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Vertical farming is an innovative approach to agriculture that involves growing plants in vertically stacked layers or on vertical surfaces, using artificial lighting and precise environmental controls. Before you embark on vertical farming, here are some key considerations to know:

1. Space and Infrastructure: Vertical farming requires dedicated space and infrastructure to accommodate the vertical growing systems. Consider the available space, whether it’s an existing building, a retrofit project,clone racks or a purpose-built structure. Ensure the space has adequate height, structural stability, and access to utilities like water and electricity.
2. Lighting and Energy: Vertical farms rely heavily on artificial lighting, such as LED grow lights, to provide the necessary light energy for plant growth. Assess the lighting requirements, including light intensity, spectrum, and duration, and factor in the associated energy costs. Optimize your lighting setup to ensure efficient energy usage and maximum plant productivity.
3. Climate Control and Environmental Factors: Maintaining optimal environmental conditions is crucial for successful vertical farming. Control factors such as temperature, humidity, air circulation, and CO2 levels to create a controlled and stable microclimate that suits the specific needs of your crops. HVAC systems, ventilation, and sensors can help regulate and monitor these factors.
4. Growing Systems and Technology: Explore various vertical farming systems, such as hydroponics, aeroponics, or aquaponics, and choose the one that aligns with your goals, available resources, and expertise. Research and invest in appropriate technologies, such as automated irrigation systems, nutrient delivery systems, and data monitoring tools, to streamline operations and optimize plant growth.
5. Crop Selection: Consider the types of crops you intend to grow in your vertical farm. Some crops, like leafy greens and herbs, are well-suited for vertical farming due to their compact size and high yield potential. Assess the market demand, growth cycle, profitability, and feasibility of growing specific crops in a vertical farming setup.
6. Cost and Financial Viability: Vertical farming can involve significant upfront investment, especially in terms of equipment, infrastructure, and energy consumption. Conduct a thorough cost analysis, including operational expenses, maintenance costs, and potential returns. Explore funding options, grants, or partnerships to support your vertical farming venture.
7. Expertise and Training: Acquire the necessary knowledge and skills to operate a vertical farm successfully. Understand plant physiology, hydroponic or aeroponic principles, lighting technologies, and pest management strategies. Consider attending workshops, training programs, or partnering with experienced growers to gain insights and practical knowledge.
8. Sustainability and Resource Management: Vertical farming offers the potential for efficient resource utilization,hydroponic shelves reduced water consumption, and minimal pesticide use. Explore sustainable practices like water recycling, nutrient management, and integrated pest management to minimize environmental impact and ensure long-term viability.
9. Market Demand and Business Strategy: Assess the market demand for locally grown produce and identify potential customers or distribution channels. Develop a business plan that outlines your target market, marketing strategies, pricing, and revenue streams. Consider partnering with local restaurants, grocery stores, or participating in farmers’ markets to establish market presence.
10. Regulatory Compliance: Familiarize yourself with local regulations, zoning ordinances, and any specific permits or licenses required for operating a vertical farm. Ensure compliance with food safety standards, labeling requirements, and any applicable agricultural regulations.

Vertical farming offers unique opportunities to grow fresh produce in urban environments, reduce transportation costs, and provide year-round harvests. By thoroughly researching and planning, you can set yourself up for success in this innovative and sustainable farming practice.

Vertical farming has the potential to save significant amounts of water compared to traditional agriculture methods. Here are some reasons why vertical farming is often considered a water-efficient approach:

1. Controlled irrigation: Vertical farming systems typically utilize precise irrigation techniques such as drip irrigation or hydroponics. These methods deliver water directly to the plant roots in a controlled manner, minimizing water wastage due to runoff or evaporation. Water is delivered to the plants only when needed, reducing overall water consumption.
2. Recirculation systems: Many vertical farms incorporate recirculating water systems, where the nutrient-rich water that is not absorbed by the plants is collected and reused. This closed-loop system reduces water waste and allows for efficient use of water resources.
3. Reduced evapotranspiration: In a controlled indoor environment, vertical farms can minimize evapotranspiration—the loss of water from plants and soil through evaporation and transpiration. By controlling temperature, humidity, and air circulation, vertical farms can reduce water loss through evaporation, resulting in more efficient water use.
4. Water-efficient technologies: Vertical farms often employ advanced technologies that optimize water use. For example, sensors and automation systems can monitor and adjust irrigation based on plant needs, preventing overwatering and reducing water waste.
5. Recycling and water treatment: Some vertical farms incorporate water treatment systems that filter and purify wastewater, allowing it to be reused in the irrigation process. This recycling approach further reduces the need for fresh water input and minimizes overall water consumption.

While the water savings in vertical farming can be substantial, it’s important to note that the specific water savings will depend on various factors, including the crop types grown, the efficiency of the irrigation system, the technology utilized, and the operational practices of the vertical farm. Additionally, the water required for vertical farming still needs to come from a reliable and sustainable water source.

Overall, vertical farming’s controlled environment and efficient water management strategies make it a promising approach for conserving water resources and addressing the challenges of water scarcity in agriculture.

The ecological nature of paper 3 precludes conclusive statements about the role of alcohol and tobacco retailers in neighborhoods. No direct measure of acquisition was included. This meant that I could not be certain respondents were necessarily purchasing substances from retailers closest to their home. Improvements on this dimension should include items designed to ascertain information about purchasing and/or acquiring behaviors, and whether they consumed these substances or acquired them for a third party, etc. Finally, does this obscure sample of Latino adolescents living along the US/Mexico border generalize to a larger population of Latinos? Adolescents in this study sample were recruited based on their latent tuberculosis infection diagnosis. Associations between LTBI and use of alcohol, tobacco, or marijuana have not been shown previously. As such, study sample adolescents were not expected to differ substantially from their peers. Indeed, sample characteristics were similar to the larger set of adolescents recruited for the tuberculosis screening , most of which were negative. To the extent the findings reported in these studies are consistent with extant literature there is a rational basis to believe that other findings, even if new, may be of substantial import. The true test of generalizability will be measured in the replication of these findings,horticulture trays or variants of them in future studies. As with much research, the final product raises equally as many questions as answers. Questions that in turn generate new research agendas and future directions. Results from paper 1 suggest that future studies investigate with more depth the nature of alcohol and tobacco uptake among adolescents, including an exploration of the differences between the two substances.

