高速公路交通噪声预测模型探讨

针对我国当前广泛使用的2种高速公路噪声预测模型《06规范》预测模型与《09导则》预测模型在预测时比较研究,重点利用环境现状监测数据分别对2种模型验证与对比分析.结果表明,2种模型预测值与实测值相差3dB ～5dB,车流量＞ 300辆/h,《09导则》更接近实测值；在夜间车流量＜300辆/h,《06规范》更接近实测值,2种模型结合采用《06规范》计算的车速,距离衰减考虑车流量的大小,在此基础上应用《09导则》,预测结果与实测值更为接近.     

测点高度对道路交通噪声监测数据的影响

通过在4城市对132条道路进行了道路交通噪声测点高度试验,系统分析了测点高度从手工监测的1.2 m向自动监测的4.5 m转化后,在不同道路情况对道路交通噪声监测数据的影响。结果表明,测点高度提高后监测结果变化在±3 dB(A)以内。待测道路的车道数和测点与机动车道的距离是其主要影响因素。对两测点高度进行对比,4.5 m高度噪声值随水平距离增加衰减较小,测量结果更稳定。     

机场周围区域飞机噪声监测方法对比研究

中国机场周围区域飞机噪声监测一直采用计权等效连续感觉噪声级L_(WECPN)为评价量,标准修订后拟采用昼夜等效声级L_(dn)为评价量,监测方法也相应更改。该文通过理论推导及宁波栎社机场噪声现场监测数据,系统比较了2种机场周围区域飞机噪声监测方法,并分析了监测结果的差异及影响因素。结果表明:L_(WECPN)与L_(dn)在相差10 dB的基础上,差值受到单次飞机噪声值和傍晚飞行次数2个因素影响。单次飞机噪声监测量L_(EPN)和L_(AE)在飞机匀速直线经过时差值约为3.75 dB,实际上受到飞行航迹、飞机运动状态、噪声传播环境、突发噪声干扰等因素影响,此次监测的187次飞机L_(EPN)和L_(AE)的差值范围为2.1～5.5 dB。傍晚飞行次数引起的监测结果差值范围为0～4.8 dB。     

地铁车辆段试车线噪声源强及传播规律实测分析

以深圳市前海湾车辆段和横岗车辆段试车线为例,对其试车过程中的噪声影响源强及传播情况进行实测分析。结果表明,试车线车辆行驶速度约为36 km/h,噪声源强LeqA约为73 d B左右,试车中噪声最大值约为83 d B;试车线内噪声源的传播并不完全遵循一般的线性递减规律,在距声源30 m范围内,距声源越近,衰减量越小,反之,主要是由于距离声源越远,受到墙体或立柱等阻挡引起的附加衰减就越大。     

城市轨道交通噪声测试分析

采取1/3倍距法布设测点,使用多通道噪声分析系统,对城市轨道交通噪声进行地面和轨面上的水平测试及垂向测试,取得其距离衰减规律、声级垂向分布等特性,新的测试点位布设方案取得了比传统测试方案更详细的测试结果。研究结果对于城市轨道交通噪声特性的研究及其控制方法的确定具有重要的学术意义和工程价值,同时对城市轨道交通噪声的测试方案进行了有益的探索。     

风电机组噪声特性研究

以山东某平原风电场机组为例,对机组噪声进行仿真与测试研究.研究分析了风电机组噪声源频率特性、不同风速下的噪声声功率级及噪声的衰减特性,研究结果验证了Focus仿真软件在预测风电机组噪声源的可靠性.该研究为风电场噪声预测及降噪方案的研究提供了参考.     

