Editor’s note: The mention of specific products in this article does not constitute endorsement by AIHA or The Synergist.

At AIHce 2014 in San Antonio, Texas, Dr. John Howard, the director of NIOSH, delivered a keynote address that imagined the future of industrial hygiene. The occasion for Howard’s speech was AIHA’s celebration of its 75th anniversary, and Howard took the opportunity to speculate on what the next 75 years might bring. “The next era of exposure assessment may involve continuous sensing of the working environment,” Howard told attendees. He suggested that direct-reading instruments may allow workers to control their own exposures, and that industrial hygiene sampling may evolve to incorporate the use of sensors that send exposure data to a central repository. “What are we going to do with all that data that sensor technology is going to generate?” Howard asked. “Will we start accepting correlation instead of causation because of the sheer quantity of the data? Will you be known as an ‘exposure data scientist’?” In the four years since Howard’s keynote address, his questions have become more immediate than anyone in the audience that day would have speculated. Technological advances in sensor technologies, the proliferation of connected devices, and the transformation of “Big Data” from buzzword to culture-altering phenomenon have brought the future implied by Howard’s remarks tantalizingly (perhaps worryingly) close. By 2020, a staggering number of devices will be connected to the internet—an article in IEEE Spectrum set the range at 20 to 30 billion, including not only phones and tablet computers but everyday appliances like toasters and washing machines as well as manufacturing equipment. The days of connected workplaces, where endless streams of data from machines and monitors feed into software that mashes the numbers together for predictive purposes, appear to be right around the corner. As direct-reading instruments and wearable sensors for exposure assessment gain acceptance and worker health information becomes part of routine data collection, industrial hygienists stand to benefit from the predictive powers of Big Data. Running algorithms against disparate data streams to pinpoint not only where health and safety risks exist but where they’re likely to exist in the future has obvious potential to protect workers. Yet the challenges are also clear. They include concerns about data privacy and security, and questions about the role of industrial hygienists in a world where exposure measurement is not a discreet activity carried out at select times by trained professionals but a feature baked into the workplace itself. STEEPED IN DATA For some industrial hygienists, one of the challenges related to Big Data may be simply understanding what the term means. IHs are steeped in data, and many have collected what seem to be large datasets of exposure measurements over the years. Do such data qualify as Big Data? Probably not, according to Russ Hayward, CIH,  AIHA’s managing director of Scientific and Technical Initiatives.
“Just because you have big spreadsheets doesn’t mean you have Big Data,” Hayward explains. “It’s not just a dataset that has a thousand data points.” In his role at AIHA, Hayward has worked closely with the Content Portfolio Advisory Group, which identified Big Data as one of AIHA’s research priorities a few years ago. Hayward organized a meeting on Big Data at AIHA’s Falls Church, Va. headquarters in November 2017. The participants at that meeting included representatives from CPAG, the AIHA Computer Applications Committee, the International Safety Equipment Association, NIOSH, software vendors, and technology companies, including Google. The goal of the meeting was to identify opportunities for AIHA to play a leading role in the application of Big Data to industrial hygiene. As was discussed at the meeting, Big Data has several characteristics that differentiate it from regular data. The most important characteristics are known collectively as the “three Vs”: volume, variety, and velocity. Volume is the amount of data being generated in real time; typically, Big Data is measured in terabytes (one terabyte equals 1,000 gigabytes). Variety refers to the types of data; Big Data usually entails many types of data, including images and videos in addition to text and numbers. Velocity is the speed with which the data is generated and processed. Big Data analysis typically involves the collection of vast amounts of data from many sources in real time or near-real time, which is processed and output in a matter of seconds according to predetermined algorithms.  The literature on Big Data identifies at least four other Vs (veracity, value, variability, and viability), but the participants at the AIHA meeting agreed that the three-V model is sufficient for understanding how Big Data might apply to industrial hygiene. Do these characteristics exist for the kinds of data most industrial hygienists have access to today? Hayward doesn’t think so, but he stresses that it won’t be long before they do. “We’re on the cusp of having variety,” Hayward says. “We have some instruments that are able to take data and correlate it to GPS coordinates, which are then manually put on maps or process diagrams. And for years, we’ve been putting noise measurements on unit plot plans. What we haven’t been able to do is combine those processes into one step and then analyze dozens, hundreds, or thousands of data points to form exposure estimates. Right now a lot is still manual, but the instruments we have today are continuing to evolve, with both volume and velocity increasing. Variety still needs to be further developed.” Hayward adds that