For example, are family influences really more important in determining alcohol use than tobacco? If so, what aspects of that influence? Part of clarifying these differences may include more precise measures related to alcohol and tobacco use. For example, the quantity, frequency, and conditions under which it was consumed. Such items may help to discriminate between low-risk experimentation and high-risk experimentation or use . These discriminations are important as the set of risk factors for the respective behaviors are different. Nevertheless, these findings suggest that tobacco prevention interventions be focused entirely on influences in the peer domain. Whereas alcohol prevention efforts must be multidimensional, addressing family, school and peer influence domains. In the event future science confirms family and parental influences are valid intervention targets for alcohol prevention , researchers should be aware of the potential in congruence between reports from parents and their own children about parenting practices. Such findings should serve as impetuses for the creation of new measures, or at least the application of existing measures that minimize error, e.g., direct observation. The added dimension of paper 3 in this dissertation, especially considering its classification as an exploratory study, generates many unanswered questions. Upon first inspection, it is confusing to think that females living in high-risk areas report lower rates of gateway drug use. This may be an example of the built environment interacting with the social environment, on multiple levels. Determining these mechanisms will most definitely require continued research in this area, and the ability to balance technology and theory. Future measures may include such things as markers of neighborhood social acceptability of drug use, and parental involvement. Once again, I see parents as a key influence in this process. A new research agenda moving forward should include inspection of differential parental controls of males versus females in high-risk neighborhoods. With future refinements and sequential iterations in future studies, measures, results, and intervention implications will become more precise and prescriptive, making meaningful behavior change through intervention more achievable.

The homeless population is aging . People born in the second half of the “baby‐boom” have an elevated risk of homelessness . Homeless adults develop aging‐related conditions, including functional impairment, earlier than individuals in the general population. For this reason, homeless adults aged 50 and older are considered “older” despite their relatively young age . The homeless population has a higher prevalence of mental health and substance use problems than the general population . Individuals experiencing homelessness report barriers to mental health services, due to lack of insurance coverage, high cost of care, and inability to identify sources of care . These barriers can prevent their using services to treat mental health and substance use problems, such as outpatient counseling, prescription medication, and community‐based substance use treatment. Without these, homeless populations may experience more severe behavioral health problems and rely on acute care to address these chronic conditions. Homeless individuals have higher rates of Emergency Department use for mental health and substance use concerns , and are more likely to use psychiatric inpatient or ED services and less likely to use outpatient treatment than those who are housed . Homeless adults with substance use disorders face multiple barriers to engaging in substance use treatment. Competing needs , financial concerns, lack of knowledge about or connection to available services, and lack of insurance are barriers to substance use treatment among homeless adults . Older adults face additional barriers to mental health or substance use treatment due to cognitive and functional impairment, such as difficulty navigating and traveling to healthcare systems . However, there is little known about older adults experiencing homelessness. According to Gelberg and Anderson’s Behavioral Model for Vulnerable Populations, predisposing factors, enabling factors, and need, shape health care utilization . Although prior research has used this model for homeless populations, this work has not included older homeless adults .

Little is known about the prevalence of mental health or substance use problems in older homeless adults, the level of unmet need for services, or the factors associated with that need. To understand the factors associated with unmet need for mental health and substance use treatment in older homeless adults, in a population‐based sample of homeless adults age 50 and older, we identified those with a need for mental health and substance use services. Then, we applied the Gelberg and Anderson model to examine predisposing and enabling factors associated with unmet need, which we defined as not receiving mental health and substance use treatment among participants with mental health or substance use problems .In a population‐based sample of older adults experiencing homelessness, we found a high prevalence of unmet need for mental health and substance use treatment. While the majority of participants had mental health and substance use problems, few received treatment. One‐third of those with mental health need received mental health care. Fewer than 13% of those with substance use need received substance use treatment. We identified predisposing and enabling factors associated with unmet treatment need. Adults aged 65 and over had a higher odds of unmet need for mental health treatment. Older adults are more likely to have competing demands, including higher physical health needs, which can interfere with receiving behavioral healthcare . Due to a shortage of geriatric psychiatrists and geriatric mental health care services,sliding grow tables older adults may not have access to treatment when they seek care . The homeless population age 65 and older is expected to triple by the year 2020 . Thus, there is a need to design care that meets the needs of this growing, but under served, population. We found that having a regular healthcare provider was associated with less unmet need. Having a regular provider can increase engagement because primary care providers may help identify needs and refer to care. In safety‐net systems, such as the ones in which our participants receive care, primary care providers may be the primary source of mental health treatment, by prescribing psychotropic medication. Primary care providers are responsible for an increasing proportion of prescriptions for psychotropic medication . In addition to prescribing medication for mental health conditions, primary care providers can refer patients to outpatient mental health counseling and treatment with specialist staff or providers. In some safety‐net settings, mental health services may be colocated with physical health services via collaborative care models.Collaborative care models can enhance information sharing and treatment plan collaboration and reduce barriers to care .CCMs are cost‐efficient and can increase the capacity of resource‐constrained settings to provide care for patients with complex needs .

