Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.     

Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

Use of time series analysis for the identification of tidal effect on groundwater in the coastal area of Kimje, Korea

The purpose of this study is to apply time series analysis to investigate whether the groundwater quality in the coastal area is affected by the tide. Continuous and regular in situ monitoring data of electrical conductivity (EC) and groundwater level, and tidal level data measured by the National Oceanographic Research Institute were used for the time series analysis. Through the time series analysis, it is known that EC and groundwater level conspicuously fluctuate with two periodicities (15.4 and 0.52-day), which is very similar to those of the tide. Also the behaviors of their fluctuations vary in accordance with the tidal period. These indicate that the groundwater quality has been mainly controlled by the tidal level, and the strength of tidal effect on the groundwater quality is different according to the tidal period.     

无人机三维倾斜摄影测量助力自然资源调查——以不动产调查为例

随着第一次地理国情普查与监测、第三次全国土地调查等重大自然资源调查类项目的实施,以无人机作为搭载平台的遥感影像获取手段在不断发展并深入应用,特别是无人机航测技术和地理信息技术的结合应用越来越紧密.本文以不动产调查为例,重点论述结合无人机三维倾斜摄影测量技术在内的测绘地理信息新手段的作业原理及应用情况,并对其优缺点进行总...     

地理信息数据质量的概念、评价和表述——地理信息数据质量控制国家标准核心内容浅析

介绍已经报批的国家标准<地理信息质量原则>和<地理信息质量评价过程>所确定的地理信息数据质量元素、子元素及其描述符,数据质量评价的方法、内容和要求,以及数据质量评价结果的表达方法,并就我国这一领域存在的问题和相关标准及今后的发展简单地进行了分析.     

土地利用变化对土地生产力的影响研究进展

土地利用变化对土地生产力的影响是土地利用/覆被变化(LUCC)效应研究的重要内容之一。本文基于对土地利用变化和土地生产力概念的辨析, 着重从土地利用变化对土地生产潜力、粮食生产能力和土壤质量3 个方面的影响, 总结梳理了当前的主要研究内容和方法, 探讨了研究的影响尺度特征。最后指出今后的研究应加强土地利用结构、方式和强度变化对小尺度范围内耕地生产力的影响, 强化不同尺度的综合以及空间分析和动态评估方法的应用。     

加标回收在水质分析中的应用及回收率计算方法

阐述了水质分析中加标回收分析的方法步骤、条件控制及计算方法。当加标体积影响试样在加标水样中的浓度时,直接使用回收率理论计算公式的计算值,均较其真实的回收率偏低,且其偏低值随加标体积的增加而增大。根据加标回收分析的定义,分别以加标前后被测组分质量浓度(mg/L)的变化或含量(μg)的变化为依据,导出两个计算加标回收率的实用公式。采用该公式,加标体积可以不受严格控制,其计算结果与真实的回收率一致,适用于日常水质分析工作。     

18—23MSP 多光谱航空摄影技术

此项研究是国家科技攻关项目。在研究此课题以前,世界上采用的多光谱航空摄影机一直是小像幅的(大部分为6cmx6cm,代称为6MSP)。6MSP不能测图,分光“有害吸收”大,故图像质量差,且像幅小,飞行和后期加工量极大。本研究采用18cm×l8cm和23cm×23cm像幅航空摄影测量像机组成多光谱像机组,并设计了“截止滤光镜一特制感光胶片”组合的无“有害吸收”的分光系统,使本多光谱航空摄影像幅比6MSP加大了9倍和14.7倍,并能“一摄多用”,既能测图,又能进行资源调查和环境监测,其图像因无“有害吸收”,分光质量高,可供目视判读,也可输入计算机进行准确地物分类解译。18-23MSP是一项有效的航空遥感技术。     

兰州市气象与污染环境背景综述

利用 196 0～ 2 0 0 0年气候统计资料和 1975～ 2 0 0 0年环境监测资料 ,从地理位置、大、小尺度气候背景、兰州市区污染源概况及主要污染物的时空分布等方面 ,分析该市的气候与污染环境背景 ,为兰州市空气质量预报方法和模式的建立提供依据     

贵州省优质烤烟气候区划指标的补充和修正

从贵州省优质烤烟气候区划指标出发,结合威宁县高海拔地区种烟的实际情况,详细分析了贵州省优质烤烟气候区划指标。得出表征热量与作物生长发育的关系,不仅要考虑日平均温度和持天数,还要考虑积温、昼夜温差、地温及叶面温度等。通过分析,对原指标中成熟期平均温度。大田生长期温度、旺长－成熟期天数等指标进行了修正。补充≥１０℃活动积温≥２２００℃,移栽期日平均气温≥１４℃,月平均日较左≥８℃等指标。