Phylogeny-based genome mining is based on the understanding of the mostly modular structure of bio-synthetic gene clusters.It is theorized that this mostly modular structure comes from a quickly evolving defense system where new molecules are produced by randomly swapping and shuffling domains and modules. As an example, the program Natural Product Domain Seeker , constructs a phylogenic tree based on ketosynthase domains and condensation domains of PKS and NRPS genes, respectively. KS and C domains are two of the enzyme families used to construct phylogenic trees in order to predict compound structures. This phylogenic tree can be utilized to give information about the function of the PKS and NRPS gene searched for, its evolutionary history, and the novelty of products produced in the secondary metabolite cluster containing the gene. Lastly, resistance gene directed genome mining and target directed genome mining involve identifying bio-synthetic gene clusters that contain self-resistance genes. For organisms that produce antibiotics or anti-fungals, there needs to be a development of a self-resistance method to avoid suicide. One self-resistance mechanism is the use of efflux pumps to transport the compounds to extracellular space. Another self-resistance mechanism involves the inclusion of self-resistance enzymes in bio-synthetic gene clusters. These SREs are mutated copies of the housekeeping target that retain activity and are not inhibited by the natural product produced, thereby keeping the organism alive. These SREs are typically found in secondary metabolite clusters. Therefore, an approach searching for these SREs can be developed to find bio-synthetic gene clusters.

Utilizing this knowledge, Moore et al. were one of the first groups to utilize a targeted genome mining approach. They screened for housekeeping copies of genes in 86 similar strains of Salinospora and screened for location near bio-synthetic gene clusters. They identified the second copy of a bacterial fatty acid synthase colocalized within a cluster that contained a PKS-NRPS hybrid gene. They annotated the cluster, heterologously expressed the genes,flood table and after chemical characterization, elucidated that the cluster produced thiolactomycin, which is a fatty acid synthase inhibitor. To demonstrate its capabilities, target directed genome mining has been used to locate bio-synthetic gene clusters with known bio-molecular targets, for discovering natural products with desired bio-molecular targets, and for discovering the bio-molecular targets of known natural products. Therefore, searching for resistance enzymes in a secondary metabolite cluster has become an increasingly appealing genome mining approach for finding new clusters and subsequently, novel natural products. In recent years, tools and programs have been developed to search for new bio-synthetic clusters more quickly. These programs have the ability to predict the entire secondary metabolite gene cluster.Anti-SMASH identifies polyketide synthase and non-ribosomal peptide synthetase core genes in potential clusters and then outputs the cluster information in a user-friendly interface that can be readily searched through. Secondary Metabolite Unknown Regions Finder is another one of these programs. SMURF evaluates secondary metabolite gene clusters by scoring the nearness of core genes with the different tailoring genes near the core gene. Additionally, there is another program that is more specified in its search called Antibiotic Resistance Target Seeker . ARTS specifically queries for antibiotic resistance genes in bacteria that can lead to bio-synthetic gene clusters for possible novel drug targets.

We utilized anti-SMASH to elucidate the non-plant olivetolic bio-synthetic pathway.Since its inception, the Tang lab has utilized various methods of genome mining to identify many natural products and novel enzymes, as well as elucidate the bio-synthetic pathways of natural products in addition to the production of novel natural products through the engineering of bio-synthetic genes. One such example is the further characterization ofthe bio-synthetic pathway of zearalenone, a member of the resorcylic acid lactone family of products produced from the fungal species Gibberella zeae, and production of novel resorcylic acid lactones achieved through the reconstitution of the polyketide synthase involved in the biosynthesis of zearalenone. RALs are polyketides, exclusively produced by fungi, consisting of a macrolactone ring with a 2,4-dihydroxybenzoic acid moiety embedded. The first discovered RAL, radicicol was characterized from the fungal species Monocillium nordinii in 1953, with 200 more RALs having been identified from a variety of fungal species since then. RALs are potent molecules that exhibit of variety of biological activities including having antimalarial, anti-cancer, anti-microbial, mitogen activating protein -kinase inhibitor, TAK1 inhibitor, heat shock protein inhibitor, and estrogen receptor against properties. Many RALs consist of 14 membered lactone rings, although there also exists RALs consisting of 10, 12, and 16 membered lactone rings. The RAL bio-synthetic gene cluster typically consists of two polyketide synthases: a highly reducing polyketide synthase and a non-reducing polyketide synthase . Regarding the bio-synthetic pathway of RALs, the HRPKS generates the terminal hydroxyl group that becomes the macrocyclizing nucleophile. The chain is then transferred to the NRPKS where it is further elongated and then goes through aldol cyclization to form the enzyme bound resorcylic thioester. A fused thioesterase domain in the NRPKS then performs macrocyclization to release the final RAL product. Furthermore, considerable structural diversity at the C6 position of the RAL can be generated by utilizing different HRPKSs that are able to synthesize a variety of reduced products.

Type I polyketide synthases contain multiple functional and catalytic domains, generating most of the polyketides that have been characterized. Furthermore, type I polyketide synthases are divided into two separate categories: iterative type I and modular type I. Modular type I polyketide synthases are more commonly found in bacteria. They are large multimodular enzymes having assembly line like characteristics, condensing acyl substrates module by module, where the order of the module defines the order of the functional groups of the final elaborated compound. Iterative type I polyketide synthases, more commonly found in fungi, contain a single multidomain, and iteratively use the domain, similarly to fatty acid synthases operate, to generate the programmed polyketide product. Type III polyketide synthases are found in plants , although a few have been elucidated from microbes, and are much smaller than type I and type II PKSs. They are homodimers of ketosynthases; therefore, they extend chain length through iterative decarboxylative Claisen condensation and are responsible for producing compounds such as stilbene, flavonoids, and alkylresorcinols from plants. Type III PKSs release their products to either the active site cysteine of the enzyme or the carrier molecule, coenzyme A. There have also been reports of Type III PKSs utilizing an acyl carrier protein bound substrate as the starter substrate, similar to type I and type II PKSs.Since our platform utilizes two type I iterative polyketide synthases, it is appropriate to go into more detail concerning these megasynthase enzymes. Fungal PKSs resemble bacterial type II PKSs in that the catalytic domains of both classes of enzymes are iteratively utilized during polyketide synthesis and resemble bacterially type I modular PKSs in that the catalytic domains of both fungal PKSs and bacterial type I modular PKSs are linearly arranged. However, fungal PKSs differ from bacterial type I modular PKSs in rules dedicated to chain elongation, regioselective cyclization, and starter-unit selection.There are three types of fungal polyketide synthases: highly reducing polyketide synthases , partial reducing polyketide synthases , and non-reducing polyketide synthases .HRPKSs generate highly reduced compounds that can be furthered modified to produce compounds such as lovastatin. Fungal HRPKS domains contain, minimally, a ketosynthase domain, a malonyl-CoA: acyl carrier protein transacylase domain, and an acyl carrier protein domain. These HRPKSs also contain tailoring domains such as an enoyl reductase domain, a dehydratase domain, a methyltransferase domain, and a ketoreductase domain. These domains are interactively utilized to produce the reduced polyketide product, with the HRPKS employing the tailoring domains in different arrangements for each extension cycle. PRPKSs typically synthesize phenolic aromatic compounds such as 2,4-dihydroxybenzene and 6-methylsalicylic acid . As their name implies, these enzymes utilize their iterative domains to generate partially reduced polyketide compounds. The ketoreductase domain is the key domain controlling the reductive programming in PRPKSs, through judicious reduction of the polyketide compounds.

6-MSA is a perfect example of this, with the PRPKS responsible for producing 6-MSA undergoing just one round of reduction by the KR domain and one round of dehydration by the DH domain. NRPKSs, similar to HRPKSs and PRPKSs, minimally contain KS, AT, and ACP domains. Separate from the other two polyketide synthase types, however, NRPKSs also harbor a starter unit: acyl carrier protein transacylase domain that takes up the starter unit,4×8 flood tray and a product template domain which acts as an aldol cyclase. They also may contain a methyltransferase domain and usually contain a domain for product release such as a thioesterase domain. The SAT domain’s role is to take up the starter unit, and to transfer the starter unit onto the ACP domain where it is moved to the KS domain, undergoing decarboxylative Claisen condensation with an extender unit transferred from the AT domain. An example of a starter unit would be a malonyl-CoA unit or if in conjunction with a HRPKS, the product produced from the HRPKS. Iterative use of these domains of the NRPKS extend the chain and the PT domain cyclizes the product and then the product is programmed for release by the releasing domain. All the RALs elucidated contain the 2,4-dihydroxybenzoic acid moiety otherwise known as the β-resorcylic acid moiety, the same moiety comprising the core of tetrahydrocannabinol, cannabidiol, cannabigerol, and the rest of the cannabinoids from the Cannabis sativa plant. Furthermore, the first key intermediate in the cannabinoid bio-synthetic pathway is olivetolic acid, a β-resorcylic acid with a pentyl alkyl chain at the C6 position. Olivetolic acid is found in small quantities in Cannabis sativa extracts; therefore, this key intermediate is expensive. Additionally, although not fully studied for its biological activity, it is proposed to have antimicrobial, photoprotective, and cytotoxic activities. Due to the similarities between olivetolic acids and RALs which the Tang lab is quite familiar with, we hypothesized that fungal bio-synthetic pathways containing a tandem PKS pair may be able to produce olivetolic acid or related molecules that vary in the C6 position chain length and saturation. Therefore, we hypothesized that, by using genome mining to look for tandem fungal polyketide synthases, we could find a bio-synthetic gene cluster in fungi that produces olivetolic acid. The terminal TE domains in the NRPKSs that produce RALs are responsible for the macrocyclization reaction. In order to produce resorcylic acid instead of RALs, the releasing enzyme must catalyze a hydrolysis reaction instead of esterification. In fungal PKSs, TEs that catalyze hydrolytic release have been characterized and are typically free-standing enzymes. With this in mind, we performed genome mining of sequenced fungal genomes for bio-synthetic gene clusters that encode a HRPKS, a NRPKS, and a standalone TE. Among theclusters identified by antiSMASH,one set of homologous clusters satisfied this particular criterion . The ova cluster from Metarhizium anisopliae encodes a typical HRPKS and a NRPKS that is not fused to a terminal TE domain. Instead, a didomain enzyme Ma_OvaC containing an N-terminal ACP and a C-terminal TE is present in the cluster. Further sequence analysis of the ACP domain showed the well-conserved DSL triad in all functional ACPs, in which the serine is post-translationally phosphopantetheinylated, is mutated to NQI.This suggests the ACP domain is unlikely to carry out the canonical function of acyl chain shuttling, thus the enzyme is designated as a ψACP-TE. Previously, a ψACPmethyltransferase fusion enzyme was found in a fungal PKS pathway, in which the ψACP facilitates protein-protein interactions between the NRPKS and the ψACP- MT to enable methylation of the growing polyketide intermediate.Hence, we hypothesize the ψACP domain in Ma_OvaC may have a similar role in facilitating the catalytic function of the TE domain on a PKS-bound intermediate. The M. anisopliae cluster contains additional genes encoding a transcriptional factor and a flavin-dependent monooxygenase. Alignment of homologous clusters from various fungal species showed that HRPKS, NRPKS, and ψACP-TE are conserved , including the inactivated ACP triad . None of these clusters have been characterized and no product has been reported in the literature. Based on these analyses, we predict that the trio of HRPKS, NRPKS, and ψACP-TE will make resorcylic acids that are structurally related to OA.To examine the product profile of the ψACP-TE containing pathways, we heterologously expressed Ma_OvaA, B, and C in the model fungus Aspergillus nidulans A1145 ΔSTΔEM strain.This strain has been used in reconstitution of fungal bio-synthetic pathways, and contains genetic deletions that inactivated biosynthesis of endogenous metabolites sterigmatocystin and emericellamide B.

During the dehumidification process, the liquid desiccant is in contact with air through a permeable membrane that allows water vapor interaction but prevents the flow of LiCl into the air. LiCl absorbs the water vapor in the air until it reaches water vapor pressure equilibrium with the air. This process is exothermic. The desiccant is kept cool by the evaporation of water to the exhaust heat stream. Figure 34 shows the dehumidifier unit schematic. Equations for each stream are presented below. Mass transfer is driven by a vapor pressure differential between the air and desiccant solution as shown in Equation 66. The supply air side heat transfer for the dehumidifier is given by Equation 68. The heat transferred to the solution includes the sensible heat due to the temperature difference between the desiccant and air, and desiccant and water, plus the latent heat of absorption and enthalpy of dilution, given by Equation 72.Absorption process weakens the desiccant solution and reduce its ability to absorb water vapor. To desorb water vapor from LiCl, the desiccant is heated to have equilibrium water vapor pressure that is higher than that of the air. This regeneration process is the reverse of dehumidification and can use low grade heat sources. In this study, the SOFC system exhaust heat is used in this regeneration process to increase the concentration of LiCl in solution. Then, the concentrated liquid desiccant solution is stored. When moisture must be removed, the high concentration solution is used to dehumidify the outside air. Figure 36 shows the regenerator schematic.The dehumidifier’s model inlet parameters are the weather conditions, the return air condition,cannabis drying rack desiccant temperature and concentration, and cold water temperature and flow. The model output is supply air temperature, desiccant outlet temperature and exhaust air temperature.

