长江南通段污染带影响因素研究

通过建立应用贴体坐标系和有限体积法求解的二维水流－水质方程的模型，对潮汐河口南通段污染带的影响因素进行研究。揭示了水深、水面比降、纵向及横向混合系数以及污染物的排放时段与污染带范围的相关关系，并进行了各影响因素的灵敏度分析，对灵敏度大小进行了排序：水深＞水面比降＞横向混合系数＞纵向混合系数＞排放时段。     

基于“压力-状态-响应”模型的宝鸡市生态环境敏感性评价

生态环境敏感性评价研究对于区域社会、经济与生态环境可持续发展具有重要现实意义。基于"压力-状态-响应"(PSR)模型构建相关指标体系,运用均方差赋权法对选取的2001~2013年宝鸡市生态环境"压力-状态-响应"层共25项指标进行评价研究。结果表明:生态环境压力指数和响应指数是影响宝鸡市生态环境敏感性变化的主要因素;受人类活动影响,2001~2013年宝鸡市生态环境敏感性不断波动,其发展大致经历了4个阶段:即2001~2004年属于较高敏感阶段,2005~2009年属于中度敏感阶段,2010~2011年属于较低敏感阶段,2012~2013年属于低敏感阶段。调整区域产业结构、优化经济发展方式、加强生态环境治理力度,是降低宝鸡市生态环境敏感性,实现区域人与社会、经济、资源和生态环境持续协调发展的有效途径。     

Management-oriented sensitivity analysis for pesticide transport in watershed-scale water quality modeling using SWAT

The Soil and Water Assessment Tool (SWAT) was calibrated for hydrology conditions in an agricultural watershed of Orestimba Creek, California, and applied to simulate fate and transport of two organophosphate pesticides chlorpyrifos and diazinon. The model showed capability in evaluating pesticide fate and transport processes in agricultural fields and instream network. Management-oriented sensitivity analysis was conducted by applied stochastic SWAT simulations for pesticide distribution. Results of sensitivity analysis identified the governing processes in pesticide outputs as surface runoff, soil erosion, and sedimentation in the study area. By incorporating sensitive parameters in pesticide transport simulation, effects of structural best management practices (BMPs) in improving surface water quality were demonstrated by SWAT modeling. This study also recommends conservation practices designed to reduce field yield and in-stream transport capacity of sediment, such as filter strip, grassed waterway, crop residue management, and tailwater pond to be implemented in the Orestimba Creek watershed.     

A Sensitivity Analysis of an Integrated Modelling Approach to Assess the Risk of Wind-borne Spread of Foot-and-mouth Disease Virus from Infected Premises

The sensitivity of an integrated model to assess the potential for wind-borne spread of foot-and-mouth disease (FMD) to variations in key parameters controlling different physical and biological processes was evaluated. The estimated number of farms at risk is sensitive to the virus strain used and the accompanying effective contact rate. The C Noville strain increased the estimated number of exposed farms ranked as high and medium risk of being infected by a factor of 5, compared to the baseline, based on the O UKG 2001 strain. The inclusion of a model for biological ageing of the virus can also have a significant effect on the concentration patterns arising from transport and dispersion of the virus. Its inclusion has the practical advantage of markedly reducing the time required for the calculations. The estimated number of farms affected by exposure to high and medium virus concentrations is not grossly sensitive to attenuation caused by temperature or relative humidity effects. Changes in susceptibility to infection, as determined by the parameter θ in the exposure-risk model, does not change the configuration of the virus plumes, but it does change the distribution of farms at risk by risk category. These findings suggest that a good understanding of characteristics (excretion rates from infected animals, susceptibility of different species to infection, virus survival, etc.) of the virus strain involved in an FMD outbreak is necessary to provide a reliable assessment of the risk of wind-borne spread. In the event of an incursion of FMD, provision for laboratory studies on the virus will be an essential component of the disease response and should be factored into contingency plans.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Year-round observation of atmospheric inorganic aerosols in urban Beijing: Size distribution, source analysis, and reduction mechanism

