More than half of the world’s population is living in towns and cities. The United Nation predicted that 60% of the world’s population will be living in urban regions by the year 2025, and the urban population will grow to 4.9 billion by 2030. On the one hand, the large proportion and rapid growth in the urban population will result in a potentially higher level of total harmful emissions, thus more citizens will suffer from deteriorated air quality. On the other hand, the spatial configurations and arrangements of different urban elements, collectively termed urban form, may dramatically affect human activities and the distribution of air pollutants. Thus, urban air quality varies with different designs of land uses pattern and urban infrastructure. However, urban form characteristics have not been widely applied in air quality research and their relationships with air quality have not been widely applied in related fields, such as urban planning, exposure assessment, and environmental justice, due to complicated environmental factors and individual behavioral patterns. As a result, applying GIScience to study the urban form-air quality relationship is crucial for modeling spatiotemporal heterogeneity of air pollutants, understanding human mobility patterns, and improving the accuracy of risk assessment. In this study, we try to analyze the urban form-air quality relationship, improve the understanding of spatiotemporal disparities of human exposure to urban air pollutants based on multiple geospatial data sources, such as remote sensing imagery, portable sensors, crowdsourced database, and mobile monitoring, and to make effective strategies to improve urban ecology in global cities. We find that urban form does impact air quality from both static and dynamic perspectives and meteorological factors have synergic effects on the urban form-air quality relationship.