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。     

南京市典型快速路交通噪声监测与影响分布研究

为科学全面地开展城市道路交通噪声影响评估，更精确地服务道路交通噪声污染治理工作，选取南京市具有代表性的道路交通干线，采用手工监测和自动连续监测的方法，进行不同路段噪声污染程度及其随时间、空间变化规律的研究。结果表明，地面快速路段、高架路段以及衔接路段两侧噪声敏感建筑物受道路交通噪声影响较大，夜间均超标；相邻区域要达到2类声环境功能区昼间标准限值要求，需距离地面快速路慢车道约45 m，距离高架路段慢车道约92 m；要达到2类声环境功能区夜间标准限值要求，需距离地面快速路慢车道约175 m，距离高架路段慢车道约172 m；敏感建筑物所受噪声影响随楼层高度升高而增大，同时噪声影响情况与道路两侧建筑物密度、道路车辆行驶速度有关。建议严格道路及噪声敏感建筑物规划控制，采取阻断传声路径、受声建筑物强化保护等措施控制噪声影响。     

兰州市七里河区交通噪声污染现状及防治对策

文章作者对兰州市七里河区城市道路交通噪声噪声问题进行了论述，其污染己成为七里河区的首要环保问题，提出了降低噪声污染的建议和治理措施     

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。     

深圳市罗湖区道路交通噪声地图的制作与应用

以 SoundPLAN 软件为依托，将罗湖区城市主干道路及周边地物进行概化、建模，并根据实际监测数据调试参数，制作出罗湖区主干道路噪声分布地图，并对噪声污染严重路段防护措施进行效果模拟，为罗湖区道路交通噪声控制规划和整治以及建设项目审批管理等提供依据。     

应用GIS技术研究车流量与交通噪声相关性

对无锡建成区交通噪声等效声级(L_(eq))与车流量进行监测,应用GIS技术分析交通噪声与车流量的空间分布,为交通噪声污染防治工作提供科学参考。结果表明:噪声L_(eq)峰值及车流量峰值均集中在高速公路出入口必经路段,此区域为噪声污染防治重点区域。同时发现,噪声L_(eq)与车流量在空间分布上并非成正比关系,在时间趋势上车流量上升但噪声值下降,说明在机动车保有量不断攀升的背景下,采取多种防治措施可以有效缓解甚至改善交通噪声污染。     

道路交通噪声对城市环境质量的影响

指出在城市环境噪声中，声级最强、干扰最大且较难解决的交通噪声对城市声环境质量的早影响，并提出了在防治措施中，禁鸣是管理好交通秩序，防治噪声污染最经济、效果较好的一条措施。     

"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。     

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.     

兰州市交通噪声污染现状及评价

分析了兰州市交通噪声污染现状和危害，并运用模糊数学原理对兰州市交通噪声污染现状进行评价。得出兰州市总体交通噪声目前属于中度污染，在总结国内外城市交通噪声最新研究成果的基础上。提出了提高兰州市声环境质量的综合治理思路。     

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.     

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。     

城市典型高架复合道路交通噪声污染模拟及防治对策

与一般城市道路相比,城市高架复合道路通行能力大、行车速度高、车辆行驶状态复杂,交通噪声污染极为突出。选取深圳市典型的高架复合道路——春风高架和爱国高架进行实地监测,同时运用SoundPLAN软件模拟其噪声污染现状与安装声屏障后的降噪效果。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面等方面提出高架复合道路噪声污染控制的对策建议。     

南京市城市典型道路交通噪声控制措施效果初探

针对南京市典型道路的交通噪声控制措施,分别选取低噪声路面、声屏障、隔声窗3种噪声控制措施进行监测,监测显示低噪声路面对整体声级降噪有限,声屏障对于1kHz倍频带以上的中高频隔声相对较好,真空玻璃隔声窗能对低频噪声有显著改善。     

北京市典型道路交通噪声排放特征

采用北京市道路交通噪声自动监测系统2013—2017年采集的等效连续A声级数据,对城市快速路、城市主干线、城市次干线、城市支路的代表性站点噪声排放情况进行了统计分析,结果显示,北京市不同等级的道路噪声排放具备一定的特征,排放水平从大到小依次为城市快速路城市主干线城市支路和城市次干线,道路噪声随时间变化存在较为一致的周期性排放特征,24 h变化特征比较明显。个别道路排放特征存在特异性,如城市主干线道路的一个代表监测站点噪声监测值出现了逐年下降趋势,分析发现,北京市非首都功能疏解对其噪声值的下降有一定贡献。采取一定的规划和管理措施有助于减少道路交通噪声的排放。     

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。     

Study on the effect of porous fence on air quality and traffic noise level around a double-decked road structure

Fence for traffic noise control sometimes causes adverse effect on air pollution. Thus in this study, performance of porous fence as a tool for control of both air pollution and noise pollution was evaluated. A two-dimensional numerical model for flow and pollutant concentration and an analytical model for traffic noise were utilized in the analysis of a double-decked road structure with fences only at ground (Case 1) and at both ground and upper deck (Case 2). Porous fences were assumed only at the ground level since the solid fences at the upper deck usually leads to desirable result on air pollution. Effects of the variable porosity on air quality and noise level near road were evaluated. Obtained results showed: (1) flow pattern in leeward of fence was drastically changed at 40–50% porosity in Case 1 and 50% in Case 2. The porosity larger than 40% excluded presence of a circulation behind the fence. (2) Effect of porous fence on air pollution was different in Cases 1 and 2. In Case 1, the porous fence generally resulted in the reduction of air pollution at the ground level; on the other hand, in Case 2, it rather led to increase of the concentration. (3) Traffic noise level was also largely changed by the porosity of the fence; an example of simultaneous evaluation of the effects of porous fence on both air and noise pollution in Case 1 showed that the fence of 60% porosity leads to reduction of air pollution by 20% compared with solid fence case, and reduction of noise pollution by 4–6% in dB compared with no fence case, at l m high and 10 m from the road.     

A comparative study of noise pollution levels in some selected areas in Ilorin Metropolis, Nigeria

The noise pollution is a major problem for the quality of life in urban areas. This study was conducted to compare the noise pollution levels at busy roads/road junctions, passengers loading parks, commercial, industrial and residential areas in Ilorin metropolis. A total number of 47-locations were selected within the metropolis. Statistical analysis shows significant difference (P < 0.05) in noise pollution levels between industrial areas and low density residential areas, industrial areas and high density areas, industrial areas and passengers loading parks, industrial areas and commercial areas, busy roads/road junctions and low density areas, passengers loading parks and commercial areas and commercial areas and low density areas. There is no significant difference (P > 0.05) in noise pollution levels between industrial areas and busy roads/road junctions, busy roads/road junctions and high density areas, busy roads/road junctions and passengers loading parks, busy roads/road junctions and commercial areas, passengers loading parks and high density areas, passengers loading parks and commercial areas and commercial areas and high density areas. The results show that Industrial areas have the highest noise pollution levels (110.2 dB(A)) followed by busy roads/Road junctions (91.5 dB(A)), Passengers loading parks (87.8 dB(A)) and Commercial areas (84.4 dB(A)). The noise pollution levels in Ilorin metropolis exceeded the recommended level by WHO at 34 of 47 measuring points. It can be concluded that the city is environmentally noise polluted and road traffic and industrial machineries are the major sources of it. Noting the noise emission standards, technical control measures, planning and promoting the citizens awareness about the high noise risk may help to relieve the noise problem in the metropolis.     

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.