Novel Systems for Rapidly Identifying Toxic Chemicals During Emergencies

Details

• Personal Author:
  Bhavnani SK ; Richardson R ; Scott C ; Zheng K
• Description:
  During chemical emergencies such as those resulting from plant explosions and bioterrorism, speed and accuracy in detecting unknown chemicals are critical for reducing injury and death of chemical workers, first responders, and the larger public. Unfortunately, less than 10% of toxic chemicals can be automatically identified through chemical detectors. Furthermore, current systems designed to help search toxic chemical databases currently use rudimentary search algorithms and interfaces, which require too many user inputs, do not guide users to select the most discriminatory inputs, and therefore do not facilitate an efficient response. To address the above problems we conducted a feasibility study to design, implement, and evaluate novel algorithms and interfaces with the goal of helping first responders and plant safety managers to rapidly identify unknown toxic chemicals. This was achieved through three specific aims: 1. Quantify the complex relationship between chemicals and their symptoms/properties in well-known public health databases. This aim was achieved through the use of advanced bipartite network visualizations and quantitative analysis methods, and through the invention and use of three novel bipartite quantitative approaches to analyze the overlap of symptoms/properties across chemicals. The results revealed the precise nature of the overlap of symptoms/properties across chemicals in two large databases, which led to insights for efficient algorithms and interfaces to identify unknown toxic chemicals. 2. Identify the needs of first responders, and of safety managers at chemical plants. This aim was achieved through focused semi-structured interviews of 20 first-responders from Texas and Michigan, followed by a nation-wide survey. Our focused plus broad data collection of information/tool needs of first responders provided a detailed understanding of (a) the subpopulation of first responders who would most likely use advanced decision-support tools for toxic chemical identification, (b) the context in which such tools will be used, and (c) the specific interface features that are critical for decision-support tools. 3. Develop and evaluate novel algorithms and interfaces to aid in the rapid identification of toxic chemicals based on symptoms and properties. This aim was achieved by developing four novel algorithms designed to enable the rapid identification of (a) individual chemicals, (b) classes of chemicals, (c) combination of chemicals with different probabilities of occurring during any specific incident, and (d) individual chemicals that were robust under incorrect inputs. We also developed a prototype for a visual analytical decision-support system based on user needs, which provided a framework to enable the above algorithms to be useful and usable for first-responders on laptops, and mobile devices. Our results, based on the integration of computational, cognitive, and contextual dimensions, have the potential to substantially reduce the time it takes to identify toxic chemicals during chemical incidents, and therefore reduce injury and death of workers in chemical plants, and first responders. The results have been published in peer-reviewed national conferences and high-impact journals, has received a national poster award, received a student poster award, resulted in a US patent application (US 2013/0245959 A1), and has been applied in other biomedical domains. [Description provided by NIOSH]
• Subjects:
  Database Emergency Responders Hazardous Substances Manufacturing And Industrial Facilities Occupational Health Safety
• Keywords:
  Chemical Factory Workers Chemical Hazards Chemicals Databases Detectors First Responders Hazard Identification Toxic Materials
• Series:
  Grant Final Reports
• Publisher:
  National Institute for Occupational Safety and Health
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Final Grant Report
• Place as Subject:
  OSHA Region 6 ; Texas
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  OEP - Office of Extramural Programs
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  1-39
• NIOSHTIC Number:
  nn:20055932
• NTIS Accession Number:
  PB2019-101110
• Citation:
  Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, R21-OH-009441, 2014 Apr; :1-39
• Contact Point Address:
  Suresh K. Bhavnani, Institute for Translational Sciences, University of Texas Medical Branch, 301 University Blvd. Galveston, TX 77555-0129
• Email:
  skbhavnani@gmail.com
• Federal Fiscal Year:
  2014
• NORA Priority Area:
  Services
• Performing Organization:
  University of Texas Medical Branch, Galveston
• Peer Reviewed:
  False
• Start Date:
  20100901
• Source Full Name:
  National Institute for Occupational Safety and Health
• End Date:
  20130131
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:b9e6bf5d714c18b0a920eb9acae4575a53dd294bf64273929eb4136b9faa8cca2cb5b3148a4cdc841e6ddbd6e24cf8d31042e8a9d53c47d276bfbd8eb881e87d
• Download URL:
  https://stacks.cdc.gov/view/cdc/218872/cdc_218872_DS1.pdf
• File Type:
  [PDF - 3.12 MB ]