In order to keep the humidity of the air cooling the servers below the allowable limits, the air humidity in the dehumidifier is controlled by manipulating the percentage of return air. In this model, the desiccant outlet concentration is also controlled by the desiccant flow rate. In the regenerator system the inputs of the model are weather condition, desiccant inlet temperature and concentration, and hot water temperature and flow. The outlets are air temperature, desiccant temperature, and hot water temperature. To use the desiccant for dehumidification purposes, it is required to regenerate it to a certain concentration. In this model the manipulating parameter to control the desiccant concentration is the desiccant flow rate. The validity of the model can be assessed by comparing its predicted supply conditions to the measured supply conditions. These comparisons are done for each stage independently: the first-stage dehumidifier and the second-stage Indirect Evaporative Cooler . Experimental data from DEVap prototype testing is used to verify the dehumidifier and indirect evaporative cooler in the next two section, respectively.For indirect evaporative cooler, 5 different cases from have been used to verify the model. Table 7 shows the input conditions as well as experimental outcome and model output for supply air temperature and relative humidity. Figure 38 compares the model predictions and the experiments of the relative temperature of the supply side air. For the Indirect evaporative cooler, the measured supply-side temperature change predicted by the model matches the experiments within 10% except for test number 3. In case 3 the temperature difference is higher and has low mass flow which shows the weakness of bulk model to predict the result and the need for a discretized model. Modern data centers try to use adiabatic cooling whenever the weather condition allows. However, adiabatic cooling is not possible in all locations at all times. Different types of common data center cooling systems were presented in chapter 2.

This chapter presents the method for calculating the cooling demand of data center at various locations. Also, the cooling demand for seven different data center locations that are used as case studies of this research are analyzed. In order to calculate the amount of cooling required for data center a MATLAB data center model has been developed which calculates the amount of cooling required by a data center. The inputs of the model are weather data associated with data center locations including temperature, pressure, and relative humidity. The weather data are obtained on an hourly basis from Typical Meteorological Year data from 2006 to 2016. The model takes this data and contains multiple functions that have been developed for calculating thermodynamic parameters such as saturated temperature, wet bulb, and dew point temperatures based upon the knowns weather data. In order to calculate the load, the acceptable operating conditions for servers within a data center are required. ASHRAE is the association that updates and releases an industry standard for data center operations every couple of years, based upon industry technology improvements. Table 8 shows the boundaries that define the ASHRAE recommended and allowable environmental envelope from the 2016 standard.In order to calculate the number of hours that the data centers in each location need mechanical cooling, TMY data for seven locations in the United States that are home to Microsoft data centers have been used as the input for the code. The number of hours of each cooling type that is required in each location based on both allowable and recommended envelope is shown in Figure 39. As expected, by expanding the range of temperature and humidity, the number of hours that mechanical cooling is required decreases. For data centers located in California, Seattle, and Wyoming a mechanical cooling system is barely required, while economizer and evaporative cooling will be sufficient throughout the year to keep the servers in acceptable range. However, Illinois, Iowa, Virginia and Texas require between 1000hr to 4500hr of mechanical cooling based on the location and ASHRAE requirements. Server load profiles tend to be confidential information that are rarely published.

However, the profile is roughly constant and utilization changes between 60% to 80%. For the current work and data center simulation results, either NREL published server load profiles as shown in Figure 40 are used by scaling it to the size of the targeted data center, or it is assumed that the utilization is constant at 70% throughout the entire operating period. The following data center simulation results for each location are based upon the assumption of a 50MW designed data center that follows the load demand of. The designed temperature difference of air entering and leaving the servers is 15℃. The number of cooling hours and cooling device correspondent to that is based on ASHRAE recommended envelope. The mechanical cooling system in these results is assumed air cooled chiller. Figure 41 to Figure 44 show the results for California and Texas which are the two ends of the spectrum with California being the location with the lowest overall energy use and Texas the highest. Figure 41 shows the TMY dry bulb and wet bult temperature for California and Texas which are the parameters that determine what type of cooling is required for the data center. California and Texas average dry bulb temperature are 14℃ and 20.2℃ and wet bulb temperature are 11.6℃ and 15℃, respectively. California has the least variation in temperature throughout the year while Texas temperature changes more than 40℃ during the year which has a significant impact on change in the cooling required for Texas.PUE is the ratio of total energy used by facility to energy used by the serves. This parameter shows how effectively a data center uses energy. As the number gets closer to 1 it means that the facility becomes very efficient, with most of the energy being directly converted in the servers for the computational demands of the data center. The following two graphs shows the PUE for the entire year. The spikes that bring PUE up to the 1.4- 1.5 range are because of energy being consumed by mechanical coolers. The average PUE for California and Texas for the whole year as simulated with the current model are 1.16 and 1.32, respectively. The results for the TMY data, Power usage breakdown,vertical grow system percentage of energy usage, and PUE for the other 5 locations are presented in APPENDIX A. Figure 45 shows energy use for each location for a 50MW designed data center following Figure 40 load demand. The air temperature difference is 15℃ and ASHRAE 2016 standards is followed for temperature and humidity limits. Table 12 shows the average PUE for all the locations with California having the lowest PUE at 1.167 and Texas the highest at 1.315.The type of cooling system, designed temperature difference, and changing allowable range has a significant effect on the amount of energy that data center consumes. For example, as the technology is rising IT manufacturers are pushing the boundaries on safe temperature that IT equipment’s can tolerate. Figure 46 and Table 13 show the energy used and percentage consumed by different part of data center for various combinations. Water cooled system use less energy than air cooled system. Increasing the temperature difference for the air entering and leaving the server room means less flow of air is required, which leads to less energy required for cooling the air. As the IT technology progresses, the IT equipment can tolerate higher temperature which leads to higher range of acceptable temperature and humidity. This means wider range of outside temperature is acceptable for cooling the server, leading to lower energy usage. In this chapter, a data center cooling model has been developed to calculate the amount of cooling required by a data center.

The model takes the weather data for each location and acceptable range of temperature and humidity for data center to calculate the load. In addition, the cooling demand for California, Seattle, Wyoming, Illinois, Iowa, Virginia, and Texas have been calculated and analyzes. Texas had the highest cooling demand with a PUE of 1.315 and California had the lowest with a PUE of 1.167. Energy usage of data center based on different types of cooling device and at different designed temperature difference has been compared. Results showed that higher temperature difference and water-cooled system lead to less energy consumed by the cooling system. In this section, the possibility of using a highly efficient, zero emission SOFC system to produce electricity and cooling in various amounts to meet electricity and cooling demands of a data center is investigated. In this configuration each fuel cell powers one server rack and heat from each individual SOFC system in used in a small-scale LDD to produce cooling for one server. Figure 47 shows the integrated system configuration for rack level power and cooling. For this analysis, each server rack power is considered nominally 12kW. We assume that a fuel cell equivalent to eight 1.5kW BlueGEN SOFC systems is used to meet the server electrical demand . The exhaust of the SOFC is used to regenerate LiCl liquid desiccant to provide 1400CFM cold and dehumidified air for each server rack. Figure 48 shows the integrated SOFC-LDD system. The SOFC exhaust gas produces hot water that will supply the heat demand for regenerating the liquid desiccant. The regeneration process occurs within a heat and mass exchanger where the vapor desorbs from the desiccant and is carried away by an air stream due to desiccant solution that has higher water vapor pressure than the air. The high concentration LiCl is stored in a tank. When air conditioning is required, the high concentration LiCl is used to dehumidify the air. As mentioned before the ASHRAE recommended suitable range of temperature and humidity for all environmental classes inside the data centers is 18˚C to 28˚C dry bulb temperature and 9˚C to15˚C dew point and 60% RH . Figure 49 shows the model results of the first test on a psychrometric chart. The green line labeled ‘LiCl – 35%’ shows the humidity ratio of the air in equilibrium with the liquid desiccant at a mass fraction of 0.35 LiCl and at each of the temperatures considered. Line black shows the first stage dehumidification process for supply air and then the cooling air process. The dehumidification process is internally cooled by evaporation of water to exhaust air to keep the desiccant cooler and increase its dehumidification potential. Then, the supply air is cooled by indirect evaporative cooling at constant humidity ratio. Horizontal black line shows the second stage process for supply air, which goes through indirect evaporative cooling at constant humidity ratio.

Furthermore, female rats that were permitted to self-administer nicotine beginning in later adolescence exhibited higher levels of nicotine intake compared to those that initiated self-administration in adulthood. Thus, the stage of development when nicotine and cannabinoid exposure occur as well as the duration of the exposure are important factors that impact later drug-taking. Cannabinoid and nicotine co-exposure in adulthood also appear to alter later drug related behaviors. Of further interest, while WIN exposure decreased nicotine self administration in adult male rats at a moderate nicotine dose, this effect was reversed when the level of effort required to obtain drug infusions was increased under a progressive ratio schedule of reinforcement. Similarly, under operant conditions requiring high levels of behavioral effort, a brief history of THC administration in adulthood increased subsequent nicotine self-administration in male rats. Thus, in high effort situations, cannabinoid exposure can drive an increase in effort to obtain nicotine. Finally, cannabinoid signaling may also be involved in cue-associated nicotine seeking. Male rats administered WIN prior to a cue-induced reinstatement session exhibited increased nicotine-seeking behavior. This suggests that acute cannabinoid receptor activation heightens the responsivity to cues in triggering reward-seeking behaviors. Taken together, these studies highlight the importance of prior drug history at varying developmental stages and level of effort required on the effectiveness of cannabinoids in modulating nicotine reinforcement.Nicotine and/or cannabinoid use may also alter cognitive and emotion-associated behaviors,vertical growing systems which are often correlated with substance use disorders.

Acute cannabinoid or nicotine exposure has been shown to induce either anxiolytic or anxiogenic effects dependent on dose, age, or sex. For example, nicotine decreased anxiety associated behaviors in adolescent male rats, but paradoxically increased anxiety-associated behaviors in females. Further, male and female adolescent rats exposed to cannabinoids exhibited a decrease in short-term and spatial working memory but an increase in depressive-like behaviors. In a study assessing chronic co-exposure of nicotine and the synthetic cannabinoid CP 55,940, both male and female adolescent rats developed increased anxiety-like behavior that was further reflected physiologically by elevated corticosterone, a stress-associated hormone. In contrast, in adult mice, chronic co-exposure to both nicotine and THC decreased anxiety-like behaviors. Similarly, nicotine treatment can reduce some of the anxiogenic effects of acute THC exposure, and THC treatment can attenuate the anxiogenic effects of acute nicotine exposure. Finally, nicotine and/or cannabinoids can induce a significant developmental impact on cognitive outcomes when consumed during pregnancy. Chronic in utero exposure to nicotine, THC, or co-exposure to both drugs has been associated with long-term effects into adolescence. Specifically, adolescent male and female rats exposed prenatally to THC exhibited deficits in short-term memory. Interestingly, the adolescent male rats with a prenatal history of nicotine and THC co-exposure exhibited similar deficits in short-term memory, as well as a deficit in pre-pulse inhibition, a behavioral outcome associated with schizophrenia symptomology. It is worthwhile to note that the nicotine and THC prenatal co-exposure condition only induced memory-related effects in the males but not females, suggesting that nicotine may have buffered the effects of THC on the developing female brain.

Together, these findings indicate that nicotine and cannabinoids induce complex interactions on the brain across various stages of development.Less than 10% of those who want to quit smoking cigarettes are successful in the long-term. Most people attempt to quit ‘cold-turkey’, without the help of any nicotine replacement therapies , other pharmacotherapies, or behavioral support programs. Unfortunately, this cold-turkey approach induces significant nicotine withdrawal symptoms, such as cravings, irritability, difficulty concentrating, headaches, and insomnia, which can promote relapse as the user attempts to alleviate symptoms with drug re-exposure. By using NRTs, such as nicotine patches, lozenges, or gum, the success of quitting increases to 50-60% at the six-month time point. This is likely due to smokers being able to obtain nicotine from a source other than cigarettes, thereby reducing withdrawal symptoms and easing the transition to abstinence. ENDS were also developed as a type of NRT for adult smokers. It was proposed that this method of administration may be more successful given that the same physical and sensory cues are present as with smoking cigarettes, such as raising the hand to the mouth and inhaling/exhaling smoke. In 2014, 4% of adults in the US reported using ENDS for cigarette cessation, but by 2018, the percentage decreased to 3.2%. Furthermore, about half of adults who vape nicotine also smoke tobacco cigarettes, a behavior known as ‘dual use’. Surprisingly, a recent study found that people who quit smoking for more than a year have an increased risk of relapse if they vape nicotine during that time. Additionally, there is increasing evidence of cannabis use in vaping devices among teens and adults. People who use THC vapes report a high incidence of tobacco product use as well. As such, the absolute effectiveness of ENDS for tobacco cessation remains to be determined. Regardless of the smoking cessation tools implemented, high rates of nicotine relapse remain prevalent.

