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).     

海口市声环境影响因素分析及预测

噪声污染一直是海口市主要的环境问题之一。主要原因是城市纵深度太低，道路密度太高，交通布局不合理，1991年－2000年城市区域环境噪声和道路交通噪声的平均值分别为59．0dB（A）和69．5dB（A）。利用城市区域环境噪声预测方法和道路交通噪声预测方法对该市噪声进行预测，2001年－2005年该市的区域噪声昼间平均等效声级综合预测值在57．6dB（A）－56．7dB（A）之间；道路交通噪声昼间平均等效声级综合预测值在68．2dB（A）－68．3dB（A）之间。     

降雨对噪声自动监测数据的影响分析

选取北京市近5年夏半年(4—9月)的降雨数据及相关噪声自动监测小时等效声级,利用数学统计软件进行有雨-无雨声级差异性分析、声级-降雨相关性分析及平均声级-雨量变化趋势分析等,提出降雨确实对噪声自动监测小时数据有一定贡献,不同雨量对不同功能区噪声影响不同,建议噪声自动监测系统建设时应考虑雨噪声影响,以保证对声环境质量评价的科学性和准确性。     

城市轨道交通噪声评价方法及其限值的研究

根据城市轨道交通噪声时、空分布特点及污染规律,对轨道交通噪声评价量作了分析研究,提出了列车通过时的最大声级作为城市轨道交通噪声的评价量,并根据噪声时问分布特性曲线提出了与之相对应的等效声级简易计算方法.通过对上海轨道交通3号线沿线居民区的主、客观调查,得到主观烦恼度阈值和干扰睡眠阈值,提出了适用于我国城市轨道交通的环境噪声限值(建议).     

南京市地铁噪声影响调查

为了解城市地铁在正常营运时噪声对外部环境的影响,对南京地铁1号线高架段、车厢内和站台上环境声进行了调查.结果表明,地铁高架段噪声对外环境的影响达到4类区标准,其对声环境的影响明显小于快速道路高架段;车厢内和站台上的噪声对乘客的影响不容忽视.指出,地铁高架两侧100 m范围内有敏感点的路段应全部设置隔声屏障,地铁运营部门应提高地铁驾驶人员的技术和责任心.     

地铁振动引起的二次辐射噪声适用标准研究

研究了城市地铁产生的二次辐射噪声测量现状及评价方法,依据现行地铁二次辐射噪声监测相关标准,对城市地铁产生的二次辐射噪声进行测量,将JGJ/T 170—2009、GB 50118—2010和GB/T 50355—2018中规定的限值与地铁二次辐射噪声的特征频谱、环境影响评价以及噪声限值进行了对比分析。结果显示,现行标准限值在执行中存在一定的不完善之处,需要根据实测数据重新分析评价标准的适用性、准确性和合理性。结合当前噪声管理的社会需求,提出了声级增量和特征频谱增量的概念,并将分析结果与现行标准值进行了比较分析,表明声级增量和特征频谱增量能更好地反映二次辐射噪声的影响,对于地铁二次辐射噪声标准的制修订具有参考价值。     

高速公路交通噪声的多普勒效应

通过对高速公路交通噪声现场测定与研究分析,结果表明,由于高速公路上行驶的车辆速度很快,致使在高速公路两侧近距离接收点上,当车辆迎面驶来到背离驶去时,交通噪声出现明显的频率漂移和A声级的显著差异,即出现高速公路交通噪声的多普勒效应。     

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。     

机场周围航空噪声的影响规律初探

以机场航空噪声为对象,以计权等效连续感觉噪声级(LWECPN)作为基本评价量,在典型机场的大量实测数据和相应评价指标参数基础上对影响航空噪声值的各主要因素进行分析,得出主要影响因素与航空噪声值间的相应关系。     

噪声监测中的布点问题与对策

GB12349-90《工业企业厂界噪声测量方法》(以下简称《方法》)和GB12348-90《工业企业厂界噪声标准》(以下简称《标准》)是1990年颁布实施的,《方法》和《标准》在噪声监测中的使用率最高,但也常常遇到现行《方法》难以解决的问题,甚至影响《方法》执行的严肃性。因此,应该对执行了10多年的《方法》或《标准》给予补充和完善。1　问题1.1　测点定于界外1m处噪声测量最关键、最核心的问题之一是选择合适的测量点,现行《方法》中用测点位置在界外1m处测得的等效连续A声级作为评判量的科学依据不足。甲、乙单位厂界噪声监测点见图1。 ——…     

Noise pollution in textile,printing and publishing industries in Saudi Arabia

A study of 20 factories belonging to textile, printing, publishing and paper products industries in Jeddah was conducted.Data on Leq, Maximum and Minimum SPL at different octave bands, as well as dBA, were collected. It has been found that textile,publishing and paper products industries are the most noisy industries. The paper is concluded with suitable recommendationfor noise control and worker protection.     

