During the last couple of years air pollution on the earth owing the several sources are at very serious levels. The air pollution sources include mostly vehicles, factories, power plants, construction work and lastly fires, particularly wildfire/bushfire (bushfire is commonly known in Australia), which is discussed here for the subject point of interest. The air pollution occurred by bushfire has same health and environmental issues as other sources of air pollution. The general air pollutants of ambient air include, Ozone-O3, Particulate Matter PM10 and PM2.5, Nitrogen Dioxide-NO2, Sulphur Dioxide-SO2 and Carbon Monoxide-CO.
In latest research, different researcher uses gaussian dispersion model is based on the analysis of air pollution measurement with different concentration at different levels. The gaussian dispersion model uses three-dimensional and lagrangian view-points. The modified model known as CALPUFF, approved by USEPA. But Gaussian dispersion model only gives average values without considering any instantaneous values, and it’s uses are limited due to single source of receptor and gives one estimate for an interval of ten minutes to one hour. So, it is one of the drawbacks of using only gaussian model for evaluation of the pollution data. So, the detailed study of smoke concentration, the path of smoke, instantaneous meteorological parameters, and wind data have vital significance for the study of air quality in bushfire.
The gaussian dispersion model used with spatial parameters studies (Jan Bitta et al., 2018) shows the better results rather then alone gaussian model and box models used previously by different researchers, but they are not performed it yet. This combined gaussian dispersion model with spatial parameters is able to improve the modelling quality then earlier. In a latest investigation for air quality model, the novel linear and nonlinear ensemble model (A. Shishegaran et. al. 2020), which predicts strong results than earlier models. The data collection is observed for four years (2012-2015) used to regulate the innovative model. These calibrated data further validated with observed data in 2016. Here, the investigators used combination of GeneExpression Programming (GEP) and Auto Regressive Integrate Moving Average-ARIMA is the best model to predict the AQI.
In current scenario, the Gaussian Dispersion Model with Spatial Parameters Studies were not performed practically for AQI in case of bushfire conditions. So, the researchers can practically validate this model with the mathematical modelling of ARIMA-GEP approach to confirm performance of both novel methods for the proposed problem of AQI measurement in bushfire.
The aim of the proposed research is to perform and validate the novel methods in bushfire situation in many parts of the world, because these methods are basically designed in case of routine AQI measurement not for severe bushfire situation, because bushfire has spread over large area of land masses. So, it is very important to perform the practical as well as mathematical approaches to validate the methods.
In proposed research methodologies of air quality measurement, two groups are formed 1) Gaussian Dispersion Model with spatial parameters and 2) ARIMA-GEP approach, for comparision of air pollution model. Further this experiment will be executed in two group individually, 1) small scale group, and 2) Large Scale Group, to measure the performance of both the methods in any adverse conditions. The proposed method of air pollution modelling will provide, the comparison between two models, in measurement of PM2.5, PM10 and other plumes of smoke.
The modelling theory for AQI measurement is selected as Gaussian Dispersion model with Spatial Parameters and ARIMA-GEP approach, for performance comparison between two models. Both this method is not used to measure air quality in bushfire. The comparison with both the methods provides a clear picture, the ability of new methods in case of bushfire.
Instruments used for measurements
Air pollutant measurement techniques such as the
All the data are analysed using Auto Regressive Integrate Moving Average (ARIMA) method to getting accurate results
Prediction of air quality in Tehran by developing the nonlinear ensemble model, Aydin Shishegaran, 2020, Journal of Cleaner Production.
A REVIEW ON AIR POLLUTANTS MEASUREMENT TECHNIQUES AND ITS FUTURE ADVANCEMENT, Herman Wahida, 2016, Jurnal Teknologi
Inquiry into The impacts on health of air quality in Australia Senate Standing Committees on Community Affairs, Dr Helen Cleugh, 2013, CSIRO.
Bushfire smoke and public health, Dr Rachel Tham, 2008, Bushfire CRC Ltd 2008
Air quality modelling: a technical review of mathematical approaches, Richard S Collett, Meteorol
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