Federally Qualified Health Centers can bill for both a medical and mental health visit on the same day , and recent changes to FQHC payment codes allow billing for behavioral health care management services in addition to the FQHC billable visit. Pay‐for‐performance programs link public hospitals’ payments to care coordination and mental health treatment metrics . It is possible that participants in our study were obtaining care in safety‐net primary care settings with CCMs. Alternatively, the reduced odds of unmet need amongst those who had regular care providers could reflect other factors that we did not measure. For example, having a regular care provider may be a marker for increased system engagement and reduced barriers to any type of care. Those who seek primary care may be more organized, knowledgeable about safety‐net service availability, and have more access to transportation and other enabling resources. . Having a case manager was associated with less mental health and substance use treatment need. In the case management brokerage model, case managers help people navigate care systems and provide a linkage to services. In the clinical case management model, case managers serve as care providers and may provide both mental health and substance use services directly . In some models, such as intensive case management, case managers provide both brokerage and direct services . It is possible that the association between having a case manager and decreased odds of unmet need for both mental health and substance use services is a result of reverse causality; treatment programs may assign a case manager. We found that participants who first became homeless at age 50 or older had a higher odds of unmet substance use treatment need. Those with late onset homelessness had led more “typical” lives, with a higher likelihood of having been continuously employed and having been married or partnered . They were less likely to have had early onset of substance use problems, thus, they may have developed substance use problems more recently. These individuals may have been less aware of safety‐ net resources in general or resources for substance use treatment in particular. Spending time in jail/prison in the past 6 months was associated with reduced unmet substance use treatment need. It is possible that participants initiated substance use treatment while incarcerated. However, most incarceration settings do not provide adequate treatment services. Alternatively, as a condition of release, participants may have been required to engage in substance use treatment. Our findings indicate there is a lack of community‐based pathways into substance use care. By giving medication‐assisted treatments, such as buprenorphine for opioid use disorder and naltrexone for alcohol use disorder in primary care settings, primary care providers can begin to address this unmet need . However, there is a need for greatly expanded substance use services. Our study has several limitations. We did not use a full psychiatric diagnostic interview. However, screening measures are important empirical tools for the referral of individuals to mental health treatment, especially when integrated care is available . We did not ask participants where they received mental health services, thus we cannot determine whether they received care colocated with primary care, or treatment in mental health specific settings.Methamphetamine is a potent psychostimulant and complications of chronic use and abuse include addiction, psychosis, and depression, as well as increased risk of medical problems including HIV, impaired immune system functioning, cardiomyopathy, neurocognitive dysfunction, and Parkinson Disease . Current treatment is limited to behavioral therapies and risk of relapse following behavioral treatment is high . Pharmacotherapy may improve outcomes with behavioral treatment but despite numerous clinical trials no effective medication is available for methamphetamine use disorder . Negative clinical trials to date have primarily tested medications approved for other indications and focused on medications targeting the monoamine neurotransmitter systems suggesting that the identification of new targets for medications is necessary for the successful development of effective medications for methamphetamine use disorder. Substance use disorders are influenced by both biological and social factors although studies estimating heritability in excess of 50% for substance use disorders suggest an important role for genetic influences . For example, a recent study estimated heritability for stimulant use disorder at 68% .

The cost of vertical farming can vary depending on several factors such as the scale of the operation, the technology used, the location, and the specific crops being grown. Here are some key cost considerations in vertical farming company:

1. Initial Investment: Setting up a vertical farm requires significant initial investment in infrastructure, equipment, and technology. This includes constructing or retrofitting a suitable building, installing climate control systems, lighting systems, irrigation systems, and automation systems. The costs can vary greatly depending on the size of the facility and the complexity of the setup.
2. Operating Costs: Vertical farming entails ongoing operating expenses such as electricity for lighting and climate control, water and nutrient costs, labor, maintenance, and pest management. Energy costs, in particular, can be a significant expense due to the need for artificial lighting.
3. Technology and Automation: Advanced technology and automation systems are often utilized in vertical farms to monitor and control environmental factors, optimize resource usage, and streamline operations. These technologies can contribute to higher upfront costs but can also improve efficiency and reduce long-term labor costs.
4. Crop Selection: Different crops have varying requirements in terms of lighting, climate control, and nutrient solutions. Some crops may require more specialized equipment or additional resources, which can affect the overall cost of vertical farming. High-value crops may yield higher profits but can also require more investment.
5. Location: The location of the vertical farm can impact costs. Setting up a vertical farm in an urban area may require higher land or building costs compared to a rural location. Additionally, the availability and cost of utilities like water and electricity can vary depending on the region.

It’s important to note that while vertical farming initially involves higher capital investment compared to traditional farming, it can offer advantages such as higher crop yields, year-round production, and reduced resource usage,vertical farming systems which can lead to improved profitability in the long term. As the technology and practices of vertical farming continue to advance, costs are expected to decrease, making it more accessible and economically viable.

Overall, the cost of vertical farming is influenced by various factors, and it is crucial to conduct a detailed feasibility analysis and cost projection specific to the intended operation to get a more accurate estimate of expenses.

The modeling mechanism involves behavioral cues, then consumption that is then reinforced by norms and behaviors of members within the peer community . This sequence of events is referred to as a contingency. Contingencies employed by parents sometimes compete directly with contingencies in the social or peer network . The complex relationship between these competing contingencies is further compounded by the concept of MO discussed previously. At given times during adolescence peer contingencies may have enhanced reinforcing effects. Health behavior as a field is relatively new. Numerous theories and models have emerged during its short existence, each employing unique vocabulary to emphasize key features. Nevertheless, the most prominent health behavior theories and models have all demonstrated a propensity for highlighting such constructs as attitudes, intentions, and beliefs. This stems mostly from presumptions that internal constructs mediate behavior. Perhaps one of the most well-known health behavior theories is Albert Bandura’s Social Cognitive Theory.Both theories, SCT and operant psychology, profess that consequences such as rewards and punishment affect the likelihood that similar behaviors will occur again. One of the stark differences between these two theories is the locus of control. Operant psychology puts the locus of control in the environment and SCT focuses on internal intermediary causes for behavior, things like attitudes and emotions. The principal construct in SCT is self-efficacy. This construct is believed to portray one’s confidence for performing a given behavior, i.e., self-assessment about perceived abilities. This cognitive construct is usually measured via self-report. Pieters suggested that most theorists support the notion that people can access their internal states ,farm shelving and that there is a relationship between these internal states and behavior.

