菏泽市气象因子与空气质量相关性研究与应用

利用气象因子与空气中污染物之间的相互关系,建立了旨在反映两者内在关系的多元回归方程模型。利用气象参数预报值和当日污染物监测结果,对次日空气中污染浓度进行预测。     

地下水环境智慧监管技术集成与平台应用研究

针对"双源"地下水(地下水污染源和集中式地下水型饮用水源)环境监管需求,研发了一套集地下水污染在线监测预警、评价溯源、预测应急于一体的可视化技术集成与应用平台。该平台将物联网、地下水数值模拟、大数据、云计算等技术集成应用于环境监管,实现了工业园区地下水环境实时动态监测评价及预警、地下水污染路径溯源计算、地下水事故污染预测及应急支撑等网络服务与计算功能,并通过地下空间三维数字化处理,构建了"所见即所得"的三维虚拟现实界面,实现了对地下水环境的便捷、高效监管与决策。该平台兼容手机、平板电脑、台式计算机等固定和移动设备,可为"双源"地下水污染监控、管理和应急提供实时、高效的科技支撑。     

基于PSO-SVM算法的环境监测数据异常检测和缺失补全

针对环境监测数据异常和数据缺失问题,提出了基于支持向量机的粒子群优化数据异常检测和缺失补全算法。利用粒子群优化算法选取较优的支持向量机训练参数组合,以此建立非线性的支持向量机模型,并利用结果模型对测得的真实数据拟合预测。以宁夏回族自治区某污水处理厂的污染物测量数据作为实验数据,结果表明,利用该算法预测数据的准确率可达97.977%,检测异常数据准确度高,缺失数据补全正确。     

基于MODIS的烟台近海水体叶绿素浓度分布

利用实测光谱数据及水体叶绿素浓度数据建立了基于MODIS数据的叶绿素反演模型,并利用MODIS L1B数据对研究区的叶绿素浓度进行了反演。通过分析烟台近海水体叶绿素浓度分布得出,烟台近海水体叶绿素浓度由沿岸向海延伸,叶绿素的浓度逐渐增加;通过不同月份的叶绿素浓度分布状况发现,夏季水体叶绿素浓度含量最高,冬季最低。     

深圳市饮用水源中CODMn与TOC的相关性研究

通过对深圳市饮用水源中CODMn和TOC测定结果的相关性分析,结果表明,水库内监测点位及监测区域CODMn与TOC相关性良好,水库外监测点位及监测区域CODMn与TOC相关性不显著,而整个监测区域的CODMn与总有机碳相关性令人满意。因此,提出了TOC值可直接作为饮用水源在线监测有机污染物的综合评价指标,同时也实现了湿式氧化法测定TOC在地表水监测中的应用。     

城市河道水质模拟研究

对玉绣河研究河段进行长期监测,确定玉绣河研究河段的主要水质问题是富营养化.选取叶绿素a作为富营养化的指示参数,并确定NH3-N为限制富营养化的控制性因子.以NH3-N为对象建立玉绣河水质模拟模型,利用监测数据在MATLAB环境中通过矩阵计算验证了所建模型的适用性.并利用MATLAB软件模拟了不同情况下的玉绣河水质变化趋势.进而分析了玉绣河目前存在的问题及需要采取的控制措施.     

基于大数据智能技术的空气质量综合分析系统设计和应用

基于“数字政府”统一框架,设计并建立了基于大数据智能技术的空气质量综合分析系统,集空气质量大数据集成、数据审核、监控报警、综合分析、达标管理、污染溯源功能于一体,建立城市空气质量综合监管数学模型,实现空气质量大数据快速分析及可视化、空气质量预测及评价,可以全方位提升城市环境空气质量综合监测与分析能力,及时、迅速地对城市环境空气质量异常状况作出应对,为环境空气污染精准治理及确保空气站监测数据准确性提供技术支撑。     

上海市烟气排放连续监测系统的数据审核机制

随着烟气排放连续监测系统(CEMS)的广泛安装和验收,CEMS数据的应用将成为污染源管理最直接最有效的手段.在国家规定的数据有效性审核基础上,结合实际工作遇到的问题和长期实践经验,提出每日审核对于数据有效性的重要作用,并通过上海市特有的CEMS数据审核机制,介绍该审核机制的建立背景、内容、流程及取得的成果.     

