## 5 Empirical Results and Discussion
### Bivariate Local Moran's I Results  
Figure 2 illustrates the relationship between PM\({}_{2.5}\) concentration and population density from 2012 to 2022. The dark red areas on the map indicate regions with a high PM\({}_{2.5}\) concentration and high population density (High-High clusters), which are primarily clustered in central Thailand, particularly in the BKK&VIC and CE regions. In contrast, the dark blue areas on the upper side of the map represent the NO region of Thailand, which is predominantly mountainous and forested. Most residents in this region are farmers engaged in sugarcane and corn cultivation for animal feed, and forest burning is commonly practiced to collect forest products. These areas have a low population density compared to the BKK&VIC region, where business districts offer diverse job opportunities. As a result, many workers from rural areas migrate permanently to the BKK&VIC region in search of employment. This migration trend explains why NO has a lower population density [10; 49; 50]. Figure 3 illustrates the relationship between the PM\({}_{2.5}\) concentration and GPP per capita from 2012 to 2022. The dark red areas on the map represent those with a high PM\({}_{2.5}\) concentration and high GPP per capita (High-High clusters), which are primarily found in central Thailand, particularly in the BKK&VIC and CE regions. These regions are Thailand's major economic and industrial hubs, characterized by high economic activity, shopping malls, residential areas, and business districts [10; 50; 51]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **Year** & **Moran’s I** & **E(I)** & **SE(I)** & **Z(I)** & \(p\)**-Value** \\ \hline \multirow{3}{*}{PM\({}_{2.5}\)} & 2012 & 0.945 & \(-\)0.013 & 0.092 & 10.435 & 0.000 \\ \cline{2-7}  & 2017 & 0.928 & \(-\)0.013 & 0.090 & 10.460 & 0.000 \\ \cline{2-7}  & 2022 & 0.935 & \(-\)0.013 & 0.090 & 10.499 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Empirical results (spatio-temporal characteristics of PM\({}_{2.5}\) pollution) of Moran’s I statistical test. Figure 2: Local Indicators of Spatial Association (LISA) cluster maps of bivariate analyses: PM\({}_{2.5}\) and population density during 2012–2022. In contrast, the light blue areas at the bottom right of the map represent areas with a low PM\({}_{2.5}\) concentration but high GPP per capita (Low-High clusters). These regions (EA) include industrial estates, special economic zones, and the Eastern Economic Corridor (EEC), which are close to the Gulf of Thailand. The sea breeze helps disperse pollutants, resulting in lower PM\({}_{2.5}\) concentrations despite high economic activity [10; 52; 51]. On the right-hand side of the map, the pink areas in the NE region of Thailand contains areas with a high PM\({}_{2.5}\) concentration but low GPP per capita (High-Low clusters). The dark blue areas indicate regions with low PM\({}_{2.5}\) concentration and low Energy consumption per capita (Low-Low clusters). These areas are predominantly agricultural, and most residents are farmers engaged in rice cultivation and gardening. Due to the low market value of agricultural products, these provinces have a low GPP per capita. Additionally, the open burning of agricultural waste is a common practice in these areas, as farmers clear fields quickly for the next planting season [10; 50; 53]. Figure 4 illustrates the relationship between PM\({}_{2.5}\) concentration and industrial density from 2012 to 2022. The dark red areas, the BKK&VIC and CE regions (High-High clusters), and the light blue areas, the EA region (a Low-High cluster), on the map are interpreted similarly to those in previous figures. Found on the upper half of the map, the NO region of Thailand is characterized by mountainous and forested landscapes. Most residents in this area are farmers engaged in sugarcane and corn cultivation for animal feed. Additionally, forest burning is commonly practiced for agricultural purposes and to collect forest products.