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Principal neurons in the hippocampus and cerebellum use endocannabinoids to carry out a signalling process that is analogous in mechanism, but opposite in sign, to DSI, called depolarization-induced suppression of excitation. Like DSI, DSE is induced by neuronal depolarization, it consists of a transient depression in neurotransmitter release, and it requires a retrograde endocannabinoid messenger. But unlike DSI, DSE targets glutamatergic rather than GABA axon terminals, and results therefore in reduced excitatory input to the affected cell108 . Do DSI and DSE occur simultaneously in a single neuron and, if so, how are they coordinated? In cerebellar Purkinje cells, the two opposing phenomena can be elicited by similar stimulation protocols and so are likely to coexist. Although they might be topographically segregated along the longitudinal axis of the neuron, the significance of their coexistence is not known. On the other hand, in the hippocampus, the induction of DSE requires longer periods of depolarization than does DSI, and its magnitude is smaller. This could be explained by the lower sensitivity of glutamatergic terminals to endocannabinoid activation, which would indicate that a switch from DSI to DSE might occur when endocannabinoid concentrations at hippocampal synapses attain a certain threshold value. Again, the role of such a switch, if any, is undefined.Inhibition of glutamatergic neurotransmission by cannabinoid agonists has been documented in a variety of brain structures besides the hippocampus and cerebellum. These include the prefrontal cortex, amygdala, nucleus accumbens,trimming cannabis striatum and substantia nigra pars reticulata. Whether such effects reflect the existence of regional DSE-like phenomena is an important question that remains to be addressed.The ability of cannabinoid agonists to inhibit the release of neurotransmitters in the CNS is not restricted to glutamate and GABA.

A particularly convincing case has been made for acetylcholine, the release of which is reduced by cannabinoids both in vitro and in vivo, and is enhanced by inactivation of CB1 receptors. Acetylcholine release in the neocortex and hippocampus facilitates learning and memory, so disruption of this facilitatory process might contribute to the detrimental effects of cannabinoid drugs on cognition. Cannabinoids also reduce the release of the biogenic amines noradrenaline and serotonin136, and the neuropeptide CCK-8 . Analogous, but as yet unknown, actions on peptide release in the hypothalamus might underlie the central involvement of the endocannabinoid system in the secretion of stress hormones and regulation of appetite .High-frequency stimulation of cortical fibres that innervate the striatum leads to a form of persistent synaptic plasticity called long-term depression. Like its hippocampal counterpart, striatal LTD is induced when Ca2+ enters the somatodendritic compartment of projection neurons, and is expressed as a decrease in glutamate release from axon terminals of corticostriatal fibres. These analogies with DSI are suggestive of an endocannabinoiddependent process, an idea that has been confirmed experimentally. Striatal LTD is absent in CB1 -deficient mice and is blocked by the CB1 antagonist rimonabant; moreover, it is induced in a CB1 -dependent manner by anandamide or AM404 . A similar form of endocannabinoid-dependent LTD can be produced by low-frequency stimulation of cortical fibres that innervate the nucleus accumbens. Despite differences in induction protocols in vitro __ one is produced by high-frequency,the other by low-frequency, stimulation __ striatal and accumbal LTD could serve complementary functions. For example, they might both contribute to habit formation, a type of striatumdependent learning that underlies the development of motor skills and is implicated in the pathogenesis of drug addiction. Notably, cannabinoid drugs provoke in rats a relapse to drug-seeking behaviour after prolonged periods of abstinence, whereas CB1 antagonists attenuate the relapse induced by drug-associated cues.

These findings have provided the rationale for current clinical trials of rimonabant as a treatment for alcohol and tobacco addiction .In the hippocampal CA1 field, stimulation protocols that cause long-term potentiation at excitatory synapses onto pyramidal neurons simultaneously produce LTD at adjacent inhibitory synapses. Like striatal LTD, I-LTD might be endocannabinoid-mediated, but its molecular mechanism seems to be remarkably different. According to a current model, glutamate released from excitatory terminals activates metabotropic receptors on dendritesof pyramidal neurons, which in turn stimulates 2-AG formation through the DGL pathway. The newly formed endocannabinoid can then depress GABA release by engaging CB1 receptors on inhibitory nerve endings. How this long-lasting disinhibitory process interacts with other forms of endocannabinoiddependent plasticity and contributes to the overall effects of cannabinoids on hippocampus-dependent learning will surely be the object of future discussion and experiments.We have learned that the brain contains multiple endocannabinoid lipids, and that neurons produce them using membrane constituents as starting material. We have also discovered that these lipids behave differently from traditional transmitters. Rather than being secreted from vesicle stores, they are released in a non-synaptic manner and combine with cannabinoid receptors located near their sites of synthesis. Despite this progress, many crucial pieces of the endocannabinoid puzzle are still missing. For example, we need to map the neuronal circuits that produce anandamide and 2-AG, and this requires in turn the molecular characterization of the synthetic enzymes involved. We also need to understand how classical neurotransmitters and drugs of abuse interact with these circuits, and to explore the functional consequences of such interactions. Last, but not least, we must continue to develop selective pharmacological tools that target not only the different cannabinoid receptor subtypes, but also the mechanisms of endocannabinoid synthesis and deactivation. Although these tasks are far from trivial, what is already known about the endocannabinoid system indicates that they are well worth pursuing.

The use of cannabis by older adults increased sharply over the past two decades in the United States 1 with the legalization for medical and recreational purposes in many states. While there is limited evidence that cannabis may be helpful for specific conditions,older adults are increasingly using cannabis to treat a wide range of symptoms and conditions, including recreationally,and their perceived risk of regular cannabis use is decreasing.However, older adults, due to the physiological changes related to aging, medication use, and increased comorbidity, are at high risk for adverse effects of any psychoactive substance, including cannabis.Cannabis is associated with a range of acute adverse effects that can require emergency care and detrimental for older adults, who are already the most frequent utilizers of the emergency department .Cannabis can slow reaction time and impair attention,leading to injuries including falls. Cannabis use is also associated with increased risk for psychosis, delirium, paranoia, and other acute psychiatric symptoms.The use of cannabis can cause acute physiological changes that can exacerbate cardiovascular and pulmonary diseases.Additionally, there are potential drug interactions that can lead to adverse effects and cannabinoid hyperemesis syndrome is related to cannabis use.Many of these complications have resulted in the need for acute clinical care in EDs.Cannabis-related ED visits have increased in the US, with one study finding a 12.1% average annual increase from 2006 to 2014 in cannabis-associated ED visits.This included a sharp increase among adults aged ≥65 who, while having lower overall rates of cannabis associated ED visits compared to younger adults, had the largest one-year increase from 2017 to 2018 compared to all other age groups.A study focused on adults aged ≥50 found that cannabis use increased the likelihood of ED visits due to injury.Despite the increase in cannabis use and its potential for adverse effects requiring emergency care in this age group, there has been little research focusing on cannabis-related ED visits among older adults. In 1996, California became the first state in the country to legalize medical cannabis and in 2016 passed Proposition 64, which legalized the use, sale, and cultivation of recreational cannabis.We aim to help fill this knowledge gap by examining trends in the rates of cannabis-related ED visits among older adults aged ≥65 and to examine trends among subgroups of older adults in the state of California.This was a retrospective cohort study of adults aged ≥65 using visit-level data from 2005 through 2019 from all non-federal acute care hospitals across the state of California using non-public data from the California Department of Healthcare Access and Information . All licensed hospitals in California are subject to mandatory reporting of utilization data in a standardized format to HCAI. The number of hospitals with EDs ranged from 316 to 335 facilities during the study period. Data presented in this study represent unique ED encounters from hospitals providing emergency medical services licensed by the State of California,gardening rack which were available in two separate non-public HCAI research data sources: Patient Discharge Data and Emergency Department and Ambulatory Surgery Data . ED encounters resulting in admission to the same hospital are combined with the inpatient record and only reported in the PDD; all other ED encounters are reported in the EDAS. For this study, same-hospital ED admissions from the PDD were combined with ED encounters from the EDAS to construct a complete ED utilization database for analysis including all non-duplicative ED encounters reported to HCAI.

Detailed descriptions of these data sources can be found elsewhere.This study followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline for cross-sectional studies. For each calendar year, we determined the number and rate per 100,000 ED visits for overall cannabis-related ED visits among adults aged ≥65 and estimated trends between 2005–2019. We stratified individual trends of cannabis-related ED visits per 100,000 ED visits by age groups , race/ethnicity , sex , and payer/insurance . The Charlson comorbidity index score was calculated with the enhanced coding algorithm provided by Quan et al25 for both ICD-9 and ICD-10 coding and stratified by the following scores: 0, 1, 2, and ≥3. Finally, cannabis diagnoses were divided into three categories: 1. cannabis abuse and unspecified use, 2. cannabis dependence, and 3. poisoning by cannabis, lysergide, and psychodysleptics . Linear trend p-values were calculated for the overall trend and across subgroups. Statistical significance was defined as a p-value <0.05 All statistical analyses were conducted with IBM SPSS Statistics . This study was approved by UCSD’s Human Research Protections Program. Cannabis-related ED visits significantly increased in California among adults aged ≥65 from a total of 366 visits in 2005, a rate of 20.7 per 100,000 ED visits to 12,167 visits in 2019; a rate of 395.0 per 100,000 ED visits , which is an absolute increase of 374.3 and a 1808.2% relative increase. Table 1 presents cannabis related ED visit trends stratified by patient characteristics. There were significant increases for all subgroups. By age group, adults aged 65–74 had the highest rate in 2019 while also having the largest increase in absolute increase compared to adults aged 75–84 and those aged ≥85. Adults aged 75–84, meanwhile had the largest relative percent change with a 2208.3% increase. By race/ethnicity, older Black adults had the highest rate in 2019 and the largest absolute increase compared to older adults of other races/ethnicities. Older males had a higher ED visit rate in 2019 compared to older females although older females had a larger relative percent increase . Older adults without health insurance had the highest rate in 2019 and the largest absolute and relative increases compared to those with health insurance. Older adults with a higher Charlson comorbidity index score also had the highest rate in 2019 and the largest absolute increase compared to those with lower comorbidity scores; however, those with the lowest comorbidity score had the highest relative increase . Finally, the “cannabis abuse and unspecified use” category comprised nearly all cannabis-related ED visits each year with 369.4 per 100,000 ED visits in 2019 with the largest absolute and relative increases compared to the other categories.Cannabis-related ED visits increased sharply in California among older adults over a 15- year period. While there was a significant increase in cannabis-related ED use among all subgroups of older adults, we identified key subgroups with higher rates and larger increases in cannabis-related ED visits . We found that adults aged 65–74, older males, and those with more comorbidities had higher ED visit rates in 2019, while those with marked relative increases included adults aged 75–84, older females, older adults without health insurance, and those with the lowest comorbidity. Older Black adults had the highest rate of ED visits among all subgroups examined during the study period, consistent with a previous study that showed that among older adults who used cannabis, being Black was associated with an increased likelihood of ED visits.

Such tests of complete SOFC systems are both expensive and complex; therefore, novel modeling practices were developed to avoid costly testing. The balance of plant typically includes thermal insulation, pipework, pumps, heat exchangers, heat utilization plant, fuel processors, control system, start-up heater and power conditioning. Arguably, the BOP is the dominant part of the system. The Engen-2500 system includes a host of BOP components that are commonly used to sustain the SOFC operation. More specifically, these BOP components consist of an external reformer, an oxidizer, a heat exchanger, a condenser with a water drain, a desulfurizer, an evaporator, a pump, two blowers, and three valves. For a visual illustration of the Engen-2500 system setup, refer to Figure 22, “Reproduced design schematic for the Engen-2500 system,” in section 4, Experimental Setup and Results. It is important to note that the Engen-2500 was originally designed for CHP applications and therefore the system is currently designed to produce high-quality waste heat so that the end-user can recycle the heat for additional purposes like building heating. Furthermore, only the external reformer, thermal oxidizer, heat exchangers, and blower components were included in the Engen-2500 system model. The additional BoP components such as the valves, pump, desulfurizer, evaporator, condenser, and water drain were not included in the system model in order to significantly reduce computational time and because these components do not have a significant impact on the overall thermodynamics and performance of the system. Its primary purpose is to chemically convert a readily available fuel such as a hydrocarbon fuel into a hydrogen-rich fluid that can be oxidized at the fuel cell’s anode. It also serves to convert fuel or oxidant not consumed at the fuel cell’s anode and cathode into useful energy. A fuel processing subsystem consists of a series of catalytic chemical reactors that convert hydrocarbon fuel into a low impurity, high-hydrogen content gas.

