We continue our discussion of the pitfalls and practicalities of quantitative spectroscopy with a discussion of several practical tips to help you improve your analyses. We will cover the following topics: the importance of cleanliness, how many standards to run, how to spot outliers, and important experimental variables to control. Some of these tips seem obvious, but either myself or people I know have made these mistakes, so now is your opportunity to learn from our mistakes. A lot of these tips apply to developing chromatography methods as well, so if you are a chromatographer, please read this column.

This may seem obvious, but you would be surprised how often lack of cleanliness can mess up a calibration. Simply put, cleanliness means every surface that comes into contact with a sample needs to be clean. This includes sample container walls, sample container caps, syringes, weighing pans, extraction funnels, sample cells, syringes, and so on. If not, contamination can introduce impurities into your sample that can throw off your quantitation.

Also, all parts of the instrument that come into contact with the sample need to be clean. For chromatographs this includes the injector, all tubing, the column, and the detector. For high performance liquid chromatography (HPLC), both refractive index and ultraviolet-visible (UV-vis) detectors use light to analyze the sample. Therefore, there are detector cell windows through which the light must pass to do its job. It is easy for these windows to become dirty, which completely throws off quantitation. For spectroscopy, all sample windows and cells need to be cleaned before and after the sample comes into contact with them. Be how your mother encouraged you to be—clean up after yourself—and you will be fine.

Be Aware of Chemistry Within Samples and Between Samples and Cells

As stated by protagonist Mark Watney in the excellent sci-fi novel by Andy Weir, 

, “Chemistry is a messy bitch” (1). What this messiness means for us is that chemical reactions within a sample, and between a sample and anything it contacts can spell disaster for a calibration. When we make up standards for a calibration, we often tacitly assume that the standards are stable over time, but chemistry within a sample can cause composition to change over time. A way to test sample stability is to make up your standards, take spectra of them, and then continue to take spectra over the ensuing hours and days. Then, compare the spectra to each other. If the spectra look the same, great! However, if they have changed over time you need to take that into account when generating a calibration. For example, if a sample is not stable over the course of hours or days, make sure to scan it immediately after creating it. Better yet, try to determine why the sample is unstable and fix it. At minimum, the chemical composition of standards and unknowns must remain the same on the time scale needed to make and analyze a sample. Also, if standards are not stable over time, you will need to make fresh ones each time you recalibrate or perform a calibration check.

Samples can also react with containers and sample cells. In infrared spectroscopy, we commonly use sodium chloride (NaCl) and potassium bromide (KBr) windows as sample cells because they are transparent in the infrared. However, these materials are water soluble, and any sample containing an appreciable amount of water will damage them. Back in graduate school when I was a teaching assistant, I can’t tell you how many sets of KBr windows I lost by freshmen washing them with water in the sink and being surprised when the windows dissolved and went down the drain. All this despite clear warnings about keeping water away from the windows!

Same Chemical Environment for Standards and Unknowns

In previous columns on Beer’s Law and quantitative pitfalls to avoid (2–7), we discussed that the absorptivity is matrix sensitive. In other words, the absorptivity of a molecule is sensitive to its chemical environment. Since the fundamental assumption of quantitative spectroscopy is that the absorptivity is the same for the standards and unknowns, keeping the chemical environment constant is particularly important. As much as possible then the temperature, pressure, pH, and composition of standards and unknowns need to be as similar as possible.

Thus, do not measure the standards at room temperature and the unknowns at 100 °C. Additionally, do not measure the standards in water solution if the unknown will be an acetone solution. In the end you will have a calibration applicability problem, which means you will be applying the wrong calibration to your unknowns. The tricky part here is that the inapplicable calibration may give good results on the standards which will mislead you into thinking it will work well on the unknowns. Ultimately, here the problem is that you will be using the wrong calibration for the unknowns.

Same Instrumental Parameters for Standards and Unknowns

An important experimental parameter to control is the instrumental resolution, which is a measure of how well an instrument distinguishes peaks that are close together (8). This parameter is measured in wavenumber units (cm

). For example, a spectrum measured at 8 cm

 resolution means the scan can distinguish between peaks that are 8 cm

 apart or more. Changing the instrumental resolution used to measure a spectrum has a huge impact on spectral appearance as seen in 

The top of the figure shows the mid-infrared spectrum of water vapor measured at 0.5 cm

 resolution. Note that there are a series of nice sharp peaks that baseline resolved, any of which could be used for a quantitative analysis. The bottom of Figure 1 shows the spectrum of the same sample of water vapor, measured on the same instrument, but at an instrumental resolution of 32 cm

. Note that rather than there being a series of nice sharp lines this spectrum contains a big, broad blob. This is because at a resolution setting of 32 cm

 the instrument cannot resolve peaks that are close together, thus they combine into the broad peak seen. The point is that the peak heights and areas in the two spectra are different, so using different resolution settings for the standards and unknowns adds a huge source of error to a calibration.

Another scanning parameter it is important to control is the number of scans used. This is because the number of scans affects a spectrum’s signal-to-noise ratio (SNR). We have discussed SNR previously (5). The SNR of a spectrum depends upon the number of spectra averaged together according to 

where SNR is the signal-to-noise ratio and N is the number of scans averaged together.

If the standards and unknowns are measured with a differing number of scans, they will have different noise levels affecting accuracy.

Lastly, the spectra of the standards and unknowns should be measured on the same instrument. In theory, if you scan the same sample on two different instruments, you should obtain the same results. However, the reality is that despite the best efforts of instrument manufacturers there are small variations between instruments, or as a colleague of mine likes to say, “instruments have offsets.” This means typically one instrument will give results a fixed amount and direction from another. For example, one instrument might consistently measure the tetrahydrocannabinol (THC) content in cannabis buds 2 weight percent lower than another. There exists a whole field of analytical chemistry devoted to determining how best to transfer calibrations from one instrument to another. Rather than struggling with this problem, it is best to measure all standards and unknowns on the same instrument.