The prediction of behavior from attitudes has been studied extensively. Ajzen and Fishbein stated that attitudes predict and explain behavior. This belief led them to develop the Theory of Reasoned Action . The RA is an explanation of the relationship between attitudes, defined as behavioral intentions, and behaviors. A person’s behavior is a function of attitudes, intentions, and beliefs. This point of view is heavily supported in social psychology, but greatly contested by theorists in behavioral psychology . Lloyd contended that attitudes and behavior share, at best, a small correlation. He states there is little evidence of a relationship between reported attitudes and observable behavior. Harrison, Mullen and Green demonstrated almost no relationship between beliefs and behavior in value expectancy models; except for cross-sectional designs and only when important factors are not controlled. They found positive relationships between beliefs and health behaviors, but the relationship was relatively weak. Furthermore, Lloyd reported that attitudes are weak behavioral predictors. However, the dynamics of the relationship are enhanced when the order is reversed; when behaviors are considered as predictors of attitudes. Paniagua called the verbal-nonverbal relationship correspondence. Correspondences, he argues, can be developed so that the verbal correspond reliably with the non-verbal . Wilson, Rusch and Lee also reported an increase in correspondence between verbal-nonverbal behaviors. Given the difficulty of assessing attitudes, and the precarious correlation with behavior, other theories have emerged that remove the focus on intermediary constructs and place it on environmental variables and observable behavior. The most prominent among these is operant theory . In operant theory subjects operate on their environment and consequences result. Such contingencies alter the future probabilities of that same behavior occurring. Operant theory employs a parsimonious approach in exclusively focusing on observable variables.

An obvious advantage of observable variables is the ability to measure them while simultaneously measuring changes in behavior, thereby establishing temporal order. This eliminates capricious assumptions involving intervening variables so prevalent in other health behavior theories. The Behavior Ecological Model , a model that explains behavior in terms of antecedents and consequences is based on environmental determinants of behavior. It extends the logic of contingent relationships to cultural influences and population behavior. Antecedents gain their effectiveness to control behavior as they are tied to consequences. The BEM does not rule out mediating variables conceptually. However, it does ignore them due to inability to validate the concepts or their operational measures. The model also explicitly assumes that ignoring cognitive model variables does not compromise prediction or control of behavior. In actuality, such contingencies are not limited to just one individual. As a result, the BEM claims that antecedent, response, and consequence contingencies are in effect in populations and cultures. The value of the BEM is its application of behavioral principles to populations, including a focus on metacontingencies formed from social and cultural interactions sometimes omitted in traditional operant models. As it pertains to adolescent substance use, the BEM enhances prediction of the environment’s role in with features such as social norms and standards through behavioral cues. Some consequences are visible to external observers, yet others may occur within the individual e.g., drug high. Peer interactions are ideal venues for reinforcement to occur, often in the form of praise with others present. Peer praise will likely increase future use. Biologic responses that occur simultaneously may be synergistic and establish behavioral patterns that are difficult to discontinue, and once established, can persist absent peer praise. Density of modeling and peer reinforcement are important determinants of behavioral persistence.

Behavioral cues necessarily precede behavior, and can therefore be regarded as antecedents. Their occurrence in the everyday built and social environment is almost continuous. The ability to attend to them as stated previously, is related to the consequences that follow the behavior being prompted. Antecedents tend to be of two varieties, grossly defined as distal or proximal. In truth these are two points on a continuum, but they represent the temporal relationship with the behavior and ensuing consequence. Peer antecedents reliably predict substance use behaviors, because they are conceptually very proximal . Distal antecedents by definition are more general. Alcohol advertisements exemplify such antecedents. During the latter part of the 1990s and into the first half of the current decade, there have been substantial reductions in the proportion of daily smokers among adolescents in the United States. But according to a recent survey, this trend is beginning to slow, and adolescent rates of experimentation, which may lead to daily smoking, remain over 50% . When comparing rates by race/ethnicity, highest rates were observed among Hispanic adolescents , followed by non-Hispanic Whites and African Americans . The same pattern holds true for alcohol experimentation: highest rates among Hispanic adolescents , followed by non-Hispanic Whites and African Americans . A number of risk and protective factors related to alcohol and tobacco use among adolescents have been identified in the literature. Adapting from a landmark publication by Hawkins, Catalano and Miller ,hemp drying racks many risk factors fall into a class of interpersonal environmental influences in three domains: family influences, school influences, and peer influences, with an additional dimension encumbering demographic related characteristics. Evidence of these domains from the literature is first presented, followed by a description of how constructs in these domains were measured for this study.The BEM that guides the present study suggests several mechanisms by which the above-listed domains may influence adolescent risk behaviors. The model, which draws heavily upon learning theory suggests that powerful influences of behavior are found in the environment. Environments change within an individual’s lifetime , and the importance of any one specific domain or environment may change at different developmental stages. For example, the transition from preteen to adolescence is characterized by increasing independence , probably enhancing the influence of the peer social network. Based on an extensive review of the literature, few studies, including those that identify risk factors for alcohol and tobacco use, have used theory to guide testing of multivariate models. In addition,few studies are specific to Latino adolescents . The purpose of this study is to use theory to identify factors that influence Latino adolescents’ risks for alcohol and tobacco use. Advancements to this end may inform future interventions to curb rates of risk behavior earlier in adolescence, especially among Latino adolescents. The sample of 252 Latino adolescents in this study were high school students ages 13 to 19. They were attending school in south San Diego County, tested positive for LTBI, and volunteered to participate in a medication adherence trial for LTBI treatment. The San Diego State University Institutional Review Board approved the study. Adolescents were ineligible to participate in the study if they had definite plans to relocate from the area within 12 months and/or to receive LTBI treatment in Mexico.

