特殊区域地表水环境背景值表征技术方法研究与应用

选取黑龙江省源头水保护区作为研究对象,根据DEM数据对保护区内随机布设的水质监测点控制单元进行划分并提取单元内的相关指标建立监测点背景特性量化模型,基于量化结果提出地表水环境背景值监测方案,建立背景值数据库.选取研究区自然属性指标与空间属性指标,以空间叠置技术与聚类分析方法对研究区地表水进行背景值分区,最终将黑龙江省分为六大地表水水质背景值地理分区,并计算了各分区地表水环境背景值表征范围.表征范围显示：依据现行标准进行水质评价,保护区内水质背景值已超出Ⅱ类水质标准限值.因此,基于水质背景值研究成果提出了考虑背景值影响下的水环境质量评价方法,并将方法应用于研究区,结果表明方法切实可行且优于单因子评价法.研究成果可为区域制定背景值影响下的水环境评价方法提供科学的数据支撑与理论依据.     

基于PCA-SOM的北京市平谷区地下水污染溯源

为了解北京市平谷区地下水污染物来源,以平谷区2010—2018年监测数据为基础,使用PCA(主成分分析法)识别了地下水水质指标因子,使用自组织映射识别了污染物的空间分布.结果表明:通过监测指标间的Pearson检验发现, 平谷区地下水电导率与ρ(Ca2+)(p=0.936)、总碱度与ρ(HCO₃2-)(p=0.981)、ρ(Mg2+)与总硬度(p=0.944)指标之间显著相关.地下水化学类型主要以HCO₃-Ca型为主,其次为HCO₃-Mg型.NH₄+、SO₄2-、Cd、Fe(Ⅱ)、NO₂指标空间分布离散性和差异性较大,存在局部富集现象.通过因子分析法筛选出影响平谷区地下水水质的8个公因子,首要影响因子为溶滤-富集作用(贡献率为22.398%),次要影响因子为农业、养殖业和填埋场等人为活动作用(贡献率为16.533%),雨水下渗作用(贡献率为8.035%)、工业源人为活动(贡献率为7.466%)对地下水也有一定影响.通过比较各指标的SOM(Self-Organizing Map,自组织映射)特征图像和监测井映射特征图像,发现NH₄+受山前地带林业、种植业和平原地带农业、养殖业的双重影响,Na+、Mn受平原地带人为活动的影响;同时,NH₄+、NO₃-、NO₂三者之间及Fe(Ⅱ)与Fe(Ⅲ)之间来源不同,Cd、Al、氰化物三者具有同一来源.研究显示,PCA-SOM(PCA与SOM相结合)可以对地下水化学组分来源进行定性识别与定量分析.      

京津冀西北典型区域地下水位时空演变及驱动因素

京津冀西北地区是北京、天津重要的水源涵养区和生态屏障,也是“京津冀协同发展”战略重要的支撑区域。为揭示京津冀西北地区地下水位时空演变规律及其驱动因素,以张家口地区为研究区域,选取1981—2015年56个地下水位观测井连续观测数据、水文地质相关资料、气象数据、地下水资源开采数据和社会经济数据,对研究区内坝上高原、柴宣盆地、涿怀盆地和蔚阳盆地四个地貌单元地下水位时空演变规律和驱动因素进行分析。结果表明：张家口地区四个地貌单元及下辖各区县地下水位呈整体下降趋势,且下降速率逐步加快。35年间坝上高原水位降幅3.59 m<蔚阳盆地水位降幅3.6 m<涿怀盆地水位降幅7.17 m<柴宣盆地水位降幅20.41 m。张家口地区四个地貌单元地下水位动态变化与降雨量、蒸发量、气温等自然因素相关性较差,但与第一产业产值、常用耕地面积、有效灌溉面积、粮食总产量、蔬菜总产量、猪牛羊肉总产量、第二产业产值、第三产业产值和年末总人口等社会经济因素存在显著的相关性。在主成分分析中四个地貌单元前两个主成分的累计方差贡献率均>80%,其中第一个主成分主要为蔬菜总产量、猪牛羊肉总产量、年末总人口和第一、第二、第三产业产值等社会经济因素,不同地貌单元均可解释66%以上,表明社会经济因素是造成张家口地区地下水位持续下降的主要驱动因素。研究成果可为该地区未来社会经济的可持续发展和区域水资源的合理利用、调配以及京津冀西北地区水源涵养和生态功能保持提供理论依据。     

