Alternation emerges as a multi-modal strategy for turbulent odor navigation

Foraging mammals exhibit a familiar yet poorly characterized phenomenon, "alternation", a momentary pause to sniff in the air often preceded by the animal rearing on its hind legs or raising its head. Intriguingly, rodents executing an olfactory search task spontaneously exhibit alternation in the presence of airflow, suggesting that alternation may serve an important role during turbulent plume-tracking. To test this hypothesis, we combine fully-resolved numerical simulations of turbulent odor transport and Bellman optimization methods for decision-making under partial observability. We show that an agent trained to minimize search time in a realistic odor plume exhibits extensive alternation together with the characteristic cast-and-surge behavior commonly observed in flying insects. Alternation is tightly linked with casting and occurs more frequently when the agent is far downwind of the source, where the likelihood of detecting airborne cues is higher relative to cues close to the ground. Casting and alternation emerge as complementary tools for effective exploration when cues are sparse. We develop a model based on marginal value theory to capture the interplay between casting, surging and alternation. More generally, we show how multiple sensorimotor modalities can be fruitfully integrated during complex goal-directed behavior.

Spatial information from the odour environment in mammalian olfaction

The sense of smell is an essential modality for many species, in particular nocturnal and crepuscular mammals, to gather information about their environment. Olfactory cues provide information over a large range of distances, allowing behaviours ranging from simple detection and recognition of objects, to tracking trails and navigating using odour plumes from afar. In this review, we discuss the features of the natural olfactory environment and provide a brief overview of how odour information can be sampled and might be represented and processed by the mammalian olfactory system. Finally, we discuss recent behavioural approaches that address how mammals extract spatial information from the environment in three different contexts: odour trail tracking, odour plume tracking and, more general, olfactory-guided navigation. Recent technological developments have seen the spatiotemporal aspect of mammalian olfaction gain significant attention, and we discuss both the promising aspects of rapidly developing paradigms and stimulus control technologies as well as their limitations. We conclude that, while still in its beginnings, research on the odour environment offers an entry point into understanding the mechanisms how mammals extract information about space.

Correlated decision making across multiple phases of olfactory guided search in Drosophila

All motile organisms must search for food, often requiring the exploration of heterogeneous environments across a wide range of spatial scales. Recent field and laboratory experiments with the fruit fly, Drosophila, have revealed that they employ different strategies across these regimes, including kilometer scale straight-path flights between resource clusters, zig-zagging trajectories to follow odor plumes, and local search on foot after landing. However, little is known about the extent to which experiences in one regime might influence decisions in another. To determine how a flies’ odor plume tracking during flight is related to their behavior after landing, I tracked the behavior of individually labelled fruit flies as they explored an array of three odor emitting, but food-barren, objects. The distance flies travelled on the objects in search of food was correlated with the time elapsed between their visits, suggesting that their in-flight plume tracking and on-foot local search behaviors are interconnected through a lossy memory-like process.

TROPOMI Aerosol Index: detailed aerosol plume tracking and plans for future development

<p>The aerosol index (AER_AI) as calculated using data from the Tropospheric Monitoring Instrument (TROPOMI) onboard the ESA Sentinel 5 Precursor (S5P) platform was publically released in July 2018. The operational AER_AI dataset is available from May 2018 through the present. It is a useful data product not only for tracking ultraviolet (UV) absorbing aerosol plumes of desert dust, volcanic ash, and smoke from biomass burning but also for monitoring the quality of the TROPOMI Level 1b (L1b) data since the AER_AI calculation is very sensitive to the absolute calibration of irradiance and radiance. The aim of this work is first to highlight the new level of detail seen in aerosol plume events based on the recent switch to a reduced pixel size of 3.5 x 5.5 km. Such high spatial resolution also presents specific challenges as non-Lambertian cloud features and 3-D effects of clouds are now visible in the TROPOMI AER_AI data. Plans for an approach to flag and correct these features in future AER_AI updates will be given. Secondly this work will include an overview of the impacts on AER_AI due to observed degradation in the TROPOMI measured irradiance and wavelength-dependent features in the radiance. As a result of these L1b effects, there is a steadily increasing negative bias in the global mean AER_AI value. Examples are given how the new version of the L1b data (2.0.0) will be used to correct for this degradation-driven bias. Recommendations are also given to guide data users looking to perform trend analysis or those using AER_AI as a filter for aerosol removal or detection in other L2 data products.  </p>

Modelling and Simulation Platform for Chemical Plume Tracking and Source Localization

Using autonomous robot to detect chemical emissions and track plumes caused by fire, toxic gas leakage and explosive at their early stages, and swiftly localize their sources can avoid risking human health and potentially save lives. The benefits of deploying autonomous robot(s) rather than human beings in performing such hazardous tasks are obvious. Even though using real robots to research, develop, and experiment in real environment are normally preferred, modelling and simulation are indeed sometimes better options when such as a consistent and repeatable complex environment with controllable variables (i.e. wind velocity and plume propagation in this case) for experiments is important. This chapter presents one out of many possible modelling and simulation approaches for the research related to chemical plume tracking and source localization using robots, and covers the modelling of robot, the modelling of the environment, and the integration of both to become a platform.