影响系数矩阵在河流水质规划中的应用

河流水质规划中常常要用水质数学模型来模拟水体的水质、刻划岸边污染带、对水质进行预测，并结合其它模型对污染物排放进行总量控制，以及对污染方案进行水质验证。由于水质数学模型的单向性（即数据只能从输入流向输出），在实际工作中当需要逆计算时（如计算水环境容量等）往往采取试算法，工作量较大。本文介绍的影响系数矩阵法可以方便快捷地完成上述各项工作。此法在中英国际合作项目“长江、嘉陵江水污染控制规划研究”中已采用，效果较好。     

陕西关中地区水环境承载力研究

从水环境、人口、经济发展之间的关系入手，分析探讨水环境承载力的内涵，并在此基础上，建立了研究区域水环境载力的大系统分解协调模型，将模型应用于关中地区，得到不同方案下关中地区水环境承载力及提高关中水环境承载力最优策略。     

Modeling the Effects of Air Quality Policy Changes on Water Quality in Urban Areas

This paper describes the development and application of an integrated modeling framework composed of an urban air chemistry model, an urban runoff model, and a water-quality model. The models were linked to simulate the fate and transport of air emissions of nitrogen compounds in the air, urban watershed, surface water runoff, and in a coastal receiving-water body. The model linkage is demonstrated by evaluating the potential water quality implications of reducing NO _x emissions by 32%, volatile organic compound emissions by 51%, and ammonia emissions by 30%, representing changes from 1987 levels to proposed 2000 target levels in Los Angeles, California, USA. Simulations of the Los Angeles dry season during the summer of 1987 (June 1 to August 31) indicated that by reducing emissions from 1987 to proposed year 2000 levels, the dry deposition nitrogen loads to Santa Monica Bay and the Ballona Creek watershed were reduced 21.4% and 15.0%, respectively. Water quality modeling results indicated that dry season atmospheric load reductions to the Ballona Creek Estuary did not reduce chlorophyll-a levels or significantly raise nighttime dissolved oxygen levels because the magnitude of the reductions was negligible compared to non-atmospheric inputs of nitrogen compounds. Simulations of the time period from November 18, 1987 to December 4, 1987 during the Los Angeles wet season indicated that air emissions reductions produced an 18.6% reduction in the dry deposition nitrogen load to Santa Monica Bay, a 15.5% reduction in the dry deposition nitrogen load to the Ballona Creek watershed, a 16.8% reduction in the wet deposition nitrogen load to the Ballona Creek watershed, and a 16.1% reduction in the stormwater discharge load from the Ballona Creek watershed. Although the wet season load reductions are significant, modeling results of the ultimate effect on the Ballona Creek Estuary water quality were inconclusive.     

A method for assessing generalised nitrogen leaching estimates for agricultural land

The purpose of this study was to develop a method for assessing generalised N leaching estimates from large areas of agricultural land. The system developed was based on calculating a number of N leaching estimates for different typical cropping situations. The estimates were normalised with respect to varying weather conditions and crop production. The different cropping situations were described by setting up a matrix consisting of crucial factors influencing leaching such as soils, crops and climate. Nitrogen leaching was then estimated for a number of combinations of these factors. Calculations were made for three different regions where all the major crops were cultivated on soils with seven different textures and four different organic-N classes and two fertilisation regimes. The three regions are representative of climates and agricultural practices in some of the major agricultural areas in Sweden. The model used was the SOILN model. Leaching of nitrogen from the root zone showed large variations. The range was from 1 to 50 kg ha⁻¹ for different soils and crops when only fertiliser N was applied. Leaching varied both due to different climates and differences in cultivation practices between the regions. Leaching decreased in a south-north gradient. Leaching increased as a result of greater mineralisation when the organic matter content in the soils was increased, leaching was less from soils with a high clay content and was very small for the heavy clay soil. This revised version was published online in July 2006 with corrections to the Cover Date.     

Distributed process modeling for regional assessment of coastal vulnerability to sea-level rise

Sea-level rise involves increases in the coastal processes of inundation and erosion which are affected by a complex interplay of physical environmental parameters at the coast. Many assessments of coastal vulnerability to sea-level rise have been detailed and localised in extent. There is a need for regional assessment techniques which identify areas vulnerable to sea-level rise. Four physical environmental parameters – elevation, exposure, aspect and slope, are modeled on a regional scale for the Northern Spencer Gulf (NSG) study area using commonly available low-resolution elevation data of 10 m contour interval and GIS-based spatial modeling techniques. For comparison, the same parameters are modeled on a fine-scale for the False Bay area within the NSG using high-resolution elevation data. Physical environmental parameters on the two scales are statistically compared to coastal vulnerability classes as identified by Harvey et al. [1] using the Spearman rank-correlation test and stepwise linear regression. Coastal vulnerability is strongly correlated with elevation and exposure at both scales and this relationship is only slightly stronger for the high resolution False Bay data. The results of this study suggest that regional scale distributed coastal process modeling may be suitable as a first cut in assessing coastal vulnerability to sea-level rise in tide-dominated, sedimentary coastal regions. Distributed coastal process modeling provides a suitable basis for the assessment of coastal vulnerability to sea-level rise of sufficient accuracy for on-ground management and priority-setting on a regional scale.     

