Air Quality Forecasting in Northern India

Posted on February 28th 2016
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Himalayan Mountains seen from Space

During the past months, the World Air Quality team has been working on analyzing several new Air Quality forecast models, as well as improving the Air Quality forecast model demonstrator.

This article will present the latest forecast model demonstrator, which is based on the Gridded Population of the World (GPW), and which will be applied to analyze the Air Quality forecast for the Northern India region (including Bangladesh, Pakistan and Nepal).

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The forecast model and computation is still based on the GFS Wind Forecast, as we demontrated in the previous article about the forecast in Beijing region that the wind is an essential component for Air Quality forecasting.

However, unlike in the previous simulation where the pollution sources where arbitrarily located in specific locations in the Hebei region, the model used for this Northern India forecast is based on the Gridded Population of the World (aka GPW 2015) from the University of Columbia CIESIN[1]: The assumption is that the higher the number of person living in a given area, the higher the chance of anthropogenic pollution beeing generated.

It is indeed not a 100% correct assumption since the pollution generated by heavy industries can be much higher than the pollution generated by the population, but that's something we will address in our next article. So, for this article, what is wanted is to verify the impact of wind on the pollution under the assumption of a correlation between population density and pollution concentration.

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The image below is showing the density model (at 0.2° resolution) used for the simulation. Each "pixel", or point on this gridded map, is considered as a pollution source. The green color is used for low density regions, which are generating very small amount of pollution, while darker colors represent zones where both population and generated pollution is higher.

Population Density (persons per square meter)
The animation below is showing the real-time concentration based on actual[2] wind data. Note that the color coding and the associated concentration levels is arbitrary - and can not (and should not) be associated one-to-one to AQI levels without further work. The essential idea is to plot the zones which are more likely to have
high
or
very high
pollutant concentration based on the wind condition forecast.

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Particule Concentration Scale:
Air Quality Forecast Viewer
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Loading ...

Forecast Time:


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Without too much surprise, New Delhi is seeing a high level of pollution concentration, but what is really interesting is to compare the situation in New Delhi compared to Beijing: In Beijing, there is literally almost no anthropogenic pollution in the near north, so, when the wind blows from the north, the air gets immediately cleaned. But for New Delhi the population density in the north in still quite high, so the chance of getting immediate clean air from the North are much lower. In order words, New Delhi requires a much higher amount of ventilation (or cumulated wind power) to get its air cleaned-up.

The second observation is the situation in Bangladesh: From the above simulation, the pollution is getting obviously trapped in Bangladesh by the proximity of mountains in the East and North. That's actually not a surprise to anyone who has been living in Dhaka. Unfortunately, there is actually not any available monitoring stations in Bangladesh / Dhaka at the time of writing, so it is not possible to verify forecast accuracy vs the actual observations. (Note: Few days after this article was written, the US Consultate in Dhaka started to publish their Air Quality Data, which you can find from this link: city/bangladesh/dhaka/us-consulate). For more information about the Air Quality in Bangladesh, you can refer to this page: country/bangladesh.

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As a conclusion, this forecast model is still far from being complete, but at least, it has the advantage of showing the impact of wind the pollution concentration in Northern India, and especially how the Himalayan mountains are trapping the air pollution. In the next version, we will introduce a enhanced version for the pollution sources, taking into account known positive flux which we can be deducted from observations.

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Note: In order to get the real-time forecast viewer to be able to handle such a wide region, our team had to work hard on a quite a few improvements and optimizations. We are now working on an even further optimized version able to handle more than 10K particles, and we are considering making its code open source, so if you are interested, drop us a message via the "discuss" board below (we will will only make it open source only if there is enough demand for it).


[1] Center for International Earth Science Information Network
[2] so, if you check this animation tomorrow, you might see a very animation


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    大気汚染指数の測定方法:

    大気汚染レベルについて

    指数大気質指数の分類(米国)健康影響 / カテゴリ粒子状物質(PM10,PM2.5)
    0 - 50良い - Good通常の活動が可能なし
    51 -100並 - Moderate特に敏感な者は、長時間又は激しい屋外活動の減少を検討非常に敏感な人は、長時間または激しい活動を減らすよう検討する必要がある。
    101-150敏感なグループにとっては健康に良くない - Unhealthy for Sensitive Groups心臓・肺疾患患者、高齢者及び子供は、長時間又は激しい屋外活動を減少心疾患や肺疾患を持つ人、高齢者、子供は、長時間または激しい活動を減らす必要がある。
    151-200健康に良くない - Unhealthy上記の者は、長時間又は激しい屋外活動を中止
    すべての者は、長時間又は激しい屋外活動を減少
    心疾患や肺疾患を持つ人、高齢者、子供は、長時間または激しい活動を中止する必要がある。それ以外の人でも、長時間または激しい活動を減らす必要がある。
    201-300極めて健康に良くない - Very Unhealthy上記の者は、すべての屋外活動を中止
    すべての者は、長時間又は激しい屋外活動を中止
    心疾患や肺疾患を持つ人、高齢者、子供は、全ての屋外活動を中止する必要がある。それ以外の人でも、長時間または激しい活動を中止する必要がある。
    300+危険 - Hazardous上記の者は、屋内に留まり、体力消耗を避ける
    すべての者は、屋外活動を中止
    全ての人が屋外活動を中止する必要がある。特に、心疾患や肺疾患を持つ人、高齢者、子供は、屋内に留まって激しい活動を避け静かに過ごす必要がある。
    (Reference: see wikipedia,and cn.emb-japan.go.jp/)

    大気汚染についての更なる詳細をお知りになりたい方は、WikipediaAirNowを参照してください。

    北京在住の医師Richard Saint Cyr氏による大変役に立つ健康上のアドバイスは、 www.myhealthbeijing.com をご覧ください。


    使用上の注意: すべての大気質データは公開時点では妥当性が担保されていないため、これらのデータは予告なしに修正することがあります。 世界大気質指数プロジェクトは、この情報の内容を編集に最善の注意を尽くしておりますが、いかなる状況においても World Air Quality Index プロジェクトチームまたはそのエージェントは、このデータの供給によって直接的または間接的に生じる損失や損害について責任を負いません。



    設定


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    Temperature unit:
    Celcius