Same Sampling Cells for Standards and Unknowns

Everything a light beam sees on its way from source to detector will become part of the measured spectrum. This includes the cell and window materials used to contain the sample, and the crystals used in attenuated total reflectance (ATR) experiments (8). Varying these things will add error to your quantitative measurements. For example, don’t use a NaCl cell for the standards and a KBr cell for the unknowns. Or for ATR don’t use a diamond crystal for the standards and a zinc selenide crystal for the unknowns. Changing cell and window materials will change the spectra measured and add error to your analysis.

This is a common question in quantitative spectroscopy. 

gives the minimum number of standards you should run to obtain a calibration.

where n is the number of analytes being predicted.

For a single analyte quantitation the minimum number of standards is (2 + 2) or four. However, I always use more because when it comes to calibrations the more data points the better. We have not discussed multianalyte quantitative analysis in this column series because it is beyond the scope of what I want to do here. However, my own work has shown that spectroscopy can accurately quantitate multiple analytes including in cannabis flower, extracts, distillates, and tinctures (9–15).

 shows a calibration line for the prediction of the percent of isopropyl alcohol (IPA) in a water solution.

Note that for this one analyte I used five data points. However, there are spaces between the data points, and by drawing the line seen we are assuming the behavior of the calibration between these data points follows the line drawn, which may or may not be the case. Thus, the reason more data points are better in a calibration is that by filling in the gaps in a calibration line we can be sure of the shape of the line rather than assuming it.

Measure Two Spectra of Each Standard and Then Subtract Them

The idea behind measuring two spectra of each standard is to see if they are stable, as was discussed above. Spectra of the same standard should be the same, so long as you measure them on the same instrument under the same conditions. However, if the two spectra turn out different it means there is a variable in your experiment that you do not have under control. A good way to determine if two spectra are the same is to subtract them. A detailed description of spectral subtraction is beyond this column, but can be found elsewhere (8). Briefly, there exist software packages where the absorbances in one spectrum are subtracted from another. An illustration of subtracted spectra is shown in 

If the two standard spectra are the same your subtraction result should be a flat line because all the absorbances at all wavenumbers in both spectra should be identical. The reality is that the noise characteristics in spectra are always different, so if you subtract two spectra of the same standard and see nothing but noise, as seen in the top of Figure 3, the two spectra measured were identical. The bottom of Figure 3 shows the result of subtracting two spectra of the same standard where the spectra were different. Note that in addition to noise there are real spectral features that upon inspection were from the analyte (in Figure 3 we are ignoring the CO

 peaks, which are an artifact). In this case, you should not build a calibration, but instead go back to the drawing board and figure out what variables are out of control.

We have added seven things to our list of quantitative spectroscopy tips and pitfalls to avoid. We began with emphasizing that cleanliness is important, being aware of the chemistry of your sample and sampling device, and making sure the chemical environment is as similar as possible in the standards and unknowns so that the absorptivity is reproducible. I then emphasized the importance of controlling experimental parameters, including scanning conditions and sampling cells. Lastly, we discussed the importance of using many data points in a calibration, and why it is a good idea to measure two spectra of each standard and subtract them to see if anything has changed.

 (Crown Publishers, an imprint of the Crown Publishing Group, a division of Random House LLC, a Penguin Random House Company, New York, New York, 2014).

Fundamentals of Fourier Transform Infrared Spectroscopy,

 2nd Edition (CRC Press, Boca Raton, Florida, 2011).

Quantitative Spectroscopy: Theory and Practice

 (Elsevier, Boston, Massachusetts, 2002).

B.C. Smith, M. Lewis, and J. Mendez, “Optimization of Cannabis Grows Using Fourier Transform Mid-Infrared Spectroscopy,” PerkinElmer Application Note (2016).

B.C. Smith., C.A. Fucetola, K. Ehrmantraut, and T. Hagan, 

, PhD, is Founder, CEO, and Chief Technical Officer of Big Sur Scientific. He is the inventor of the BSS series of patented mid-infrared based cannabis analyzers. Dr. Smith has done pioneering research and published numerous peer-reviewed papers on the application of mid-infrared spectroscopy to cannabis analysis, and sits on the editorial board of 

. He has worked as a laboratory director for a cannabis extractor, as an analytical chemist for Waters Associates and PerkinElmer, and as an analytical instrument salesperson. He has more than 30 years of experience in chemical analysis and has written three books on the subject. Dr. Smith earned his PhD on physical chemistry from Dartmouth College.
Direct correspondence to: 

Articles

Cannabis Analysis 

The final part in this series of pitfalls to avoid and practicalities covers the importance of cleanliness, how many standards to run, how to spot outliers, and important experimental variables to control.

Quantitative Spectroscopy Pitfalls and Practicalities, Part IV: The Finale

Greenhouses are the future of commercial cannabis cultivation, and for good reason. As cannabis normalization spreads across the globe, operators will come under increased pressure to control their energy consumption. As competition increases, growers will also be looking for ways to lower their cost of production. Greenhouses can successfully address both issues.

But not just any greenhouse will do. There is a myriad of options for every crop and growing region, and selecting the best model is vital to ensuring energy and production efficiency.

The semi-closed greenhouse is a relative newcomer with great potential for cannabis growers looking to improve energy efficiency, crop health, and plant productivity. The structure uses a climate chamber to condition air before it’s sent to the crop through distribution tubes under the greenhouse benches or grow gutters. This delivery method creates a uniform climate throughout the crop and positively pressurizes the growing structure. Semi-closed greenhouses have fewer vents than traditional greenhouses, so insect pressure is also decreased. Greenhouse air can be reused and mixed with outside air, allowing growers to maintain high CO

 levels while minimizing their heating costs.

In this article, Casey Houweling, the inventor of the semi-closed greenhouse, explains how this greenhouse concept came about, the challenges he’s had developing it, and the potential it holds for the future of the cannabis industry.



How did you get started in the greenhouse business?