After consenting, bilingual interviewers interviewed participants and completed baseline self-reported interviews. Age, gender, foreign-born status, acculturation level, and receiving an allowance were selected to represent demographic characteristics. Foreign-born status was ascertained by asking their country of birth; respondents born outside of the United States were coded as foreign-born. Acculturation was measured using the Bidimensional Acculturation Scale for Hispanics . The acculturation scale consists of 24 questions regarding language use , linguistic proficiency , and electronic media use . Each question had four possible responses: very poorly, poorly, well, or very well. The questions are separated into 2 domains, Hispanic and non-Hispanic , with 12 items in each. For each cultural domain, an average of the 12 items is calculated, obtaining a mean range of scores between 1 and 4. Scores on both domains were used to determine the level of acculturation. Acculturation categories are computed using a 2.5 cutoff score to indicate low or high level of adherence to each cultural domain. Individuals scoring higher than 2.5 in both domains are considered bicultural .The purpose of this study was to explore theoretical correlates of alcohol and tobacco use. The sequential regression approach allowed for a conservative estimate of each block’s association with the dependent variable. In the final model, parental consistency was protective and decreased relative risk of alcohol use by 18%. In terms of increasing risk, skipping school in the last 12 months and friend’s use of alcohol were both associated with an almost threefold increased risk for reported use of alcohol. Representation of significant variables from three different blocks, including the parenting block, school block, and the peer block suggest the many different areas in which alcohol use may be affected. Four variables were significant in the final model; three of them part of the peer block. The peer block clearly emerged as being most important among the correlates of tobacco use, in contrast to the multi-dimensional correlates of alcohol use. The presence of a peer model of alcohol use doubles an adolescent’s likelihood of using tobacco, and a peer model of tobacco use resulted in a threefold increase in likelihood. As a protective factor, having more close friends accounts for a 39.5% decrease in the likelihood of tobacco use. The positive association with age indicates that risk for tobacco use increases with age, an observation supported in the literature. Thus, the most precarious situation for a teenager at risk for tobacco use would include a small social network , late adolescence, and friends that use alcohol and tobacco. Parental consistency represented parents’ consistent use of contingencies and was only related to alcohol use. The observed relationship between parental consistency and alcohol use is supported by previous research and underscores the important influence that parents can have in preventing alcohol use. The finding that parental consistency was only related to alcohol use and not tobacco is very interesting and may highlight some inherent differences in the nature of tobacco use versus alcohol use. A variant of this finding has been demonstrated previously, but never with an exclusively Latino sample. Replicating this unique effect across ethnic groups provides confirmation of this construct selected for this analysis for theoretical reasons. Peer influence comprised the final block, and was considered to be theoretically most proximal to the outcomes of interest, alcohol and tobacco use . This was confirmed and is consistent with previous reports . Comparing peer correlates for alcohol and tobacco reveals interesting findings. It appears tobacco use may fall under greater peer control based on the number of significant correlates in their respective peer blocks. As demonstrated here and in numerous published reports, peer modeling of alcohol and tobacco use is related to adolescent use of both substances. It is plausible that similar associations exist for parent modeling of substance use.

However, experiments in rodents, such as those outlined above, are insufficient to model human reward based behaviors and to predict the addictive potential of drugs. Thus the present results provide the first unequivocal demonstration that URB597 lacks THC-like reinforcing properties, and suggest that this FAAH inhibitor might be used in therapy without anticipated risk of abuse or triggering relapse to drug use. Exogenous anandamide exerts potent reinforcing effects in monkeys . Thus, it may be surprising that the ability of URB597 to potentiate brain anandamide signaling does not translate into overt rewarding properties. However, there are two plausible reasons why URB597 does not support self-administration responding. First, exogenous and endogenous anandamide might each access distinct sub-populations of CB1 receptors in the brain. In particular, systemic administration could allow anandamide to reach a receptor pool that is normally engaged by 2-AG. In this context, the observation that treatment with URB597 decreases 2-AG levels in the monkey brain suggests the existence of a compensatory mechanism aimed at reducing 2-AG signaling in the face of enhanced anandamide signaling. Such a mechanism might account, at least in part, for the inability of URB597 to serve as a reinforcer. Consistent with this idea, a recent report suggests that pharmacological or genetic disruption of FAAH activity causes a down-regulation of 2-AG production in acutely dissected rodent striatal slices, which is reportedly due to vanilloid TRPV1 receptor activation . However, rolling benches for growing we were unable to replicate this observation in live animals even when using doses of URB597 that completely suppressed FAAH activity and significantly increased anandamide levels . Another possibility is that the kinetics of CB1 receptor activation may differ between anandamide and URB597 administration, as the former is likely to produce a more rapid recruitment of CB1 receptors than the latter.

It is well established that effectiveness of drug reinforcement in monkeys depends on a rapid drug distribution throughout the brain . Irrespective of the mechanism involved, the impact of 2-AG down-regulation on the broad pharmacological properties of URB597 in primates remains to be determined. In conclusion, our findings with URB597 unmask a previously unsuspected functional heterogeneity within the endocannabinoid signaling system in the brain, and suggest that FAAH inhibitors such as URB597 might be used therapeutically without risk of abuse or triggering relapse to drug abuse. Big cities produce a lot of sewage, which often contains pharmaceuticals, illicit drugs, and caffeine. These flushed pollutants can remain in wastewater even after processing by a wastewater treatment plant, and may have negative effects on marine organisms and ecosystems if introduced into the marine environment. California is home to Los Angeles, San Diego, and San Jose—three of the ten most populous cities in the United States.1 All three are located in coastal counties2 and utilize wastewater treatment plants 3 that discharge treated wastewater effluent directly into the Pacific Ocean.4 As our cities grow, municipal wastewater is expected to contain increasing concentrations of flushed pollutants, posing a heightened threat to the health of our coasts and the marine environment more broadly. However, monitoring and regulation of flushed pollutants is currently insufficient, allowing them to be introduced into the marine environment undetected. This raises serious concern that flushed pollutants may devastate the marine environment beyond repair. The precautionary principle, a central tenet of environmental law and policy, “asserts that regulators and decision makers should act in anticipation of environmental harm, without regard to the certainty of the scientific information pertaining to the risk of harm.”In the face of great uncertainty as to the amounts of flushed pollutants being introduced into the marine environment and the effects they will have on marine organisms and ecosystems, a precautionary approach is necessary to ensure adequate protection.

