Cannabis Grow

These are just a couple of examples on how we had to conceptualize the plasmid making process differently on the LAS than how we typically do on the benchtop.After the construction of mutation plasmids both manually and utilizing the Living Biofoundry, we heterologously expressed the mutated Ma_OvA plasmids with Ma_OvaB and Ma_OvaC in Aspergillus nidulans. Out of the many mutation plasmids we constructed, we identified two that produced olivetolic acid analogs. Heterologous expression of first plasmid, containing the F418A and Y420A mutations, with Ma_OvaB and Ma_OvaC produced the nonyl and undecyl variants of olivetolic acid, both previously mentioned as having antibacterial activity. Heterologous expression of the second plasmid, containing the T318W and S347W mutations, with Ma_OvaB and Ma_OvaC produced orsellinic acid and divarinic acid which were confirmed by analytical standard and heterologous expression also produced what we propose to be ethyl variant based on mass and UV, although at low quantities so NMR was not taken. We also further utilized genome mining to elucidate new clusters producing olivetolic acid analogs. As previously detailed, we had identified three clusters homologous to the Metarhizium anisopliae cluster containing the Ma_OvaA, Ma_OvaB, and Ma_OvaC genes. As described, we identified the Tolypocladium inflatum and Metarhizium rileyi clusters in which heterologous expression produced the same product profile as Metarhizium anisopliae albeit at lower titers and the Talaromyces islandicus cluster which selectively produced olivetolic acid.

From the percent identity comparisons between the enzymes,2×4 flood tray we determined that clusters harboring close to 50% or less homology to the Metarhizium rileyi cluster would produce the greatest variety in products different from the product profile of the Metarhizium anisopliae cluster. Equipped with this knowledge, we utilized the Targeted Genome Mining Information Finder program, a MATLAB based program develop by Dr. Nicholas Liu, an alumnus of the Tang lab. Employing MATLAB’s Bioinformatics Toolbox which includes the ability to use Basic Local Alignment Search Tool to analyze FASTA formatted sequences, Dr. Liu developed a program to elucidate possible bio-synthetic gene clusters based on a target queried for. Although this program was developed to query for target resistance gene clusters, it can be used for a variety of different purposes. We employed the program to query for tandem polyketide synthases and used the Tang lab’s in-house fungal strain list as the database. We elucidated a cluster in Penicillium thomii containing 48%, 41%, and 36% homology to Ma_OvaA, Ma_OvaB, and Ma_OvaC, respectively. We heterologously expressed this cluster in Aspergillus nidulans and produced the nonyl olivetolic acid variant with a diene at the C1 and C3 positions of the alkyl chain as well as the heptyl variant unsaturated at the C3 position of the alkyl chain and a hydroxy group at the C2 position, both of which are non-native to the Cannabis sativa plant and therefore can be further processed to new to nature cannabinoids.We have detailed ways in which we were able to be diversify our product profile to produce rare olivetolic acid analogs. There are still more mutations near the active site of the KS domain as well as the AT domain that can be made to produce even more olivetolic acid analogs with different alkyl chain lengths.

With regards to homologous cluster expression, we only searched through the100+ sequenced fungal strains that we have. Therefore, there is a large number of fungal strains that can be queried to search for homologous clusters that can producer are olivetolic acid analogs. Testing of these variants and the ones produced utilizing the Living Biofoundry can lead to potentially promising results with regards to biological activities. As previously detailed, microbial production of naturally occurring and novel cannabinoids has potential to be a disruptive technology to the ~$10 billion global cannabis industry. From olivetolic acid, the next step in the cannabinoid bio-synthetic pathway is the geranylation of olivetolic acid to produce cannabigerolic acid , known as “the mother of all cannabinoids”CBGA can be decarboxylated to form cannabigerol , a cannabinoid with intriguing therapeutic potential. In the Cannabis sativa plant, CBG is produced in larger quantities in the early stage of the plant but in minute quantities in the mature stage of the plant. Preliminary research has indicated that CBG is non-psychoactive but has anti-oxidant, antimicrobial, anti-inflammatory, anticancer, photoprotective, and appetite-enhancing properties.Studies on the effect of CBG on the cannabinoid receptors have shown that CBG is a partial agonist for the CB2 receptor but cannot bind to the CB1 receptor, hence its nonpsychoactive properties.As previously described, the CB2 receptors are primarily located in the nervous system and agonists of the receptors provide anti-inflammatory and anti-oxidant effects. CBG was first isolated in 1970 and was fully characterized shortly after.We therefore sought to produce this cannabinoid microbially. To do so, we had to identify the prenyltransferase responsibly for geranylating the C2 carbon position of olivetolic acid.

This prenyltransferase activity was first demonstrated in the Cannabis plant and was proposed to beattributed to cannabis sativa prenyltransferase 1 and was soon patented. However, when Luo et al heterologously expressed CsPT1 in Saccharomyces cerevisiae, they observed no activity. Therefore, they mined for prenyltransferases with predicted activity similar to CsPT1 and heterologously expressed those candidate prenyltransferases. They demonstrated that the Cannabis sativa prenyltransferase 4 was able to successfully prenylate olivetolic acid to produce CBGA. CsPT4 was found in the Cannabis sativa plant and is part of the UbiAmembrane bound family of prenyltransferases, predicted to contain eight transmembrane helices. When expressed in Saccharomyces cerevisiae, and with the plastid targeting amino acid sequence removed, the CsPT4 enzyme was found to be located in the microsomal fractions of the yeast strain. Luo et al performed in vitro assays with the microsomal fraction harboring CsPT4 and demonstrated that the enzyme displayed Michaelis Menten behavior when olivetolic acid concentration varied whilst GPP concentration stayed constant but deviated from Michaelis Menten behavior when olivetolic acid concentration was held constant while GPP concentration varied.Aromatic prenyltransferases capable of geranylating olivetolic acid to produce CBGA outside of the Cannabis sativa plant have also been discovered. There are three classes of aromatic prenyltransferases: ABBA-type prenyltransferases, UbiA-type prenyltransferases, and dimethylallyl tryptophan synthase -type prenyltransferases. ABBA-type and DMATStype prenyltransferases are found in bacteria and fungi and UbiA-type prenyltransferases are found in fungi, plants, and bacteria. These aromatic prenyltransferases catalyze formation of carbon nitrogen, carbon oxygen, and carbon-carbon bonds between the prenyl donor’s carbonand the aromatic substrate.ABBA-type and DMATS-type have been elucidated as soluble aromatic prenyltransferases while UbiA prenyltransferases are membrane bound aromatic prenyltransferases. UbiA-type prenyltransferases are membrane bound prenyltransferases found in a variety of organisms such as bacteria, fungi, plant, human, etc. These prenyltransferases have been observed to be involved in menaquinone and ubiquinone biosynthesis as well as fungal meroterpenoid biosynthesis, archaeal membrane lipid biosynthesis, and prenylated aromatic secondary metabolites biosynthesis in plants, among other biosynthesis reactions. These UbiAtype prenyltransferases typically contain eight to nine transmembrane helices. Regarding their structure, enzymes in the family contain two conserved aspartate rich motifs with the first used for Mg2+ binding in order to catalyze the reaction; therefore, these prenyltransferases are metal dependent.DMATS-type prenyltransferases have been elucidated in fungal and bacterial species. These aromatic prenyltransferases are metal independent although addition of metal ions like Ca2+ and Mg2+ have been reported to have enhanced the catalytic activities of several of these prenyltransferases.DMATS-type prenyltransferases primarily act upon indole derivatives such as tryptophan,food and drain table indole terpenoids, and cyclic dipeptides that contain tryptophan by prenylating these compounds. Reports have demonstrated that DMATS-type prenyltransferases have the ability to prenylate all positions of the indole ring and characterization of its structure have revealed that these prenyltransferase also have the α-β-β-α prenyltransferase folds that ABBA-type prenyltransferases have.Similarly to ABBA-type prenyltransferases, DMATS prenyltransferases show selectivity in prenyl donor, with most enzymes in the family utilizing dimethylallyl pyrophosphate for prenylation, but have great flexibility with regards to prenyl acceptor, capable of prenylating not only those indole derivative previously mentioned but also xanthones, tricyclic and tetracyclic aromatic compounds, and tyrosine.ABBA-type prenyltransferases are found in both fungi and bacteria. They primarily utilize DMAPP and geranyl pyrophosphate as the prenyl donor. All the members of the ABBA-type family of prenyltransferases, except for NphB, do not need metal to assist in catalyzing the reaction.

Although CloQ from Streptomyces roseochromogenes var. oscitans was the first member of the ABBA family of prenyltransferases to be discovered, NphB was the first in the family to have its crystal structure. The crystal structure revealed a structure containing an unique three dimensional α-β-β-α prenyltransferase fold, hence the name ABBA.156 NphB, a member of the ABBA family of prenyltransferases, from the bacteria Streptomyces sp. CL190 was discovered to have non-specific prenylation activity for the formation of CBGA.These prenyltransferases are soluble and are capable of catalyzing the transfer of dimethylallyl , geranyl , or farnesyl prenyl groups onto a diverse set of electron-rich aromatic acceptors. Genome mining for analogs to CloQ, the first gene identified as part of the ABBA family of prenyltransferases, led to the discovery of NphB in Streptomyces sp. CL190.Wildtype NphB is specific in prenyl donor, preferring the geranyl group but is promiscuous with regards to aromatic acceptor although the major substrate is 1,6- dihydroxynapthalene.Wildtype NphB was shown capable of prenylating olivetolic acid toCBGA although at low catalytic efficiencies .Wildtype Nphb was also non-specific in prenylation of OA, capable of producing not only CBGA when reacted with GPP but also 2-O-geranyl olivetolate.Therefore Valliere et al. preformed mutations on NphB to increase specificity for the production of CBGA. They docked the olivetolic acid structure to the NphB crystal structure and then utilizing Rosetta, developed a 22-construct library, constructed the library, and screened for CBGA production. They identified two amino acid mutations that greatly increased specificity to CBGA. From the initial library, then they constructed a focused library and discovered that all but one of the mutations in the focused library had 100-fold higher activity than wildtype NphB with regards to kcat value. Ultimately, they determined that their two best mutations were Y288AG266S and Y288VA232S. Both mutations selectively produced CBGA and both had kcat values 1000-fold higher than the wildtype. Valliere et al. demonstrated that they were capable of producing 1.25g/L of CBGA in a cell free manner utilizing their mutated NphB enzyme.Based on the Tang’s lab collaboration with the Bowie lab, from where Valliere et al developed the mutated NphB enzyme, and the company that he helped found, Invizyne, we were given the mutated NphB enzyme which we used in order to test its ability to prenylate the olivetolic acid analogs in vitro as well as test for functional expression in vivo. We purified the enzyme and performed in vitro assays with our olivetolic acid analogs and GPP. Based on LCMS/HPLC data, NphB was able to prenylate olivetolic acid analogs that we produced as well as other analogs bought commercially, although the final prenylated products were not confirmed by NMR. However, the CBGA product produced by the NphB reaction with olivetolic acid and GPP was confirmed by an analytical standard. Additionally, based on the masses of the in vitro assays, we observed an interesting trend: the shorter alkyl chain variants reacted with NphB and GPP generated not only the C3 geranylated product but also the C3 geranylated product with an O-geranylation. As the alkyl chain length increased, the less appearance of this double geranylated product, with the C3 geranylated product being the major product. We then tested for the functional expression of NphB in vivo in Aspergillus nidulans. We heterologously expressed NphB and the GPP synthase enzyme vrtD from Penicillium aethiopicum , along with Ma_OvaA, Ma_OvaB, and Ma_OvaC in Aspergillus nidulans, expecting to observe the CBGA and CBGA analog products. However, we did not observe any of the geranylated products. We also heterologously expressed Nphb and vrtD with Ti_OvaA, Ti_OvaB, and Ti_OvaC in Aspergillus nidulans and did observe production of CBGA but it was very minute. We additionally expressed CsPT4 as well as an aromatic prenyltransferase from Aspergillus terreus recorded to have prenylation activity in vitro with our platform. However similar to NphB, we observed no production of CBGA. These results led us to postulate that the enzymes are not being properly expressed in A. nidulans, especially engineered NphB, after all a bacterial gene; therefore, we sought to look at its transcription.

They demonstrated that ∆9 -THC synthetic mimics were able to bind to a specific site in the membranes of the brain, inhibiting synthesis of cyclic adenosine monophosphate through a G-protein mediated mechanism. Furthermore, molecular cloning of the first cannabinoid receptor gene as well as brain mapping of the cannabinoid binding sites in rats confirmed the existence of the cannabinoid receptor in the brain, known as the CB1 receptor. The cannabinoid receptors are part of a larger family of receptors known as G-protein coupled receptors . G-protein coupled receptors total about 800 in number and are divided into five major families: secretin, frizzled/taste 2, adhesion, glutamate, and rhodopsin. Majority of GCPRs including CB1 and CB2 belong to the rhodopsin like family. Rhodopsin is a pigment found in the rod photoreceptor cells of the retina and its role is to change photons into chemical signals, allowing vertebrate animals and humans to sense light by stimulating cellular biological processes in these organism’s central nervous systems. With regards to structure, GCPRs contain a transmembrane unit harboring seven alpha-helices conjoined with a G-protein that itself has three subunit proteins: Gα, β, and γ. Once an agonist binds to the GCPR transmembrane domain, the G-protein subunits conjoin with another cellular protein such as a protein kinase or adenyl cyclase to catalyze downstream functions. GCPRs role in regulating functions in the cells is so important that drugs targeting GCPRs make up 30- 40% of all the drugs in the market. 20 The CB1 receptor’s discovery was in 1984 through the observation that cannabinoid binding to the receptors reduced cyclic adenine monophosphate concentrations in neuroblastoma cells.