: I’m a Canadian and I started in greenhouse production here in Canada. I was born into it basically; I was in the floral industry originally and ended up building a six-acre greenhouse in 1985 after purchasing some property in Delta, which at that time was the largest greenhouse ever built in British Columbia. So that six acres within 16 years turned into 50 acres after just expanding it over several years.

What led you to create the semi-closed greenhouse?

: I ended up buying some property in California and started to build there. That was in the 1980s recession, so we got some property for a pretty good price. We built the first 20 acres, and within a period of 10 years, we had it up to 125 acres.

We had it up to 80 acres, and then we got what I call “The 11

 Plague:” whitefly. They were so thick that sometimes it looked like it was snowing. The worst thing is that they carry a virus, and we had no insect screens. But we got the virus and had to figure out what in the world we were going to do about this.

How did you handle that traumatic crop event?

: I did some research in Europe—Southern Europe, particularly in Sicily and Spain—in more southern climates to figure out what these guys were doing about bugs. Well, everybody over there had insect screening.

So, I went out and purchased 22,000 insect screens to cover the roof vents. But six months later, it was all falling apart because the manufacturer never put UV inhibitor in the insect screening.

So, we had to do it again, but in the interim, I learned to hate insect screening. First of all, it cuts your airflow by 50%, sometimes even more if there’s no wind. But the other thing is that they collect dust, and you lose about 30% of your light.

How did you get around the need for a lot of insect screening in your greenhouse?

: I went to Holland and looked around, and at that time, they were doing some fully closed greenhouse testing. Basically, greenhouses with no roof vents. The theory was that if they captured the heat in the summer and injected into an aquifer in the ground, then they could pull it back up in the winter and use it to heat the greenhouse.

It was great in theory, but practically, it was way too expensive and incredibly difficult to cool a greenhouse with no vents. I went through that facility and thought, “This is never going to work,” but I took some of the concepts from that and I rolled it around in my head, and said we have to pressurize the greenhouse. That’s one thing we’ve got to do.

So, we took one zone (of my greenhouse in California) and we did some testing. We put a bunch of cooling pads in with fans to take the air from outside and put it through tubes underneath the plants on grow gutters. That way you could pressurize them and, with this mechanical system, you could get rid of the problems of a fan and pad system where you have a completely different climate on one end than you do on the other.

This basically gave us a way to equalize the climate. After a bunch of tweaks and a bunch of other things that we had to change, we got to a point where in a 20-acre facility, there was about a half a degree of difference between the warmest and coolest areas.

The great news was that we only needed 4% roof vents! When you have a system like this, you can completely control the air exchanges, you can control the humidity with the fan and pads, you use way less CO

, and you get 30% more light in your greenhouse because you don’t have insect screens all over the place.

After two years of trying this, I felt comfortable enough to build 40 acres of semi-closed greenhouse, what today is coined “Ultra-Clima.”

Is a semi-closed greenhouse easy to operate?

: I often use this analogy: You buy a little Toyota or a Maserati. If you want to get top performance out of the little Toyota, it’s not hard to do. But if you want to get top performance out of the Maserati, you’d better be a good driver.

It’s the same thing with a greenhouse; the more dials you have, the more difficult it is to get top performance, but you have a lot more opportunity.

The whole idea now is how can we grow in a greenhouse and be comparable to indoors but use natural light, utilize way less energy, and get to a point where quality is the same level.



Are there any environmental challenges that a semi-closed greenhouse can’t overcome?

: The only impediment is when the humidity is high, and that’s no different than a fan and pad system. They don’t work when the humidity is high, and that’s the same thing with semi-closed greenhouses and the same challenge you’ll have whether you have roof vents or not. When your humidity is 85%, cooling pads don’t really work. Temperature is a plant’s enemy, but humidity and temperature make it way worse.

High humidity can’t be lowered in a semi-closed greenhouse?

: Sure, you can put in a chiller with a radiator in front to get the water out of the air and then heat it back up again, and then you’re going to lower the humidity, but all those things come at a cost. It’s big energy, and you start becoming like an indoor grow. It is possible, but it’s expensive.

Do you see more cannabis growers using semi-closed greenhouses in the future?

: It has a lot of potential, but like I said, you need to have people that know how to grow in it. It’s going to take a different mindset to figure it out because traditionally, most of the industry is all using Dutch technology, and Dutch technology is all roof vents.

With semi-closed greenhouses, you’ve got to learn how to grow upside down because your cooling is coming from the bottom, not from the top, as it does in a regular greenhouse. So, you have to be able to control that, but I think there’s a lot of possibility of what can be done with this greenhouse.

Do you hold a patent on the semi-closed greenhouse?

: Correct, and we’ve sub-licensed it to a bunch of greenhouse builders. You’ve probably got a choice of 12 or 13 different greenhouse companies that are licensed to build these kinds of greenhouses. But there are still a lot of them out there doing it clandestine and hoping nobody sees. If you’re planning on doing this, make sure they’re licensed.

Where can people learn more about your semi-closed greenhouses?

 That’s the company we did a lot of the research with. We had an engineering company that we worked with to get the whole system in place and do the engineering on it, and then the greenhouse builder we worked with was Kubo, so we did all the research and development of that together.

Cannabis is not a traditional horticultural crop, so it’s reasonable to expect that standard greenhouse solutions won’t meet the needs of growers in the coming years.

Though not a perfect solution for every situation, the semi-closed greenhouse shows great promise for cannabis cultivators focused on minimizing their energy requirements while producing the healthiest crop possible.

 is a horticulture expert who helps businesses cultivate a profitable future in the cannabis industry.
Direct correspondence to: 

The Cultivation Classroom

Casey Houweling, the inventor of the semi-closed greenhouse, explains how this greenhouse concept came about, the challenges he’s had developing it, and the potential it holds for the future of the cannabis industry.

The Semi-Closed Greenhouse: A Revolution in Cultivation and Energy Efficiency

Presenting new environmental studies for a safer indoor grow operation.

