A Computer Model Designed to Predict India's Air Pollution Level in Advance

As we had already discussed about the severe effects of air pollution on health in our previous article, there’s need to be more cautious towards the increasing level of air pollution. Government have deployed some parameters in different cities of India to monitor the PM level in the air. And recently obtain the more accurate level of air pollution a new computer model is proposed by US and Chinese scientists. The new computer model will be able of predicting accurate air pollution levels in advance.

Image Credit: Washingtonpost

India is one the most polluted countries. During winter in India, northern states like Delhi, UP, etc. faces the hazy environment which occurs frequently due to the burning of crop residue by farmers. This hazy conditions includes many tiny particle suspended particulate matter like PM 2.5 and PM 10, which is very dangerous for every living organism.

The statistical model of newly developed computer project is described in the journal Science Advances. This computer model will use certain climate patterns related to the ocean, as oceans have a regulatory effect on the air pollution of India during winter season.

As per the researchers “this model will make it possible for government to forecast aerosol pollution conditions in winter and hence it will help to improve pollution control plans”. Meng Gao, school of engineering and Applied sciences Harvard university said that “we built a statistical prediction model, which uses two autumn temperature variation patterns as predictors. With it, we will know air pollution in winter”.

The study found out that over northern India, inter annual variability of wintertime aerosol pollution is regulated by combination of two factors:
  1. El-Nino: It is a climate cycle in the Pacific Ocean with a global impact on weather patterns. It occurs when sea surface temperatures in the tropical Pacific Ocean rise to above-normal levels for an extended period of time.
  2. Antarctic Oscillation(AAO): AAO is the dominant pattern of natural variability in the Southern Hemisphere outside the tropics.

Researchers wrote in the research paper that, “Both El  Nino sea temperature (SST) anomalies and AAO induced anomalies can persist from autumn to winter, offering prospects for pre-winter forecast of winter time aerosol pollution over northern India”.

 The researches constructed a multivariable regression mode incorporating the El Nino and Antarctic Oscillation indices for autumn to predict wintertime Aerosol Optical Depth (AOD). The prediction exhibits a high degree of consistency with observation, with a correlation coefficient of 0.78 (P<0.01).

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