The Study Of The Model Between The Aerosol Optical Thickness And The Inhalable Particles Based On The Satellite Remote Sensing Technology

In recent years, with the rapid development of China’s economy, and the continuous expansionof city scale, in most of our cities, the haze phenomenon occurs frequently. The Inhalable Particlescausing haze phenomenon has become the primary pollutant of air quality. Air quality issues haveincreasingly become the focus of attention. According to the current status of air pollution, usingsatellite remote sensing technology to monitor air quality is very significanct in practicalapplications.In this study, the aerosol optical thickness(AOT) of Hangzhou during January2011toDecember2012was investigated based on dark pixel algorithm using remote sensing imageprocessing software (ENVI4.5). The relationship between the AOT and the Inhalable Particles(PM10) concentration was analyzed and the fitted model was established after monitoring theground data and the meteorological data. The optimal model was obtained after the dominantmeteorological factors in the model. This study demonstrated that the use of satellite remotesensing technique to achieve real-time dynamic monitoring of air quality is possible. The maincontents of this paper are as follows:(1) Total241AOT data were obtained after preprocessing the aerosol products of Hangzhoubased on the ENVI4.5platform, The data was divided into month and season, and the change ofAOT according to month and season was investigated. The results show that the averaged monthvalue ofAOT of Hangzhou changed like a wave,and the AOT is varied with season: the aerosoloptical thickness in spring and summer is higher than that in autumn and winter.(2) Through the analysis of2011to2012years’ PM10datas, we can find that the primarypollutant of Hangzhou was Inhalable Particles PM10; dividing the PM10datas into month andseason, the results show that the average month value of Inhalable Particles has a double parabolicchange in Hangzhou, averaged PM10cocentration shows certain season variations, the InhalableParticles in spring and autumn is higher than that in summer and winter.(3) The correlation study was investigated among the AOT, PM10concentrations, andcommon meteorological factors. The results show that we can select air pressure and wind speed asdominant meteorological factors, and can use the annual PM10concentration data andmeteorological factors to set up multivariate regression analysis. The regression model wassuccessfully fitted.(4) Based on the annual and seasonal data, five regression models were constructed, that is,exponential model, linear function model, logarithmic function model, quadratic function model, and power function model. In the annual regression models, model comparison analysis shows thatthe power function model is the best-fitting model between the annual AOT and the PM10, and thebest fitting model for each season is also found. In order to improve accuracy of the model, themeteorological factors were added in the model reconstructions. The study shows that, afterconsideration the dominant meteorological factors, the correlation coefficient and fitting accuracyhas improved to some extent, which can more accurately monitor atmospheric air quality usingsatellite remote sensing technique.