How to identify and handle hidden lithium-ion batteries

Lately, the regulatory spotlight has been shining on one particularly confusing chemical in consumer products: 1,4-dioxane. 

Both California and New York have already issued new guidance targeting 1,4 dioxane. These new restrictions will mean that both retailers and suppliers need to pay attention to where the substance may be, and be ready to confirm that it is not in their products at a certain level.  So, we’ve put together a quick look at the reasons behind the urgency around 1,4 dioxane and some tips for how to stay ahead of the curve.  

1,4 dioxane: What is it? And why does it matter? 

1,4-dioxane is a substance that can be created when making detergents, soaps, and creams, and it’s now considered an environmental contaminant and a probable human carcinogen. 1,4-dioxane contamination can occur rather easily – as a byproduct of the manufacturing processes when making these products. It is also intentionally used at higher concentrations as a solvent in industrial manufacturing processes. Studies have also shown that the harmful substance can easily dissolve in water, which means it could be found in unsafe amounts in drinking water.  

All of this has, naturally, made consumers and regulatory bodies worried about finding it in things they use and its possible effects on our health. However a risk evaluation published by the US EPA in 2020 found no unreasonable risks to consumers or bystanders from any conditions of use, including eight consumer uses of surface cleaners, laundry/dishwashing detergents, and paint/floor lacquer where 1,4-dioxane is present as a byproduct.

Regulatory changes and how to keep up 

While there is still a lot of unknown around the future of 1,4 dioxane, specific new regulations have started to gain traction. The Food and Drug Administration had previously encouraged suppliers to minimize 1,4-dioxane content, but New York has now taken a proactive approach by implementing a restriction on dioxane contamination levels in products. As of December 31, 2022, cleaning and personal care products are limited to 2 parts per million (ppm), while cosmetics are limited to 10 ppm. And the cleaning and personal care limit was set to reduce to 1 ppm by December 31, 2023.

1,4-dioxane has also been getting attention because of the California Cleaning Product Right-To-Know Act of 2017. Under this new regulation, it must be disclosed on a products website as a “nonfunctional constituent” when  it's present at or above 10 parts per million (ppm). Since this chemical is also a carcinogen on the California Proposition 65 list, it might be subject to labeling requirements even below this threshold.

There a few simple - but crucial - steps both suppliers and retailers can take to stay ahead of these changes: 

1. Brush up on which product categories are affected by these bans. You can find product categories that are likely to fall under the ban on the NY State Department of Conservation website. 
2. Suppliers: Be ready with specific evidence proving the dioxane content for your products is below the allowable threshold. If a product uses a “ethoxylated” ingredient (commonly employed in the production of personal care, household care products) it might contain a regulated amount of 1,4 dioxane. Determine which products you have contain these ingredients, so you can narrow down which items you need to obtain evidence for and make sure you have that on-hand. 
3. Retailers: Maintain an open-line of communication with suppliers. Your suppliers know their products’ best and have the information you need to prove that the dioxane levels are below allowable thresholds. 

The NY Dioxane Ban, along with evolving regulatory guidelines, can be confusing to navigate and leave more questions than answers. Lean on your suppliers, retailers and regulatory partners to help translate and prepare for these new guidelines as effectively as possible. 

By staying informed and maintaining close collaboration with your partners, you can successfully navigate these regulations, avoid costly fines or product delays, and provide consumers with safe and transparent choices. 

INTRODUCTION

California State Toxicity is a notoriously difficult waste classification to master for retailers and waste professionals.

Classifying State Toxicity correctly can serve as a major benefit to waste generators by reducing regulated waste volumes, hauling costs, and associated fees.

California’s Department of Toxic Substances Control (DTSC) has published aquatic toxicity results for several hundred consumer products. The study evaluated the products’ aquatic toxicity and assigned labels of “Pass” to products that are non-toxic to the aquatic environment and “Fail” to products that are toxic to the aquatic environment.

