## 4 Conclusions
Continued investigation on visibility in the eastern region of Thailand or extension to other areas can broaden the understanding of visibility quality at a regional scale. Linking visibility to synoptic circulation patterns can enhance the current observational analysis framework. Advanced air quality modeling and source apportionment for visibility can be utilized to specifically determine emission sources contributing to visibility degradation. Given that human-based visibility observation has its own limitation in terms of uncertainty and reliability, automated visibility measurement may offer an alternative source of data for future comparison and application, as the TMD has recently installed automated visibility sensors at its standard weather stations although only at a small number of stations. Based on the results in this study, we recommend: (a) Apart from particulate pollution, emissions of precursor gases for secondary aerosols should also be paid attention to improve visibility. (b) Regional transport of air pollutants also play important role in visibility degradation posing a challenge to its improvement with the measures of reducing local emission. (c) The negative trends and persistently low tendency in visibility over the past years provide a clear evidence to policy makers regarding the impact of industrialization and urbanization. Given that there is a larger-scale regional developers (i.e., Eastern Economic Corridor), the awareness of visibility degradation should be integrated into policy making. (d) Policy makers should also think about amenity effects of higher air pollution apart from the health effects while deciding the framework for managing air quality. The following are available online at [[http://www.mdpi.com/2073-4433/10/3/122/s1](http://www.mdpi.com/2073-4433/10/3/122/s1)]([http://www.mdpi.com/2073-4433/10/3/122/s1](http://www.mdpi.com/2073-4433/10/3/122/s1)). Section S1: Change point detection for changed or modified targets; Table S1: Numbers of days before and after the met-screening by station and by season; Table S2: Explanatory variables initially tried and finally used for the generalized linear models of visibility (_VIS_) and PM\({}_{10}\); Table S3: Visibility (_VIS_) and seasonality index (_SI_) by station and by season; Figure S1: Diurnal variation of visibility and other meteorological variables at Chon Buri (CB), Laem Chabang (LC) and Rayong (RY); Figure S2: Frequency (days) of each visibility class (I: Good, II: Moderate, and III: Poor) by station in the dry season; Figure S3: Chon Buri (CB), Laem Chabang (LC), and Rayong (RY) and their nearby PCD (Pollution Control Department) stations (Google Maps); Figure S4: Diurnal variation of air pollutants at Chon Buri (CB), Laem Chabang ( LC), and Rayong (RY). **Author Contributions:** [PERSON] initiated and conceptualized the study. [PERSON]. ([PERSON]) implemented it, performed all analyses, and drafted the manuscript. [PERSON], P.S., [PERSON], and [PERSON] ([PERSON]) helped in discussing the results during manuscript preparation. **Funding:** This study was financially supported by the Joint Graduate School of Energy and Environment, the Postgraduate Education and Research Development Office, and partly by the National Research Council of Thailand. * _[PERSON] et al. (2010)_ [PERSON]; [PERSON]; [PERSON] The challenge of improving visibility in Beijing. _Atmos. Chem. Phys._**2010**, _10_, 7821-7827. [CrossRef]
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