How much does cannabis cultivation really affect the environment? And what should cultivators do about that?

Some cultivation facility builders believed that they already determined those factors in the design phase of their grow facility. The reality is that the myriad of environmental factors inside a grow operation is a crucial issue that the cannabis industry is still trying to manage (1).

There are also employee health considerations to consider as well. Workers inside indoor cannabis grow facilities need to know more about what they are facing in their work environment.

According to the Colorado Department of Public Health and Environment (CDPHE), the top occupational hazards of cannabis cultivation are biological (heat, molds, bacteria, fungus, and allergens), and chemicals to support crop production (carbon dioxide, carbon monoxide, volatile organic compounds, biogenic volatile organic compounds [BVOCs], fertilizers, and pesticides).

Cannabis emits BVOCs, which can alter the ozone by forming toxic air pollutants. That’s why emission controls and respiratory protection are essential for worker and public health.

This “Tech Innovations” column discusses what top environmental issues still face the cannabis industry, how they can help reduce emissions in their grow facility, the different effects of various strains on carbon emissions, and how the government is finally helping the industry do a deeper dive into understanding these issues.

Greenhouse gases (GHG) are gases that trap heat in the atmosphere (2). So, how much GHG is generated by a cannabis cultivation facility?

That was what Jason Quinn set out to discover. Quinn is the associate professor in mechanical engineering and director of the Sustainability Research Laboratory at Colorado State University in Fort Collins, Colorado. He is one of the authors who recently published a study into greenhouse gases for indoor cannabis grows in Colorado because the state is such a good place to find and study greenhouse gas emission data (3). Colorado grew more than 1.8 million pounds of legal cannabis in 2020 (4), with Denver, Pueblo, and El Paso growing the most number of plants in 2020—over half a million plants in Denver alone. Quinn’s study included the results of a recent survey that showed 41% of producers in North America indicated that their grow operations occur solely indoors.

It is well known that indoor cannabis cultivation requires significant energy input, reflected in high utility bills. However, many of these large energy loads, along with other material inputs required to cultivate indoor cannabis, have not yet been equated to GHG emissions.

Some quantifications of GHG emissions from indoor cannabis have been performed by equating emissions with electricity use from monthly bills. But that’s not enough.

According to Quinn’s study, this quantification approach omits additional GHG emissions from other energy sources, such as natural gas, upstream GHG emissions from the production and use of material inputs, and downstream GHG emissions from the handling of waste.

To help develop data on these sources and their effects, Quinn and his associates quantified the GHG emissions of commercial indoor cannabis production using life cycle assessment (LCA) methodology (a technique for assessing the environmental aspects associated with a product over its life cycle [5]), and expanded the scope to include geographic effects across the US.

An indoor cannabis cultivation model was developed by Quinn and his researchers to track the necessary energy and materials required to grow cannabis year-round in an indoor, warehouse-like environment. This environment maintains climate conditions as required for the cannabis plants, yielding a consistent product regardless of weather conditions.

The model calculates the necessary energy to maintain these indoor climate conditions by using a year’s worth of hourly weather data from more than 1000 locations in the US. (The researchers analyzed locations independent of current legal status, and represent hypothetical grow facilities in all 50 US states.)

The model then converts the required energy, supplied from electricity and natural gas, into GHG emissions through electrical grid emissions data from 26 regions in the US and life cycle inventory (LCI). Additionally, the model accounts for the upstream, or cradle-to-gate, GHG emissions from the production and transportation of material inputs such as water, fertilizers, fungicides, and bottled carbon dioxide (CO

) supplied for increased plant growth, as well as the downstream process of waste handled in a landfill.

The resulting cumulative GHG emissions for annual indoor cannabis cultivation across the US was represented as kilogram CO

e per kilogram of dried cannabis flower (kgCO

e kg–1). “Carbon dioxide equivalent” or “CO

e” is a term for describing different greenhouse gases in a common unit. For any quantity and type of greenhouse gas, CO

 which would have the equivalent global warming impact (6).

Quinn explained that the primary conclusions of the study were that greenhouse gas intensity varies dramatically geographically across the US for two reasons. “If you have an indoor grow facility in San Diego, California compared to Fargo, North Dakota, the energy intensity of maintaining that comfortable cannabis growing environment is very different between those two locations just based on the heating, venting, and air-conditioning (HVAC) intensity associated with those two locations,” Quinn said. “The second thing is what I will call the cleanliness of the grid. So, the emissions intensity associated with the grids in different regions also has a major impact as well.”

He said that a lot of the grow facilities use a higher concentration of CO

 in the room. “The big issue here is the number of air exchanges that the cannabis industry uses in order to maintain a mold-free environment,” said Quinn. “They’re in a situation where the revenue they can make enables them to not require optimization of the facility in terms of energy. And so, they’re putting in rich CO

 but they’re doing massive air exchanges, like more air exchanges then most hospitals. That CO

Are any indoor cultivation facilities trying to do anything about these emissions? Quinn said that there are certain retailers and growers who are trying to make a name for themselves in this area. “As a part of this study, I found out that there was a grow facility in Colorado that is 100% hydro-power,” he said. “They reached out to me to say, ‘Hey, listen, we’re hydropower. So therefore, our emissions are much lower.’ That’s a true story, because that dramatically impacts the overall emissions associated with that specific grow facility.”

The study also noted that geographic variations of GHG are most noticeable in Colorado, where the mountainous locations of Leadville, Aspen, Gunnison, and Alamosa lead to significantly more GHG emissions than locations on the plains of Pueblo, Trinidad, or Denver. For example, the practice of growing cannabis in Leadville leads to 19% more GHG emissions than in Pueblo.

Grow operations could consider moving to other areas of the state and operate with lower GHG emissions. For example, the savings in GHG emissions from moving indoor cultivation to Pueblo and away from Leadville are likely to be much greater than the GHG emissions of transporting the final product to retail locations in Leadville.