This Article advocates for policy reform to increase monitoring and regulation of pharmaceuticals, illicit drugs, and caffeine in wastewater, and to ultimately minimize the amounts of these flushed pollutants that are discharged into California’s coastal waters. Part I provides an overview of the wastewater life cycle as a pathway for flushed pollutants to enter the marine environment. This is followed by a discussion of the effects that pharmaceuticals, illicit drugs, and caffeine may have on marine organisms. Part II discusses the current legal and regulatory landscape for managing pollutants in municipal wastewater and its inadequacies in preventing flushed pollutants from harming marine organisms and ecosystems. Part III proposes various legislative tools that can be used to address this issue and suggests topics for future research.In addition to their contributions to tourism and the economy, coastal ecosystems offer unique recreational and educational opportunities, hold important cultural value, and provide a variety of ecosystem services. California’s ocean economy produced over $44 billion in 2013 and its coastal counties are home to almost three-quarters of the state’s population, despite comprising less than a quarter of the state’s land area.Coastal ecosystems can be incredibly complex and marine organisms vary in their sensitivity to pollutants. The discharge of treated wastewater effluent containing pharmaceuticals, illicit drugs, and caffeine into coastal waters raises concerns for the health of these ecosystems and humans alike. This Part introduces three categories of flushed pollutants that are of particular abundance and concern and provides an overview of the means by which flushed pollutants are discharged into the marine environment. It then discusses the potentially devastating effects that these pollutants may have on marine organisms and ecosystems. When humans consume pharmaceuticals, illicit drugs, and caffeine they excrete a portion of these substances as waste. In turn, this waste is flushed down toilets. Flushing is also a common means of disposal of unwanted pharmaceuticals and illicit drugs. In the context of large cities, flushed wastewater is generally transported through a network of sewers to a POTW.These facilities employ a variety of technologies and processes designed to remove solid waste, bacteria, and other pollutants from municipal wastewater.The levels of pharmaceuticals, illicit drugs, and caffeine that remain in treated wastewater effluent largely depend on the technology used.

POTWs located in coastal regions, such as the Hyperion Treatment Plant in Los Angeles, the Point Loma Wastewater Treatment Plant in San Diego, and the San José/Santa Clara Water Pollution Control Plant in San Jose, often discharge treated wastewater effluent directly into coastal waters.This makes POTWs the last safeguard to prevent flushed pollutants from reaching coastal waters. Although POTWs reduce the concentrations of pharma ceuticals, illicit drugs, and caffeine in wastewater, some measure of these pollutants still remains in wastewater after treatment and is thus introduced into the marine environment. A recent study in Southern California tested effluent from four large POTWs that discharge into coastal waters through marine outfalls.The study found pharmaceutical hormones such as estradiol, testosterone, progesterone, and estrone in 63–100% of effluent samples.Another alarming study detected cocaine in 36% of mussel tissue samples collected along the California coast and caffeine in 19% of the samples.As more research is conducted to determine the levels of pharmaceuticals, illicit drugs, vertical cannabis grow and caffeine in POTW influent and effluent, concern is growing over the effects these pollutants may have on marine organisms and ecosystems. Pharmaceuticals, illicit drugs, and caffeine have been detected in California’s coastal waters, but little research has been done to determine the rate at which these pollutants are being introduced into the marine environment. The persistence of these pollutants and the effects they have on marine organisms and ecosystems are also largely unknown. If these pollutants prove to be harmful and persistent, exposure and bio accumulation could threaten the collapse of ecosystems already imperiled by climate change, overfishing, and other anthropogenic impacts. This section introduces pharmaceuticals, illicit drugs, and caffeine, and summarizes what is known about the concerning effects these pollutants may have on marine organisms and ecosystems. Pharmaceuticals include both prescription and over-the counter medications. Antibiotics, antidepressants, and repro ductive hormones are a few examples of particular concern. In a 2012 study, almost half of Americans reported using at least one prescription medication in the past 30 days,and pharmaceutical use in the United States continues to rise.The body absorbs only a portion of pharmaceutical compounds that are consumed and excretes the remainder as waste.With prescription drug abuse and overdose rates on the rise,16 unused pharmaceuticals are also commonly flushed as a method of disposal. Pharmaceutical pollution has been detected in treated wastewater effluent and in surface waters throughout the nation,and has been documented in detail along the South Florida Coast.In fact, according to the National Centers for Coastal Ocean Science, pharmaceutical pollution may be as common in the marine environment as agricultural pollution.However, as compared to agrochemicals, far less research has been conducted on the effects of pharmaceuticals on marine organisms and ecosystems. Pharmaceutical antibiotics, antidepressants, and reproductive hormones are endocrine-disrupting chemicals that have been detected in treated wastewater effluent.EDCs interfere with “the production, release, transport, metabolism, binding, action, or elimination of natural hormones in the body responsible for the maintenance of homeostasis and the regulation of developmental processes.”EDCs have been linked to reproductive and developmental toxicity, carcinogenesis, immunotoxicity, and neurotoxicity in humans,and research indicates EDCs may have similar impacts on wildlife.Exposure to pharmaceutical EDCs has been shown to have devastating consequences for freshwater organisms and preliminary research indicates marine organisms may be similarly affected.Appropriately, there is growing concern in the environmental community over the introduction of pharmaceutical EDCs into coastal ecosystems through treated wastewater. For the purposes of this paper, illicit drugs are those for which non-medical use or possession is prohibited by federal law.