Two years later,seedling starter trays work was done to demonstrate that the cAMP concentration reduction by cannabinoid binding could be reversed through the exposing of the cells to the pertussis toxin, a Gαi protein inhibitor. However, it wasn’t until 1990 that the CB1 receptor was cloned and elucidated from a cDNA library of cerebral cortex tissues with studies that same year demonstrating that CB1 receptors in the brain were nearly as prevalent as gammaaminobutyric acid receptors and glutamate receptors. The prevalence of CB1 receptors and their localization in the brain allowed researchers to correlate their expression to the subsequent pharmacological effects. For example, localization and expression of CB1 in the hippocampus and cerebral cortex correlated to memory and cognition effects whereas localization and expression of CB1 in the cerebellum and basal ganglia was correlated to stride or gait effects. Therefore, binding of the CB1 receptors in the brain correlates to the psychoactive effects of cannabis. Although CB1 receptors are prevalent in the brain, they also have been found to be located in the uterus, prostrate, adrenal glands, tonsils, gastrointestinal tract, spleen, and vascular smooth muscle cells. The discovery of the CB2 receptor answered the question of why cannabis was reported to have immunomodulatory effects. The CB2 receptor was discovered three years after the CB1 receptor and was found in a human promyelocytic leukemia cell line. Unlike the well characterized CB1 receptor, CB2 has not been as well as characterized due to numerous conflicting reports about the effects of its expression but one thing is known for sure: the CB2 receptor plays a strong role in immunomodulatory effects with great implication for example, in Alzheimer’s and Huntington’s disease. The CB2 receptor is known as the peripheral receptor for cannabinoids and is primarily found and expressed in immune tissues.

When these cannabinoid receptors are activated, as previously mentioned with regards to CB1, there is a decrease in cAMP levels and there is also modulation of potassium and calcium levels in the cells. When these receptors are stimulated, p38 mitogen activated protein kinases , c-Jun N-terminal kinases, and p42 and p44 MAPKs are activated. The p42/44 MAPKS are also referred to as extracellular signal regulated kinases 1 and 2 and they are involved in transcription regulation, cell differentiation, downstream regulation of genes, and cytokine synthesis regulation.Both cannabinoid receptors utilize the transducing G proteins, G1 and Go, responsible for a wide range of cellular functions such as response to environmental stimuli and responses to hormonal signals. ∆ 9 -THC binding to these receptors causes the opening of potassium channels, inhibition of adenylyl cyclase activity, closing of voltage gated calcium channels, and stimulation of mitogen-activated protein kinases, among other responses. The characterization of these cannabinoid receptors in human signaled to researchers that there are likely some endogenous ligands capable of binding to these receptors. These ligands are part of the endocannabinoid system and include anandamide which was found to produce similar effects as the cannabinoids from Cannabis sativa, O-arachidonoyl ethanolamine, and 2- arachidonoylglycerol , and 2-arachidonoyl glycerol ether. Additionally, reports have been made indicating that cannabinoids have been able to bind to other receptors in the body outside of CB1 and CB2. Cannabinoids have been found to be capable of binding to the transient receptor potential cation channel vanilloid type 1 , 5- hydroxytryptamine -3A ligand-gated ion channel, G-protein-coupled receptor 55 , 5-hydroxytryptamine -3A ligand-gated ion channel, and transient receptor potential cation channel Ankyrin type 1 . Both ∆ 9 -THC and CBD were isolated from the hemp oil plant in the 1940s. Unlike ∆ 9 -THC, CBD has no psychoactive properties and has low affinity to both CB1 and CB2 receptors, whereas ∆9 -THC binds effectively to the CB1 receptor.

Although CBD has low binding affinity to the endocannabinoid receptors, there is some data that indicates that CBD has some beneficial properties in the treatment of seizures and epilepsy, movement disorder, psychosis, anxiety, multiple sclerosis, and Huntington’s disease, once again highlighting the therapeutic potential of cannabinoids. The therapeutic potential of cannabinoids has led to the development of cannabinoidbased medicines . Currently, in the United States, there are 3 licensed CBMs approved by the Food and Drug Administration: nabilone , a synthetic analog of ∆9 -THC, used as an antiemetic, preventing vomiting and nausea caused by cancer medications, dronabinol, synthetic ∆9 -THC , used to treat lack of appetite leading to weight loss in AIDS victims as well as treat nausea and vomiting like nabilone, and a liquid formulation of dronabinol. The FDA has also placed on the fast track a few more CBMs and has also approved investigational drug studies of CBD due to its ability to treat chronic pain and help prevent seizures in childhood epilepsy cases. All in all, the global pharma cannabinoid industry is projected to exceed $102 billion by 2030 indicating a significant interest in the development of CBMs; therefore, methods for the efficient large-scale production of cannabinoids are necessary.In recent years, there has been significant interest from the synthetic biology community to produce cannabinoids using microbial and cell-free strategies because of the flexibilities in engineering the pathway to access rare or unnatural cannabinoids, the challenges associated with chemical synthesis, and the inconsistent and relatively low production of cannabinoids from plants.Owing to the identification of important enzymes in the cannabinoid biosynthesis pathway such as olivetolic acid synthase and olivetolic acid cyclase , which converts hexanoyl-CoA to olivetolic acid, microbial hosts have been utilized to produce cannabinoids.Another key finding was the discovery of the prenyltransferase responsible for prenylating olivetolic acid with geranyl to form cannabigerolic acid . With these key enzymes identified, botanicare trays different groups have attempted to microbial produce cannabinoids and their precursors. Tan et al., employing olivetolic acid synthase and olivetolic acid cyclase from the Cannabis sativa plant in addition to further engineering, were able to produce 80 mg/L of olivetolic acid from E. coli. To date, that is the highest literature recordedamount of olivetolic acid produced in vivo in any wild type or engineered organism. Additionally, Luo et al. were able to express the entire cannabinoid pathway in Saccharomyces cerevisiae and produce 8 mg/L of tetrahydrocannabinolic acid , the direct ∆9 -THC precursor, and 4.3 ug/L of cannabidiolic acid, the direct CBD precursor; this was the first time that ∆9 -THC and CBD were produced by yeast. Furthermore, Valliere et al., utilizing commercial olivetolic acid and the synthetic biochemistry approach of cell-free systems, showed that they were able to produce 1.25 g/L of cannabigerolic acid , by engineering an aromatic prenyltransferase from Streptomyces sp. CL190 to efficiently prenylate olivetolic acid, another important intermediate in the cannabinoid biosynthesis platform. 42 Additionally, Jimbo Ma et al engineered the yeast Yarrowia lipolytica to improve the biosynthesis of olivetolic acid and achieved an 83 fold titer increase giving a final titer of .11 mg/L. 43 E.coli and yeast strains have not been the only microbial organisms utilized to produce olivetolic acid and cannabinoids. Two groups have demonstrated that amoeba can also be engineered for the production of olivetolic acid. Reimer et al. engineered Dictyostelium discoideum, due to its ability to produce polyketides and terpenoids, to ultimately produce olivetolic acid. By fusing the OLS to the C-terminal of the D. discoideum StII gene , they were able to engineer an amoeba/plant hybrid gene for the production of olivetolic acid. The group utilized the StII gene, an enzyme consisting of a type III PKS and FAS and swapped the type III PKS domain with OLS and overexpressed this fused hybrid gene to produce olivetolic acid.

Kufs et al also utilized Dictyostelium discoideum to produce olivetolic acid , developing a scaled approach in bioreactors able to achieve a titer of 4.8 µg/L. Although novel with the use of Dictoystelium discoideum to produce this cannabinoid intermediate, such a result underscores the need to increase production of olivetolic acid. To date, these approaches for the production of olivetolic acid and cannabinoids all rely on the plant pathway with the enzymes olivetolic acid synthase and olivetolic acid cyclase catalyzing the formation of olivetolic acid from hexanoic acid and have heavy intellectual property surrounding them so a novel way of producing olivetolic acid is preferred. We were able to discover a novel pathway to olivetolic acid by focusing on fungal natural product bio-synthetic pathways, a vast field with promising capabilities. Natural products from fungi have demonstrated great therapeutic and agricultural potential with two of the currently used anti-fungal drugs being the natural products echinocandins and amphotericin. Echinocandins, referred to as the “penicillin of antifungals”, and amphotericin are also included in the World Health Organization’s List of Essential Medicines, showcasing the powerful potential of fungal natural products. Natural products, also referred to as secondary metabolites, are most times not essential for the organism’s life but do still have important roles such as acting as metal-transporting agents, symbiosis facilitators, sexual and differentiation effectors, and metal-transporting agents, among other functions. Fungi produce a vast variety of natural products that can be classified as terpenes, polyketides, sugars, and alkaloids. There are 100,000 known fungal species, although upwards of one million fungal species are expected to be in existence. Of the known 100,000 fungal species, only a fraction of their natural products and bio-synthetic pathways have been elucidated. Therefore, there is great potential to search for more fungal natural products with interesting bio-activities, underscoring the importance of genome mining. These bio-synthetic pathways encode secondary metabolite genes that produce natural compounds. Genome mining has been used to search for bio-synthetic pathways for known products as well as undiscovered bio-synthetic pathways that can produce novel natural products. Genome mining answers the question of how natural products are formed. It describes the utilization of genomic information to search for bio-synthetic gene clusters responsible for producing natural products. Over the years many different methods of genome mining have been developed. Among them include classical genome mining, comparative genome mining, phylogeny-based genome mining, and resistance gene/target directed genome mining. Classical genome mining involves the search for genes involved in a bio-synthetic pathway. Typically, the process consists of querying for a desired gene across many genomes and then querying for that gene in the context of a bio-synthetic gene cluster. NCBI Basic Local Alignment Search Tool is widely utilized for classical genome mining. Comparative genome mining involves comparing multiple genomes in different organisms to identify similar clusters. It differs from classical genome mining in that instead of searching for single genes, one is searching for partial or whole gene clusters across various genomes. Gene clusters in a genome are then prioritized based on their homology to other clusters in other organisms’ genomes.

The models that have been used in previous studies are simple steady state models for the fuel cell and liquid desiccant systems. In this study, I investigate the integration of rack and row level fuel cell powered servers with Liquid Desiccant Dehumidifier technology that can be dynamically dispatched to produce electricity and cooling in various amounts to meet power and air conditioning demands of data centers. In addition, the storage capacity to meet the demand of data center for the entire year is evaluated. The objectives of this phase of the study focus on theoretically evaluating the integrated system concept and to assess the achievable air conditioning from SOFC waste heat. To explore the feasibility of thermally integrating SOFC with LDD, a spatially resolved physical model developed in MATLAB is used to simulate the operating characteristics of this SOFC system. A corresponding physical model is developed to simulate the liquid desiccant air conditioner for dehumidification. This study considers SOFC systems capable of powering single server rack and row of servers and the operation of an LDD for cooling and dehumidification of that same configuration. The small-scale LDD operation is based on distributed waste heat from each individual SOFC at the rack level. The analysis will indicate whether waste-heat based cooling and dehumidification can power the servers and maintaining server operating temperatures and humidity in the safe range for different weather conditions.A spatially and temporally resolved fuel cell stack model has been developed based on the developed model in NFCRC using the MATLAB software. The model is developed for one unit cell that describes the response of the entire stack. 51 unitary anode-supported planar rectangular cells are assembled into one-unit SOFC stack.

Figure 18 presents one crossflow cell geometry with its components as well as a depiction of two repeated cells in series. The planar rectangular cells are flexible, compact,how to cure cannabis easy to produce, and have lower manufacturing cost compared to tubular cells. The unit cell consists of five layers i.e., cathode flow, anode flow, Positive electrode, Electrolyte, Negative electrode , fuel bipolar plate and air bipolar plate. The spatially and temporally resolved model uses different states to calculate parameters at different nodes of a cell. These nodal states are calculated at each time step among set of points in the time vector using the MATLAB Ordinary Differential Equation solver. These nodal states are composed of nodal temperatures of fuel and air separator plates , temperature of fuel and air flows, nodal temperature of the PEN, nodal concentration of different species in both cathode and anode flow sides , nodal current and cathode/anode flow inlet pressure. The evaluated parameters based on the mentioned states are nodal Nernst voltage, nodal voltage losses, nodal operating voltage , nodal molar flow rate of ions crossing the PEN, nodal heat generation in the PEN, nodal heat and mass transfers and many other parameters. A two-dimensional multi-layer approach is accomplished for both the electrochemical modeling and heat transfer modeling resulting in a quasi-3-D representation of an electrochemical cell. As an assumption, instantaneous electrochemical reactions are assumed, due to relatively fast electrochemical reactions compared to cell and stack thermal response dynamics. Using Faraday’s law, the rate of electrochemical reactions is directly proportional to the cell current. Furthermore, it is considered that electrical current flows only in one direction from one electrode to the other one along the PEN, as the electrical potential is assumed constant on the electrode surfaces.