Modulation of the cannabinoid receptor has also been employed as a novel approach for smoking cessation. In rat models, CB1R antagonists were shown to decrease nicotine self administration and reduce nicotine-induced dopamine release in the NAc, which then led to the progression along the drug development pipeline. Two different CB1R antagonists, rimonabant and taranabant, underwent clinical trials for smoking cessation and were found to be marginally effective. However, both drugs have now been withdrawn from the market due to adverse psychological side effects in humans, including increased anxiety and depression. Cannabidiol, a CB1R and GPR55 antagonist and CB2R reverse agonist, has also been assessed as a modulator for nicotine withdrawal symptoms in a pre-clinical study. Coexposure to cannabidiol during chronic nicotine exposure reduced the somatic signs of nicotine withdrawal, including paw tremors, head shakes, jumps, and abdominal contractions, in rats, suggesting that cannabidiol may be a potential therapeutic in future clinical studies. Individuals with cannabis use disorder exhibit similar withdrawal symptoms as nicotine, including increased irritability, aggression and depression, sleep difficulty, and physical symptoms. Indeed, daily cannabis users who attempted to quit report similar withdrawal symptom severity as daily cigarette smokers attempting to quit. A preliminary study has shown that synthetic cannabinoids, such as nabilone, may be used to attenuate these withdrawal symptoms, which was demonstrated in a small sample of cannabis users in a clinical setting. Targeting nAChRs may also be effective as a treatment for cannabis use disorder. A pre-clinical study in rats showed that blocking a7 nAChRs with a selective antagonist, methyllacotinine, reduced self administration of the synthetic cannabinoid WIN and prevented THC from increasing dopamine in the NAc shell. This is quite promising because this putative therapeutic did not result in any depressant or toxic effects. More recently, nicotine patches have been examined for alleviation of cannabis withdrawal symptoms. A low-dose nicotine patch was shown to reduce negative affective withdrawal symptoms in subjects that were not heavy tobacco users, but a side effect of nausea was also observed. Importantly, in consideration of the co-use condition, adult tobacco smokers who also smoke cannabis are twice as likely as non-cannabis users to continue smoking tobacco even years later. This could be due to the cannabinoids enhancing the effects of nicotine-associated cues in reinstating the drug-seeking behavior after a quit attempt. However, one study found that people attempting to quit or reduce cannabis intake also report using less tobacco on abstinent days. Thus, research on effective cessation methods for co-users is heavily understudied and needs to be conducted to aid in the smoking cessation of people suffering from co-occurring cannabis use disorder and nicotine use disorder. People battling with nicotine, cannabis, or co-occurring substance use disorders may try to quit taking the drugs, but the risk of relapse is quite high as most people begin smoking again within the first week [90]. Relapse can occur due to trying to alleviate the negative withdrawal symptoms or it can be triggered by things like stress, acute exposure to the drug, or certain cues that were previously associated with drug-taking. Cues can include the physical environment, people with whom the drug taking typically occurs, as well as any associated auditory, visual, olfactory, or tactile signals. In animal models, this phenomenon is known as incubation of drug craving in which cue-induced drug-seeking behavior increases over time during abstinence after drug self-administration. In other words,pruning cannabis a rodent is allowed to intravenously self-administer a drug of abuse for a while, such as cocaine or nicotine, then the drug is taken away. During this abstinence period, when the rodent is put back in the same environment with the same sensory cues as when receiving the drug, they will actively seek it more.

This active drug seeking is usually measured in lever pressing or nosepokes . The cue-induced portion of this paradigm is highly pertinent as it is the visual, auditory, and olfactory cues that trigger this drug-seeking behavior. This phenomenon was first seen in humans experiencing progressively higher rates of cue-induced cocaine craving and cigarette craving during the first few weeks of abstinence. The incubation of drug craving effect has now been replicated in rodent models using a wide variety of drugs of abuse, including heroin, alcohol, and nicotine. Understanding how prior drug history might impact this cue-induced drug-seeking behavior can help create more effective relapse interventions for those in the early stages of abstinence.Beyond the research itself, it is important to have insight into the scientists conducting the research, the populations being studied, the dissemination of this new scientific knowledge, as well as the people being impacted by the findings. The final chapter of this dissertation explores a broader perspective of these crucial issues and the necessity of more support for people from historically marginalized backgrounds in the field of neuroscience at every level. Publications within this chapter include discussions on Black, Indigenous, and Hispanic early career scientists being cited less often, receiving less grants, having fewer authorships, and receiving lower salaries while still doing the majority of diversity, equity, and inclusion work to make academia more accessible for subsequent generations. This section also discusses the high attrition of trainees from disadvantaged backgrounds as well as the need for stronger community, professional resources, and culturally competent mentors to advocate on their behalf. Finally, it delves into the necessity of representation and accountability in the scientific community as well as the systemic issues that inhibit this progress. Valuing diversity, equity, and inclusion principles, not only makes academia more welcoming while bringing in a wealth of knowledge and unique perspectives, but it also strengthens the research being done. In fact, studies have shown how productivity, innovation and the success of research is enhanced when these ideals are embraced. Furthermore, it allows those researchers to then disseminate information effectively about critical research progresses to their own communities while conducting meaningful outreach and mentoring the next generation of researchers.Nicotine, the main psychoactive component in tobacco, is considered to be responsible for the development and maintenance of dependence in humans. Nicotine’s effects on adolescent development have become of increasing concern given the emergence of ecigarettes, which deliver vaporized nicotine. According to a nationwide CDC survey, ~30– 45% of high school students self-reported prior use of cigarettes, vaporized nicotine products, and/or cannabis. Given that legalization of recreational cannabis across states since the time of this survey, the number of adolescents exposed to this drug will likely continue to increase through both primary and second-hand exposure. Importantly, studies in humans examining co-use of these drugs have found that individuals who reported smoking both cannabis and tobacco cigarettes consumed more cigarettes than those using tobacco alone. Furthermore, the practice of mulling has been reported as frequently occurring in adolescent users, with high incidence among daily cigarette smokers in some populations. Interestingly, chronic male cannabis users show decreased activation of the caudate nucleus in relation to reward anticipation as compared to nicotine users and non-smokers [6], suggesting altered function of reward-related circuitries dependent on prior drug exposure. Chronic use of cannabis during adolescence has also been linked to an elevated risk of psychosis, anxiety disorders, and depression. For instance, Crane and colleagues found that symptoms of depression were positively correlated with both cannabis use and tobacco smoking frequency in male, but not female, subjects. In contrast, Wright and colleagues report that cannabis use predicted increased depressive symptoms in both males and females, but increased anxiety symptoms and behavioral disinhibition were only found in females.

In the original test of social disorganization theory by Sampson and Groves , low SES was found to be significantly associated with crime. The link between economic deprivation and crime may be more complex than the simple explanation that poor people have higher incentives to commit crime and suffer lower opportunity costs for doing so. For example, Sampson reports in an earlier work that low SES neighborhoods have higher levels of police supervision, independent of actual law violative behavior . Sampson concludes that “the influence of SES on police contacts is contextual in nature, and stems from an ecological bias with regard to police control” . Whether economic deprivation has a direct relationship with crime through individuals’ behavioral mechanisms , or an indirect effect on crime rates through the operation of police bias, there is a strong theoretical basis for the finding that poor neighborhoods report higher crime rates than rich ones. Mollie Orshansky developed the original poverty thresholds in 1963-64 when she was an economist working for the Social Security Administration, shortly before the declaration of a “War On Poverty” by President Johnson . An individual or household is in poverty when its total cash income falls below the applicable threshold, determined by family size and composition . The 2010 poverty thresholds range from $11,139 for a single individual living alone to $42,156 for a family of eight or more people living in the same household.Indicators of socioeconomic disadvantage, including poverty and unemployment, have been associated with higher crime rates in Miami, Florida and Columbus, Ohio . Other studies show that rates of crime and violence are extremely high in neighborhoods containing public housing developments,cannabis dryer which are areas of extremely concentrated socioeconomic disadvantage .

In their study of medical marijuana dispensaries in Sacramento, Williams and colleagues found that violent crimes were significantly associated with “concentrated disadvantage”, a variable constructed from 2008 poverty guidelines. Property and violent crimes were not associated with density of marijuana dispensaries, but both categories were significantly related to unemployment rate . Further evidence of the link between crime and unemployment is found in crime data from across the United States in the 1990’s, a decade of incredible crime reduction. Raphael and Winter-Ebmer found that a substantial amount of the reduction in property crimes could be explained by the corresponding decline in unemployment rate. A weaker relationship existed between unemployment and violent crime, according to the researchers. Freeman presents similar findings and concludes that as much as one-third of the drop in crime in the 1990’s can be explained by the expanding job market. Theorizing from earlier empirical work by Sampson , which found that macrolevel indicators of family disruption were related to rates of juvenile crime, Sampson and Groves include family disruption among their “exogenous sources of social disorganization” . The theoretical basis for this lies in the notion that “traditional” families provide their communities with greater parental supervisory resources, compared to single-parent families. Additional supervision results in greater social control and more effective prevention against crime . According to social disorganization theory, communities with higher levels of residential turnover suffer from correspondingly lower levels of social control and are therefore likely to report higher rates of crime . The present study conceptualizes “residential instability” as an index of the percent of housing units in a given census tract that are vacant and the percent of individuals living within the tract who are between the ages of 18 and 29. These variables are also used by Martínez and colleagues in their study of crime and drug use in Miami neighborhoods. Williams and colleagues found that property crimes—but not violent crimes— were significantly associated with percent of owner-occupied households, which is a measure of residential stability. Hipp and colleagues examined residential turnover in an ethnic context and found it to be significantly associated with crime.

They conceptualized “residential stability” as the average length of residence of households in the relevant census tract .Although it is not included in the forthcoming analysis, there is one final measure of social disorganization that appears in the theoretical literature which is relevant to the present discussion of crime in city neighborhoods: population heterogeneity . The argument here is that segregated communities suffer from lower rates of communication and interaction, which prevent them from organizing collectively to reduce crime and delinquency—even when the different population groups have a shared interest in law and order. A number of studies have found that population heterogeneity is associated with higher crime rates .In this chapter I have reviewed the literature on cannabis, MCDs, and crime that is relevant to the present study. Particular attention has been given to routine activities theory and social disorganization theory . In the next chapter I extrapolate from these theories in developing a conceptual model of crime to test for the criminogenic effect of MCDs.This study examines the spatial relationship between medical cannabis dispensaries and crime across 189 census tracts in San Francisco in the year 2010. I test two competing hypotheses drawn from the theoretical literature on routine activities theory , controlling for neighborhood characteristics drawn from social disorganization theory . The first is that MCDs increase crime by attracting likely offenders and presenting them with suitable targets; the second is that MCDs actually decrease crime by protecting their surrounding community with adequate security measures and thereby providing capable guardianship. The theoretical bases for these claims are discussed in greater length in the previous two chapters. In this chapter I discuss the research methodology employed by this study and present results. Data are collected from the San Francisco Police Department, Planning Department, and Department of Public Health; the California Department of Finance; the American Community Survey ; and the United States Census Bureau. Linear regression models are tested using four dependent variables at the census tract level: total property crimes, property crimes per 1,000 residents, total violent crimes, and violent crimes per 1,000 residents.

