The work of this project included maintaining and operating the Ministry of Environment visibility monitoring network (VMN) in Banqiao, Xitun, and Xiaogang stations, implementing the data quality assurance and quality control, investigating the measured information between Ministry of Environment air quality monitoring stations and VMN, and providing the analysis and application of the VMN optical monitoring data. From 2019 to the implementation of the project this year, monitoring results showed that the visibility of the three measuring stations showed an improving trend. The air visibility was estimated by the visual range constant divided the extinction coefficient. The multivariate regression relationship between the extinction coefficient and regular air quality data was established to assess the feasibility for simply and rapidly simulating visibility variations. The extinction coefficient is estimated based on the chemical composition of PM2.5. Banqiao, Zhongming, and Xiaogang stations use the empirical estimation formula for the atmospheric extinction coefficient to estimate the extinction coefficient value ratio using the revised IMPROVE estimation formula It is more in line with the actual local extinction coefficient value. In the application of the absorbance coefficient, the three-dimensional indicators of CO/NOx, AAE and BC/PM2.5 are verified by the pollutant events of Ministry of Environment announcements to identify the characteristics of long-range transport pollutants, local pollutants, and traffic emissions. The positive matrix factorization (PMF) was used at three stations for source apportionment of pollution. The extinction coefficients were then reconstructed from PMF factors modified with RH to understand the source contributions of the visibility degradation in the field atmospheric environment. During the heavy pollution period in winter and spring, the highest contributor to the reconstructed extinction coefficient at three stations was the RH-modified nitrate. The results highlight the importance of nitrate pollution source control in improving visibility policies. Using high-resolution images to identify visibility, currently using the ResNet50 pre-trained model (feature extractor), most of the daytime images can achieve 80% classification accuracy. Using the LiDAR (Light Detection And Ranging) system of Academia Sinica for near-surface atmospheric observation, in addition to observing the vertical data lacking in the existing instrument LPV-4 of this project, it can also complement the 250-meter near-earth observation network of the Environmental Protection Agency. Insufficient data, and provide higher resolution monitoring data of visibility changes in the vertical profile of the atmosphere.

Extinction coefficient, Scattering coefficient, PM2.5 chemical component, Relative humidity