concerns around the privacy and security of sensitive information need to be a top priority in this discussion before Big Data analysis could be applied across the board to industrial hygiene datasets. Some companies consider exposure data to be medical or personal information. In the U.S., the Health Insurance Portability and Accountability Act of 1996 requires the safeguarding of medical information. Privacy laws in Europe are even more strict. In addition, individual companies likely have internal policies that govern the sharing of personal information and data. Security and privacy need to be factored into any attempt to use Big Data analysis, Hayward says. SIGNALS IN THE NOISE Big Data originally grew out of the efforts of internet companies such as Google, Amazon, and Facebook to monetize vast amounts of data about individuals’ online behaviors. For example, consider someone who needs new shoes. He searches online for local shoe stores, and then he finds an ad for Converse Chuck Taylors in his Facebook feed. Clicking on the ad opens a page on Amazon’s website where he can purchase the shoes. Meanwhile, he is served suggestions for products that other Converse customers bought on Amazon. This is Big Data as marketing: automatic, algorithm-based delivery of the “right” ads at the right time, determined by voluminous data about the online behaviors of similar customers. In recent years, as many kinds of data, including data collected by the federal government, have become available online, the computing tools for discovering signals in the noise have been refined. The same or similar tools that the internet companies use to parse online behaviors are available in “the cloud”—a network of servers hosted on the internet—and can be used for other purposes.  Other companies see potential in harnessing those tools for worker protection. One such company, Corvex, has created an open-license software platform that connects workers’ smart devices to a system of beacons that tracks their location as they move through a facility. According to Corvex’s founder, Joe O’Brien, who attended AIHA’s meeting on Big Data, the Corvex platform is being piloted at a million-square-foot manufacturing facility in Minneapolis. The facility is divided into 32 zones, some with specific requirements for entry. As workers approach a zone, the platform delivers information to their smart device about the safety plan for that zone, as well as the training and personal protective equipment required to be present in that zone. Workers can also take pictures of hazards—a spill, for example—and submit them to the platform, where they join a queue for resolution. The goal is to encourage workers to participate in their own health and safety. “While this platform is being used, it’s collecting and distributing massive amounts of relative data,” O’Brien says. “It alerts the entire work force to potential risks and ongoing warnings. And it allows for a predictive element that can help improve policies and processes.”
The days of connected workplaces, where endless streams of data from machines and monitors feed into software that mashes the numbers together for predictive purposes, appear to be right around the corner.
O’Brien says that Corvex is working with behavioral economists to develop a scoring model for risk that could predict, based on operational and environmental conditions, where problems are likely to occur. He says it won’t be long before software platforms like Corvex are collecting health-related information about workers and data from machines in the workplace. The platforms will send that data up to the cloud, where it will be processed according to algorithms and output to a dashboard, where an industrial hygienist or safety manager could use the information to predict where problems are likely to occur. And it will all happen in real time. “The equipment’s going to be connected, and so is everyone on the shop floor,” he says. “Very soon here, anything that’s useful is going to be collected in real time with basically continuous frequency. You’re going to see the industrial landscape change dramatically in the coming years.” LEVERAGING DATA Cority, a company that designs software for EHSQ (environmental, health, safety, and quality) applications, represents another approach that could allow industrial hygienists to take advantage of Big Data analysis. Cority’s clients use its software platform to store, manage, and analyze their EHSQ data. According to Monica Melkonian, MS, CIH, the industrial hygiene product manager at Cority, some of the company’s clients are interested in leveraging data they’ve stored in Cority’s system for Big Data analysis. The data typically includes occupational health information, safety information, and measurements from IH instruments. Cority has also partnered with Fatigue Science, a company that manufactures a wearable device for monitoring worker fatigue. “With our safety suite, we’re looking at mashing the data together and helping present analytics on the likelihood of someone becoming injured, for example, based on the level of fatigue,” Melkonian explains.  