Findings are largely but not perfectly consistent across these different models with respect to the spatial relationships between crime, MCD density, and the eight other neighborhood characteristics analyzed: poverty, unemployment, family stability, vacancy rate, percent of the population ages 18-29, percent of the population that is male, total population size, and percent of land commercially zoned.As a matter of simple spatial correlation, MCD-containing tracts have higher rates of both property crime and violent crime than tracts that do not contain MCDs. But this relationship may be obscured by the limited number of cases under review—26 dispensaries across 16 census tracts, compared to 173 non-MCD-containing tracts—and the fact that MCDs are clustered in busy downtown areas . This highlights the need to consider other variables related to crime. A more nuanced approach reveals that crime is more strongly predicted by certain “exogenous sources of social disorganization” than by MCD density. Poverty is a strong predictor of high crime rates across all four of the regression analyses conducted by this study—much stronger than MCD density. By “stronger” I mean that it has a larger correlation coefficient and a higher degree of statistical significance . “Family stability” is negatively associated with crime across all four models. Again,vertical farming systems this link is stronger than the link found between MCD density and crime. Residential instability is not as strongly predictive of crime in the present model as socioeconomic disadvantage or family disruption. In the following sections I discuss the current research design in greater detail and present empirical findings. In a recent working paper for the California Center for Population Research at UCLA, Nancy Williams and colleagues present a routine activities approach for examining the link between MCDs and crime. They examine 95 census tracts in Sacramento using data for the year 2009. Their findings indicate that tracts containing dispensaries are not significantly associated with higher rates of crime when controlling for neighborhood characteristics associated with crime . This study borrows from their work in conducting an observational study of the spatial relationship between MCDs and crime in 189 San Francisco census tracts for the year 2010. All measures are aggregated to the level of census tracts. Census tracts are convenient units of analysis because they have similar population sizes, their boundaries align with the physical environment, and they are intended to be homogenous with respect to population characteristics and living conditions . Thus they roughly approximate city neighborhoods. From a routine activities perspective, I argue that it is reasonable to assume in the case of densely populated cities like San Francisco that likely offenders, in choosing whether, where, and when to commit a crime—that is, in weighing the target suitability and guardianship of potential victims—are going to consider targets within an area roughly the size of a census tract. Maps presented in the forthcoming analysis should illustrate the geographic implications of this assumption. Another advantage to using census tracts as the spatial unit of analysis is that there is an abundance of demographic information available at the census tract level via the U.S. Census Bureau and ACS. This provides for an excellent range of control variables.Greenbaum’s second line of contention. The present model does not account for criminal activity in other tracts and therefore misses the “spillover effects” that a land use such as MCDs may have on crime in neighboring tracts.

This presents a significant limitation for the present model—although one that could theoretically be corrected for, to some extent, through more sophisticated spatial analyses. Considering the lack of empirical evidence currently available with respect to this issue—and its significant implications for policy making and future research—I argue that, as a preliminary analysis, this study has tremendous value despite this and other limitations. It may not account for inter-tract crime, but it does provide new knowledge about the nature of intra-tract crime. City residents probably are concerned about businesses in adjacent neighborhoods; but when it comes to crime they are concerned, first and foremost, with the people next door.In this study I examine the relationship between MCDs and crime rates across San Francisco neighborhoods in the year 2010. This provides an excellent case study for analyzing the criminological impact of MCDs because it offers a high level of certainty and relevance. It can be said with a high level of certainty that there were 26 dispensaries operating in San Francisco in 2010 and that they were open for most or all of that year; similar statements are difficult to make with respect to other jurisdictions. In some other municipalities, governments and MCD operators have undergone heated legal battles with one another. This has resulted in a “regulatory vacuum” with respect to MCDs in many jurisdictions . In such a vacuum, it is difficult if not impossible to determine exactly how many MCDs are open at a given time . Perhaps the most notable example of this is Los Angeles, where MCDs have been in legal limbo for years. Studies of MCD density and local crime rates in Los Angeles, while certainly compelling, would require a substantial amount of field research. San Francisco provides a case in which important information can be determined with high certainty and low cost. Other cities may provide even higher certainty, but these generally suffer from limited relevance because of the small number of dispensaries that they contain. San Francisco is not the only municipality to escape the “regulatory vacuum”. Other California cities have enacted similar MCD ordinances, including two prominent examples that can be found directly across the water from San Francisco in the cities of Berkeley and Oakland. But as a case study San Francisco has several advantages over these and other alternatives. First and foremost, it is a major city with a large sample of MCDs in the year for which data are collected. By comparison, Berkeley and Oakland have smaller populations and “hard caps” on the number of dispensaries allowed.So although they present interesting pieces of the legal, social, and political puzzles presented by California’s medical cannabis law, their small sample size limits the extent to which they are useful cases for empirical study. Unlike the “regulatory vacuum” experienced by MCDs in some other jurisdictions, MCDs in San Francisco face a number of local regulations. Thus San Francisco is not only a convenient case to study, it is also relevant and important—it touches directly on whether, and to what extent, locally regulated MCDs are related to crime.

All pollen samples were stored at −20°C after collection. Our collection protocols were as follows, during hand collection, we placed a 50-mL centrifuge vial at the base of inflorescences with dehiscent anthers and used tweezers to tap or brush the dehiscent flowers, allowing pollen to fall into the collection vial. During the water collection method, we flipped plants upside down and dipped them into a 100-mL graduated cylinder filled with 50 mL of distilled water to wash all the pollen off the plant. The water sample containing pollen was then transferred into a 50-mL centrifuge vial for storage. During bag collection, we placed brown paper bags on the plants and loosely tied the base of the bag using twine. We then flipped the plants upside down and lightly shook them for 10 s to encourage dehiscence of pollen, after which we untied the twine from the base of the bag to remove the plant, and quickly sealed the bag to prevent pollen loss. After the first trial was completed and it appeared that bag and water collection methods would likely be less successful and/or efficient than hand collection, we focused on comparing the efficiency of vacuum to hand collection in the second trial. We retrofitted small paper cups to act as filters inside a hand-held vacuum by reducing the height of the cup to 2 cm and poking four holes along the cup’s sides to allow a small amount of airflow through the filter. Retrofitted cup filters were then placed inside the body of the vacuum, between the nozzle and the motor, intercepting and storing all of the vacuumed particles. Pollen was then collected by vacuuming the leaves and inflorescences of male plants, after which the filter was carefully removed and transferred to plastic containers for storage.Using a stopwatch, we recorded the time spent collecting pollen for each method. For the treatments that did not involve water collection,ebb and flow rolling benches we mixed each pollen sample into 50 mL of distilled water, and then vortexed the samples for 30 s to create a liquid suspension with a consistent distribution of pollen grains.

To quantify the relative grain density in each sample, we used visible light spectroscopy, employing the absorption reading as a response variable. We pipetted 2 mL of the vortexed suspension into a 3-mL cuvette and then used the light spectrometer to quantify the proportion of light that was reflected by the sample. We ultimately chose 425 nm as the reflectance wavelength for the absorption reading by testing multiple wavelengths on the first sample and then identifying the wavelength region that corresponded to the peak in the absorption curve. To verify that our light spectroscopy readings were truly indicative of the amount of pollen in each sample, we pipetted 5 μL of the suspension in each cuvette onto a glass slide and used a light microscope at 10× magnification to count the number of grains contained in the sample. Counting was done using a hand-held tally counter , and counting extended across the entire length and width of the slide cover . To determine if any of the pollen grains had burst and, if so, what proportion of the total sample they represented, we counted the number of burst grains on each slide using a second hand-held tally counter. All data used in this paper are provided Appendix S1.All analyses were run in R version 3.6.0 . To test if spectroscopy readings were correlated with microscopy-derived pollen count data, we used a linear regression model. We used the adjusted R2 value and correlation of the linear model to evaluate how well reflectance predicts pollen counts. A strong positive relationship was confirmed , and so we used this method to compare yield and efficiency for the four different collection protocols. We compared the effectiveness of the collection methods using repeated measures analysis of variance , where method was a fixed effect, collection event was the repeated measure, and plant ID treatment was used as an error term. We log-transformed the response variables, i.e., the spectroscopy reading and efficiency , to satisfy the assumption of normally distributed residuals.

We then compared transformed estimates of pollen yield and collection efficiency with a repeated-measures multivariate ANOVA  followed by individual repeated-measures ANOVAs when factors were found to be statistically significant, using the manova and aov functions. Any experimental factors that were determined to be statistically significant underwent subsequent post-hoc analysis using Tukey’s honest significant difference test function in the R stats package, version 3.6.0.Visible light spectroscopy readings strongly predicted microscopy-derived pollen counts in liquid samples , indicating that this method accurately quantifies pollen abundance. Correlation between the two variables was high , implying that there was a strong linear relationship between pollen counts and light spectroscopy readings. On average, only 0.88% of pollen grains had burst. Both trial 1 and trial 2 showed significant differences between collection protocols in the initial repeated measures MANOVAs . Trial 1, which compared hand, bag, and water collection, showed significant differences between methods for relative yield , with post-hoc analysis revealing that hand collection yielded significantly more pollen than the other two methods . Water collection resulted in a somewhat higher yield than bag collection . Collection yield did not differ across time points, nor was there a time point by-method interaction . Collection efficiency was not affected by collection method, time, or their interaction . Trial 2, which compared hand collection to vacuum collection, showed no significant influence of collection method, time, or their interaction for pollen yield or collection efficiency , implying that increases in collection time directly resulted in increases in relative yield .Artificial selection for preferential traits in wind-pollinated species like cannabis critically depends upon effective and efficient methods for pollen collection and storage so as to prevent unintended genetic contamination of selected lines . Similarly, methods for pollen handling are also essential in cannabis production, where growers have conflicting needs: to maximize yield of the current crop, pollen must be excluded from production plants, but to generate future crops, pollen is essential. A gap in the literature comparing the relative success and efficiency of pollen collection methods highlighted the need to explore the often laborious process of mass collection of pollen for controlled cross-fertilization. A key step is to determine the best method for the controlled capture of pollen. Here we compared the yield and efficiency of multiple collection methods , and also compared two approaches for quantifying the relative pollen yield of different methods.

We found that light spectroscopy was an effective method for quickly and easily quantifying the abundance of pollen when suspended in distilled water. Light spectroscopy is a much faster method for quantifying pollen abundance than microscopy and is successful in predicting the pollen abundance in a collection sample. We anticipated this result, as light spectroscopy has often been used for measuring the abundance of particles in a suspension , and variations have previously been used on pollen . Hand collection resulted in a higher pollen yield than water or bag collection in our first trial, but the efficiency with which they collected pollen did not differ. In the second trial, hand collection and vacuum collection did not differ in their yield or efficiency, implying that they are equally suitable for pollen capture. Bag and water collection did require significantly less time for pollen capture; however, the substantially lower yield inhibits their application as an effective method of controlled capture. We further note that while our vacuum device did not outperform hand collection, improvement of the design to engineer a better filtration system and tailor suction power to individual growers’ needs could improve yield and efficiency. Ultimately, the results of these experiments serve as an important early step in the establishment of a practical framework for breeding cannabis,rolling grow benches as well as other economically valuable wind-pollinated crops.The coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 . COVID-19 was declared as a global pandemic by the World Health Organization . The main cause of the disease was a previously unknown coronavirus which was first identified and reported in Wuhan, China in late December 2019 . The possible zoonotic nature of the disease and viral spillover have made it more serious due to the transmission patterns from animals to humans and with the progression of the COVID-19 pandemic, transmisson from humans to animals and spill back events to other animal species were reproted . Calssification of coronaviruses within the family Coronaviridae is shown in Fig. 1. The possible transmission patterns of SARS-CoV-2 from human-to-animal and animal-to-human are shown in Fig. 2. Based on the available data at the time of this review, COVID-19 is believed to have emerged in a seafood market in Wuhan, China . Bats could be the proximal origin of SARS-CoV-2 and pangolins could be a potential intermediate host because of high genome sequence similarities of isolated SARS-related viruses with the SARS-CoV-2 genome . However, the origins of SARS-CoV-2 were recently reviewed elsewhere , yet the host range and intermediate hosts of SARS-CoV-2 remain unknown . SARS-CoV-2 is not the first coronavirus to cross species and infect humans leading to the first pandemic in history to be caused by a coronavirus. Previously, two highly pathogenic coronaviruses, severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus infected humans and caused severe diseases . As a result of the uncontrollable spread of COVID-19, countries imposed lock downs, postponed or banned international travel, and limited exports and imports to control the transmission of SARS-CoV-2 , 2020. There were significant concerns on whether food and animal products may contribute to the transmission of SARS-CoV-2 and whether the disruption of the agricultural production chain including livestock production systems would significantly harm the global economy. The effect of the COVID-19 pandemic on agricultural production, including crop and animal products, depends on the product, the location, and the economic status of the impacted location . The current pandemic has had serious impacts on animal production, animal health and welfare, global food safety, and the global economy. Impacts include the disruption of food supply chain, shortage of labor, reduced access to markets and veterinary health services, in addition to movement restrictions and limitations on international trade.

