奎屯市主要交通干线噪声状况与分析

通过对奎屯市6条主要交通干线3年的交通噪声监测与分析，得出各条道路车流量、车型、车速和路况的差异。使交通噪声在空间上差异明显，各交通干道交通负荷不是很重，但二侧噪声污染却比较严重，而且污染水平逐年提高。     

基于异质人群响应特征的交通噪声暴露评估研究

考虑异质人群不同声功能需求和时空分布,对异质人群交通噪声暴露特征进行评估。通过集记人口高斯分解和噪声预测,获取特征人群分布数据和交通噪声数据;基于特征人群年龄和声功能需求,标定各年龄段人群噪声响应函数并进行归一化处理,构建异质人群噪声响应曲线;构建交通噪声暴露评估模型,结合获取数据及噪声响应曲线进行噪声暴露评估。结果表明,3类声功能区中人群噪声暴露与年龄变量均呈现类抛物线趋势,40岁左右人群暴露影响较儿童和老人低59.9%左右。人均噪声暴露在夜间明显偏高,尤其在声功能需求较高的第1类声功能区,其人均噪声超标值比昼间高7 d B。特征人群的空间分布对噪声暴露影响显著,工作时段学校区域适学人群集中,其总噪声暴露风险为同等状况住宅区的1.2倍。综合考虑人群特征和时空分布等因素,可更科学地进行区域交通噪声污染评估。     

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。     

城市快速路交通噪声影响分析——以深圳市罗湖区罗沙路为例

城市快速路是城市交通运输网的重要组成部分,其产生的交通噪声相当严重。评价城市快速路交通噪声对其两侧交通噪声敏感点的污染状况是目前面临的一个难题。文章以深圳市罗湖区罗沙路为例,对其周边的交通噪声敏感点进行现状调查,利用SoundPLAN软件模拟噪声污染状况,并对安装声屏障前后的降噪效果进行评价,为城市快速路交通噪声污染防治措施的制定提供基础数据和建议。     

噪声地图在环境噪声监测中的应用

以青岛理工大学新校区为例,采用变网格划分法,研究了基于地理信息系统(GIS)的噪声地图在区域环境噪声评价方面的应用。结果表明,噪声预测系统结合GIS,以数字与渲染图的方式能够直观地展现噪声污染在环境区域的分布状况,可用于指导区域的规划和环境噪声评价。     

浅析新疆城市声环境现状与对策

研究了新疆近五年来的城市声环境变化趋势,并对城市噪声污染现状进行综合分析评价,提出相关的治理对策与建议.从区域环境噪声状况看,噪声值呈下降趋势;从声源强度看,对城市声环境冲击最大的是交通噪声源;从道路交通噪声状况看,近五年交通噪声污染呈逐年下降趋势,但全区各年度均有超标路段,全区城市道路交通噪声仍存在污染.因此,整治城市噪声污染应贯彻"预防为主、防治结合"的方针,综合利用科技、法律手段来改善城市声环境.     

无锡市区域噪声现状及防治对策

城市区域噪声体系是一个复杂的噪声污染系统。由于噪声污染的偶发性,分散性等原因,现阶段有效控制措施较薄弱,噪声污染已经影响到人们的生活、工作、学习。由于城镇化的推进,人们生活质量的提高,人们对噪声污染控制和治理要求越来越高,噪声污染的投诉也成为环境保护部门关注对象。本文针对无锡市区区域噪声现状开展评价,从评价结果来看,无锡市区区域噪声呈逐年改善趋势,并对改善城市区域噪声环境现状提出了防治建议。     

北京市交通环境监测点大气污染特征分析

研究分析了2013—2017年北京市交通环境点位大气污染物浓度分布特征,结果发现:交通监测点NO、NO_2与PM_(2.5)浓度时间变化特征与城区总体状况基本一致,与交通环境密切相关的NO_2浓度采暖季高于非采暖季,重污染日期间交通监测点峰值浓度也明显偏高;周末交通监测点NO浓度在5:00—23:00低于工作日4.9%～32.1%,周末NO_2浓度在7:00—23:00低于工作日0.7%～7.4%,NO_x浓度周末偏低与车流量降低密切相关;重大活动期间空气质量减排措施实施后,北京市作为区域NO_2浓度高值区中心明显消失,PM_(2.5)浓度分布梯度减小,本地减排效果明显。     

道路交通噪声相关因素的研究

道路交通噪声相关因素的研究王振中（云南个旧市环境监测站，个旧）在噪声环境评价、预测以及噪声污染防治工作中，首先必须对影响道路交通噪声的主要因素，如机动车类型、车流量、行车速度、鸣喇叭频率、道路状况、声学环境等诸多相关因素进行深入的分析研究后，才能够较...     

基于GIS的区域环境噪声分析与评价

噪声污染具有复杂的空间分布特征,传统的噪声评价方法难以达到准确、直观的效果。噪声分布的空间特性和GIS强大的空间数据处理和分析功能及可视化图像操作界面,使得GIS在处理与空间位置有关的环境噪声问题方面体现出巨大优势。以大学校园为例,探讨了GIS方法在区域环境噪声评价方面的应用。研究结果表明,GIS 方法评价噪声高效、准确、直观,是区域环境噪声评价理想的技术工具并具有良好的应用前景。     

城市道路交通噪声分布模拟研究

通过对梅州市中心城区7条道路的噪声监测,分析了中心城区道路的噪声污染水平。采用道路交通噪声预测模型,以实测交通流数据对中心城区的噪声污染进行模拟和减噪措施评估。结果表明,采用限速措施和安装声屏障措施均有降低噪声污染的效果,为管理部门防治噪声污染提供了参考。     

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.     