A study about cannabis cultivation facilities and air quality impact in Colorado (7) first noted that terpenoids are highly reactive compounds with atmospheric lifetimes ranging from seconds to hours. They are primarily biogenic in origin (emissions that come from natural sources [8]), and their reactions alter the atmospheric oxidizing capacity, resulting in a range of low volatility products that can partition into the aerosol phase and, depending on the concentration of nitrogen oxides, lead to the formation of ozone.

Various studies have found that ozone concentrations in Denver are volatile organic compound (VOC)-sensitive, meaning that an increase in volatile organic compound concentrations will increase ozone production. When combined with VOCs and sunlight, nitrogen oxides help form ground-level ozone, a major component of smog (9). Ozone can cause or exacerbate chronic lung diseases like asthma, chronic obstructive pulmonary disease, or emphysema, especially among vulnerable populations like children and the elderly, for whom it may prove deadly. Researchers attributed 254,000 premature deaths to ozone pollution in 2015.

The location of cannabis indoor grow facilities in a VOC-sensitive region in Denver suggests a potential emission source that may impact regional air quality.

William Vizuete, a former professor in Department of Environmental Sciences & Engineering at the University of North Carolina, and a chemical engineer by training, co-authored that air quality and cannabis study (10). He noted that there were few assessments to date on the environmental impacts of the production of cannabis, especially about the possible BVOCs emitted from the growing of cannabis, and its impact on indoor and outdoor air quality.

Vizuete watched as the legalized cannabis market came to Denver in 2014, and warehouse space was being snapped up because growers were regulated to have a secure perimeter around their grow. Growing indoors was the best way to achieve that requirement, he explained.

Many warehouses in Denver are built near transportation hubs or zones, such as the intersection of I-70 and I-25 in downtown Denver. “Ozone requires both VOCs and nitrogen oxides (11), which are coming from car and truck engine combustion,” said Vizuete, “Cities have lots of nitrogen oxide because there are lots of cars and industry. The bulk of VOCs come from cars. But if you’re in Atlanta, Georgia, there’s also a huge amount of VOCs that come from trees.”

What’s interesting about Denver is that it’s high desert and doesn’t have a huge input of VOCs from trees. “What they did was build all these grow plants right where the highest concentration of nitrogen oxide was,” Vizuete said. “If this was any other industry, like a gas station or power plant, I could go to the EPA and look up the emission factor and know exactly what emissions it produces for every mile driven. I can build an inventory for the entire industry. And then I can input that into my model and see if it makes a difference for ozone.”

He eventually published some of the first studies about emission factors for cannabis, such as the cannabis emissions research project for Santa Barbara County, California that he designed and oversaw (12).

One of the most surprising things he heard from a colleague is that he’s never seen a plant like the cannabis plant as far as emissions. “There are a wide variety of types of VOCs that come off the plant,” he said he found out from his colleague. “The amount varies by lifecycle, whether it’s young or old, whether it’s indoor or outdoor. And what’s surprising is that it varies pretty significantly by strain.”

If you look at Colorado’s 600-plus strains, Vizuete said, every single one of them is different as far as what they emit, when they emit it, and how much they emit. “That was really, really surprising,” he said. “The second thing that was really surprising is that it’s very difficult to build an emission inventory if you don’t know what plants they are, how many of them there are, and what strain they are. That information is actually very difficult to get from the state. So, there’s a lot of uncertainty in building these inventories for the state.”

There is also an interest in mitigating smell from cannabis grows, as more and more growers must address smell complaints (13) by the communities where their grow operations are located.

Vizuete formed a company to continue his research into VOCs after wrapping up his university studies, and “bring science to the wild west of this industry.”

His company was the very first to identify and confirm, with both gas chromatographs and with a nose test, what created the cannabis smell. “I had a hypothesis that there’s no way that this smell from the plant, this skunky smell, is a terpene, which is the conventional wisdom of the industry,” he said. “I know the chemistry that forms that skunky smell for beer in bottles and I know the molecules that it generates. The hypothesis I had was that the cannabis plant is producing those same kind of molecules but are emitting it directly, and are not having to occur via chemistry in a beer bottle.”

Since there is the ability to measure every molecule that’s coming off a cannabis plant, he said, he believed that those molecules could be found. “Sure enough, we found those in the sulfur, and sulfur smells. Just a variety of VOCs, the amount and type and variety of styles varies by strain, and accounts for some of the differences in strength and odor.”

If a cultivation operator knows what the molecule is, there is a test standard available, and they can design their carbon sequestration, the air exchange—basically everything can be designed around that molecule.

“As far as smell mitigation, what we suggest is a combination of a couple of things and it always depends on where you are,” Vizuete said. “We would recommend that you measure and know exactly how much your plants are breathing out, like how many of these gas molecules are breathing out. Then we can size the mitigation to take just enough to capture what they’re breathing out. Not to take too much because then you’re over-designing and you’re wasting energy. You’re drying out your plants. Knowing the amount of gas molecules that are coming off and knowing which molecules to look for helps us in designing and sizing our (mitigation) controls.”

Vizuete hopes to continue his research understanding the consequences to the air quality by cannabis cultivation, but bumps up against limits that are imposed by the US government. For example, he said that, when he looked up the emission factors for cannabis, there’s “absolutely zero” literature that told him anything about any gases. Getting a study done to find those gas measurements was problematic because it’s cannabis. No federal facility or academic institution that relies on government grants would allow him or other researchers to touch it.

Researchers have needed expanded legal access to modern cannabis strains for research on cannabis and cannabis-derived products for more than 50 years. Vizuete and other researchers are always frustrated by the lack of access to cannabis for their work, and, when they finally do get federally-approved cannabis from a legal grow operation in the US, the quality is so poor that it becomes problematic to do the kind of new and advanced tests that the industry needs.

That is finally changing... slowly but surely.

The Drug Enforcement Administration (DEA) just authorized another legal grow operation—their 7th—since announcing that they would make more legal grows available in 2021 (there has been only one DEA-legal grow facility since 1968 [14]).

