This study takes Taichung City as an example and aims to investigate the long-term impact of urban development on air pollution. By establishing a spatial model, we estimate the concentration trends of fine particulate matter (Particulate Matter 2.5, PM2.5) over the past 25 years (from 1994 to 2020) and further assess the influence of urban development on air quality. Various databases were utilized as sources of spatial predictor variables, including the Environmental Resources Database, meteorological database, land-use inventory, landmark database, digital road network map, digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and power plant distribution database. The spatial hybrid model in this study combines Hybrid Kriging/Land-Use Regression and machine learning methods. Initially, important predictor variables were determined using traditional Land-Use Regression (LUR) and Hybrid Kriging-LUR. Subsequently, prediction models based on the selected variables from LUR models were fitted using Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (Light GBM), and CatBoost algorithms. Validation methods such as data splitting, 10-fold cross-validation, and external data verification were employed to confirm the robustness of the developed models. The results indicate that the model is stable and reliable, with an Adj-R2 of 0.93. Through linear regression, it was observed that the estimated values of the predictor variable ‘year’ for most locations in the city are negative, indicating a significant improvement in air pollution over the past 25 years. This study emphasizes the importance of managing and controlling air pollution in urban development planning.

Nitrogen dioxide (NO2) pollution is a concerned public health issue in urban areas. Children may experience more severe health effects when exposed to NO2. Furthermore, heavy traffic during the morning commuting time leads to peak outdoor NO2 emissions. The limited number of monitoring stations poses a challenge in assessing NO2 exposure during children's school commutes. To accurately depict the spatial distribution and variation of NO2 concentration during elementary school children's commutes, this study estimated NO2 distribution in Chiayi City using Geo-AI technology. Air pollution monitoring data during morning commuting time from 2015 to 2020, land use and potential geospatial-related variables were collected. Machine learning algorithm were then used for variable selection and model development. The results reveal that the main model and Chiayi City both had high explanatory power, with R2 values of 0.94 and 0.93, respectively. The estimations are accurate and reliable. Higher NO2 concentrations are clustered in the southern-central part of Chiayi City. The averaged NO2 levels in Western District is slightly higher compared to the Eastern District. Furthermore, concerning the land use distribution patterns within elementary school districts, a positive correlation was observed between NO2 concentrations around schools and road density and residential area density.

The study area is located in the flat artificial forest of Swietenia macrophylla King in Guken Township, Yunlin County. TLS and ALS were used to estimate the stand structure and evaluate their efficiency and accuracy. Height-diameter equations and local volume equations for this tree species were established, and the ALS was used to estimate the volume. The results showed that the point cloud can compensate for the limitations of TLS in obtaining tree height and ALS in obtaining diameter at breast height and tree position. The efficiency of data acquisition was also improved by combining the point cloud, and height-diameter equations and local volume equations were established using the point cloud. Using ALS alone can also establish the airborne volume equation. Choosing the appropriate LiDAR system according to different survey purposes and site conditions, and combining TLS and ALS point clouds can effectively improve the efficiency and accuracy of surveying stand structure.

Approximately 99% of the global population resides in areas with air pollution higher than World Health Organization (WHO) guidelines. Among these, school children are particularly vulnerable due to their immature immune systems. This study focused on 62 school children recruited in 2022 from Tainan city. For air pollutant data, it was based on the estimated concentrations of PM2.5 model. from previous study. It had been published to the journal and its R2 is about 94%, which demonstrated high explanatory power. In commuter route planning, the least PM2.5 exposure routes can not only reduce average PM2.5 exposure by 6.84% but also 6.13% decrease in commuting distance. If we divide school children into 3 categories with different commute modes, we found that 37 school students go to school by motorcycle, which is the predominant way, followed by car and walking those 23 and 2 students travel by these ways. Among these commute modes, walking commute featured its shorter distances, however, it also has longer outdoor exposure time, while motorcycle commute is more directly exposed to vehicular emissions. These factors mentioned above collectively contribute to increasing potential air pollution exposure. Thus, we need to give school children proper route suggestion to improve this situation.