Cannabis Grow

Marijuana smoking, the predominant method of use, causes a 5-fold increase in the blood carboxyhemoglobin level and a 3-fold increment in the quantity of tar inhaled compared with tobacco . Studies on secondhand marijuana smoke have found endothelial dysfunction in rats after exposure . Given the myriad ways in which marijuana might potentiate vascular disease, we conducted a systematic review to assess the effect of regular marijuana use on cardiovascular outcomes and their associated risk factors. The protocol was registered at PROSPERO  at the start of our investigation. This review focuses on studies examining marijuana use and cardiovascular risk factors and outcomes; our protocol also includes searches and a review of hemodynamic changes associated with marijuana use that are not reported here. We searched several online databases for titles and abstracts between 1 January 1975 and 30 September 2017. We chose a 1975 start date because that was the year the Alaska Supreme Court ruled that the “Alaska constitution’s right to privacy protects an adult’s ability to use and possess a small amount of marijuana in the home for personal use” . We also conducted reference and author tracking to identify additional articles and searched Clinical-Trials.gov and the National Institutes of Health Research Portfolio for ongoing or completed studies not reported in the literature. For search terms and details, see Supplement 1 . All titles and abstracts were independently screened by 2 reviewers . We included observational studies and interventional studies that enrolled participants older than 12 years and were published in English. The exposure criterion was any form of marijuana .

The main outcomes of interest were cardiovascular risk factors and outcomes. We excluded case reports, case series, review articles, editorials,grow table and in vitro and animal studies. The same 2 investigators independently reviewed the full texts of selected articles to identify those that met our inclusion criteria. Disagreements regarding inclusion were resolved by a third reviewer . Inter rater reliability for the abstract selection process and the concurrent decision to include the article in the review was excellent . For the selection process, see Supplement 2 . Eleven studies provided data on 1 or more metabolic parameter outcomes, including hyperglycemia, dyslipidemia, and diabetes . Five cross-sectional studies examined the association between marijuana use and hyperglycemia, dyslipidemia, metabolic syndrome, or diabetes . Marijuana use was measured by self-report in all studies. Four studies were based on 3 different waves of the NHANES . Three of the 4 used multi-variable analysis to examine the association between marijuana use and metabolic parameters after adjustment for baseline characteristics. All 3 studies reported that marijuana use had different favorable associations, including a lower prevalence of diabetes , lower glucose levels , or higher high-density lipo-protein cholesterol concentrations . The fourth NHANES study used both regression models and an instrumental variable analysis to examine associations . Marijuana use was associated with a beneficial metabolic effect in the regression model evaluation; no such effect was seen in the instrumental variable analysis. The final cross-sectional study was an exploratory analysis based on a small sample of 30 persons who were heavy marijuana users and 30 control participants matched for age, sex, ethnicity, and body mass index . The authors identified no differences between groups in glucose tolerance or fasting glucose, total cholesterol, or triglyceride levels. Three prospective studies examined the association of marijuana use with risk factors .

Two were based on the CARDIA cohort study, which examined the development and determinants of clinical and sub-clinical cardiovascular disease and its risk factors . The CARDIA study began in 1985 to 1986 with 5113 black and white men and women aged 18 to 30 years. It included comprehensive in-person baseline and outcome data and several exposure assessments during a long follow-up. Questions pertaining to marijuana use lacked detail on the form used, and exposure was quantified differently in each study. The low-ROB CARDIA-based study reported no associations between marijuana use and changes in glucose, high-density lipoprotein cholesterol, or triglyceride levels among heavy users compared with nonusers during 15 years of follow-up . The moderate-ROB CARDIAbased study examined the association between marijuana use and diabetes and pre-diabetes . Marijuana use was ascertained in year 7 of the prospective cohort, and exposure was very limited: The highest category of use was a lifetime frequency of more than 100 times. Incidence of diabetes and prediabetes assessed at 4 subsequent follow-up examinations over 18 years was based on laboratory assessment . A greater risk for prediabetes was identified among participants who reported using marijuana 100 or more times during follow-up compared with nonusers. The final prospective study followed 18 000 Swedish men and women aged 18 to 84 years over 10 years but assessed marijuana exposure only once, at baseline . Measures of socioeconomic factors, diet, or other drug use at baseline were limited. No definite relationship was found between marijuana use and diabetes; CIs around the risk estimate were wide and compatible with either increased or decreased risk for diabetes with marijuana use . Two experimental studies examined the effect of cannabis-related compounds on metabolic factors . Both had small sample sizes, and neither identified a measurable effect on metabolic parameters.