SO2 modeling in İzmit Gulf, Turkey during the winter of 1997: 3 cases

In this study, SO₂ dispersion over İzmit Gulf is simulated by California Puff (CALPUFF) model for three air pollution cases, which occurred on January 28, February 12, and February 26, 1997. These days are generally characterized by dominant high-pressure systems – pressure values reaching 1032 mb, low wind speeds and sometimes calm conditions, and low temperatures with a minimum of 0°C. Hourly simulations during those critical cases were carried out and results revealed very high concentrations of SO₂ transported to the downwind regions of Tüpraş and Gebze, and values sometimes exceeded 1,000 μg/m³. Nighttime and morning simulations associated with inversion produced considerably higher values of SO₂ than the afternoon simulations associated with breeze. Model verification was conducted by comparing the measured daily average values of eight stations with the model predicted values at the same receptor points. Results showed that the model well predicted the values at station Gebze in all three cases. The model sometimes underestimated and sometimes overestimated the concentrations at other receptor stations.     

Evaluating response surface designs for uncertainty analysis and prescriptive applications of a large-scale water quality model

Environmental models are often too large and cumbersome for effective use in regulatory decision making or in the characterization of uncertainty. This paper describes and compares four response surfaces that could complement a large-scale water quality model, the U.S. National Water Pollution Control Assessment Model (NWPCAM), in simulation and regulatory decision support applications. Results show that a physically based reduced-form model that exploits the mathematical structure of the underlying water quality model is a better predictor of policy-relevant outputs than the polynomial expansions that are frequently used in response surface studies.     

Stream Temperature Modeling Using Functional Regression Models

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.     

Toward Explaining Nitrogen and Phosphorus Trends in Chesapeake Bay Tributaries, 1992–2012

Understanding trends in stream chemistry is critical to watershed management, and often complicated by multiple contaminant sources and landscape conditions changing over varying time scales. We adapted spatially referenced regression (SPARROW) to infer causes of recent nutrient trends in Chesapeake Bay tributaries by relating observed fluxes during 1992, 2002, and 2012 to contemporary inputs and watershed conditions. The annual flow‐normalized nitrogen flux to the bay from its watershed declined by 14% to 127,000 Mg (metric tons) between 1992 and 2012, due primarily (more than 80% of the decline) to reduced point sources. The remainder of the decline was due to reduced atmospheric deposition (13%) and urban nonpoint sources. Agricultural inputs, which contribute most nitrogen to the bay, changed little, although trends in the average nitrogen yield (flux per unit area) from cropland and pasture to streams in some settings suggest possible effects of evolving nutrient applications or other land management practices. Point sources of phosphorus to local streams declined by half between 1992 and 2012, while nonpoint inputs were relatively unchanged. Annual phosphorus delivery to the bay increased by 9% to 9,570 Mg between 1992 and 2012, however, due mainly to reduced retention in the Susquehanna River at Conowingo Reservoir.     

Modeling the probability of freeway lane-changing collision occurrence considering intervehicle interaction

Objective: To better capture the relationships between lane-changing collisions and explanatory variables, a microscopic model is developed for freeway lane-changing collisions based on the interactions between lane-changing vehicles.

Methods: The model applies an intervehicle interaction structure to account for the occurrence mechanism of lane-changing collisions. The occurrence mechanism can be described as the failure of a vehicle driver of an adjacent lane in avoiding the lane-changing vehicle, which disturbs the smooth movement of the adjacent lane vehicle and requires the driver's brake action to avoid an angle collision. This model is examined using data collected from freeways in Washington State during 2010 to 2011 and validated using lane-changing collision data for the SR 520 freeway.

Results: The findings of this study show that generalized truck percentage has a significant decreasing effect on lane-changing collision risk, whereas average spacing and several roadway characteristics have significant increasing effects. The frequency of slight collisions during peak hours is higher than that during off-peak hours. Young female drivers are more likely to be involved in collisions during lane-changing than young male drivers, but the result for senior drivers is opposite, with older male drivers having a higher probability of lane-changing collisions than female drivers in the same age group.

Conclusion: The process of lane-changing collisions is a complicated maneuver. Truck percentage, average spacing, and good roadway characteristics, such as straight and level segment, in the target lane have a significant effect on the occurrence of lane-changing collisions. Age and gender are also 2 important factors contributing to the relationship between lane-changing collisions and explanatory variables.