In recent years, air quality has gradually deteriorated, which has affected human behavior patterns, and made air pollution become an important issue. This study took New South Wales, Australia as our study area to define the spatial relationship between air pollution levels and indoor- and outdoor- crimes. By using the Ordinary Least Square (OLS) and Geographically Weighted Regression model (GWR), the Air Quality Index (AQI) and other relevant factors are used to predict the number of crimes. The results show that the areas with severer degree of air pollution tend to have more domestic violence (indoor crimes) but less robberies (outdoor crimes).

Lung cancer was ranked as the top-3 leading cause of cancer-related deaths in Taiwan in the last decade. Signs and symptoms of lung cancer typically occur only when the disease is advanced, therefore elevating the difficulties in early-stage diagnosis. Moreover, the prognosis is usually poor for most patients. Recently, studies have suggested that exposure to areas with higher amounts of distributed community vegetation has health benefits. To understand the cardiovascular health effects related to greenness exposures is important in developing the lung cancer preventive medicine. In this study, township level lung cancer incidence from 2000 to 2010 for Taipei and Chiayi areas were obtained from the Longitudinal Health Insurance Database (LHID). The MODIS Normalized Difference Vegetation Index (NDVI) database was used to identify the long-term exposure to greenness of the study population. A global Moran’s I was used to examine the spatial clustering effects of lung cancer incidence during the study period. Then, a Generalized Additive Mixed Model (GAMM) was applied to assess the association between surrounding greenness and lung cancer incidence. While all the calculated Moran’s I achieved statistical significance (p < 0.01), lung cancer represented a spatial clustering trend regardless the season or study year. The results of GAMM indicates that, greenness exposures showed a negative association with lung cancer. The adjusted relative risk for NDVI was 0.68 (95% confidence interval [CI] = 0.66 to 0.70; p < 0.01) per NDVI interquartile range (IQR) increment (0.27) exposure. Consistently negative association was found regardless the study cities. The results demonstrated long-term greenness exposure decreased the risk for lung cancer.

This study used MODIS satellite imagery and government open data to discuss the relationships among urban green areas, PM2.5 and land surface temperature in the former Taichung city in winter and summer. We demonstrated the relationships with five different grid sizes in the general spatial model. According to the results of the general spatial model, there was a certain relationship between land surface temperature and PM2.5 concentration and also a positive effect on each other. The Moran’s I index indicates that 500 m is the most suitable grid size to demonstrate the relationships between factors and land surface temperature and deal with the influence of spatial autocorrelation; the values of Moran’s I is 0.008 and 0.018 in winter and summer, respectively. As for PM2.5, the most suitable grid size is 250 m with the values of Moran’s I -0.046 and -0.032 in winter and summer, respectively. In addition, we found that the positive and negative effects of NDVI on PM2.5 concentrations were different in the winter and summer seasons. It is speculated that dry riverbed during the low flow season and barren land in land consolidation area in winter caused raised dust pollution, which is the reason why the dust retention with plants could not be effectively exerted.

Air pollution is a global environmental problem and causes numerous adverse health effects. Recent studies indicated that prenatal and perinatal air pollution exposure decreased the neurodevelopment of children. Thus, we conducted a study to evaluate the effects of prenatal and early childhood exposure to air pollution and land use characteristics near residence on neurodevelopment at six months old (adjusted age) among very low birth weight (VLBW) preterm children in the Greater Taipei area. We used geographic information system to estimate air pollution concentrations and land use types around the study children’s residences during pregnancy and in the first six months of life. Neurodevelopment of the study children were evaluated using Bayley Scales of Infant and Toddler Development (Bayley-III). Other important information of the children were collected using a structured questionnaire and medical records including prenatal and postnatal residential addresses, basic social demographic characteristics, pollutants at home, etc. We used multiple regressions to examine the relationships of interest. According to the results of regression analyses, several air pollutants (O3, PM10, NO, and NO2) had adverse effects on children’s neurodevelopment. Among different exposure periods, air pollutant exposure during pregnancy had the most significant impact on children’s neurodevelopment. In addition, children’s neurodevelopment was negatively associated with area of freeways near residence, and positively related to living near parks and greens. Based on the results of this study, reduction of air pollution emission and increase of greenbelts in the urban areas can promote the neurodevelopment of VLBW preterm children.

Fine particulate matter PM2.5 is one of the most concerned environmental issues in central and southern Taiwan. Except for Taichung Thermal Power Plant, the exhaust emissions from motor vehicle are often attributed to the main cause. The ground and point monitoring approaches of air quality and traffic flow are generally used while may not fully explore whether traffic flow has a significant impact on PM2.5. Therefore, this study used Landsat 8 OLI image and dispersion coefficient method to retrieve aerosol optical depth. We also included 25 air quality stations in Taichung and neighboring counties as the reference to estimate PM2.5 for holiday and normal day in Taichung City. The passenger car equivalent was estimated with average congestion level from Google Map general road traffic as well as actual road width and the seconds of stopped-time to estimate the congestion of the traffic. Finally, considering the spatial autocorrelation and different spatial units, the spatial regression model decision process is used to explore the most suitable regression model to explain the local PM2.5 affected by the local and neighboring factors.