和生成实验区人口密度空间分异渲染图,初步抽取了人口分布与土地利用之间的关系。实验表明,基于多级网格的统计方法能更好地表达土地利用及其相关数据的空间分异性,利于对土地资源数据的进一步挖掘以抽取所需知识。     

Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

Modelling the forest carbon budget of a Mediterranean region through the integration of ground and satellite data

This paper introduces an innovative modelling strategy aimed at simulating the main terms of net forest carbon budget (net primary production, NPP and net ecosystem exchange, NEE) in Tuscany (Central Italy). The strategy is based on the preliminary calibration and application of parametric and bio-geochemical models (C-Fix and BIOME-BGC, respectively), which simulate the behaviour of forest ecosystems close to equilibrium condition (climax). Next, the ratio of actual over-potential tree volume is computed as an indicator of ecosystem distance from climax and is combined with the model outputs to estimate the NPP and NEE of real forests. The per-pixel application of the new modelling strategy was made possible by the collection of several data layers (maps of forest type and volume, daily meteorological data and monthly normalized difference vegetation index (NDVI) images for the years 1999–2003) which served to characterize the eco-climatic and forest features of the region. The obtained estimates of forest NPP and NEE were evaluated against ground measurements of accumulated woody biomass and net carbon exchange. The results of these experiments testify the good potential of the proposed strategy and indicate some problem areas which should be the subject of future research.     

Bootstrap methods for simultaneous benchmark analysis with quantal response data

A primary objective in quantitative risk assessment is the characterization of risk which is defined to be the likelihood of an adverse effect caused by an environmental toxin or chemcial agent. In modern risk-benchmark analysis, attention centers on the “benchmark dose” at which a fixed benchmark level of risk is achieved, with a lower confidence limits on this dose being of primary interest. In practice, a range of benchmark risks may be under study, so that the individual lower confidence limits on benchmark dose must be corrected for simultaneity in order to maintain a specified overall level of confidence. For the case of quantal data, simultaneous methods have been constructed that appeal to the large sample normality of parameter estimates. The suitability of these methods for use with small sample sizes will be considered. A new bootstrap technique is proposed as an alternative to the large sample methodology. This technique is evaluated via a simulation study and examples from environmental toxicology.

水环境监测结果的审核重点与审核方法

环境水质监测分析过程中水质指标多,监测数据多,数据的审核工作比较复杂。数据审核是水质分析工作质量保证的一个重要环节,是整个质量保证体系中有效的控制手段。主要从理论上分析环境监测中水质指标之间的关系,为水质监测人员监测和数据审核提供参考,从而提高数据质量,为环境水质管理服务。     

生命周期清单分析的数据质量评价

生命周期清单分析(LCI)数据质量的分析方法可概括为两类:采用诸如地理代表性、数据年代或数据获取方式等一系列指标来表示;根据不确定性来综合反映LCI质量.笔者在分析了这两类方法各自所存在缺陷的基础上,提出了将这两者相结合的评价方法:采用5个独立的反映数据质量的指标,根据系统各单元各数据属性对各指标从1～5进行打分,形成数据的质量指标向量元素.根据数据质量向量元素的算术平均在总指标范围中所占的百分数将质量指标向量转化为对应的综合数据质量指标(DQI),继而根据DQI可得出每个数据的随机分布,以便进行清单结果的不确定性随机模拟.最后将方法应用于钢铁生产生命周期清单数据中.