While the traditional aquatic toxicity test is a widely accepted testing standard, it necessitates the usage of live animal testing on fish. More and more, this practice is being viewed as unethical, costly and unnecessary by manufacturers and consumers alike.

Computational methods of toxicology testing are increasingly being considered as more effective and humane alternatives to traditional methods.

To verify this claim, Smarter Sorting applied an automated toxicity calculation to the same DTSC dataset and evaluated the efficacy of our calculations in determining California aquatic toxicity.

The results are promising:

• Precision = 100%
• Recall = 77%
• Specificity = 100%
• Balanced accuracy = 89%

Keep these numbers top of mind – we’ll be addressing them soon and explaining exactly what they mean for the aquatic toxicity determination, and more importantly, for fish.

BACKGROUND

Determining State Toxicity in California requires the evaluation of many criteria, which include carcinogenicity, toxicity, flammability, and corrosivity to name a few.

The DTSC outlines the myriad ways in which a waste qualifies as a California State Toxic on their website. One of these criteria is called Acute Aquatic Toxic, where a “waste is hazardous by aquatic toxicity if a 96-hour LC50 is less than 500 mg/L.”

The DTSC does not provide guidance on the methodological evaluation of aquatic toxicity as it pertains to consumer products. Whether computational methods are permitted or not is left unanswered. The text suggests that each and every consumer product should undergo live animal testing to evaluate the aquatic toxicity and determine if the product is hazardous in California.

Live animal testing a substance for aquatic toxicity is not a trivial process. The EPA claims that each test could cost well over $14,000. These tests take time and resources that many retail businesses simply do not have.

When faced with onerous or complicated state hazardous criteria, many retailers will skip the hazardous evaluation process all together. Instead, they opt to just consider all potentially hazardous waste as hazardous. In this manner, many retailers are over-regulating their waste streams, and overpaying for expensive waste treatment in the process.

Smarter Sorting has automated the calculation of toxicity-based state regulations in the past with great success. In our Computer Bits vs. California Fish article, we demonstrated the difficulties associated with determining aquatic toxicity classifications for consumer products, and the accuracy of estimating toxicity calculations in a sample product.

We also demonstrated how Smarter Sorting’s ability to make complex state regulatory calculations typically reduces the amount of regulated waste retailers generate.

For this article, we applied our aquatic toxicity methodology to a larger sample of 100 test products to determine the accuracy and viability of computerized testing as an alternative to live fish testing.

In the following analysis, the terms “non-toxic” and “pass” are synonymous and indicate aquatic toxicity values greater than 500 mg/L. Conversely, the terms “toxic” and “fail” indicate aquatic toxicity values of less than 500 mg/L.

DATA

Check out the original dataset used in our analysis.

The 100 sample products were randomized and satisfied the following criteria:

• Smarter Sorting has the product Safety Data Sheet (SDS) on file
• At least one of the Eurofins Calscience or Aquatic Testing labs had conducted a pass or fail study on each product

Learn more about sample products and results!

METHODOLOGY

Smarter Sorting used the Acute Toxicity Estimation (ATE) to arrive at our aquatic toxicity estimates. The chemicals and associated concentrations for each product were derived from SDSs and ingredients labels. For ingredients derived from product labels in lieu of the SDS, we deduced ranges of potential concentrations based on regulatory requirements for listing chemicals on SDSs and ingredients labels. For more information on how concentrations were estimated, refer to our previous paper on calculating aquatic toxicity.

Using the calculated aquatic ATE, we assigned products with ATE values greater than 500 mg/L a score of PASS. For products with ATE values of less than 500 mg/L we assigned a score of FAIL. This threshold is based on information listed on the DTSC website. (It should be noted that the published study labels several products as FAILS despite having LC50 values greater than 500 mg/L (e.g., sample numbers 180, 220, 296, 374, 389)).

When a product received both a PASS and FAIL from our calculation and the standard lab test, we considered the product a FAIL.