Some of these chemical processes release heat , while others require heat to be supplied . For high-temperature fuel cells,drying weed the required heat may be supplied by the fuel cell itself. The size and complexity of an external fuel processor depend on the type of fuel reformed, whether impurities or poisons need to be removed, and how much reformate needs to be produced. SolidPower’s Engen-2500 system uses an annular external reformer located in the HotBox section of the system and encompasses the oxidizer component. Placing the external reformer in contact with the oxidizer suggests that the heat produced from the combustion of fuel and air within the oxidizer is transferred via conduction across the walls of the oxidizer and to the reformer. The heat transferred from the oxidizer to the reformer functions as the heat input required for the endothermic steam-methane reformation reaction to occur. Reforming hydrocarbon fuel to hydrogen-rich fuel in the Engen-2500 system is accomplished by two subsequent reactions: steam-methane reformation and water-gas shift . Realistically, while fuel cells stacks cannot use all of the fuel in the anode compartment, fuel cell systems are unable to make use of all the fuel provided. Therefore, the remaining fuel that leaves the anode is sent to the thermal oxidizer – where it is usually mixed with cathode off gas – and undergoes combustion to produce heat and clean the anode off-gas emissions. The oxidizer releases the remaining fuel energy of the anode off-gas as heat and reduces the composition of methane and carbon monoxide to satisfactory levels. The Engen-2500 system has three gas streams that enter the oxidizer: extra natural gas, anode off-gas, and ambient air. A valve controls the addition of natural gas to the oxidizer when additional heating is necessary. As mentioned previously, the external reformer encompasses the thermal oxidizer and therefore the heat produced from the thermal oxidizer is used for the endothermic reformation process occurring within the external reformer component. Therefore, the valve controller controlling the input of extra natural gas to the burner must consider the instantaneous degree of heat being provided to the external reformer so that the system maintains a desired external to internal reforming ratio. The anode off-gas has very little heating value because the majority of its composition is steam and carbon dioxide.

Mixing of these three streams is determined by solving the conservation of mass equations to find the molar flow rate and mixed species concentrations. In any fuel cell system, there are process streams that must be heated. The challenge imposed on the system designer is to use the heat available from one stream to heat another in the most efficient way. The gas or liquid to be heated passes through pipework that is heated by the gas or liquid to be cooled. The pipework is generally referred to as a heat exchanger and is a mechanical device that conveys thermal energy or heat from a hot-fluid stream on one side of a barrier to a cold-fluid stream on the other side without allowing the fluids to directly mix. There are many types of heat exchangers such as shell and tube, plate, fin, tubular, regenerative, phase change, and printed circuit exchangers that all use different geometries and physical means of energy transfer. The key features to consider for selecting a heat exchanger are the surface area available for heat transfer, the types of fluids/solids exchanging heat, inlet temperatures and flow rates, and geometric configuration. The heat exchanger design implemented by SolidPower for their Engen-2500 system is assumed to be a typical cross-flow plate and fin heat exchanger. Entering on the hot side is the thermal oxidizer exhaust stream mixed with the cathode outlet exhaust. The combination of these streams carry high quality heat that preheats the ambient air before it enters the cathode. The ambient air is blown through the cold side of the heat exchanger via a blower. The high operating temperatures of SOFC systems melt most common metals, therefore, high temperature heat exchangers are typically manufactured from high temperature stainless steel or ceramic materials, for which the planar configuration is most common. Incorporating a heat exchanger into the model involved discretizing each plate into a specified number of control volumes, referred to as nodes, along the length of the air flow. This gives detailed temperature profiles, and avoids pinch-point limitations of a bulk counter-flow heat exchanger model. Heat exchanger size varies with the number of plates, allowing dimensional constants to remain fixed. This modeling technique gives an approximation of the surface area required to meet the needs of each particular design and scale.

The following energy balance equations are applied to each control volume. The most general function of a condenser is to change the physical state of gas to its liquid state by cooling it. The condenser can be used to capture the latent heat of condensation. In a fuel cell system, a condenser is important for both recapturing heat and recovering liquid water to achieve neutral system water balance. Neutral water balance is achieved when all of the water that is consumed by the system components is produced by other components internal to the system. In other words, no additional water needs to be suppled from an external source. The Engen-2500 system utilizes a condenser at the tail-end of the SOFC system cycle after the mixing of the cathode outlet exhaust and thermal oxidizer exhaust streams flow across the hot side of the heat exchanger. The hot side exhaust is comprised of mostly carbon dioxide and steam, with extremely small concentrations of methane, carbon monoxide, and hydrogen. The high concentration of steam present in the hot side exhaust is mixed with additional ambient tap water via a valve to help reduce the temperature and condense the high steam concentration to liquid water in the condenser. A water drain is included to capture all the liquid water that leaves the condenser. The remaining gases pass through and are emitted into the atmosphere as exhaust. Two blowers are included in the Engen-2500 system to take ambient air and direct it into the system components. One blower provides ambient air to the oxidizer for combustion of the anode off-gas and extra natural gas to produce enough heat for the endothermic external reformer. A second blower provides ambient air to the heat exchanger so that the air can be preheated before entering the cathode of the SOFC stack. Blowers operate much like a compressor, but with a much lower pressure ratio. Both blowers have their own controllers to provide the necessary pressure rise to its specific component. For the blower that directs air to the oxidizer component,vertical growing systems this blower’s controller attempts to regulate the heat produced by the oxidizer such that it maintains a desired external to internal reforming ratio designated by SolidPower. Considering the blower that directs air to the heat exchanger, this blower’s controller simply regulates the air flow required by the SOFC stack to maintain a constant cathode outlet temperature. A simplified thermodynamic analysis was used to analyze the blowers operation. The dynamics of the blowers are assumed sufficiently quick to minimally affect the remainder of the system due to low rotational inertia of the blowers. The blowers are treated as compressors with an isentropic efficiency factor and are provided enough power to generate the necessary pressure gain. The calculation of the necessary power for the blower is performed by applying an energy conservation analysis to the control volume with the following expression accounting for energy lost to inefficiencies and cooling. Sulfur in any form is harmful to the SOFC stack. It poisons the nickel-containing anode and reduces the stack performance. The Sulphur level must generally be below 0.1 ppm to avoid a performance loss. Natural gas and petroleum liquids naturally contain organic sulfur compounds that normally must be removed before any further fuel processing can be carried out. Even if sulfur levels in fuels are extremely low, 0.2 ppm, some deactivation of steam reforming catalysts can occur. The most common desulfurization techniques use conventional sorbents such as activated carbon, alumina and/or zeolites at low capacity.

Another common technique is to use zinc oxide and/or copper oxide at high capacity with hydrogen and an operating temperature >200°C. The latter reactively adsorbs sulfur and is regenerable. Although the specific type of desulfurizer is not explicitly known, it is acceptable to assume the desulfurizer is removing enough sulfur for continuous, unhindered operation of the SOFC system. Fluid systems generally involve a method of increasing the pressure, velocity, and/or elevation of a fluid. This can be accomplished by supplying mechanical energy to the fluid via a pump, fan, or a compressor. Pumps require mechanical work in the form of shaft work produced by an electric motor, which is transferred to the fluid as mechanical energy. There exists only one pump in the Engen-2500 system, the purpose of which is to pump recycled liquid water from the water drain to the evaporator before it enters the external reformer. Throttling valves are any kind of flow-restricting devices that cause a significant pressure drop in the fluid without involving any work. There are three valves included in the Engen-2500 system, the purposes of which are all the same: vary the pressure change of the fluid to increase or decrease thereby influencing the mass flow rate. Two separate valves vary the mass flow rate of methane going to the external reformer and the thermal oxidizer, independently. A SOFC system controller controls one valve to vary the mass flow rate of methane entering the external reformer such that the system maintains a steam-to-carbon ratio greater than two after taking into account the mass flow rate of steam. Fuel flow delay to the fuel cell can be a primary fuel cell transient limitation. In fuel cell systems, the most significant fuel flow delay is due to fuel flow control valves and pressure transients in the fuel processing system. Another system controller controls a second valve that adjusts the mass flow rate of methane entering the thermal oxidizer such that enough methane is being supplied along with air and anode off-gas to produce enough heat through combustion that maintains a designated external to internal reforming ratio. A third system controller simply controls a third valve to adjust the addition of tap water entering the SOFC system in order to maintain a neutral system water balance. To begin the verification process, the most influential piece of information is verifying that the model performance curve matches that of the experimental results.

In an attempt to achieve net zero emissions, tech companies have strongly considered fuel cell technology as a greener and more efficient alternative to energy conversion than the traditional combustion methods of power plants. Over the past several years, the data center industry has experimented with centralized fuel cells through simulation and pilot installations. There are a few reasons to consider fuel cells for data center power production, the first of which is its reliability when connected to the natural gas grid. Reliability of individual fuel cells themselves is mediocre at best; however, when connected to the natural gas grid their reliability improves dramatically since fuel cells can operate indefinitely as long as they are provided sufficient fuel and air. In addition, because data centers will require multiple fuel cell systems, adding a few redundant ones can easily account for any individual fuel cell failures. The reliability of fuel cells is heavily influenced by the reliability of the gas grid, which is known to be high, exhibiting greater than five nines reliability, much higher than three nines for the electric grid. The second benefit is that gas distribution within a data center is much cheaper than the high voltage switch gear, transformers, and copper cables required to connect to the electric grid. If fuel cells are placed closer to the power consumption units , then data centers can easily eliminate the power distribution system, including the backup power generation system. This is highly favorable because the electrical infrastructure accounts for over 25% of the capital cost for state-of-the-art data centers. The third benefit is that fuel cells are environmentally friendly. Although initial phases of introducing fuel cells into the data center would require that they operate on natural gas, even with this source of fuel, fuel cell emissions are much cleaner and far more efficient than those from traditional combustion methods. Carbon dioxide emissions have the potential to be reduced to 49%,heavy duty propagation trays nitrogen oxides by 91%, carbon monoxide by 68%, and volatile organic compounds by 93% when compared with a combustion cogeneration plant.

With respect to architecture, there are several designs for incorporating fuel cells into data centers: at the utility power level, rack level, or server level. The utility power level combines groups of fuel cells to achieve a power rating on the order of megawatts to replace the traditional electric utility power input, therefore disconnecting the data center from the electric grid, or operating in parallel with the grid. The rack level design uses fuel cells with a power rating on the order of kilowatts to power one or a few nearby server racks, eliminating the entire power distribution network within the data center and replacing it with a fuel distribution network . The server level design integrates small fuel cells on the order of watts into the servers, which is similar to the rack level design but eliminates the short distance power cabling needed from the rack level fuel cells to the servers – helping to minimize DC transmission losses. Reliability can be maximized using the server level design because fuel cell failures would only affect a single server; however, it is worth noting that smaller fuel cells may not be as energy efficient and cost effective as their larger counterparts. Microsoft has envisioned a new concept for their data centers, aptly labelled as the ‘stark’ design. They proposed a direct generation method that places fuel cells at the server rack level, inches from the servers. The close proximity allows for the direct use of DC power without the large capital cost, potential for failures, and efficiency penalties associated with AC-DC inversion equipment. As a result, power distribution units, backup power generation equipment, high voltage transformers, expensive switch gear, and AC-DC power supplies in the servers can be completely removed from the data centers. Previous analysis and experiments have shown that low cost, low greenhouse gas, high reliability , and high efficiency can be achieved by using mid-sized fuel cells at the rack level, directly supplying DC power to the servers, and effectively replacing the power distribution system in a data center with a gas distribution network. Data centers are facilities that contain information technology devices used for data processing, storage, and communications in addition to the infrastructure equipment required to operate them as reliably as possible.

The infrastructure equipment typically consists of specialized power conversion and backup equipment and environmental control equipment . Within the data centers, the volume storage servers and cooling system infrastructure are by far the largest consumers of electricity; together they account for over 70% of current energy demand. As servers become more powerful, more power is needed to run and cool them. Therefore, the biggest consumer of square footage in data centers is not by servers but by the power infrastructure. From an outside perspective, Microsoft’s data center in Tukwila, Washington looks like a nondescript sprawl of beige boxlike buildings arranged to be inconspicuous like remote warehouses. Like most data centers, the Microsoft Tukwila facility comprises a sprawling array of servers, load balancers, routers, fire walls, tape-backup libraries and database machines, all resting on a raised floor of removable white tiles, beneath which run neatly arranged bundles of power cabling. To keep the servers cool, Tukwila has a system of what are known as hot and cold aisles where cold air seeps from perforated tiles in front and is sucked through the servers by fans, ultimately expelled into the space between the backs of the racks and ventilated out and away. Tukwila can be thought of as less a building than a giant machine built for computing. Ranging from small computer server rooms to mammoth server farms, data centers now house more than several millions of computer servers. Even more startling is that in 2013 alone, roughly 3 million server rooms used enough electricity to power all households in New York City for 2 years – equivalent to the annual output of 34 large coal-fired power plants. According to a McKensey Quarterly report, the annual CO2 emissions of data centers will reach approximately 1.54 metric gigatonnes by 2020, which could make IT companies among the biggest greenhouse gas emitters. This would be equivalent to the amount of electricity generated by 50 large coal-fired power plants – each with 500 megawatts of capacity – emitting nearly 150 million metric tons of CO2 emissions per year. There continues to be several persisting issues slowing the progress of energy efficiency in data centers: comatose servers, peak provisioning, limited deployment of virtualization technology, failure to power down unused servers, and shortsighted procurement practices.

