大庆油田道路交通噪声的相关分析

根据大庆油田主要交通干线噪声的监测结果与机动车流量、重车率、机动车运行速度及道路宽度等因素进行相关分析，找出了影响交通噪声的主要因素。回归分析表明：交通噪声与上述４个参数均呈正相关，其中机动车流量、道路宽度对交通噪声的影响较小，机动车运行速度对交通噪声的影响较大，对交通噪声影响最大的是重车率。根据分析结果，提出了交通噪声预测模式及控制交通噪声的建议。     

基于Cadna/A软件的110kV户外变电站噪声预测模型研究

简述了110kV变电站噪声源特性。以邻港变电站作为噪声预测研究对象,利用Cadna/A软件分别对5种主流的110kV户外变电站噪声预测模型进行仿真预测。通过预测值与实测值的对比分析表明:110kV户外变电站采用水平+垂直面声源模型能较好地反映变电站噪声衰减情况,并对声源尺寸及源强选取、噪声控制方案提出了建议。     

几种大气预测模式预测结果比较分析

估算模式、AERMOD模式系统、ADMS模式系统均是HJ2.2-2008《环境影响评价技术导则大气环境》中推荐的大气预测模式,为探求此3种大气预测模式预测结果的大小关系规律,选用估算模式、AERMOD模式系统、ADMS模式系统,在简单地形和复杂地形两种条件下,结合一般工业类环评项目中常见的点源、面源案例,对不同预测模式的大气预测结果进行比较分析,得出相应的规律,对环评工作中进一步预测模式的选用具有一定的参考借鉴意义。     

机场飞机噪声的预测研究

本文以机场飞机噪声为研究对象,采用国际民航组织推荐使用的计权等效连续感觉噪声级(LWECPN)为评价量,以广汉机场为例,通过定量分析,利用MATLAB拟合出机场周围各监测点飞机噪声与跑道中心线对数距离的线性方程,然后将该方程用于对未知监测点的LWECPN预测,并与实测数据对比检验其准确度。结果表明,采用该线性拟合预测方法所得到的未知监测点的噪声预测值与实测值较为接近,两者的相对误差保持在1.0%到2.0%之间,预测准确度较好。值得一提的是,该方法操作相对简便,可以达到快速预测机场周边噪声敏感点LWECPN的目的,为今后飞机噪声的预测研究提出一种新的预测方法,具有一定的实际意义。     

博乐市城市环境噪声分析及防治对策探讨

论述了博乐市自1992-2006年城市环境噪声状况,对交通噪声、生活噪声、建筑施工噪声及发展趋势进行了分析,并提出必须从城市总体规划的战略高度统筹实施综合整治,有效地控制噪声污染.     

兵团农五师师部周围主要交通干线噪声污染状况分析

通过对农五师师部周围8条主要交通干线近3年的交通噪声监测与分析,得到各条道路车流量、车速和道路的基本状况,经分析得出,各交通主干道交通压力虽不大,但两侧噪声污染却比较严重,而且污染水平呈逐年上升趋势.     

新建铁路邻近既有铁路的声屏障降噪效果研究

以某既有铁路及新建铁路并行穿越某居民区为例,对该区域的交通噪声现状进行监测分析,表明环境噪声受铁路噪声影响较大,对于临街建筑的影响为从低楼层至高楼层呈逐渐增大的趋势。考虑既有铁路和新建铁路的共同影响,建立了Cadna/A噪声预测模型,预测了采用声屏障降噪措施前后区域的环境噪声空间分布特征。结果表明,设置声屏障措施能有效减少铁路噪声的影响,预测点昼间均能达标,夜间达标率为50%,但超标点位的预测值基本相当或优于现状值。     

成都市新建过境二环路交通噪声预测评估讨论

本文对新建过境二环路交通噪声的影响,用类比测量一环路交通噪声的方法,通过理论和实测数据的统计分析,预测机动车辆流量、种类、速度、道路宽、快慢车道分隔绿化带宽、两侧建筑高与距离等,确立计算公式,预测二环路建成后交通噪声影响的趋势。为控制交通噪声,计算了交通噪声辐射的影响;干线平均车流量与高峰的实际噪声表减:道路两侧建筑物受交通噪声影响的最小距离。为城市规划和建设部门提供声学环境的参考依据。     

基于RS和GIS的山东省17城市生态环境质量动态变化研究

运用遥感动态监测与地理信息系统技术相结合的方法,以2005年和2009年遥感解译数据、土地侵蚀数据及环境统计数据为数据源,依据《生态环境状况评价技术规范(试行)》(HJ/T192-2006),对山东省17个城市生态环境质量现状及动态变化趋势进行了评价。结果表明:2009年17个城市生态环境状况指数在59.81～78.08之间,生态环境质量状况总体良好;2005-2009年17城市生态环境状况指数变化值在0.06～3.5之间,生态环境质量状况基本稳定。     

