Water quality of reclaimed water from treated urban wastewater in Chaobai River Basin,North China

The utilization of reclaimed water could be an efficient tool to alleviate water scarcity,especially for dry river augmentation.However,it is crucial to monitor water quality to ensure safety to human health and to avoid negative effects on the environment.Reclaimed water samples were collected bimonthly from May to November in 2010 in Chaobai River,and the physiochemical parameters were determined.The main results are as follows:The parameters exceeding the threshold value of the water guidelines are mainly nutrition related to nitrogen and phosphorus,which are known to increase the risk of eutrophication in surface waters.Additionally,nitrite and nitrate can be detrimental to human health.The majority of the parameters have a peaking concentration in May,whereas others either show significant temporal variation over the entire period or remain relatively constant in all four months.Correlation analysis shows that some parameters(pH,T and B) have no significant correlation with others,whereas significant positive correlation was found for Sr with EC and TDS,for CI with TDS,for Si02 with TP and for NO3-N with TN and a significant negative correlation between SO4 and Ba.According to principal component analysis,60.108%of the total data is represented by dominant solutes,and the second principal component with a percentage of 31.876 comprises parameters related to nitrogen.Subsequent cluster analysis of parameters identified four groups,which represent different compositions,and samples in May differ from others.     

Dissolved heavy metal concentrations of the Kralkızı, Dicle and Batman dam reservoirs in the Tigris River basin,Turkey

Water samples were collected at monthly intervals during 1 year of monitoring from Kralk?z?, Dicle and Batman dam reservoirs in the Tigris River basin to assess the concentrations of dissolved heavy metals and to determine their spatial and seasonal variations. The results indicated that dissolved heavy metal concentrations in the reservoirs were very low, reflecting the natural background levels. The lowest total metal concentrations in the three dam reservoirs were detected at sampling sites close to the dam wall. However, the highest total concentrations were observed at sites, which are located at the entrance of the streams to the reservoirs. Fe, Cr and Ni were the most abundant elements in the reservoirs, whereas Cd and As were the less abundant. The mean concentrations of dissolved metals in the dam reservoirs never exceeded the maximum permitted concentrations established by EC (European Community), WHO and USEPA drinking water quality guidelines. All heavy metals showed significant seasonal variations. As, Cd, Cr, Cu, Fe, Ni and Pb displayed higher values in the dry season, while higher values for Zn in the wet season. Cluster analysis grouped all ten sampling sites into three clusters. Clusters 1 and 2, and cluster 3 corresponded to relatively low polluted and moderate polluted regions, respectively. PCA/FA demonstrated the dissolved metals in the dam reservoirs controlled by natural sources.     

道路交通噪声预测模式预测结果的比较

为了分析实际环境影响评价中常用的各种公路交通噪声预测模型预测结果之间存在的差异,并验证各预测方式与实测值之间的相符性,通过对选取的高速公路和市政快速公路采用各种预测模型计算比较,并用实际监测值对各模型预测结果进行验证,结果发现,不同的预测方式会造成预测结果之间昼间4～9 dB、夜间5～10 dB的差异,采用2006版规范计算车速和单车噪声源强,距离衰减考虑车流量大小的预测方式得到的预测结果与实测值最为接近。     

Mathematical Modeling of Leachates from Ash Ponds of Thermal Power Plants

The present study describes the development of empirical models for the prediction of various trace metals i.e., Mn, Cu, Fe, Zn and Pb found in the leachates generated from the ash ponds of various thermal power plants. The dispersion phenomenon of these trace metals followed first order reaction rate kinetics. The empirical models for individual trace metals derived from the lab scale models data correlate well with the real field data with regression coefficients varying from 0.93 to 0.98. The predicted concentrations of the trace metals varied within ±3% of the observed values in the leachates generated from the ash ponds of four thermal power plants with standard deviation varying from 0.001 to 0.032. The empirical models derived from the study can be applied for prediction of trace metals in leachates generated from similar thermal power plants.     

Long-range transport of acidifying substances in East Asia—Part II: Source–receptor relationships

Region-to-grid source–receptor (S/R) relationships are established for sulfur and reactive nitrogen deposition in East Asia, using the Eulerian-type Community Multiscale Air Quality (CMAQ) model with emission and meteorology data for 2001. We proposed a source region attribution methodology by analyzing the non-linear responses of the CMAQ model to emission changes. Sensitivity simulations were conducted where emissions of SO₂, NO_x, and primary particles from a source region were reduced by 25%. The difference between the base and sensitivity simulations was multiplied by a factor of four, and then defined as the contribution from that source region. The transboundary influence exhibits strong seasonal variation and generally peaks during the dry seasons. Long-range transport from eastern China contributes a significant percentage (>20%) of anthropogenic reactive nitrogen as well as sulfur deposition in East Asia. At the same time, northwestern China receives approximately 35% of its sulfur load and 45% of its nitrogen load from foreign emissions. Sulfur emissions from Miyakejima and other volcanoes contribute approximately 50% of the sulfur load in Japan in 2001. Sulfur inflows from regions outside the study domain, which is attributed by using boundary conditions derived from the MOZART global atmospheric chemistry model, are pronounced (10–40%) over most parts of Asia. Compared with previous studies using simple Lagrangian models, our results indicate higher influence from long-range transport. The estimated S/R relationships are believed to be more realistic since they include global influence as well as internal interactions among different parts of China.     