A 2013 report prepared by the Substance Abuse and Mental Health Services Administration estimated roughly 9.4% of Americans aged 12 or older had used at least one illicit drug in the past month.Similar to its treatment of pharmaceuticals, the human body does not absorb a large portion of illicit drugs that are consumed. Illicit drugs thus “enter the wastewater network . . . by human excretion after illegal consumption or by accidental or deliberate disposal.”A variety of illicit drugs have been detected in surface waters and treated wastewater effluent from POTWs across the United States, including cannabis,cocaine, MDMA,methadone, and methamphetamine.However, the effects of illicit drugs on marine organisms and coastal ecosystems are not well studied. Research indicates that exposure to illicit drugs could produce devastating effects in freshwater organisms, including genetic damage and mutation.Illicit drugs may have similarly disastrous consequences when introduced into the marine environment.The consumption of caffeine, which is found most notably in coffee and tea, has been linked to reduced fatigue and heightened mental alertness.Caffeine is widely enjoyed throughout the United States, with roughly 85% of Americans consuming at least one caffeinated beverage daily.Caffeine originating in human waste has been detected in coastal waters across the United States, including Puget Sound,Boston Harbor, and the Oregon coast.The ubiquitous nature of caffeine in our nation’s surface waters and treated wastewater effluent is well-documented and concern over the continuous discharge of caffeine into coastal waters is tempered as compared with concerns over pharmaceuticals and illicit drugs. However, the effects of caffeine on marine organ isms and coastal ecosystems have not been well-researched and are largely unknown. One laboratory study indicated that a seven-day exposure to environmental concentrations of caffeine induced a stress syndrome in Mediterranean mussels, causing the mussels to undergo detoxifying processes.Bioluminescence inhibition, fertilization impairment, algal bleaching, and mortality have also been observed in marine species as a result of exposure to high concentrations of caffeine.Although current environmental concentrations of caffeine are thought to be too low to significantly affect the survival, growth, and reproduction of marine organisms, there is concern that higher concentrations of caffeine released into the marine environment may have devastating effects. This Part provides an overview of the primary state and federal laws that address pollutant flushing, wastewater treatment, and discharges of treated wastewater effluent into the marine environment. When it comes to flushed pollutants, the Clean Water Act and Porter-Cologne Water Quality Control Act provide the most effective protection for the marine environment through the regulation of discharges of pollutants by hospitals and other large-scale flushers into the sanitary sewer system, as well as discharges of effluent from POTWs into the ocean. The Controlled Substances Act and California Uniform Controlled Substances Act help to reduce household flushing of unwanted pharmaceuticals by providing alternative mechanisms for safe and legal disposal.

The mean age for meeting criteria for Moderate AUD progresses from 19.1 in Moderate to 17.3 in Severe . The age of onset for Severe AUD is 18.5. This age relationship is detailed in Figure 3. Figure 3 represents the onset of alcohol use and alcohol problems in 3286 adolescents observed over a ten year period. It includes 1870 who remained unaffected, 684 who developed mild alcohol use disorder, 415 who developed moderate alcohol use disorder, and 317 who developed severe alcohol use disorder. The ANOVA for onset of first drink among the unaffected, mild, moderate, and severe cohorts shows p < 0.001. The ANOVA for onset of regular drinking among the unaffected, mild, moderate, and severe cohorts shows p < 0.001. The ANOVA for onset age of mild AUD among the mild, moderate, and severe cohorts shows p < 0.001. The t-test for onset age of moderate AUD between the moderate and severe cohorts shows p < 0.001. These data suggest a strong effect of externalizing and internalizing disorders on prevalence and age of onset of Alcohol Use Disorder among adolescents/young adults at risk for the development of AUD on the basis of family history. Externalizing disorders were clearly associated with an increased risk for AUD and for earlier development of AUD. Internalizing disorders by themselves did not show a significant effect, but in combination with externalizing disorders they were associated with an earlier onset for severe AUD . When we considered all internalizing disorders together a clear effect on onset of moderate AUD was seen as well. By the end of the follow-up period, more than 60% of young people with both externalizing and internalizing disorders at baseline had developed alcohol dependence in comparison with about 30% of young people with neither type of comorbid disorder. The effect of comorbidity was stronger in more severe forms of AUD,marijuana drying rack with a 6-fold increase in risk for Severe AUD among subjects with both externalizing and internalizing disorders compared to subjects with neither form of comorbid disorder. There was also evidence for an earlier developmental course in more severe forms of AUD compared to less severe.

Persons with Severe AUD were likely to have their first full drink prior to the age of 13 and be drinking regularly prior to age 16 and experiencing 1–2 alcohol problems by that same age. In contrast young people who did not demonstrate any AUD were likely to have their first drink at 16 and start regular drinking just prior to age 19. Median and mean ages of onset for each type of AUD were 18–19, though the range extended through the follow-up period. Those at greatest risk for an AUD were males of European descent from an alcohol dependent proband family with one or more childhood onset psychiatric diagnoses. Those at least risk were females of African-American ancestry from a non-case family with no childhood onset diagnosis. Limitations of the study include the fact that all analyses are based on self-report and there is no independent corroboration of diagnoses or symptoms. Subjects interviewed in their late 20s may have had more difficulty with accurate reporting of events in early teenage years in comparison to subjects in their mid-teens. Retention rate from baseline interview to two-year interview was 85%, the majority of subjects completed at least four interviews . Families in the COGA study tend to be densely affected and results may not be generalizable to persons with alcohol use disorder in the general population. The subjects were ascertained at 7 University-based clinical sites and the populations studied reflect those sites. The magnitude of these effects was substantial, and this information may be helpful in targeting efforts at education and prevention. In this sample most of the AUD-affected subjects had a comorbid psychiatric disorder at baseline. Many such subjects may come to clinical attention for their childhood-onset disorders and it may be worth educational efforts targeting AUD, especially for those at increased familial risk. It has been argued, though, that more intensive interventions are not likely to be cost- effective at this time . It seems to be of value to continue to try to quantify risk in various clinically and biologically identifiable groups. Polygenic risk scores, especially as they increase in power with data from expanding clinical samples, will likely be of use . It would also be of value to attempt to separate AUD effects from other forms of SUD, since we know that they are highly comorbid in many samples, including the sample studied here. Since the late 1990’s, there have been dramatic increases in alcohol-related problems in the United States. Between 1999 and 2016 annual deaths from liver cirrhosis increased by 65% and doubled for liver cancer .