Fortunately, Microsoft has taken these issues seriously and has worked with vendors to reduce power use when processors are idle. Dileep Bhandarkar, a distinguished engineer at Microsoft who oversaw the company’s server hardware architecture in 2011, says, “It used to be [that] an idle server would be [at] 50% of the power . We’ve pushed that down to about 30%”. Despite this achievement in reducing power consumption of idle servers, many servers remain “comatose” and no longer needed. If half the savings potential from energy efficient best practices are realized, then America’s data center industry could slash their electricity consumption by as much as 40%. In the last decade, there have been dramatic advances in data center design. Previous methods of using outside air directly to cool servers has been replaced by computer room air conditioning systems, evaporative cooling methods have replaced absorption chillers, and power over subscription is used to better utilize power capacity. The data center industry measures their building system efficiency using the power utilization effectiveness measurement. The PUE measurement must consider all the equipment necessary to maintain the daily operation of the data center after receiving power from the utility grid. A diagram of the major components considered in a PUE calculation is shown in Figure 5. A PUE of 2.0 means that for every watt of IT power consumed, an additional watt is required to cool, distribute power to the IT equipment, and operate the data center. A PUE closer to 1.0 means nearly all of the energy is used for computing – the ideal case. Industry-wide,vertical cannabis the PUE ratio between overall facility power consumption and the power used by servers has improved from a 2.0 to a best practice of 1.11. Despite this significant achievement, the fundamental data center power infrastructure, consisting of transformers, power distribution units, uninterruptible power supply systems, and backup generators has changed very little. The power distribution chain starting from the utility grid to the server racks is illustrated in Figure 6. This power system remains necessary to deal with the high-voltage AC power utility grid and its relatively low reliability of three nines . In an attempt to reduce the PUE measurement of Microsoft’s data centers, Microsoft has decided to pursue alternative means of generating power in order to reduce the power infrastructure required to maintain high reliability. According to the United States Environmental Protection Agency , adapting distributed generation methods in data center design could achieve great energy savings, significant environmental benefits, and high power reliability. Therefore, Microsoft has chosen to focus their research on a novel and promising distributed generation technology: high temperature fuel cell systems . They have proposed to place the HT-FCs at the rack level, significantly close to the servers. This new design layout limits the failure domain to just a few dozen servers instead of the entire data center like what would typically occur if the electric grid were to experience failure. Placing the HT-FCs at the rack level would allow Microsoft to eliminate the backup generators, high-voltage transformers, expensive switch gear, transfer switches and AC-DC conversion systems within a data center.

Currently, less than half of the interior square footage within a data center is dedicated to the electrical infrastructure; therefore eliminating this can significantly shrink the physical space requirements. Additionally, shifting the UPS and battery backup functions from the data center into the server cabinet can reduce the power losses from the multiple AC-DC conversions that occur between the utility power grid and the data center equipment . Currently, over 85% of the world’s energy needs are met and will continue to be met in the coming decades through the consumption of fossil fuels . These fossil fuels have become a very reliable fuel resource that is used to produce power through combustion processes , which transforms the fuel’s chemical energy into other forms of energy for everyday use . The relative ease and low cost of producing power through combustion-based processes has made developed and developing countries extremely reliant on the availability and access to the limited fossil fuels on the planet. Yet, the benefits of combustion-based energy generation do not exist without the consequences of harmful criteria pollutant and greenhouse gases emissions. Combustion emissions continue to remain the major source of urban air pollution leading to respiratory health problems and the primary source of greenhouse gas emissions contributing to global warming. Nevertheless, energy conversion and combustion emissions continue to increase simultaneously as the world’s population increases because combustion-based power production is quick and easy to set up compared to alternative methods of energy conversion.As the inadvertent effects of climate change become a very grim reality in this lifetime, great strides have been made for reducing the pollution emitted by combustion sources thanks to scientific and public awareness of federal and state governments. The first major step toward climate change legislation was made when dense visible smog in many of the nation’s cities and industrial centers circa the 1960’s prompted the United States Environmental Protection Agency to pass the Clean Air Act in 1970 at the height of the national environmental movement. With this legislation, the states were now required to adopt enforceable plans to achieve and maintain air quality meeting the air quality standards for the six common “criteria pollutants”. Following with the federal mandate, California implemented the most progressive power generation legislation in 2001 with the introduction of the California Public Utilities Commission , Self-Generation Incentive Program . The SGIP provided incentives to support existing, new, and emerging distributed energy resources, such as wind turbines, recycling waste heat, microturbines, gas turbines, fuel cells, and advanced energy storage systems.

Uncertainty in when, how and where water is being used, however, threatens the security of water rights — particularly when water is substantially over allocated relative to natural supplies. During the 2012–2016 drought, for example, the SWRCB issued notices of curtailment to water rights holders to protect endangered fish species within priority watersheds. Less controversial targeted cutbacks to individuals might have been sufficient if the agency had more accurate information on how water rights were being exercised. As the 2012–2016 drought progressed, flaws in the state’s accounting system for tracking water rights be came more apparent. This study, together with other policy reports , articulated the need for water accounting reforms, raised public awareness and helped to mobilize support for new legislation in 2015 , which significantly increased water-use monitoring and reporting requirements for water rights holders. The new regulations also extended reporting requirements to senior water rights holders , which are among the largest individual water users in the state.The legalization of recreational cannabis in 2016 with passage of State Proposition 64 prompted state agencies to develop new policies to regulate the production, distribution and use of the plant. For example, California Senate Bill 837 directed the SWRCB to establish a new regulatory program to address potential water quality and quantity issues related to cannabis cultivation. The subsequently enacted California Water Code Section 13149 in 2016 obliged the SWRCB,hydroponic drain table in consultation with the California Department of Fish and Wildlife, to develop both interim and long-term principles and guidelines for water diversion and water quality in cannabis cultivation.

As a result, in 2017, the SWRCB adopted the Cannabis Cultivation Policy: Principles and Guidelines for Cannabis Cultivation . The Cannabis Cultivation Policy’s goal is to provide a framework to regulate the diversion of water and waste discharge associated with cannabis cultivation such that it does not negatively affect fresh water habitats and water quality. A key element of the Cannabis Cultivation Policy is the establishment of environmental flow thresholds, below which diversions for cannabis irrigation are prohibited . During the dry season , no surface water diversions are permitted for cannabis cultivation. Diversions from surface water sources to off-stream storage are allowed between Nov. 1 and March 31. However, water may only be extracted from streams when flow exceeds the amount needed to maintain adult salmon passage and spawning and winter rearing conditions for juvenile salmon. Environmental flow requirements for the winter diversion season were determined by an approach known as the Tessmann Method , which uses proportions of historical mean annual and mean monthly natural flows to set protective thresholds. Because flows are not measured continuously in most streams in California , including at most points of diversion, the Cannabis Cultivation Policy instead relies on using the predictions of natural flows from the models described above. Predicted natural mean monthly and annual flows are used by the SWRCB at compliance gauge points to calculate the Tessmann thresholds. Cannabis cultivators seeking a Cannabis Small Irrigation Use Registration permit from the SWRCB are assigned a compliance gauge near their operation and can legally divert water only when flows recorded at the gauge meet or exceed the Tessmann thresholds during the diversion season . The motivation for developing natural stream flow models and data rests on the premise that rivers and streams can be managed to preserve features of natural stream flow patterns critical to biological systems while still providing benefits to human society . For any stream of interest, balancing the needs of humans and nature requires an understanding of its natural flows, whether observed conditions are modified relative to natural patterns and what degree of modification harms its health. As noted in the examples above, this work has both direct and indirect implications for policy and decision-making.

A database of natural stream flows developed by machine-learning models was used to help define cannabis policy to set minimum flow targets — a direct application of the technique. However, this work also influenced policy and decision-making in more subtle ways, including building awareness of shortcomings in the state’s water rights accounting system. This form of engagement with government agencies and the broader public helps define the agenda early in the pol icy-making process , although quantifying the degree to which our research contributed to policy outcomes such as SB 88 is difficult. The future impact of our work on environmental flow management remains unclear, but early engagement with state and federal agencies through the Environmental Flows Work group suggests that our flow modeling tools and data will have an important role in future policy development. Recognizing there are likely other applications for our modeling tools, we have been working to make the data available to the public. Model predictions have now been generated for every stream in California, including values of mean monthly, maximum and minimum monthly flows and confidence intervals for California’s 139,912 stream segments in the National Hydrography Database . A more dynamic spatial mapping tool has been developed to explore the data in individual rivers, watersheds or regions. An online interactive visualization tool is also available that allows a user to select one or several stream gauges and generate the corresponding hydrograph of observed and expected monthly flows . An immediate next step for this project is to expand the natural flows dataset to include predictions of additional stream flow attributes that are relevant to environmental water management. This will support the Environmental Flows Work group’s goal of defining ecological flow criteria in all rivers and streams of the state and can help inform a variety of programs including, for example, water transactions and stream flow enhancement programs.

Other direct applications of the natural flows data may be in hydropower project relicensing, which requires consideration of environmental flow needs. In addition, under the Sustainable Groundwater Management Act , groundwater sustainability agencies are required to avoid undesirable rsults including depletions of interconnected surface water that have significant and unreasonable adverse impacts on beneficial uses of the surface water. Because environmental flow criteria have not been established for most streams in California, GSAs are rightfully confused as to the standards they are expected to meet. Statewide environmental flow criteria may help to define management targets required for SGMA implementation. Looking to the future, society will continue to face challenges in balancing environmental protections with the demands of a growing population. Tools that make use of long-term monitoring data and modern computing power, such as the models described here, can help inform policy and management intended to achieve this balance.Future high-performance computing systems are1 driven toward heterogeneity of compute and memory resources in response to the expected halt of traditional technology scaling, combined with continuous demands for increased performance and the wide landscape of HPC applications. In the long term, many HPC systems are expected to feature a variety of graphical processing units , partially programmable accelerators, fixed-function accelerators, reconfigurable accelerators such as field-programmable gate arrays, and new classes of memory that blur the line between memory and storage technology. If we preserve our current method of allocating resources to applications in units of statically configured nodes where every node is identical, then future systems risk substantially underutilizing expensive resources. This is because not every application will be able to profitably use specialized hardware resources as the value of a given accelerator can be very application-dependent. The potential for waste of resources when a given application does not use them grows with the number of new heterogeneous technologies and accelerators that might be co-integrated into future nodes. This observation, combined with the desire to increase utilization even of “traditional” resources, has led to research on systems that can pool and compose resources of different types in a fine-grain manner to match application requirements. This capability is referred to as resource disaggregation. In datacenters, resource disaggregation has increased the utilization of GPUs and memory. Such approaches usually employ a full-system solution where resources can be pooled from across the system. While this approach maximizes the flexibility and range of resource disaggregation,rolling benches hydroponcis it also increases the overhead to implement resource disaggregation, for instance by requiring long-range communication that stresses bandwidth and increases latency. As a result, some work focuses on intra-rack disaggregation. While resource disaggregation is regarded as a promising approach in HPC in addition to datacenters, there is currently no solid understanding of what range or flexibility of disaggregation HPC applications require and what is the expected improvement of resource utilization through this approach.

