# How May Building Morphology Influence Pedestrians' Exposure to PM2.5?
## 4 Discussion  
Despite the moderate variation in building morphology, the study observed relatively uniform PM2.5 levels within an area of about 0.5 km\({}^{2}\) across all data collection runs. The consistent PM2.5 levels observed in this study agree with the findings of a study by [PERSON] et al. [2] aimed at understanding spatial variability at a neighborhood scale. [PERSON] et al. [2] collected daily on-road PM2.5 measurements over a month within an area of 100 km\({}^{2}\) (equivalent to MODIS aerosol product's single pixel), encompassing the University of Texas at Dallas. Their study found that depending on the weather condition, the spatial scale of PM2.5 variation in the area varied from 0.8 km to 5.2 km, suggesting uniform PM2.5 levels over a distance of 0.8 km or smaller. The building morphology accounted for 33.22% of the variations in the fixed effects, suggesting that the built environment could affect PM2.5. Regression results from the fixed effects showed that the building coverage ratio had a positive impact on PM2.5. Thus, the greater built-up area likely hindered the dispersion of the pollutants. Despite the negative impact of the number of buildings per 1000 m\({}^{2}\) on PM2.5, a similar inference could be drawn. A negative correlation between the number of buildings per 1000 m\({}^{2}\) and mean building area, mean building height, and building coverage ratio in the study area denoted the contrast between areas with many smaller buildings and areas with fewer larger buildings, suggesting areas with smaller and shorter buildings experienced better dispersion than other areas. Nevertheless, the study could not assess each variable in isolation, as an inference about it could change based on its relationships with other variables in the model. The insignificant impact of mean building height and mean building area could be attributed to limited variability among building morphological characteristics in the study area. The negative effect of wind speed on PM2.5 in the model reaffirmed findings from the previous studies that increasing wind speed improved dispersion and hence reduced the PM2.5 concentration [10; 11]. Increased temperature promoted air circulation and was negatively related to PM2.5 measures [10]. Therefore, the temperature was expected to have a negative effect on PM2.5 contrary to the positive effect found in this study. Several factors could contribute to this discrepancy. One possible explanation is that elevated temperature contributed to chemical reactions that might have formed new particles including PM2.5 [30]. Other reasons could be the blocking of the airflow by surrounding buildings or the desorption of particles from different surfaces due to increased temperature. As for the relative humidity, depending on its value, it could increase or decrease in PM2.5 mass. Increased humidity promoted particle size growth, but after a certain threshold, very high humidity could lead to particle deposition due to heavy particle growth [30]. Moreover, [PERSON] et al. [31], in their 3-year study in the Yangtze River delta, described the relationship between relative humidity and PM2.5 as an inverted U-shape, where the hygroscopic growth of particles continued with an increase in relative humidity until it reached 70%, and then it started decreasing. The relative humidity observed in the study ranged from 39 to 70%--not high enough to cause particle deposition--and resulted in a positive correlation with PM2.5. The positive coefficient associated with directional viewshed suggested that a more open area in the direction of the wind was consistent with additional incoming pollution from the other areas, as confirmed by [PERSON] et al. [11]. Of the different viewshed distances used in the model, model performance consistently