The association between marijuana use and obesity was evaluated in 1 prospective study; 1 retrospective study; 1 randomized controlled trial; and 4 cross-sectional studies, 2 of which were based on NHANES . None of these studies found an association between marijuana use and BMI. Another cross-sectional study of 786 Inuit adults found that participants who used marijuana in the past year had a lower BMI than nonusers . Although this study included important baseline characteristics, such as physical activity and dietary intake, the marijuana exposure assessment that divided the population into ever- and never-users was inadequate . Another study examined the charts of 297 women referred for weight management and found that marijuana use was associated with a lower BMI . This trial was limited by lack of adjustment for baseline characteristics and biased sample selection . One prospective cohort study found no association between marijuana use and changes in BMI . In a longitudinal pre birth study in 7223 women and their offspring , the children were administered health, sociodemo-graphic, and lifestyle questionnaires at ages 14 and 21 years . Although BMI was measured at both ages,4×8 grow table with wheels a retrospective assessment of marijuana use was conducted only at age 21. Daily cannabis users were less likely to have a BMI greater than 25 kg/m2 than were never-users. This study was limited by inadequate baseline data on the children. In a small double-blind placebo-controlled randomized trial , the effect of 5 mg of dronabinol on BMI was assessed at 28 days in 13 of the 19 participants who completed follow-up . No statistically significant association was found between marijuana use and BMI. The MIOS was a case-crossover study that examined marijuana use as a potential trigger for myocardial infarction . In this multi-center trial, 3882 patients with acute myocardial infarction were interviewed, on average within 4 days of their infarction, about their history, timing, and frequency of marijuana smoking. Marijuana use in the 1 hour immediately preceding the onset of myocardial infarction symptoms was then compared with its expected frequency on the basis of self-reported use during the previous year. Of the 3882 patients, 9 and 124 reported smoking marijuana within 1 hour of the onset of myocardial infarction symptoms and in the previous year, respectively. The myocardial infarction risk in the first hour after smoking was greater than that expected among users . That individuals served as their own control helped limit confounding from other behaviors that may be associated with marijuana use. The study, however, was assessed as moderate ROB, primarily because of recall bias.Two prospective studies examined the effect of marijuana exposure on stroke and transient ischemic attack .

