Nitrogen Dioxide (NO_2) is one of the major air pollutants in urban area. In this study, NO_2 concentration observations during 2000 to 2013 were obtained from 18 EPA monitoring stations. Geographically Weighted Regression (GWR) coupled with GIS land-use data was then applied to assess the spatial-temporal variability of NO_2 in Taipei metropolis. The results showed that, a slightly decreasing trend was found in the pollutant level during the studied fourteen years. For example, the averaged NO_2 concentration level was 25.49ppb in 2000 but decreased to 21.48ppb in 2013. Several land-use related variables were selected as important predictors in the developed GWR model. Among them, roads were positively corelated to pollutant levels. Forests and waterbodies showed a negative association. Moreover, the resultant model had a highly explanatory power with the model R^2 of 0.89. Finally, NO_2 variability was illustrated using the developed model. Higher pollutant levels were clustered in the densely populated areas with heavy traffic.

The petrochemical industry is a major contributor to air pollution emissions, so determining the variations of these pollutants in the petrochemical industrial area is needed to reduce health risks due to PM_(10) exposures. In this study, a specific industrial monitoring database was used to collect PM_(10) data from May 2015 to September 2018 in Kaohsiung Petrochemical Parks area (including Linyuan Industrial Park and Linhai Industrial Park), Taiwan. Conventional land use regression and hybrid kriging approach which consider both the spatial and temporal heterogeneity of air pollutants was then conducted to develop the prediction model for estimating the spatial-temporal variability of PM_(10) concentrations. The resultant models showed a high-estimate performance level with the value of R^2, Adjusted R^2, RMSE and cross-validation for conventional approach was 0.89, 0.89, 7.29 μg/m^3 and 0.83, 0.83, 9.0 μg/m^3, respectively; and for hybrid approach was 0.95, 0.95, 5.2 μg/m^3 and 0.94, 0.94, 5.41 μg/m^3, respectively. The hybrid approach showed better explanatory power than conventional approach. Moreover, the results of hybrid model simulation showed that the highest concentrations of PM_(10) formed in the pure residential area, the manufacturing areas and agricultural fields; a similar decreasing trend was observed as well.

The research undergoes crop recognition and classification through seven CNN architecture models: 2DCNN, AlexNet, VGG16, ResNet50, Inception-v1, Inception-v3, and InceptionResNet-v2 to help the government agency in tabulating the crop production and solve the imbalance between production and sales. Besides, our research also designed the imagery classification of the machine learning method by pre-extracting crop’s texture, shape and size, color, and 11 vegetation indices features. We compared the advantages and disadvantages between the two. The result of the experiment shows that the training model with CNN as the basic architecture is better than the traditional imagery classification method.

Social inequality occurs when resources in a given society are distributed unevenly. The assignment of environmental resources may also be unfair. Previous studies pointed out that green resources and environmental greenness can provide various benefits on human being. However, no research investigates the environmental resources inequality. The objective of this study is to integrate the remote sensing, Geographic Information System (GIS), and statistical tests to assess the greenness resources inequality among socially/demographically defined categories of populations in Taipei metropolis. Several databases were used including NASA MODIS Normalized Difference Vegetation Index (NDVI) database and socioeconomic from the Ministry of Interior. The results showed that, green resources represented spatial variation among the townships in Taipei metropolis. In New Taipei City and the whole Taipei Metropolis, townships with higher proportion of female or higher income level shared fewer green resources. Finally, the results of correlation analysis again confirmed the unequal allocations of greenness resources among people with different socio-economic and demographic characteristics. We suggest future urban planning should consider environmental inequality issues.