The COVID-19 pandemic has also resulted in ominous impacts on food security, leading to hunger and increased poverty in resource-limited countries . Milk and meat industries, animal and animal-product processing industries such as slaughterhouses, and poultry sectors were negatively impacted during the course of the COVID-19 pandemic. Smashing of eggs, dumping of milk, inhumane culling of animals, and disruptions of animal feed supply chain have resulted in crisis in the global economy . In the current review, we highlight different routes of transmission of SARS-CoV-2 in animals and humans, possible ways COVID-19 can disrupt the animal production chain, and effects of COVID-19 pandemic on animal health and welfare, diagnosis and treatment of diseases, and the global economy. We also provide recommendations for the prevention and control of the COVID-19 pandemic and for boosting up animal production and ultimately global economy. Since SARS-CoV-2 is primarily a respiratory, not a food borne, pathogen, the risk of food borne transmission of COVID-19 is negligible . The possibility of SARS-CoV-2 being transmitted via feces is much lower than enteric viruses which are transmitted via the fecal-oral route which may be explained by the lower relative amounts of infectious viruses in feces . In addition to the droplet transmission, the National Health Commission of the People’s Republic of China confirmed that aerosol transmission of SARS-CoV-2 is possible in special circumstances including long exposure to high concentration in a closed environment . Additionally, it was recently reported that aerosole transmission plays a role in the spread of COVID-19 . Food industry premises can be considered a closed workplace setting where infected workers can transmit the virus to their co-workers through air due to the close proximity. Viral particles of SARS-CoV-2 can also end up on surfaces of food preparation areas, meat, dairy, or other animal products. In addition, SARS-CoV-2 viral particles can also be present on swab samples from isolation wards, other hospital wards, sewage treatment units, and nursing homes . The deposition on surfaces can lead to subsequent hand-to-mouth, hand-to-nose, or hand-to-eye transmission .

Delaware and Rhode Island permitted courts to waive requirements to pay existing legal obligations. Colorado vacated underlying convictions as part of its expungement process, and voided remaining fees, fines, and restitution associated with vacated cases.Among the 39 states and Washington DC that decriminalized or legalized cannabis use, we found that 36 permitted some type of expungement of conviction records related to cannabis and four states prohibited the expungement of existing convictions. Within the 36 states that allowed expungement, 34 offered general expungement, 21 offered cannabis-specific expungement, and 11 offered general drug offense expungement . Among the 34 states with general expungement programs, 33 allowed expungements by petition, 9 expunged records automatically , and 7 allowed pardons as part of records expungement. Among the 21 states with cannabis specific expungement programs, 17 allowed expungements by petition, 9 expunged records automatically, 5 allowed pardons, and 7 provided multiple expungement programs. States with petition systems require applicants to navigate complex procedures that consume time and resources . Administrative burdens are broadly defined as the compliance, learning, and psychological costs endured by persons interacting with civic institutions that are correlated with reductions in uptake of government services . Petition systems increase administrative burdens, and as a result navigating them may require record holders to secure legal assistance,cannabis grow systems further increasing costs and limiting relief for people who are socioeconomically disadvantaged due to their convictions . Cannabis laws have imposed disproportionate criminal burdens on minorities,making accessible expungement important to improving racial equity outcomes as cannabis legalization expands .

Limited knowledge of expungement, the education needed to navigate petition systems, stress incurred during the process, the requirement to wait a specific period before seeking relief, and the payment of fees likely contributes to limited use of petition-based expungement . State policymakers may create, or retain, systems based on petitions that make expungement more difficult in the belief that this will encourage lawful behavior, and that it will prevent those who are likely to reoffend from having their records expunged . However, research suggests that expungement reduces recidivism , improves earnings, and increases employability by removing criminal records that limit the ability to obtain work, housing, or secure education funding . Expungements are also low-cost relative to job training programs and improve economic outcomes, as persons with criminal records earn less over time . Our findings suggest that potential beneficiaries may have been denied this economic and social relief, since the majority of states require record holders to petition for expungement. The majority of expungement programs also imposed administrative burdens in the form of waiting periods and fees. Administrative burdens can compound racial disparities associated with stigma, and even small administrative burdens can limit access to government aid and services . For example, waiting periods established by some states, in the expectation that these will ensure only “truly reformed” persons obtain record relief, can perpetuate inequalities as racial minorities are disproportionately criminalized by cannabis offenses . Among the states offering expungement, all but two had waiting periods, which varied in duration by offense levels and types. Waiting periods are administrative burdens because they create a psychological hurdle and sustain stress related to criminal record stigma and penalties . Shorter waiting periods improve record holders’ long-term earnings , while longer waiting periods can increase recidivism by hindering reentry into employment. Some states have reduced waiting periods to improve access to expungement . Waiting periods could arguably be eliminated for former cannabis offenders in states where use is legal and or decriminalized, given that criminalization has already been overturned .

We also found that most states required applicants to pay administrative fees and resolve outstanding judgments and legal financial obligations, pay restitution, or all of the above. Fees constitute another burden, either directly by increasing financial costs or indirectly by requiring applicants to expend time to learn how to navigate indigency waiver processes. We were unable to find prior research assessing the impacts of administrative fees on cannabis-related record holders or applicants for expungement. However, legal financial penalties disproportionately burden minorities and persons of lower socioeconomic status and are associated with increased poverty . Although multiple states permit courts to grant indigency waivers, judges do not always allow this assistance . Many record holders are unaware of indigency waivers, creating an invisible barrier to expungement . The inability to pay financial penalties may also inflict collateral consequences, such as driver’s license revocation or having debt reported to credit agencies . States could reduce these harms by eliminating administrative fees for expungement and postponing or eliminating payment of outstanding legal financial obligations. Previous research has suggested that states explore automated expungement systems to reduce administrative burdens and increase rates of expungement . Reducing administrative burdens has historically increased records expungement . Nonetheless, further research is needed on automatic expungement programs. For example, California’s historic petition-based expungement program for cannabis offenses resulted in 5% to 7% of eligible candidates applying . The state implemented automated expungement for cannabis offenses in 2018 to provide greater relief , but progress on automatically clearing the 220,000 cannabis conviction records estimated to qualify for clearance has been uneven . As of July 2021, 34,000 records remained unprocessed, spurring the introduction of new legislation to provide further automatic relief by January 1, 2023 .Our study has limitations. It focused on expungement for cannabis offenses in states that decriminalized or legalized cannabis use and sales by September 2022 and cannot be generalized to other offenses. People with cannabis offenses are more likely to secure records expungement than people with non-cannabis offenses, given the growing social permissiveness around cannabis and the expansion of legalization.

States also have expungement practices that this study did not capture, such as filing and dissemination requirements for expungement petitions and orders, mandatory hearings, imposing a burden of proof on applicants, or program qualifiers limiting expungement to either first-time offenders or persons that are diverted to and complete probation or drug treatment. In addition, we did not explore regulations that prevent legal aid organizations from filing petitions or other factors that could increase or reduce expungement access, such as prohibiting relief for people with unrelated offenses, imposing caps on total offenses, or on certain types of offenses. These requirements are administrative burdens that further complicate the process of records expungement. Although we cataloged expungement provisions pertaining to violations, misdemeanors, and felonies, we did not survey the levels and types of cannabis offenses in each state or how those may have changed over time . Instead, our paper surveyed all potentially applicable statutes and restrictions that could apply to a cannabis offense. Finally, cannabis is a dynamic policy area and legalization laws as well as expungement statutes have changed rapidly since 2012. Nonetheless,ebb and flow tables this research suggests how current expungement programs may affect the ability of cannabis-related record holders to secure relief in states that have attempted to reduce cannabis criminalization, and provides the groundwork for further exploration.Warehouse storage occupancies are currently reaching heights on the order of 24 to 30 m high stacks of storage commodity, which have not been considered by existing fire codes and engineering correlations. Over the last 50 years, fire protection engineers have relied on large-scale tests to classify commodities into one of seven classes that are representative of their fire performance under specific geometric configurations and ignition conditions. This classification process, which relies on expensive full scale testing, results in increased safety gaps as the industry creates new and untested materials that are stored in large quantities. Only a limited amount of fundamental science has been performed in this area, which is largely due to the range of complexities that occur in large-scale fire phenomena. Some correlations for large-scale flame heights of some commodities and fuels are present in the literature, but they are limited to specific fuels/configurations and some of the correlations require heat release rate values from full-scale tests. These correlations will be discussed further in the following sections of this paper. Currently, no tests that are known to the authors provide a complete set of fundamental, non-dimensional parameters that can be used in engineering calculations towards the safer design of large storage facilities. Efforts that result in the development of such test methods and classification methodology with a sound scientific basis may fulfill an urgent need to improve upon the current warehouse design methods. In most of these incidents, which were reviewed in Part I of this paper, the facilities were protected by automatic sprinkler systems that were installed in accordance with their respective current codes and standards. The negative impacts of these devastating fire incidents were felt by the occupants, firefighters, insurance interests, and local environments. From a business aspect, millions of dollars of materials or products are lost, and operations may be halted. Furthermore, insurance premiums are increased as a result of the fire, and the lost time can never be recovered. From a life-safety aspect, the lives of workers and responding firefighters are endangered, which can result in injuries or death.

The water runoff from firefighting operations and the resulting smoke plumes can also adversely affect the surrounding environment. The development of an approach to protect these facilities based upon the combustible materials that are stored, the layout of these materials, and the complex interaction with potential suppression systems is a critical step towards reducing the amount of devastating warehouse losses. As a first step towards improving the methods for commodity classification, two existing non-dimensional parameters are used to represent the physical phenomena present at the large-scale and model one part of large-scale flame spread. In Part I of this paper, a method was developed to experimentally quantify the burning rate of a material based upon the non-dimensional comparison of a materials chemical energy released during the combustion process with the energy required to vaporize the fuel, which was measured as a B-number. Commodities are classified to design sprinkler protection systems for most warehouse scenarios, and because such a sprinkler systems goal is to suppress or control a fire, the ranking of materials based upon the burning and spread rates of a potential fire is appropriate. Experiments were performed on a standard warehouse commodity, a Group A plastic, which is typically used to represent the worst-case commodity in large-scale tests. The commodity consisted of a single corrugated cardboard box that measured 53 x 53 x 51 cm and contained 125 crystallized polystyrene cups that were segregated by corrugated cardboard dividers. All of the faces except for the front face of the commodity were uniformly insulated, and the front face of the commodity was ignited at its base. The experimental observations of the Group A plastic commodity resulted in a qualitative description of the burning process over three distinct stages of burning. The first stage was characterized by upward flame spread over the front face of the corrugated cardboard, followed by a decreased burning rate as the cardboard smoldered and the polystyrene heated, and finally a sharp increase in the burning rate after ignition of the polystyrene. Despite the complex configuration, each stage resulted in distinct material involvement, which indicates the potential to model distinct material involvement from each stage using parameters derived from bench-scale testing. Fluctuations between the repeated tests also indicated the difficulty in obtaining repeatable measurements during these larger tests; therefore, small-scale test methods that can be repeated at a level of statistical accuracy may greatly improve the applicability of the results. Part II of this study continues the development of a non-dimensional approach to characterizing the burning behavior of materials. The bench scale tests that were performed in this study involved a small, flat sample of corrugated cardboard or polystyrene oriented vertically in which the burning was isolated to the front surface of the sample. The flow was considered to be laminar due to the observed behavior of the flow in the experiments. At the bench scale, the tests captured the effects of the commodity material properties on the flame spread process while separating the large-scale effects such as turbulence and radiation. Non-dimensional B-numbers were experimentally determined for the samples with greater accuracy than previous experiments. A flame spread model was then utilized to demonstrate the application of the experimentally measured B-numbers to predict in-rack flame heights in large-scale configurations.

The amount of dilution required to achieve odorless air delivers a crude point of departure but should acknowledge the large error and be presented with only one significant figure in the final results. Thresholds for odorants typically vary by several orders of magnitude, especially the ODTC50 . Further, reliable methods may not have been used, and results for odor detection and odor recognition are sometimes mixed up. Within a controlled setting, two approaches to sensory testing of dilutions by panelists are used. One is the Odor Profile Method , which uses sugar solutions for calibration, and the other uses odor disappearance upon sufficient dilution. Both rely on dilution equipment, typically a dynamic dilution olfactometer that delivers sampled air diluted with odorless air to a nose port where the dilution is smelled by the panelist. Concentrations are presented in ascending order to avoid desensitization and anticipation bias. Statistics are then used on the panelists’ results to determine the ODTC50 for the odorant or mixture tested. For a single odorant, OPM is used to assign an odor note and intensity to each dilution. The Weber-Fechner law is applied, meaning that the logarithm of the concentration is taken and then the intensity results are fit to a line through linear regression. Extrapolation to intensity score 1 yields the ODTC50 value. Each odorant can have a vastly different slope. For example, a 200-fold change in concentrations of 1-propanol and n-amyl buterate caused a 15-fold and 0.5- fold change in odor intensity, respectively . For mixtures , the ODTC50 can be determined by forced choice, typically “triangle,” methods using the same dynamic dilution equipment . While inhaling at the nose port,commercial racks the panelist rotates between three choices and then selects which is the diluted sample. A point estimate is generated rather than a curve, and no odor description is gathered.

