编程

geoskill-noise-pollution-mapping

试用

交通噪声衰减模型制图 dB(A) 等级:FHWA 简化声源级、点/线源几何发散、建筑屏障衰减与地面吸收。Maps traffic noise levels with distance attenuation, building shielding and terrain. 输出噪声级 GeoTIFF + 参数 JSON。

它能做什么

交通噪声衰减模型制图 dB(A) 等级:FHWA 简化声源级、点/线源几何发散、建筑屏障衰减与地面吸收。Maps traffic noise levels with distance attenuation, building shielding and terrain. 输出噪声级 GeoTIFF + 参数 JSON。

技能文档

噪声污染制图 | Noise Pollution Mapping

Source level is estimated with the FHWA simplified formula L = 10·log10(flow) + 20·log10(speed/50) + 30; geometric divergence attenuates point sources by -20·log10(R/R0) and line sources by -10·log10(R/R0) (6 dB / 3 dB per doubling of distance, respectively); each row of building barriers attenuates 5 dB (capped at 20 dB); soft-ground absorption is 0.5 dB/100 m. The synthesized noise level is clipped to [0,120] dB(A).

Use cases: road environmental impact assessment, acoustic environment functional zoning, and noise-barrier siting.

Dependencies / 依赖

pip install numpy rasterio scipy

Usage / 使用方法

Example 1: default traffic flow (1000 veh/h, 60 km/h)

python geoskill-noise-pollution-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./output

Example 2: highway (high-flow line source)

python geoskill-noise-pollution-mapping.py --bbox 116 39 117 40 --synthetic --flow 5000 --speed 100 --source-type line --output-dir ./highway

Example 3: distance/barrier raster input (band0=distance cell, band1=number of barriers)

python geoskill-noise-pollution-mapping.py --input dist_barriers.tif --flow 2000 --output-dir ./real

Example 4: low-flow residential road

python geoskill-noise-pollution-mapping.py --bbox 121 31 122 32 --synthetic --flow 300 --speed 40 --output-dir ./residential

Example 5: quiet batch run

python geoskill-noise-pollution-mapping.py --bbox 113 23 114 24 --synthetic --quiet --output-dir ./batch

Output / 输出

FileFormatDescription
noise_level.tifGeoTIFF (float32)Noise level dB(A), EPSG:4326
noise_params.jsonJSONSource level, pixel scale, noise statistics
output-manifest.jsonJSONRun manifest (inputs/outputs/QA/software versions)

Data Source / 数据源 / Source

Local GeoTIFF (band0=pixel distance to road, band1=number of barriers, optional); attenuation coefficients follow public FHWA traffic noise model literature; synthetic mode generates a road network + building belts locally, with no external data source.

Privacy / 隐私声明 / Privacy

  • Runs fully offline by default; no network requests are made.
  • --synthetic mode reads no external data.
  • All computation is performed locally; user data is never uploaded.

License / License

MIT



name: geoskill-noise-pollution-mapping description: '交通噪声衰减模型制图 dB(A) 等级:FHWA 简化声源级、点/线源几何发散、建筑屏障衰减与地面吸收。Maps traffic noise levels with distance attenuation, building shielding and terrain. 输出噪声级 GeoTIFF + 参数 JSON。'

噪声污染制图 | Noise Pollution Mapping

声源级按 FHWA 简化式 L = 10·log10(flow) + 20·log10(speed/50) + 30 估算;几何发散点源 -20·log10(R/R0)、线源 -10·log10(R/R0)(距离加倍分别衰减 6 dB / 3 dB);建筑屏障每排衰减 5 dB(上限 20 dB);软地面吸收 0.5 dB/100m。合成噪声级 clip 到 [0,120] dB(A)。

适用场景:道路环评、声环境功能区划、降噪屏障选址。

依赖

pip install numpy rasterio scipy

使用方法

示例 1:默认车流(1000 辆/h, 60 km/h)

python geoskill-noise-pollution-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./output

示例 2:高速公路(大流量线源)

python geoskill-noise-pollution-mapping.py --bbox 116 39 117 40 --synthetic --flow 5000 --speed 100 --source-type line --output-dir ./highway

示例 3:距离/屏障栅格输入(band0=距离像元, band1=屏障数)

python geoskill-noise-pollution-mapping.py --input dist_barriers.tif --flow 2000 --output-dir ./real

示例 4:低流量居住区道路

python geoskill-noise-pollution-mapping.py --bbox 121 31 122 32 --synthetic --flow 300 --speed 40 --output-dir ./residential

示例 5:静默批量

python geoskill-noise-pollution-mapping.py --bbox 113 23 114 24 --synthetic --quiet --output-dir ./batch

输出

文件格式说明
noise_level.tifGeoTIFF (float32)噪声级 dB(A),EPSG:4326
noise_params.jsonJSON声源级、像元尺度、噪声统计
output-manifest.jsonJSON运行清单(输入/输出/QA/软件版本)

数据源 / Source

本地 GeoTIFF(band0=距道路像元距离、band1=屏障数,可选);衰减系数参考FHWA 交通噪声模型公开文献;合成模式本地生成路网+建筑带,无外部数据源。

隐私声明 / Privacy

  • 默认完全离线运行,不发起任何网络请求
  • --synthetic 模式不读取任何外部数据
  • 所有计算在本地完成,不上传用户数据

License

MIT

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