One study , based on CARDIA, reported that marijuana was not associated with stroke ; however, the exposure was minimal and the population was young and healthy . Another study enrolled 49 321 Swedish men conscripted into compulsory military service between the ages of 18 and 20 years. They were followed until age 59 to assess the initial occurrence of stroke. No association between cannabis use and stroke was identified, but the study was limited by potential misclassification of the exposure, given that it was not reassessed over 25 years of follow-up and adjustment for baseline characteristics was inadequate . A third study using a case–control design compared patients admitted to the hospital for stroke or transient ischemic attack with other, matched hospitalized patients. It found no association between stroke and plant-based marijuana use ; however, the study was limited because it measured use with urine toxicology screens, and although all case participants were screened, it is unclear why the control participants underwent screening. The urine drug screen may have misclassified exposure, because results may remain positive for up to 10 weeks . Two prospective cohort studies involving myocardial infarction survivors enrolled in MIOS between 1989 and 1996 examined the association between marijuana use and mortality . Marijuana use in the year before the first myocardial infarction was self-reported at baseline and was not evaluated again. Cause of death was assessed by physician review of death certificates. In the study that followed patients for a median of 3.8 years, baseline use of marijuana once weekly or more and less than once weekly was associated with an increased risk for cardiovascular mortality compared with nonuse. This study also found an association between marijuana use and an increased risk for all-cause mortality . In the other MIOS-based study, which followed patients for a median of 12.7 years, any marijuana use was associated with an increased risk for all-cause mortality compared with nonuse, although the finding was not statistically significant . Another investigation used CARDIA data to examine the association between cumulative lifetime marijuana use and cardiovascular mortality . This study measured exposure several times and had robust assessment of baseline characteristics and outcomes. It found no association between marijuana use and cardiovascular mortality . The study also included a composite outcome of cardiovascular mortality, stroke, and coronary heart disease and, again, found no association between 5 or more years of marijuana use and this combined outcome . However, median cumulative marijuana exposure in the cohort was minimal . Further, although participants were followed for 26 years, the median age at recruitment was 18 to 30 years. Because of these factors, the study probably was under-powered to assess the association between marijuana use and cardiovascular disease. Finally, a retrospective cohort study linking NHANES to the National Center for Health Statistics survey found that users were at higher risk than nonusers for “hypertension-related” mortality. However, the marijuana exposure assessment was flawed, the outcome definition unclear, and the adjustment for baseline differences inadequate . Four studies examined the association between marijuana use and various outcomes, including peripheral arterial disease , irregular heartbeat , multi-focal intracranial stenosis , and aneurysmal subarachnoid hemorrhage . All 4 studies were rated as high ROB, primarily because their marijuana exposure assessments and adjustments for baseline risk factors were inadequate.Evidence that marijuana use either increases or decreases most cardiovascular risk factors is insufficient, as is evidence regarding any association between marijuana use and adverse cardiovascular outcomes . The current available literature is limited by a preponderance of cross-sectional study designs. Although the literature includes several long-term prospective studies, they are limited by recall bias, a lack of robust longitudinal assessment of marijuana use, participants with infrequent marijuana use, and the relative youth of some of the cohorts. A MEDLINE search revealed a recent systematic review of marijuana harms that identified 2 studies on the relationship between marijuana use and cardiovascular events . We included both articles in our systematic review and assessed 1 of them differently, assigning its ROB as moderate rather than high . The strength of this study lies in the minimization of confounding.The use of a case-crossover design in the study of marijuana compares each participant to him- or herself and eliminates this problem.

The participants came from two studies, including 686 adolescents or young adults between 12 and 21 years old, who received care at the Children’s Health Center at Zuckerberg San Francisco General Hospital Center . The study was conducted from 2013 to 2014, prior to the legalization of recreational marijuana in California. Adolescents attending the CHC in 2013 included 62.4% Latino, 15.9% non-Hispanic Black , 11.0% Asian, 5.1% White and 5.5% other race/ethnicity children. In the first study, adolescents who had surplus urine during a clinic visit for both sick and well care were included. Urine samples were collected for clinical indications, including urinary tract or sexually-transmitted infection screening and diagnosis, abdominal pain evaluation, trauma and pregnancy screening. There was no direct patient contact, and after chart review, all patient identifiers were deleted from the database and research charts. This study was conducted anonymously and participants were not consented. In the second study, adolescents participated in a consented study in which they were asked to provide urine samples and complete a questionnaire regarding tobacco use, secondhand smoke exposure, and the use of marijuana products, including blunts, and alcohol. Participants were told that their study data would not be shared with their doctors or other health care providers. Each question had an option not to answer. Information on race/ethnicity, sex,plant bench indoor age and self-reported tobacco use history was collected on all participants. These studies were approved by the Institutional Review Board at the University of California San Francisco.

