Study On The Correlation Between MODIS Aerosol Optical Thickness And Air Pollution Index In Nanjing

The fast development of national economy, the unceasing expansion of city scale, the unceasing development of all kinds of urban projects, as well as the unceasing development of motor vehicles, all of which had brought the stern challenge to the city air quality. The practice proved that remote sensing had played an important role in urban air quality monitor. This paper used the MODIS Aerosol Optical Thickness data and the ground monitor Air Pollution Index data, attempting to establish the corresponding relational model by the correlation analysis between API and AOT.In addition, the air pollution condition or the aerosol optical thickness was related to certain meteorological condition, so the meteorological factors must be discussed when studying the relationship between the API and AOT. Then established the corresponding model to find out the good model of API and AOT, thus realized the purposes of using remote sensing to monitor the status of urban air pollution. The main conclusions of this paper were drew as follows:1. Through regression analysis to API and AOT of season or year to compare each model's precision, found that the predicted accuracy of summer and autumn model were good.2. Through discussing the conventional meteorological observation data, the wind speed and the barometric pressure were selected as the main meteorological factor. Then further considered the above two meteorological factors in the model, which were reconstructed by the whole year and the season data, it was found that the precision of the model involved the meteorological factor was higher than which no meteorological factors involved.3. The sample data were grouped according to the main meteorological factor such as wind speed and barometric pressure. Through regression analysis to each group's API and AOT, and discussed its accuracy. It was found that the model's precision between API and AOT was the highest and the forecast accuracy reached 95.4%, when the barometric pressure was more than 1020hpa and the wind speed was less than 1m/s.4. Through discussing seasonal sample data after the meteorological factor grading, found that the forecast results of the predictive API by meteorological factor grading model was better than their own model in the summer and autumn, which the accuracy averagely increased of 2 percents points. The effect of spring and winter were not very significant.5. Through the research of this paper, we can find that discussing the relationship between API and AOT with the meteorological factors will obtain a better effect, and even really reflect the air pollution condition on the ground.