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How Emerging Tech Can Assist the Environmental Health Industry


The fight to address the many kinds of pollution affecting our societies is a rapidly changing one, with new research, tools, and technology constantly emerging. A 2019 report by the National Association for Environment, Health and Safety and Sustainability Management (NAEM) revealed a number of the trends shaping the environmental health and safety industry, including a new wave of companies experimenting with some high-tech solutions to improve data collection, reduce risks and monitor environmental impacts in real time.

So what tech is emerging? And what kind of impact can it have? Let’s take a look.

Smart Sensors

With the development of “internet of things” architectures, sensor devices have become smarter than ever over the past decade. They have a broad range of applications: monitoring and categorising noise spikes in problem areas; tracking factory workers’ movements on the production line to boost training efficiency and avoid injury; monitoring the structural integrity of sewage systems; if it needs to be monitored, categorised, and inter-connected, smart sensors are being developed to do so.

Smart sensors are a key development in the environmental health industry due to their capacity to assist researchers in developing further tools to combat more specific problems. Legislators, too, can access smart sensor data when designing regulations for things like noise levels, automotive exhaust, or urban greenery density.

Drones

Electrically-powered drones can be equipped with a broad range of sensors to allow remote monitoring of any environment which would be difficult for humans to access, from huge swathes of farmland to active disaster zones. They allow environmental enforcement officers, researchers, and other industry professionals to capture real-time data while reducing the labour and financial costs associated with in-person surveillance, and also massively boosting personal safety. They also allow travel time to be cut from the equation, boosting problem-identification speeds and lowering containment costs.

Drones going hand-in-hand with smart sensors drives the producers of both devices to keep them relatively cheap, reliable, and consumer-friendly – after all, if they can’t be effectively used in the field, they won’t be used at all.

Machine Learning

Machine learning is the term used to describe a complex algorithm that changes and grows with data input. It brings automation and reduces human error in a number of routine environmental health & safety tasks, such as combing through decades of research for particular findings or modelling the effects of different pollutants on a range of animal or plant cell types. Smaller-scale offerings are becoming available for markets like small businesses that help inform them about, for example, correct recycling practices for individual products.

Machine learning is still in its infancy, but is offering a number of bureaucratic solutions that allow researchers and officers to focus more of their time and energy on developing real solutions to environmental issues.

Virtual Reality

Workplace safety awareness and training is an important area of the environmental health officer’s expertise, covering everything from correct lifting and stacking techniques to hazardous materials compliance. It’s no surprise, then, that many EHS officers are turning to virtual reality to more effectively educate workplaces on correct safety practices. Virtual reality gives educators a safe and clear way to communicate safety risks – far better than just words or diagrams could. By participating in a virtual reality ‘walkthrough’ of a facility before it’s built, EHS officers can also assist construction firms in flagging compliance issues and offering advice on modifications to the project while it’s still in the design stage.

Data Management

Not so much a new technology as a new concern for the environmental health sector, managing the massive quantities of data being produced by the above innovations is becoming more and more pressing. While machine learning algorithms are capable of taking some of the load off, those algorithms, as well as the security protocols surrounding them, need to be very carefully crafted to avoid violating privacy policies or developing biases.

Ultimately, it’s not the data that you gather, but what you use it for that makes all these new technologies most impactful.


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