Without any data-driven analysis of the workload, we risk over designing resource disaggregation that will make it not only unnecessarily expensive but also may overly penalize application performance due to high latencies and limited communication bandwidth. To that end, we study and quantify what level of resource disaggregation is sufficient for typical HPC workloads and what the efficiency increase opportunity is if HPC embraces this approach, to guide future research into specific technological solutions. We perform a detailed, data-driven analysis in an exemplar open-science, high-ranked, production HPC system with a diverse scientific workload and complement our analysis with profiling key machine learning applications. For our system analysis, we sample key system-wide and per-job metrics that indicate how efficiently resources are used, sampled every second for a duration of three weeks on NERSC’s Cori. Cori is a top 20, open-science HPC system that supports thousands of projects, multiple thousands of users, and executes a diverse set of HPC workloads from fusion energy, material science, climate research, physics, computer science, and many other science domains. Because Cori has no GPUs, we also study machine learning applications executing on NVIDIA GPUs. For these applications, we examine a range of scales, training, and inference while analyzing utilization of key resources.Based on our analysis, we find that for a system configuration similar to Cori, intra-rack disag gregation suffices the vast majority of the time even after reducing overall resources. In particular, in a rack configuration similar to Cori but with ideal intra-rack resource disaggregation where network interface controllers and memory resources can be allocated to jobs in a fine-grain manner but only within racks, we show that a central processing unit has 99.5% probability to find all resources it requires inside its rack. Focusing on jobs, with 20% fewer memory modules and NIC bandwidth per rack, a job has an 11% probability to have to span more racks than its minimum possible in Cori. In addition, in our sampling time range and at worst across Haswell and KNL nodes, we could reduce 69.01% memory bandwidth, 5.36% memory capac ity, and 43.35% NIC bandwidth in Cori while still satisfying the worst-case average rack utilization. This quantifies how many resources intra-rack disaggregation can reduce at best.Future HPC systems are expected to have a variety of compute and memory resources as a means to reduce cost and preserve performance scaling. The onset of this trend is evident in recent HPC systems that feature partially programmable compute accelerators, with GPUs quickly gaining traction. For instance, approximately a third of the computational throughput of today’s top 500 HPC systems is attributed to accelerators. At the same time, recent literature proposes fixed function accelerators such as for artificial intelligence. Consequently, past work has examined how job scheduling should consider heterogeneous re source requests, how the operating system and runtime should adapt, how to write applications for heterogeneous systems, how to partition data-parallel applications onto heterogeneous compute resources, how to consider the different fault tolerances of heterogeneous resources, how to fairly compare the performance of different heteroge neous systems, and what the impact of heterogeneous resources is to application performance.Resource disaggregation refers to the ability of a system to pool and compose resources in a fine grain manner and thus be capable of allocating exactly the resources an application requests. This is in contrast to many systems today where nodes are allocated to applications as a unit with identical fixed-sized resources; any resources inside nodes that the application does not use have no choice but to idle. Following the trend for hardware specialization and the desire to better utilize resources as systems scale up, resource disaggregation across the system or a group of racks has been actively researched and deployed in commercial hyperscale datacenters in Google, Facebook, and others. In addition, many studies focus on disaggregation of GPUs and memory capacity. Resource disaggregation is slowly coming into focus for HPC in addition to the existing hyper scale datacenter deployments.

Several social behavioral theories have placed perceived risks as a precursor to risky behavior,with lower risk perceptions leading to increased substance use.For example, young people who perceive long-term tobacco use as low risk are nearly four times more likely to start smoking than peers with high risk perceptions.Perception of harm for marijuana use has been decreasing in the United States, even among young people .The proportion of high school seniors reporting that regular marijuana use poses little to no health risks more than doubled between 2004 and 2014, from 20% to 45%.468 Youth at low risk for drug use report greater intentions to use marijuana if full legalization of medical and/or retail marijuana occurred.469 Data from Colorado show that risk perceptions among 18-25 year olds decreased from 2006 to 2014, with 18.5% of young adults perceiving “great risk” from once-per-month marijuana used in 2006 to 8.4% in 2014; among 26 years and above from 32.8% in 2006 to 19.8% in 2014 . 406 While use among youth has not increased since legalization of recreational use in 2013, perception of “great risk” from monthly marijuana use declined from 30% in 2006 to 17% in 2014, and past 30-day use was 12.6%, well above the national average of 7.2% . By 2014, almost 100% of the 10th and 12th grade current users reported no perceived harm. The 10th grade students reported no risk at 95%, 8th grade students reported no risk at 90%, and 6th graders reported no risk at 75%.407 Given the association between reduced risk perceptions and substance use, it is likely that as social norms on marijuana use increase and access becomes more widespread,hydroponic table use among youth will also increase in Colorado and Washington. US youth perceive marijuana to be either harmless or less risky than tobacco or alcohol.

Data from California, which legalized medical use in 1996 show that teens perceive marijuana and blunts as more socially acceptable and less risky than cigarettes. Exposure to positive messages on therapeutic benefits of marijuana use was associated with a 6% greater odds of marijuana use while peer use was associated with 27% greater odds of use.Similarly young adults in Colorado acknowledged the harmful effects of tobacco use, including secondhand exposure, while exposure to marijuana smoke was perceived as benign.The trend in marijuana legalization may contribute to shifts toward reduces risk perceptions and more permissive norms among young people in the US.Indeed, a 2014 Canadian study with adults found that social normalization of cannabis is driven and reinforced by its perceived widespread use, low incidence of harm from use, and positive social norms surrounding medical use. Canadians were also skeptical of the media’s “exaggerated” portrayal of the harms and risks of cannabis use, although some users did acknowledge health risks, particularly for smoked marijuana. Health risks commonly cited in the public discourse, including respiratory problems, mental health problems, cognitive and memory deficits, were not salient to cannabis users who perceived use was associated with a low incidence of cannabis related harm. Some participants in the study perceived risks of cannabis to be modest compared to tobacco and alcohol.Marijuana use in the United States has been rising since 2002 in both young and older adult populations, while days of use among past year users has also increased. Hall and Pacula’s initial comparisons of young adults in the United States found few differences between use in decriminalization versus prohibition states. Williams and Bretteville-Jensen used the 2001 National Drug Strategy Household Survey to assess the impact of marijuana decriminalization policy on marijuana smoking prevalence in Australia and found that decriminalization is associated with earlier youth marijuana use,and short-term increases in the population prevalence of useLiving in a medical marijuana state was associated with an increased likelihood of initiating marijuana use among young adults, although states with medical marijuana laws had higher rates of use before legalization.

No clear increases have been found since legalization of medical marijuana, especially in youth. Marijuana prevalence among young adults in Colorado went from 21% in 2006 to 31% in 2014 and among adults from 5% in 2006 to 12% in 2014.406 In 2014, 14% of adults were regular marijuana users , with 33% reporting daily use.In Washington young adult use went from 11% in 2011 to 15% in 2013, and older adult use from 4% in 2011 to 8% in 2013.Eighteen percent of young adults in Oregon and 21% in Alaska reported past 30-day marijuana use in 2014, prior to state implementation of retail marijuana laws. In Uruguay, marijuana use has been increasing since 2001, with 23% reporting ever use, 9.3% reporting past year use, and 6.5% reporting current use in 2014 . Of note, since Oregon, Alaska, and Uruguay had not fully implemented marijuana regulatory frameworks these data provide very little information about the direct impact of legalization laws on risk perceptions and use. While previous research argued that marijuana prevalence is unrelated to legalization because higher use rates were generally found prior to legalization,392 data from Denver and Seattle suggest that youth perceptions of risk have decreased and adult use has increased since implementation of retail marijuana laws.480 Moreover, while prevalence was indeed higher than the national average in the four US states that legalized recreational marijuana, liberalizing marijuana laws in 2013 and 2014 has led to dramatic increases in young adult prevalence in Colorado and Washington after the retail market opened. Notably, in Oregon, marijuana use among those 26 years and older nearly doubled between 2006–2007 and 2012-2013 , while national use has increased only slightly .Noncombustible forms of marijuana are increasing in popularity.Even though the use of noncombustible products might be increasing, their overall share is still very low among youth and adults compared to combustible product use in the four US states and Uruguay. Among current marijuana users in Colorado, young adults were more likely to report smoking marijuana than vaporizing and consuming edibles .Cross-sectional data show similar findings among high school seniors with 74% in Washington and 88% in Alaska reporting combustible product use as the preferred mode of consumption.

Similar findings were noted in Oregon in 2015, with nearly 90% of adults and youth reporting combustible marijuana use .In Washington, in 2014 high school seniors were less likely to report oral ingestion , vaporization , or other modes of administration than combustible product use.In Oregon, adults were less likely to report edible use , vaporization , while 25% reported using multiple routes of administration.Multiple administrative routes was most frequent among heavy marijuana users than less frequent users. Among frequent cannabis users in Montevideo,greenhouse tables use of joints and pipes were two of the most widely reported modes of administration in the past 12-months. Other modes of administration that were less popular include: edibles , vaporization , drinks , tinctures , and creams .368 Thus, while consuming edibles and vaporizing marijuana may be less dangerous in terms of cancer, heart disease, and lung disease than using smoked products, smoking remains the dominate mode for consuming marijuana. In addition, it is unknown what the health impacts of these forms of administration are on cardiovascular health or brain function.Marijuana commercialization was associated with a significant increase in annual hospitalizations from 803 to 2,413 in Colorado following the opening of the commercial retail market in 2013. In addition, emergency room visits increased from 739 per 100,000 to 956 per 100,000 ED visits .There was also an increase in emergency room visits for burns, cyclic vomiting syndrome, and marijuana intoxication. At the University of Denver’s burn center, 31 people were treated for marijuana related burns as a result of unexperienced users experimenting with chemical extraction using butane.Some of the increase in hospital utilization could be explained by an increase in new users experimenting with alternative ways to use and produce marijuana.The prevalence of cyclic vomiting syndrome increased after legalization of for-profit medical dispensaries in Colorado in 2010.Since 2012, when retail marijuana laws were implemented, cyclic vomiting syndrome has doubled from 41 per 113, 262 ED visits in to 87 per 125, 095 after medical marijuana was legalized. Legalization of retail marijuana in Colorado was associated with a 44% increase in marijuana-related auto fatalities, from 55 in 2013 to 79 in 2014. In Washington, auto fatalities that involved drivers with active THC in their blood increased by 122.2% from 2010 to 2014 .The interpretation of marijuana-related traffic fatalities is difficult because, unlike alcohol, there is no scientific consensus on what defines “THC impairment,” and THC can be found in the blood or urine several days after use.Legalization may also have resulted in ascertainment bias in that police in Colorado were testing more frequently for THC levels in drivers than prior to legalization. Rather an increase in drivers who tested positive for THC may better explain an increase in marijuana use generally rather than marijuana-impaired drivers specifically. The available epidemiological data on risk perceptions and use patterns from the four US states are limited in their ability to provide a comprehensive overview of the effects of state implementation of marijuana laws because legalization has only been in place for a relatively short period of time. The best that public health authorities can do is provide evidence from the tobacco control experience to have at least an understanding of what potentially the impact of these laws could be on marijuana risk perceptions, use, social norms, and harms associated with use. These shortcomings in the available literature indicate the importance of collecting adequate baseline data before enacting policy change .

Identifing proximal measures of harm with which to measure impacts of legalization would also facilitate evaluating the effects of marijuana policy change.In many ways the state of the marijuana market is similar to where tobacco was at the turn of the 20th Century, before corporatization of the market, with industrialized product design and production and mass marketing.The result was the rise of a sophisticated and politically powerful tobacco industry that led to the death and suffering of hundreds of millions of people worldwide. It took nearly a century to begin to bring the tobacco industry under control as a result of the combined forces of national and international public health advocacy and policy making, as exemplified by the WHO Framework Convention on Tobacco Control.The four US states that have legalized retail marijuana to date have used regulatory regimes largely modeled on alcohol policy regimes. There has not yet been a legalized nationwide market available for entry of major corporations. It is likely that large corporations, including the tobacco industry,with the product engineering and marketing power to quickly transform the market, could capitalize on the opportunities that such a market represents. In part because of relatively low use and the fact that marijuana and tobacco are often used together, the specific health dangers of marijuana are not yet fully defined. We do know that marijuana smoke is toxicologically similar to tobacco smoke and had been identified as a human carcinogen by the California Environmental Protection Agency72 since 2009. There is also evidence of risk of heart and lung disease as well as psychological issues. Other forms, such as edibles, oils, and vaporized marijuana have other risk profiles that are not yet well defined. The question from a policy making perspective is whether to apply the precautionary principle and develop policies to minimize use based on the existing evidence base or wait, likely 20 to 30 years, until the specific risks of marijuana and secondhand exposure have been quantified as precisely as they have been for tobacco today. There is evidence to support the conclusion that without adequate public health controls a newly legalized marijuana market will transform into one modelled on the tobacco market. There are enough similarities between tobacco and marijuana products that the evidence and experience from successful tobacco control programs could form the basis for a public health approach to legalizing marijuana. principles defined in the WHO Framework Convention on Tobacco Control486 could form the basis for a public health approach to legalizing marijuana, which would seek to minimize industry influence in the policy process and to minimize consumption of marijuana products and the associated health risks of a new legal marijuana market.