RESULTS

Smarter Sorting Aquatic ATE Test vs. Traditional Lab Testing

Data Analysis Metrics

DISCUSSION

To make sense of the data, let’s first unpack the results from Smarter Sorting’s aquatic ATE test as compared to traditional lab testing:

• In 47 cases, we said an item was non-toxic and the labs agreed
• There were zero cases where we said an item was non-toxic and the labs disagreed
• In 39 cases, we said an item was toxic and the labs agreed
• In 14 cases, we said an item was toxic and the labs disagreed

Smarter Sorting Aquatic ATE Test vs. Traditional Lab Testing

Let’s break down the data science jargon to understand how these metrics are applied to their respective probabilities:

• Precision of 100%: Given Smarter Sorting’s classification of Non-Toxic, the likelihood that the product is actually non-toxic is 100%.
• Recall of 77%: Given a laboratory classification of Non-Toxic, the likelihood of Smarter Sorting’s classification being non-toxic is 77%.
• Specificity of 100%: Given a laboratory classification of Toxic, the likelihood of Smarter Sorting’s classification being Toxic is 100%.
• Balanced Accuracy refers to the accuracy of Smarter Sorting’s Toxic versus Non-Toxic calculations normalized to account for the number in each class.

One important to note is that Smarter Sorting’s calculations appear to be more conservative (more likely to indicate that a product is Toxic) than laboratory testing.

CHANGING THE STATUS QUO

By working with several retailers, Smarter Sorting has observed that regulated state waste identification remains a persistent and costly problem. The status quo for many retailers is to “play it safe” and lump nearly all non-RCRA wastes together with regulated state wastes.

In other words, retailers are over-regulating their waste. They do not have the time nor resources to evaluate every state’s hazardous waste criteria to determine if some of their wastes are non-hazardous. This is precisely where computational methods of toxicology stand to help waste professionals and retailers the most.

THE SMARTER SORTING ADVANTAGE

Smarter Sorting’s aquatic toxicity calculations detect aquatic toxicity as defined by DTSC when sufficient ingredient information is available.

Using Smarter Sorting’s aquatic toxicity calculations, complicated and burdensome waste regulations can be calculated instantly, accurately, and without harming any fish. In the process, retailers may see a dramatic decrease of up to 47% in their state regulated waste.

Leveraging Smarter Sorting’s automated classifications for California State Waste could change the way regulated state waste is classified and ultimately handled in California. Most importantly, it could eliminate the need for animal testing in the evaluation of hazardous wastes.

That’s a huge win for retailers, waste professionals, and fish everywhere.

‍

Can computational toxicology reproduce lab tests for consumer products?

In 2017, the California Department of Toxic Substances Control (DTSC) published a study on the aquatic toxicity of 417 consumer products.

They sent each product – vitamins, soaps, cleaners, ink cartridges, toothpastes, and more –  to multiple labs and ran tests on fish to determine the products’ toxicity. The tests implemented a measurement called LC50 – or Lethal Concentration 50 –which is the amount of a chemical needed to kill 50% of a tested species (in this case fish). Conducting these lab tests is technically a DTSC requirement for determining whether a substance is hazardous waste.

That got us thinking.

Is it possible to reproduce those lab results using computational toxicology instead of killing fish? And is it possible using only publicly available data?

The answer, we think, is “yes”.

Here’s our stepwise analysis of one product to see what is possible.

Picking Our Product

We limited our set to products that have two valid LC50 measurements from the DTSC study, as well as a Safety Data Sheet (SDS) and a full ingredients list. Seems reasonable.

Then we picked a lucky winner: Febreze Fabric Refresher Allergen Reducer Clean Splash.

Here’s what you need to know about it:

Pay particular attention to the last three rows of the table. Those are the results of DTSC’s lab tests on our Febreze product. In two tests, it took 201 mg/L and 330 mg/L to kill 50% of fish in the respective test. The average is 266 mg/L. Hold those numbers in your brain.

Step 1: What does the Safety Data Sheet tell us?

