Role of the floodplain lakes in the methylmercury distribution and exchanges with the Amazon River, Brazil

Seasonal variability of dissolved and particulate methylmercury(F-MeHg, P-MeHg) concentrations was studied in the waters of the Amazon River and its associated Curuai floodplain during hydrological year 2005–2006, to understand the MeHg exchanges between these aquatic systems. In the oxic white water lakes, with neutral pH, high F-MeHg and P-MeHg concentrations were measured during the rising water stage(0.70 ± 0.37 pmol/L, n = 26) and flood peak(14.19 ± 9.32 pmol/g, n = 7) respectively, when the Amazon River water discharge into the lakes was at its maximum. The lowest mean values were reported during the dry season(0.18 ± 0.07 pmol/L F-MeHg, n = 10 and 1.35 ± 1.24 pmol/g P-MeHg, n = 8), when water and suspended sediments were outflowing from the lakes into the River. In these lakes,the MeHg concentrations were associated to the aluminium and organic carbon/nitrogen changes. In the black water lakes, with acidic pH and reducing conditions, elevated MeHg concentrations were recorded(0.58 ± 0.32 pmol/L F-MeHg, n = 16 and 19.82 ± 15.13 pmol/g PMeHg, n = 6), and correlated with the organic carbon and manganese concentrations. Elevated values of MeHg partition coefficient(4.87 Kd 5.08 log(L/kg) indicate that MeHg is mainly transported associated with the particulate phase. The P-MeHg enrichment detected in all lakes suggests autochthonous MeHg inputs from the sediments into the water column. The MeHg mass balance showed that the Curuai floodplain is not the source of P-MeHg for the Amazon River.     

污染物在地下水毛细饱水带的运移规律浅释

分析了毛细饱水带的水动力学特性，指出在地下水污染研究中，污染物在毛细饱和水带和潜水怪具有相同的水平运动规律，并以实例分析说明该带对污染物运移的重要性。建议在研究和一非饱和条件地下水及污染物运动问题时把毛细饱水带与潜水含水层统一为饱马毛细饱水带顶面作为饱水面。     

Monitoring Air Quality in Mountains: Designing an Effective Network

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.     

青铜峡铝厂氟污染对广武乡玉米影响的调查分析

监测了青铜峡铝厂东南方向４．５ｋｍ的广武乡玉米叶，得知含氟量为４３．９４－１６６．１ｍｇ／ｋｇ，对照样品树新林场玉米叶的含氟均值为１３．１５ｍｇ／ｋｇ，广武乡的玉米受到了严重的氟污染，农作物减产严重。     

水中Mg~(2+)的测试管快速测定

研制了一种测定水中Mg2 + 的测试管 ,测定范围为 0 .5mg/L～ 2 .0mg/L。该测试管适用于现场应急监测 ,具有快速、简便、抗干扰能力强和价格低廉等特点     

经济手段防治白色污染的博弈分析

经济手段在环境管理中的作用及涉及到政府与各利益关系人之间的博弈过程。本文就政府与一次性餐具生产者，销售者之间的博弈关系，分析如何应用经济手段使其在防治“白色污染”中发挥更好的作用。     

新疆河流洪水水污染分析

洪水期间的水污染主要来源于面源。其特点是污染物的浓度高、负荷量大。给工业、渔业和人民生活都会带来不同程度的危害。悬浮物是洪水过程中的主要面污染产物，同时还作为载体吸附和携带大量的有机物、无机盐、重金属类和微生物进入水体，致使河流水质下降。在研究洪水灾害的同时。不应忽略洪水期间的水污染问题。     

阿图什市空气质量变化趋势分析

分析了阿图什市“八五”至“九五”期间空气质量监测数据不同年度、不同季节及不同污染因子的动态变化趋势．结合当地能源结构、气候特征、城市环境综合发展水平，指出影响阿图什市空气质量的主要因素，为防治和减轻阿图什市的空气污染提供了科学依据。     

乌鲁木齐市餐饮业油烟污染的调查及防治初探

通过对乌鲁木齐市20家餐饮业油烟污染的调查分析，结果表明，乌鲁木齐市餐饮业油烟污染严重，并提出了防治措施。     

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.