To investigate particle characteristics and find an effective measure to control severe particle pollution, year-round observation of size-segregated inorganic aerosols was conducted in Beijing from January to December, 2016. The sampled atmospheric particles all presented bimodal size distribution at four pollution levels (clear, slight pollution, moderate pollution and severe pollution), and peak values appeared at the size range of 0.7-2.1 μm and >9.0 μm, respectively. As dominant particle compositions, NO₃⁻, SO₄²⁻, and NH₄⁺ in four pollution levels all showed significant peaks in fine mode, especially at the size range of 1.1-2.1 μm. Secondary inorganic aerosols accounted for about 67.6% (36.3% (secondary sulfates) + 31.3% (secondary nitrates)) of the total sources of fine particles in urban Beijing. Severe pollution of fine particles was mainly caused by the air masses transported from nearby western and southern areas, which are industrial and densely populated region, respectively. Sensitivity tests further revealed that the control measures focusing on ammonium emission reduction was the most effective for particle pollution mitigation, and fine particles all showed nonlinear responses after reducing ammonium, nitrate, and sulfate concentrations, with the fitting curves of y = -120.8x - 306.1x² + 290.2x³, y = -43.5x - 67.8x², and y = -25.8x - 110.4x² + 7.6x³, respectively (y and x present fine particle mass variation (μg/m³) and concentration reduction ratio (CRR)/100 (dimensionless)). Overall, our study presents useful information for understanding the characteristics of atmospheric inorganic aerosols in urban Beijing, as well as offers policy makers with effective measure for mitigating particle pollution.     

基于重要性分析的甲烷/乙烯混合燃烧框架机理构筑

建立了基于重要性分析的详细化学机理分析平台,利用耦合组分化学存活时间和敏感性系数的重要性参数,确定详细机理中的准稳态组分,通过移除这些组分及其相关反应,得到了计算精度较高的框架机理。针对目前燃烧学界较为关注的混合燃烧问题,以甲烷、乙烯这两种典型低碳碳氢燃料为研究对象,对其详细化学反应机理进行了分析,利用重要性分析法构筑框架机理,并对甲烷/空气和甲烷/乙烯/空气预混火焰进行了数值计算。与详细机理相比,框架机理所涉及的组分数与基元反应数都得到了大幅度的降低,计算时间明显减少,但对火焰温度及反应物、生成物、中间组分浓度的预测与采用详细机理得到的结果吻合良好,证明了重要性分析法的有效性与可靠性。     

Quantitative Pollution Spill Risk Assessment: Using A Gis-Based System

Federal and State agencies have recently advocated risk-based analysis as a mechanism for advancing regulatory reform and safety determination in marine systems. the present investigation promotes this objective through the development of risk-based environmental planning strategies for oil spill contingency plans. This alternative approach to contingency planning departs from conventional methodology by employing quantitative risk assessment methods to identify hazardous oil spill zones and sensitive environmental areas, R_o and R_e respectively. the product of this conversion is referenced on a single “Risk” layer within a Geographic Information System (GIS) framework allowing coastal managers to evaluate natural resource data with associated elements of oil spill risk. As a new tool for coastal pollution management, risk-based environmental planning strategies have shown potential for evolving more efficient oil spill contingency plans.     

Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis

Identifying source information after river chemical spill occurrences is critical for emergency responses. However, the inverse uncertainty characteristics of this kind of pollution source inversion problem have not yet been clearly elucidated. To fill this gap, stochastic analysis approaches, including a regional sensitivity analysis method, identifiability plot and perturbation methods, were employed to conduct an empirical investigation on generic inverse uncertainty characteristics under a well-accepted uncertainty analysis framework. Case studies based on field tracer experiments and synthetic numerical tracer experiments revealed several new rules. For example, the release load can be most easily inverted, and the source location is responsible for the largest uncertainty among the source parameters. The diffusion and convection processes are more sensitive than the dilution and pollutant attenuation processes to the optimization of objective functions in terms of structural uncertainty. The differences among the different objective functions are smaller for instantaneous release than for continuous release cases. Small monitoring errors affect the inversion results only slightly, which can be ignored in practice. Interestingly, the estimated values of the release location and time negatively deviate from the real values, and the extent is positively correlated with the relative size of the mixing zone to the objective river reach. These new findings improve decision making in emergency responses to sudden water pollution and guide the monitoring network design.

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Sensitivity analysis of the two dimensional application of the Generic Ecological Model (GEM) to algal bloom prediction in the North Sea

Harmful algae can cause damage to co-existing organisms, tourism and farmers. Accurate predictions of algal future composition and abundance as well as when and where algal blooms may occur could help early warning and mitigating. The Generic Ecological Model is an instrument that can be applied to any water system (fresh, transitional or coastal) to calculate the primary production, chlorophyll-a concentration and phytoplankton species composition. It consists of physical, chemical and ecological model components which are coupled together to build one generic and flexible modelling tool. In this paper the model has been analyzed to assess sensitivity of the simulated chlorophyll-a concentration to a subset of ecologically significant input factors. Only a small number of approaches could be considered as suitable for several reasons including the model complexity, engagement of numerous interacting parameters and relatively long time of a single simulation. Thus, sensitivity analysis has been carried out with the use of the Morris method and later enriched by the computation of the correlation ratios of the selected parameters on the model response at more than a few locations in the modelled area. The obtained results are in agreement with expert knowledge of the ecological processes in the North Sea and correspond well with local characteristics.