Evaluation of Noise Pollution in Urban Parks

The present study provides an evaluation of noise pollution in six Urban Parks located in the city of Curitiba, Brazil. Equivalent noise levels (L _eq) were measured in 303 points (each point measured during 3 min) spread throughout the Parks. Measured values were confronted with local legislation (Law 10625) allowed limits, and the Parks were thus classified as “acoustically polluted or unpolluted”. Measured values were also evaluated according to international legislation: Decree no. 12 of the City Council of Rome, DIN 18005 for German cities, the World Health Organization, and the United States Environmental Protection Agency. Urban parks in the downtown area of Curitiba, surrounded by roads of heavy traffic and in the midst of intense commercial activities, do not satisfy any of the standards used. The most noise-polluted parks in Curitiba were the Public Walk Park and the Botanical Garden Park, with measured L _eq of 64.8 dB(A) and 67 dB(A).     

广州市昼夜道路交通噪声的监测与分析

对广州市的昼夜交通噪声污染现状进行了分区域分道路等级的实地监测,得到共53个监测点位白天和夜晚的等效声级及其统计声级,同时对每个监测点展开了交通流调查,并分析交通流特征对交通噪声的影响。监测结果表明, 白天快速路、主干路、次干路及支路的平均等效声级分别为74.2、72.2、67.8、65.1 dB,快速路及主干路沿线的交通噪声污染比次干路及支路的严重。夜晚所有测点的噪声值均超过55 dB,快速路、主干路、次干路及支路的平均等效声级分别为72.2、72.3、66.3、64.5 dB,广州市夜晚的交通噪声污染较为严重。     

Assessment of Traffic Noise Pollution from 1989 to 2003 in Lanzhou City

On the basis of the continuous traffic noise data observed at 142 sites distributed in 52 roads from 1989 to 2003, the characteristics of traffic noise and effect factors were analyzed through traffic noise indices, such as Lep, L10, L50, L90, TNI, and Pn. Our findings allow us to reach a number of conclusions as follows: Firstly, traffic noise pollution was serious, and its fluctuant characteristic was obvious, resulting in a great intrusion to public in Lanzhou City during last 15 years. Secondly, traffic noise made a distinction between trunk lines and secondary lines, west-east direction roads and north-south direction roads. Thirdly, spatial character and time rule of traffic noise were obvious. In addition, traffic volume, traffic composition, road condition, and traffic management were identified as the key factors influencing traffic noise in this city.     

"九五"期间乌鲁木齐市交通噪声的控制措施及效果

在分析交通噪声影响因素的基础上，对乌鲁木齐市近十年控制交通噪声污染的措施和效果进行了探讨。     

Urban daytime traffic noise prediction models

An evaluation was made of the acoustic environment generated by an urban highway using in situ measurements. Based on the data collected, a mathematical model was designed for the main sound levels (L _eq, L ₁₀, L ₅₀, and L ₉₀) as a function of the correlation between sound levels and between the equivalent sound pressure level and traffic variables. Four valid groups of mathematical models were generated to calculate daytime sound levels, which were statistically validated. It was found that the new models can be considered as accurate as other models presented in the literature to assess and predict daytime traffic noise, and that they stand out and differ from the existing models described in the literature thanks to two characteristics, namely, their linearity and the application of class intervals.     

Traffic noise assessment based on mobile measurements

Continuous noise monitoring based on mobile systems could provide a quick feedback to assess the effect of policies implemented by authorities to mitigate noise pollution. The present research verifies that mobile measurements taken along a main street and aggregated in time and space can accurately estimate noise levels at static points. As a consequence, mobile sensors would be suitable to build, and continuously update, noise maps. Furthermore, the experiment computes the optimum aggregation distance of the mobile measurements.To perform the mobile noise measurements, a low-cost noise sensor with an integrated GPS was mounted on a bicycle. One hour worth of measurements was taken along a main avenue with the mobile receiver simultaneously to 6 static measurement points. For the mobile receiver, the L_Aeq was computed aggregating samples within a radius from 1 m to 100 m around the static measurement points. Then, the error between the aggregated L_Aeq of the mobile and the static receivers for the same time period was computed. It is observed that the RMSE and the measurement uncertainties decrease as the aggregation distance increases, having a minimum at an aggregation radius of 33 m and reaching a stabilization due to the constant traffic of the studied street.     

Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks

The analysis aims to evaluate which is the most important among traffic parameters (flows, queues length, occupancy degree, and travel time) to forecast CO and C₆H₆ concentrations. The study area was identified by Notarbartolo Road and bounded by Libertà Street and Sciuti Street in the urban area of Palermo in Southern Italy. In this area, various loop detectors and one pollution-monitoring site were located. Traffic data related to the pollution-monitoring site immediately near the road link were estimated by Simulation of Urban MObility (SUMO) traffic microsimulator software using as input the flows measured by loop detectors on other links of road network. Traffic and weather data were used as input variables to predict pollutant concentrations by using neural networks. Finally, after a sensitivity analysis, it was showed that queues length were the mostly correlated traffic parameters to pollutant concentrations. An erratum to this article can be found at