基于HRS和GIS的城市污染场地动态监测原理分析

选择有代表性的城市场地,运用网格法土壤采样,并通过实验室化学分析得出土壤的重金属等污染物质含量,运用地物光谱仪在实验室设定条件下测定土壤反射光谱,应用偏最小二乘法（PLS）建立高光谱遥感（HRS）影像定量反演模型,预测场地土壤中各种污染物的含量,并在遥感影像中直观表示。结果表明,HRS技术能提供大量、连续性的光谱信息;PLS模型为大尺度城市场地污染快速评价与监测提供了可能性;并通过结合G IS技术可实现城市污染场地的动态监测,为城市公共管理提供决策支持。     

Roadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia

Currently, vehicle-related particulate matter is the main determinant air pollution in the urban environment. This study was designed to investigate the level of fine (PM_2.5) and coarse particle (PM₁₀) concentration of roadside vehicles in Addis Ababa, the capital city of Ethiopia using artificial neural network model. To train, test and validate the model, the traffic volume, weather data and particulate matter concentrations were collected from 15 different sites in the city. The experimental results showed that the city average 24-hr PM_2.5 concentration is 13%–144% and 58%–241% higher than air quality index (AQI) and world health organization (WHO) standards, respectively. The PM₁₀ results also exceeded the AQI (54%–65%) and WHO (8%–395%) standards. The model runs using the Levenberg-Marquardt (Trainlm) and the Scaled Conjugate Gradient (Trainscg) and comparison were performed, to identify the minimum fractional error between the observed and the predicted value. The two models were determined using the correlation coefficient and other statistical parameters. The Trainscg model, the average concentration of PM_2.5 and PM₁₀ exhaust emission correlation coefficient were predicted to be (R² = 0.775) and (R² = 0.92), respectively. The Trainlm model has also well predicted the exhaust emission of PM_2.5 (R² = 0.943) and PM₁₀ (R² = 0.959). The overall results showed that a better correlation coefficient obtained in the Trainlm model, could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.     

北京市公交车噪声测试与分析

公交车已成为当前北京市道路交通噪声的主要束源之一,针对公交车声源模型缺乏而沿用大型车声源模型所致的噪声预测误差问题,在北京市选取了两类常见公交车进行了537辆车的单车通过噪声测试,在无效数据剔除和背景噪声修正后,利用回归分析法获得了北京市公交车声源模型,通过与现有《公路建设项目环境影响评价规范》中大型车声源模型的比较,显示出建立北京市公交车噪声声源模型的必要性。基于《公路建设项目环境影响评价规范》中的道路交通噪声预测方法,提出了符合北京市实际情况的道路交通噪声预测模型。     

关于“十四五”生态环境监测支撑能力提升的思路与目标

生态环境监测是推进国家生态环境治理体系和治理能力现代化的重要支撑。党中央、国务院高度重视监测事业发展,将生态环境监测纳入生态文明改革的大局统筹推进。"十四五"时期,生态文明改革持续深入,生态环境治理将向精准治污、科学治污、依法治污转变,生态环境监测面临着全新的压力与挑战。因此,笔者以习近平生态文明思想为指导,按照"面向发展、服务公众、智慧监测、精准支撑"的原则,针对大气、地表水、土壤、地下水、生态等重点领域,提出了"十四五"时期生态环境监测的重点目标任务,为生态文明和美丽中国建设提供技术支撑。     

Highway-rail grade crossings accident prediction using Zero Inflated Negative Binomial and Empirical Bayes method

Introduction: Recently the Federal Railroad Administration (FRA) released a new model for accident prediction at railroad grade crossings using a Zero Inflated Negative Binomial (ZINB) model with Empirical Bayes (EB) adjustments for accident history (2). This new model is adopted from the work that was conducted by the authors (3–6). The unique feature of the new FRA model is that it has a single equation for all three warning devices (crossbuck, flashing light, and gates) and uses the same variables regardless of the warning devices at the crossing. Since the New FRA model incorporates the warning device category as one of the variables in its model equation, the predicted accident frequency is higher when a crossing has crossbucks than flashing lights, and higher when it has flashing lights than gates. While this model is significantly better than the old USDOT model (7), its shortcoming is that the single equation does not accurately represent the field condition. Method: This paper presents the ZINEBS model (Zero Inflated Negative binomial with Empirical Bayes adjustment System). The ZINEBS model gives three different equations depending on the type of warning device used at the crossings (gates, flashing lights, and crossbucks). The three equations use variables, some of which are common across all warning devices, while other variables are specific to a warning device. The predicted values for the ZINEBS model show a closer agreement with the field data than the new FRA model. This observation was true for all three warning device types analyzed. Practical Applications: Based on the results of this study, the ZINEBS compliments the new FRA model and should be used when the single equation is not adequately representing the role of traffic control device types and relevant variables associated with that device type.     