As described in previous publications in this study population, we categorized urine cotinine levels in urine as follows: Active smoking for values >30 ng/ml, significant SHS exposure as 0.25 to 30 ng/ml, and light SHS and/or thirdhand smoke exposure as 0.05 to 0.25 ng/ml . Including covariate categories of race/ ethnicity, sex and age , we present a descriptive analysis of THC detection frequencies and median for THC-COOH concentrations. We tested differences in THC-COOH detection frequency with the χ2 test or fisher’s exact test as appropriate. To evaluate whether absolute measured values of THCCOOH differed across covariates we used Kruskal-Wallis tests. Spearman’s correlations were used to describe the correlation of tobacco biomarkers with THC-COOH and this relationship between smoking status and THC-COOH detection was further assessed using logistic regression. Both an unadjusted and an age- and race category-adjusted model of the odds ratio for THC-COOH detection given active smoking are provided with 95% confidence intervals. All statistical analyses were carried out using SAS v. 9.4 . All statistical tests were considered significant at p < 0.05. For the full study group, prevalence of THC exposure based on THC-COOH increased with age, and was highest in Black and Mixed/Other groups, but did not differ by sex . Among those who were positive for THC-COOH, absolute levels of THC-COOH followed a similar pattern, with highest levels in older adolescents, in Blacks and Mixed/Other group, and in males. Based on a urine cotinine cut-point of 30 ng/ml, 66 participants were determined to be active tobacco cigarette smokers. Frequency distributions for total THC-COOH among smokers and non-smokers are shown in Fig 1. The prevalence of THC exposure was much higher among adolescent tobacco cigarette smokers compared to those who did not use tobacco . Among biochemically-determined cigarette smokers, 81.8% were positive for marijuana use by total THC-COOH and 71.2% were positive using the THC screen.

The proportion of THC-positives did not vary significantly by sex or age, but was highest in Black and Mixed/Other groups. The absolute levels of THC-COOH were much higher in cigarette smokers compared to non-smokers, and increased with age . Comparison by race/ethnicity was not possible due to small numbers in some groups. Using urine cotinine, we were also able to examine the relationship between extent of secondhand tobacco smoke exposure to THC use . Based on THCCOOH, the prevalence of THC exposure was 2.8% among those with no tobacco smoke exposure, 8.7% with light SHS exposure, 33.9% with heavy SHS exposure, and as noted above 81.8% in active smokers . As shown in Fig 3, across all levels of tobacco exposure, there was a moderately strong quantitative correlation between tobacco use and THC biomarkers, with r = 0.60, p <0.001 for cotinine and r = 0.55 for NNAL, respectively, vs total THC-COOH . The odds ratio for a urine cotinine level of > 30 ng/ml as a predictor of COOH-THC positivity was 18.9 unadjusted and 13.2 after adjusting for age and race . In a subset of participants we were able to compare self-report of various self-reported behaviors to THC biomarker levels . For those who reported marijuana use in the past three days , 91.7% were positive and 8.3% negative for total THC-COOH. For those who reported marijuana use in the past 3 months , 53.8% were positive and 46.2% negative for THC-COOH. Among all self-reported ever marijuana users , 77% had smoked marijuana in the form of blunts . For those who reported blunt use in the past 30 days , 74.1% were positive THC-COOH. For those who reported alcohol use in the past 3 months , 32.3% were positive; while among those who report alcohol use in the past 3 days , 54.5% were positive for THC-COOH. Our study provides novel information in several areas. We show that the commonly used urine immunoassay screen for THC exposure with a cut-off of 50 ng/ml substantially underestimates actual THC exposure, as measured by a sensitive chromatographic method.