Greater exposure reduced the odds of current cigarette use and smokeless use by 30% and 45%, respectively. Anti-smoking media campaigns help to shape social norms and institutional policies around smoking, which in turn change smoking behavior at the population level,including adult quit attempts.Several studies have found that youth are equally likely to report favorable responses to adult-targeted ads as to youth targeted ads,Studies from California,Massachusetts,and Australia demonstrate that exposure to adult-targeted mass media campaigns is associated with reduced smoking initiation and smoking behavior among youth. Even in countries where comprehensive tobacco control policies have been in effect for decades , intensive mass media campaigns have a positive additional influence on smoking behavior outcomes.Tobacco taxes are used to provide an annual revenue stream to support implementation of government media campaigns that consist of paid radio, television, billboard, internet and social media, and print advertising. Media campaigns with greater impact also include public relations campaigns for general market and population-specific communities, including various ethnic populations, young adult, and lesbian, gay, bisexual, transgender, and queer communities.Social norm change has been one of the most effective tobacco control strategies in the United States. The most successful application of the social norm change strategy took place in California, where in 1989 a statewide tobacco control program was implemented to transform the social environment where tobacco use is not socially desirable or acceptable.The key to the success of the California Tobacco Control Program has been its design as a broad-based campaign focused on reinforcing the nonsmoking norm aimed at the population as a whole – not just smokers or youth,for each element of the program, including the statewide hard hitting, evidence-based media campaign. Indeed,greenhouse growing racks by focusing on adults through its comprehensive tobacco control program, California has achieved one of the lowest youth smoking rates in the United States.Advertising bans are another important policy to denormalize tobacco use.

Like large graphic warning labels and plain packaging, they are inexpensive for governments to implement, and generally apply to all products Point of sale tobacco display bans in Ireland and Australia were both followed by reduction in perceived smoking prevalence among youth and young adults, which reflects lower normalization of tobacco use. In contrast to media campaigns, which require regular appropriations and create ongoing opportunities for the tobacco industry to weaken, block, or eliminate funding, advertising bans, once enacted, are legally binding.Promoting understanding of the industry’s predatory behavior has been a central theme of the California Tobacco Control Program since it started in 1989 and the Truth Initiative “truth” campaign. The messaging frame on industry behavior is an important reason for these campaigns’ success at preventing smoking initiation and promoting quit attempts, likely because they reduce the attractiveness of affiliating with the tobacco companies’ brand images. In contrast, programs that focus solely on individual, peer and family influences on youth smoking prevention and understate or ignore the effects of tobacco industry advertising are less effective than campaigns that highlight the role of the tobacco industry. Indeed, when Florida – where the “truth” campaign first originated in 1999– shifted its media messaging away from confronting the tobacco industry to a softer “kids shouldn’t smoke” message, it lost its effectiveness.Tobacco companies use product engineering to maximize consumption and profits.Large corporations have the scientific and technical capacity to undertake research and development programs that aim to identify which characteristics of a product to manipulate, and use sophisticated manufacturing processes to accentuate product features that maximize addictive potential. The cigarette companies invested heavily in their secret internal R&D departments to understand the addiction process, and modified their products to increase their addictiveness.Reviews of internal industry documents show that cigarette companies manipulate nicotine levels, cigarette length, chemical additives to alter nicotine absorption, improve the flavour of the smoke, reduce harshness,and increase puff intensity.

They also use ventilated filters, manipulation of nicotine levels,and other product modifications to attract novice smokers and to increase addictive potential by optimizing nicotine delivery and dosing.Cigarette companies also designed their brands to meet psychological and psychosocial needs of consumers.In addition to attracting youth,product design technology was used to recruit and socially normalize smoking among women,African Americans,Latinos,Asians,LGBTQ,low income groups, and veterans. Cigarette companies have also taken advantage of weak cigarette testing protocols around the world to conceal the actual toxicity of their products to consumers and regulators.In the process of manufacturing cigarettes to enhance nicotine delivery, and so the addictiveness and sales of cigarettes, tobacco companies have reduced particle size and made many other design changes which , while good for the cigarette business, resulted in a more dangerous cigarette in 2014 than in 50 years earlier in 1964. Changes in tobacco blends and curing of tobacco has caused US cigarettes to have higher levels of tobacco specific nitrosamines , a group of carcinogens found in tobacco and nicotine products. Surgeon General Report observed that “[f]or Kentucky reference cigarettes, mutagenicity per mg of total particulate matter was 30–40% lower for unfiltered cigarettes than for the same cigarette with a filter added.”These design changes have not only made cigarettes become more dangerous in terms of rising lung cancer rates,but also contributed to an increase in overall mortality, chronic obstructive pulmonary disease and heart disease. The rising risks correspond to changes in cigarette design – unfiltered to filtered, higher tar to lower tar, introduction of filter vents, among other changes to cigarette design. Deeper inhalation of more dilute smoke increases exposure of the lung parenchyma. These and other design changes in cigarettes may also have contributed to the shift, beginning in the 1970s, in the histologic and topographic features of lung cancers in male smokers, with an increase in the incidence of peripheral adenocarcinomas that largely offset the decrease in squamous-cell and small cell cancers of the central airways.The tobacco companies use menthol and other flavour additives including fruit and candy flavouring as marketing tools to attract young smokers.National survey findings from the United States and Japan confirm that menthol cigarette use is disproportionately common among younger and newer adolescent smokers.

Tobacco products that disguise the taste of tobacco through flavouring agents and palatability enhancers create products that largely appeal to youth and young adults.Menthol is the most popular characterizing flavour of cigarettes in the US, with more than 90% of all cigarettes containing menthol.Tobacco companies use menthol’s analgesic effects to mask acute effects of smoking . Such harsh effects, if experienced by the smoker, could encourage quit attempts and cessation among menthol users.Women perceive the minty aroma of menthol cigarettes to be more socially acceptable than non-menthol cigarettes, which complicates public health efforts to denormalize tobacco use. In the US, the tobacco companies intensely market menthol cigarettes in predominately black communities through price discounts, signage, and through associations of menthol use with hip hop lifestyle and culture.Family and social factors that prevented smoking among African American teens do not seem to carry over into young adulthood likely due to tobacco company targeted marketing.In 2012, teenage smoking prevalence among whites was twice as high as black smoking prevalence .While use rates among young adults remains higher for whites than blacks ,compared to white smokers, menthol cigarettes are disproportionately used among black smokers. National data from the United States show that around 80% of African American smokers use menthol cigarettes compared to around 30% of whites. Tobacco-caused morbidity and mortality rates are disproportionately higher among African Americans compared to whites,and menthol cigarette smoking is disproportionately high among African Americans, which may help to partly explain the disproportionate tobacco-related disease burdens.These rapid changes in medical costs are due to the fact that risks of cardiac events,non-cancer lung disease, complications of pregnancy,and effects on children begin to appear almost immediately when people stop smoking or being exposed to secondhand smoke. Cancer is also affected, albeit more slowly over time. Hospitalizations for heart attacks, other cardiovascular conditions, stroke, and pulmonary conditions drop immediately following implementation of smoke free laws, as do need for treatment of respiratory conditions,plant bench indoor and complications of pregnancy and hospitalizations for childhood illnesses. The fact that marijuana smoke exposure has similar – indeed larger – effects on vascular function73 suggests that there may be similar adverse consequences and medical costs if marijuana use increases following legalization and expansion of the market. Tobacco control policy change in Australia between 2001 and 2011 played a substantial role in reducing smoking prevalence among Australian adults between 2001 and 2011. During that time, the Australian government increased tobacco taxes, adopted more comprehensive smoke free laws, and increased investment in mass media campaigns, which can explain 76% of the decrease in smoking prevalence from 23.6% to 17.3% . Comprehensive tobacco control policies may have an even greater impact on cigarette consumption and demand reduction in low and middle income countries compared to high income countries.For example, there has been a 50% reduction in male and female smoking prevalence in Brazil between 1989 and 2010, which represents a 46% relative reduction compared to the 2010 prevalence under the counterfactual scenario of policies held to 1989 levels.Combined these policies had averted 420,000 deaths by 2010, with estimates of an almost 7 million deaths averted projected by 2050.

Uruguay, an international leader in tobacco control, became one of the first countries to fully implement the Framework Convention on Tobacco Control. In 2006, Uruguay implemented its national smoke free law, and in 2009 the government implemented the largest graphic warning label, covering 80% of the package. In that same year Uruguay prohibited use of false or misleading statements on tobacco packages . There were three tobacco tax increases in June 2007, June 2009, and February 2010, which made tobacco products in Uruguay the highest in the region. In 2012, the Ministry of Health launched an aggressive mass media campaign308 and in 2014 the government prohibited all forms of tobacco marketing including advertising, promotion and sponsorship, product promotion, and point-of sale displays. Since implementation of its comprehensive tobacco control program, tobacco consumption, risk perceptions, and social acceptability of use and the tobacco industry have shifted dramatically. From 2003 to 2011, adult smoking dropped by 3.3 percent each year while youth smoking dropped by 8 percent, from 39% to 31% for males and from 28% to 20% for females. 308 In 2012, 75% of Uruguayans favored a total ban on all tobacco products within 10 years and 60% of the population believed the tobacco companies were unethical. Support for comprehensive smoke free laws among smokers increased from 54% in 2006 to 90% in 2012.After Uruguay implemented its smoke free law, hospital admissions for heart attacks dropped 20%309 and non-hospital emergency visits for bronchospasm dropped by 15%. 303A 2000 study on marketing restrictions in OECD countries found that the effects of marketing bans are cumulative and that partial bans were not associated with reductions in tobacco use. Overall, comprehensive bans on advertising and promotions were associated with a significant reduction in tobacco consumption since implementation, with larger effects for more comprehensive bans. Tobacco companies use market research to understand smoking behaviour among different segments of the population,and, in turn, use such research in future marketing campaign messages.This information can be used to design advertising campaigns that circumvent partial advertising restrictions by shifting expenditures toward other media outlets .For example, after the 1998 Master Settlement Agreement in the United States, in which the tobacco companies agreed to some limitations on their advertising and promotional activities, the tobacco industry shifted marketing expenditures to direct mailings and online marketing.Partial advertising restrictions permit cigarette companies to target young adults through lifestyle magazines created by the industry,event sponsor ships,and low income and less educated women through distribution of coupons with food stamps, direct mail, and bundle offers at the point-of-sale.Following implementation of a 2012 law that prohibited point-of-sale tobacco displays in New Zealand the odds dropped significantly for experimentation with smoking , smoking initiation , and smoking prevalence , among adolescents, consistent with similar studies from Ireland,Norway,and Australia.There was a marginal decrease in perceived peer smoking among New Zealand smokers, which may have been greater if all forms of tobacco marketing had been prohibited simultaneously.Because the tobacco industry continuously seeks to evade any advertising restrictions, the World Health Organization recommends that governments license tobacco manufacturers and retailers, with penalties and sanctions for noncompliance, including license suspension and revocation for repeat violations commensurate on the nature and seriousness of the offence, to assist with enforcement efforts to control tobacco advertising.

Given the exploratory and cross-sectional nature of this study, we could not examine a causal relationship between perceived risks/benefits and cannabis use behavior. Longitudinal studies have found a bidirectional relationship between perceptions and tobacco use, in which perceived risks and benefits predicted adolescent cigarette smoking , and on the other hand, AYAs’ personal smoking experience decreased their perceived risks and increased their perceived benefits of cigarette use over time ; however, less is known for the longitudinal relationship between perceptions and AYA cannabis use. In addition, we did not collect data on potential confounders which may have affected participants’ perceived risks and benefits of cannabis use. Longitudinal and more comprehensive data are needed to better understand how the perceptions impact initiation and continued use of cannabis among AYAs. Findings from our small school-based sample in California may not generalize to other young populations or geographic locations that have different demographic characteristics and cannabis-related policies. Self-reported data might be subject to recall and social desirability biases. The small sample size did not allow us to examine perceived risks and benefits in a more comprehensive set of user categories . Future research should examine perceived risks and benefits among these groups of cannabis users to elucidate an association between cannabis-related perceptions and use patterns. In conclusion, this study indicated that AYAs’ perceptions of risks and benefits differ by cannabis product and use status, with greater perceived risks and benefits for combustible cannabis and blunts than for vaporized and edible cannabis. Prevention efforts should take into account perceptions of both risks and benefits and tailor educational messages to specific products to prevent all forms of AYA cannabis use. The coronavirus disease 2019 pandemic has had wide-ranging impacts on society, particularly among vulnerable populations. People who misuse substances may be particularly susceptible to social isolation and other pandemic-related hardships.

