李旭, 张书景. 西三旗南路交通噪声预测及治理的研究[J]. 环境卫生学杂志, 2013, 3(6): 532-534.
    引用本文: 李旭, 张书景. 西三旗南路交通噪声预测及治理的研究[J]. 环境卫生学杂志, 2013, 3(6): 532-534.
    Li Xu, Zhang Shujing. Research on Traffic Noise Prediction and Control for Xisanqi South Road[J]. Journal of Environmental Hygiene, 2013, 3(6): 532-534.
    Citation: Li Xu, Zhang Shujing. Research on Traffic Noise Prediction and Control for Xisanqi South Road[J]. Journal of Environmental Hygiene, 2013, 3(6): 532-534.

    西三旗南路交通噪声预测及治理的研究

    Research on Traffic Noise Prediction and Control for Xisanqi South Road

    • 摘要:
      目的 对西三旗南路道路交通噪声预测的全过程, 根据预测结果提出针对性的、有效的噪声治理措施。
      方法 选择"NMPB-Routes-96(SETAR-CETRU-LCPC-CSTB)"模型作为本次环境评价中的环境噪声预测模型, 应用Lima软件对西三旗南路营运期环境噪声计算评价预测, 选择2014年(项目建成年)、2020年和2028年分别代表营运初期、中期、远期进行评价。
      结果 运营远期相比近期, 昼间60个声环境敏感点处的噪声值增量在0.2~5.4 dB(A)之间, 夜间在0.2~4.9 dB(A)之间。在运营远期, 60个声环境敏感点处的昼间噪声预测值最高为72.0 dB(A), 夜间噪声预测值最高为65.2 dB(A)。10层以下, 随着预测点高度的升高, 噪声预测值增大, 10层以上, 随着预测点高度的升高, 噪声预测值减小。将面向道路一侧的窗户全部改造为IV级隔声窗, 室内噪声预测值最高为42.0dB(A), 夜间噪声预测值最高为35.2 dB(A)。
      结论 应用Lima软件预测城市道路的交通噪声环境影响评价取得了很好的效果, 不仅可以预测平面上各敏感点的噪声值, 还可以预测垂直方向的各敏感点的噪声值; 采用安装隔声窗的降噪措施, 有效降低了噪声对居民生活的影响。

       

      Abstract:
      Objectives Investigate the whole process in noise prediction of city road traffic, proposing targeted, effective noise control measures according to the results.
      Methods Assess the early, middle and late stage of operation in 2014, 2020 and 2028, respectively, using Lima software and taking "NMPB-Routes-96(SETAR-CETRU-LCPC-CSTB)" as environmental noise prediction model.
      Results Noise value increment of 60 acoustic environment sensitive points in operation late stage ranged from 0.2~5.4 dB(A) during daytime and.2~4.9 dB(A) during nighttime, compared with the early stage. In operation late stage, the highest values of noise prediction were 72.0 dB(A) during daytime and 65.2 dB(A) during nighttime. With increasing the height of predicted point, values of noise prediction increased below 10 floors and deceased above 10 floors. After modifying the window on the side of the road into Sound-proof Windows of class IV, the highest indoor values of noise prediction were 42.0dB(A) in the daytime and 35.2 dB(A) in nighttime.
      Conclusions Applying Lima software to predict the noise of city road traffic, the noise values of sensitive points can be predicted in both the horizontal and vertical plane. The noise effect was significantly decreased by installing Sound-proof Windows.

       

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