On the other hand, in a subset of adolescents in whom use of marijuana was queried, misreporting was relatively low, with only 4.6% and 1.8% of those who reported no marijuana use in the past 3 days and 3 months, respectively, biochemically positive for THC exposure. Thus, the routine urine THC screen has a high degree of specificity, but only a moderate degree of sensitivity. The THC screen presumably has lower sensitivity because many urines had THC-COOH values between 3 and 50 ng/ml. It should be noted that other immunoassays might have lower cut-off values and have a higher levels of sensitivity. We found that 25% of adolescents had biochemical evidence of marijuana use, which is slightly higher than the 15 to 17% self-reported prevalence of past month use reported in the Monitoring the Future study and in a recent survey in Washington State , but similar to the 24% biochemically-assessed exposure in adolescents attending a hospital clinics by Silber et al in Washington D.C. in 1987 . Consistent with other studies,greenhouse rolling racks we found an increased prevalence of marijuana use with increasing age and in Blacks, compared with other racial/ethnic groups. Black adolescents in particular have a much higher use of use of blunts compared to other groups, which may account for the higher prevalence of THC exposure . We found an extremely high biochemically-determined prevalence of THC use in active cigarettes smokers. A strong association between tobacco and marijuana use was expected based on other studies of self-reported behaviors, but the strength of the association was remarkable. The odds ratio of active cigarette smoking predicting THC use was 13.2 . Thus, cigarette smoking was a strong surrogate marker for marijuana use. Whether this remains the case as the prevalence of cigarette smoking declines in youth in the future and the extent to which the use of electronic cigarettes in youth are predictive of marijuana use remain to be determined. Another novel finding was the association between the level of SHS exposure, determined by urine cotinine, and biochemically-determined prevalence of THC use, with a progressive increase in prevalence from no exposure to light, and to heavy SHS exposure, and to active smoking. A limitation of our study is that we cannot tell if the source of nicotine exposure is cigarette smoking or use of electronic cigarettes. Additionally, we cannot determine if cotinine levels consistent with high levels of SHS exposure actually represent low-level nicotine exposure from SHS or intermittent non-daily smoking vaping, or whether the low levels of cotinine reflect the use of blunts. One laboratory study reported no detectable plasma nicotine after three puffs of a blunt, suggesting that cotinine levels attributable to blunt use would be low .

Across all categories of tobacco exposure as indicated by either urine cotinine or NNAL, there was a moderately strong quantitative correlation between tobacco biomarkers and level of THC exposure. The pharmacologic bases and health implications of this association remain to be explored.As expected, a history of use of blunts in the past 30 days was associated with a high prevalence of biochemically-determined marijuana use . A history of alcohol use in the past 3 days was also associated with a high prevalence of marijuana use . The concordance of marijuana and alcohol use has been well described in other studies . Limitations of our study include the use of a convenience sample of adolescents seeking medical care in an urban public hospital outpatient clinics in one city. Our study was conducted before recreational use of marijuana was legalized in California. The prevalence of marijuana use might be even higher now that recreational marijuana is legal. Further, we did not collect data on level of dependence on tobacco or marijuana. One of the more controversial questions in drug policy today is whether the trend toward legalizing marijuana for medicinal and adult recreational use could increase illicit marijuana use among young people . Since 1996, 28 U.S. states and the District of Columbia have legalized the production and sale of marijuana for medicinal use , and eight have legalized marijuana for adult recreational use. Medical marijuana laws could potentially increase the availability of marijuana and reduce perceptions of its harmfulness, leading more young people to try it. State medical marijuana laws include regulations that protect young people from illegally obtaining marijuana . But if these restrictions are not carefully enforced, young people could gain increased access to marijuana through the diversion of medical marijuana into illegal markets, which could also lower its price . Marijuana use by young people is associated with lasting detrimental changes in cognitive functioning of the developing brain, and poor educational and occupational outcomes . Use increases the risk of unintentional injuries and auto fatalities, mood and psychotic disorders, and drug dependence, especially when use is initiated at a young age . Long term marijuana smokers have a disproportionate burden of upper respiratory illnesses, including chronic bronchitis and some cancers, and an increased risk of cardiovascular disease . Medical marijuana producers and retailers are promoting new, more potent products such as oils often used as inhalants with tetrahydrocannabinol concentrations ranging from 40 to 70 percent . They are also developing new products that appeal to youth, such as packaged edibles and candies, that may increase the hazard of overdose due to their relatively slow rates of absorption and perceived intoxication . These products could increase the risk of overdose in young people, who tend to be less experienced users with low tolerance levels. Studies have reported little evidence that medical marijuana laws increase marijuana use among young people , although well controlled studies consistently report increased consumption in adults . Using the Youth Risk Behavior Survey, researchers have compared high school students’ consumption before and after medical marijuana laws were enacted, finding no evidence of rising consumption on a national basis . A national study of 12–17 year old found that medical marijuana laws had no causal impact on consumption , as did a carefully controlled national study of 13–18 year olds . Wen et al. reported a five percent increase in the likelihood of trying marijuana among 12–20 year olds who dwell in states with medical marijuana laws, but the study was limited by the need to pool such a broad range of ages. Prior studies have ignored or been unable to detect age related variation in the impact of medical marijuana laws by pooling children aged 12–17, or even 12–24, or by studying particular age groups in isolation. Age-related variation is important to capture because young peoples’ access to marijuana and their developmental susceptibility to drug related harms differs by age .