A recent U.S. report found that 13% of adults started or increased substance use to cope with COVID-19. The stress of the pandemic,cannabis growing systems combined with the social isolation resulting from essential public health strategies to contain transmission, may contribute to worsening mental health and/or increased substance misuse. Prior work has identified links between social isolation and these outcomes. Increased negative emotions due to the pandemic are also likely, which could increase coping-related substance use motives that precipitate use. Other than alcohol and tobacco, cannabis is the most commonly consumed drug in the U.S., with prevalence typically highest among emerging adults , who may be especially impacted by social isolation. Given that smoking is a primary method of cannabis consumption, individuals who consume cannabis may also have higher risk for respiratory and pulmonary complications of COVID-19 infection. To date, studies examining smoking and COVID-19 have focused on tobacco rather than cannabis. It remains crucial to examine the association between substance use and other related behaviors among broad samples of cannabis-using emerging adults during this pandemic, with most research to date focusing solely on college students or other age groups. For example, in a survey of college students, both binge drinking and illicit drug use declined after COVID- 19 onset. Another study of university students in Russia and Belarus found that one-fifth to one-third reported pandemic related increases in tobacco, alcohol, cannabis, and other drugs. Among Canadian adolescents , recent 3 week prevalence of binge drinking, cannabis use, and vaping were lower compared to the 3 weeks prior to the pandemic, with increases in mean alcohol and cannabis use days. Importantly, findings for pandemic-related changes in substance use may differ among higher risk samples engaging in regular substance use or misuse.

Therefore, to contribute to the nascent literature on COVID-19 among vulnerable substance-using populations of youth, we examined self-reported perceptions of changes in cannabis and alcohol use and other psychosocial outcomes, among emerging adults who regularly use cannabis. We collected data as part of an ongoing cannabis intervention study initiated just before the COVID- 19 pandemic hit the U.S. Given the limited prior literature, we had no a priori hypotheses and rather sought to provide a descriptive exploration to inform future research and prevention services. Note that we examine perceptions of behaviors before/ during the pandemic and do not examine outcomes relative to the pilot randomized controlled trial as follow-ups are ongoing.The present data were collected within an ongoing pilot RCT of an online cannabis intervention for emerging adults; all procedures occurred online. We recruited participants using social media ads that led to an online consent and screening survey to determine RCT eligibility 3+ times per week. Advertisements included photos and headlines, such as: “Use weed? Participate in a research study, earn $$$. See if you’re eligible, click here!” and participants were recruited regardless of their intentions to change cannabis use. The RCT involves group-based intervention and control conditions conducted separately by age and residence ; eligibility criteria were the same regardless of state residence. Recruitment procedures paralleled prior work and took place in two waves. Wave 1 was recruited in February 2020 prior to full emergence of COVID-19 in the U.S. Wave 2 recruitment occurred in May 2020 . This study was approved by our institutional review board and we received a standard Certificate of Confidentiality from the National Institutes of Health.As the pilot RCT is ongoing, we cannot examine outcomes. Currently, we focus on data related to COVID-19 only; nonetheless, we provide a brief description of the study conditions for context. Participants in each wave were randomized to either an 8 week intervention or control group, separated by age group. The 8 week intervention occurred in secret private groups on Facebook, moderated by health coaches who posted content for 56 days straight .

Content addressed cannabis use directly as well as upstream motives for cannabis use and prevention of related consequences using a motivational interviewing style where participants and coaches interacted. Consistent with MI, participants were informed that any changes they might consider making to cannabis use or other health behaviors would be completely up to them. The control group was parallel in length and format, except coaches posted entertaining social media content unrelated to substance use or mental health.COVID-19 items were designed to assess the prevalence of COVID-19 and perceived impacts. We modified available items to assess whether participants experienced COVID-19 symptoms , contacted a health care provider due to symptoms, and if they engaged in pandemic-related quarantine or isolation. We developed items to assess the following: COVID-19 hospitalization, known infections in participants’ households and social networks, changes in employment status, lost childcare and school closures, and dates of quarantine/social isolation . Among those reporting isolation, we asked about their cannabis use during isolation compared to their usual use of cannabis in the 3 months before the pandemic affected their geographic area . We asked participants about their emotions and behaviors in the 30 days before the pandemic came to their area relative to the past 30 days . Emotions assessed included feeling: lonely, stressed, anxious, depressed, hopeful, and happy. Among those who endorsed each of the following in the past year, we queried changes in: cannabis smoking, vaporizing, dabbing, and eating; using cannabidiol , drinking alcohol, smoking tobacco, vaping nicotine, and exercising. We assessed changes in eating and social activities. Participants rated the degree to which they agreed or disagreed that COVID-19 had impacted their lives in positive and negative ways . We asked an open-ended question, “please describe the ways that the coronavirus pandemic has or has not affected your life,”flood table which is presented in the qualitative analysis below.Participants completed an online Timeline Follow Back assessing past 30 day cannabis and alcohol use days. Items regarding past 30 day cannabis use methods, medical cannabis certification, sources of cannabis acquisition, hours high per day, and time to first use upon waking were adapted from prior work. When answering questions about cannabis, participants were prompted to respond about products containing THC and to exclude reporting on “CBD-only” products.We provide quantitative data in the form of means, standard deviations , and proportions of participants. After using chi-squared and t-tests for preliminary examination to conclude that Wave 1 and Wave 2 participants did not substantially differ on demographics and cannabis use indicators , we pooled data from the two cohorts for this descriptive paper since each group completed measures close in time . We used chi-square analyses to examine relationships between perceived changes in cannabis consumption and negative emotions. We conducted content analysis of responses to the single qualitative item. The first author reviewed ~50% of responses and noted emerging themes for a code book of potential response categories, then incorporated the last author’s feedback. The first author trained the second and third authors in the coding scheme. The two coders independently coded 10 responses, then resolved discrepancies and clarified code definitions with the first author. Next, they coded 15 responses and met with the first author to resolve discrepancies and refine code definitions prior to coding the remaining responses. The first author reviewed this coding and resolved discrepancies, which, out of 291 codes applied , occurred on 70 occasions . Codes were enumerated to assess the prevalence of themes in participants’ responses.

Participants’ agreement with the statement “The coronavirus pandemic has impacted my life in positive ways” was as follows: 6.4% strongly agreed, 29.8% agreed, 22.7% were neutral, 21.3% disagreed, and 19.9% strongly disagreed. Their ratings for a parallel statement focused on negative impacts were: 30.5% strongly agree, 46.1% agree, 18.4% neutral, 3.6% disagree, and 1.4% strongly disagree. Table 5 provides exemplar quotes from openended responses about the impact of the pandemic on participants’ lives. Overall, themes reflecting negative impacts were most prevalent, although positive aspects were mentioned. Negative impacts on employment and finances , social isolation , and stress or negative emotions, including worsening mental health were most frequently mentioned. Perhaps of interest given the developmental age of the sample, uncertainty about the future and lost opportunities or milestones came up less frequently. Among positive themes, employment and finances were mentioned most frequently with some participants having increased income due to stimulus checks and federal unemployment benefits during the initial pandemic response. Very few spontaneously mentioned changes in cannabis use as a positive or negative impact of the pandemic, although they had already reported on this in the quantitative survey.We have provided a unique snapshot of the perceived impact of the COVID-19 pandemic on the lives of emerging adults across the USA who regularly use cannabis. A few months into the events of the pandemic unfolding in the USA, many of these emerging adults were experiencing significant changes, including ongoing social isolation, increased loneliness, anxiety, and depression, lost wages or jobs, and/or changes in school or residence. Many participants felt that their use of cannabis increased during the pandemic, particularly when socially isolated , with rates similar to those reported previously. Descriptively, there were more participants reporting perceived increases in cannabis use than there were reporting increased alcohol or tobacco/nicotine use, which was consistent across cannabis consumption methods. It is possible that the minority of the sample who felt their cannabis consumption decreased had limited access to cannabis during the pandemic; however, nearly all participants reported accessing cannabis in the prior month . Perhaps most concerning are the third to half of the sample who felt they increased their cannabis consumption due to the pandemic, given that greater frequency of consumption is correlated with greater severity of cannabis use disorder , which has a mean age of onset around 21 years and is associated with greater risk for depression and anxiety disorders. However, we did not assess the diagnosis of CUD, which should be included in future research to characterize the severity of cannabis use. Nonetheless, the clinical features of the sample and the large proportions reporting increased depressive feelings raise alarm given the association between mood disorders and escalation of cannabis use disorder severity. Although we could not examine causal effects in these one-time COVID-19 survey questions, because the majority of participants reported changing their cannabis use in the wake of the pandemic, it seems clear that COVID-19 has far-reaching impacts on other areas of public health beyond disease transmission. This concern is underscored by the finding that pandemic-related increases in negative emotional states coincided with reports of increased cannabis smoking in particular.

In addition to choosing attributes based on significance, we considered the importance of attributes for policy implications. For this reason, we kept the densities of bike lanes and bike racks even though they were not statistically significant in the final model. The major limitations of this study stem from the nature of the bike sharaing activity data that is used. The time period observed is the first full month of JUMP operations in San Francisco, which is likely to include travel behavior of early adopters and novelty rides that do not reflect more regular patterns that may have emerged among JUMP users since its launch. In addition, by comparing JUMP and GoBike trips during this time period, we observe the interdependent effects of both the dockless model and the electric pedal-assist bicycles on JUMP travel behavior compared to that of GoBike, which used non-electric bicycles with a stationbased model during the study period. While differences in travel behavior related to elevation may be more directly linked to the e-bikes in the JUMP system, most other trip attributes examined may be influenced by a number of variants in the operation and/or ridership across the two bike sharing systems. The lack of user data linked to the trips we observe constrains our ability to account for socio-demographic differences across the riders of the two systems. We use census data to differentiate bike sharing trip destinations by the socio-demographic makeup of the surrounding census tracts, though we cannot directly draw conclusions about the sociodemographic characteristics of riders, nor of the actual points of interest visited during each trip. In addition, we used suggested bike routes from the Google Directions API to estimate trip distances, durations, and elevation gain in the absence of trajectory data. However,trimming tray we chose not to incorporate bike path availability along these suggested routes in the DCA model due to a concern that the results would overestimate the use of bike routes. Lastly, there is a degree of endogeneity in our DCA results for GoBike, as the destination choices of GoBike users are completely constrained to the station locations of the GoBike system.

We begin with a visual analysis of the geographical and temporal distribution of demand for each bike sharing system. Figures 4.a – d. display heat maps of bike sharing activity during February 2018 by time of day. Areas in which the departures constitute the majority of activity are shaded green, while areas in which arrivals constitute the majority of activity are shaded red. Thirty-two percent of JUMP trips, 33% of GoBike non-member trips, and 43% of GoBike member trips took place during the AM period . Both JUMP and GoBike exhibit concentrated AM demand destined for dense employment centers along Market Street and in the South of Market and Financial District areas just South-East and North-West of Market Street, respectively. These neighborhoods are home to many large office buildings housing numerous corporate headquarters and branch offices. The intensity of trip arrivals around the Civic Center could represent multi-modal trips, as bike sharing users may choose to transfer to the Bay Area Rapid Transit line at this most North-Western access point. There is a clear difference in the trip origins of JUMP and GoBike in the AM period, where we see a concentration of GoBike trips departing from the CalTrain and Embarcadero BART stations, while JUMP trip departures were spread out in neighborhoods outside of the CBD. In the PM period , we observed both systems servicing riders originating in the CBD, but the destinations of JUMP trips were again spread out in neighborhoods farther away from the CBD, while GoBike trip destinations were concentrated at the Caltrain and Embarcadero BART stations.The distribution of bike sharing trip distance and duration for each of the two systems exemplifies the behavior observed in the visual analysis. We assess the trip characteristics of GoBike members and non-members separately, noting that 95% of GoBike trips were made by members. JUMP trips tended to be longer in distance and duration than GoBike trips . The average JUMP trip was about a third longer in distance and about twice as long in duration as the average GoBike member trip. While this may be a result of the newness of JUMP in February 2018, the similarity implies that JUMP tended to be used for longer, potentially more recreational trips, which are more similar to GoBike non-member trips.

