环境监测数据分析和监测网设计中SPSS 10.0的应用

SPSS10.0是目前功能齐全、便于应用的优秀统计软件。文章介绍了该软件在环境监测数据分析和监测网设计中的应用。文中简述了该软件的功能及优点，并通过实例，展示其在环境监测数据分析和监测网设计中的应用，这对于优化监测数据分析结果和监测网的设计均有较好的借鉴作用。     

机场飞机噪声自主验收监测难点与解决对策探讨

中国机场建设高速发展,但竣工环保验收制度执行不理想。在自主验收的大背景下,飞机噪声问题将直接制约机场建设项目的环保验收。笔者分析了验收监测布点要求不明确、验收监测工况要求不完善、飞机噪声监测计算方式不统一、降噪措施落实难效果差等自主验收难点,并提出了合理选择测点位置、完善验收工况调查内容、根据机场类型和敏感点分布特征选择飞机噪声监测计算方式以及智能化大数据协管主动降噪措施的解决对策。     

实验室监测数据的合理性检验

通过比较总含盐量与溶解性总固体的关系、阴阳离子的平衡关系、总硬度与钙镁离子的关系及三氧之间的关系,进而检验实验监测数据的合理性和可靠性。     

Assessment of Wet Sulfate Concentration Changes at CAPMoN Sites, Canada

This paper describes a statistical analysis of wet sulfate deposition data sampled by the Canadian Air and Precipitation Monitoring Network (CAPMoN) since 1988 till 1997. The goal of the investigation is to detect presence of prevailing significant changes in the probability distribution of annual samples collected by the network at each site. The considerations are based on a first order autoregression model with second order polynomial trend and methods used for analysis of variance and multiple comparison. Unlike studies suggesting existence of long term trends in the data, methods applied here indicate absence of any systematic changes in the observed annual concentration patterns at most of the sites.     

Uncertainty Assessment of Voc Emissions from Paint in the Netherlands Using the Nusap System

We demonstrate an innovative approach to uncertainty assessment known as the NUSAP system, to assess qualitative and quantitative uncertainty for the case of emissions of volatile organic compounds (VOC) from paint in The Netherlands. Using expert elicitation, we identified key sources of error, critical assumptions, and bias in the monitoring process. We assessed pedigree and probabilistic uncertainty of all input quantities. We used four pedigree criteria to assess the strength of the knowledge base: proxy representation, empirical basis, methodological rigour and degree of validation. Using Monte Carlo analysis, we assessed sensitivity and propagation of uncertainty. Results for sensitivity and pedigree were combined in a NUSAP Diagnostic Diagram, which effectively highlighted the assumption for VOC percentage of imported paint as the weakest spot in the monitoring of VOC emissions. We conclude that NUSAP facilitates systematic scrutinization of method and underlying assumptions and structures creative thinking on sources of error and bias. It provides a means to prioritise uncertainties and focus research efforts on the potentially most problematic parameters and assumptions, at the same time identifying specific weaknesses in the knowledge base. With NUSAP, nuances of meaning about quantities can be conveyed concisely and clearly, to a degree that is not possible with statistic methods only.     

A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables

In estimating spatial means of environmental variables of a region from datacollected by convenience or purposive sampling, validity of the results canbe ensured by collecting additional data through probability sampling. Theprecision of the estimator that uses the probability sample can beincreased by interpolating the values at the nonprobability sample points tothe probability sample points, and using these interpolated values as anauxiliary variable in the difference or regression estimator. Theseestimators are (approximately) unbiased, even when the nonprobability sampleis severely biased such as in preferential samples. The gain in precisioncompared to the estimator in combination with Simple Random Samplingis controlled by the correlation between the target variable andinterpolated variable. This correlation is determined by the size (density)and spatial coverage of the nonprobability sample, and the spatialcontinuity of the target variable. In a case study the average ratio of thevariances of the simple regression estimator and estimator was 0.68for preferential samples of size 150 with moderate spatial clustering, and0.80 for preferential samples of similar size with strong spatialclustering. In the latter case the simple regression estimator wassubstantially more precise than the simple difference estimator.     