基于环境一号卫星CCD数据的巢湖叶绿素a的动态监测

环境一号卫星CCD数据具有获取周期短、空间分辨率高等特点,能够及时准确地监测叶绿素a的浓度变化和分布,其在内陆湖泊水质遥感监测方面具有良好的应用前景。文章通过星地同步地面实验,建立起巢湖水体的叶绿素a浓度遥感反演模型,利用2009年4月至2010年3月的环境一号卫星CCD数据,分季节对巢湖叶绿素a行动态监测和分析。结果表明,巢湖叶绿素a具有明显的时空分布特征,夏季叶绿素a浓度最高,冬季最低,秋季高于春季;西半湖湖区叶绿素a浓度一般高于东半湖湖区,西北部和中部湖区空间变化比较大,东部湖区变化较小。     

灰色理论及其模型在北京典型城市区域声环境研究中的应用

以北京市某典型区域作为研究对象，在收集大量相关资料与实测历史噪声数据的基础上，对研究区域内的声环境质量影响因素进行灰色关联度分析，并运用灰色理论建立GM（1,1）模型进行预测。结果表明，影响城市区域声环境质量因素从大到小的排序依次为：机动车辆﹥常住人口数量﹥平均车流量﹥地区生产总值﹥城市道路桥梁﹥基础设施投资﹥治理噪声环保投资；以研究区域内噪声污染实测历史数据建立的GM（1,1）模型精度符合要求标准，根据GM（1,1）模型预测北京市“十二五”期间声环境质量达标且有轻微下降趋势。     

Application of statistical modeling to optimize a coastal water quality monitoring program

The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.     

江苏省空气质量预报准确度及改进方法分析

介绍了江苏省重污染天气监测预报预警系统以及大气重污染预警会商流程,将2015年13个地级市的模式预报、人工预报结果分别与实际观测值进行比较。结果表明:人工预报更准确,PM_(2.5)日均值、臭氧日最大8 h平均值、AQI 3个指标人工预报和实况的相关性分别比模式预报高出12.8%、0.3%、11.4%,平均标准误差(MNE)分别低20.7%、3.1%、23.1%。依据国家空气质量预报技术指南评分办法,对各市2015年全年空气质量级别为"良"时进行评分。通过开展07∶00预报更新,使2015年上半年空气质量预报级别得分平均提高了0.9分,全年级别得分平均提高了2.6分;通过改进模式预报参数,使PM_(2.5)日均预报值、臭氧日最大8 h平均预报值、AQI预报值和实际观测值的相关性比上年同期分别提高26.0%、5.0%、33.9%,MNE分别降低3.6%、31.3%、7.6%。     

基于OPAQ的城市空气质量预报系统研究

空气质量预测在国内的关注度日益提高,传统的空气质量预测系统通常运用数值化学传输模型,利用物理方程来计算污染物的扩散、沉降和化学反应。而化学传输模型的预测准确性很大程度上需要依赖详细的污染源排放信息和气象模型的输出结果。基于统计模型的OPAQ空气质量预报业务系统,采用人工神经网络算法,可预测各污染物的日均值或日最大值。并对北京空气质量预报的结果进行了评价,OPAQ空气质量预报业务系统对空气质量预测的准确性较高,能够利用较低的计算资源得到较为准确的预测结果。与数值预报相比,OPAQ空气质量预报业务系统不需要大量的基础数据作为输入,可弥补数值预报的不足,并成为数值预报的有力补充。     

河流水质预测模型的应用研究

河流水质预测模型使用的参数未引入预测断面现状监测结果 ,使预测结果可能与现状监测结果发生矛盾。针对这一情况 ,通过对预测模式的推导转化 ,在模型中引入污染源现状源强和预测断面现状监测浓度 ,将预测结果同现状监测结果联系起来 ,使预测结果更贴近实际     

A feasible method to assess inaccuracy caused by patchiness in water quality monitoring

Patchiness is a typical property of water quality in lakes. However, in conventional water quality monitoring, patchiness is usually too expensive to take into account, due to the high number of required samples. This study examines a feasible methodology developed for estimating the representativeness of discrete chlorophyll a measurements. Four spatially extensive data sets were collected from the Enonselkä basin of Lake Vesijärvi in Southern Finland, using a flow trough system with a fluorometer in a moving boat. Data sets were used to estimate (1) the spatial representativeness of discrete sampling; (2) the effect of varying sample size on the detected mean chlorophyll a concentration and on the observed proportion of variance. Spatial representativeness was assessed using semivariogram analysis. Results indicate that the spatial representativeness of discrete sampling can remain undesirably low. Furthermore, in monitoring programs involving just one or only a few samples, there is a significant risk of obtaining a false estimate for the mean value and variance of chlorophyll a concentration over the whole monitoring area.     

大宁河水体营养盐状况与水华爆发之间关系分析

通过对大宁河常规监测和水华爆发期间加密监测断面的氮、磷营养盐指标统计,运用综合评价、相关性分析等方法对大宁河氮、磷营养盐分布和叶绿素a与总磷、总氮的关系进行了分析.结果显示,水华爆发期间叶绿素a与总磷、总氮呈正相关关系,总磷是大宁河水华爆发的限制因子.氮磷水平呈现从上游到入长江口逐渐递增趋势,长江回灌作用对大宁河输入部分氮、磷营养盐.     