基于改进多目标粒子群算法的航空器改航研究

针对影响航行安全的动态危险天气,提出了一种基于改进多目标粒子群算法的改航路径规划方法。首先,获取实时动态气象数据,利用栅格法对改航环境进行建模并采集危险天气区域初始边界点的历史气象数据。然后采用灰色预测模型对上述初始边界点坐标进行位置预测,进而建立改进后的实时动态环境模型。最后利用改进环境模型的多目标粒子群算法对改航路径进行动态规划。在考虑改航路径角度和距离等约束条件的基础上,确定了改航路径危险系数和距离最优的双目标函数。对中东部沿海某次短时危险天气下的航空器改航进行仿真分析,仿真结果表明改进后算法具有一定的有效性和可行性。     

隧道内不同行驶工况下CO浓度分布及预测模式研究

本文通过现场实测,获取了在不同行驶工况下深圳市横龙山隧道内CO浓度分布情况.结果表明,在堵车、缓行及正常行驶3种情况下,隧道入口、隧道中及隧道出口的CO浓度均逐渐升高,堵车时隧道内CO浓度最高,正常行驶时的浓度最低.采用环保部机动车排污监控中心关于在用车综合排放因子的研究成果中第3阶段汽车尾气排放标准计算隧道内CO浓度,结果与实测数据相符,CO在纵向全射流通风隧道内扩散规律符合一维纵向空气质量扩散方程.     

基于航班运行规律的典型机场NOx浓度临近预测方法研究

针对天津机场区域考虑航班运行影响,应用广义加性模型（Generalized Additive Model,GAM）建立污染物浓度预测模型,对因子间的共线性问题和交互作用进行改进,得到最优的NO_x浓度预测模型.选取天津机场区域2019年11月—2020年3月环境、气象及航班数据,建立改进的GAM.结果显示：（1）改进的GAM预测效果优,可以更加准确地预测浓度峰值及变化趋势;（2）样本量会影响模型选择的因子数量及模型性能;（3）改进模型adj-R²为0.940,实测和预测NO_x浓度的相关系数为0.975,预测效果好;（4）航班活动对机场区域污染物浓度影响较大.改进的GAM考虑污染物浓度与影响因子之间的复杂非线性关系及影响因子之间交互作用对污染物浓度变化的影响,使模型精度进一步提升. GAM对污染物浓度的准确预测可为机场区域污染防治提供依据.     

我国土壤受试植物筛选与毒性预测

生态毒性数据缺乏是我国土壤基准与生态风险评估研究中一直存在的问题,开展本土受试植物的筛选可提供更多的生态毒理试验选材,从而获得不同物种的生态毒性数据.鉴于植物对土壤污染物的敏感性,从被子植物中依据分布范围、代表性和易于获得性等原则,对我国潜在的受试植物进行筛选,结果发现,13科53种被子植物分布广泛且易于获取,可作为本土受试植物;结合生态毒性数据的搜集与分析,其中12种受试植物的生态毒性数据相对丰富,并对部分典型污染物表现高敏感.此外在受试植物生态毒性预测模型研究方面,对12种受试植物两两进行建模预测,共得到132个物种种间关系估算模型(Interspecies Correlation Estimation,ICE),其中88个为显著性模型(F检验P<0.05);此处,回归分析了已构建ICE模型的评价参数,得出预测效果较好的ICE模型应满足交叉验证成功率≥ 80.00%、MSE ≤ 0.62、R² ≥ 0.76和分类学距离≤ 4的标准.最终筛选出25个符合上述标准的ICE模型,涉及禾本科-禾本科、十字花科-十字花科的相互预测,其中当燕麦Avena sativa、芜青Brassica rapa、普通小麦Triticum aestivum、玉蜀黍Zea mays和黑麦草Lolium perenne等作为替代物种时,预测物种的实际生态毒性值与预测值较为接近.受试植物的筛选与生态毒性预测模型的建立有助于生态毒性数据的产生,并为土壤污染管理和生态风险评估提供科学依据.     

基于粗集理论算法-脊波神经网络的深基坑变形预测与应用

基坑开挖变形具有非线性特性,在脊波神经网络的基础上,采用粗集理论算法优化初始权值和阈值,建立了基于粗集理论算法-脊波神经网络的深基坑变形预测模型,应用该模型对西南地区某市火车站综合交通换乘中心南广场的基坑开挖过程进行了变形预测。结果表明:粗集理论算法能够对脊波神经网络进行优化,提高了脊波神经网络基坑变形预测结果的收敛速度和泛化能力;脊波神经网络能逼近基坑变形的非线性部分,避免了模型误差影响基坑开挖变形预测精度,提高了系统整体抗干扰性能。模型的预测值与实测值之间的误差在5%以内,满足实际工程的要求。