便携β射线法手工监测PM_(2.5)的可行性研究

提出利用自动射线原理结合重量法的简捷便携特点的便携射线仪手工监测PM2.5的思路,平行比对了便携射线法和手工重量法所测60组PM2.5浓度的监测结果,采用线性回归及T检验、F检验等理论分析了两者数据结果间的有效性和可比性。结果表明,便携式射线仪监测结果与重量法监测结果之间有效可比且得到了两者间的线性回归方程;此外进行了20组数据的实样验证,换算值和真值之间相对误差在8%以内,因而确定了便携射线仪手工监测PM2.5的可行性。     

丹东生态环境监测业务系统设计与实现

丹东生态环境监测业务系统是用于生态环境监测日常业务观测,编制各类观测报文及数据上网传输的实用型业务系统。系统采取多窗157结构框架,合理布局功能窗口界面,兼容了地面气象观测数据文件格式,统一数据检查标准,观测数据处理更为准确有效。并以VB6．0可视化语言为开发工具适应不用版本Windows操作系统。基于生态环境监测数据统计、数据分析和数据发布为一体的多功能业务应用软件。     

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.     

基层环境统计年报中的问题及建议

环境统计工作是环境保护工作中的基础,是各级政府和环境保护行政主管部门制定环境保护政策和计划、加强环境监督管理和污染防治的重要依据。本文结合日常工作经验,就目前基层环境统计工作中存在的问题进行探讨,并提出一些建议。     

Advancing injury and violence prevention through data science

Introduction: The volume of new data that is created each year relevant to injury and violence prevention continues to grow. Furthermore, the variety and complexity of the types of useful data has also progressed beyond traditional, structured data. In order to more effectively advance injury research and prevention efforts, the adoption of data science tools, methods, and techniques, such as natural language processing and machine learning, by the field of injury and violence prevention is imperative. Method: The Centers for Disease Control and Prevention’s (CDC) National Center for Injury Prevention and Control has conducted numerous data science pilot projects and recently developed a Data Science Strategy. This strategy includes goals on expanding the availability of more timely data systems, improving rapid identification of health threats and responses, increasing access to accurate health information and preventing misinformation, improving data linkages, expanding data visualization efforts, and increasing efficiency of analytic and scientific processes for injury and violence, among others. Results: To achieve these goals, CDC is expanding its data science capacity in the areas of internal workforce, partnerships, and information technology infrastructure. Practical Application: These efforts will expand the use of data science approaches to improve how CDC and the field address ongoing injury and violence priorities and challenges.     

An innovative computer design for modeling forest landscape change in very large spatial extents with fine resolutions

Although forest landscape models (FLMs) have benefited greatly from ongoing advances of computer technology and software engineering, computing capacity remains a bottleneck in the design and development of FLMs. Computer memory overhead and run time efficiency are primary limiting factors when applying forest landscape models to simulate large landscapes with fine spatial resolutions and great vegetation detail. We introduce LANDIS PRO 6.0, a landscape model that simulates forest succession and disturbances on a wide range of spatial and temporal scales. LANDIS PRO 6.0 improves on existing forest landscape models with two new data structures and algorithms (hash table and run-length compression). The innovative computer design enables LANDIS PRO 6.0 to simulate very large (>10⁸ ha) landscapes with a 30-m spatial resolution, which to our knowledge no other raster forest landscape models can do. We demonstrate model behavior and performance through application to five nested forest landscapes with varying sizes (from 1 million to 100 million 0.09-ha cells) in the southern Missouri Ozarks. The simulation results showed significant and variable effects of changing spatial extent on simulated forest succession patterns. Results highlighted the utility of a model like LANDIS PRO 6.0 that is capable of efficiently simulating large landscapes and scaling up forest landscape processes to a common regional scale of analysis. The programming methodology presented here may significantly advance the development of next generation of forest landscape models.     

Assessment of the clear-sky bias issue using continuous PM10 data from two AERONET sites in Korea

A bias in clear-sky conditions that will be involved in estimating particulate matter(PM)concentration from aerosol optical depth(AOD) was examined using PM_(10) from two Aerosol Robotic Network sites in Korea. The study periods were between 2004 and 2007 at Anmyon and between 2003 and 2011 at Gosan, when both PM_(10) and AOD were available. Mean PM_(10) when AOD was available(PM AOD) was higher than that from all PM_(10)data(PM all) by 5.1 and9.9 μg/m~3 at Anmyon and Gosan, which accounted for 11% and 26% of PM all, respectively.Because of a difference between mean PM_(10) under daytime clear-sky conditions(PM clear)and PM AOD, the variations in ΔPM_(10), the difference of PM all from PM clear rather than from PM AOD, were investigated. Although monthly variations in ΔPM_(10)at the two sites were different, they were positively correlated to those in ΔT, similarly defined as ΔPM_(10)except for temperature, at both sites. ΔPM_(10)at Anmyon decreased to a negative value in January due to an influence of the Siberian continental high-pressure system while ΔPM_(10)at Gosan was high in winter due to an effect of photochemical production at higher temperatures than at Anmyon.     

道路交通噪声自动监测应用探讨

结合城市道路路网状况及实际噪声监测数据和历史实验数据,对道路交通噪声自动监测数据的有效性、监测点位布设进行了研究,对道路交通噪声监测点位优化提出建议。     

振动试验数据管理系统概述

随着近些年振动试验技术的快速发展,对于振动试验数据的保存、管理和统计提出了更高的要求。鉴于此,附属于试验室信息管理系统的振动试验数据管理系统作为振动试验有效的管理工具,必然会成为提升试验室振动试验能力、规范试验技术的有效工具。     

A database for integrated assessment of European agricultural systems

A major bottleneck for data-based policy making is that data sources are collected, managed, and distributed by different institutions, residing in different locations, resulting in conceptual and practical problems. The use of dispersed data for agricultural systems research requires the integration of data sources, which means to ensure consistency in data interpretations, units, spatial and temporal scales, to respect legal regulations of privacy, ownership and copyright, and to enable easy dissemination of data. This paper describes the SEAMLESS integrated database on European agricultural systems. It contains data on cropping patterns, production, farm structural data, soil and climate conditions, current agricultural management and policy information. To arrive at one integrated database, a shared ontology was developed according to a collaborative process, which facilitates interdisciplinary research. The paper details this process, which can be re-used in other research projects for integrating data sources.