Long-term trends and short-term variability of water quality in Skive Fjord, Denmark – nutrient load and mussels are the primary pressures and drivers that influence water quality

Nineteen years of monitoring data from the eutrophic Skive Fjord, Denmark were examined for linkages to external pressures and drivers, including nutrient inputs, meteorology and stocks of blue mussels. Linkages were examined by: 1) time-series analysis to document effects of nutrient reduction programs, 2) Pearson Rank correlations, 3) multivariate statistical analysis (PLS) to identify water quality variables with high predictability and their linkages to pressures, and 4) regression analysis to quantify relationships between pressures and water quality. Freshwater input, nitrogen load and phosphorus load showed decreasing trends through the period 1984–2002. The load reductions were only partially translated into trends in water quality: phosphorus decreased in most seasons, while total nitrogen decreased during winter and spring only. Phosphorus concentration had the highest predictability (explained by seasonal temperature variation) followed by transparency, silicate, tot-N, chlorophyll-a, primary productivity, phytoplankton diversity and phytoplankton turnover. The variation in pressures other than nutrient input confounded the relations between loads and water quality. High biomass of mussels led to reduced chlorophyll-a and increased transparency, while short-term variability in water column mixing led to changes in chlorophyll-a due to nutrient entrainment and coupling to benthic mussels.     

An overview of systems analysis methods in delineating environmental quality indices

Environmental quality indices (EQIs) have been developed for a variety of purposes ranging from enforcement of environmental standards, to analysis of trends of environmental degradation or improvement, to scientific research. EQIs currently in use are not organized within an integrated framework and thus it has been difficult to analyze adequately complex, multidisciplinary, large-scale, global phenomena. In this paper we compare four different approaches to developing EQIs within a systems perspective. Our analysis suggests that: (1) non-linear regression models that represent an ecosystem's response to different impacts within a stress-response framework (method of response functions) are useful tools for analysis of environmental data; (2) non-equilibrium thermodynamics models based on the concept of exergy, which represents the free energy a system possesses in relation to its environment, provide a common basis for representing many aspects of ecosystem development and response to environmental impacts as a single measure; (3) diagram models based on the concept of emergy, which represents both environmental values and economic values with a single measure, provide a common basis for integrating economic development and environmental protection values into one index; and (4) complex systems simulation models based on general systems theory, which use the methodologies of systems analysis and simulation to identify, quantify, and interrelate EQIs within a dynamic systems context, provide explicit linkages between causes and effects (vertical integration) and identify cross-linkages among different environmental issues (horizontal integration).     

Modeling and risk assessment for soil temperatures beneath prescribed forest fires

Prescribed fire is a management tool used by wildland resource management organizations in many ecosystems to reduce hazardous fuels and to achieve a host of other objectives. To study the effects of fire in naturally accumulating fuel conditions, the ambient soil temperature is monitored beneath prescribed burns. In this study we developed a stochastic model for temperature profiles (values at 15 minute intervals) recorded at four depths beneath the soil during a large prescribed burn study. The model was used to assess the temporal fit of the data to particular solutions of the heat equation. We used a random effects model to assess the effects of observed site characteristics on maximum temperatures and to estimate risks of temperatures exceeding critical levels in future similar prescribed fires. Contour plots of estimated risks of temperatures exceeding 60°C for a range of fuel levels and soil depths indicated high risks of occurrence, especially when the moisture levels are low. However, the natural variability among sites seems to be large, even after controlling fuel and moisture levels, resulting in large standard errors of predicted risks.     

Short-term wind speed forecasting: Application of linear and non-linear time series models

This study forecasts day-ahead wind speed at 15 minute intervals at the site of a wind turbine located in Maharashtra, India. Wind speed exhibits non-stationarity, seasonality and time-varying volatility clustering. Univariate linear and non-linear time series techniques namely MSARIMA, MSARIMA-GARCH and MSARIMA-EGARCH have been employed for forecasting wind speed using data span ranging from 3 days to 15 days. Study suggests that mean absolute percentage error (MAPE) values first decrease with the increase in data span, reaches its minima and then start increasing. All models provide superior forecasting performances with 5 days data span. It is further evident that ARIMA-GARCH model generates lowest MAPE with 5 days data span. All these models provide superior forecasts with respect to current industry practices. This study establishes that employing various linear and non-linear time series techniques for forecasting day-ahead wind speed can benefit the industry in terms of better operational management of wind turbines and better integration of wind energy into the power system, which have huge financial implications for wind power generators in India.