接种不同普通活性污泥培养厌氧氨氧化污泥的研究

采用厌氧氨氧化反应器（ASBR）,分别以普通厌氧活性污泥、混合污泥、好氧活性污泥为种泥,通过对氨氮,NO2^--N等指标监测、分析及污泥颜色的观察,研究采用不同普通活性污泥为种泥启动ASBR的可行性及差异。结果表明．ASBR反应器A和B成功启动,C因反应器故障．启动失败。采用厌氧活性污泥为接种污泥（反应器A）,当进水N的容积负荷为kg／（m^3·d）时,氨氮平均去除率为14.4％。P（NO2^--N）：p（NH4^＋-N）变化量为1．24。采用混合污泥为接种污泥（反应器B）．N的容积负荷于前者相同时,氨氮去除率平均29．7％,p（NO2^--N）：p（NH4^＋-N）变化量为1．27。采用混合普通活性污泥作为种泥培养厌氧氨氧化污泥优于单一厌氧普通活性污泥。     

Assessing the Effects of Nutrient Management in an Estuary Experiencing Climatic Change: The Neuse River Estuary, North Carolina

Eutrophication is a serious water quality problem in estuaries receiving increasing anthropogenic nutrient loads. Managers undertaking nutrient-reduction strategies aimed at controlling estuarine eutrophication are faced with the challenge that upstream freshwater segments often are phosphorus (P)-limited, whereas more saline downstream segments are nitrogen (N)-limited. Management also must consider climatic (hydrologic) variability, which affects nutrient delivery and processing. The interactive effects of selective nutrient input reductions and climatic perturbations were examined in the Neuse River Estuary (NRE), North Carolina, a shallow estuary with more than a 30-year history of accelerated nutrient loading and water quality decline. The NRE also has experienced a recent increase in Atlantic hurricanes and record flooding, which has affected hydrology and nutrient loadings. The authors examined the water quality consequences of selective nutrient (P but not N) reductions in the 1980s, followed by N reductions in the 1990s and an increase in hurricane frequency since the mid-1990s. Selective P reductions decreased upstream phytoplankton blooms, but increased downstream phytoplankton biomass. Storms modified these trends. In particular, upstream annual N and P concentrations have decreased during the elevated hurricane period. Increased flushing and scouring from storms and flooding appear to have enhanced nutrient retention capabilities of the NRE watershed. From a management perspective, one cannot rely on largely unpredictable changes in storm frequency and intensity to negate anthropogenic nutrient enrichment and eutrophication. To control eutrophication along the hydrologically variable freshwater–marine continuum, N and P reductions should be applied adaptively to reflect point-source–dominated drought and non–point-source–dominated flood conditions.     

A Mixed-Order Model to Assess Contaminant Declines

We introduce a generalized form of the common first-order (exponential) decay model, that has potential utility for describing contaminant declines in environmental applications, particularly when declines are a mixture of many underlying processes. The exponent on contaminant concentration is left as a free parameter allowing the order of the reaction to be determined by the data. The mixed-order model is more flexible than models with the exponent determined a priori, facilitating an improved fit to observed behavior. We demonstrate the utility of this model, and compare it to two other models, by estimating PCB concentration declines in Lake Michigan fishes.     

Monitoring habitat preserves in southern California using high spatial resolution multispectral imagery

Habitat preserve systems have been established adjacent to the densely populated regions of southern California to support indigenous plant and animal species that are listed as rare, threatened, or endangered. Monitoring the condition of habitat across these broad preserves is necessary to ensure their long-term viability and may be effectively accomplished using remote sensing techniques with high spatial resolution visible and near-infrared (VNIR) multispectral imagery. The utility of 1 m spatial resolution VNIR imagery for detailed change detection and monitoring of Mediterranean-type ecosystems is assessed here. Image acquisition and preprocessing procedures were conducted to ensure that image-detected changes represented real changes and not artifacts. Change classification products with six spectral-based transition classes were generated using multiband image differencing (MID) for three change periods: 1998-1999, 1998-2001, and 1998-2005. Land cover changes relevant to habitat quality monitoring such as human-induced disturbance, fire, vegetation growth/recovery, and drought related vegetation stress were readily detected using the multitemporal VNIR imagery. Suggestions for operational habitat monitoring using image products and mobile geographic information system technologies are provided.     