ASTM Method E679-04 has been used to determine the ODTC50 values for a range of odorants . Such methods have been used in the drinking water industry to set ODTC50 values for methyl tertiary butyl ether and improvements to ASTM Method E679-04 have been suggested for drinking water . In Europe, under EN 13725, the final dataset typically only includes the data for the four or more panelists whose results are the most consistent with the overall panel’s geometric mean value. Also, panelists may be presented with 2 samples instead of 3. The dilution result is called “European odour unit” , which is defined as “the amount of odorants that, when evaporated into 1 m3 of neutral gas at standard conditions, elicits a physiological response from a panel equivalent to that elicited by 1 European reference odour mass [123 μg n-butanol] evaporated in 1 m3 of neutral gas at standard conditions” . Thus, the European approach accounts for the variation in detection thresholds of the panelists. Despite these strictures, proficiency tests in 2007 and 2008 found two thirds of the European laboratories claiming to work in accordance to the EN 13725 standard failed to demonstrate compliance with the required performance criteria . Dilution equipment can also be used to evaluate the odor hedonic tone, as is required in the Netherlands . Panelists express the degree of pleasantness using a 9-point scale ranging from H -4 to H +4 . The logarithm of the odor concentration is plotted versus the H-value, which approximates a straight line. Using linear regression, the level of dilution required to achieve Dutch regulatory criteria can be estimated. OPM used to determine the ODTC50 goes against the guidance upon which it was built, yet still delivers useful results . Both the FPA and APHA Method 2170 advise against conducting statistical analysis on the intensity scores. The scores are categorical rankings and not an interval scale. In other words, an intensity score of 8 is not necessarily twice as intense as a score of 4. The intensity scale was not designed for 1 to be the ODTC50 value, and historically used the symbol. Surprisingly, such “against the guidance” extrapolation was found to be not off-the-mark from ODTC50 values from other studies , which may be confirmation of the enormous range of such values .

Day-to-day variation by panels is typically within an order of magnitude . Faint environmental odors are sensed but cannot be measured by dynamic dilution olfactometry . Typically, dynamic dilution values are never given for levels lower than 10 OUE/m3 , and generally the lower values start in a range of 50–100 OUE/m3 . The use of human subjects as panelists is strictly controlled . International codes of conduct apply as do reviews by a human subjects review boards to protect participants.Exposure limits are intended to protect the health of populations and arrive at quite different results for workers versus the general public. Workers are considered healthier and less diverse in susceptibility than the general population. They also only spend a portion of their day and week on-the-job. Political pressures, too, may influence worker limits to prevent onerous restrictions to industry . The morbid or lethal outcomes of occupation exposures likely overshadowed concerns about impact on olfactory function. The end result is that worker exposure limits are usually many times higher than those for residential exposures. A survey by National Geographic revealed that factory workers reported poorer senses of smell , although sensory loss is typically gradual and may be confused with aging . Sensory-impaired workers can miss out on warning signals that impact their nutrition and quality of life. Monitoring of workplace air for hazardous chemicals cannot be replaced by sensory cues, due to a portion of the workforce having little or no sense of smell , odors can create a workplace nuisance for most employees. Workers, too, are subject to the psychological strain of odor exposure, and setting and occupational exposure limit above the odor threshold may lead to perceived risk and well as physical response . For these reasons, occupational exposure limits consider odor. OELs have long considered irritation . In the United States and Europe, about 40% of the OELs are set to avoid sensory irritation . Sensitization is especially a concern, and OELs should be set low enough to avoid such initial, triggering exposures .

Only three chemicals are regulated by OSHA based on “obnoxious odor” and worker complaints: isopropyl ether, phenyl ether, and vinyl toluene . Critical reviews suggest setting OELs by taking into account both irritation and odor thresholds . For residential exposures, sensory effects are considered in emergency situations but rarely for ongoing, long-term exposures . The latter protect from cancer and non-cancer effects by focusing on system organ toxicity. Whether odor is a health-protective signal varies from odorant to odorant. Carcinogens have especially low exposure limits, so the ODTC50 tends to be well above the residential exposure limit. Benzene and formaldehyde are prime examples of such carcinogens . Because ODTC50 values range over several orders of magnitude, they commonly overlap with exposure limits. Re-visiting the work by Rosenfeld et al. and adding the ODTC50 ranges from AIHA , this overlap became apparent for hydrogen sulfide and ammonia .Exposure assessment involves the measurement of odorant concentrations in the hope the subset measured is responsible for the odor. It also involves the use of human panels to identify odor characteristics . Such categorical measures cannot be used in a quantitative risk assessment; however, they do provide qualitative information. The measurement of the number of dilutions required to remove an odor is plagued by inconsistent results and driven by the final remaining odorant in the dilution . When exposure measurements have been determined for a subset of odorants, the readings can be compared to the exposure limits presented in Section 4.4. This is the risk characterization. For residential exposure, the comparison is with the ODTC50 by a ratio known as the “Odor Activity Value” . Such a comparison, however, carries with it the inherent weakness in the numerator and denominator values in the ratio, undermining some of its usefulness. Specifically, ODTC50 values usually span odors of magnitude, and measured concentrations are typically a snapshot in time. Some studies find the inputs to OAVs too variable and are now pursuing other methods instead . In addition, for ongoing rather than intermittent odor exposures, comparisons with health based exposure limits for residents, such as the USEPA Reference Concentration or California EPA Reference Exposure Level ,greenhouse rolling benches can be performed for non-cancer effects. For carcinogens, the cancer risk estimate involves the inhalation USEPA Unit Risk Factor or equivalent. Note that exposure assessment usually has less uncertainty than other portions of risk assessment . Mixtures are challenging for risk assessment. Combined effects of sensory irritants can be considered additive as a first approximation . The interplay of odorants, however, is often unknown . Hydrogen-sulfide-equivalents are a proposed approach , similar to the dioxin equivalents for the 17 dioxin-like congeners and the carbon dioxide equivalents for various greenhouse gases.As a literature review, where no exposure data were generated, the case-study approach is used to illustrate how odorant concentration data can be used in conjunction with odor threshold information to characterize the health risks.

Although the most significant risks are due to chemical exposures in the workplace , the focus of this paper is on residential exposures to environmental odors, so worker exposure studies were excluded .Not only have OAVs been evaluated for on-site worker exposures , they also have been evaluated for odorants from sewers across Australia over a period of 3.5 years . Both efforts were to prioritize the subset of odorants monitored within the mixture to identify “high priority” odorants for further analysis. For the sewer study, the upper bound concentrations were compared to the ODTC50 values from Nagata and Takeuchi . The ranking by OAV reduced the number of compounds from 31 to 8 “high-priority” odorants. Hydrogen sulfide and methyl mercaptan dominated across all sites , and other volatile sulfur compounds also ranked high for most sites. Diethyl sulfide, limonene, toluene and m,p-xylenes each ranked high for fewer sites. The limitations of such ranking are substantial. The choice of ODTC50 value is paramount and typically ranges over several orders of magnitude across studies, complicating OAV calculations and their utility as a ranking scheme. Although Sivret et al. acknowledged the enormous ranges of ODTC50 values available in the compilation by van Gemert in several graphs, the final ranking was performed using the single values found in Nagata and Takeuchi for simplicity. Presenting the final OAVs as ranges would have muddied the results. Finally, those compounds without ODTC50 values available in the literature were dropped from the ranking, essentially rewarding lack of data for potentially odorous compounds. As with any study of mixtures, the analyzed odorants are only a subset and may not reflect the overall, total odor experienced by residents. Risk assessment is often predictive rather than retrospective. The impacts of a proposed oil well in Hermosa Beach, California, were forecast . Except for upset conditions, the anticipated negative health outcomes were largely nuisance-related . The evaluation concluded that the oil well would have no substantial effect on community health, which was based in part on a risk assessment of hydrogen sulfide as the odorant of primary concern. The results of the modeling indicated that fugitive emissions from normal operations could produce concentrations greater than the odor threshold without mitigation, which would reach nearby residences. Concentrations could be as high as 6 times the odor threshold, primarily driven by hydrogen sulfide. The acute REL for hydrogen sulfide would only be exceeded, according to the model, during accidental or unplanned release. Odor impacts from normal operations were, therefore, considered potentially significant without mitigation, so an Odor Minimization Plan was required.Over years, the residents of communities north of Denver have complained of intermittent, unpredictable “tar” and “asphalt” odors. The symptoms in Globeville, Colorado, included burning eyes and throat, headaches, skin irritation and sleep problems. A USEPA funded environmental justice study was conducted there in 2012 . Air samples were collected from locations near potential sources and within the community over a period of 7 months. Out of a list of 23 analytes, the most prevalent were hexane, heptane, benzene, toluene, m,p-xylenes and naphthalene. The maximum concentrations were below odor and toxicity thresholds , except for the carcinogens , which were considered within normal ranges for urban air.

The inclusion of an intercept, however, fundamentally changes the slopes of the lines and does fundamentally alter the relationship. The original Weber-Fechner law included no intercept . Researchers added the 0.5 intercept to account for “the definition of the odour threshold concentration which states that 50% of the panellists perceive weak odour while the others perceive no odour” and proceeded to use an intensity scale that ranged from 0 to 5 . Other researchers also used the equation with the 0.5 intercept, but the intensity scale ranged from 0 to 6 . The effect of the 0.5 intercept in both of these studies was to assign an intensity score of 0.5 to the ODTC50 concentration, which had nothing to do with the percent of the panelists perceiving or not perceiving and odor. Another researcher allowed the intercept to float uniquely for each odorant and used a 0 to 12 intensity scale , which scale been used in flavor and drinking water profiling . The results of these three approaches are dramatically different because the fixed intercept forces all lines to the same point while the floating intercept allows for very different linear fits to the data. Further research is needed into the equation that best fits the relationship between concentration and intensity scales.To use the above equations, an intensity scale needs to be used that offers interval scaling, i.e., a doubling of the score means a doubling in perceived intensity. Initial work by Arthur D. Little avoided such scaling and applied symbols and words to relative intensity bins . Researchers turned this into a mathematical scale and chose at one point to multiply the scores by a factor of 4 . After this arbitrary expansion, the score 1 was then deemed to be the odor detection threshold concentration for 50% of panelists , although such an approach is advised against in the guidance . This approach to odor intensity is included in the Odor Profile Method,vertical grow rack which is presented in Section 4.2.The predicted ODTC50 using the 0-12 scale for several chemicals was within the range of the values from more traditional methods .

Although the intensity scale is ordinal , it is handled as a metric scale , presumably because it simply works. As with analytical instruments, the zero intensity score is not included in the linear regressions. Note that the above equations use the base-10 logarithm rather than the natural logarithm, which is used in most science, perhaps because decibels use the base-10 logarithm. Another method of finding the intensity of an odorant is through repeated dilutions of the sample . These dilutions are presented to panelists from high concentration to low concentration using continuous airflow to a nose port by a dynamic dilution instrument . Two odorless blanks and the sample are presented, and the panelist chooses which one is odorous . In Asia, the standard method is 3 bags instead of continuous flow. Points along the intensity curve can be observed, but it is typically the ODTC50 that is sought. Both methods, using an intensity scale or dilutions, are used to analyze mixtures as well. Mixtures are more complex and do not necessarily follow the above associations. Specifically, the number of dilutions required to reach the odor-detection threshold for a mixture does not properly reflect the actual sensory intensity , as demonstrated by a study of fecal odorants . In other words, odor intensities increase and decrease with concentration at different rates for different odorants, not to mention the antagonistic and synergistic effects that also occur. A comparison of two intensity methods commonly used to evaluate single odorants were compared . One method used various vials of n-butanol as reference points to provide a sniffed intensity scale for panelists , while the other used three tasted solutions of sugar at different concentrations . The latter approach was applied to odor research based on the cross-modal premise that our senses are linked, so taste can be used to inform the sense of smell . Although the two anchors were not necessarily parallel at the high end of their scales , they compared favorably . Therefore, the result of this study cannot be used to say the two scales are similar beyond this one study’s approach.