In the developed model, gases behave as ideal gas due to high operating temperature of the SOFC. In this study a 5×5 spatial resolution is accomplished for all 5 layers . The solid oxide fuel cell has an active surface area of 139.24cm2 . Fuel cell module material properties are presented in Table 2 and geometry parameters are presented in Table 3. Figure 19 depicts schematically the geometric parameters of the fuel cell.The developed model takes into account the conservation of energy within each node and energy transfer between control volumes to calculate the nodal temperature changes during dynamic operating conditions for all 5 layers which are fuel plate, air plate, cathode flow, anode flow and the PEN. Nodal energy balance equation for the cathode/anode stream includes convective heat transferred to the solid walls , enthalpy flux due to the electrochemical reaction, mass transfer of oxygen ions from/to the PEN, and inlet/outlet enthalpy flux of the bulk flow from/to adjacent nodes. It is assumed that temperature in each control volume decreases/increases linearly along the control volume of each node . A nodal energy balance for the PEN includes convective heat transferred to the PEN from streams, conductive heat transferred to the PEN from bipolar plate, conductive heat transferred to adjacent nodes in PEN and heat generation due to the electrochemical reactions and electrical resistances. Radiation heat transfer between PEN and bipolar plate is negligible. A nodal energy balance equation for the bipolar plate includes convective heat transferred to bipolar plate from streams, conductive heat transferred to bipolar plate from PEN and conductive heat transferred to adjacent nodes in bipolar plate. Adiabatic boundary condition is considered for all the nodes located at the beginning and at the end of the cell on PEN and bipolar plate, as the cell height is relatively thin compared to its length. Also, it is considered that the stack is well insulated such that heat loss to the environment is negligible. The nodal energy balance equation for cathode and anode flows, PEN and bipolar plate are presented below.

Note that all nodal specific heat capacities are calculated at the node temperature. Also, for each cathode/anode node, inlet flow enthalpy is the outlet flow enthalpy of the previous node in the flow direction as follows. As flows move from inlets to the cathode and anode outlets, the composition of fuel in anode side and the composition of oxygen/nitrogen at cathode side will be changed. The developed model takes into account the conservation of mass within each node and mass transfer between control volumes to calculate the nodal species’ concentration’s changes . Mass balance in cathode/anode flow side includes inlet/outlet molar flow rates from/to adjacent nodes and variation due to the existing electrochemical reaction. The nodal dynamic mass balance equations for steam, hydrogen, carbon monoxide, carbon dioxide, methane, oxygen and nitrogen species are presented below. Note that for each node, the inlet molar flow rate is the outlet molar flow rate of the previous node in the flow direction as follows. Figure 20 to Figure 24 show the spatial distribution of temperature, Nernst voltage, losses, voltage, and current density in a unit SOFC. The temperature changes between 1000K- 1040K. The temperature has its lowest amount at the corner close to the cathode and anode streams inlet where the air which is cooling stream has its lowest temperature and the reformation which is endothermic is still occurring at the beginning of the cell. Temperature has the highest amount at the corner close to the cathode and anode streams outlet where air has its highest temperature and electrochemical reaction which is dominant at the end of cell generates heat due to its exothermicity. The Nernst voltages are among 0.9V-0.98V. The Nernst voltage is highest at the corner close to the cathode and anode inlet streams where both fuel and oxidant partial pressures are high leading to high thermodynamic potential with the lowest Nernst potentials realized at the corner close to the cathode and anode streams outlet. The lowest amount of losses is 0.065V captured where the temperature is highest. High temperature increases the thermal energy available in the system,trimming cannabis generally resulting in the fact that all the particles in the system now move and vibrate with increased intensity. This higher level of thermal activity will possess sufficient energy to overcome activation polarization and most importantly increases electrolyte ionic conductivity, which decreases the ohmic loss. Figure 22 shows the spatial distribution of cell operating voltage which is uniform along the cell as expected due to the equipotential surface that is established by the good electronic conductivity of the electrodes and bipolar plates. The current density along the direction of fuel flow decreases as the fuel gets consumed and has less potential to produce current.The SOFC system model contains a fuel cell stack, an anode off-gas oxidizer, air preheating heat exchangers, recirculate valve, mixer, blower, and reformer. A system diagram is provided in Figure 25. The oxidizer outlet preheats air and fuel first, and then, the leftover heat from the SOFC exhaust is recovered for regenerating desiccant liquid in the LDD system.Cell temperature control is one of the key issues involved in the dynamic operation of high temperature SOFC systems. In this study, the stack is thermally managed by manipulating one actuator, which controls the blower power. A variable speed blower enables control of blower dynamics that consider the inertia of the blower as described above. Increasing the blower power ultimately increases the blower speed, which in-turn increases the air flow rate introduced to the stack. The air flow rate has two functions in the proposed system, i.e., providing the oxygen for the electrochemical reactions and providing cooling or heating to the stack. The temperature control strategy consists of two parts. Stack temperature gradient is controlled with blower power. If the temperature gradient goes higher than the set point, the blower power increases which increases the air flow rate which cools the stack. To control stack average temperature, the valve position changes to increase or decrease the air flow to oxidizer which affects the cell inlet air flow temperature. To decrease the cell average temperature, the valve opens more, to decrease the hot flow temperature in the air heat exchanger. In this study, the controller set-point temperature difference constraint is 50K. Also, the inlet temperature of both anode and cathode temperature are controlled to the set point of 1023K. The system model is based on a commercially available SOFC systems called BlueGEN. BlueGEN CHP unit, originally manufactured by Ceramic Fuel Cells Ltd . BlueGEN is a commercially available SOFC CHP system, now built and sold by SOLIDpower, designed for small- to medium-scale building applications. Operating on natural gas, the unit can produce power modulated from 500We to 2kWe ; however, it achieves its highest net electrical efficiency of 60% at a 1.5kWe output. The BlueGEN SOFC unit consists of 51 planar type Yttria Stabilized Zirconia electrolyte-supported cell layer sets and operates at around 750℃. Hydrogen is produced from natural gas by external and internal steam reforming. The polarization curve data from the BlueGEN SOFC system tested at the NFCRC and the developed model results are presented in Figure 26. Note that the V-j curve obtained from the test covers only the operating envelope of the SOFC system. The steady state performance of the system was calculated under the standard operating conditions of the BlueGEN at 85% fuel utilization and cathode outlet temperature of 750˚C. The SOFC system is controlled to keep the stack temperature difference at 50˚C. The SOFC parameter and standard operating conditions are presented in Table 4. The electrical efficiency of the stack is over 61% under standard operating conditions. The steady state performance parameters of the model for 1.5kW SOFC system and experiment values are compared in Table 5. Note that experimental values well match those of the model. Note also that the exhaust gas temperature and flow rate were not measured during the experiment. The particular SOFC system that was evaluated in this study was designed for CHP, and thus produces more heat than would be used for preheating the SOFC inlet air and fuel. The heat produced from the SOFC system in the current case is then used for producing hot water for LiCl regeneration purposes. This model indicates that a nominal 1.5kW system would produce 0.0104kg/s of exhaust gas and that the temperature of the exhaust would be 100˚C. Electricity demand for a single residential unit is used as a desired demand applied to the spatially resolved dynamic SOFC model. SOFC model results that generated output power follows the desired demand quite closely except for very short periods of high ramp rate operating conditions. Figure 28 shows the electrical efficiency of the system during the dynamic operation. The average efficiency during dynamic operation is 71%. The high efficiency is due to high fuel utilization and part load operation of the system which also lead to a lower exhaust temperature.

The vast majority of adult smokers began smoking during adolescence. Each day, approximately 2,000 youth smoke their first cigarette and over 300 become daily smokers. Previous studies have shown that the younger people are when they begin using tobacco products, the more likely they are to develop nicotine dependence and other substance use disorders. It has also been shown that adolescents can become nicot inedependent very quickly, even after occasional intermittent use. Furthermore, a meta analysis of longitudinal studies confirms that ENDS use is associated with an increased likelihood of future cigarette smoking. Although the percentage of teen ENDS use had been consistently increasing over the years, the percentage of high school seniors vaping nicotine actually decreased from 34.5% in 2020 to 26.6% in 2021 during the COVID-19 pandemic in the US. This decrease in adolescent drug use was surprisingly consistent across many substances, including alcohol and opioids. This phenomenon could possibly be attributed to limited access to drugs during government ‘stay at home’ orders, reduced in person peer pressure, increased messaging of the harmful effects of drug products targeted at youth, and/or potential survey response biases as adolescents are likely responding in the presence of a parent or guardian at home. According to the World Health Organization, cannabis, also known as weed or marijuana, is the most abused illicit drug. As of 2021,air racking only eight countries have at least partially legalized cannabis for recreational use, but approximately 200 million people report using cannabis worldwide.

Cannabis is derived from the Cannabis plant which contain over 100 compounds called phytocannabinoids, with Δ9-tetrahydrocannabinol and cannabidiol being the most well-characterized. Cannabis use may induce sensations of euphoria, altered sense of time, distorted sensory/body perception, and mood changes. THC is the main psychoactive component in cannabis and can result in feeling ‘high’, along with increased anxiety and paranoia, altered perception, impaired working memory, slower movements, and cognitive deficits, depending on the dose. Among youth, smoking cannabis appears to be becoming somewhat less prevalent, but this downward trend has been paralleled by a general upward trend in consumption of edibles and use of THC vapes. Of concern, high frequency adolescent cannabis use has been linked to deficits in attention, learning, and memory, as well as mental health issues including increased depression, anxiety, suicidal ideation and schizophrenia. Similar to nicotine, however, in correlation to the COVID-19 pandemic, there was a decrease in the percentage of adolescents who report using cannabis in 2021. Interestingly, there is evidence that low doses of THC in older mice may help restore cognitive function. Similarly, in older humans , short-term low-dose cannabis consumption does not seem to have adverse effects on cognition and can aid in pain management. THC can be also used to help counteract weight loss in HIV/AIDS patients and to alleviate nausea for patients undergoing chemotherapy. Synthetic cannabinoids are those created in a laboratory and fall under the drug classification ‘New Psychoactive Drugs’. Examples include WIN 55,212-2 and CP- 55,940 – which are compounds that can be used in the street drug termed ‘spice’.

Some forms of spice are created by spraying synthetic cannabinoids onto shredded plant material, with users smoking the resulting combination like a joint. However, it has more intense psychoactive and physiological effects than THC. Synthetic cannabinoids were initially created for pharmaceutical research and were not intended for human consumption. However, because synthetic cannabinoids have similar physiological effects as THC and were not federally illegal, they have been sold in US and European street drug markets since the early 2000s. Further, these synthetic cannabinoids are not typically tested for during routine drug screens and often remain undetected when law enforcement or hospitalization is involved. Synthetic cannabinoids induce many more intense effects than merely altering one’s mood and perceptions, such as tachycardia, psychosis, hallucinations, respiratory distress, and in some cases, death. In 2012, 26 different formulations of spice were banned with the Synthetic Drug Abuse Prevention Act in the US, classifying them as Schedule I drugs. Nevertheless, as different versions of these synthetic compounds become illegal, new modified versions created by underground chemists consistently reappear in the market. They have not yet been classified as illegal, but still can be quite harmful. Drug co-use is also of concern, given the potential synergistic effects on the user. For instance, a study in youth aged 12-17 found that those who used cannabis were more likely to use nicotine products at the same time, or initiate nicotine consumption within one year. Daily cannabis use has also been associated with co-use of opiates, cocaine, and/or inhalants, and approximately half of young cannabis users report simultaneously consuming alcohol and cannabis. Moreover, young adults co-using tobacco and cannabis were more likely to use nicotine ENDS, cocaine, and greater amounts of cannabis than those that just consumed cannabis alone. In older adults, using cannabis is associated with an increased likelihood of being diagnosed with a substance use disorder for either nicotine, alcohol, or cannabis.