城市公交车定置噪声测试与特征分析

对北京市典型类别公交车的噪声污染水平及排放特征进行了测试和研究。研究和统计结果表明,北京城市在用公交车的定置噪声排放水平总体处于90～103 dB(A),其A计权频域特征以人耳敏感的中高频噪声为主,对城市居民的环境影响较大,是目前城市内需要重点关注的噪声污染源。相关数据将为交通和环保部门有针对性地提出噪声控制措施和制订相关法规提供有效支撑。     

城市快速路交通噪声分布特征及污染现状

为了解快速路交通噪声的分布特征及污染现状,对城市快速路的交通噪声进行监测,结果表明,噪声随车流量的增大而升高,同时受车辆类型等其他因素的影响;随着与快速路水平距离的增大,交通噪声值呈下降趋势,特别是在距离快速路路沿前40 m内噪声衰减尤为明显;距高架道路不同水平距离处的噪声声场垂直分布规律基本一致,但距离高架道路较远处的敏感建筑物噪声值最高点楼层有所上移;快速路旁噪声敏感点交通噪声超标情况严重。     

乌鲁木齐市市区学校环境噪声现状调查与评价

对学校环境噪声作了初步调查表明:学校环境噪声主要污染源来自交通噪声,危害最大的是位于交通干线两侧的学校.上课时间能持续在64.5～66.9dB(A)之间的较高水平.其噪声污染与距噪声源距离呈负相关(r=-0.679).     

乌鲁木齐—成都 114／113次 旅客列车噪声监测分析

对乌鲁木齐至成都旅客列车车厢内的噪声监测表明,一般情况下车厢内噪声等效A声级不超过铁道部部颁标准要求(TB1932-87).在车厢机械陈旧、路况复杂、隧道、狭谷等情况下,车厢乘务室内噪声等效A声级可达74.9～84.6dB(A),最高超标9.6dB(A),全程超标2.2dB(A).     

风电场风机噪声监测方法初探

据现场实测,风电场300 m范围内社会噪声测值水平较低,且昼夜变化较小,对风电场背景噪声贡献不大;风机停运时背景噪声值随风速递增,二者之间存在较好的多项式回归性。在测得风机运行时噪声值和测点风速的情况下,根据回归方程式得到背景噪声,当风机运行时测得的噪声值与背景噪声之差大于1.5 dB(A)时,可以运用噪声叠加原理计算风机噪声,并且其计算误差在可接受范围之内。     

Evaluation of Traffic Noise Pollution in Amman, Jordan

The City of Amman, Jordan, has been subjected to persistent increase in road traffic due to overall increase in prosperity, fast development and expansion of economy, travel and tourism. This study investigates traffic noise pollution in Amman. Road traffic noise index L ₁₀(1 h) was measured at 28 locations that cover most of the City of Amman. Noise measurements were carried out at these 28 locations two times a day for a period of one hour during the early morning and early evening rush hours, in the presence and absence of a barrier. The Calculation of Road Traffic Noise (CRTN) prediction model was employed to predict noise levels at the locations chosen for the study. Data required for the model include traffic volume, speed, percentage of heavy vehicles, road surface, gradient, obstructions, distance, noise path, intervening ground, effect of shielding, and angle of view. The results of the investigation showed that the minimum and the maximum noise levels are 46 dB(A) and 81 dB(A) during day-time and 58 dB(A) and 71 dB(A) during night-time. The measured noise level exceeded the 62 dB(A) acceptable limit at most of the locations. The CTRN prediction model was successful in predicting noise levels at most of the locations chosen for this investigation, with more accurate predictions for night-time measurements.     

低噪声路面降噪效果研究

通过对公路交通噪声特征,低噪声路面的特点、结构、降噪机理及国内外研究现状的分析,对宁杭高速二期工程低噪声路面与普通路面噪声监测结果进行对比分析,确认低噪声沥青路面具有比较明显的降噪效果,路肩处噪声可降低3～4 dB(A),路外15 m处可降低1.1～3.5 dB(A).     