Further, the n-butanol method advises against translating the results into perceived odor intensities . A comparison with the other anchors would be informative .Note the similarities between odor-detection thresholds and method detection limits for analytical chemical analyses. Both are statistical measures that become less precise and highly variable when near these limits, with sensory thresholds showing greater variability, often of two orders of magnitude . Both the physical setting and panelist’s unique situation can lead to such large variability, even from one day to the next. Therefore, presenting an ODTC50 as a range rather than a single value is recommended . If data are only generated suprathreshold, then a Weber-Fechner plot can be used to crudely estimate an ODTC50. Quality controls should include method blanks and spiked samples, and analyzing samples in triplicate allows for the calculation of standard deviations .For risk assessment of conventional air pollutants, frequency and duration are temporal aspects used in the calculation of exposure and indicate the appropriate hazard benchmark . For odor assessment, frequency and duration also are part of the sensory experience because they alter the perception of odor . An individual can become “used” to an odor and no longer able to detect it. Because odors are sensed within a few seconds or less, any averaging times applied to measurements may miss the peaks that trigger the complaints. Noting the frequency and duration of odor events can help inspectors and facility operators identify odor sources based on operation schedules and weather patterns.English speakers typically refer to a smell by its source . When forced to avoid using source terms, the descriptors “stinky,” “fragrant” and “musty” are used most often. Subsistence hunter-gatherers in the Malay Peninsula, by comparison, have a rich olfactory language, naming smells as easily as colors .

Dutch participants in a study had the same facial expressions as the hunter-gatherers when smelling odorants; however, the words selected to describe the odors were vastly different, the Dutch words tending be vaguer . Overall, smell tends to be poorly coded in languages as compared to other senses, yet this seems to be based in cultural norms rather than any neurological reason .As a starting point, the previous reviews of odor-measurement methods conducted by doctoral students were consulted . These reviews built upon a substantial review , an effort sponsored by groups in Australia and Spain. To update the prior work, a literature search was conducted post-2010 to gather the most recent methods and critiques. The search was conducted online and at the UCLA and CARB physical libraries. When relevant articles or books were found, the “cited by” function was used to discover even more up-to-date information. Reviews of the latest approaches to odor exposure measurement and risk assessment were sought. Finally, relevant websites and posted materials that are not typically available in scientific journals were searched. The starting point for risk assessment methodology was the foundational work by the National Research Council , now updated for the 21st century . The programs that grew out of the original work have issued their own guidance, which was also consulted. Such programs include pesticides , site remediation , exposure factors and California-specific work . International efforts, such as the review of the chemicals in commerce in the European Union , were also consulted. The 1,556-page tome edited by Dennis Paustenbach titled “Human and Ecological Risk Assessment: Theory and Practice” was an additional starting point. Risk assessment principles and terminology will be used to organize and structure the field of odor exposure assessment. The risk assessment framework will not be applied per se to environmental odor cases but, rather,hydroponic shelf system offer well-established concepts, conventions and terminology that can be applied to odors. As one example, the challenge of evaluating real-world chemical mixtures rather than a single chemical at a time applies to both fields. At the core of the evaluation will be the scientific merits of the various approaches to ensure any recommendations are evidence-based. The strengths and shortcomings of popular sensory and instrument methods will be reviewed.Sensory methods use the human nose as the detector. Because odor complaints arise from this same detector, it is the “gold standard.” As discussed in Section 2.3, our sense of smell can detect odor notes, although our vocabulary may struggle to supply the right words. The odor hedonic tone is more easily assigned. The odor intensity may be assigned categorical words or scores or placed on a scale. The scaling of intensity has led to the techniques discussed in this section. As with all else pertaining to odors, evaluating individual odorants is an oversimplification of their contribution to the total odor of a mixture.While chemical analyses can identify a subset of odorants and their concentrations in ambient air, chemical analyses often do not directly relate to human sensory experiences .

Odor samples usually contain multiple odorants that can have synergistic and/or antagonistic effects on each other, altering the overall perception. Odor is not simply additive, unlike concentration. Further, the human nose is usually more sensitive than analytical techniques. Therefore, the most accurate way to evaluate an odor it to judge its properties “as is.” This direct approach is called “odor profiling” and can be performed at the location where the odor is observed or by capturing a sample and transporting it to where a trained observer is located . A specific version of this approach, the Odor Profile Method , has been developed based largely on flavor profiling for the food and drinking water industries, specifically Method 2710 “Flavor Profile Analysis” by the APHA . OPM includes two parts: first, identifying one or more odor notes in the sample and, second, determining the odor intensity for each odor note. Duration of the odor at the site fenceline can also be included as a third factor . OPM can be part of a diagnostic investigation or an ongoing monitoring program. Rather than have panelists use their own natural, naïve language to describe the odor notes, a standardized vocabulary has been developed. Note, however, that only up to 2 odorants per mixture can be recognized by trained panelists . The standardized vocabulary has been tabulated for several industries and displayed graphically as wheels. Appendix A includes a collection of odor wheels, and Table 3.4 is a side-by-side presentation of their content. Over the years, such wheels have contributed a standardized way to classify, communicate, and identify odor notes, and sometimes the underlying odorants, in emissions . Odor wheels consist of three rings: an inner ring of general odor notes, a middle ring of more specific odor notes within each segment and an outer ring of potential odorants associated with each odor note. Their development has been described . The OPM intensity scale, described and critiqued in Section 2.3 , is used for each odor note. This scale is anchored on three sugar solutions tasted by mouth with cross-modal sensory translation to smell. The 7-point OPM intensity scale is: threshold , slight , weak , medium , medium strong , strong , and very strong . Before an individual becomes a panelist, their sense of smell is verified. The OPM uses the University of Pennsylvania Smell Identification Test , which is the best-known test of smell that uses micro-encapsulated odorants . As such, it is highly portable and often used in field studies. It has been well-standardized against age, gender and correlates well with the results of quantitative odor-detection tests . A passing score is 70% of the 40 questions. Further guidance on the selection of panelists is provided by ASTM Method E1440 . A minimum of four panelists is required by OPM. They may not have a cold, mustache, wear perfume, eat food or drink during the session. They are trained using the applicable odor wheel as well as the intensity scale .

Sociostructural factors that contribute to substance use-related risk factors, such as incarceration, community context, and social networks, are gaining increasing research focus. Social networks contribute to the context and uptake of drug use via modeling and the sharing of information on how to use substances, drug procurement, and how to sustain usage. Previous studies show that Black men report learning about cannabis from peers and family members and use cannabis to cope with racial discrimination, financial challenges, pain, and other stressors; but the perceptions and attitudes toward cannabis use and other “harder” drugs are not well described. Whether consumed for self-medicative or recreational purposes, many participants in prior studies endorse daily usage as being a non-problematic, normalized, and a beneficial part of their lives. Many providers utilize the Diagnostic and Statistical Manual of Mental Disorders fifth edition to determine problematic cannabis use, and assessments are supported by varied metrics and assessment tools. The DSM-5 determines a person may be diagnosed with cannabis use disorder if they meet two of their designated nine criteria that primarily evaluate ability to cease/decrease cannabis use, frequency and amount consumed, and impairment of ability to achieve daily tasks. A person’s social network can influence that person to use substances in ways that are problematic, but these networks have also been found to mediate problematic substance use and recidivism by supporting persistent recovery. By characterizing social networks and contexts to inform the development of effective interventions, researchers and policymakers can leverage social structures for behavioral change. To generate further understanding of this topic as well as support potential interventions,plant growing rack this paper explores how one’s social network contributes to cannabis perception and usage patterns among BMSM who have been involved with the criminal legal system.These data were collected via a supplement to a NIDA funded three-site study designed to develop agent-based models to examine the impact of various interventions on HIV in BMSM who experience incarceration.

The parent study involved three of the four largest jail systems in the country . Interviews from Chicago and Houston were included in this analysis because they were collected first using similar interviewing approaches. Los Angeles data collection was incomplete at the time of this paper and used more in depth approaches, building on what was learned from the Chicago and Houston interviews.We used these data to qualitatively examine perceptions of cannabis use in BMSM in Chicago, IL, and Houston, TX. Eligibility criteria were: age 18–34 years old; Black or African American identified; cisgender male; had sex with a man in the 12 months prior to their interview; incarcerated for at least 24 h; and misuse of opioids or use of illegal drugs other than opioids or marijuana in their lifetime. Researchers conducted 25 interviews that lasted approximately 60 min each using a semi-structured interview guide to explore participants’ experiences with substances, social networks, and incarceration. Participants received a $50 incentive. University of Chicago institutional review board provided oversight to all study procedures.Interviews were audio-recorded and transcribed, then coded and analyzed. Minority Stress Teory and the Social Ecological Teory were used to understand how contextual factors produce a social environment that encourages sustained or exacerbated substance misuse. By using a modified version of Braun and Clarke’s six-phase guide to a deductive-inductive thematic analysis, we systematically identify and offer insights into themes observed across the sample. Dedoose was used for data coding and management.This paper expands on how social networks and socioenvironments can influence drug use; importantly, we emphasize how a majority of participants reported that their peers and family both positively influenced their desire to consume cannabis as well as negatively influenced their desire to consume “heavier” drugs . Participant narratives of their cannabis usage further emphasized their drug-use behavior as being agentic; rather than passively absorbing influential factors, interviewees endorsed actively considering social contexts and potential consequences when engaging in cannabis related decision-making and risk-assessment.

While most participants endorsed a generally positive relationship with cannabis and did not find their usage disruptive to their daily lives, a small number felt their reliance on cannabis to manage physical and emotional needs was problematic. Social networks as an influencing force for cannabis use is of interest as these same networks could also be leveraged as an intervention for cannabis use disorder. For example, there are several network interventions that could be utilized to promote treatments or health care seeking for cannabis use disorder. A community opinion leader has been successfully utilized for other HIV prevention interventions, and is currently being tested to promote COVID-19 prevention behaviors. Criteria for diagnosing cannabis use disorder vary and effective and durable cannabis use disorder treatments are elusive and impact the likelihood that individuals’ will seek treatment or stop using, particularly when these individuals are embedded in environments where use is pervasive and accepted. Future research and practice examining the role of mediating factors for cannabis usage, such as anxiety and depression management, has the potential to improve treatments. Our findings add to a growing body of research elucidating how social relationships, including peer and family relationships, as well as negative life events influence cannabis usage patterns. Shifting statutes around the legality of cannabis and efforts to reduce recidivism highlight the importance of research on the interplay of incarceration, substance use and cessation, and recidivism. Our findings did not reveal significant differences in how the legality and illegality of cannabis in Chicago and Houston respectively influenced the cannabis use contexts and perceptions of our participants; this may be because all already had experienced incarceration and used substances as a study entry criterion. Analysis of how cannabis consumption behavior differs between legislative jurisdictions should be pursued in future studies, as the landscape of cannabis acceptability and availability is rapidly yet inconsistently evolving across cities.ceptions and consumption in the life course of BMSM who had been incarnated. As such, cannabis perceptions and experiences were not explored in-depth during interviews; however, our analysis is strengthened by not having used leading questions during participant interviews. Additionally, all participants resided in two large metropolitan areas and had a history of incarceration, which may affect generalizability to broader BMSM populations.

Last, we did not have other information to corroborate whether participants’ cannabis consumption was problematic, neutral, or effective as self-treatment.use as being influenced by social and familial networks, the desire to satisfy physical and emotional needs, and preconceptions of the risks and benefits involved in “heavy” drug and cannabis usage. Furthermore, we expand on previous findings with the novel observation that participants behaviors related to “heavy” drug and cannabis usage were the result of their own risk assessments versus passive reactions to impersonal socio-environmental factors. Given the expanded availability of cannabis, with expanding legalized recreational use, and marketing that is often directly speaks to the rationales expressed by our participants, further qualitative research will facilitate understanding of the nuanced of risk-assessments people make related to cannabis use. This is particularly important for individuals who were previously incarcerated for cannabis-related charges. Study findings strengthen and expand our understanding of drug consumption-related decision-making, revealing opportunities for future targeted behavioral interventions.Cannabis sativa has a long history of cultivation for a variety of uses including food, fibre, medicine,indoor vertical garden system and recreational drugs . Cannabis produces many different secondary compounds such as cannabinoids, flavonoids, stilbenoids, alkaloids, lignanamides, and phenolic amides . D9 -Tetrahydrocannabinolic acid , a product of the cannabinoid class, is the primary psychoactive agent. This compound is produced as an acid in the glandular trichomes of in- florescence bracts and undergoes decarboxylation with age or heating to D9 -tetrahydrocannabinol . Cannabis cultivars differ substantially in economic traits that range from marijuana, arguably the most widespread illicit drug, to hemp fibre derived from the stems of the plant. Marijuana consists of the dried female inflorescences in which the quantity of THC exceeds that of cannabidiol, and potency varies among cultivars by several orders of magnitude . Marijuana cultivars are known to have THC levels exceeding 2–24% of inflorescence dry weight whereas hemp cultivars produce substantially less THC but rather high levels of CBD . THCA and CBDA share the same biosynthetic pathway except for the last step in which THCA synthase and CBDA synthase produce THCA or CBDA, respectively . Recent evidence suggests that the genes encoding the two synthases are allelic . CBD and THC are enatiomers, but only THC elicits psychotropic effects, whereas CBD may mediate anti-psychotropic effects , a difference highlighting the stereo-selectivity of receptors in the human body that bind these compounds. Although classified as a drug without therapeutic value in the United States, ingestion of THC is widely regarded as having effects including pain relief and appetite stimulation, that may, among other things, increase the tolerance of cancer patients to chemotherapy . Dronabinol, a synthetic analogue of THC, is approved for use as an appetite stimulant in the United States as a Schedule III drug . Cesamet , another synthetic analogue, is used as an anti-emetic for patients undergoing cancer therapy. The natural product Sativex is approved for use in the UK and is derived from Cannabis cultivars containing both THC and CBD, and is used to treat pain symptoms associated with multiple sclerosis. Compounds from Cannabis sativa are of undeniable medical interest, and subtle differences in the chemical nature of these compounds can greatly influence their pharmacological properties. For these reasons, a better understanding of the secondary metabolic pathways that lead to the synthesis of bio-active natural products in Cannabis is needed .