Thus, the drug co-use condition represents a significant health concern among various age groups. When inhaled, nicotine readily enters the bloodstream through the alveoli in the lungs and becomes absorbed into the brain within seconds. Nicotine selectively binds to nicotinic acetylcholine receptors , which are pentameric ligand-gated ion channels located on either the presynaptic or postsynaptic membrane. The nAChR subtype may be heteromeric or homomeric, containing a combination of a and b subunits or containing all the same subunit, respectively. The a subunits present in nine different types, a2 – a10; whereas the b subunits present in three different types, b2 – b4. Various combinations of these subunits result in diverse effects on the pharmacokinetics of the receptor with ligand binding. The a7, a4 and b2 subunits are the most prevalent in the central nervous system. The homomeric a7 nAChR has a relatively lower affinity for nicotine and is important for modulating inflammation. The a4 and b2 subunits combine to form a functional heteromeric receptor with a high affinity for nicotine; the a4b2 nAChR is involved in mediating nicotine’s reinforcing and rewarding properties through receptor localization in the mesolimbic circuit. Further, receptors containing the a5, a3 and b4 subunits have been shown to modulate aversive signaling that limits nicotine intake and aspects of the withdrawal syndrome via receptor localization in the habenulo-interpeduncular circuit. Like nicotine, when cannabis smoke is inhaled, active phytocannabinoids pass from the lungs into the bloodstream and are carried throughout the brain and body. THC acts on the cannabinoid 1 receptor and cannabinoid 2 receptor. In the brain, CB1Rs are mainly localized in neurons and astrocytes, whereas CB2Rs are primarily found on immune cells. In humans and rodents, CB1Rs are highly expressed on neurons in the neocortex, hippocampus, amygdala, cerebellum, and basal ganglia. Since endogenous cannabinoids engage in retrograde signaling from the cell body to the presynaptic axon terminal, activation of CB1Rs results in inhibition of presynaptic neurotransmitter release. CB1Rs are also located on postsynaptic membranes and astrocytes. Of note, THC is a partial agonist of the CB1Rs and CB2Rs, whereas synthetic cannabinoids are typically full agonists. Cannabinoids can also act on other receptors, including GPR55 and TRPV1, although the functional role of these receptors is lesser known. When individuals co-use nicotine and cannabinoids, one would expect activity at both the nAChRs and CB1Rs. Both of these receptor types exhibit overlapping expression patterns in drug addiction-associated brain regions, such as the prefrontal cortex, nucleus accumbens ,drying weed ventral tegmental area , and amygdala. In particular, the mesolimbic dopamine pathway, a circuit from the VTA to the NAc, controls reward processing and the reinforcement of natural rewards and most drugs of abuse. Mechanistically, as nicotine and cannabinoids bind to nAChRs and CB1Rs in the VTA, they increase the firing rate of dopamine neurons and trigger the release of dopamine to the NAc which subsequently reinforces the drug-taking behavior. Nicotine acts on the a4b2-containing nAChRs to mediate dopamine signaling via their locations on both dopaminergic and GABAergic neurons in the VTA and neuron terminals in the NAc . Due to a lack of CB1R expression on VTA dopamine neurons, cannabinoids likely act on this circuit indirectly. Through retrograde mechanisms, cannabinoid binding to presynaptic CB1Rs expressed on GABAergic presynaptic terminals decrease GABAergic inhibition, thereby increasing dopamine release in the NAc.

In addition, it is important to acknowledge the involvement of other neurotransmitters, such acetylcholine, glutamate, and serotonin in consideration of the intricate complexity of the projections among brain regions. Thus, nicotinic and cannabinoid signaling within reward-related brain regions may lead to interactions among signaling mechanisms that modulate various aspects of drug-taking behaviors.Approximately 20-30% of people who experiment with cigarettes will meet criteria for nicotine use disorder during their lifetime. Around 30% of cannabis users are anticipated to develop some degree of cannabis use disorder. Individuals who begin using cannabis prior to 18 years of age have a four to seven times increased likelihood of developing the use disorder. Moreover, as noted above, the co-use of both substances is quite frequent. Around 60% of cigarette smokers reported ever using cannabis, and 90% of cannabis users reported ever smoking cigarettes in their lifetime. While individual drug use has its own potential to develop into a substance use disorder, co-users of both substances are at an increased risk of developing both nicotine dependence and cannabis dependence. This increased risk is of further concern because adults with cooccurring cannabis use disorder and nicotine dependence are more likely to have bipolar, anxiety, and personality disorders than those with only nicotine dependence. Among youth and young adults, the concern of these co-occurring substance use disorders is also prevalent. Teens who vape nicotine or smoke hookah are four times more likely to start smoking cannabis within two years, and current adolescent tobacco smokers who frequently use cannabis are more likely to report nicotine dependence. Likewise, adolescents who use cannabis are more likely to become daily cigarette smokers and develop nicotine dependence. Additionally, a longitudinal study looking at the trajectories of nicotine and cannabis vaping from adolescence into early adulthood revealed that those who frequently vape were also very likely to be users of both substances. Adolescent and young adult cannabis and tobacco cigarette co-users exhibit increased cannabis use disorder symptoms including continued use despite negative consequences, developing a tolerance, and inability to reduce cannabis use, in comparison to those who only use cannabis. Moreover, young adults who co-use cannabis and nicotine together report consuming more cannabis and nicotine in the past year than those who only use one of the drugs. In sum, across age ranges, nicotine and cannabis co-use conditions increase the risk of individual drug use and the development of substance use disorders. Furthermore, sex differences have emerged with the prevalence and patterns of substance use disorders in the population. Adult men are more likely to initiate drug use, but adult women develop substance use disorders more rapidly. Interestingly, during adolescence, drug use is initiated at similar rates between sexes, but boys appear to escalate their drug use faster than girls. According to a recent report, men are more likely to smoke cigarettes, vape nicotine, use smokeless tobacco, and be daily cannabis users. Women with a history of cannabis use are four times more likely to become regular cigarette smokers and almost three times as likely to develop nicotine dependence. However, women report experiencing more intense nicotine withdrawal symptoms.While the above findings suggest an important role of age or sex in nicotine and cannabis co-use, many individual, societal, and familial factors also influence drug taking behaviors in humans, and as such, animal models are important to delineate the precise effects of such factors on drug taking behaviors. Findings from rodent models have established that cannabinoids and nicotine exert unique effects on drug-related behaviors based on the duration and timing of exposure, in addition to the animal’s sex. Interestingly, adolescent male and female rats will self administer greater amounts of nicotine than adults, and nicotine exposure during adolescence can lead to increased self-administration of other drugs of abuse, including alcohol, methamphetamine, and cocaine. Drug exposure during adolescence can also induce long-lasting effects on the animal into adulthood. Adolescent male rats exposed to THC were found to self-administer higher levels of the synthetic cannabinoid WIN or heroin in adulthood.

Taylor and colleagues report that between 34% and 61% of adult males used cannabis at least once in the year 2000, including between 17% and 63% of violent criminal offenders and between 34% and 74% of offenders in property crimes . These findings, while far from definitive, do not suggest a clear and direct link between cannabis use and crime. Many arrestees who test positive for cannabis also test positive for alcohol or other drugs . This makes it difficult for researchers to infer the extent to which certain behaviors can be attributed to specific substances. Furthermore, because of the way that the human body metabolizes marijuana, positive tests for marijuana only indicate use within the past month, and therefore do not necessarily signify that a particular crime was committed while under the drug’s influence . In fact, in some studies cannabis has been shown to inhibit aggression and violence . Thus, although it may appear that marijuana use is associated with criminal arrests, there are many reasons to be skeptical about that correlation. The drug-crime link, in psychopharmacological terms, is stronger for alcohol than it is for illicit drugs . “In the case of marijuana,” Resignato concludes, “the assumed psychopharmacological connection between use and violent crime has been almost completely disproved in the research” . Research on economic compulsive drug related crime tends to focus on drugs with higher addictive potentials than cannabis,how to cure cannabis most notably heroin and cocaine .

Perhaps the most compelling mechanism through which drugs might lead to crime is through the occurrence of systemic violence . The violence associated with markets for illegal drugs is at least indirectly caused by drug prohibition and enforcement, “a relationship that may be underestimated by many policy officials” . It is plausible that this would apply to retail vendors of medical marijuana—although arguably less so than the illicit dealers of the drug, who are displaced by legal or quasi-legal cannabis businesses . I conclude from the available literature that cannabis has no significant relationship with crime via its psychopharmacological effects or its capacity to bring about economic-compulsive criminal behavior in its users . I argue that the only plausible link between cannabis and crime lies in the extent to which the drug attracts “systemic violence”, due to its illicit status and high market value. The following chapter presents a conceptual model that can be used to test empirically whether or not medical cannabis dispensaries are associated with crime in city neighborhoods. In the remaining sections of this chapter, I discuss potential linkages between MCDs and crime and present two theoretical approaches to urban criminology: routine activities theory and social disorganization theory.Do medical cannabis dispensaries increase crime? It depends who you ask. Empirical evidence is scant and is almost entirely overshadowed by anecdotal reports. For example, a 2009 report compiled by the Coalition for a Drug-Free California alleges that “marijuana escalates the level of mental illness, crime and all related problems” . But the report offers little empirical evidence in support of these claims.

The California Police Chiefs Association —an organization representing California’s municipal law enforcement agencies—has also been a vocal critic of dispensaries. Its 2009 White Paper on Medical Marijuana Dispensaries concludes that the presence of dispensaries “poses a clear violation of federal and state law; they invite more crime; and they compromise the health and welfare of law-abiding citizens” . The underlying evidence is entirely anecdotal: the CPCA presents no statistical analysis supporting the conclusion that medical cannabis dispensaries cause crime at a higher rate than any other business. The CPCA’s assertion that dispensaries increase crime stands in conflict with an analysis conducted by the Police Department of Denver, Colorado, which in 2010 projected that dispensaries attract crime at a similar rate as pharmacies and a lower rate than banks or liquor stores.A recent study conducted by researchers at the UCLA Luskin School of Public Affairs examined crime data in 95 census tracts in Sacramento . No clear link was found between the density of marijuana dispensaries and the rate of violent or property crime, suggesting that dispensaries do not increase crime and that some security steps taken by dispensaries may actually reduce local crime. Despite the insistence of organizations such as the Coalition for a Drug-Free California and the California Police Chiefs Association, there does not appear to be an obvious link between medical cannabis dispensaries and criminal behavior in surrounding areas. Limited reports from California and Colorado suggest a neutral or negative correlation between MCDs and crime, but more empirical research is needed to substantiate a generalized claim to that effect. This study aims to extend current knowledge about the relationship between MCDs and crime by conducting a spatial analysis of crime data from San Francisco.Thus routine activities theory attempts to explain crime by looking at the ecological circumstances in which it occurs, while other criminological approaches tend to focus on the characteristics of criminal offenders.

Both are useful to the present study, which analyzes whether crime is related to MCD presence in city neighborhoods but is also interested in controlling for demographic characteristics related to crime across those neighborhoods. Regarding the number and concentration of motivated offenders in an area, MCDs could have a positive, negative, or neutral effect on crime. If cannabis use directly or indirectly leads to criminal behavior, then MCDs might increase crime in a neighborhood by concentrating a large number of cannabis users in one place. It is also possible that MCDs could reduce criminal behavior in a neighborhood by displacing illicit dealers of the drug. And it is of course possible that MCDs have no effect whatsoever on the number or concentration of motivated criminal offenders in an area. From a routine activities perspective, the most likely link between MCDs and crime is probably in the extent to which MCDs present suitable targets for crime. Components of target suitability include value, visibility, and accessibility . With respect to value, MCDs tend to possess large amounts of cash and cannabis, which are high-value targets for theft. As Felson notes, “lions look for deer near their watering hole” .Patients, vendors, and especially staff are potential targets of crime: “Employees of the dispensaries can be at-risk for violent crimes, such as robbery or assault, as they are gatekeepers to both the marijuana products and the cash at the site” . In California it is estimated that MCDs conduct roughly $1.9 billion in gross sales annually.Beyond the fairly obvious point that criminals like to steal things of value—which is just as true for MCDs as it is for banks, pharmacies, and other retail businesses—there are other valid reasons to believe that criminals would perceive MCDs as suitable targets. Most MCDs are open systems that are constantly seeking new members through advertising and other means. Thus they present, in many cases, highly visible targets for crime. Although few empirical studies have examined the relationship between MCDs and crime, existing research has explored the criminogenic effects of other land uses. A study by Roncek and Lobosco reported that high schools elevate local crime rates, probably through the concentration of likely offenders . Brantingham and Brantingham found an inverse relationship between crime rates and distance from the nearest McDonald’s. As Felson explains, the restaurants themselves may be safe—but they attract customers who are “in prime offending and victim ages,indoor grow methods producing high crime risk for nearby properties” . There is strong evidence to suggest that bars and other establishments that serve alcohol meet the criteria for target suitability .

