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Tourism contributes to groundwater pollution, but quantifying its exact impact is challenging due to the presence of multiple pollution sources. However, the COVID-19 pandemic presented a unique opportunity to conduct a natural experiment and assess the influence of tourism on groundwater pollution. One such tourist destination is the Riviera Maya in Quintana Roo, Mexico (specifically Cancun). Here, water contamination occurs due to the addition of sunscreen and antibiotics during aquatic activities like swimming, as well as from sewage. In this study, water samples were collected during the pandemic and when tourists returned to the region. Samples were taken from sinkholes (cenotes), beaches, and wells then tested using liquid chromatography for antibiotics and active ingredients found in sunscreens. The data revealed that contamination levels from specific sunscreens and antibiotics persisted even when tourists were absent, indicating that local residents significantly contribute to groundwater pollution. However, upon the return of tourists, the diversity of sunscreen and antibiotics found increased, suggesting that tourists bring along various compounds from their home regions. During the initial stages of the pandemic, antibiotic concentrations were highest, primarily due to local residents incorrectly using antibiotics to combat COVID-19. Additionally, the research found that tourist sites had the greatest contribution to groundwater pollution, with sunscreen concentration increasing. Furthermore, installation of a wastewater treatment plant decreased overall groundwater pollution. These findings enhance our understanding of the pollution contributed by tourists in relation to other pollution sources.
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Mapping Spatiotemporal Tourist Behaviors and Hotspots Through Location-Based Photo-Sharing Service (Flickr) Data.
Social media services and location-based photo-sharing applications, such as Flickr, Twitter, and Instagram, provide a promising opportunity for studying tourist behaviors and activities. Researchers can use public accessible geo-tagged photos to map and analyze hotspots and tourist activities in various tourist attractions. This research studies geo-tagged Flickr photos collected from the Grand Canyon area within 12 months (2014/12/01–2015/11/30) using kernel density estimate (KDE) mapping, Exif (Exchangeable image file format) data, and dynamic time warping (DTW) methods. Different spatiotemporal movement patterns of tourists and popular points of interests (POIs) in the Grand Canyon area are identified and visualized in GIS maps. The frequency of Flickr’s monthly photos is similar (but not identical) to the actual tourist total numbers in the Grand Canyon. We found that winter tourists in the Grand Canyon explore fewer POIs comparing to summer tourists based on their Flickr data. Tourists using high-end cameras are more active and explore more POIs than tourists using smart phones photos. Weekend tourists are more likely to stay around the lodge area comparing to weekday tourists who have visited more remote areas in the park, such as the north of Pima Point. These tourist activities and spatiotemporal patterns can be used for the improvement of national park facility management, regional tourism, and local transportation plans.
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- Award ID(s):
- 1634641
- PAR ID:
- 10065923
- Date Published:
- Journal Name:
- Progress in Location Based Services 2018. LBS 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-319-71470-7_16
