喻家湖水质时空分布特征和影响因子分析

通过设计合理的水质监测网,采用多元统计分析,并结合地理信息技术对武汉市喻家湖在2011年-2012年期间12个监测点、13个水质参数监测值进行水质时空分布特征研究.结果表明,喻家湖13个水质指标概括为4个主成分:第一主成分代表喻家湖的重金属污染,第二主成分代表其富营养化水平,第三主成分代表有机污染程度,第四主成分间接指示富营养化程度;在时间和空间变化上都可分为二组,显著性指标的时空差异较明显,水质污染程度从南至北逐渐减弱,湖溪河是喻家湖的最主要污染源.并对水质参数,监测点位和频次进行了优化.     

扬州地区酸雨现状及成因分析

根据2006年1月—2010年12月的酸雨监测数据,对扬州地区酸雨的变化规律和趋势进行了统计分析。结果表明:扬州地区酸雨主要集中在扬州市区与仪征城区,"十一五"期间,扬州市区酸雨呈逐年减少趋势,仪征城区大气污染严重,降水pH年均值均小于5.6,经评价扬州市区属于非酸雨区-较重酸雨区,仪征城区属于轻酸雨区-重酸雨区。高邮、江都、宝应城区从2007年起无酸雨出现。仪征城区酸雨主要是由燃煤型和燃油型混合空气污染造成的,近年来随着机动车拥有量的迅速增加,各种机动车排放的尾气已成为形成酸雨的重要原因,应引起重视。     

基于顺序注射分析技术的总氮在线分析仪

针对环境监测领域,研制了一种基于顺序注射分析技术的总氮在线分析仪。该分析仪以注射泵为分析过程的液体驱动定量单元,以多通道选向阀为流路切换单元,以发光二极管和硅光电管为检测单元。在该分析平台上,含氮化合物在高温高压条件下经过硫酸钾氧化成硝酸根,硝酸根被还原为亚硝酸根后与N-(1-萘基)乙二胺盐酸盐生成偶氮染料,再由光电检测单元测定吸光度值,经计算得到总氮含量。与现有的总氮在线分析技术相比,该分析仪具有检测单元简单、生产成本低、测量精度高、试剂消耗和废液产生量少等优点,适合于环境监测等领域的长时间在线分析。     

乌鲁木齐冬季雾天可吸入颗粒物透射电子显微镜研究

利用透射电子显微镜(TEM)对乌鲁木齐冬季雾天采集的可吸入颗粒物(PM_2.5、PM_2.5~10)的形貌特征和集聚状态进行分析。将乌鲁木齐大气可吸入颗粒物分为烟尘集合体、飞灰、矿物颗粒、硫酸盐和有机颗粒等5种单颗粒类型,并讨论了其来源。TEM分析表明,PM_2.5中烟尘集合体占14%,飞灰占7.4%,矿物颗粒占24%,硫酸盐占16.7%,有机颗粒占20.4%;PM_2.5~10 中烟尘集合体没有观察到,飞灰占4.9%,矿物颗粒占26.8%,硫酸盐占12.2%,有机颗粒占58.5%。     

室内空气中的气态多环芳烃的被动采样监测

利用自制被动采样装置,在2011年秋冬季对南京市部分地区室内空气中5种气态多环芳烃(PAHs)(萘、苊烯、苊、芴、菲)进行了为期100d的连续采样检测,被动采样器的采样速率为0.012m3/d,5种PAHs的回收率在63%～105%之间,方法检出限在1.1～2.4ng范围内。结果表明,南京市5处不同室内环境空气中萘的浓度最高,占总量的90%以上。室内环境空气中5种PAHs的总浓度为230～1564ng/m3。住宅内人体对5种PAHs的暴露速率为479～560ng/h。     