Nutrient criteria for lakes,ponds, and reservoirs: A Bayesian TREED model approach

We develop regional-scale eutrophication models for lakes, ponds, and reservoirs to investigate the link between nutrients and chlorophyll-a. The Bayesian TREED (BTREED) model approach allows association of multiple environmental stressors with biological responses, and quantification of uncertainty sources in the empirical water quality model. Nutrient data for lakes, ponds, and reservoirs across the United States were obtained from the Environmental Protection Agency (EPA) National Nutrient Criteria Database. The nutrient data consist of measurements for both stressor variables (such as total nitrogen and total phosphorus), and response variables (such as chlorophyll-a), used in the BTREED model. Markov chain Monte Carlo (McMC) posterior exploration guides a stochastic search through a rich suite of candidate trees toward models that better fit the data. The Bayes factor provides a goodness of fit criterion for comparison of resultant models. We randomly split the data into training and test sets; the training data were used in model estimation, and the test data were used to evaluate out-of-sample predictive performance of the model. An average relative efficiency of 1.02 between the training and test data for the four highest log-likelihood models suggests good out-of-sample predictive performance. Reduced model uncertainty relative to over-parameterized alternative models makes the BTREED models useful for nutrient criteria development, providing the link between nutrient stressors and meaningful eutrophication response.     

Nutrient fluxes in a eutrophic coastal Louisiana freshwater lake

Nitrogen and phosphorus cycling in a eutrophic Louisiana freshwater lake system (Lac des Allemands) was studied. Nutrients from runoff entering the lake, as well as sediment-interstitial and lake water nitrogen and phosphorus fractions, were measured seasonally. Sedimentation rates in the lake were determined using¹³⁷Cs dating.Phosphorus levels in the lake were found to be largely dependent on concentrations in the incoming bayou water from upland drainage. Lake water concentrations appear to respond to fluctuations in incoming waters. Laboratory equilibrium studies showed bottom sediments in the lake are a major sink for the incoming dissolved orthophosphate phosphorus. Total nitrogen concentrations in the lake water generally exceeded incoming runoff concentrations, suggesting fixation by the large blue-green algae population in the lake as being the major source of nitrogen to the system.Sedimentation ranged from 0.44 cm/year to 0.81 cm/year, depending on the proximity to the inlet bayous. Even though the lake is eutrophic the sediment served as a buffer by removing large amounts of carbon, nitrogen, and phosphorus through sedimentation processes. Carbon, nitrogen, and phosphorus were accumulating in the sediment at rates of 60, 7.1, and 1.1 g/m²/year, respectively.The water quality of the lake is likely to continue to decline unless measures are taken to reduce municipal, industrial, and agricultural inputs of phosphorus into the lake.     

Approaches to Evaluate Water Quality Model Parameter Uncertainty for Adaptive TMDL Implementation1

Abstract: The National Research Council recommended Adaptive Total Maximum Daily Load implementation with the recognition that the predictive uncertainty of water quality models can be high. Quantifying predictive uncertainty provides important information for model selection and decision‐making. We review five methods that have been used with water quality models to evaluate model parameter and predictive uncertainty. These methods (1) Regionalized Sensitivity Analysis, (2) Generalized Likelihood Uncertainty Estimation, (3) Bayesian Monte Carlo, (4) Importance Sampling, and (5) Markov Chain Monte Carlo (MCMC) are based on similar concepts; their development over time was facilitated by the increasing availability of fast, cheap computers. Using a Streeter‐Phelps model as an example we show that, applied consistently, these methods give compatible results. Thus, all of these methods can, in principle, provide useful sets of parameter values that can be used to evaluate model predictive uncertainty, though, in practice, some are quickly limited by the “curse of dimensionality” or may have difficulty evaluating irregularly shaped parameter spaces. Adaptive implementation invites model updating, as new data become available reflecting water‐body responses to pollutant load reductions, and a Bayesian approach using MCMC is particularly handy for that task.