Knowledge of genetics underlying cannabinoid biosynthesis is also needed to engineer drug-free and distinctive Cannabis varieties capable of supplying hemp fibre and oil seed. In this report, RNA from mature glands isolated from the bracts of female inflorescences was converted into cDNA and cloned to produce a cDNA library. DNA from over 2000 clones has been sequenced and characterized. Candidate genes for almost all of the enzymes required to convert primary metabolites into THCA have been identified. Expression levels of many of the candidate genes for the pathways were compared between isolated glands and intact inflorescence leaves.Seeds from the marijuana cultivar Skunk no. 1 were provided by HortaPharm BV and imported under a US Drug Enforcement Administration permit to a registered controlled substance research facility. Plants were grown under hydroponic conditions in a secure growth chamber yielding cannabinoid levels in mature plants as reported in Datwyler and Weiblen . Approximately 5 g of tissue was harvested from mature female inflorescences 8 weeks after the onset of flowering. Tissue was equally distributed into four 50 ml tubes containing 20 ml phosphate buffered saline as described by Sambrook et al. , but made with all potassium salts and mixed at maximum speed with a Vortex 2 Genie for four repetitions of 30 s mixing followed by 30 s rest on ice, for a total of 2 min of mixing. Material was sieved through four layers of 131 mm plastic mesh and the flow-through was split into two 50 ml tubes and spun in a centrifuge for 30 s at 500 rpm. Supernatants were decanted and pellets were resuspended in PBS. The suspensions were combined into one tube and pelleted as before. The resulting pellet was diluted into 100 ll of PBS. Five ll were used for cell counting with a haemocytometer, and the total suspension was estimated to contain 70 000 intact glands. Plant residue was incinerated by a DEA-registered reverse distributor .Full-length cDNAs from CAN24, 383, and 1069 contigs encoding putative polyketide synthases were used as templates for PCR reactions. Sequences identical to CAN24 and 1069 are available in GenBank with accession numbers AB164375 and AAL92879, respectively. The GenBank accession for CAN383 is GQ222379. The PCR reactions were designed to add 5# NcoI and 3# BamHI restriction enzyme sites to the ends of each sequence. After digestion with NcoI and BamHI, the inserts were cloned into the corresponding sites of pHIS8 , which adds eight histidine residues to the N-terminus of the encodedprotein. Clones corresponding to each PKS-related gene were sequenced. The Lasergene MegAlign program using the Clustal W algorithm was used to generate the alignment of PKS genes.

Cannabis farmers and the cannabis industry in general have to navigate the same pitfalls that we see [elsewhere] in the California regulatory landscape. But when you add in [the cannabis industry’s] lack of access to banking, and [its] inability to transfer product across state lines — it makes it even more difficult for folks involved in this industry.An overarching issue is the tendency of localities to move toward indoor cultivation. We only have about 17 localities in the state — counties or cities — that allow sun grown cultivation [cultivation without supplemental light]. We’re not doing enough to educate localities and regulators about the energy impacts of high-intensity lighting, or [the drawbacks] of setting up systems where the only way you can cultivate cannabis commercially is through very energy-intensive methods — which go very much against California’s goals [for reducing] carbon emissions. I think the California Department of Food and Agriculture could talk more about sustainable cultivation — about implementing [incentive programs similar to those developed for] other industries — so that, from the get go, we’re establishing sustainable systems, rather than going back 10 or 15 years later to do a greening of the cannabis industry.If I had to rank the type of cultivator that licensed cultivators are okay seeing law enforcement go after, number one would be people that are doing environmental degradation on public land. Folks in the forest or the national park who are harming the environment — I would say that almost no one would ever disagree [with enforcement against them]. The attitude changes when we talk about people on private land. Because even though nobody wants folks to be diverting from streams,mobile vertical grow racks there is a sense that “It’s their land,” and maybe they’re trying to do better.

That’s part of the culture up here [in the Emerald Triangle]. A lot of folks came up here to buy big pieces of land [partly because] they wanted privacy, and to be themselves on their land. No one wants to see environmental degradation, but when it comes to private land, they may say “Is there a way you can go in and try to help [noncompliant growers] before law enforcement comes in? Can you go and give them a warning?” In terms of people on private land who are not harming the environment, there is a strong belief that law enforcement should not be involved. Maybe these individuals want to become compliant but can’t afford to become compliant. So instead of law enforcement prioritizing them, we should instead offer support and say, “What can we do to support you in transitioning to the regulated market?” It’s not an all-or-nothing thing. There are definitely people cultivating without a license who are way more egregious than others.Over the next decade, I do. I think that there are prob ably two main components that have to happen before we can start thinking about cannabis like other industries — one, of course, being banking. We cannot be treated like any other industry when we cannot bank. Until banking is allowed and we can get small business loans, we will not be like any other industry. The second thing is being able to ship across state lines. You can’t ship wine to every state, but you can ship it to most states, and the ability of states like California and Oregon, or California and Washington, to enter into an agreement so cannabis can flow across the borders — that’s another way that we will be able to be treated like every other industry. Until then, you have to cap production [at the level] your state can consume. Do we say, “Florida, you can only grow so many oranges because [your oranges] all have to stay in Florida?” That doesn’t make sense. I’m hoping that both [banking and interstate shipments] will happen in the next 10 years. I think banking will happen this year — the SAFE Banking Act [a cannabis banking bill] was introduced in Congress this year with over 100 sponsors from both sides of the aisle.Public complaints about odors dominate the complaints received by air pollution agencies in the United States and Europe .

As populations urbanize and move closer to industry, the volume of complaints has risen. For example, in Europe between 13 and 20% of the population is affected by odor annoyance, while in highly urban areas the fraction has risen to 25% . The inherent variability in the perception of odors makes the regulation of odors a challenge. This means that odors cannot be regulated directly like other air pollutants by setting concentration limits. Instead, human perceptions of odor have to be considered within the regulations. Some neighborhoods have highly sensitive residents, while other communities have become accustomed to an odor and rely on its source for their jobs.Fugitive emissions from such industries can lead to odor complaints due to the extremely low sensory thresholds. Further, upsets and their associated larger releases are especially problematic at oil refineries , where it can take a long time to fix the problem. Historically, odors have been regulated under public nuisance law or when permitting a new or modified facility by conducting dispersion modeling within a theoretical odor impact assessment. The latter approach is outside the scope of this paper because it has been reviewed elsewhere . The use of dispersion modeling within a theoretical odor impact assessment has only achieved partial success as evidenced by the ongoing volume of odor complaints. Odor as regulated under public nuisance law for existing facilities, rather than proposed new or modified facilities, is the focus of this paper. Public nuisance law originated in British Common Law. Such law distinguishes between “private nuisance” and “public nuisance” . Almost always, nuisance odor complaints fall under the latter category rather than individual rights . To address nuisance odor complaints, regulators may set subjective or objective standards. Based on population-based health studies of toxic air pollutants, objective standards have been set for pollutants. Odors, however, are multidimensional and require difficult-to-enforce standards. Unsurprisingly, the setting of odor standards varies greatly throughout the world and depends heavily on the industrial sector, type of activity generating the odor and level of public outcry.

Applying public nuisance law to odors has been criticized : “A direct measure of annoyance is typically made by an inspector or authorized officer of the state,indoor farming systems and if there are no complaints there is by definition, no problem. The inherent subjectivity built into the approach is a problem, as is its susceptibility to political influence or community pressure. It lacks continuity of regulation for both the community and the industry concerned and does not offer a ‘target’ for the design and management of odor control systems.” As an alternative, odor intensity limits have been set in some jurisdictions. Such limits remain sensory-based, bringing with them the inherent variability in human responses to odors, and typically are set at a certain number of volumes of odorless air required to dilute one volume of sample air until the odor is no longer detected by 50% of panelists . Dynamic dilution olfactometry, which requires a dilution instrument and evaluation by a trained panel, is typically used to set this limit.In air quality guidelines for Europe, the World Health Organization recommended guideline concentration values for six odorants , which are presented along with their sensory thresholds in Table 2.3. Five of the odorants have their WHO guidelines near or below their odor detection thresholds. The exception, hydrogen sulfide, has a WHO guideline slightly above the odor recognition threshold. Developing a guideline value for each of the thousands of odorants would be daunting. Further, odorants are typically present as mixtures, which alters the overall perceived odor. Therefore, the WHO recommended that future work for sensory annoyance should probably focus on odors as perceived by individuals rather than on the component substances .Based on the information gathered from around the world, a targeted literature search and phone interviews of odor experts and air inspectors were conducted to provide current perspectives on odor investigations. Literature searches were conducted online and at the University of California, Los Angeles, and California Air Resources Board physical libraries. The focus was post-2010, and when relevant articles or books were found the “cited by” function was used to find even more up-to-date information. A California-specific literature search was conducted as well. Reviews of the latest approaches to odor investigation were sought. Finally, relevant websites and posted materials not typically available in scientific journals were searched. To collect the latest information on nuisance odor investigations from California’s air districts and international experts, phone interviews were conducted. The interviewees’ insights were gathered and any available guidance documents and relevant case studies were requested.California regulates odors under California Health and Safety Code Section 41700, which states “A person shall not discharge from any source whatsoever such quantities of air contaminants or other material which cause injury, detriment, nuisance, or annoyance to any considerable number of people.” California primarily regulates its air quality through 35 local air districts, although the California Air Resources Board addresses odor complaints from mobile sources. In addition, CalRecycle offers odor guidance to composting operations.

CARB has a long history of addressing nuisance odor complaints. In the 1970s, questionnaires were used to collect information from residents near pulp mills on exposure to the odor and on health and annoyance reactions . In general, the frequency with which odor was noticed and the frequency and intensity with which respondents were bothered by the odor were correlated with perceived odor intensity and frequency as measured by dynamic dilution olfactometry within each community. More recently, an agreement was formed between CARB and the local air districts regarding the handling of odor complaints . It stated that CARB handles mobile source odor complaints and, for stationary sources, the local air district is phoned as soon as possible, but at least within one business day. It then gives local air districts 15 working days after receipt of the CARB letter to provide a written complaint resolution or summary progress report. Finally, CARB agreed to subscribe to an over-the-phone verbal translation service that it would make available to the local air districts. Several of the local air districts were contacted to gather information on how they currently investigate nuisance odor complaints. The information gathered is summarized next. One observation during the interviews was that CARB trains the air district staff on smokestack opacity measurements using their own eyes . Training regarding environmental odor evaluation could be developed as well. In 2008, SCAQMD Rule 410 took effect to address odors from trash transfer stations and material recovery facilities. Sufficiently large facilities were required to have an approved odor management plan with extra requirements for even larger facilities. In 2009, a panel of 10 air inspectors used odor profiling to evaluate a trash transfer station . Based on the observed odors, specific odorants were suggested and then confirmed analytically. In 2017, SCAQMD adopted Rule 415 to address odors from rendering facilities . The rule requires building enclosures with ventilation to odor control systems for odorous operations and best management practices, such as covers for trucks and trailers and time limits for moving materials during the receiving and rendering process. Also in 2017, Rule 1430 was adopted to reduce odors from grinding operations at metal forging facilities . Four confirmed odor complaints over six months trigger additional odor management controls, such as enclosures.Air inspectors use their own noses to confirm the complaint, which does not necessarily need to match the complainant’s description of the odor. Each person’s ability to describe odors varies too greatly to require consistent descriptions. Rather, sensing that “something” is there is sufficient. The next step is to identify the source and move upwind to verify that the odor is not coming from elsewhere. Air inspectors are assigned to specific areas and know the sources within their area well. They are trained on how to respond by pairing junior staff with more senior mentors. For more complex odors, monitoring and meteorological data are used. Hydrogen sulfide monitors have been used for direct measurements and SUMMA evacuated canisters and Tedlar™ bags have been used to collected air samples. Sometimes a SUMMA canister is given to a complainant to collect a sample over time.