This provides an interesting point of comparison for medical cannabis dispensaries and other retail businesses. A criminal might reasonably perceive that a patron entering or leaving such businesses would be in possession of some amount of cash and therefore be an attractive target for theft . The establishments themselves operate largely in cash and are accessible to the public, in many cases advertising themselves in an effort to increase their accessibility. The perception that these would be easy targets is compounded by the perception that patrons and staff might be medically ill or intoxicated . Thus, as Roncek and Maier conclude in the case of bars, “the patrons and the businesses have all the components of target suitability” . The degree to which MCDs present suitable targets for crime—a notion that is subject to considerable debate—is largely dependent on the effectiveness of MCD security measures. Here we see an inverse relationship between target suitability and the third core component of routine activities theory, capable guardianship. As guardianship increases, a potential target becomes less and less accessible to predatory crime. By definition, members of MCDs suffer from medical conditions that qualify for medical cannabis treatment. This could create a perception of vulnerability that is attractive to criminals seeking to obtain cannabis and/or cash without a fight. It is also possible that criminals perceive MCD operators to be less likely to report crime due to their precarious legal existence and a resulting incentive not to attract attention from law enforcement. Thus it is plausible that the presence of MCDs might increase crime in a neighborhood by presenting, in the terms of routine activities theory, accessible targets for crime. But it is also possible that criminals do not perceive MCDs to be suitable targets for crime, or that they consider them no more attractive than other retail businesses with high-value inventories and large amounts of cash. Furthermore, it is possible that—compared to other potential targets in the neighborhood—criminals actually perceive MCDs to be unsuitable targets. This largely depends on the effectiveness of guardianship in a neighborhood to prevent crime. Compared to other businesses, MCDs might represent superior guardianship in a neighborhood and thereby serve to reduce the frequency of crime. Security protocols are one way in which an MCD might do this.Many dispensaries implement security protocols—in order to comply with local mandates or simply to protect their patients, vendors, and staff—that might serve to increase the level of guardianship in a community and thereby reduce crime in a neighborhood, as routine activities theory suggests. These range from I.D. verification to external lighting and alarms; some dispensaries even conduct external patrols in order to demonstrate guardianship over their property and improve the surrounding community. 23 Unlike pharmacies and liquor stores, MCDs verify their customers’ qualifications before they can enter the premises and make a purchase. It is plausible that these and other security protocols have a deterring effect on crime. If these outweigh the criminogenic aspects of MCDs—that is, the potential for MCDs to attract likely offenders to suitable targets—then it is likely that the net effect of MCDs on crime is neutral. I examine this possibility in the present study. Social disorganization researchers have identified several neighborhood characteristics that are positively associated with crime. This study borrows from their work in selecting control variables to address the fact that social and demographic characteristics vary widely across city neighborhoods, with significant implications for social and economic activity, public health and safety, and other outcomes. Whereas routine activities theory focuses on the circumstances in which crime occurs, social disorganization theorists point to the social and demographic characteristics of surrounding communities in explaining outcomes of high crime and violence. Social disorganization has been concisely summarized by Sampson and Groves: “The general hypothesis is that low economic status, ethnic heterogeneity, residential mobility, and family disruption lead to community social disorganization, which, in turn, increases crime and delinquency rates” . This approach is summarized in Figure 2.2. According to this theory, crime is associated with what Sampson and Groves call “exogenous sources of social disorganization” . This study controls for three such categories of neighborhood characteristics: economic deprivation, residential instability, and family disruption. Low socioeconomic status translates to lower levels of formal and informal social control. Higher rates of family disruption, i.e. a higher proportion of single-parent versus married-couple households, results in decreased parental supervision and a relative lack of guardianship . These conceptual links to crime, expounded originally by Shaw and McKay , have received more nuanced theoretical attention from Kornhauser , Sampson and Groves , and Veysey and Messner . The explanatory power of their empirical correlates is discussed in the following section, which describes the measures to be analyzed in the present study.

The overwhelming majority of this sample reported using cannabis to treat multiple health conditions. This result is unsurprising given increased cultural attention to the wide range of conditions for which cannabinoids may be therapeutic. However, panacea-like use may prove problematic. The same cannabinoid preparation that might be helpful for one condition [e.g., high THC and neuropathic pain ] could exacerbate symptoms of another [e.g., high THC and anxiety ]. Indeed, the current study found limited variability in choice of cannabinoid content; the overwhelming majority of veterans preferred cannabis with high THC relative to CBD. This quantity and frequency of use are consistent with other populations of medicinal cannabis users. For example, Bonn-Miller et al. documented average rates of cannabis use of 2–3 times per day, consuming between 6 and 12 grams of cannabis per week, in a general sample of medicinal cannabis users. The sample’s strong preference for frequent use of high THC-containing cannabis raises concerns for long-term outcomes of self-medication. While THC-rich cannabis may provide acute relief for symptoms often experienced by veterans [e.g., nightmares ],metal greenhouse benches it is also more likely to cause intoxication and associated with increased risk of developing symptoms of CUD relative to CBD-rich cannabis . Indeed, CBD may reduce anxiety , depression , and inflammation , as well as improve cognition and extinction learning .

This dichotomy could explain why veterans who use cannabis to self-treat mental health symptoms, like PTSD, often show worse long-term outcomes and report higher rates of problematic use , despite preclinical and human experimental evidence of potential therapeutic utility of certain cannabinoids. Likewise, while the majority of participants preferred using an inhalation method for administration of cannabinoids, a nontrivial number reported that they prefer highly concentrated “dabs” , which is associated with greater risk of tolerance and withdrawal . Moreover, those who typically chose an inhalation method of administration reported a strong preference for smoking cannabis over vaporization. Smoking cannabis carries significantly greater health risks compared to vaporization . Perhaps more troubling is the finding that over 40% of the sample either didn’t know or didn’t care what type of cannabis they were using. This may be a function of the lack of sufficient science and education within this space. Given the historic barriers to conducting well-controlled trials with cannabinoids, even savvy patients have limited information to inform their choice of cannabinoid product, which might lead patients to choose at random. Moreover, while it is likely that many providers are rightly hesitant to make recommendations without the results of well-controlled clinical trials, there is also an enormous gap in the knowledge and training of those who interface with patients in terms of best practices given the current evidence base .

The primary limitation of the current study is that it assessed cannabis use and related behaviors entirely using self-report with no ability to verify cannabinoid constituents in products typically used. This is a major limitation of the current study because there is large variability in cannabinoid content within “strains” . Oversight of non-FDA approved cannabinoid products is lacking, and recent reports suggest that many of these products are often mislabeled . However, current legal prohibitions made collection and objective testing of participants’ products impossible. While using product names to assess preferred cannabinoid ratio provides only a gross approximation of possible cannabinoid content, the results of the current study offer more information on choice of cannabinoid products among veterans than exist in the literature to date. Substitution behavior was also assessed through self-report and was assessed broadly by asking participants if they had ever substituted cannabis for other substances. Substitution, however, can occur in a multitude of ways. It is unclear whether veterans who endorsed substitution were completely abstaining from the substance that they endorsed substituting cannabis for, or whether they interpreted substitution as reduction of quantity or frequency of use. Likewise, retrospective recall of substance use is often inaccurate . Substitution data collected via self-report might not reflect these veterans’ true behavior. Finally, the current study did not collect data on age of first initiation of use of these other substances. It is unclear whether these participants started using these substances before or after initiation of cannabis use, and whether substitution behaviors co-varied with combat exposure or other military-related experiences.

Despite these limitations, the current study’s findings highlight an ongoing issue among veterans, namely the possible gravitation toward addictive substances that provide acute relief yet potential long-term exacerbation of symptoms . Coupled with the need for improved science in this domain, findings highlight the importance of training providers in the nuances and differential effects of specific cannabinoids, as well as steering patients toward cannabinoid-based products that, while less rewarding in the short term, may be associated with reduced risk and long-term therapeutic gains. Future research might focus on the development of interventions that disseminate information on cannabis and cannabinoids to providers and patients. For example, vaporization of flower cannabis is associated with significantly lower risks of bronchial symptoms compared to combusted cannabis , but a very small proportion of this sample noted that they preferred vaporization to other inhalation methods. This suggests one specific target for possible intervention. The current study also confirms the findings of previous studies that have documented a trend in substitution behavior, where cannabis is substituted for other drugs, which, if associated with reduced harm, could be beneficial for overall health. Future studies might attempt to categorize which specific medications veterans who use medicinal cannabis are substituting cannabinoids for and whether those changes are associated with improvements in functioning. Cannabis sativa L. is a dioecious plant, producing male and female flowers on separate unisexual individuals . Although both male and female plants are capable of producing cannabinoids in equal concentrations , female plants produce greater floral biomass than male plants and thus are exclusively used in commercial marijuana production facilities. Moreover, after pollination, female plants alter their relative investment in phytochemicals by reducing the production of secondary metabolites like cannabinoids, flavonoids, and terpenoids . In the absence of pollen, stigmas on female plants continue to grow and thus produce more surface area on which cannabinoids can be produced . Because of this negative impact of pollination on cannabinoid yield, industrial growers rarely maintain male plants in production facilities, and instead propagate their stock of female plants by vegetative cloning . However, the “mother” plants used to produce clones eventually become non-regenerative and new mother plants are grown from seed, which necessitates pollination . Therefore, careful consideration must be given as to the most effective and efficient ways to collect pollen for controlled crosses while preventing pollen escape into production areas. Cannabis is anemophilous ,rolling greenhouse tables and therefore relies on air movement for pollen transfer from male to female plants, sometimes across long distances . Pollen dispersal mechanisms often reflect pollen ornamentation, as seen in C. sativa’s smooth exine layer, triporate morphology, and low mass—features intended to maximize pollen dispersal distance and chance of successful ovule fertilization . The aerodynamic morphology of C. sativa’s pollen highlights the difficulty associated with controlling its movement, as any airflow following anther dehiscence can result in pollen movement, a frequent issue when studying dispersal in anemophilous species .

It is therefore important to determine the most efficient method of capturing wind borne pollen upon anthesis, in terms of both the number of pollen grains collected and the time spent collecting pollen. Procedures for controlled pollen capture are typically required in crop breeding programs to ensure precise knowledge of paternity so as to breed progeny with preferred traits . For example, standard methods for maize breeding were established in the early 1900s, with an abundance of literature outlining the procedure for controlled crosses . However, because corn is monecious, breeding procedures prioritize avoidance of self-fertilization , with controlled capture of pollen samples as a secondary goal . Studies on controlled pollen capture in other species have developed methods based on species-specific traits, such as the clipping of large anthers in Eucalyptus L’Hér. . Although some literature related to maximizing pollen capture from trees describes methods that may be applied to cannabis , these would require modification based on the scale of collection and organismal size. In addition, most research on determining optimal methods for controlled pollination relates to pollen storage and germination conditions rather than optimizing controlled pollen capture. One of the largest barriers to comparing the efficiency of pollen collection methods is quantifying relative pollen yield. Previous research on pollen production in cannabis, which estimated the number of pollen grains per anther, relied on hemocytometers , a method frequently employed for counting pollen grains . More broadly, light scattering as a method for rapidly estimating particle abundance is well documented , and laser scattering has been used to analyze the physical properties of pollen grains . Relative to direct pollen counting using a hemocytometer, visible light spectroscopy could allow for the rapid quantification of particles in a liquid suspension. Here, we compared several existing methods used to collect pollen in other species, i.e., hand collection , vacuum collection , bag collection , and water collection , and explored their use in cannabis. Notably, we could not find any peer-reviewed publications that directly compared the efficiency of such methods , although many have examined pollen collection using a single methodology . Collecting pollen in large quantities may be of use in commercial crop breeding programs, especially when creating a repository of genetic stock for later use, and as such, we were interested in both the relative yield and efficiency of various methods. Hand collection, while simple in practice, may be inefficient because, in cannabis, it relies on pollen removal from individual flowers, one by one. Comparatively, vacuum collection may be more efficient but could be prone to sample contamination if male plants are not properly isolated from each other. Bag collection, similar to vacuum collection, is efficient, but the plant must be able to hold up bags; in the case of cannabis, male plants are so diminutive, and the flowers are so dispersed on a plant, that this is a difficult endeavor . Bag collection also could result in reduced yield if issues such as static charge of pollen grains are not sufficiently addressed .We used two hemp cultivars of C. sativa , both possessing an expected total tetrahydrocannabinol content of less than 0.01%; we grew CFX-1 in the first trial and CFX-2 in the second trial . Following germination in a two-tier terracotta germination pot , which took three days, we planted the seedlings in SC-10 containers filled with 200 mL of PRO-MIX BX mycorrhizae peat moss growing medium . A week later, we transplanted seedlings into 1-L pots filled with the same growing medium. We applied 250 mL of filtered water twice weekly and applied 250 mL of 0.4% diluted Miracle-Gro once weekly. For four weeks, plants grew under 24-h lighting from high-pressure sodium fixtures . Male floral development was visible in the third week, and we selected early-flowering males for use in our experiments to minimize variability in the number of inflorescences on each plant. We pruned the apical meristems of male plants twice, once in week 3 and once in week 4, to promote increased branching and thus inflorescence growth. After approximately four weeks, we switched plants to 12-h lighting to induce anthesis under visible spectrum LED fixtures .In the first trial, we used three pollen collection methods : hand collection , bag collection, and water collection . In the second trial, we maintained hand collection as a control and tested vacuum collection. To compare the yield and efficiency of collection methods, we imposed each pollen collection treatment on a randomly selected subset of experimental plants, each of which we collected three times during the course of the trial. Cannabis anthers dehisce non-concurrently, and as such we initiated pollen collection when at least 33% of visible male flowers were releasing pollen to ensure there was enough pollen to collect in the context of a breeding program. From each plant, we performed three collections over a seven-day period, where initial sufficient anther dehiscence occurred on day zero, the first collection occurred on day 1, and subsequent collections on days 4 and 7. In our first trial, we attempted to perform a fourth collection on day 10 but found that by this point the plants were no longer producing enough pollen to warrant a fourth collection from that point onward.

Even in the United States, new research is needed that more accurately evaluates real differences across the states in terms of the legal status of possession offences, how these laws are enforced and interpreted by police and prosecutors, how these differences get translated into arrest patterns, and how these differences in laws and their enforcement are perceived by citizens. Only then can we hope to accurately assess the real impact of a policy change on the primary outcomes of interest: consumption and harms. Strides are being made within particular countries to better understand these issues, but much work remains.Cannabis criminalization disproportionately harms minorities. African Americans composed 30% of cannabis arrests while comprising 14% of users between 1990 and 2002 . Despite consuming cannabis at similar rates to Whites in 2018, African Americans were 3.64 times likelier to face arrest for cannabis offenses . Criminal justice system contact negatively affects health , social welfare , and economic outcomes . Holding a criminal record imposes over 44,000 potential consequences . Criminal justice advocates have increasingly advocated for policies decriminalizing or legalizing cannabis possession and sales at the state level to reduce arrests; such policies prevent future harm but fail to assist existing record-holders. As a result, some states have provided general and cannabis-specific criminal record relief for former cannabis offenders . Forty-five states and Washington DC allow some degree of expungement , which is defined as the destruction or sealing of criminal records . Expungement lowers recidivism ,rolling greenhouse benches enhances earnings and employability , and improves people’s ability to obtain work, housing, and education funding .

