The nighttime boundary layer was studied in an urban area surrounded by tropical forest by use of a copter-type unmanned aerial vehicle (UAV) in central Amazonia during the wet season. Fifty-seven vertical profiles of ozone concentration, potential temperature, and specific humidity were collected from surface to 500 m above ground level (a.g.l.) at high vertical and temporal resolutions by use of embedded sensors on the UAV. Abrupt changes in ozone concentration with altitude served as a proxy of nighttime boundary layer (NBL) height for the case of a normal, undisturbed, stratified nighttime atmosphere, corresponding to 40% of the cases. The median height of the boundary layer was 300 m. A turbulent mixing NBL constituted 28% of the profiles, while the median height of the boundary layer was 290 m. The remaining 32% of profiles corresponded to complex atmospheres without clear boundary layer heights. The occurrence of the three different cases correlated well with relative cloud cover. The results show that the standard nighttime model widely implemented in chemical transport models holds just 40% of the time, suggesting new challenges in modeling of regional nighttime chemistry. The boundary layer heights were also somewhat higher than observed previously over forested and pasture areas in Amazonia, indicating the important effect of the urban heat island.

Nighttime vertical profiles of ozone, PM_2.5 and PM₁₀ particulate matter, carbon monoxide, temperature, and humidity were collected by a copter-type unmanned aerial vehicle (UAV) over the city of Manaus, Brazil, in central Amazon during the dry season of 2018. The vertical profiles were analyzed to understand the structure of the urban nighttime boundary layer (NBL) and pollution within it. The ozone concentration, temperature, and humidity had an inflection between 225 and 350 m on most nights, representing the top of the urban NBL. The profile of carbon monoxide concentration correlated well with the local evening vehicular congestion of a modern transportation fleet, providing insight into the surface-atmosphere dynamics. In contrast, events of elevated PM_2.5 and PM₁₀ concentrations were not explained well by local urban emissions, but rather by back trajectories that intersected regional biomass burning. These results highlight the potential of the emerging technologies of sensor payloads on UAVs to provide new constraints and insights for understanding the pollution dynamics in nighttime boundary layers in urban regions.