Crowdsourcing dust storms utilizing social media data

In the past we have explored how social media can be used to delineate earthquakes, study human-wildlife interactions, understand urban morphology, urban smells or  locating wildfires among many other things. 

Keeping with the last topic (i.e., locating things), in a new paper published in GeoJournal entitled "Crowdsourcing dust storms in the United States utilizing social media data," Stuart Evans, Festus Adegbola and myself explore how we can use X (formerly Twitter) and Flickr  to source observations of windblown dust. 

As such the paper demonstrates how social media data can act as supplementary source for dust events monitoring and captures the seasonal trends of such events. Furthermore, the paper highlights the potential of using crowdsourced data for the often overlooked field of dust monitoring that has substantial health and economic impacts. 

If this sounds of interest, below we provide the abstract to the paper along with some figures which showcase our methodology and comparison with National Weather Service dust advisories and VIIRS satellite data. At the bottom of the post, you can find the full reference to the paper along with a link to it. 

Abstract: 

Dust storms and other dust events are natural phenomena characterized by strong winds carrying large amounts of fine particles which have significant environmental and human impacts. However, capturing the occurrence of such phenomena is a challenge. Previous studies have limitations due to available data, especially regarding short-lived, intense dust storms and events that are not captured by observing stations and satellite instruments. In recent years, the advent of social media platforms has provided a unique opportunity to access vast amounts of crowdsourced data. This paper explores the utilization of Flickr and X (Twitter) data to study dust event occurrences within the United States and their correlation with National Weather Service (NWS) advisories. The work ascertains the reliability of using crowdsourced data as a supplementary source for dust events monitoring. Our analysis of Flickr and X indicates that the Southwest region is most susceptible to dust events, with Arizona leading in the highest number of occurrences. On the other hand, the Great Plains show a scarcity of crowdsourced data related to dust events, which can be attributed to the sparsely populated nature of the region. Furthermore, seasonal analysis reveals that dust events are prevalent during the Summer months followed by Spring. These results are consistent with previous traditional studies that did not use social media of dust occurrences in the U.S., and Flickr-identified images of dust events show substantial co-occurrence with regions of NWS dust warnings. This paper highlights the potential of using crowdsourced data for the often overlooked field of dust monitoring that has substantial health and economic impacts.

Keywords: Dust storms, Crowdsourcing, Social media, Weather. 

 

Flowchart of our workflow

Selected posts retrieved from X showing active dust events.

Selected images retrieved from Flickr showing active dust events.

Map showing the distribution of flickr-identified dust event occurrences, X-identified dust event occurrences, National Weather Service dust advisories, including dust storm (DS) warnings and blowing dust (DU) advisories.

Seasonal cycle of dust events using social media metadata, the National Weather Service advisories, and the VIIRS satellite data.

Examples of social media identified dust events and satellite observations for the same day. Brown shaded pixels indicate locations Suomi-VIIRS observed dust particles. Any VTEC warnings issued by NWS for the location are shown after the date of each dust event, with HWW and DSW indicating High Wind Warning and Dust Storm Warning, respectively.

Full Referece: 

Adegbola, F., Crooks, A.T. and Evans, S.M. (2025). Crowdsourcing dust storms in the United States utilizing social media data. GeoJournal, 90(3), pp.1-18. Available at https://doi.org/10.1007/s10708-025-11359-9 (pdf)