Expungements are less expensive than work training programs , and improve economic outcomes since holding a criminal record lowers employability and earnings over time . Expungement is relatively underused. A 2020 study found that under 20% of eligible people with a record of conviction in 10 states petitioned for expungement . This low uptake is driven by a lack of awareness, the complexity involved in navigating the expungement process, and regulatory and financial hurdles . In 2016, the American Bar Association found that 13 states required either payment of a fee for expungement, or payment of all existing fines or fees related to their conviction, to qualify for relief . A majority of states’ expungement systems rely on petitions, although some, such as California, have automated expungement. Additionally, some states require that persons first obtain a Certificate of Eligibility to apply for expungement, which often requires its own process and fees. While some states offer expungement programs that address a broad range of offenses, other states offer expungement programs specific to cannabis records. There is limited research assessing how such programs operate in practice or the extent to which record-clearing processes are automated across states . We analyzed expungement statutes in states that decriminalized or legalized cannabis for medical and or recreational use to determine the availability and accessibility of expungement relief. We defined expungement as record sealing or destruction available to former cannabis offenders. We assessed whether states had automated or petition-based processes, as well as waiting periods, using model guidelines created by the nonprofit organization Alliance for Safety and Justice . We also reviewed policies to identify whether states that provide conviction record relief offered general expungement, general drug expungement, or cannabis-specific expungement programs.

We assessed whether states required Certificates of Eligibility to begin the expungement process, relied on pardons to grant relief, or vacated court rulings to provide expungement , a system known as vacatur. A review was also conducted of potential financial barriers, including administrative fees, and payments of existing financial obligations associated with convictions. Given the low expungement relief rates identified in previous research, we anticipated that most states would have expungement programs that allowed cannabis record relief, but that they would be petition-based and involve waiting periods and financial barriers.We conducted a retrospective qualitative survey of expungement laws in the US of states, and Washington DC, that had decriminalized or legalized cannabis use. Our goals were to determine whether states allowed expungement of prior cannabis offenses, whether states had generalized offense expungement regimes, general drug offense expungement regimes, cannabis-specific expungement regimes, or a combination of regimes, whether they had automated or petition-based expungement, the length of waiting periods required before seeking expungement relief, and the existence of financial requirements for persons seeking relief. Our focus on expungement automation and waiting periods were informed by guidelines produced by the Alliance for Progress and Safety . As of September 2022, 26 states and Washington DC had decriminalized cannabis possession , 38 had legalized medical cannabis and 20 had legalized recreational cannabis . We refer to the 39 states and Washington DC with some form of cannabis decriminalization or legalization, representing 40 jurisdictions, collectively referred to as “states” in this paper. To identify expungement policies, we collected each government’s statutes pertaining to general expungement, general drug expungement, and cannabis-specific expungement from state legislative websites or the NexisUni database.

We also coded the pardon application for North Dakota to capture its provisions, a web page from the Pennsylvania Board of Pardons to obtain relevant data for cannabisspecific pardon relief, and legislative text applying to cannabis expungement provisions in Vermont when the statute itself could not be located. Search terms included “cannabis”, “marijuana”, “expungement”, “record seal”, “set aside”, “vacatur”, legalization”, “recreational marijuana”, “retail marijuana”, “medical marijuana”, “decriminalization”, “statute”, and “pardon.” Google searches using these terms in combination with each state were used to find statutes from state government websites. Statutes that were not found through a web search were triangulated using government or legal websites that provided statute codes or names, then accessed through state legislative websites or NexisUni. If a state had expiring statutes that would be superseded by a new statute, we excluded the expiring statute and analyzed the new legislation. Only Washington State was in this category . Statutes were selected if they were relevant to general, drug, or cannabis-specific expungement regimes related to convictions, wait periods, fees, and fines. The research was approved by the University of California, San Francisco Institutional Review Board . The research used data that can be accessed freely by the public without special permission or application, the information was defined as not “private” and not involving human subjects. One author , who had previously written a report on state tobacco control policymaking that required interpreting legal regulations, and analyzing their impacts , imported the text of each statute into Atlas.ti for descriptive coding between February 25, 2021, and August 25, 2022. Statutes were iteratively coded to determine the characteristics and restrictions of state expungement programs until default codes were developed for program component themes. The identified themes included the type of substance , the mechanism , waiting period , and financial requirements . We indicated whether expungement was automated, and the presence and length of waiting periods for all states,commercial drying racks using guidelines created by the Alliance for Progress and Safety . Details relevant to waiting periods were further refined through a review of records. The resulting codes categorized statutes by whether they targeted relief for convictions, general offenses, general drug offenses, cannabis-specific offenses, or provided non-conviction expungement mechanisms. We classifiedprograms providing relief for cases that were deferred in exchange for probation, or for participation in treatment programs, as “conviction expungement” since a sentence was assigned, and served, to avoid entry of a guilty verdict into government records. Waiting periods were categorized by expungement program type, by their duration, and by the level of offense targeted . Financial requirements were coded to reflect whether they constituted an administrative fee or involved a financial obligation related to conviction that had to be resolved before expungement relief was received. The research team reviewed a subset of initial statutes together, then after an agreement was reached regarding themes and categorizations, the remaining statutes were read and reviewed by a single author . When there was uncertainty regarding the coding or categorization of a statute or law, the authors discussed it until they reached a consensus.General expungement waiting periods could extend up to 20 years. For violations and infractions, waiting periods ranged from less than 1 year to 5 years, for misdemeanors waiting periods ranged from less than 1 year to 10 years, and for felonies waiting periods ranged from 1 year to 20 years . States often set waiting periods that varied by offense and severity . In total, 33 governments set waiting periods for petition expungement, generally setting multiple durations based on offense level and type. The most common waiting periods were 1-2 years , 2-5 years , and 10 or more years .

Michigan automatically expunged general misdemeanor convictions after 7 years and felonies after 10 years, Pennsylvania expunged misdemeanors after 10 years, South Dakota expunged violations and misdemeanors at 5 years, and Vermont expunged convictions within 30 days for people who were between the ages of 18-21 years at the time they were charged. Arkansas permitted courts to immediately expungean offender’s record after completing drug or other court-ordered treatment . Four states explicitly established waiting periods for expungements after a pardon. Two states, Colorado and Illinois, allowed expungement at any time following a pardon. Alabama allowed expungement 180 days after a felony pardon, and Maryland required a 10-year waiting period before records associated with pardoned offenses were expunged.The 7 states that set waiting periods for cannabis-specific expungement programs set them to shorter durations relative to waiting periods for general expungement, as shown in Table 4. The 6 states with petition mechanisms set waiting periods ranging between no wait to 4 years. The 3 states with automated expungement waiting periods set waiting periods ranging between 1-2 years or tied waiting periods to the date of the offense. Three states with automated expungement, California, Illinois and New Mexico, set waiting periods for the expungement of cannabis records. Illinois automatically expunged certain cannabis-related offenses after 1 year, while California and New Mexico expunged certain cannabis-related offenses after 2 years. Illinois expunged cannabis offenses dated between 2013 and 2019 by 2021, offenses dated between 2000 and 2013 by 2023, and offenses dated before 2000 by 2025. Arizona and New Jersey imposed cannabis-specific waiting periods for expungement, with New Jersey also requiring a 3-year wait, completion of probation, or resolution of financial assessments, for cannabis offenses involving distribution or intent to distribute.Of the 34 states offering general expungement programs, 19 required that people pay administrative fees to procure relief, as shown in Table 5. Administrative fees collectively refer to filing and processing fees incurred during the expungement process. Among these 19, 11 charged a filing fee, 13 instituted a processing fee, and 5 states required both a filing fee and a processing fee. Nine of these 19 states charging administrative fees offered waivers for indigence. Oklahoma reimbursed filing fees upon expungement, and West Virginia waived administrative fees if an applicant participated in a treatment or diversion program. Nevada and Rhode Island did not charge administrative fees for expunging offenses that had been decriminalized at the state level. Compared to general expungement programs, cannabis-specific expungement programs were less likely to require payment of administrative fees. Among the 21 states with cannabis-specific expungement programs, only 4 required payment of a filing or processing fee . Three states required payment of filing fees to have records expunged, and 2 others required payment of processing fees. Delaware was the only state that set both filing and processing fees. Arizona offered an indigent waiver for cannabis offenses, and Virginia reimbursed filing fees after expungement. Three states required that a person seeking expungement first obtain a Certificate of Eligibility: Illinois, Louisiana, and Utah. Of these three, Utah imposed associated administrative fees but waived certificate requirements for offenses involving cannabis possession as well as for persons previously charged for using cannabis for qualifying health conditions. None of the pardon-based expungement programs required that pardon petitioners or recipients pay fees to receive an expungement. Seventeen states required that people seeking expungement pay other legal financial obligations to secure relief, as shown in Table 6.Of these, 16 required that any outstanding financial judgments, legal obligations, and restitution be paid before granting general expungement, and New Jersey required these obligations be paid before granting cannabis-specific relief . Although Oregon historically required that applicants for cannabis record expungement pay outstanding financial obligations, the state repealed that requirement in 2022. Only Illinois explicitly permitted the expungement of records if financial obligations remained unpaid.

The review not only examined the mechanisms by which odors induce a health response, but also identified effective risk-based approaches for regulating health impacts from exposures. The main outcomes of interest in the review were health symptoms, physiological responses, annoyance, mood and psychological health, quality of life, cognition , athletic performance, and brain activity. Alberta Health chose not to review animal studies, occupational exposures, hypersensitivity, commercial uses of aromas or potential systemic organ toxicity from odorant exposure. Of these gaps, occupational exposures are addressed in this paper due to their sentinel value for lower exposed residential populations. Non-sensory endpoints are addressed as well. Such information was found in other reviews and the post-2013 literature search.To understand the adverse effects from exposure to odors, the human sense of smell is introduced. Humans have around 5 million olfactory receptor neurons, and they are directly connected to the most ancient, primitive part of the brain. By comparison, dogs have around 220 million olfactory receptor neurons and rabbits have around 100 million. It takes around 1 second to respond to an odor. Olfaction relies on two neural systems and two routes of entry to the nasal cavity. Air enters either through the nostrils or the mouth . Volatile chemicals in the air bind to olfactory neuron receptors and to trigeminal neuron receptors . The combination of olfactory and trigeminal neuron receptors explains why menthol produces a minty smell as well as a tingling in the nose .

The human nose contains roughly 400 different types of receptor neurons,cannabis grower supplies each sensitive to specific types of odorants . The neural receptors signal the brain, which then associates the perceived odor with past experiences once the signal becomes strong enough. Environmental odors are typically a complex mixture of multiple odorants. The processing of odor mixtures involves activation of more brain regions compared to single odorants . Odorants can bind to one or more receptors, and receptors can bind to one or more odorants. Only a few odorants, however, are discerned within a mixture . Some odorants dominate while others are masked, and factors such as concentration, temperature and humidity all play roles.Human olfactory mucosa occupies 3% of the nasal cavity and is protected high in the nasal vault , so only an estimated 5 to 10% of air entering the nostrils reaches this region . The olfactory mucosa is composed of the olfactory epithelium and the underlying olfactory neurons. See Figure 4.2 for an overview.When sensed orthonasally, odors are perceived as coming from the environment, while when perceived retronasally, they are perceived as coming from food in the mouth . Our two nostrils help us stereoscopically locate the source of the odor . The sinuses, a connected system of hollow cavities in the skull lined with mucosa tissue that has a thin layer of mucus, may help humidify air in the nasal cavity. In 2015, a $15-million grant by the National Science Foundation kicked off further research into how animals, including humans, locate the source of an odor, such as food . The research focuses on how odors move in the landscape and how animals use spatial and temporal cues to move toward a target. The research is just one part of the federal BRAIN Initiative that studies olfaction as a window into understanding the brain, because olfaction is considered the most primal pathway to understanding brain evolution.

At present, such information is not available for e-nose development. The olfactory epithelium contains three types of cells: olfactory receptor neurons, their precursors and supportive cells. The cilia are constantly exposed to the nasal environment and are continually replaced, even their basal cells, possibly indicating frequent damage. A layer of mucus 10 to 40 µm thick coats the mucosa epithelium, and odorants must pass into this layer to interact with the sensory neurons through a series of poorly understood “perireceptor” events . Each sensory neuron, covered in cilia, projects down from the olfactory epithelium into the mucosa. The cilia form a network covered in receptor proteins. These proteins thread back and forth across the outer membrane of the cilia and interact with odorants. Various theories have been put forward on how exactly odorants interact with the proteins, and this remains an area of research. Receptor cells of the same type are randomly distributed in the nasal mucosa but converge on the same glomerulus. Each type of neuron frequently responds to more than one odorant, even from different chemical classes, so the overall odor signal must be integrated bythe olfactory bulb . Integration includes both olfactory and trigeminal signals, and workers often report odor and irritation as a combined, singular perception . The olfactory bulb also receives information from other areas of the brain to filter out background odors and enhance perception. Fascinatingly, none of the physical stimuli themselves ever reach the brain. Instead, a host of proteins transduces captured molecules into a small change in voltage that can be deciphered by the brain . The unpleasant and pleasant aspects of mixtures are represented separately in the brain .