泸州市降水监测布点优化研究

在泸州市2016—2020年大气降水监测数据的基础上,借助聚类分析、多元方差分析等统计分析手段,对原有降水监测点位进行优化研究,并验证优化结果。在遵循《酸沉降监测技术规范》(HJ/T 165—2004)点位布设要求的前提下,建立了降水监测点位评价体系,从原有7个降水监测点位中优化筛选出3个。优化后的点位对监测结果的影响显著性均>0.05,表明优化前后全市降水数据无显著性差异,该优化方案不会影响区域整体代表性。     

基于多种方法的长江上游小流域水质综合评价

选取大河和巴关河流域2018—2022年水质监测数据,运用单因子评价法、Spearman秩相关系数法、综合污染指数法和主成分分析法,对流域水质状况、变化趋势及主要污染物特征进行综合评价分析。结果表明:2018—2022年,大河流域水质未达到地表水Ⅲ类标准;流域整体综合污染指数超过了1,为重污染,下游污染程度较重;营养指标和有机污染物指标存在显著正相关。巴关河流域水质达到了地表水Ⅲ类标准;流域整体综合污染指数未超过1,为中污染,上游污染程度较重;营养指标、有机污染物指标和重金属指标等均呈现出不同程度的相关性。巴关河流域水质总体优于大河流域。TP、NH₃-N、COD_Mn、COD_Cr和BOD₅是影响大河流域和巴关河流域水质的主要因子,均属于有机型及富营养化污染指标,主要来自生活源和农业源。建议选择适宜的评价方法开展系统性小流域污染溯源,分河段有针对性地开展小流域水环境治理。     

以江苏省河流湖泊为例探讨基于物种与面积关系确定大型底栖动物最适及高效采样量

以江苏省河流湖泊为研究对象,选取不同水体代表性点位,运用多生境采样法和形态学鉴定获取淡水大型底栖无脊椎动物的物种数及对应的采样面积,拟合实际结果的物种数与面积关系对数函数曲线。所研究采样量包括采样面积值、样方及样带数。研究发现,不同水体点位最适采样量面积为1.45~12.75 m²,样方及样带总数为6~16个,其要求十分耗时耗力。为解决这一问题,以物种数与面积关系对数函数的一阶导数曲线y≈1值为临界点,提出"高效采样量"概念,并提出3条采样原则供讨论。同时得到江苏地区高效采样量:涉水可过河流采样面积为1.36 m²,涉水不可过河流采样面积为1.5 m²,不可涉水河流和浅水湖泊采样面积为1.75 m²,均需采集4个样方,1个样带。     

Water Quality Evaluation and Trend Analysis in Selected Watersheds of the Atlantic Region of Canada

Water quality indices (WQIs) have been developed to assess the suitability of water for a variety of uses. These indices reflect the status of water quality in lakes, streams, rivers, and reservoirs. The concept of WQIs is based on a comparison of the concentration of contaminants with the respective environmental standards. The number, frequency, and magnitude by which the environmental standards for specific variables are not met in a given time period are reflected in WQIs. Further, the water quality trend analysis predicts the behavior of the water quality parameters and overall water quality in the time domain. In this paper, the concept of WQI was applied to three selected watersheds of Atlantic region: the Mersey River, the Point Wolfe River, and the Dunk River sites. To have robust study, two different water quality indices are used: Canadian Water Quality Index (CWQI), and British Columbia Water Quality Index (BWQI). The complete study was conducted in two steps. The first step was to organize and process the data into a format compatible with WQI analysis. After processing the input data, the WQI was calculated. The second step outlined in the paper discusses detailed trend analysis using linear and quadratic models for all the three sites. As per the 25 years trend analysis, overall water quality for agriculture use observed an improving trend at all the three sites studied. Water quality for raw water used for drinking (prior to treatment) and aquatic uses has shown improving trend at Point Wolfe River. It is further observed that pH, SO₄, and NO₃ concentrations are improving at Dunk River, Mersey River, and Point Wolfe River sites. To ascertain the reliability and significance of the trend analysis, a detailed error analysis and parametric significance tests were also conducted It was observed that for most of the sites and water uses quadratic trend models were a better fit than the linear models.     

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.