A visual study of Wind impact on PM2.5 Concentration

Posted on November 5th 2015
Share: aqicn.org/faq/2015-11-05/a-visual-study-of-wind-impact-on-pm25-concentration


A perfect dust storm (attribution)
We have been writing quite a few times about the influence of wind on air pollution, and how strong winds (or, to be more precise, strong ventilation) can help to clean the air in a very short time. But we never had the opportunity to create on a dynamic visualization of this phenomenon, so this is what this article will be writing about.

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When it comes to Air Quality forecasting, the key to a better accuracy is refine the forecasting model, and create a specific modelisation for each country, and, even better, for each city. For instance, in Beijing, it is the proximity of the montains in the North and Hebei in the south which defines the model:
  • South wind tend to increase the pollution in Beijing: If the wind is not strong enough (i.e. not ventilating enough), then the particules will get blocked by the montains and will not be able to move further to the north, thus creating a dense particule concentration in Beijing.

  • North wind tend to clear the pollution: When the wind blows sufficiently from the North, the air gets almost immediately cleared since there is no "pollution source" in the north (or, at least, much less than in the south).
This is what one can see in the animation below, in which the pollution sources are arbitrarilly located where the monitoring stations are located in hebei. Each pollution source in emmitting one particule every hour. The more the number of particules in a zone, the higher the pollution (blue corresponds to low concentration, red ~ brown to high concentrations). The wind model is based on the Global Forecast System (aka GFS).

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Air Quality Forecast Viewer
version 1.2 (2016/2/18)
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This is of course a very light model compared to the complex models which do require super computer processing power to be able to compute the whole world air quality forecast. But it has the advantange of visually explaining the basic concept behind air quality forecasting.

To be more precise, the model should include vertical wind profile, as well as the forecasting for several heights (layers) - currently, the model is only using the forecast at 10 meters, 100 meters and 5KM. Moreover, the pollution sources should be more complete and include the overall world sources - currently, only sources from Hebei are included.

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Last, many research reports have investigated Machine Learning or Artificial Intelligence based Air Quality forecast systems. The concept behind is to "learn" by comparing the observed data with the forecasted data and identify repetitive patterns (as shown on the diagram on the right).

On the paper, Machine Learning based forecasting system look good, but in actual fact, are they any better than the tradtional deterministic models (which we do prefer at the World Air Quality Index project)? Refering to the excellent TED talk from Talithia Williams on 'Own your body's data', our answer to this question is "show us the data!", and that's something we will be writing about in our next article on forecasting!


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    關於空氣質量與空氣污染指數

    本網站採用的污染指數和顏色與EPA是完全相同的。 EPA的指數可以從 AirNow上查到

    空气质量指数空气质量指数级别(状况)及表示颜色对健康影响情况建议采取的措施
    0 - 50一级(优)空气质量令人满意,基本无空气污染各类人群可正常活动
    51 -100二级(良)空气质量可接受,但某些污染物可能对极少数异常敏感人群健康有较弱影响极少数异常敏感人群应减少户外活动
    101-150三级(轻度污染)易感人群症状有轻度加剧,健康人群出现刺激症状儿童、老年人及心脏病、呼吸系统疾病患者应减少长时间、高强度的户外锻炼
    151-200四级(中度污染)进一步加剧易感人群症状,可能对健康人群心脏、呼吸系统有影响儿童、老年人及心脏病、呼吸系统疾病患者避免长时间、高强度的户外锻炼,一般人群适量减少户外运动
    201-300五级(重度污染)心脏病和肺病患者症状显著加剧,运动耐受力降低,健康人群普遍出现症状儿童、老年人及心脏病、肺病患者应停留在室内,停止户外运动,一般人群减少户外运动
    300+六级(严重污染)健康人群运动耐受力降低,有明显强烈症状,提前出现某些疾病儿童、老年人和病人应停留在室内,避免体力消耗,一般人群避免户外活动
    (参考详见http://zh.wikipedia.org/wiki/空气质量指数)

    如果你想了解更多有關空氣質量與污染,詳見維基百科或者 AirNow

    有關健康建議詳​​見北京的Richard Saint Cyr MD醫生的博客:www.myhealthbeijing.com


    注意事項: 所有空氣品質數據在發佈時均未經驗證,且為了確保資料準確性,這些數據可能隨時被修改,恕不另行通知。 世界空氣品質指數專案在編制這些訊息內容時已經謹慎的運用了所有適當的技巧,在任何情況下世界空氣品質指數在任何情況下,世界空氣品質指數專案團隊或其代理人均不對因提供此數據而直接或間接引起的任何損失、傷害或損害來承擔契約、侵權或其他責任。



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