That newly licensed facility, Maridose (15), a biopharmaceutical research and product development company focused on cannabis and cannabis-derived products operating out of Brunswick Landing, Maine, was notified by the DEA of its selection in late August after the company spent 5 years working with the DEA.

The criteria for the selection by the DEA was a bit confusing, according to Richard Shain, founder of Maridose. “The way they phrased it was if you’ve been involved in the state cannabis markets, even if it’s legal within the states, the DEA would look unfavorably on the application,” he said. “They didn’t say they would refuse it. Then the next criteria was that they were looking for people that had experience within the industry. That kind of goes against each other.”

The DEA is not going to issue an unlimited supply of licenses, but are limited just to what was needed to supply the needs of the research community.

The way it works is each manufacturer can provide their own strains to the Maridose grow facility. But Maridose has also licensed all the intellectual property from Tikun Olam (16), Israel’s largest medical cannabis provider, providing Maridose with a bank of some of the most researched strains in the world. “So, we have access to all of these strains and research, and we’re going to make those available to the DEA researchers,” Shain said.

Because Maridose is a DEA-licensed manufacturer, the company can only sell to DEA researchers. “As of a year or so ago, that number was a little more than 600 licensed researchers. So, when you look last year at the total quota for the entire research, DEA researchers got 3.2 million grams. That’s not a lot in the scheme of things when you look at sales out there to dispensary. You really don’t need a lot of land to satisfy how much you can grow.”

Their grow operation right now is inside Brunswick Landing, which is an old naval base with its own airport that was decommissioned years ago. “Right now, we haven’t built our larger facility. Our facility now is inside the building. But from a security standpoint, there is phenomenal security.” He said that he wanted to keep the size of the grow confidential for now.

The DEA-approved researchers can research only the dry plant and the extracts. Only DEA-approved manufacturers can grow the live plants. “For example, if the Dana Farber Cancer Institute has run a study and they want to try a different combination of active ingredients and a different strain, they can’t grow it. One of the manufacturers has to grow it. We plan on doing that and having that be something that we made as something of value for our customers.”

https://pubs.acs.org/doi/10.1021/acs.estlett.0c00844

https://www.epa.gov/ghgemissions/overview-greenhouse-gases

https://dx.doi.org/10.1038/s41893-021-00691-w

https://sbg.colorado.gov/sites/sbg/files/210916%202020%20MED%20Annual%20Final.pdf

https://www.sciencedirect.com/topics/earth-and-planetary-sciences/life-cycle-assessment

https://ecometrica.com/assets/GHGs-CO2-CO2e-and-Carbon-What-Do-These-Mean-v2.1.pdf

https://acp.copernicus.org/articles/19/13973/2019/

https://www.epa.gov/air-emissions-modeling/biogenic-emission-sources

https://theicct.org/stack/vehicle-nox-emissions-the-basics/

https://byers-scientific.com/dr-william-vizuete-ph-d/

https://carpgrowers.org/wp-content/uploads/2019/08/Public-Comment-Vizuete.pdf

https://www.achrnews.com/articles/143060-reducing-cannabis-odors-is-a-growing-concern

https://news.olemiss.edu/federally-funded-marijuana-turns-50/

https://www.prnewswire.com/news-releases/maridose-announces-federal-license-from-the-us-department-of-justice-and-drug-enforcement-agency-for-bulk-manufacture-and-sale-of-cannabis-and-cannabis-derived-products-301609001.html

 has written for many cannabis publications, and organized or moderated sessions at national and international cannabis trade shows. He was voted the 2018 Journalist of the Year by Americans for Safe Access, the world’s largest medical cannabis advocacy organization.

Tech Innovations

Here, we take a look at the top issues related to cannabis cultivation: what top environmental problems still face the cannabis industry, how to reduce emissions in grow facilities, the different effects of various strains on carbon emissions, and how the government is finally helping the industry do a deeper dive into understanding these issues.

New Data on the Environmental Impact of Cannabis Cultivation

Cannabis products are commonly labeled for sale with a single number as an indicator of potency, or one number for each cannabinoid listed. These potency values are derived from testing that is done on multiple samples for each production lot, but information on the range of potencies found in those samples is generally not reported. By collecting reports from the New Jersey State laboratory (the Department of Health’s Environmental and Chemical Laboratory Services), which has conducted testing for the state’s medicinal program, I documented an average variance of 20% in potency levels from different samples of the same test lot.Surprisingly, a greater degree of variance was found in strains that had mid-level potency (<15% THC) than in higher potency strains.

Many companies that sell cannabis products advertise the consistency of their products, especially for medicinal use. Rigorous quality control testing is needed to back up such claims, and that testing should be done on several randomly chosen samples of the product. This has long been the standard for manufactured and processed products, but is even more important for natural, unprocessed products.

All natural agricultural products can be expected to have plant-to-plant variation in their properties. Because of variable growing conditions, this is even true of plants that are clones of the same mother plant. In recognition of this natural variation, most state guidelines for cannabis testing specify the number of separate samples that must be compared to produce potency ratings, with more samples needed for larger lot sizes.

While testing guidelines are written to consider natural variation between different plants, the literature on this subject appears to lack well-controlled documentation of how much variation should be expected. In a research document detailing recommendations for a sampling program for the state of Washington, Sexton and Ziskind (1) documented variation in the quoted tetrahydrocannabinol (THC) levels between different production lots of the same strains but cited no evidence for variation within each lot. In fact, they wrote that, “... there is little literature to tell us that plants of the same strain grown under identical conditions can be assumed homogenous, or to what extent they differ.” Experienced growers and testers are surely familiar with the degree of variation that exists, but a well-controlled study may provide some further insight.

The variance across samples does not present a methodological problem for determining a single representative value: the samples can be ground together to produce a composite with greater homogeneity, just as consumers are able to do when they receive the product. Although testing laboratories have ways to derive a single representative number for a batch, that does not take away the variance between different portions (buds) of the product that consumers will confront when they acquire it.

