Spatial distributions of particulate matter in neighborhoods along the highway using unmanned aerial vehicle in Shanghai.

Road traffic pollution has become a major source of pollution in the city. As a result, the residents of near-road neighborhood are inevitably exposed to high air pollution. However, due to lack of the measured data especially in vertical direction, the distribution of traffic-related air pollutants (TRAP) in three-dimension in community along the road is not understand in details. Hence, in this paper, we use an unmanned aerial vehicle (UAV) to reveal the impact of the TRAP on the community. Firstly, an in-situ observation was adopted using UAV and portable air pollutants sensors to realize three-dimensional monitoring of air pollutant distribution from traffic in near-road area. Secondly, a statistical analysis was conducted to reveal near-road PM 1 distribution in different wind directions and time periods based on 14 days' observation. And results showed that PM 1 concentration dropped 44% after 5 rows of high residence buildings beside an urban expressway while slightly fluctuated in the open field next to the residence community. Thirdly, a model based on CALINE4 was established to verify the PM 1 concentration in the neighborhood, providing quantitative data support and theoretical basis for the planning and design of neighborhood to reduce pollutants' concentration level. We found the UAV platform is quite applicable in field measurement, and that traffic emissions provide most PM 1 source in near-road area, while wind and building layout have a great influence on its distribution pattern. This research approach has the potential to be further investigated not only to support the design and development of urban building projects, but also to aid the location choice and alignment of new transportation infrastructures from the perspective of mitigating their environmental impacts. • UAV platform is used to realize three-dimensional distribution of air pollutant in near-road area. • Spatial distribution of PM 1 is dependent with wind directions and traffic emission intensity. • PM 1 in community exhibited linear variations related to the distance of expressway. [ABSTRACT FROM AUTHOR]