Precipitation Modeling by Polyhedral RCMARS and Comparison with MARS and CMARS

Climate change is becoming an ever important issue due to the possibility that it may result in extreme weather events such as floods or droughts. Consequently, precipitation forecasting has similarly gained in significance as it is a useful tool in meeting the increasing need for the efficient management of water resources as well as in preventing disasters before they happen. In the literature, there are various statistical and computational methods used for this purpose, including linear and nonlinear regression, kriging, time series models, neural networks, and multivariate adaptive regression splines (MARS). Among them, MARS stands out as the better performing precipitation modeling method. In this article, we used a recently developed method called robust conic mars (RCMARS), based on MARS (also on CMARS), to forecast precipitation owing to its ability to model complex uncertain data. In CMARS, which was developed as a powerful alternative to MARS, the model complexity is penalized in the form of Tikhonov regularization and studied as a conic quadratic programming. In RCMARS, on the other hand, CMARS is refined further by including the existence of uncertainty in the future scenarios and robustifying it with a robust optimization technique. To evaluate the performance of the RCMARS method, it was applied to build a precipitation model constructed as an early warning system for the continental Central Anatolia Region of Turkey, where drought has been a recurrent phenomenon for the last few decades. Furthermore, the performance of the RCMARS precipitation model was also compared to that of MARS and CMARS. The results indicated that RCMARS builds more accurate, precise, and stable precipitation models compared to those of MARS and CMARS. In addition to these advantageous features of the RCMARS precipitation model, it also provided a good fit to the data. As a result, we propose its use in precipitation forecasting for the region studied.     

Landsat and limnologically derived water quality data: A perspective

Fixed station sampling is the conventional method used to obtain data on the median water quality of reservoirs. A major source of uncertainty associated with this technique is that water quality at the fixed stations may not be representative of the ambient water quality in the reservoir at the time of sampling. This problem is particularly relevant for water quality variables such as chlorophyll, which have a markedly patchy spatial distribution. The use of Landsat reflectance data to estimate median chlorophyll concentrations in Roodeplaat Dam was investigated. A linear polynomial regression model for estimating chlorophyll concentrations from Landsat reflectance data, was firstly calibrated with chlorophyll concentration data obtained by sampling seven fixed stations on the reservoir at the time of the satellite overflight to produce an individual calibration. Secondly, the model was calibrated with a pooled set of sampled data obtained from five separate overflights, to obtain a generalised calibration.It was found that median chlorophyll concentrations determined from Landsat-derived data were similar to median chlorophyll concentrations estimated from fixed station data. However, the range of chlorophyll concentrations in the reservoir estimated from Landsat data was considerably larger than that estimated from fixed station data. Landsat derived estimates of chlorophyll concentrations have the added advantage of providing information on the spatial distribution of chlorophyll in the reservoir.     

The Contribution of Acadia PRIMENet Research to Science and Resource Management in the National Park Service

Acadia National Park was one of the 14 sites included in the Park Research and Intensive Monitoring of Ecosystems network (PRIMENet). For eight years the EPA monitored ultraviolet (UV) radiation at this site, with the National Park Service (NPS) sponsoring a total climate and air monitoring station. Under the auspices of PRIMENet, research projects were initiated that investigated the effects of UV on amphibians, determined watershed mass balances, and developed a model of deposition along an elevational gradient. The monitoring data and research results have been used by park management to protect vegetation and water resources from ozone and deposition. These data are now being used to develop a “vital signs” monitoring program under the NPS’ Inventory and Monitoring Program. These data sets have been used in regional, national and international programs to protect human health and resources from air pollution. Public outreach has been accomplished through web site resources and via the Schoodic Education and Research Center.     

基于WRF-NAQPMS的一次沙尘天气预报评估

为提高沙尘天气的预报准确率,利用ECWMF再分析资料和近地面PM₁₀小时质量浓度监测数据,评估WRFNAQPMS模式对2021年3月15—21日甘肃强沙尘过程的预报能力。结果表明,WRF-NAQPMS能够在一定程度上模拟此次污染过程:WRF对“3· 15”天气系统的模拟与实况整体趋势较为一致,随着预报时效延长,气象模拟场移动偏快,导致沙尘预报场发展偏快、沙尘二次传输影响下游时间提前;近地面风向的局地偏差是导致甘肃中东部地区沙尘浓度出现预报误差的主要气象因素。NAQPMS模式对PM₁₀小时质量浓度的模拟随着预报时效增加和离沙源地距离的增大,预报误差逐步增大:在河西地区,沙尘影响时段和起沙浓度的模拟值均接近监测值,其中嘉峪关、酒泉、张掖的PM₁₀小时质量浓度模拟值与监测值相关系数r>0.8;中部地区城市的沙尘影响时段预报略有偏差,且模拟值低于监测值;受复杂下垫面和气象场预报误差影响,省内其他地区沙尘预报结果参考性较低。