A different kind of variation, that between ratings made by different laboratories on the same batch (2), has been a recurring concern in the industry, but is a different problem than the variation inherent in the plant. There are ways to address concerns about different laboratories producing different results, but having the means to derive a “right” answer should not obscure the inherent variances in the plant material itself. Consumers should be given the most accurate picture of the product, including how much variance they might expect from bud to bud.

One benefit of a careful examination of sample-to-sample variability inherent in cannabis products may be as a guide for regulations that results in more informative labeling. While every product is labeled with its THC content, this single value does not convey the variation that exists between different portions of the same lot. Rather, the numbers on labels, particularly when they quote cannabinoid percentages with one or two decimal places, likely give the consumer a false sense of accuracy. No one familiar with the production of these products could confidently state that the detailed THC levels on the labels of flower product is applicable for each portion of each package. Perhaps a detailed examination of sample variance will provide a useful alternative.

A good data set for examining sample-to-sample variability in cannabis comes from New Jersey’s medical program. All of the growing is done under highly controlled conditions, and for five years, all of the product testing has been done by a single, publicly funded laboratory (the Department of Health’s Environmental and Chemical Laboratory Services). This laboratory has publicly posted strain test results for patients to review (3).

The tests results posted from June 2016 through March 2019 included results for each of the five samples for a given strain, as well as results for a composite made of the five samples. Six concentration levels were thus reported for all eight cannabinoids being measured (after March 2019, the posted results only contain a single measure for each cannabinoid). Reviewing all the reports currently posted, 100+ sets of data points were recovered which included cannabinoid levels on each of five samples of the same batch, plus the composite. Six producers were active in New Jersey throughout 2019, and at least 10 reports were collected from each. Previous studies on this data set (4) reported on the relative frequency of strain types (overwhelmingly THC-dominant) and the covariance of cannabinoids.

In the present study, only the THC-dominant strains were examined, reducing the data set to 98 entries. From each of the five sample reports plus the composite test, the THC percentage and the tetrahydrocannabinolic acid (THCA) percentage were recorded. As a first step in analysis, the THC and THCA values were summed for a total THC value (after the 0.877 weight correction for THCA). To analyze each strain report, a calculation was made of the average of the five total THC values as well as the standard deviation across the five values. In addition, the relative standard deviation (RSD) was calculated by dividing the standard deviation by the average value. In this way, the amount of variation can be compared across strains with a range of potencies.

A new calculation was also made that perhaps best characterizes the information consumers would find useful: how much variance can be expected between those portions with the highest concentrations and those with the lowest? This high–low variance was calculated by dividing the highest value of the five samples by the lowest, minus 1. Importantly, this measure of variance is relative to the expected concentration. If a consumer is expecting an 11% product and instead gets 14% (a 27% variance), that will likely be much more noticeable than expecting 22% and getting 25% (a 13% variance). The high–low variance, being relative to overall potency, might reflect the degree of subjective variance that patients could experience.

To present data on individual strains that contain several data points, a charting tool was adopted from the world of finance: the stock price candlestick chart. These charts are typically used to graphically present the several values that a stock price has during a single day: the stock’s high price for the day, its low price, its opening price, and its closing price all in a single image. I have adopted that format to show, for a single strain, the highest total THC concentration recorded from one sample, the lowest sample value, the average of the five sample values, and the concentration measured in the composite of the five samples (

A reasonable hypothesis one might have about the degree of sample-to-sample variation in THC levels is that it would be higher for strains with higher THC values. The evidence does not support this idea. Figure 2 shows two depictions of the information from one of the six growers operating in New Jersey. The strain data is the same but are arranged differently on the 

axis: in the first they are arranged by the average potency of the strains, and in the second they are arranged by the standard deviation for each set of samples. In the first example, it is apparent that the bars move up as you go left to right across the chart, but there is no trend of a greater spread between the high and low entries, nor the variance between average and composite values. In the second chart, arranged by increasing standard deviation, the entries with the wider gaps are clustered to the right, but not a trend toward higher potency values: the higher potency strains are at the mid-range of standard deviation.

The data from just one grower is displayed in 

, and it appears that there are nearly the same number of white bars as black bars (that is, as many cases of the average being above or below the measure of the composite of five samples). This is not the case for the entire data set, however. Out of the 98 entries, in 57 cases the average value was greater than the composite, and for 41 it was less. There was no clear relationship between either overall potency or spread as to whether the composite or the average was greater.

Another feature of the candle charts is that it draws attention to anomalous figures, where the central bar is at the extreme of the five sample values. In each case, this is where either the composite value is lower than the lowest sample value (or nearly so), resulting in a black bar touching the bottom; or the composite value is higher than the highest sample (or nearly so), resulting in a white bar reaching to the top of the entry. Remarkably in this data set of 98 entries, there were 16 examples in which the total THC values for the composite of the five samples was outside the range of the samples themselves (eight cases of higher than the highest sample and eight lower than the lowest; each of these instances was double checked in the original posting of laboratory data). The conclusion to draw may only be that the laboratory’s process for compositing the samples was not very thorough; but it also supports the idea that the mathematical average of the samples, equally taking into account of the high and low samples, is the best single number to represent one set of samples.

Relative Standard Deviation and High–Low Variance

Relative standard deviation (RSD) is a common measure of the divergence of values in a sample set. Similar to the high–low variance measure described above; it provides added information by normalizing the variance to the base level of the sample set (a deviation of five units has a very different meaning if the base value is 10 rather than 100). The RSD for the subset from the single grower used in Figure 2 were plotted against the high–low variance for those same samples, resulting in the scatter plot in 

 = 0.95) of these two measures of divergence, a choice can be made based on which measure is more meaningful for this purpose. The high–low variance measure has the advantage that it is likely more intuitive for the interested audience: a 30% variance between extreme samples has more practical meaning than a measure of standard deviation. In addition, the high–low variance measure is not predicated on their being a “right answer” from which the values deviate; it simply describes the observed natural variation.

