scholarly journals Participatory Air Monitoring in the Midst of Uncertainty: Residents’ Experiences with the Speck Sensor

2017 ◽  
Vol 3 ◽  
pp. 464 ◽  
Author(s):  
Jacob Robert Matz ◽  
Sara Wylie ◽  
Jill Kriesky

How do participants engage in at-home air monitoring in the midst of uncertain exposures to airborne emissions associated with unconventional natural gas development (UNGD) activities? We investigate residents’ experiences with the “Speck” particulate matter sensor with an emerging environmental health resource center called the Southwest Pennsylvania Environmental Health Project (EHP).  In response to the gaps in knowledge about the health impacts of UNGD and the growth citizen science tools, participatory environmental monitoring (PEM) projects have taken off in shale gas communities. Using interview and survey data from residents, advocates, and activists we show that residents use the Speck as: 1) “environmental health thermometers” to make real time decisions based on readings; 2) real-time tools of exposure-validation to immediately validate or invalidate suspicions of exposure; 3) “epistemic objects” or tools manipulated in exploratory ways to understand their efficacy in monitoring UNGD; and 4) passively by those who chose to rarely interact with the monitors and rather waited for overall analysis of results.  While PEM’s have been critiqued for potentially passing the burden of monitoring onto communities, our research shows PEM, when connected with research and public health organizations like EHP, can both empower individuals by increasing their perceived and actual agency and build collective knowledge by producing novel scientific findings. The modes of participation identified here each imply individual and community-level outcomes. When connected with an organization like EHP, Speck monitoring enabled participating individual the latitude to develop their own research and make immediate use of the data, while also creating data useful for aggregated scientific analyses that provoke new questions about the health risks associated with UNGD.

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110138
Author(s):  
Erika Bonnevie ◽  
Jennifer Sittig ◽  
Joe Smyser

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.


Indoor Air ◽  
2021 ◽  
Author(s):  
Yuan Shao ◽  
Lucy Kavi ◽  
Meleah Boyle ◽  
Lydia M. Louis ◽  
Walkiria Pool ◽  
...  

2007 ◽  
Vol 40 (5) ◽  
pp. 363 ◽  
Author(s):  
Ju-Hee Seo ◽  
Eun-Hee Ha ◽  
Ok-Jin Kim ◽  
Byung-Mi Kim ◽  
Hye-Sook Park ◽  
...  

2015 ◽  
Vol 1 (1) ◽  
pp. napoc.2015.1468
Author(s):  
Matteo Tozzi ◽  
Marco Franchin ◽  
Vincenzo Formica ◽  
Matteo Ganna ◽  
Gabriele Piffaretti

Stenosis and thrombosis are common causes of prosthetic vascular access (pVA) failure. The role of arteriovenous fistula (AVF) surveillance is widely debated. The aim of this paper is to present a new real-time application designed for AVF surveillance called SPIDER. Surgical staff and hemodialysis nurses are responsible for data entry. SPIDER automatically analyses data and generates alerts in case of abnormal trends. Surgical evaluation and duplex Doppler ultrasonography are then immediately performed to confirm presence of stenosis or other possible pVA defects. Surgery can be performed if required. A preliminary analysis of results will be completed at 12 months after the program begins and subsequently after 24 months. Primary assisted patency will be compared with historical using multivariate analysis. Expected results are an improvement in primary assisted pVA patency and reduction of hospitalizations. Simultaneous management of a high number of patients can become difficult due to the large amount of data required for surveillance. We want to demonstrate whether a real-time automated system could help to prevent thrombosis and graft loss.


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