From a study conducted in Munich, Scherer et al. reported mean concentrations of nicotine in the living room at about 4 µg/m3 in 20 smokers’ homes, about 200´ as large as the mean for ten nonsmoking homes. Matt et al. used passive samplers to measure gas-phase nicotine concentrations in San Diego County homes with infants of mothers who did or didn’t smoke. The study sites included three groups: “no exposure” with non-smoking mothers and no indoor smoking; “indirect exposure” with maternal smokers but no indoor smoking; and “direct exposure” , with maternal smokers and unrestricted indoor smoking. The indoor air nicotine levels varied systematically across the three groups households, with geometric means reported for both living rooms and the infant’s bedroom. For the no exposure group, levels were 0.10 µg/m3 in the living room and 0.09 µg/m3 in the infant’s bedroom. For the indirect exposure group, geometric mean concentrations were 0.32 µg/m3 in the living room and 0.23 µg/m3 in the infant’s bedroom. The direct exposure group had geometric mean concentrations of 2.6 µg/m3 in the living room and 1.5 µg/m3 in the infant’s bedroom. Gehring et al. measured nicotine concentrations in the homes of 347 German and 335 Dutch children using passive samplers. In homes with a light to moderate smoker , the median nicotine concentrations were 0.59 µg/m3 in Germany and 0.25 µg/m3 in the Netherlands. In homes with a heavier smoker the median nicotine concentrations were 1.4 µg/m3 in Germany and 0.65 µg/m3 in the Netherlands. A Korean study reported a median concentration of 3.2 µg/m3 in ten homes with smokers consuming ≥ 6 cigarettes/day. In summary, in homes where no smoking occurs and no smokers live, gas-phase nicotine levels tend to be less than about 0.1 µg/m3 ; in homes with moderate smoking, nicotine levels are commonly between 0.3 and 1 µg/m3 ; and in homes with heavy smoking,flood table levels are between 1 and 5 µg/m3. At 25 °C, nicotine’s vapor pressure is reported to be 0.11 mm Hg and 6 Pa. Its log value has been calculated to be 8.1.

Nicotine’s partitioning to airborne particles and settled dust, if water contents are low, is expected to be dominated by sorption to weakly polar organic matter in the particles or dust, and can be estimated using the octanol-air partition coefficient . When this condition holds, a semivolatile compound’s dust/gas partition coefficient is expected to be lower than the particle/gas partition coefficient , with reported central tendencies of Kd/Kp being 0.12 and 0.25.Based on several studies, in environmental tobacco smoke , ~ 95% of indoor airborne nicotine is gaseous . Typical indoor particle concentrations are lower than particle levels in smoking environments, which would further favor nicotine’s presence in the gas phase. However, indoor particles may have a larger water content and lower pH than the particle phase of ETS. Hence, it is difficult to estimate the fraction of airborne nicotine that is gaseous in indoor environments without active smoking. Regardless, nicotine is often detected in samples of household dust. The total amount of nicotine in dust or particles is expected to be enhanced if the dust is acidic and if there is sufficient associated water for ionization to occur. Matt et al. collected vacuumed floor dust samples from the same San Diego homes where they measured gas-phase nicotine with passive samplers. In the 13 homes with indirect exposure, the geometric mean nicotine levels in dust were 1.8 µg/m2 in the living room and 0.7 µg/m2 in the infant’s bedroom. Corresponding results in the 15 homes with direct exposure were 6.9 µg/m2 in the living room and 5.4 µg/m2 in the infant’s bedrooms. Willers et al. collected dust samples from the homes of 23 children with asthma using two different methods. Among these households, n = 8 had no current household ETS exposure, based on self-report, whereas n = 15 were classified as having current ETS exposure. Samples from vacuum cleaner bags had a median nicotine mass fraction of 31 µg/g for the no-current-exposure group as compared to 121 µg/g for the exposure group.