With the higher standard deviations seen for strains with mid-level potencies, it is not surprising that the degree of variance between high and low samples, in percentage terms, was also found to be higher in the middle range of potencies. This is illustrated for the same single grower data sub-set used in Figure 2. The range of high–low variance in this set of 13 strains was from 6.4% at the low end to two cases of variances over 50%.

A view of the entire data set of 98 strains tests bears out the impression from the subset: greater variance is not found in the highest potency strains. A candlestick graph for all strains, arranged on the 

-axis by the high–low variance, shows a trend toward lower potencies toward the right side of the chart, with higher potency strains toward the left (lower variance).

The degree of variance between samples of a single lot of cannabis flower may come as a surprise to those who have not worked in producing or testing the material. Fully 30% of the strains in this data set have a variance between high and low samples of 25% or greater, and 9% of strains had greater than 50% variance. The median variance is 19.8% (the mid-point along the 

), meaning that a randomly chosen strain could be expected to have nearly 20% variance in the potency of individual portions; if one chooses lower potency strains, the chances of having higher than 20% variance increase.

The degree of intersample variance might not be a matter of chance, however, and the factors that influence it bear further investigation. One possible source of variance was investigated and is reported here, but does not prove to be helpful. Six licensed growers of medical cannabis in New Jersey were the source of all the strains tested. It may be that some growers consistently had better or worse control of the variance in their product. A graph of the median variance for each grower, plotted with the number of strain tests that they contributed to this data set, shows a comparable degree of variance among them. It does not appear growers with a greater number of strains tested over this period produced tighter variances.

The flip side to the observation that mid-potency strains had higher variances is that high-potency strains had, on average, lower variances. If this observation is confirmed in larger data sets, it may be thought to reflect a property of the plant: the combination of strain type and growing conditions necessary to yield high potency also yields high consistency.

The evidence presented shows that there is considerable inter-sample variation in THC levels from single lots of medical cannabis tested for the New Jersey program. The possibility that the variation is an artifact of poor consistency in the testing methods, rather than in the plants themselves, was investigated by comparing strain results obtained on the same day by the laboratory. For four strains that had samples prepared and high performance liquid chromatography (HPLC) tests run on the same day, even in the same hour, the high–low variances were 10%, 25%, 51%, and 53%. It does not appear that method or machine performance can be looked at as a factor producing such disparate results. Other single day results also showed a spread of variances that mirrored the data set as a whole. Finally, if the variances were a product of laboratory performance rather than natural, we should not expect the observed relationship between potency and high–low variance that was observed.

Many laboratories and growers must have data that can support or refute these observations and conclusions, but few are publicly available. One other source of public data that allows inter-sample comparisons is the published test results generated in Washington State between June 2014 and May 2017 (5). This set includes 140,000 entries for flower product. Included with each entry is a unique identification number, strain name, the name of the grower and the testing laboratory, the test date, and reported values for total THC and total cannabidiol (CBD) concentrations. While the dataset does not identify entries as being samples of a single lot, there are numerous entries in which the same strain name, or with an identifier variant (for example, BigStrain A1 and BigStrain A2) from the same grower were tested by the same laboratory on the same day, time stamped within an hour of each other (6). A quick assessment of 12 sets of entries, which had at least five entries per strain name, revealed a median high–low variance of 28%.

Another source of data pointing to high sample-to-sample variance comes from the test of the consistency of different laboratories run by Gieringer and Hazekamp (2). Though they used blended composite samples, they reported that “lab results were consistent to within 20% of each other. To some degree, the differences in results might be explained by natural variations in the consistency of the cannabis samples used; to some degree, by differences in lab procedures.” The current study removes the variable of laboratory performance by drawing all the data from a single laboratory, leaving the observed variance to be attributed only to the natural variance inherent in the product.

The degree of variation seen in this collection of strain results from New Jersey’s medical program might be considered a best, or lowest, case for natural variation between samples. The product was all grown in highly controlled, indoor conditions by tightly regulated growers; the testing was all done by a single, highly professional laboratory with consistent, simultaneous preparation of each sample before testing. Within the strains that the laboratory tested on the same day can be found the full range of inter-sample variance.

This information might be regarded as “well-known” by growers and laboratories, but it has not been well documented, as it seems not to have influenced the way that legal cannabis is labeled for sale. It is not uncommon for package labels to be very specific about THC levels, to a degree of specificity (19.62%) that cannot be supported based on the natural variance that can be expected from most strains. In contrast, in the Netherlands products regulated by the Office of Medicinal Cannabis are labeled with an “approximate” THC level, with an allowed test variance of 20% from the label. That Dutch maximum allowed variance is essentially the same as the median of high–low variance seen in the New Jersey data set.

An alternative labeling scheme, that would provide a more accurate picture for the consumer, would be to label the product in a way that informs them of the degree of variance that was found at testing. It would require no more space on a package label to report the THC level as “15-17%” than to provide false confidence with a label that reads “16.53%.” A similar suggestion was made to authorities in the state of Washington by Sexton and Ziskind (1).

Analysis of medical cannabis test results show that there is a considerable range of potencies between different samples of the same production lot. Consumers might benefit from having information about how much variation they might expect in different portions of a product that is labeled with a single, but misleadingly precise number.

In the work presented here, the author shares research from collecting reports from the New Jersey State laboratory (the Department of Health’s Environmental and Chemical Laboratory Services), which has conducted testing for the state’s medicinal program. He documented an average variance of 20% in potency levels from different samples of the same test lot.

October 2022

Quantitative Spectroscopy Pitfalls and Practicalities, Part IV: The Finale

The Semi-Closed Greenhouse: A Revolution in Cultivation and Energy Efficiency

New Data on the Environmental Impact of Cannabis Cultivation

Degree of Variance in THC Levels in Samples Taken from Single Lots of Medical Cannabis

How Manufacturing, Regulations, and Sustainability Are Affecting the Cannabis and Hemp-Based Products Industry