Remote Sensing Analysis Of Aerosol Over Xianlin, Nanjing Based On LiDAR

LiDAR plays an increasingly important role in atmospheric aerosol monitoring for its high vertical spatial and temporal resolution. Taking Xianlin, Nanjing as study area, a new method of optimizing the inversion parameters to improve the accuracy of LiDAR inversion. Then, based on LiDAR inversion results, the spatial and temporal variation of aerosol over Xianlin was analyzed during March,2014 to February,2015. Combining LiDAR and other multi-source data, a typical air pollution proceed caused by particulate matter from 26th May to 1st June,2014 was analyzed. The main research results are as follows:(1) The calculation method for varied LiDAR ratio using visibility. LiDAR ratio is one of the major error source during LiDAR inversion. Taking the near surface extinction coefficient converted by visibility as a constraint, this article presents a new method to calculate the LiDAR ratio. Compared with the traditional solution, this method can understand how the LiDAR ratio continuously changed in smaller time scale. Meanwhile, the inversion accuracy was significantly improved when varied LiDAR ratio was used in the retrieval process compared to a fixed value for some time in traditional solution.(2) LiDAR can accurately detect aerosol vertical stratification structure which reflects its unique advantages for air pollution detection. Consequences of LiDAR inversion could capture the temporal and spatial variation of the distribution of pollutants. Using LiDAR detection results, the aerosol optical characteristics of vertical distribution were deeply analyzed. In near-surface under 0.2km, the extinction coefficient is significantly large which means high concentration of aerosol. The average extinction coefficient between 0-0.2km and 0.2-0.5km presents almost the same double-peak variation trend which is closely related to the surface human activities. Because of atmospheric turbulence, the average extinction coefficient in 0.5-lkm rises significantly in the afternoon. The atmospheric boundary layer also shows an obvious diurnal variation ranging from 1-2km which is mainly associate to the intensity of solar radiation. During the observation period, the average extinction profile for each month is slightly different. The minimum average near surface extinction coefficient is around 0.4km-1 in January,2015 and the maximum is about 0.7km-1 in July,2014. The average near surface extinction coefficient are all quite high in September and October, 2014 and February,2015 at about 0.6 km-1. In addition, each month in the observation period are all affected by the surface turbulence to varying degrees especially in summer when the surface temperature is higher.(3) However, the acquisition of LiDAR detection is only a point data which cannot represent the whole area. Moreover, the single band Mie scattering LiDAR used in this paper has relatively little measurement parameters. So, only a single LiDAR device is sometimes difficult to achieve a comprehensive scientific analysis of the atmospheric pollution process. Based on environmental monitoring data, meteorological data and the results of numerical simulation(NAAPS、HYSPLIT), a typical air pollution process deeply analyzed combining LiDAR system. Experimental results demonstrate that the entire pollution proceed was affected by both local pollution and exogenous inputs including dust and smoke. This indicates that the air pollution process in Nanjing area is quite complex and pollution source is multiple which make the atmospheric pollution control situation very grim. The environmental monitoring data can be applied to extract the pollution process and analysis the characteristics of different stages during the pollution. The results of numerical simulation can present the spatial distribution of pollutants and can also be used for analysis of pollutant source. Inversion result of the LiDAR can clearly reflect the time and space variation of the distribution of pollutant. In addition, meteorological elements are of great significance for atmospheric pollution monitoring. On one hand, they can influence LiDAR ratio and then affecting the inversion accuracy of LiDAR system. On the other hand, during the air pollution process, meteorological elements play an important role in the generation, evolution and dissipation of contaminants. Low pressure, temperature inversion phenomena is not conducive to the spread of pollutants which contribute to the air pollution. However, rainfall and gale can terminate the pollution apparently.