GROUNDWATER DISCHARGE AND ITS IMPACT ON SURFACE WATER QUALITY IN A CHESAPEAKE BAY INLET1

ABSTRACT: Surface water, groundwater, and groundwater discharge quality surveys were conducted in Cherrystone Inlet, on Virginia's Eastern Shore. Shallow groundwater below agricultural fields had nitrate concentrations significantly higher than inlet surface waters and shallow groundwater underlying forested land. This elevated nitrate groundwater discharged to adjacent surface waters. Nearshore discharge rates of water across the sediment-water interface ranged from 0.02 to 3.69 liters·m^?2·hr^?1 during the surveys. The discharge was greatest nearshore at low tide periods, and decreased markedly with increasing distance offshore. Vertical hydraulic heads, E_h, and inorganic nitrogen flux in the sediments followed similar patterns. Nitrate was the predominant nitrogen species discharged nearshore adjacent to agricultural land use, changing to ammonium farther offshore. Sediment nitrogen fluxes were sufficient to cause observable impacts on surface water quality; nitrate concentrations were up to 20 times greater in areas of groundwater discharge than in the main stem inlet water. Based on DIN:DIP ratios, nitrogen contributions from direct groundwater discharge and tidal creek inputs appear to be of significant ecological importance. This groundwater discharge links land use activity and the quality of surface water, and therefore must be considered in selection of best management practices and water quality management strategies.     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.     

Seasonal Variation of Inorganic Nutrient Uptake by Heterotrophic Bacterioplankton in Jiaozhou Bay,North China

The distribution patterns and factors affecting uptake rates and turnover times of dissolved inorganic nutrients by heterotrophic bacterioplankton (HB) in Jiaozhou Bay were studied based on in situ surveys and laboratory culturing. The uptake rates of dissolved inorganic phosphorus (DIP) and dissolved inorganic nitrogen (DIN) by HB are limited by the temperature of the water and the concentration of dissolved organic carbon. The turnover times of DIP and DIN by HB are regulated by the concentrations of the nutrients and the numbers of HB, which show significant spatiotemporal variability in Jiaozhou Bay. HB may aggravate the P-limitation of phytoplankton by competing for phosphorus in summer in Jiaozhou Bay. HB play an important role in the biogeochemical cycling of nutrients and may increase the N:P ratio and the accumulation of DIN, thereby accelerating the imbalance of the N:P ratio in Jiaozhou Bay.     

杭州湾潮滩表层沉积物中多环芳烃的分布及来源

对杭州湾潮滩表层沉积物样品中的多环芳烃(PAHs)进行了定量分析.结果表明,沉积物中PAHs总含量范围为45.78～849.93ng/g.PAHs的空间分布总体呈现钱塘江杭州河段>杭州湾南岸>杭州湾北岸.PAHs含量分布与有机碳(TOC)含量存在良好的线性关系,受人类活动和水动力条件的影响较大.样品中PAHs的燃烧来源所占的比重较大,呈现出油料燃烧与木材、煤燃烧的混合污染来源特征.该地区表层沉积物样品中的PAHs尚未对生物造成显著的负面影响.     

Atmospheric deposition of polycyclic aromatic hydrocarbons near New England coastal waters

Wet and dry deposition of polycyclic aromatic hydrocarbons (PAHs) was measured at Nahant, Massachusetts, a peninsula jutting into Massachusetts Bay and Wolf Neck, a peninsula jutting into Casco Bay, Maine. Wet deposition (rain and snow) was collected in a funnel which drains into a shielded, temperature controlled receiving bottle. Dry deposition of gaseous and particulate PAHs was collected onto an exposed water surface. PAHs were analyzed by solid phase extraction and gas chromatography-mass spectrometry. Sixteen PAH species were analyzed, ranging from acenaphthylene to coronene. The mean wet deposition rate of the sum of the 16 species is 720 ng m⁻² cm⁻¹ precipitation at Nahant, and 831 ng m⁻² cm⁻¹ precipitation at Wolf Neck. Wet deposition is attributed to regional PAH emitting sources. Storm patterns appear to bring somewhat higher wet deposition of PAHs to Wolf Neck than to Nahant. The mean dry deposition rate is 95 ng m⁻² h⁻¹ at Nahant and 9.3 ng m⁻² h⁻¹ at Wolf Neck. The large difference is attributed to the fact that Nahant is close to the urban-industrial metropolitan Boston area and Logan International Airport, whereas Wolf Neck has no major PAH-emitting sources nearby. Individual measurements have an error bracket of ±30%. The Chemical Mass Balance model was used to apportion the dry deposition to source categories. At Nahant, nine samples gave valid statistical attributes with a mean apportionment: jet exhaust 35%, gasoline fueled vehicles 32%, diesel fueled vehicles 17%, wood combustion 13%, others 3%. At Wolf Neck, six samples yielded a mean apportionment: jet exhaust 30%, gasoline vehicles 28%, diesel vehicles 18%, wood combustion 16%, others 8%. There is a considerable variation between the samples. The apportionment is greatly dependent on the quality and selection of the model inputs, i.e. source signatures, which for PAHs are questionable.     

