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Extreme Value Analysis of Urban Air Quality using Internet of Things

Anurag Barthwal, Debopam Acharya


Extremely poor levels of air quality are often experienced in high pollutant concentration regions like Delhi. The analysis and forecasting of such extreme events is suitably performed using probability distributions. In this work, an end to end IoT based system has been developed to collect, visualize and analyze air pollution data in Delhi and NCR. Central fitting and extreme value distributions, Lognormal, Gumbel, Frechet and Weibull have been compared using goodness of fit criteria to select the best fit model for computation of exceedance probabilities and return periods for air pollution extremes. The exceedance probabilities and return periods have been compared to actual occurrences of extreme events. The results indicate that the Gumbel distribution based air quality model is best suited for forecasting air quality in this high pollutant concentration region.

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