To compute the aquatic toxicity for Febreze, we first need to know something about the chemicals in the  product.

Let’s start by looking at the product’s Safety Data Sheet.

Section 3 gives us our first glimpse of chemical detail:

‍

The SDS says the product has 1%-5% ethanol. If you’re surprised that only one ingredient is listed, don’t be. You’ll soon see this Febreze has far more ingredients, but due to OSHA’s requirements for Section 3 of an SDS, only the ethanol must be listed here.

We can work with that. We’ll start measuring the product’s aquatic toxicity immediately and then see how that measurement evolves as we uncover more data. If you’re a toxicology nerd, this may even be fun.

We measure aquatic toxicity using Acute Toxicity Estimate (ATE) – a formula that weighs the toxicity of a mixture’s chemical constituents and estimates the overall toxicity of the mixture.

ATE is a broadly accepted alternative to conducting live animal tests, and is the preferred method for evaluating toxicity by many state, federal and international regulatory bodies. And – good news – it is measured in the same units as LC50 (milligrams per liter or mg/L).

Before we can measure, we need to know something about ethanol, so we consult our vast library of toxicology studies, limiting our results to aquatic studies.

Here’s what we find:

‍

A little help reading this: We have 29 distinct aquatic toxicity values for ethanol. The distribution covers a lot of ground – from 100 mg/L to 28,100 mg/L – which isn’t uncommon. We take the median (11,200 mg/L).

Now we can take our first hack at calculating ATE, using both the minimum and maximum percentages listed in the SDS (1%-5%).

• ATEmix(min concentration) = 100 / (1 / 11,200) = 1,120,000 mg/L
• ATEmix(max concentration) = 100 / (5 / 11,200)  = 224,000 mg/L

Higher ATE values indicate that more of the substance is needed to be lethal. So the ATE values ranging from 224,000 – 1,120,000 mg/L are almost entirely non-toxic, meaning that our estimate is very “fish-friendly”.

Remember, the DTSC lab results had a range of 201 – 330 mg/L, which shows that this ethanol-only mixture isn’t telling the full story. We’re currently way off.

Let’s throw our data on a graph – with a logarithmic axis – so we can begin measuring our computational ecotoxicity against the lab results, shown here as horizontal red lines.

That’s not satisfying yet. Let’s go further.

Step 2: Can we extract more useful data from the SDS?

Take a look at Section 15.

We picked up two new ingredients. CAS Numbers 111-46-6 and 110-16-7 are both listed as Pennsylvania Right To Know chemicals.

The fact that these ingredients are listed in Section 15 – but not Section 3 – tells us something.

OSHA rules regarding SDS authorship state that potentially hazardous chemicals must be listed in Section 3 if they exceed concentrations of 1%, unless they are carcinogenic, in which case they must be listed in Section 3 if they exceed concentrations of 0.1%.

Logically, what this means is that if these two chemicals are carcinogens, then they have a theoretical maximum concentration of 0.1%. If they are not carcinogens, then they have a theoretical maximum concentration of 1%.

A quick check of our two carcinogen databases (EPA and IARC) lets us know that our chemicals are non-carcinogenic.

‍

‍

Great! We have two more chemicals to add to our ATE formula. We can assume a maximum concentration of 1% for both of these chemicals.

We’ll also assume a minimum concentration of 0.01% based on standards set by the California Cleaning Products Right to Know Act. More on this later.

Now let’s update  our picture of this product:

‍

Time to run our ATE calculator:

• ATEmix(min concentration) = 100 / ((1 / 11,200) + (.01 / 500) + (.01 / 5)) = 47,409 mg/L
• ATEmix(max concentration) = 100 / ((5 / 11,200) + (1 / 500) + (1 / 5)) = 494 mg/L

And update our visualization:

‍

‍

The ATE for the assumed maximum concentration (494 mg/L) is far more in line with the lab average (266 mg/L). That made a difference. But the range is still vast.

Let’s keep digging.

Step 3: What does a publicly-available ingredients list tell us?