Relatedly, from 2006 to 2016 the death rate from alcoholic liver disease increased by over 40% from 4.1 per 100,000 to 5.9 per 100,000 . An increase of nearly 62% in alcohol-related emergency department visits was also found between 2006 and 2014 from 3,080,214 to 4,976,136 visits per year, with the increase occurring predominantly among people aged 45 and older . Further, an analysis of data from two waves of the National Epidemiologic Survey of Alcohol and Related Conditions showed a nearly 50% increase in the prevalence of past year alcohol use disorder from 2002 to 2013 among adults aged 18 and above . Surprisingly, these increases in alcohol-related morbidity and mortality did not occur alongside notable increases in per capita alcohol consumption estimates. These estimates, based on beverage sales data collected by the Alcohol Epidemiologic Data System , increased by approximately 6% over the 2002- 2013 time period . This represents an increase of approximately28 drinks per person per year . This increase seems insufficient to explain the observed increases in alcohol-related morbidity and mortality, as we would expect a notable increase given that the heaviest drinkers consume the vast majority of alcohol . Indeed, the increase in the rate of alcohol-related ED visits between 2006 and 2014 was considered unrelated to the concomitant 1.7% increase in PCC . A possible explanation for the discrepancy between alcohol-related problems and PCC may lie in how PCC estimates are calculated. Per capita alcohol consumption is typically constructed as an aggregate measure using national and state population estimates from the U.S. Census Bureau and alcohol sales data . The state-level alcohol sales figures are from either state-provided taxable withdrawals from bonded warehouses or industry sources for states that fail to provide data. Alcohol sales-based consumption estimates are considered more complete and objective than survey data on alcohol use, which is subject to substantial under-reporting . This consideration is also due to the widespread availability of alcohol tax information and the low level of unrecorded alcohol use in the U.S. . However, the precision of typical PCC estimates is challenged by the fact that they use invariant estimates of the mean percentage of alcohol by volume , i.e. they do not use annual estimates of the alcohol content of the beer, wine, and spirits sold in each state to convert beverage volume into ethanol. The conversion factors used in the typical PCC estimate approach are based on estimates of %ABV for each beverage type and have not been updated since the 1970s. These values are 4.5%, 12.9%, and 41% for beer, wine, and spirits, respectively.

Further complicating the issue is that each beverage type is comprised of several sub-types each with different %ABVs. Thus,vertical grow rack system actual PCC is also influenced by changes over time and place in beverage sub-type preferences. Failing to acknowledge these changes in %ABVs and beverage preferences risks underestimating important changes in actual PCC that could potentially explain observed changes in alcohol-related morbidities and mortality. Additionally, PCC estimates are key to the estimation of the alcohol-attributable morbidity and mortality used to assess the global burden of disease due to alcohol . Indeed, PCC estimates are the marker against which the estimation of an exposure distribution of alcohol are based . Our previous work has demonstrated meaningful changes in the alcohol content of beer, wine, and spirits during the last half of the 20th century. The mean %ABV of beer and spirits sold in the U.S. have each declined between 1950 and 2002 . The %ABV of wine declined between 1950 and the mid- 1980s to 10.5%, where after it began and continued to increase to 11.5%. Beyond 2002 there is reason to believe there have been further changes in the %ABVs of beverage types with the emergence of high %ABV craft beer and a likely continued increase in the %ABV of wine . The aim of this paper is to extend our previous work estimating the mean alcohol concentration of the beer, wine, and spirits sold in the U.S. and PCC to the period 2003 to 2016. We present the variation in %ABV over this time period for each beverage type and examine this variation in light of changes in beverage sub-type preferences and mean %ABV. We compare PCC estimates based on our ABV-variant methods to estimates from ABV-invariant methods nationally and for each state. The general methodology we employed to obtain PCC estimates that account for variations in the mean %ABV for each beverage type is as follows. First, we estimated a sales-weighted mean %ABV for each industry-defined beverage sub-type based on leading brands sold for each year. We then applied these mean beverage sub-type %ABV values to the calculation of each state’s and the nation’s mean %ABV for each beverage type for each year using the market shares of each beverage sub-type sold in each state and nationally. Finally, we used these annual mean %ABV estimates for each beverage type in the calculation of beverage-specific and total PCC estimates for each state and nationally for each year from 2003 to 2016. These methods are based on those employed in previous publications for beer , wine , and spirits . Data sources for beer. 

We used the Beer Handbooks to obtain data on which brands were the leading brands, the volume sold of each leading brand, and state and national annual market shares of each beer sub-type . As of 2002, the Beer Handbooks no longer included %ABV values , and The Siebel Institute of Technology did not produce new editions of the reports we used previously . Therefore, we obtained brewer-reported %ABV values from brewer websites, or the Liquor Control Board of Ontario’s website, or, in the case that %ABVs could not be identified from these sources, we carried forward the 2002 %ABV value. Between 2000 and 2010 the Beer Handbooks grouped the sale of beer into the following 7 categories: Super premium, micro/specialty, flavored malt beverages; premium beer; light beer; popular beer; malt beer; ice beer, and imported beer . In 2011 the “super premium, micro/specialty, flavored malt beverages” category was divided into the categories “craft beer” and “flavored malt beverages”, and “super premium beer” was included in the “premium beer” category . Thus, between 2011 and 2016 there were 8 industry-defined categories of beer. We calculated sales-weighted mean %ABV values for each beer sub-type according to these industry-defined categories as they changed over time. Data sources for wine. For wine, we identified data on top-selling varietals from the leading wine brands from the National Alcoholic Beverage Control Association database.We chose leading brands based on sales in Pennsylvania because only 5 states control wine sales, and of those Pennsylvania is the largest . We did not use national wine sales data because such data were available only for general brands which included multiple varietals with differing %ABVs. We obtained the annual market shares of each wine sub-type in each state and nationally from the Wine Handbooks . These industry-defined wine sub-types are table wine, wine coolers, champagne and sparkling wine, dessert and fortified wine, and vermouth/aperitif. Pennsylvania as a state alcohol monopoly follows NABCA sub-types for wine that differs from those used in the Wine Handbooks. Because annual market shares are based on the Wine Handbook’s industry-defined wine sub-type categories, we first matched the sales and %ABV data for each brand varietal and then grouped the matched brands according to the Wine Handbook’s categories.