For dust samples collected from household surfaces, the median nicotine level was 20 µg/g for the unexposed and 212 µg/g for the exposed groups. They found that urinary concentrations of cotinine, a nicotine metabolite, were strongly associated with the mass fraction in dust. Kim et al.463 collected dust samples from 37 Baltimore homes. Among the 7 homes that were self-reported as nonsmoking, the median mass fraction in dust was 12 µg/g. Among the 30 homes self-reported as smoking, the median was 43 µg/g. They found a significant association between the mass fraction of nicotine in dust and the self-reported number of cigarettes smoked per day per home. In contrast, they did not find such an association for gas-phase nicotine, nor did they find an association between gas-phase nicotine and mass fraction in dust. Whitehead et al. measured total nicotine in dust samples collected from homes in Northern California during the period 1999-2007. In homes where no one had smoked in the month prior to dust collection , the median mass fraction of nicotine in dust was 0.26 µg/g. In homes where smoking had occurred , the median mass fraction was 1.26 µg/g. Based on concurrently self-reported household cigarette smoking, the authors concluded that the mass fraction of nicotine in indoor dust was a reasonable surrogate for indoor smoking. In dust samples collected in 2008 from bedrooms of Danish children, the median mass fraction of nicotine was 1.1 µg/g. In the homes of smokers , the median level was 6.6 µg/g, while in the home of nonsmokers the median level was 0.53 µg/g. It is instructive to compare these values to an earlier study of dust samples collected in 72 Danish homes in 1989. In that study, the median level in all homes was 50 µg/g; it was 242 µg/g in the homes of smokers and 18 µg/g in the homes of nonsmokers .

While there are likely multiple factors responsible for the lower levels in 2008 compared to 1989, these results are consistent with a decrease, among smokers with children, of smoking inside their homes. When comparing the homes of nonsmokers, the lower levels in 2008 may partially reflect less nicotine exposure outside the home and subsequently less nicotine brought into the home sorbed to clothing. As the abundance of water on or in a surface increases and as a surface becomes more acidic, its capacity for total nicotine increases .Several studies have examined the sorption of nicotine to indoor surfaces. Van Loy et al. reported the sorption of nicotine to the surfaces of a 20-m3 stainless-steel chamber with 45.2 m2 of nominal surface area. They found that the amount of nicotine in the gas phase was small compared to that sorbed on chamber walls. For example, at a gas-phase concentration of 33 µg/m3, the surface level was 660 µg/m2 indicating that 98% of the nicotine was sorbed to chamber surfaces. In a follow-up study, Van Loy et al. measured the dynamics of nicotine and phenanthrene sorbing to and desorbing from three different surface materials: stainless steel, a deep pile nylon carpet and gypsum wallboard covered on one side with latex paint. The results were reported, in part, in terms of a partitioning coefficient,hydroponic flood table representing the sorbed surface density normalized by the gas-phase concentration. The partitioning coefficient of stainless steel for sorbed phenanthrene was about 3.6 times as large as for sorbed nicotine. Remarkably, however, for nylon carpet and painted gypsum board, partitioning coefficients for nicotine were 2-3 orders of magnitude greater than for phenanthrene. Van Loy et al. did not address the underlying reason for these differences. At the relative humidities of these experiments, the amount of water on the stainless-steel surface is anticipated to be small while that on and within the deep pile nylon carpet and gypsum wallboard is likely large, since both of these materials are hygroscopic, and gypsum wallboard is porous. Hence, the larger capacity of nylon carpet and gypsum board for nicotine compared to phenanthrene may be partially due to the higher water solubility of nicotine.