Indeed, 7% and 8% of JUMP and GoBike non-member trips, respectively, are longer than one hour in duration, compared to less than a third of a percent of GoBike member trips. Unlike GoBike members who pay on an annual basis and are incentivized to make the most out of their membership regardless of trip length, JUMP users pay per trip and thus may prefer to make longer, less frequent trips. Next, we present the results of the destination choice model estimation to better understand the influence of different factors on bike sharing users’ destination choices in the GoBike and JUMP systems . The final log-likelihood values for the destination choice models for GoBike and JUMP trips were -1,402 and -1,713, respectively. The R squared values for the models were 0.26 for GoBike and 0.27 for JUMP, while the R bar squared values for each were 0.23 and 0.24, respectively. Across both systems, increase in estimated trip distance and elevation gain were both strong negative factors in bike sharing users’ destination choices. The estimated total elevation gain was by far the most negative coefficient in the GoBike model, indicating that destinations that involved climbing in elevation were very unpreferable to GoBike users. The coefficient for estimated distance in the JUMP model was more negative than that of estimated total elevation gain. The range of estimated trip distances for the destination choice set was inherently larger for JUMP than for GoBike, since JUMP users were not entirely restricted by the service area of the system. Seven percent of JUMP trips in our dataset were completed outside of the service area. JUMP users were fined for ending a trip outside of the service area, but they were not prohibited from doing so. The large positive coefficient for the JUMP service area indicator reflected this incentive. Factors indicating the level of activity at a destination were significant and positive across models. In addition, the density of the resident population and the ASC for low-density residential census tracts were both significant, negative coefficients in the JUMP model, suggesting an affinity of JUMP users to travel to lower-density destinations. Conversely, the model results support our findings from visual spatial analysis that GoBike users were largely bike sharing to work, as the activity level parameters were the two most positive coefficients, and the employment center ASC was positive and significant.

The age and income characteristics of destinations were mostly insignificant in the model. JUMP destination choices were significantly negatively influenced by the fraction of residents over the age of 55 in a destination census tract. The median income of destinations is not a significant factor in the destination choice models for either system, with coefficient estimates close to zero in both models. While bike rack density is unsurprisingly an insignificant factor in the GoBike model, it is a significant positive factor in the destination choices of JUMP users. Since JUMP users were instructed to lock the bikes to public racks,weed trimming tray this finding has two possible implications for the destination choices of dockless bike sharing users: 1) JUMP users may prefer destinations with a higher availability of public bike racks with which to easily end their trips, and/or 2) the spatial distribution of public bike racks is well suited to the preferred destinations of dockless bike sharing users. On a related note, the density of GoBike stations in a destination tract was a significant positive factor in the GoBike model. Again, the location of docking stations may attract users, and/or they are well-placed to serve the destination preferences of GoBike users. The insignificance of bike lane density in both destination choice models may be an artifact of our choice to model this factor as an alternative attribute rather than a trip attribute. Bike lane density along suggested destination routes or even the cumulative bike lane density across each of the census tracts along the destination route may provide a more explanatory variable with which to assess the bike sharing user sensitivity to bike lane availability. The composite suitability maps reveal the geospatial distribution of the “bike ability” for users of JUMP and GoBike in San Francisco. In particular, residential neighborhoods in the Northwest and along the Northeast of the city provide opportunity for expansion for both systems to improve equity based on physiological and economic factors. The distribution of the population over 55 and elevation in these neighborhoods appear to be the main constraints in these areas, while considerable job density and available bike facilities provide opportunities. Though e-bike sharing has potential to overcome physiological barriers for older residents in these areas, considerable social barriers may exist since JUMP is only accessible through a smartphone application. Additional social constraints, which are not visualized in the bike ability maps, may stem from language barriers or broader cultural differences across the city. Finally, introduction of temporal variables would aid in assessing the opportunities and challenges for equitable bike sharing based on the time of day. Bike ability may vary across time periods with different levels of congestion, or across hours of daylight versus darkness. Shared micromobility service models are growing across the U.S. including: docked, dockless, and e-bike sharing models.

Our research analyzes the trip making behavior of JUMP dockless ebike sharing and GoBike docked bike sharing users in the first month of the JUMP pilot program. Travel behavior and destination choice analyses reveal that the two systems appear to complement one another: GoBike trips tended to be short, flat commute trips, mostly connecting to/from major public transit transfer stations while JUMP trips were longer, more spatially distributed and more heavily servicing lower-density neighborhoods. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. In addition, JUMP trips underwent about three times the elevation gain per trip, on average, compared to GoBike trips. Our findings suggest that the assistance provided by e-bikes in addition to the flexibility afforded by the dockless model are serving mobility demand outside the dense urban core of the city, where docked models are not available. Furthermore, we found that the destination choices of docked bike sharing users are positively influenced by the density of stations, and bike rack density was a significant positive factor for JUMP users. The location of facilities necessary to use either the docked or dockless system may attract users and/or be well-placed to the destination preferences of users. While the sensitivity of destination choices to factors influencing equity, such as older age are slight, our bike ability analysis reveals that the composite effect of constraints and opportunities that impact bike sharing demand can have adverse effects in neighborhoods otherwise ripe for bike sharing expansion. Additional research is needed to more closely link the characteristics of shared micromobility users with differences in travel behavior across business models and service areas. This study focuses on San Francisco, a city with unique topographic, sociodemographic, and cultural features which have distinct effects on travel behavior that may not be generalizable to other locations. As policies and guidelines for shared micromobility are being piloted and refined, similar data sources to those used in this study complemented with user surveys can be used to monitor the emerging trends in ridership across multiple shared modes. Research into the multi-modal trip making and trip chaining using shared micromobility is needed to further the understanding of the potential positive impacts of electric and dockless models on overall mobility and accessibility across trip purposes. Finally, time series analysis of travel behavior before, during, and after the implementation of innovative policies would provide invaluable insights to help hone public interventions strategies that effectively bolster mobility while promoting sustainability and equity within the broader transportation system. The unexpected outbreak of e-cigarette or vaping-associated lung injury was reported nationwide beginning in September 2019, causing more than 2800 hospitalizations and 60 deaths.The specific biological mechanisms of EVALI, as well as the chemical causes, are still under investigation.Evidence shows that EVALI is associated with vaping tetrahydrocannabinol containing e-liquid cartridges that were obtained on the black market. 

The evidence for this is mixed, however . Whereas medical users report that alleviation of acute symptoms of these disorders was a primary motivation for permit applications , a systematic review by Walsh et al. reported that this was not consistently observed across credible studies. Of course, marijuana use itself may constitute a risk factor for suicide apart from alleviating symptoms related to depression and anxiety, at least among some populations and for some levels of suicidality . A meta-analysis by Darvishi, Farhadi, Haghtalab, and Poorolajal supports the strong consensus that alcohol use disorder “significantly increases the risk of suicidal ideation, suicide attempt, and completed suicide.” With respect to medical marijuana, of course, the theoretical prediction depends on whether marijuana is used in addition to or instead of alcohol. If marijuana and alcohol use are combined, one might expect no change in suicide risk or even an increase in suicide following legalization. If marijuana replaces alcohol, on the other hand, one might expect a decrease in suicide risk following legalization. Self-reports by medical users in California and Canada indicate that a substantial proportion substitute marijuana for alcohol and other drugs. Since the instruments included items about patients’ potentially criminal behaviors, self-reports are potentially biased. With a few exceptions, opportunity pathways have received less attention in the suicide literature. Noting that suicide risk is highest when the victim is alone in the absence of guardians who would otherwise intervene , Chew and McCleary use the routine activity theory to explain why risk is relatively lower on weekend days, when other household members are more likely to be present, and relatively higher on Mondays, when other household members are likely to be out of the home at school or work. By analogy,vertical grow system if access to medical marijuana obviates the need to leave home, one might expect a lower suicide risk following legalization.

If medical users substitute marijuana for alcohol, moreover, legalization may result in less time spent in licensed alcohol establishments. Anderson, Hansen, and Rees use this argument to explain their finding of a reduction in motor vehicle fatalities following legalization. Firearms access is a relevant opportunity pathway. The positive correlation between firearms access and suicide risk has been demonstrated for U.S. metropolitan areas and counties . These correlations are subject to the ecological fallacy, however. At a disaggregated level, compared with matched controls who live in non-gun households, individuals who live The 1993 Brady Handgun Violence Prevention Act prohibits the purchase of guns by individuals who are addicted to controlled substances. Though used for legal medical purposes, marijuana remains a controlled substance under U.S. law.1 Since California medical marijuana users were not allowed to purchase firearms in 1997, and since California firearms regulations are relatively strict, we expect a reduction in suicide risk following legalization. In sum, in 1996, California legalized marijuana use for medical purposes. Implementation was abrupt and uniform, presenting a natural experiment that we take advantage of in order to estimate the causal effect of a medical marijuana initiative on suicide risk. In the current study, we aggregate total, gun, and non-gun suicides by state for the years 1970–2004. Using a Synthetic Control Group quasi experimental design , we construct a control unit for California from time series of the 41 states that did not legalize marijuana during the time frame. We interpret post-intervention differences for California and its synthetic control time series as the effects of the medical marijuana law on suicide. Significance of the effects is assessed with permutation tests. In 1996, California voters passed an initiative Proposition 215, which legalized marijuana use for medical purposes. Because the Proposition was implemented in an abrupt and uniform manner, legalization presented a “natural experiment.”

To estimate the causal impact of legalization on suicide, annual time series of total, gun, and non-gun suicides were analyzed by comparing California with an estimated counterfactual state in a Synthetic Control Group design. The synthetic control time series for California were constructed as a weighted combination of 41 states that did not legalize medical marijuana during the time frame. Post-intervention differences between California and its constructed control time series were interpreted as the causal effect of the medical marijuana law on suicide. The statistical significance of these effects was assessed with permutation tests. Findings reveal that rates of total suicide and gun suicide dropped significantly in the aftermath of Proposition 215. Findings also reveal, however, that legalization’s impact on non-gun suicides is considerably smaller, and arguably no different than what would be expected to occur by chance. Confidence in these findings is underscored by the methodological approach undertaken in the study. A strength of the Synthetic Control Group Design is that it allows us to examine the net effect of medical marijuana legalization on suicide. Despite the strengths of this design, important limitations remain, many of which present opportunities for future directions in research. Because we examine suicide trends over eight post-intervention years, we are fairly confident that the effects are permanent. Because our time series end in 2005, on the other hand, it is difficult to generalize our theoretical result to subsequent years. We are limited by the fact that medical marijuana laws began to proliferate across the U.S. after 2005, threatening to contaminate the “donor pool” of untreated states. In virtually all the states that legalized medical marijuana after 2005, moreover, reforms were not implemented abruptly or uniformly, making confident causal interpretations more difficult. Another limitation that presents a future direction relates to the mechanisms that may account for the findings of the study. What are the mechanisms responsible for the sharp decline in total, but especially gun, suicides following medical marijuana legalization in California?

We proposed mechanisms related to the substitution of marijuana for alcohol and other related substances; marijuana use itself, which may reduce actual motivation for suicide; the inability of medical marijuana patients to purchase firearms; and changes in the culture of recreational substance use, leading to fewer unsupervised opportunities to commit suicide in the home. Each of these pathways should be tested, although many will require additional data collection. For example, one likely fruitful research direction would be to collect annual data on alcohol consumption in California and assess whether it is a plausible mechanism by which medical marijuana legalization could cause a reduction in gun suicides. Beyond adjudicating these various pathways, testing mechanisms could yield insight into why we do not find the expected reduction in non-gun suicides following legalization. Unfortunately, we do not have the data to test these mechanisms, yet it will be essential for future researchers to do so. In the U.S., use of prescription pain relievers , also known as prescription opioids and opioid pain relievers, has been increasing dramatically. Worldwide, prescriptions of PPRs have almost tripled since 1990, and the U.S. is a factor in this rise, as it has the highest percapita consumption of PPRs in the past ten years . This increase has become dangerous, as opioid use carries risks that include addiction, sedation, respiratory depression, overdose and death . Between 1999 and 2010, deaths attributed to PPRs rose five times among women and 3.5 times among men . Of all prescription drug OD deaths in the U.S. in 2013, 71.3% involved PPRs . PPRs and marijuana are biologically linked; like PPRs, marijuana induces analgesia, acts on some of the same brain regions, and partly exerts its effects via opioid receptors . This connection is especially relevant due to the changing legal status of marijuana. As of August 2016, 24 states and Washington D.C. had legalized medical marijuana. Between 2007 and 2012, the number of past month marijuana users rose from 5.8 to 7.3% 2013, and between 2001 and 2013, past year adult marijuana use increased from 4.1 to 9.5% in the U.S. . Further, legalization of medical marijuana has been associated with increased odds of marijuana use among adults ,cannabis grow equipment though no consistent association has been determined among youth/young adults . Distinct theories attempt to explain how medical marijuana legalization affects use of substances other than marijuana. The relationship between different substances can be impacted by 1) change in cost of a substance, 2) policy alterations that influence availability of a substance, 3) shifts in legal consequences of using a substance, and/or 4) the psychoactive/pharmacological effects of a substance . More U.S. states are legalizing medical marijuana , and marijuana shares some psychoactive/pharmacological effects with PPRs. The substitution theory postulates that there is a substitution effect, whereby an increase in marijuana use coincides with a decrease in the use of other substances – in this case, PPRs . There are logical reasons why individuals would opt to use marijuana instead of PPRs.

