Improving air quality through urban form optimization: A review study

Abstract

Air pollution is a significant global environmental issue. Nevertheless, the importance of rational urban planning in mitigating it is frequently disregarded. Conducting air quality optimization simulations that consider UFIs (urban form indicators) is an effective approach for air quality-friendly urban planning. Despite its potential, this technique is still in its infancy. This study aims to identify research gaps and uncertainties by summarizing the current research status of key steps in air quality optimization simulation: selecting UFIs, analyzing impact mechanisms of UFIs on air pollution, building air quality models, and proposing optimization strategies through multiple-scenario predictions. Our findings indicate a lack of consistency in selecting UFIs, and an indicator system considers their impact on air quality and urban planning standards proposed by us might be a viable option. Increases in the proportion of construction land, industrial land, and floor area ratio significantly contribute to air pollution, whereas factors such as forest land, public green space, and sky openness have a noteworthy alleviating effect. Typically, statistical modeling is preferred at the city scale while CFD simulation techniques are used at smaller scales. However, future air quality improving through UFIs optimization still lacks a multi-scale nested tool. Thus, further research is recommended to explore combined impacts of various UFIs on different air pollutants under typical scenarios, and develop intelligent, big data-driven air quality models and tools. This review might contribute to transforming air quality optimization that consider UFIs from fragmented academic research to practical application, thereby assisting in global air pollution mitigation efforts.