基于BP模型对城市交通噪声的数据处理和预测

城市交通噪声的预测和评价技术是城市交通可持续发展的重要研究内容,直接为城市交通规划中环境容量分析和环境影响评价服务。本文通过实测的大量数据,运用神经网络中的BP模型及其算法建立车辆数、道路宽度和交通噪声之间的关系,对城市道路交通噪声数据进行处理和预测。     

滇池水污染物浓度预测的人工神经网络模型

本文针对滇池日益严重的水污染现状,根据云南昆明西苑隧道断面2004年-2010年的监测资料,建立了基于BP神经网络的主要污染指标预测模型,并对其进行训练检验,研究结果表明：独立样本中pH、溶解氧、氨氮、高锰酸盐浓度的预测值与监测值的线性相关系数分别为0．952、0．967、0．945、0．936。结果证明该模型预测精度满足要求,通过准确地预测湖泊水污染物可以为治理湖泊营养化和综合利用水资源、规划管理、决策提供重要的科学依据。     

“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.     

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.     

Numerical and experimental investigation of state of health of Li-ion battery

ABSTRACT

Estimation of State of Health (SoH) of Lithium-ion (Li-ion) battery is essential to predict the lifespan of batteries of an electric vehicle (EV). The efficient prediction of battery health indicates to the effective and safe operation of EV. However, delivering an effective and accurate method for the estimation of SoH in the real condition is truly a challenging task. The present study proposed a holistic procedure of combining both experimental and numerical investigations to conduct the fundamental study on coupled mechanical-electrochemical behavior of Li-ion battery. The proposed investigation highlighted the effect of stress on the capacity of the battery, considering capacity fade as an equivalent parameter to its health for real-time estimation of SoH. Finally, a simple model of Artificial Neural Network (ANN) is provided, which shows the linear dependency of stress with the SoH. The results obtained from the ANN model are validated with a Linear Regression (LR) model for a better understanding of the inspection. The predicted value of mean Square Error (MSE) and R square error in the ANN training model are found to be 0.000309 and 0.849687, respectively. Whereas for the test model, these predicted values are found to be 0.000438 and 0.819347, respectively.     

Phosphorus loading in the Frome catchment, UK: seasonal refinement of the coefficient modeling approach

This paper describes the results of an export coefficient modeling approach to predict total phosphorus (TP) loading in the Frome catchment, Dorset, UK from point and diffuse sources on a seasonal (monthly) basis in 1998 and on an annual basis for 1990-1998. The model predicted an annual TP load of 25 605 kg yr(-1), compared with an observed (measured) value of 23400 kg yr(-1). Monthly loads calculated using the export coefficient model agreed well with monthly observed values except in months of variable discharge, when observed values were low, probably due to infrequent, and therefore unrepresentative, sampling. Comparison between filterable reactive phosphorus (FRP) and TP concentrations observed in the period 1990-1997 showed that trends in FRP could be estimated from trends in TP. A sensitivity analysis (varying individual export coefficients by +/-10%) showed that sewage treatment works (STWs) (3.5%), tilled land (2.7%), meadow-verge-seminatural (1.0%), and mown and grazed turf (0.6%) had the most significant effect (percent difference from base contribution) on model prediction. The model was also used to estimate the effect of phosphorus stripping at STWs in order to comply with a pending change in the European Union wastewater directive. Theoretical reduction of TP from the largest STW in the catchment gave a predicted reduction in TP loading of 2174 kg yr(-1). This illustrates the value of this seasonal export coefficient model as a practical management tool.     

Dynamic respiration index as a descriptor of the biological stability of organic wastes

Analytical methods applicable to different organic wastes are needed to establish the extent to which readily biodegradable organic matter has decomposed (i.e., biological stability). The objective of this study was to test a new respirometric method for biological stability determination of organic wastes. Dynamic respiration index (DRI) measurements were performed on 16 organic wastes of different origin, composition, and biological stability degree to validate the test method and result expression, and to propose biological stability limits. In addition, theoretical DRI trends were obtained by using a mathematical model. Each test lasted 96 h in a 148-L-capacity respirometer apparatus, and DRI was monitored every hour. The biological stability was expressed as both single and cumulative DRI values. Results obtained indicated that DRI described biological stability in relation to waste typology and age well, revealing lower-stability waste characterized by a well-pronounced DRI profile (a marked peak was evident) that became practically flat for samples with higher biological stability. Fitting indices showed good model prediction compared with the experimental data, indicating that the method was able to reproduce the aerobic process, providing a reliable indication of the biological stability. The DRI can therefore be proposed as a useful method to measure the biological stability of organic wastes, and DRI values, calculated as a mean of 24 h of the highest microbial activity, of 1000 and 500 mg O(2) kg(-1) volatile solids (VS) h(-1) are proposed to indicate medium (e.g., fresh compost) and high (e.g., mature compost) biological stabilities, respectively.     