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。     

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。     

公交车站对交通噪声的影响分析

首先根据公交车的运行特性提出了公交车在各行驶阶段的噪声计算方法并采用能量叠加的方法建立了公交车站附近交通噪声预测模型.然后对广州市新港西路交通噪声的实测结果与模型计算结果进行了比较,验证了该预测模型的正确性.最后分析了公交车站附近交通噪声的影响因素,通过对公交车站附近的交通噪声和远离公交车站的交通噪声的比较,得到公交车站附近交通噪声明显增大的结论.     

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。     

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.     

灰色理论及其模型在北京典型城市区域声环境研究中的应用

以北京市某典型区域作为研究对象，在收集大量相关资料与实测历史噪声数据的基础上，对研究区域内的声环境质量影响因素进行灰色关联度分析，并运用灰色理论建立GM（1,1）模型进行预测。结果表明，影响城市区域声环境质量因素从大到小的排序依次为：机动车辆﹥常住人口数量﹥平均车流量﹥地区生产总值﹥城市道路桥梁﹥基础设施投资﹥治理噪声环保投资；以研究区域内噪声污染实测历史数据建立的GM（1,1）模型精度符合要求标准，根据GM（1,1）模型预测北京市“十二五”期间声环境质量达标且有轻微下降趋势。     

Water quality characterization in the Northern Florida everglades based on three different monitoring networks

The Loxahatchee National Wildlife Refuge (Refuge) is affected by inflows containing elevated contaminant concentrations originating from agricultural and urban areas. Water quality was determined using three networks: the Northern Refuge (NRN), the Southern Refuge (SRN), and the Consent Decree (CDN) monitoring networks. Within these networks, the Refuge was divided into four zones: (1) the canal zone surrounding the marsh, (2) the perimeter zone (0 to 2.5 km into the marsh), (3) the transition zone (2.5 to 4.5 km into the marsh), and (4) the interior zone (>4.5 km into the marsh). In the NRN, alkalinity (ALK) and conductivity (SpC) and dissolved organic carbon, total organic carbon, total dissolved solids (TDS), Ca, Cl, Si, and SO₄ concentrations were greater in the perimeter zone than in the transition or interior zone. ALK, SpC, and SO₄ concentrations were greater in the transition than in the interior zone. ALK, SpC, and TDS values, Ca, SO₄, and Cl had negative curvilinear relationships with distance from the canal toward the Refuge interior (r ²?=?0.78, 0.67, 0.61, 0.77, 0.62, and 0.57, respectively). ALK, TB and SpC, and Ca and SO₄ concentrations decreased in the canal and perimeter zones from 2005 to 2009. Important water quality assessments using the SRN and CDN cannot be made due to the sparseness and location of sampling sites in these networks. The number and placement monitoring sites in the Refuge requires optimization based on flow pattern, distance from contaminant source, and water volume to determine the effect of canal water intrusion on water quality.     

列车车厢内装材料VOCs释放特性研究

列车车厢内的空气质量对乘客的健康和乘坐舒适度有明显影响.密闭车厢的内装材料所释放的挥发性有机物(VOCs)是影响车厢内环境空气质量的主要污染物.为研究25G型客车车厢内空气VOCs浓度的分布机理及扩散规律,采用环境测试舱法和扫描电镜(SEM)表征,对车厢主要内装材料(座椅坐垫、PVC地板、墙板等)的VOCs释放速率进行...     

Fungal DNA in hotel rooms in Europe and Asia--associations with latitude, precipitation, building data, room characteristics and hotel ranking

There is little information on the indoor environment in hotels. Analysis of fungal DNA by quantitative PCR (qPCR) is a new method which can detect general and specific sequences. Dust was collected through swab sampling of door frames in 69 hotel rooms in 20 countries in Europe and Asia (2007-2009). Five sequences were detected by qPCR: total fungal DNA, Aspergillus and Penicillium DNA (Asp/Pen DNA), Aspergillus versicolor (A. versicolor DNA), Stachybotrys chartarum (S. chartarum DNA) and Streptomyces spp. (Streptomyces DNA). Associations were analysed by multiple linear regression. Total fungal DNA (GM = 1.08 × 10(8) cell equivalents m(-2); GSD = 6.36) and Asp/Pen DNA (GM = 1.79 × 10(7) cell equivalents m(-2); GSD = 10.12) were detected in all rooms. A. versicolor DNA, S. chartarum DNA and Streptomyces DNA were detected in 84%, 28% and 47% of the samples. In total, 20% of the rooms had observed dampness/mould, and 30% had odour. Low latitude (range 1.5-64.2 degrees) was a predictor of Asp/Pen DNA. Seaside location, lack of mechanical ventilation, and dampness or mould were other predictors of total fungal DNA and Asp/Pen DNA. Hotel ranking (Trip Advisor) or self-rated quality of the interior of the hotel room was a predictor of total fungal DNA, A. versicolor DNA and Streptomyces DNA. Odour was a predictor of S. chartarum DNA. In conclusion, fungal DNA in swab samples from hotel rooms was related to latitude, seaside location, ventilation, visible dampness and indoor mould growth. Hotels in tropical areas may have 10-100 times higher levels of common moulds such as Aspergillus and Penicillium species, as compared to a temperate climate zone.