优化布点中模式计算结合历史数据估算的分析方法及研究

随着城市建设发展,城市建成区不断扩大,在空气质量监测中,旧的城区监测点位很难准确反映新的区域内的空气质量时空变化,文章尝试在新的发展区建立一个数学模型,预测新区域内有代表性的某一特征监测点浓度,与旧城区监测点位优选点共同构成整个区域的空气质量监测系统。     

A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling

Global sensitivity analysis can be used for assessing the relative importance of model parameters on model outputs. The sensitivity of parameters usually indicates a temporal variation due to variation in the environmental conditions (e.g., variation in weather or plant growth). In addition, the size of averaging window by which the outputs of a model are aggregated or averaged may impact parameter sensitivities. In this study, temporal variation of parameters sensitives, model performance, as well as the impact of the size of time‐averaging window on evapotranspiration (ET) prediction using the Agricultural Policy/Environmental eXtender (APEX) model are investigated. To achieve these goals, an open‐source package named PARAPEX was developed in R and used to perform dynamic sensitivity and model performance analysis of APEX using parallel computation. PARAPEX reduced the computation time from 5,939 to 379 s (using 20 and 1 computation nodes, respectively). The sensitivity analysis results indicated the parameters accounting for the reducing effect of plant cover on evaporation from the soil surface, the effect of soil on the plant root growth, and the effect of cycling and transformation dynamics of organic matter at the top soil layer as the top sensitive parameters based on the mean daily simulated ET and the Nash–Sutcliffe model performance measure. The dynamic performance analysis indicated poor ET predictions by APEX during the growing seasons. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Comparison of epidemiology and injury profile between vulnerable road users and motor vehicle occupants in road traffic fatalities

Objective: Road traffic injuries (RTIs) are a major global health issue causing a global burden of mortality and morbidity. Half of all fatalities on the world’s roads are vulnerable road users (VRUs). The targeted intervention strategies based on fatality analysis focusing on VRUs can effectively contribute to reducing RTIs. This study aimed to compare VRUs and motor vehicle occupants (MVOs) in terms of epidemiology and injury profile.

Methods: We utilized a nationwide, prospective database of RTI-related mortality cases for patients who visited 23 emergency departments between January 2011 and December 2015. All fatalities due to RTIs in the prehospital phase or in-hospital were eligible, excluding patients with unknown mode of transport and those admitted to general wards. The primary and secondary outcomes were fracture injuries and visceral injuries diagnosed using the International Classification of Diseases, Tenth Revision (ICD-10). We compared fracture injuries between VRUs and MVOs using Abbreviated Injury Scale (AIS) 2? and 2+ classification.

Results: Among a total 3,694 road traffic fatalities (RTFs), 43.3% were pedestrians, followed by MVOs (27.0%), motorcyclists (18.9), bicyclists (6.6%), and agricultural vehicle users (4.2%). The elderly (>60 years old) accounted for 54.9% of VRU fatalities. RTFs occurred most frequently in the autumn and the VRU group and the MVO group showed significant differences in weekly and diurnal variation in RTFs. The injury severities (AIS 2+) of the head, neck, and thorax were significantly different between the 2 groups (P?Conclusions: Elderly pedestrians should be targeted for decreases in RTFs, and road traffic safety interventions for VRUs should be made based on the analysis of temporal epidemiology and injury profiles of RTFs.     

基于电力历史应急大数据的应急情景规则分析与发现方法研究

为了解决电力突发事件演化过程难于预测,进而导致事件应急缺少针对性,较为被动的问题,提出在现有应急预案指导、事故模拟仿真、事故案例经验总结等方法基础上,引入基于电力历史大数据分析与预测的应急情景规则分析与发现方法,将电力应急突发事件情景构建的结果与相关大数据预测模型（汇总统计、分类与预测）相结合,提出科学、合理且具有良好操作性的情景演化规则的获取方法与技术路线,为进一步提高复杂电力突发事件的应急处置与指挥能力提供技术支撑。