Comparison of DAYCENT-simulated and measured nitrous oxide emissions from a corn field

Accurate assessment of N(2)O emission from soil requires continuous year-round and spatially extensive monitoring or the use of simulation that accurately and precisely predict N(2)O fluxes based on climatic, soil, and agricultural system input data. DAYCENT is an ecosystem model that simulates, among other processes, N(2)O emissions from soils. The purpose of the study was to compare N(2)O fluxes predicted by the DAYCENT model to measured N(2)O fluxes from an experimental corn field in central Iowa. Soil water content temperature and inorganic N, simulated by DAYCENT were compared to measured values of these variables. Field N(2)O emissions were measured using four replicated automated chambers at 6-h intervals, from day of year (DOY) 42 through DOY 254 of 2006. We observed that DAYCENT generally accurately predicted soil temperature, with the exception of winter when predicted temperatures tended to be lower than measured values. Volumetric water contents predicted by DAYCENT were generally lower than measured values during most of the experimental period. Daily N(2)O emissions simulated by DAYCENT were significantly correlated to field measured fluxes; however, time series analyses indicate that the simulated fluxes were out of phase with the measured fluxes. Cumulative N(2)O emission calculated from the simulations (3.29 kg N(2)O-N ha(-1)) was in range of the measured cumulative N(2)O emission (4.26 +/- 1.09 kg N(2)O-N ha(-1)).     

Decomposition and plant-available nitrogen in biosolids: laboratory studies,field studies,and computer simulation

This research combines laboratory and field studies with computer simulation to characterize the amount of plant-available nitrogen (PAN) released when municipal biosolids are land-applied to agronomic crops. In the laboratory studies, biosolids were incubated in or on soil from the land application sites. Mean biosolids total C, organic N, and C to N ratio were 292 g kg(-1), 41.7 g kg(-1), and 7.5, respectively. Based on CO2 evolution at 25 degrees C and optimum soil moisture, 27 of the 37 biosolids-soil combinations had two decomposition phases. The mean rapid and slow fraction rate constants were 0.021 and 0.0015 d(-1), respectively, and the rapid fraction contained 23% of the total C assuming sequential decomposition. Where only one decomposition phase existed, the mean first order rate constant was 0.0046 d(-1). The mean rate constant for biosolids stored in lagoons for an extended time was 0.00097 d(-1). The only treatment process that was related to biosolids treatment was stabilization by storage in a lagoon. Biosolids addition rates (dry basis) ranged from 1.3 to 33.8 Mg ha(-1) with a mean value of 10.6 Mg ha(-1). A relationship between fertilizer N rate and crop response was used to estimate observed PAN at each site. Mean observed PAN during the growing season was 18.9 kg N Mg(-1) or 37% of the biosolids total N. Observed PAN was linearly related to biosolids total N. Predicted PAN using the computer model Decomposition, actual growing-season weather, actual analytical data, and laboratory decomposition kinetics compared well with observed PAN. The mean computer model prediction of growing-season PAN was 19.2 kg N Mg(-1) and the slope of the regression between predicted and observed PAN was not significantly different from unity. Predicted PAN obtained using mean decomposition kinetics was related to predicted PAN using actual decomposition kinetics suggesting that mean rate constants, actual weather, and actual analytical data could be used in estimation of PAN. There was a linear relationship between predicted N mineralization for the growing season and for the first year. For this study, the mean values for the growing season and year were 27 and 37% of the organic N, respectively.     

TurfPQ, a pesticide runoff model for turf

Environmental assessments of golf courses and other turf systems must often rely on mathematical modeling. However, in the case of pesticide runoff, successful modeling applications are rare. Available models were developed for agricultural applications and have seen very limited testing for turf. TurfPQ is a pesticide runoff model developed exclusively for turf. The model is based on a curve number calculation for runoff volume and linear partitioning of pesticide into adsorbed and dissolved components during a precipitation or irrigation event. Calibration is optional, so the model can be applied, using default parameter values, to situations where runoff and chemical loss data are unavailable. TurfPQ was tested with default parameter values for 52 pesticide runoff events involving six pesticides measured in plot studies in four states. The model typically produced conservative overpredictions of pesticide runoff, particularly with strongly adsorbed pesticides. Mean predicted pesticide runoff was 2.9% [corrected] of application, compared with an observed mean of 2.1%. TurfPQ captured the dynamics of the pesticide runoff events well with R2 = 0.65 [corrected]. Sensitivity analyses indicated that prediction errors could be reduced by better estimates of adsorption parameters and runoff curve numbers. However, even with default parameters, TurfPQ predictions are at least as accurate as those produced by more complex models.