城市垃圾填埋场项目环境影响评价工作探讨

垃圾填埋场作为环保手段的同时,其排放的污染物也对周边环境产生长久的负面影响,在建设前进行环境影响评价,分析论证环保措施的有效性,可以最大限度地将环境影响控制在一定范围内。结合实际案例,从污染源分析、环保措施论证、环境影响预测等几个方面对垃圾填埋场项目环境影响评价的几个重要方面进行论述。     

合成氨厂NH3无组织排放的环境影响及污染防治措施

以某合成氨厂为例,预测分析不同的气象条件下NH3无组织排放所造成的环境影响,并提出了污染防治措施。     

侧流化学磷回收强化生物除磷的模拟预测与试验验证

为了强化污水中生物除磷作用,本研究通过模拟预测与实验室试验验证了厌氧上清液侧流化学磷沉淀与回收对强化生物除磷的促进作用.模拟预测与试验结果表明,在进水COD/P=37.5工况下,当侧流比增加至30%时,通过化学磷沉淀（调节pH＞9.0）可使出水中TP浓度从碳源抑制时的＜6.0 mg·L-1（以P计）下降至≤1.0 mg·L-1（以P计）,同时可回收进水中P负荷的64%.经验证与校正后的TUD数学模型模拟预测有着与试验结果近乎一致的效果.因此,数学模拟技术完全有可能取代中间试验过程而直接将小试结果放大至工程应用.     

Investigating the effectiveness of safety countermeasures at highway-rail at-grade crossings using a competing risk model

Introduction: Highway-rail at-grade crossings (HRGCs) are critical locations where a railway and a roadway intersect with one another. Crashes at those locations often result in fatalities and economic and social damages due to the impacts on both road and rail users. The main purpose of countermeasures at HRGCs is to permit safe and efficient rail and highway operations. Method: Countermeasures at highway-rail grade crossings (HRGCs) considered in this study include all traffic control devices and other warning and barrier devices at or on approaches to crossings. In general, active devices are commonly accepted as more effective countermeasures than passive devices. However, many of the previous effectiveness studies are either at the project level or were conducted without considering the before-improvement condition. This study focuses on the network-level marginal effectiveness of countermeasures on crash rate and severity levels during the 29-year study period from 1990 to 2018 by fully considering before-improvement control levels. A competing risk model (CRM) is able to accommodate the competing nature of crash severities as multiple outcomes from the same event of interest, which is crash occurrence in this study. Subsequently, CRM is used in this study as an integrated one-step estimation approach that investigates both crash frequency and severity likelihood over time. Results: The study findings indicate that adding audible devices to crossings already equipped with gates will result in a considerable annual decline in crash occurrence likelihood (0.25%). The same device installed at crossings already controlled by gates and flashing lights results in less reduction in crash occurrence likelihood of 0.14%. Moreover, adding a stop sign to the active crossing controls of gates, standard flashing lights, and audible devices will lead to a decrease in the probability of crash occurrence and severe crashes (injury and fatal). However, adding stop signs to crossings equipped only with crossbucks will increase the crash occurrence.     

Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.     

温岭市东环高架交通对声环境的影响评价与预测

相较于一般的城市道路,城市高架具有通行能力大、行车速度高、车辆行驶状态复杂的特点,噪声污染极为突出.温岭市东环高架穿越温岭市区,是进出市区唯一的快速通道.以温岭市东环高架道路为例,对其进行实地监测从而了解其声环境,同时运用BREEZE NOISE软件模拟其噪声污染状况.根据监测模拟结果,从合理进行道路规划、装设声屏障和铺设低噪声路到提出高架复合道路噪声污染控制的对策建议.     

基于GM(1,1)模型群的阿什河氨氮浓度的预测研究

依据灰色系统理论,以2000年-2009年10年阿什河入江口断面枯水期氨氮浓度构造了一个由6个GM(1,1)模型组成的灰色动态模型群,并运用该模型群对其变化趋势进行了预测分析,得到令人满意的结果。研究表明,灰色动态模型群法能够充分利用近期水质资料信息预测未来水质变化趋势;以模型群统计平均值作为最终预测值,避免了单一灰色模型容易利用不稳定信息的缺陷,使得预测精度更加准确,预测结果更为可信。     

震前小震调制比空间异常消失的映震能力研究

利用小震调制比方法的时空异常进行跟踪分析,通过对比发现,近几年震前小震调制比的空间异常变化与有感地震的发生具有一定的对应关系.进一步的分析研究认为,利用震前小震调制比的空间异常消失可进行有感地震甚至中强地震的地点预测.     

天然水体中铝浓度的预测方法探讨

本文概括了天然水体中铝的形态分类和地球化学行为,综述了天然水体中Ａｌ＾３＋和有机铝的浓度的预测方法,提出了由矿物浓度积,无机铝和有机铝经验关系式,有机铝模型辅以配体浓度和热力学常数预测天然水体中各形态铝的浓度的方法。     

“R1-R4” AND “BOISED” SEDIMENT PREDICTION MODEL TESTS USING FOREST ROADS IN GRANITICS1

ABSTRACT: Erosion and sedimentation data from research watersheds in the Silver Creek Study Area in central Idaho were used to test the prediction of logging road erosion using the R1-R4 sediment yield model, and sediment delivery using the “BOISED” sediment yield prediction model. Three small watersheds were instrumented and monitored such that erosion from newly constructed roads and sediment delivery to the mouths of the watersheds could be measured for four years following road construction. The errors for annual surface erosion predictions for the two standard road tests ranged from +31.2 t/ha/yr (+15 percent) to -30.3 t/ha/yr (-63 percent) with an average of zero t/ha/yr and a standard deviation of the differences of 18.7 t/ha/yr. The annual prediction errors for the three watershed scale tests had a greater range from -40.8 t/ha/yr (-70 percent) to +65.3 t/ha/yr (+38 percent) with a mean of -1.9 t/ha/yr and a standard deviation of the differences of 25.2 t/ha/yr. Sediment yields predicted by BOISED (watershed scale tests) were consistently greater (average of 2.5 times) than measured sediment yields. Hillslope sediment delivery coefficients in BOISED appear to be overly conservative to account for average site conditions and road locations, and thus over-predict sediment delivery. Mass erosion predictions from BOISED appear to predict volume well (465 tonnes actual versus 710 tonnes predicted, or a 35 percent difference) over 15 to 20 years, however mass wasting is more episodic than the model predicts.