Human sensitivity to odorants ranges across several orders of magnitude . Around 1 ppt appears to be a theoretical limit for sensitivity, and many odorants are not perceived until above 1,000 ppm. The major components of air are not sensed at all . Carbon dioxide is an interesting chemical because it is odorless at ambient concentrations yet selectively triggers only the trigeminal neurons and not the olfactory neurons when it reaches 200-fold above background levels . Describing multiple odor notes in mixtures is challenging. Fewer than 15% of the people tested could only identify one of the odorants present in a mixture, and identification of 3 to 4 components was the limit for trained experts . Even 90% of wine judges were unable to reproduce their scores . General variability in odor perception is high. Factors include age, sex, lifestyle, prior exposures, culture and health status . Approximately 3% of Americans have minimal or no sense of smell .Prolonged or repeated exposure to an odor can lead to a decreased response , which has the benefit of allowing a baseline reset in preparation for a new stimulus . Habituation happens as quickly as 2.5 second and is accompanied by decreased transduction by the neurons after 4 seconds . A growing field of research throughout public health is the microbiome, the microflora that contribute to gut, mouth and skin health. The nasal cavity, too, hosts microbes that contribute to normal functioning . Some microbes themselves emit odorants and can decrease the host’s sensitivity . Attempts to reverse engineer an odor based on the molecular properties of the odorant have been successful. Algorithms were able to predict the odor note of a given odorant based on its chemoinformatic features for 8 descriptors out of 19 total . Researchers using systems biology and computational techniques mapped odors to specific proteins on olfactory receptor neurons, which was dubbed the “odorome” . Risk assessment for estimating the non-sensory health risks of airborne chemicals has a large body of guidance and case studies. The primary focus of this paper is on the sensory health effects of odors that integrate both trigeminal response and olfactory response . In general, the olfactory pathway iscapable of informing the organism about the presence of an odorant while the trigeminal pathway helps inform the organisms about the risk of health hazards and injury .Cognitive bias plays a role in odor responses . Odors trigger memories of previous experiences and are influenced by the power of suggestion. If given a prior warning that an odor is harmful, increased irritation was reported. Fewer symptoms were reported if told the odor was healthful. Even when no odor was administered,dry racks for weed suggestion that there was a harmful odor led to symptoms. Prior experience with an odor introduces bias, too. Emotional baseline is also a factor . Sensitization to an odorant occurs when an acute exposure triggers subsequent, more-severe responses, often at lower concentrations . Desensitization can occur when chronic exposure to an odorant increases the concentration required to trigger a response. For example, workers who are habituated and desensitized to an odorant may be baffled by neighborhood complaints . The epidemiology evidence, however, indicated the full range of adverse effects from odor exposure . Such symptoms were self-reported, which means they may include bias. The distance from facility, an objective measure, contrarily did not predict the frequency of symptoms. Interestingly, the relationship between odor exposure and health symptoms appeared to be greatly influenced by odor hedonic tone, perhaps more so than odor intensity. The debate whether the purely odor-related symptoms are psychological or have an actual underlying physical cause is ongoing. In the same issue of Archives of Environmental Health in 1992, two opposing perspectives were presented. Shusterman concluded that the evidence of health effects was lacking beyond odors’ ability to inflict annoyance.

In the editorial immediately after his article, Ziem and Davidoff countered that odor, and chemical sensitivity in general, may well be based on underlying physiological responses, as was often found in the case of sick building syndrome. Both agreed that better ways to determine the impact of odors were needed, and well-controlled prospective case-control studies would be especially welcome. The psychological symptoms of odor exposure include tension, nervousness, anger, frustration, embarrassment, depression, fatigue, confusion, frustration, annoyance, and general stress . Odor frequency, odor intensity and feeling their concerns are not being heard all contribute to annoyance, which leads to stress. Health worries contribute as well. See Table 4.2.Changes in odor-induced frontal lobe activity has been linked to changes in mood, drowsiness, and alertness . Unfortunately, the studies of this connection were few and additional research in this area is needed. Odor-induced brain activity is complex, involving more than 30 different regions. Other studies reviewed found, however, that odors have no effect on task performance, so they concluded that the impact of odors on task performance may be odorant-specific. Increased prevalence of gastrointestinal symptoms were observed as a function of proximity to a wastewater treatment plant in Poland . The symptoms were correlated with both odors and microbiological pollutants and could not be disentangled to single out odors as the primary agent. Similarly, the negative effects of traffic noise and odor on residents in Windsor, Ontario, Canada, had a strong covariance between these two parameters and could not be differentiated .Some odorants and some co-pollutants within odors are considered hazardous air pollutants because they cause other adverse effects beyond smell and irritation .Air that contains odorants also is known to contain odorless co-pollutants such as particulate matter and endotoxins . There was a positive correlation between the presence of odors and the prevalence of self-reported health symptoms, such as headache and nausea, when communities near hazardous waste sites were compared . However, more serious health outcomes – cancers, mortality and birth defects – were not higher compared to the control sites .Dose-response relationships for odors aim to link the percentage of people experiencing adverse effects, such as odor annoyance and irritation, to the level of exposure. For toxic chemicals, adverse effects increase as exposure increases. Odors, however, can be more inconsistent. For example, hydrogen sulfide loses its characteristic “rotten egg” odor note as the concentration increases, leading to harmful levels going unnoticed .Such thresholds are called “suprathreshold” when above the odor is clearly perceptible. Different levels and locations of irritation may occur as well, which are also concentration dependent . Other health effects, including those from acute and chronic exposure, observe dose-dependent trends and have established thresholds by more complex, non-sensory based techniques often involving high-to-low dose extrapolation from animal studies. As an example, the thresholds for hydrogen sulfide are included in Table 4.4.The major goal of both risk assessment and odor assessment is to verify that exposures are below the thresholds of concern . For conventional risk assessment, the thresholds are health-based, often extrapolated from animal studies, and typically incorporate large margins of safety due to crude extrapolations and uncertainties. For odor assessment, achieving odorless air is the goal, yet due to the “lack of severity” of the effect, the acceptable limit is often set well above the odor-detection threshold. Given the wide variability in human response to odor, this approach is perilous, but a point of departure is needed, nonetheless.

The physical environment also plays a role. Varying wind direction and speed lead to the transitory nature of odors, and multiple sources in the vicinity lead to difficulty in source attribution. Even temperature and humidity play roles in the perception of odor, which is often overlooked during exposure sampling and analysis. In addition to the large number of chemical compounds present in malodorous air, their typically low concentrations challenge the limits of even the best instruments . Known as the “odor gap,” the human nose can usually detect odors well below analytical instrument detectors’ capabilities . Methods that use human panels to evaluate odors have been standardized over the years and can work well in parallel with traditional analytical instrument methods. The vision is to have analytical instruments that completely mimic the human nose and sense of smell. The measurement and evaluation of exposure to conventional air pollutants is considered more evolved than that for odors . The framework and methodology applied to conventional air pollutants – risk assessment – offers grounding principles and useful conventions that have evolved over time. Both fields evaluate human responses to chemicals in the air. Although risk assessments are often predictive of future events, they may also be conducted retrospectively as an investigative technique.Risk is, by definition,grow vertical is the probability of an adverse outcome and its severity. For chemical exposures, risk is a function of hazard and exposure .

The fundamental framework for risk assessment was established in the 1980s . Figure 3.1 provides an overview of the various steps. These steps begin with the generation of basic information, proceed through identifying the hazards of the chemical under evaluation, predicting how adverse effects vary with dose, and end with combining that information with exposure data to determine the incidence of adverse effects in a population. Beyond risk assessment, and beyond the scope of this paper, is subsequent regulatory, management and communication steps based on the risk assessment’s output and other factors. Given the variety of information required in a risk assessment, the field is truly multi disciplinary. The data and assumptions made along the way are evaluated for how much uncertainty they contribute to the results. Often an order of magnitude or more of uncertainty and variability are inherent in the output, which needs to be explained transparently to not “over sell” the results with a false sense of precision and accuracy.Risk assessment tends to separate exposures into acute and chronic , with sub-chronic falling in-between. A pragmatic approach to risk assessment is to first conduct a screening-level assessment based on crude approaches likely to overestimate risk. If the risk is found to be reasonable from such an approach, no further work is necessary. If not, then a more detailed, refined assessment is conducted. For the exposure assessment , the focus of this paper, a conceptual model guides the evaluation. The conceptual model traces the origin of the chemical , indicates how it is released, allows for transport of the chemical, includes possible routes of exposure, and indicates who might be exposed . Odors are released from a variety of sources, travel through the air and then are inhaled by local populations. Risk varies across a population due to biological differences , culture, lifestyle, level of exposure and prior exposures.

To protect vulnerable sub-populations, a safety factor is usually applied. Perhaps the greatest challenge for both odor assessment and risk assessment is mixtures. We are exposed to a wide variety of chemicals through food, medicine and the environment, yet risk assessment often focuses on a single chemical in isolation. Odor assessment follows suit, focusing often on only one odorant. Such an unrealistic approach is destined to produce highly skewed or biased results, probably in unknown directions . Odor assessment has the advantage of tests being performed by human panels, which can evaluate the whole mixture of the sample. Risk assessment relies on epidemiological reconstructions for human data.Risk assessment, however, has developed approaches for mixtures. A simple, screening level approach is to determine the risk-driver for the mixture. Adding up the individual effects is another crude approach. A simplifying aspect for odor exposure assessment is that human olfaction has evolved to differentiate between only a few significant stimuli. Typically, around 3 or 4 odors are sensed at a time, which decreases the complexity of the mixture . Those odorants that trigger intense, familiar or unpleasant sensations are more likely to be noticed while the remainder are lost in the signal “noise” or sensory filters. Or this limitation may due to inability to name a substance, rather than failure to detect the difference between odors . Both risk assessment and the evaluation of odors suffer from high degrees of uncertainty and variability. The personal nature of odor perception introduces fundamental variability. The health effects evaluated in risk assessment have a similar range of variability due to the biological variability of humans, which is increased further by the extrapolation of animal studies to humans. Therefore, each health effect benchmark value, such as a toxic reference dose, is typically presented with one significant figure due to the inherent uncertainty, which typically spans an order of magnitude. Exposure results, too, are uncertain due to modeling assumptions or analytical imprecision, as well as sample collection issues. In reality, one significant figure is a misrepresentation, and a range would be more accurate.

Making judgements using ranges, however, is difficult so single values are typically used. A sensitivity analysis helps show the possible range of results.Acknowledging uncertainties is key to interpreting results and making comparisons. Transparency each step of the way is paramount, otherwise overconfidence in shaky results may occur. Both the best practices and draft guidance include a tiered approach to odor evaluations. Such has long been used in risk assessment to streamline the work. First, a screening-level evaluation is performed using crude assumptions and approaches. If the exposure is deemed acceptably low, there is no need for further work. The same applies to odor investigations. If a straightforward evaluation by an air inspector identifies the source and resolves the issue, no complex further investigation need ensue. In both cases, if the screening-level approach identifies concerns, then a detailed analysis is undertaken.Describing an odor in detail is often difficult, so most complainants start with saying “something smells bad” and then struggle to give further details. Unlike other senses with broad vocabularies, smell is anchored in the source of the odor and the person’s history with that source. In a way, our sense of smell is learned. Attributing words and meanings to odors occurs over a lifetime and even changes over time. The food and beverage industry has attempted to make a science out of sensory description. Beer, wine and coffee are prime examples. Perfume formulation takes this to another level. To avoid complaints,vertical grow systems the drinking water industry has developed taste-and-odor assessment protocols.Environmental odors are typically mixtures of chemicals . The rare exception is the release of a single odorant from a chemical industry facility. The various odorants within a mixture trigger the olfactory sense in “concert” similar to the various notes in an orchestral piece of music. The perfume and fragrance industries are built largely upon this principle. The interplay of odorants in a mixture can be complex, with both synergistic and antagonistic effects taking place. Perfume has the function of covering up other odors. In odor terminology, this is called “masking.” Landfill and bio-waste sites are known to use scents such as “cherry” at their perimeter , yet in an evaluation of commercially available masking products only 4 out of 26 were able to mask odors successfully . All 4 were neutralizing agents that reacted with odorants. Within an environmental odor sample, certain odorants may mask others. Only upon dilution to a point where the major odorants are no longer perceptible are the minor odorants noticed. This dilution effect has been termed “peeling the onion” , where one layer of odor leads to another. Further discussion of this effect is in the section on odor intensity. The odorants within a mixture are subject to the same physicochemical processes and dispersion as any conventional air pollutant. The same exposure models, such as fate and transport, apply; however, the identities and concentrations of the individual odorants are often unknown, rendering such modeling impossible. To get around this issue, a pseudo-concentration approach has been developed, which is discussed in Section 4.2.