With the new legal status of medical marijuana, individuals can access it through medical dispensaries and enjoy a lower legal risk if they live in a state where it is legalized. Individuals also report switching to marijuana for pain control because when compared to prescription drugs, marijuana has fewer side effects and withdrawal symptoms . Studies supporting the substitution effect have demonstrated that either increases in the use of marijuana or the legalization of medical marijuana is associated with reductions in opioid use, hospitalizations for opioid dependence/abuse, PPR ODs, and opioid OD mortality . In contrast to the substitution effect, there may be a complementary effect, where an increase in marijuana use is associated with an increase in the use of PPRs . In support of this theory, researchers using National Survey on Drug Use and Health data found a positive association between marijuana and increased use of PPRs . In another study, researchers focused on individuals who were prescribed long-term opioid therapy and found that those who also used medical marijuana presented with greater risk of misusing prescription opioids. Additionally, a prospective cohort study using the National Epidemiologic Survey of Alcohol and Related Conditions data determined that use of marijuana was associated with a greater risk of using non-medical prescription opioids three years later . However, in these studies, researchers did not analyze how co-use of other substances would impact the direction and/or strength of the relationship between marijuana and opioids/PPRs. To determine if there is either a substitution or a complementary effect between marijuana use and PPR use, co-use with other substances needs to be studied. Additionally, there is a strong positive association between nicotine use and PPR use. When compared to non-smokers, tobacco smokers experience more intense and longer lasting chronic pain, as well as a higher frequency of PPR use . Studies have demonstrated an interaction between nicotine and opioids that is associated with an increase in the total consumption of the two substances and contributes to other effects of the drugs . The relationship between the use of these two substances has a basis in the biological connection between them, as the endogenous opioid system is an underlying mechanism for several behavioral outcomes related to nicotine . Like marijuana, nicotine is involved in anti-nociception via endogenous opioid system mediation, suggesting that nicotine is used for the self medication of pain ; and in fact, nicotine heightens the anti-nociceptive effects of both opioids and marijuana . Several studies have documented common use patterns among tobacco, marijuana, and opioids/PPRs . For example, a prospective study of NESARC data demonstrated that early-onset of smoking cigarettes increased the odds of beginning opioid use and that frequency of both cigarette and marijuana use increased the odds of beginning opioid use, re-initiating opioid use after previously stopping, and continuing opioid use among current users . Thus, the three substances share anti-nociceptive actions mediated by the endogenous opioid system, and evidence indicates that marijuana and nicotine use predict opioid use among adults. From 2003 to 2012, NSDUH data revealed a significant increase in the co-use of marijuana and tobacco . Further, smoking tobacco is significantly associated with cannabis dependence . Given the national trend toward marijuana legalization, co-use is likely to increase. Cigarette smokers and marijuana users are a crucial population to study, as nicotine and marijuana share mechanisms of action with each other and with opioids, and use of each substance has been shown to be associated with use of opioids/PPRs . However, whether there is an association between prevalence of marijuana and PPR use among current smokers has not been determined.

Residues were common in the legal cannabis supply — a 2017 investigation found that 93% of 44 samples collected from 15 cannabis retailers in California contained pesticide residues . Some studies of data from the unregulated period suggest a relationship between cannabis consumption and exposure to heavy metals , while others demonstrate that potentially harmful microorganisms may colonize cannabis flowers . A 2017 study raised concerns that in immuno compromised patients, use of cannabis contaminated with pathogens may directly affect the respiratory system, especially when cannabis products are inhaled . The currently prevailing statutes governing cannabis testing are contained in Senate Bill 94, the Medicinal and Adult-Use Cannabis Regulation and Safety Act of 2017 — which brought together all of California’s previous cannabis legislation, including Proposition 64, the Adult Use of Marijuana Act of 2016 . Since MAUCRSA, state agencies have propagated regulations for both medical use and adult use . MAUCRSA amends various sections of the California Business and Professions Code, Health and Safety Code, Food and Agricultural Code, Revenue and Taxation Code and Water Code, and introduces a new statewide structure for the governance of the cannabis industry — as well as a system by which the state may collect licensing and enforcement fees and penalties from cannabis businesses. A significant portion of MAUCRSA is comprised of testing rules that aim to certify cannabis safety . These rules, however, may increase the production cost and therefore the retail price of tested cannabis, thereby reducing demand for legal cannabis in California. Thus it is important to understand the costs of cannabis testing relative to the value of generating a safer product. We first review maximum allowable tolerance levels — that is,cannabis grow supplies the amount of contaminants permitted in a sample — under the state’s cannabis testing regulations and compare them with tolerance levels for other food and agricultural products in produced in California.

We then briefly compare testing regimes and rejection rates in other states where medical and recreational use is permitted. Finally, we use primary data from California’s major cannabis testing laboratories and from several cannabis testing equipment manufacturers, as well as a variety of expert opinions, to estimate the cost per pound of testing under the state’s framework for the cannabis business . We conclude by discussing implications of this research and potential regulatory changes.Since July 1, 2018, all cannabis products have been required to pass several tests before they can be sold legally in California. The specific test for each batch of cannabis depends on product type. Types include dried flowers , edibles , vape-pen cartridges containing cannabis oil and a wide variety of other processed cannabis goods, including tinctures, topicals and cannabis in crystallized, wax or solid hashish form. In order to enter the market legally, all these products must be tested for cannabinoids and a large variety of contaminants. Table 1 shows the substances measured in each test , provides a description of each test and specifies the products to which the test applies and the criteria for passing the test. Most tests, such as those for potency, presence of foreign materials, pesticides, heavy metals, mycotoxins, microbial impurities and terpenoids, apply to all batches. Moisture tests, however, apply only to flowers and solid or semi-solid products — while tests for solvents or processing chemicals apply only to processed or “manufactured” products. That is, the specifics of each test depend on which cannabis product is tested. Independent, licensed testing laboratories are responsible for receiving samples for testing from licensed distributors. The laboratories then conduct a full set of analyses, following the criteria established by MAUCRSA and specified by regulations. Laboratories must deliver to distributors a certificate of analysis indicating the results of each analytical test. A batch must pass all required tests before it can be released to retailers. Table 2 shows a list of residual solvents and processing chemicals, with the maximum permitted tolerance levels for legal cannabis. Tests evaluate two groups of solvents and processing chemicals , with a very low tolerance established for those in category I. Table 3 shows tolerance levels for pesticide residues and heavy metals.

The maximum permitted tolerance levels for pesticide residues are particularly tight when compared with tolerance levels for other agricultural products in California. For many pesticides, the maximum residual level is zero, meaning that very stringent tests are required and that no trace of the chemical may be found. Among pesticides with allowable limits above zero, the tolerance levels for inhalable products are particularly low. In some cases, tolerance levels for inhalable products are one-four-hundredth the levels for other products. To help interpret the cannabis tolerances, it is helpful to consider them in the context of food safety testing. The top row of table 4 shows, based on more than 7,000 samples, the percentage of California food products in which, from 2015 to 2017, any pesticide residues were detected . These percentages were above 60%. The second row of table 4 shows that, despite the high share of food products in which some pesticide residue was detectable, only 1.51% of samples in 2016 contained pesticide residue above tolerance levels set by the U.S. Environmental Protection Agency — and only 0.45% exceeded those levels in 2017. The bottom panels of table 4 show that, of the 7,000 samples tested, more than 12% of 2017 samples would have been above California’s product tolerance limits for inhalable cannabis. More than 3% of the 2017 samples would have exceeded even the less stringent tolerance levels established for other cannabis products. As shown in table 4, similar results apply to the samples for the other two years.In California’s licensed, legal cannabis channel, all products must be held by a licensed distributor while they are tested in an independent, licensed laboratory. Licensed testing laboratories do not publish their prices and the costs of testing services are not generally available. Testing prices depend on the number of samples to be tested, the type of product tested and the specifics of the contract between the distributor and the laboratory, among other factors. We collected detailed data to construct in-depth estimates of the capital, fixed and variable costs required to run a licensed testing laboratory in California. This information included the costs of equipment, facilities, maintenance, supplies, technical and non-technical labor, taxes and other inputs. We gathered data from established cannabis testing companies , new cannabis testing companies, laboratories that test other agricultural products, and other industry sources, including advisors of the cannabis industry and cannabis retailers.

We collected prices for testing equipment, supplies, chemical reagents and other cannabis testing inputs by contacting the sales representatives of large equipment supply companies . We considered the costs of sampling and transportation to and from test facilities, adjusting those costs estimates according to the geographical configuration of testing laboratories and distributors across the state. Finally,grow lights for cannabis we used data from the California Department of Pesticide Regulation and some assumptions based on experience in other states to estimate the share of cannabis that fails testing and therefore the lost inventory due to failed tests. To make these cost calculations we accounted for inventory that first fails testing, but then is remediated. In addition, to understand the opportunity cost of cannabis used in the tests or lost in the process, we use data from wholesale prices and a survey of retail cannabis prices conducted by the University of California Agricultural Issues Center . Based on this information, we developed a cost per unit of cannabis tested for representative labs of three different sizes to approximate the distribution of costs in the industry. For simplicity, we assumed that testing labs of different sizes use the same inputs, but in different proportions, to provide testing services. We assume economies of scale with higher share of capital costs per unit of output for the smaller labs. We used information reported by the Bureau of Cannabis Control in the first half of 2018 to compile a list of cannabis licensed testing laboratories and distributors in California .We used information on the geographic location of testing labs relative to cannabis production and consumption to assess the cost of transporting samples from distributors to testing labs. In March 2019, there were 49 active testing licensees and 1,213 licensed distributors. Both testing licensees and distributors are located in many areas across the state, but they are concentrated in traditional cannabis production areas in the North Coast region of California and in large population centers. Table 5 shows capacities, annualized capital costs, and other annual expenses for three size categories of testing labs: small, medium and large. The size categories are based on the number of samples analyzed annually and were chosen to represent typical firms, based on our discussions with the industry. We assume about 25% of labs are small, 25% are large and the remaining half are in the medium category. By regulation, these labs test only cannabis. The annualized cost of specific testing equipment and other general laboratory equipment is a significant share of total annual costs. The cost of equipment and installation is about $1.5 million for a small lab, about $2.4 million for a medium lab and about $3.8 for a large lab. These costs are expressed as annual flows in table 5. To account for the annual cost of investment in equipment we use a discount rate of 7.5% per year that reflects the combined effects of depreciation and interest over a 10-year horizon, using the standard equivalent annual cost formula, typically used in budgeting studies: Annual Cost = K/−10 where K is the invested capital for each of the three testing labor sizes.

These annualized costs of the invested capital for each size of testing lab operations are shown in the top row of table 5. Our survey and discussions with laboratories provide the rest of the estimated costs. Equipment maintenance costs, rent, utilities and labor also are large cost categories. Each of these costs is less than proportional to the number of samples tested and thus contributes to economies of scale. This cost of consumable supplies is calculated on a per sample basis and thus is proportional to the number of samples tested. Finally, the return to risk and profit is estimated as 15% of the sum of the foregoing expenditures. Our estimated total annual costs are about $1.6 million for small labs, $3.3 million for medium labs and $7.0 million for large labs. The scale advantage of larger testing labs is reflected in the testing cost per sample: $324 for large labs, compared with $562 for medium labs and $750 for small labs. These cost differences arise from economies in scale in the use of laboratory space, equipment and labor. Each large testing lab processes about 10 times the number of samples as a small lab but has annualized operating costs only about five times those of a typical small testing lab. That means that small-scale labs tend to specialize in servicing more remote cultivators or manufacturers that have products handled by smaller and more remote distributors located at a cost-prohibitive distance from large labs. We used data on the annual testing capacities of small, medium and large labs and our assumption about the number labs of each size to calculate the share of testing done by labs of each size category. We expect that small labs will test about 6% of all legal cannabis in the state by volume, medium-sized labs will test about 33% of legal cannabis, and large labs will test 61% of legal cannabis. Using these shares, the weighted average cost per sample tested is about $428. Let us now turn from the cost per batch tested to the cost per pound of cannabis marketed. The per pound costs of laboratory testing depends on the number of pounds tested in each test. Therefore, we must consider batch size. Regulations have set a maximum batch size of 50 pounds of cannabis flowers . We expect that the batch size will differ within this constraint depending on the product type and origin and size of the cultivator and manufacturer and explore implications of batch size differences. Using the weighted average cost per sample of $428, the testing cost for a small batch of 5 pounds is $85.60, while for the largest-allowed batch size of 50 pounds, the cost is just $8.56 per pound. Next, we turn to several costs not included in the cost of testing a sample in the lab .