Melkonian, who also participated in the AIHA meeting, sees the potential of Big Data for industrial hygienists. But she sees the difficulties just as clearly. “There are challenges with data in silos—different software solutions, different pieces of equipment where our data is stored,” she says. “Architecturally, we have challenges we have to confront to even just integrate the data to do Big Data analytics. “There can be so much nuance behind the data that if the analytics aren’t right, you could be making decisions that aren’t correct, based on improper algorithms or assumptions.” The importance of building algorithms on correct assumptions offers a career opportunity for industrial hygienists, Melkonian says. IHs can provide the knowledge and context necessary to ensure that companies using Big Data for occupational health and safety can be confident in their analysis. Melkonian thinks it would be professionally useful for industrial hygienists to develop skills in data science, similar to the vision John Howard expressed at AIHce 2014. She would like to see courses on data science offered to industrial hygienists as part of their graduate or post-graduate education, and possibly as part of a professional certification program. THE PROMISE OF BIG DATA According to Hayward, AIHA plans to explore Big Data-related activities that involve identifying publicly available datasets that AIHA could use as a test case. By partnering with a technology company, AIHA could start to understand where the association has needs in terms of education, tool sets, and the creation of algorithms that could potentially address specific problems in worker health protection. Another related development is the potential for creating an accreditation program for companies that build sensors for direct-reading instruments through AIHA Laboratory Accreditation Programs, LLC. An exploratory meeting for this program is planned for this month at AIHA headquarters. Meanwhile, as the connected world grows ever larger, companies will attempt to harness Big Data for industrial hygiene purposes. Corvex’s O’Brien predicts that Big Data will have a transformative effect on the practice of IH.  “There will be platforms like ours, and, I’m sure, others, that take that data and apply algorithms and deliver basically real-time information about health hazards, safety hazards, exposures, etcetera,” O’Brien says. “The days of walking around a facility and taking independent measurements and going back to the computer and sitting down and inputting those measurements into an Excel spreadsheet and doing the math to figure out what certain exposures are—those days are going to end pretty quickly here. That stuff is going to happen automatically, and be communicated directly to the worker in real time, and be rolled up into a dashboard so a hygienist or safety manager can see what’s going on organizationally.” Melkonian, by contrast, is more reserved about Big Data’s potential. For all of its promise, she doesn’t believe Big Data will fundamentally change the profession. “I’ve always thought of it as a tool in our toolbox,” she says. “It’s not going to be our saving grace. It’s going to be just that, a tool that we can leverage, under certain circumstances, with professional judgment—rigorous professional judgment.”    ED RUTKOWSKI is editor in chief of The Synergist. He can be reached at (703) 846-0734 or via email. Send feedback to The Synergist.
RESOURCES Computing Research Association: “Challenges and Opportunities with Big Data” (PDF, 2012). Harvard Business Review: “Big Data: The Management Revolution” (October 2012). Harvard Chan: “Big Data and Public Health” (January 2018). The New York Times: “The Age of Big Data” (February 2012). The Synergist: “Rhythms of History” (AIHA member login required, June/July 2014).
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Will Big Data Change Industrial Hygiene?
BY ED RUTKOWSKI
PREDICTIVE PURPOSES
Although the print version of The Synergist indicated The IAQ Investigator's Guide, 3rd edition, was already published, it isn't quite ready yet. We will be sure to let readers know when the Guide is available for purchase in the AIHA Marketplace.
 
My apologies for the error.
 
- Ed Rutkowski, Synergist editor
Disadvantages of being unacclimatized:
  • Readily show signs of heat stress when exposed to hot environments.
  • Difficulty replacing all of the water lost in sweat.
  • Failure to replace the water lost will slow or prevent acclimatization.
Benefits of acclimatization:
  • Increased sweating efficiency (earlier onset of sweating, greater sweat production, and reduced electrolyte loss in sweat).
  • Stabilization of the circulation.
  • Work is performed with lower core temperature and heart rate.
  • Increased skin blood flow at a given core temperature.
Acclimatization plan:
  • Gradually increase exposure time in hot environmental conditions over a period of 7 to 14 days.
  • For new workers, the schedule should be no more than 20% of the usual duration of work in the hot environment on day 1 and a no more than 20% increase on each additional day.
  • For workers who have had previous experience with the job, the acclimatization regimen should be no more than 50% of the usual duration of work in the hot environment on day 1, 60% on day 2, 80% on day 3, and 100% on day 4.
  • The time required for non–physically fit individuals to develop acclimatization is about 50% greater than for the physically fit.
Level of acclimatization:
  • Relative to the initial level of physical fitness and the total heat stress experienced by the individual.
Maintaining acclimatization:
  • Can be maintained for a few days of non-heat exposure.
  • Absence from work in the heat for a week or more results in a significant loss in the beneficial adaptations leading to an increase likelihood of acute dehydration, illness, or fatigue.
  • Can be regained in 2 to 3 days upon return to a hot job.
  • Appears to be better maintained by those who are physically fit.
  • Seasonal shifts in temperatures may result in difficulties.
  • Working in hot, humid environments provides adaptive benefits that also apply in hot, desert environments, and vice versa.
  • Air conditioning will not affect acclimatization.
Acclimatization in Workers