## 2 Materials and Methods
### Data Collection and Treatment
The \(RC\) aims to account for this local effect during daytime hours (here, 1000-1800 LT), and is computed as follows [40]:  
\[RC=1-\frac{\sqrt{X^{2}+Y^{2}}}{WR}, \tag{1}\]  
where \(WR\) is the total wind run or summed distance by an air mass over a period of time, and \(X\) and \(Y\) are the total distances travelled by the air mass in the east-west and north-south directions, respectively. In other words, \(WR=T\sum_{i=1}^{\mathrm{N}}\sqrt{u_{i}^{2}+v_{i}^{2}}\), \(X=T\sum_{i=1}^{\mathrm{N}}u_{i}\), \(Y=T\sum_{i=1}^{\mathrm{N}}v_{i}\), where \(u_{i}\) and \(v_{i}\) are the \(x\)-axis and \(y\)-axis wind components, respectively, \(N\) is the total hours of air-mass migration (here, 1000-1800 LT as the daytime hours), and \(T\) is the time interval (here, one hour). Mixing height determines the degree of atmospheric dilution. Here, mixing height was computed using AERMET (American Meteorological Society/Environmental Protection Agency Regulatory Model Meteorological Processor), the meteorological preprocessor for the AERMOD (American Meteorological Society/Environmental Protection Agency Regulatory Model) dispersion modelling system developed by the US EPA (US Environmental Protection Agency) [41], which is based on a one-dimensional heat balance in the atmospheric boundary layer. The required input to AERMET includes both radiosonde sounding and surface variables. As no radiosonde sounding is routinely conducted by the TMD in the eastern region of Thailand, reanalysis data were adopted for substitution, as in [PERSON] et al. (2016) [18] and [PERSON] et al. (2012) [19]. Climate Forecast System Reanalysis (CFSR) data [40] were chosen given their moderate grid resolution (0.5\({}^{\circ}\)). Running AERMET additionally requires long-term historical rainfall data for surface wetness characterization, and we here obtained and used TMD daily rainfall data from the past 35 years (1981-2015). The details of the AERMET setup and its input preparation are presented in the US EPA (2004) [41]. Daily daytime visibility (daily visibility) is of interest to us, and was here defined as the minimum of the visibility values at 1300 and 1600 LT. The reasons for choosing the daytime visibility are that: (1) using a single (or a few) observation(s) per day, rather than taking an average, automatically reduces the change in targets' light illumination with time; and (2) using only afternoon hours avoids the effects of lower solar elevation and diurnal change of meteorological factors over the course of the day [6; 42]. With these, diurnal characteristics are not our focal point; however, they are concisely described in the Supplementary Materials (see Figure S1). To assign daily values to other variables, those corresponding to the times at which daily visibility was chosen were used. As for data completeness, the amount of missing data (in terms of available daily visibility) at every station was found to be small (<4% and <1% in the dry and wet seasons, respectively) (Supplementary Materials, Table S1). Certain meteorological conditions (humid air, rain, fog, and mist) have the potential to reduce visibility and are thus not relevant when attention is given to visibility reduction by anthropogenic activities. Accordingly, meteorological screening was applied, in which data are excluded when rainfall, mist, fog, or very moist conditions (\(RH\) > 90%) are present, similar to other studies [5; 10; 43], highlighting the anthropogenic influence on visibility. This exclusion will hereafter be referred to as \"met-screening\". In general, moderate-to-high relative humidity can effectively impact visibility since most atmospheric aerosols can absorb humidity (i.e., are hygroscopic) and then grow in size, which in turn increases light scattering efficiency and lowers mean refractive index, reducing visibility [1].