The overwhelming majority of the molecules in air are odorless. These include nitrogen, oxygen, water, hydrogen, helium and carbon monoxide. Rather uniquely, carbon dioxide is odorless until it reaches 200-fold above background levels , at which point is triggers the nasal trigeminal receptors rather than the olfactory receptors.Colors have agreed-upon descriptions, and graphic artists often use Pantone® numbers as specific identifiers. Musical notes have frequencies assigned to them and arranged into scales . Odorants, too, have descriptors, known as “notes,” the term used in ISO 5492:2008 . For example, “fishy,” “swampy,” “rotten egg,” “pungent,” or “tingly” are odor notes. An atlas of panel-derived odor notes has been published . The odor note, however, may change with the concentration . Hydrogen sulfide at levels above 20 ppm changes from its characteristic “rotten egg” odor note to a “sweet” odor note, and at even higher concentrations, which are toxic, hydrogen sulfide becomes odorless. The response to an odor is highly personal and depends on “odor memory” – previous exposure and knowledge about the odor source . Common descriptors associated with specific odorants, however, may aid in determining the source of an odor. Odor wheels have been developed for specific odor notes associated with certain sources, such as landfills, composting and WWTPs . Odors as mixtures make assigning odor notes more complex. As with wine tasting, several dominant notes may be present, along with several subtle notes. These, too, change as the mixture is diluted or ages, or as temperature and humidity change.As with sound and color, some odor notes may be perceived as pleasant or unpleasant. This is the “odor hedonic tone,” also known as the acceptability of the odor. Dravnieks published on this topic, and a scoring system is named after him. Odor hedonic tone is a highly subjective determination, open to large variation across a population and appears to be learned rather innate . Odor hedonic tone varies as the odorants increase or decrease, sometimes progressing through flip-flops between pleasant and unpleasant .Odor intensity – the magnitude or strength of an odor – has received considerable attention. Unlike odor notes and hedonic tones, which can be fairly subjective for the untrained, odor intensity is pursued as a quantifiable, even scalable, attribute of odor perception. The belief is that odor intensity is akin to brightness or loudness, which are quantifiable through physics, yet odors are a chemical sense with accompanying complexities. Nonetheless, two approaches have been attempted: assigning words or numeric scores to intensity levels, or determining the amount of dilution required until the intensity is no longer detectable. For a single odorant, intensity appears to be linked to the odorant’s concentration. In mixtures, such a link is tenuous or absent. Although odors are typically mixtures, it is much easier to study individual odorants. A simpler, although less-precise, formula is called the Weber-Fechner law . Fechner, a student of Weber, observed that the differences in the concentration of an odorant that caused “just noticeable differences” in perceived intensity was logarithmic, meaning an intensity difference is noticeable for a small change in concentration when the starting point is low and to achieve the same “just noticeable difference” at a high starting point requires a much larger change in concentration .Although this observation applies only to the region where an odorant intensity is readily perceived, researcher have expanded it to the lower end of the range, which may be highly unreliable. As the intensity approaches the point of disappearance, panelists give very different responses. At the odor-detection threshold, up to 1,000-fold differences in odor detection have been observed in controlled human studies . Trained panels tend to give lower results as they gain experience .Where the concentration is units such as ppb or µg/m3 , and k is a constant that is unique to each odorant. The linear intercept is either fixed to 0 , 0.5 or allowed to vary uniquely for each odorant, as represented by b . Whether the concentration is used directly or divided by a reference concentration does not impact the relationship between intensity and concentration.

Social science contributions to understanding multiple drug use have lagged behind those from the natural sciences . The regular and combined use of multiple substances is disproportionately practiced by some minority and socially marginalized groups, such as gender minority individuals . The particular risks and benefits associated with multiple drug use demand a better understanding of its unique characteristics, including its specific patterns, combinations, intentions, and contexts . We use the term multiple drug use to encompass both ‘drug use repertoires’ and ‘drug use combinations’. Drug use repertoires refer to the variety of substances a person ingests during a particular time frame . ‘Drug use combinations’ refer to the ingestion of two or more substances at the same time or in close temporal proximity so that overlapping psychoactive effects are produced . Prominent methods for researching multiple drug use include retrospective surveys that inventory participants’ drug use repertoires over the past month or year, and in-depth interviews and ethnographic field work that examine practices and experiences of drug use combinations. Increasingly, mobile and geo-enabled technologies are being integrated with qualitative research methods to ground drug use practices and experiences in their social and physical environments . In the spirit of creative research methods like these ,hydroponic rack we integrated geo-enabled smartphone survey data collection with a qualitative mapping interview method and piloted it to explain tobacco use disparities among bisexual young adults.

The pilot study revealed smoking patterns and situations that reflect young adult smokers, generally, but also the unique roles that smoking plays for bisexual young adults as they navigate differently sexualized spaces in everyday life . This brief report draws from preliminary data to demonstrate how the method may also provide integrated insights into the unique patterns, intentions, and socio-structural contexts of multiple drug use for different groups of people.Smartphone apps that repeatedly administer surveys to participants and record their locations over time are often used to research recurring and episodic behaviours. These approaches can ‘reach into’ the fabric of everyday life to collect data within participants’ natural environments and routines . Smartphone ownership is increasingly ubiquitous even among low income and rural groups, making this approach feasible with diverse populations. Mobile health research methods, such as these, minimize the retrospective recall bias that occurs when participants are asked to characterize their behaviours or experiences, and can be integrated into spatial frameworks and analyses when geolocation data are also collected . The value of mHealth methods for researching tobacco use is established mHealth methods are now used to research patterns and situational predictors of use of other substances, including cannabis, opioids, cocaine, MDMA, and alcohol . Because mHealth surveys must be kept short to reduce participant burden and encourage data collection compliance, they cannot capture the richness of individuals’ experiences of use contexts and practices, nor how individuals make sense of their drug use within the context of their broader life narratives.

Integrating qualitative mapping methods with mHealth momentary assessements can provide reliable and ecologically valid measures of substance use behaviours while also revealing the richness of experiences and contexts of use. Qualitative mapping, also known as qualitative Geographic Information System , integrates mapping techniques with qualitative methods to explain the processes that produce spatial patterns, relationships, and behaviours . It has been used in research on substance use, including to understand place-based practices and norms of tobacco use , the impact of area restrictions on people who use drugs , and characteristics of drug overdose contexts . Our mixed method approach leverages the “productive complementarity” of multiple methods, acknowledging that different ways of knowing about social phenomena, like drug use, are all inherently partial and are shaped by the conditions and actors involved in the creation of knowledge . We integrate real time, smartphone-collected surveys, location tracking, and subsequent in-depth interviews that are guided by viewing maps of participants’ own mHealth data in an explanatory sequential mixed methods approach . Participants use a smartphone app for a period of time to report on the substances they used and the situations they used them in via participant-initiated real-time reports of use, prompted momentary surveys about use and non-use situations, and prompted daily diary surveys. The real-time reports and prompted surveys collected multiple choice responses with write-in options. Subsequently, real-time reports of use and location tracking data are visualized in mapping software and brought into in-depth interviews to guide and ground discussion of drug use experiences within everyday contexts and situations of use. The interviewer and/or participant toggle between map layers and zoom in and out of places in Google Earth. Together, they identify apparent spatial clusters of use of different substances and discuss what those places are, what they usually do and experience there, who they interact with, and how it is that use of particular substances fold into those experiences. This is similar to the use of travel and activity diaries to guide interviews, but further ‘grounds’ interview discussion by interacting with spatially-visualized representations of participant data.

Quantitative and qualitative data are analysed separately and then integrated in a table. Visually organizing and juxtaposing the quantitative and qualitative data sets helps to identify threads of interest to explore across the data sets and to observe the convergence, complementarity, and/or dissonance between their depictions of participant’s everyday use of and experiences with substance use. We collected data in 2019-2020 with 32 young adults in California who regularly used both tobacco and cannabis with the mixed method . We draw from that study on tobacco and cannabis use to explore one participant’s data, which provided particularly informative insights into the complex patterns, intentions, and socio-structural contexts of multiple drug use repertoires and combinations. The participant did not respond to our request for feedback on the manuscript. To protect the participant’s identity, we use a pseudonym and have added fictional details about the participant that are not relevant to interpreting the data presented below. mHealth data were descriptively analysed using STATA statistical analysis software. Transcript analysis followed an inductive-deductive thematic approach . Transcripts were coded with NVIVO qualitative data analysis software. The initial transcript coding scheme was informed by our previous studies and the literature and was used to sort content by substance type, location, social identity, and roles/intensions of use. Emergent themes regarding roles and intensions of use were identified in a series of group readings of transcript excerpts, as we have done in the past .‘Jason’ was a transgender man in his mid-twenties who lived in a rural community, had a history of homelessness, and reported having autism spectrum disorder . He worked part-time in constrution and lived with his partner in a small house. Jason completed 70% of all prompted surveys during his 30 days of data collection. Jason’s mHealth survey data indicated that he smoked a daily average of 6.5 cigarettes. He most often smoked alone, and frequently smoked at home, in a vehicle, or at someone else’s home. On the minority of occasions that he smoked cigarettes with others,vertical growing systems it was usually with friends or his partner. Jason’s mHealth survey data indicated that he used cannabis almost every day ; 4.9 times per day, on average. He frequently used cannabis in his garage or backyard, someone else’s home, or in a vehicle. He was with friends or his partner during most of these sessions , and was alone for the rest. On most days of the study he reported using cannabis and cigarettes together some or most of the time. He used alcohol on only 15 days and most of those reports were for one drink. On only 5 days did he report using alcohol and cigarettes together some or most of the time. In short, Jason’s mobile data indicated that he used cigarettes and cannabis daily or almost daily, less regularly used alcohol, and that he often used cigarettes and cannabis at the same time.This one individual’s mHealth and map-led interview data set offered an integrated understanding of the complex use patterns, combinations, and intentions within his drug use repertoire , and linked these to his intersecting identities and the particular social and structural characteristics of his environment.

Specifically, it revealed relationships between how and why he uses multiple drugs and his day-to-day experiences as a transgender person with ASD living in a rural community. A key strength of this mixed method appears to be its capacity to go beyond examination of individual substances and individual drug use ‘risk factors’, to link use patterns and intentions of multiple drugs to the intersecting characteristics and place based experiences of different people. The perspective offered by integrating mHealth and qualitative mapping methods may help identify particular drug use patterns and combinations that increase risk of drug-related harm for priority groups, like gender minority individuals, as well as provide insight into the place embedded experiences that give rise to motives for those ‘risky’ drug use practices . Our findings suggest that participant narratives of multiple drug use patterns, intentions, and experiences can be enhanced and further grounded in context by viewing and discussing maps of participants’ own data during interviews. Maps that show where and how frequently participants use different drugs provide an avenue for the participant and interviewer to organize their discussion around the complexities and diverse factors related to multiple drug use. Moreover, similar to other creative methods that integrate images or other objects into interviews , the visual representation of drug use practices in map form may help depersonalize highly stigmatized use practices, like methamphetamine use, and reinforce the participant’s role as expert while cultivating an experience of discovery, reflection, and ownership over the interpreted ‘story’ of their data. This mixed method is limited, however, by being time-consuming and resource-intensive, especially with regards to participant and investigator time effort, as well as obtaining the smartphone data collection software and mapping software. Participant burden must be considered when designing the frequency and length of smartphone-collected surveys, the duration of data collection , and participant incentives. Moreover, great care must be taken to protect participant confidentiality when using any geo-enabled data collection method. This method could be used with a larger sample size by grouping participants for comparison rather than at the individual case level, and triangulating between the quantitative and qualitative data for each group. Future studies can build on research that has identified individual ‘risk factors’ related to multiple drug use , by gaining integrated and geographically-grounded insights into how these diverse and place-embedded factors intersect and interact with one another to shape drug use repertoires and combinations. In-depth knowledge like this can inform the resources and services directed toward and tailored to the needs of diverse groups of people who experience the unique pleasures, roles, and risks of multiple drug use.Over the past decade, perceptions of cannabis and cannabis use have changed radically, with 37 states and D.C. legalizing medical usage and 21 states and D.C. allowing adult recreational usage as of January of 2023. In 2019, Illinois became the 11th state to legalize recreational cannabis for adult use, and the first in the country to adopt a regulatory system for cannabis cultivation, testing, and sales. Texas is one of 13 states without a comprehensive medical cannabis law, only allowing patients with specific debilitating medical conditions to access low-THC medical cannabis products. In May of 2021 the U.S. House of Representative introduced the “Marijuana Opportunity Reinvestment and Expungement Act” that would legalize cannabis and expunge federal cannabis arrest and offenses from individuals’ records. This bill has huge implications for Black communities who are disproportionately impacted by incarceration for cannabis-related offenses. Recent reports estimate Black people are 3.64 times as likely as their white peers to be arrested for cannabis possession despite similar rates of cannabis consumption. Research has explored the risks associated with cannabis usage in Black men who have sex with men such as homelessness, incarceration, and high risk sexual behaviors. Cannabis and sexual risk behaviors maintain a complex relationship as cannabis is often coused with alcohol and other illicit substances. In one study people who used cannabis heavily were more likely to be unaware of their human immuno defficiency virus status; whereas, associations with other HIV outcomes were inconclusive.