环境监测数值修约在Excel中的实现

本文简要介绍了环境监测技术规范中所规定的数值修约规则及相关规范要求的主要内容,并给出了相应的Excel修约公式和修约函数,可方便地用于对环境监测工作中实验测定和计算得出的各种数值进行修约。修约公式是一个利用Excel工作表函数构造的满足数值修约基本规则的工作表计算公式,在给定修约位数的前提下,可直接在工作表单元格中使用修约公式对数值进行修约,对于简单应用,具有方便、快捷、直观的特点。修约函数是一个利用Excel VBA编制的自定义函数,共有四个参数,其调用方法同工作表函数,具有针对性强、灵活、便捷的特点,可根据环境监测技术规范的特定要求,实现自动按有效位数或小数位数进行数值修约,并按相应的格式输出数据。     

Excel软件在环境监测质量控制中的应用

本文介绍了Excel软件在环境监测质量控制中的应用,结合实例详细阐述了利用Exeel软件制作Youden双样图的方法。     

环境监测数据管理系统的开发

在论述开发环境监测数据管理系统重要性的基础上,根据环境监测站的主要工作职能,确定了环境监测数据管理系统的功能,进行了系统总体设计和程序设计,开发出了操作方便、灵活实用的环境监测数据管理系统。     

试论环境监测数据审核机制的建立及运作

环境监测数据审核是环境监测质量管理的重要一环.在对环境监测数据审核重要性的认识及其内容、依据分析解释的基础上建立了环境监测数据审核运行机制.并提出了几点措施,确保环境监测数据在获取过程中得到有效监控,把监测数据差错率清除在监测过程的各个环节.     

环境空气质量自动监测系统数据的三级审核

目前,环境空气自动监测已成为我国城市环境空气监测的主要技术手段,我国已有百余个城市拥有这类监测系统。由于此类系统监测具有实时在线的特点,因此数据量很大,本文针对环境空气自动监测系统日常运行中获取的大量数据进行三级审核的特点,提出了如何加强环境空气自动监测系统中日常监测数据的审核工作。     

统计分析方法在大气环境监测数据符合性分析中的应用探讨

通过汇总分析气井的试气环境监测数据,选择AERMOD模式,运用EIAProA软件模拟预测试气SO2浓度,对实测和预测数据进行F检验和t检验,验证其方差是否符合显著性要求,同时检验其相关系数、系统均方差和非系统均方差,结果表明符合指数,满足论文预设的70%的要求。     

四川省空气自动监测数据采集系统建设

介绍了四川省空气质量监测工作的现状和存在的问题,提出了建设四川省空气自动监测数据采集系统的技术路线,建设完成了全省空气监测数据的统一采集。     

Risk-Based Analysis of Environmental Monitoring Data: Application to Heavy Metals in North Carolina Surface Waters

The state of North Carolina's Department of Environment and Natural Resources (NCDENR) conducts routine water quality monitoring throughout the state to assess the health of aquatic systems. The current study reports the results of a retrospective (1990–2000) ecological risk assessment of six heavy metals (arsenic, cadmium, copper, lead, mercury, and zinc) in 17 North Carolina basins that was conducted to estimate the risk of heavy metal toxicity to freshwater organisms and assess the sufficiency of NCDENR's monitoring data to identify water-quality-related ecological threats. Acute and chronic ecotoxicological thresholds (ETs) were calculated for each metal based upon the 10th percentile of species sensitivity distributions and were normalized for water hardness. Statewide probabilities (expressed as percentages) of a random sample exceeding acute or chronic ETs among the six metals ranged from 0.01% to 12.19% and 0.76% to 21.21%, respectively, with copper having the highest and arsenic and mercury the lowest risk. Basin-specific probabilities varied significantly depending upon water hardness and presumably watershed development. Although the majority of specific sites where data were collected were at low risk for metal toxicity, some specific sites had a high probability of toxic events associated with one or more metals. Analytical detection limits for metals were frequently higher than estimated chronic ET, limiting the ability to assess the risk of chronic toxicity in soft-water basins. Results suggest risk-based criteria may be useful for assessing and validating the sufficiency of monitoring programs and prioritizing management goals.     

Monitoring of Vegetation Impact Due to Trampling on Cadillac Mountain Summit Using High Spatial Resolution Remote Sensing Data Sets

Cadillac Mountain—the highest peak along the eastern seaboard of the United States—is a major tourist destination in Acadia National Park, Maine. Managing vegetation impact due to trampling on the Cadillac Mountain summit is extremely challenging because of the large number of visitors and the general open nature of landscape in this fragile subalpine environmental setting. Since 2000, more intensive management strategies—based on placing physical barriers and educational messages for visitors—have been employed to protect threatened vegetation, decrease vegetation impact, and enhance vegetation recovery in the vicinity of the summit loop trail. The primary purpose of this study was to evaluate the effect of the management strategies employed. For this purpose, vegetation cover changes between 2001 and 2007 were detected using multispectral high spatial resolution remote sensing data sets. A normalized difference vegetation index was employed to identify the rates of increase and decrease in the vegetation areas. Three buffering distances (30, 60, and 90?m) from the edges of the trail were used to define multiple spatial extents of the site, and the same spatial extents were employed at a nearby control site that had no visitors. No significant differences were detected between the mean rates of vegetation increase and decrease at the experimental site compared with a nearby control site in the case of a small spatial scale (≤30?m) comparison (in all cases P?>?0.05). However, in the medium (≤60?m) and large (≤90?m) spatial scales, the rates of increased vegetation were significantly greater and rates of decreased vegetation significantly lower at the experimental site compared with the control site (in all cases P?<?0.001). Research implications are explored that relate to the spatial extent of the radial patterns of impact of trampling on vegetation at the site level. Management implications are explored in terms of the spatial strategies used to decrease the impact of trampling on vegetation.     

An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China

Monitoring the dynamics of forest biomass at various spatial scales is important for better understanding the terrestrial carbon cycle as well as improving the effectiveness of forest policies and forest management activities. In this article, field data and Landsat image data acquired in 1999 and 2007 were utilized to quantify spatiotemporal changes of forest biomass for Dongsheng Forestry Farm in Changbai Mountain region of northeastern China. We found that Landsat TM band 4 and Difference Vegetation Index with a 3 × 3 window size were the best predictors associated with forest biomass estimations in the study area. The inverse regression model with Landsat TM band 4 predictor was found to be the best model. The total forest biomass in the study area decreased slightly from 2.77 × 10⁶ Mg in 1999 to 2.73 × 10⁶ Mg in 2007, which agreed closely with field-based model estimates. The area of forested land increased from 17.9 × 10³ ha in 1999 to 18.1 × 10³ ha in 2007. The stabilization of forest biomass and the slight increase of forested land occurred in the period following implementations of national forest policies in China in 1999. The pattern of changes in both forest biomass and biomass density was altered due to different management regimes adopted in light of those policies. This study reveals the usefulness of the remote sensing-based approach for detecting and monitoring quantitative changes in forest biomass at a landscape scale.