Toxic Waste Is Becoming A Massive Problem That AI Can Solve

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Four hundred million tons of toxic waste are produced each year.

Just imagine that for a moment — that’s 70 Great Pyramids of Giza combined. It’s not something most of us take into account when ordering a white-chocolate mocha latte in a plastic cup at Starbucks, but waste management is a huge problem for an entire industry tasked with preventing environmental harm.

Failing to address that problem results in things like rising methane emissions, which account for 25% of climate change. Beyond climate change, properly managing waste management directly affects the safety of the general public on an immediate level. 

Household hazardous waste (HHW), such as flammable or reactive products like paints, cleaners, oils, batteries, and other chemical products, pose a serious risk if not disposed of correctly. Despite that, between 20 and 80% of all household waste that is generated is dumped in open spaces, water bodies, or drains. What’s left is incinerated or buried, according to the U.N. 


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There simply aren’t enough humans to effectively separate recyclable material from general waste, or toxic waste from other waste. If you can catch the drift here, this is where Artificial Intelligence (AI) can help — and not at the expense of anyone’s job. 

The powers of image annotation

AI allows for a process known as image annotation, through which a machine can effectively cluster parts of an image that belong to the same object class. In plain English, this enables waste management facilities to leverage machines to identify all the recyclable material being processed. They can then separate it to be repurposed. The system can identify all the toxic waste more effectively than a human.

Annotators use small lines connected with vertices to trace the shape of objects in digital images. Additionally, annotated video helps AI algorithms understand movement and label each frame of video training footage. Correctly identifying the shape of streets and roads is useful for computer vision models trying to work out if the waste has been dumped incorrectly. Lane annotation adds lines to images and frames to show the structure of linear objects like roads and railways.

AI-powered camera systems can also help cities to combat illegal waste dumping. Using object recognition capabilities, street cameras can identify waste objects that are cluttering up sidewalks and roads and alert city services to send workers to clear them. Street-level cameras may also be able to capture number plates used by dumpers, allowing fines to be issued and acting as a deterrent. 

The fact is that government regulation isn’t seriously considered if the problem it seeks to solve seems unfeasible. And until AI emerged to help tackle the waste-management problem, regulation on a global scale has simply been insufficient. 

The challenge of regulating HHW disposal

The framework to deal with hazardous human waste (HHW) in the U.S. was set up by the 1976 Resource Conservation and Recovery Act (RCRA). Of course, those standards are only applicable to the U.S. market. There’s no global standardized definition of HHW, meaning that an item may be hazardous waste in one country but not in another. To make matters worse, most cities cannot afford to provide adequate manpower to enforce the proper disposal of HHW.

In many countries, hazardous waste is not subject to legislation unless it is separated from other household waste items. Thus, it’s difficult to craft the proper legislation to address it. Therefore, hazardous and non-hazardous waste are often mixed together and not sorted thoroughly before disposal.

Out of the waste generated by healthcare activities, for example, about 85% is general non-hazardous waste. But 15% is considered material that may be infectious, toxic, or radioactive, according to the World Health Organization. Workers at sorting plants are then unassumingly exposed to toxic waste, leading to major health issues down the line. 

The issue of HHW should not be taken lightly. If left unchecked, there are serious repercussions that could prove fatal. Not only are sorting plants (and those they employ) impacted, everyone in a city who uses potable water could feel the reverberations.

Especially when taking into account how few jobs it would actually cost — if any — the benefits of AI in this industry heavily outweigh the downside.

Arie Zilberman is CEO and cofounder of Keymakr.


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