Thanks to Procter & Gamble’s participation with SmartLabel, we found an Ingredients List for this formulation of Febreze.

Now we’ve grown our knowledge of this product from three ingredients to sixteen. Assuming minimum and maximum concentrations as we did in the last section – and adding more LC50 values from our toxicology library – we again refresh the picture.

‍

‍

A few things to note.

First, let’s return to why we’re assuming a minimum of 0.01% for these ingredients. As of this year, the California Cleaning Products Right to Know Act requires that brands list on their website all ingredients for cleaning products that meet or exceed 100 parts per million (0.01%). Therefore, we assume that P&G is listing the ingredients that meet that criteria.

Second, note that we’ve called out the order in which the ingredients are listed on the label. Brands are required to list ingredients in order of descending concentration. In the next section, we’ll use this rule to further our analysis. Or not. I don’t want to give too much away.

You’ll also note that we’ve called out the ingredients that are fragrances. Fragrances are often included in much smaller concentrations, but here we’ll still assume the same 0.01%-1% range. (There’s one exception: CAS 123-35-3 is an IARC 2B carcinogen and therefore has a maximum assumed concentration of 0.1%.)

Now with far more information than we had to start – ”Section 3 only” feels like a distant memory – we can rerun the ATE calculation, which yields the following:

• ATEmix(max concentration) = 18 mg/L
• ATEmix(min concentration) = 457 mg/L

Now we’re getting somewhere.

That’s still a wide range of values, but it’s far, far tighter and our target lab value of 266 mg/L is in between our minimum and maximum.

In fact, the median of our “SDS + Full Ingredients List” range is 238 mg/L, which is within spitting distance of our lab median.

Check out how much our predictive ability has improved as we’ve introduced new data.

‍

‍

Of course, that graph has its own lie. We’ve been using a logarithmic axis to keep everything in a readable graph that fits on your computer monitor. When we map our values on a linear axis you can see for real how much closer our computation is getting to the lab values.

‍

‍

We push on.

Step 4: What can statistical modeling tell us?

We can’t know the exact concentration of each ingredient using only publicly available information. But that doesn’t mean we’re done. Frankly it doesn’t even necessarily mean we need to know exact concentrations.

Let’s use what we know about the Rules of the Label to model informed predictions of each chemical’s concentration.

We know that the ingredients are listed in descending order of concentration. Therefore, we can apply that logic to assign randomized concentration values with a descending maximum limit. We did this 1,000 times – creating 1,000 simulations of what the concentration could be – and then arrived at a distribution of ATE values.

Here are the end results:

‍

What did we achieve? Let’s graph it again.

‍

‍

The confidence interval got slightly tighter (29.0 mg/L to 455.0 mg/L).

The median of the simulations (138 mg/L) is lower than the median of our previous range. That 75th Percentile value lines up nicely, but who’s to say what that means? Again, we’re only analyzing one product here – hardly a trend.

But there’s comfort in knowing that running 1,000 simulations – that are modeled based on the logic of industry regulations – creates a range that is so in line with the lab results that DTSC derived for this product.

Plus, the ATE values we got agree with the labs in that the product has an aquatic toxicity less than 500 mg/L, which is the threshold for a product being a hazardous waste in California due to aquatic toxicity.

Where does this leave us?

Even without exact concentration values for a product’s ingredients, one can begin replicating toxicity results from a laboratory by using publicly available product data and historical toxicological data.

The more information the better, but we saw how close we got armed only with a Safety Data Sheet, an ingredients list, our tox library, statistics and gumption. We’re actually armed with a lot, but you get the point. We didn’t do an aquatic bioassay.

We believe this analysis points toward a potential future wherein ATE could replace live fish testing. It’s just one product, but it’s a start. We’re going to keep testing and sharing our results to see if we can take this further.

Once we analyze a greater number of products we’ll better be able to spot trends and tweak our models in the name of computational toxicology for consumer products – and California’s fish.

We’ll report back soon.

‍