Measurement Diagnostics by Analysis of Last Digits

Though measurement instruments and methods have their natural precision limits, long term monitoring, which is so importantfor environmental studies, requires to set up and maintaincertain standards regarding precision. The elementaryrequirement is to produce measurements with a prescribed numberof valid digits. If the technology further develops and allowsto increase the number of valid digits, the change should bedocumented. In this paper we discuss a simple procedure for validity assessment of the last digits of data in a data base.It may be useful for both calibration control of an instrumentas well as for preliminary data analysis in scientific studies.The procedure utilizes the assumption that, under normalconditions, the last digits of the observed data can usually beconsidered as uniformly distributed random numbers. Failures ordeliberate changes in the observation mechanism can be detectedif particular digits occur more frequently than the others. Thedecision about proper representations of the last digits is doneby means of the familiar chi-square statistics.     

Standardisation of Temperature Observed by Automatic Weather Stations

Daily mean, maximum and minimum surface airtemperature data were gathered from a network ofautomatic weather stations (AWS) within the Moor HouseNational Nature Reserve in northern England. Five AWSwere installed next to the official EnvironmentalChange Network weather station at Moor House. Datawere compared graphically and correction constantswere calculated to adjust data from each AWS to thestandard of the official station by optimising theconcordance correlation coefficient. Each correctedstation was re-located next to one of five in-situstations in and around the reserve, allowingcorrection of all temperature sensors to a commonstandard. The mean error associated with measureddaily mean, maximum and minimum temperature for eachsensor does not exceed ±0.2 K. The procedurequantifies a source of systematic measurement error,improving the identification of spatial temperaturedifferences between stations.     

参数空间分布对非点源污染模拟的影响

以大宁河流域为研究区域,应用非点源模型SWAT（soil and water assessment tool）,模拟了参数空间分布对流域径流和营养物质流失的影响.利用大宁河流域巫溪水文站2000～2004年的实测数据对模型进行了率定和验证,将流域划分为6种数量不等的子流域,利用相同的模型参数输入,模拟了不同的流域划分方案对径流和营养物质流失的影响.结果表明,不同的流域划分方案,年平均流量的最大相对误差为19.6%,6种方案年平均流量的效率系数为0.52～0.82,月平均流量的效率系数为0.80～0.83,随着子流域数量的增加,径流量出现了先下降、后上升的趋势;有机氮和有机磷的最大相对误差分别为16.2%和7.7%,不同的流域划分方案对营养物质的流失产生了轻微的影响,但没有明显的变化趋势和规律.     

太湖流域水资源综合规划数模研究--水质模型的建立与率定

太湖流域水资源综合规划数模研究建立了一个覆盖全流域的，包括污染负荷模型、河网湖泊相耦合的水量水质模型的数字流域系统。该系统实现了流域的耦合求解，已在流域水资源规划中发挥了重要的作用。本文建宴了流域水质模型，并在水量模型率定的基础上进行了水质模型的率定。率定结果与实测数据吻合较好，相对误差多在30％之内，一定程度上反映了太湖流域的水质概况。     

速度场系数在烟气流速测定中的应用与研究

烟气流速测定是气态污染源排放总量监测的重要内容,关键问题是如何将连续监测系统的固定点烟气流速转化为监测断面的平均烟气流速。通过用参比方法对连续监测系统流速测定值进行校准,求出该监测断面的速度场系数。通过对两套连续监测系统的校准全过程的研究,证明速度场系数为一基本稳定值,经一段时间复验后其变化仍在容许范围之内,可用其对连续监测系统进行流速校准。      

Challenges to quantitative applications of Landsat observations for the urban thermal environment

Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program sig-nificantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature (LST). However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM + and channel differences among sensors. Based on these investigations, the concerns are to: (1) show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors; (2) emphasize efforts which should be done for the quantitative applications of Landsat data; and (3) understand the potential challenges for the continuity of Landsat observation (i.e., thermal infrared) for global change monitoring, while several climate data record programs being in progress.     

EXCEL数据处理软件在环境试验设备温度校准中的应用

人工模拟环境试验中气候环境试验的条件是否真实,主要取决于环境试验设备技术性能指标,主要包括温度偏差、温度均匀度、温度波动度.由于测试点较多,在数据处理及结果不确定度评定上工作量较大,因此使用EXCEL数据处理软件进行计算,一方面保证了数据准确可靠,另一方面提高了工作效率.     

浅谈烟气分析仪的在线校准

在分析烟气分析仪传统校准方式存在缺陷的基础上,通过引入质谱仪作为高精度的参考标准,对固定污染源排放监测系统中的烟气分析仪进行在线校准,以保证烟气分析仪的测量准确性,从而实现烟气的真正不间断测量。     

生物营养物去除(BNR)工艺模型参数校正与验证方法

研究了结合活性污泥系统物料平衡的生物营养物去除(BNR)工艺模型参数校正与验证方法.首先采用代尔夫特理工大学开发的TUD模型及其参数缺省值进行初步模拟,并将模拟结果与实测数据进行对比以明确模拟偏差.同时,对原始运行数据进行物料平衡衡算,以消除测量误差并获得TP排放量、反硝化量及氧的净消耗量等平衡计算值,为模型校正提供依据.初步模拟结果显示,TP排放量(3913mg·d-1,以P计)和反硝化量(16170mg·d-1,以N计)均超出了平衡计算值的误差范围(分别为(3762.0±43.1)mg·d-1和(14150±237)mg·d-1),且沿程溶解性COD(SCOD)、PO34--P、NH4+-N、NO3--N不能完全与实测数据吻合,表明工艺模型参数的默认值需得到必要的校正.结合TP平衡,先修正污泥龄(SRT)的计算误差;应用平衡后的运行数据,将SRT由初始值10.3d调整为10d.SRT调整后,再次模拟得出的TP排放量为3750mg·d-1,已在平衡计算结果的范围之内,TP平衡得到了闭合.将发酵过程最大反应速率常数qfe从3d-1提高到15d-1后,沿程及PO43--P拟合结果同时得到了显著改善.通过调整DO及NH4+-N的亲和系数KNH4(从1mg·L-1至0.2mg·L-1),沿程NH4+-N得到了拟合.出水及沿程NO3--N的拟合通过调整氧的亲和系数KO2从0.7至0.1mg·L-1)得以实现.最后校核反硝化负荷及氧的净消耗量,模拟计算的反硝化负荷为14090mg·d-1,氧的净消耗量为126900mg·d-1,均在平衡计算值的误差范围之内(分别为(14150±237)mg·d-1和(129700±2910)mg·d-1),说明校正过程合理、可靠.     

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N₂O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N₂O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N₂O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N₂O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N₂O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N₂O emissions from arable soils at regional or continental scales.