大亚湾日本星杆藻种群动态及其与环境因子的关系

研究了1997～1998年及1999和2000年春季大亚湾日本星杆藻(Asterionella japonica)种群动态及其与环境因子之间的关系.日本星杆藻是大亚湾浮游植物常见优势种类,可在全年温度范围(14～32.8℃)内形成数量高峰,并发生水华.日本星杆藻在1998年和2000年春季不常出现,但1999年春丰富的营养盐含量及它们之间合适的比值、多变的水温、稳定的盐度和晴朗的天气使日本星杆藻迅速生长,并发生了水华,最高细胞密度达5255cells/mL.水华消耗了大量的营养,水华期间DIN、DIP和Dsi分别下降了76.0%、69.7%和58.8%,而且营养元素的消耗特别是DIN的耗尽是水华消退的重要原因,而桡足类等浮游动物摄食也是水华消退的原因之一.     

2004年夏季太湖梅梁湾席状漂浮水华风力漂移入湾量计算

太湖北部重度水华暴发时,在夏季盛行风推动下,漂浮的席状水华可由大太湖漂移进入位于太湖北部的梅梁湾,使湾内的水华加剧.本文通过室内风箱水槽实验和野外观测,对水华的风力漂移作了定量研究,建立了风速与水华漂移速度的指数相关方程.并据此对2004年夏季太湖北部发生席状水华的6d分别计算了水华风力漂移入湾量.6d中,只有2004-07-31水华漂移是由湾内向大太湖输出,且量很小,只有0.05 km²;其余5d水华漂移进入梅梁湾的面积占整个梅梁湾面积的3.7%～13.3%,进入量最大的是2004-06-11,达17.4km²,最少的是2004-07-30,为4.8 km².     

基于Delft 3D模型的大鹏湾水环境容量分析

在污染源调查和污染负荷估算的基础上,利用Delft 3D数学模型对大鹏湾现状和未来条件下的水质进行了数值模拟,并采用模型试算法估算出大鹏湾的水环境容量.结果表明:在现状和未来条件下,大鹏湾整体水质良好,沙头角海及印洲塘的水质有下降的迹象.预测除了这2个次区外,其他次区的污染负荷到2015年仍未超出其水环境容量,建议深港双方政府采取污染物总量控制、制定社会经济发展规划等方法,以确保大鹏湾水质在现在和将来都能符合水质标准.      

同安湾海域近十年水环境质量状况分析与评价研究

根据2002年-2011年同安湾海域水质监测结果,采用单因子污染指数评价法和富营养化水平法对同安湾海域近十年水质状况进行分析与评价。结果表明,该海域主要污染因子为无机氮和活性磷酸盐,各年均值大部分超三类海水水质标准;溶解氧、化学需氧量、重金属(汞、铜、铅、镉)及砷、油类均符合一类海水水质标准。该海域目前处于N高P低的富营养状态,氮磷比趋势不够稳定,但磷限制性富营养化趋势有所下降。     

Numerical simulation of alga growth and control in Dalian Bay

WAHMO model was used to simulate the distribution of pollutants in Dalian Bay, China as to predict well as the growth and control of alga. The observed and predicted values of main pollutants showed a good trend at all study locations and the different between them can be ignored. Simulation results illustrated that phosphate was one of limited factors to control algal growth at the location near the sewage outfall, meanwhile, away from the sewage outfall, the synergy of ammonium nitrogen and phosphate was the limited factor.