典型西南工业城市春冬季PM2.5来源与潜在源区分析——以柳州市为例

为了揭示柳州城区春冬季PM_2.5的来源及其潜在源区分布和贡献,利用2018年24h自动监测数据和气象数据对柳州市大气污染物浓度变化特征进行了分析,并且使用后向轨迹模型（HYSPLIT）对春冬季柳州市PM_2.5逐日72h气流后向轨迹和前向轨迹进行聚类分析,同时结合潜在源贡献因子分析法（WPSCF）和轨迹浓度权重法（WCWT）对其潜在源区和浓度贡献进行了分析.结果显示,（1）在研究期内,不利的主导风向和工业区布局导致研究区PM_2.5在春冬季污染较严重,且工业源和交通源是其主要本地来源;（2）春冬季PM_2.5高值主要来源于西北和东南方向,其中,西北向PM_2.5主要来源于本地排放,且浓度在空间上呈现西高东低的趋势;（3）春季后向轨迹PM_2.5浓度整体大于冬季,春冬季中对柳州市PM_2.5影响最大轨迹均来自东部的短距离输送,而来自西北的气流轨迹输对PM_2.5贡献最低.春冬季柳州市大气PM_2.5通过气流传输对贵州地区大气环境有较大影响;（4）春季,柳州市PM_2.5的主要潜在源区分布在广西东南部、广东中西部、南海沿岸海域、湖南中部、江西西北部、湖北东部及安徽西北部;冬季,主要分布在广西东南部、广东西南部和南海沿岸海域.     

颗粒物分界层Mie散射激光雷达识别的sigmoid算法

基于成都市2013年6月~2015年5月期间由Mie散射激光雷达探测的大气消光系数廓线资料,发现混合层以上在颗粒物消光和分子消光之间一致存在一个S型的过渡区,利用sigmoid函数对此分布形态进行模拟,通过计算该函数上下曲率最大点所在的高度,据此提出了颗粒物分界层Mie散射激光雷达识别的sigmoid算法.针对该算法模拟效果的分析表明,颗粒物分界层过渡区附近大气消光系数理论廓线和实测廓线保持了高度的相关性,二者在春夏秋冬四季的相关系数（R）分别为0.9971±0.0052、0.9935±0.0167、0.9979±0.0038和0.9990±0.0021（均通过α=0.05的显著性检验）.基于sigmoid算法计算的颗粒物分界层过渡区与成都市温江站探空资料得到的逆温层之间存在很好的对应关系.     

考虑边界条件不确定性的地下水污染风险分析

为分析边界条件不确定性对地下水污染质运移数值模拟模型输出结果的影响,运用Monte Carlo方法对一算例进行阐明,并从污染风险预报方面对模拟结果进行分析.为减少重复调用模拟模型产生的大量计算负荷,将边界条件（第一类边界条件-水头值）作为随机变量,建立地下水污染质运移数值模拟模型的Kriging替代模型,在保证较高精度的同时,实现了Monte Carlo模拟.结果表明：边界条件的不确定性,对地下水污染质运移数值模拟模型预报的结果有很大影响,考虑与未考虑边界条件不确定性得到的研究区污染羽分布差别较大.对地下水污染质运移数值模拟模型的Monte Carlo模拟结果进行统计与分析,可以评估研究区观测井1,2,3污染物浓度预报结果的可靠程度,并且可以预报出研究区观测井1,2,3遭受不同程度污染的风险.     

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.     

新疆生态功能区划初探

“新疆生态功能区划”参考了“中国综合生态环境区划方案”，以《生态功能区划暂行规程》为依据，充分考虑新疆独特的地理分布格局，将新疆分为5个生态区，18个生态亚区，79个生态功能区。“新疆生态功能区划”对科学有效地管理新疆生态环境，因地制宜地实施保护和治理策略，保证社会经济可持续发展具有重要意义。     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Distribution of Iron, Manganese, Zinc and Atrazine in Groundwater in Parts of Palar and Cheyyar River Basins, South India

A study was carried out in a part of Palar and Cheyyar river basin to evaluate the current status of iron, manganese, zinc and atrazine concentrations, their origin and distribution in groundwater. Groundwater samples were collected during post-monsoon (March 1998 and February 1999) and pre-monsoon (June 1999) periods from 41 sampling wells distributed throughout the study area. The groundwater samples were analyzed for trace metals using AAS and atrazine using HPLC. The concentration of the trace elements in groundwater is predominant during pre-monsoon period. Distribution pattern indicates that the concentration of these elements increases from west to northeast and towards Palar river. Lower concentrations in the central part may be due to recharge of fresh water from the lakes located here. During most of the months, as there is no flow in Palar river, the concentrations of trace elements in groundwater are high. Drinking water standards indicate that Mn and Zn cross the permissible limit recommended by EPA during the pre-monsoon period. A comparison of groundwater data with trace element chemistry of rock samples shows the abundance of trace elements both in the rock and water in the order of Fe > Mn > Zn and Fe > Zn > Mn. This indicates that iron in groundwater is derived from lithogenic origin. Further, Fe, Mn and Zn have good correlation in rock samples, while it is reverse in the case of water samples, indicating the non-lithogenic origin of Mn and Zn. Atrazine (a herbicide) was not detected in any of the groundwater samples in the study area, perhaps due to low-application rate and adsorption in the soil materials.