Dynamics of traffic noise in a tropical city Varanasi and its abatement through vegetation

Noise level monitoring and its reduction with different width and height of vegetation belt were studied in the Varanasi city. Noise level monitoring of the Varanasi city revealed the fact that area category A (without vegetation) was highly polluted as compare to area category B (with vegetation) having less fluctuation of traffic load. Four plant species Putranjeva roxburghi, Cestrum nocturnum, Hibiscus rosasinensis and Murraya peniculata were tested for noise reduction study at different frequencies. Experiment revealed the fact that H. rosasinensis reduced noise highest at both low and high frequencies (100-500 Hz, 22 dB and 2.5-6.3 KHz 26 dB), followed by M. peniculata (100-500 Hz, 18 dB and 2.5-6.3 KHz 20 dB), P. roxburghi (100-500 Hz 15 dB and 2.5-6.3 KHz 17 dB) and C. nocturnum (100-500 Hz 9 dB and 2.5-6.3 KHz 14 dB). Significance of vegetation belt in noise reduction was established with multiple regression models.     

道路机动车活动水平调查及其污染物排放测算应用——上海案例研究

采用上海市车辆数据和机动车年检站实地调查方式获取车辆技术及分布，用GPS设备采集机动车行驶工况，通过交通流量录像的现场计数的方法获得道路车流分布。调查结果显示，在用汽车排放水平85％以上都达到或超过国Ⅱ标准；在低速、怠速工况区间内，社会车辆的比功率比例最低；道路轻型客车（含出租车）日平均车流量占总车流量的80％以上。机动车活动水平的详细调查将有助于上海实际道路机动车排放清单的精确计算，并获取主要的排放源及其影响因素。     

不同类型机动车尾气挥发性有机化合物排放特征研究

机动车尾气主要成分包括一氧化碳、氮氧化物、碳氢化合物、铅及苯并[a]芘等.其中,挥发性有机化合物由于其对光化学烟雾的贡献及对人体健康的影响而成为近年来大气化学研究的热点.文章首次对北京市9种车辆、5种燃料在不同工况下排放挥发性有机化合物特征进行了定量研究.结果表明,车型、燃料、净化器及工况等因素对排放量产生影响,电喷车比化油器车排放低,其中,夏利比富康与奥迪排放量高;LPG与汽油车排放量最高,柴油车与CNG车排放最低.其中,-10#柴油车比0#柴油车排放更低;使用净化器可以降低挥发性有机化合物排放量;不同工况对排放量的影响随车型、燃料类型的不同而不同.因此,使用清洁燃料、安装净化器和使用电喷装置,可减少尾气中挥发性有机化合物含量.     

Emission Factors Determination of Euro III 1,200- to 1,400-cc Petrol Passenger Cars with a PLS Multivariate Regression Model

This paper presents emission factors of a class of passenger cars obtained by applying a statistical model developed to evaluate average emission factors based on driving cycle emission measurements. A multivariate regression method based on principal components, namely, the partial least squares (PLS) method, is applied to calculate the model. The method was applied to emission data from a sample of petrol Euro III 1,200- to 1,400-cc passenger cars taken from the ARTEMIS database. A vehicle effect analysis showed that vehicle effect is considerable, in some cases comparable to or greater than the driving cycle effect. Determination of emission factors is obviously affected by these aspects. Thus, the CO2 PLS model fit results are good, CO, HC and NOX more or less sufficient. PLS-predicted quantities were compared with corresponding quantities estimated by a multiple regression model (GLM) based on a quadratic polynomial equation of sub-cycle overall mean speed. GLM goodness of fit was poorer than PLS ones. A validation effort of models is in progress, which is considering the ARTEMIS database extended with tests performed within other national or international projects. In this way, an extended population of combinations of vehicles and driving cycles will provide a better calculation of models and emission factors.     

典型街道环境空气质量分析

根据对深南东路环境空气的监测及车流量、气象条件的调查,分析机动车及气象因素对深圳市环境空气质量的影响。根据分析可知,机动车尾气是深圳市环境空气中污染物的主要来源,但污染程度不断变化的主要原因是风速,深圳市环境空气中污染物浓度与风速有着非常显著的负相关。