The pH of sorbed water may also play a role if the pH of water in the carpet and gypsum board is lower than about 7 . However, as already noted, if the water content of the carpet or gypsum board is large enough, the fraction of nicotine in the material will be much larger than nicotine’s gaseous abundance, independent of pH. It may also be that relatively polar nylon carpet and gypsum board more strongly sorb polar nicotine compared to nonpolar phenanthrene, regardless of the sorbed water content. In studies conducted in a 50-m3 room that was variably furnished, Singer and colleagues examined the influence on airborne concentrations of sorptive partitioning of nicotine and other gas-phase organics associated with tobacco smoke.Unsurprisingly, they found that for otherwise identical conditions, the sorptive loss of nicotine was largest in a fully furnished room, less in a room with just wallboard and carpet, and even less is in a room with only wallboard. Most of the nicotine emitted from cigarettes remained sorbed to room surfaces three days after smoking. Singer et al. introduced the metric exposure relevant emission factor that implicitly accounts for sorptive uptake and reemission to give the net mass of individual ETS constituents available for inhalation exposure over a day in which smoking occurs according to a representative indoor pattern. For nicotine, the EREF decreased with decreasing ventilation rate, suggesting continued sorptive uptake by the indoor surfaces over the duration of the experiments. Sorption reduced nicotine concentration during the smoking period and increased its concentration during nonsmoking periods, “resulting in fractionally higher indirect exposures.” When evaluating exposure to gas-phase constituents of ETS, nicotine, as a strongly sorbing constituent, is a poor surrogate for weakly sorbing constituents. Furthermore, given that the sorptive capacity of a surface for nicotine can be influenced by pH, it is a poor surrogate for other gas-phase constituents of ETS that do not ionize and are not affected by pH. In a related experiment, twenty volatile organics were rapidly vaporized into the fully furnished 50-m3 room and their gas-phase concentrations were measured during a sorption period, a flush period, and a desorption period.Nicotine sorbed quickly, with 99% sorbed to room surfaces within two hours of initial introduction. During the flush period, nicotine’s gas-phase concentration declined, but only a small fraction of nicotine’s total mass in the room was removed during flushing. Nicotine returned to a gas-phase concentration that was close to its pre-flush level when the room was resealed. Matt et al. measured nicotine levels on surfaces in the San Diego homes where they also measured gas-phase and dust-associated nicotine. For the no-exposure group, nicotine was below the detection limit on all sampled surfaces. For the indirect exposure group, the 40-50% of samples were quantifiable. Considering only quantifiable samples, geometric means for surface-associated nicotine levels were 10 µg/m2 in the living room and 8 µg/m2 in the infant’s bedroom. In the direct exposure group, 90% of the samples were quantifiable and the geometric mean nicotine surface levels for those samples were 51 µg/m2 in the living room and 42 µg/m2 in the infant’s bedroom. The paper does not discuss whether these surface levels include the contribution from dust on the sampled surfaces. In simulating a typical residence, using sorption parameters obtained from previous chamber experiments, Klepeis and Nazar off estimated that in a room with chronic smoking it would take more than five years for surface nicotine levels to plateau.The extensive sorption of nicotine to typical indoor surfaces indicates that in rooms where smoking regularly occurs, the surfaces remain a strong source of nicotine for extended periods after smoking has ceased. Given that the capacity of a material for nicotine increases as the pH of water in the material decreases, acidic sorptive materials are anticipated to require more time to equilibrate with ETS nicotine and to remain strong sources of nicotine for longer periods than sorptive materials that are chemically basic. Secondhand smoke is a mixture of side stream smoke from tobacco combustion and mainstream smoke exhaled by a smoker. Residual constituents of secondhand smoke that sorb to exposed indoor surfaces, including settled dust, are often referred to as thirdhand smoke .

We just don’t know enough about either population exposures or exposure-response relationships to make a satisfactory risk assessment.”Scientific knowledge about this subject improved considerably during 1985-1995 and has continued to advance during the past few decades. We can now outline major features of the system, such as what are the causes, nature, and levels of atmospheric aerosol strong acidity; and what are the relationships among indoor concentrations, outdoor concentrations, and personal exposures. But measurements remain challenging, even for stationary sites sampling outdoor air. And so Lippman’s caution retains much of its validity today, more than three decades later.The largest data set regarding particle strong acidity was acquired as part of a respiratory health study conducted at 24 outdoor sites in the United States and Canada. Samples were collected over 24-h periods every second day for one year. Measured parameters included “ozone, particle strong acidity, sulfate, and mass … In 20 of the communities, sulfur dioxide, ammonia, nitric acid, nitrous acid,hydroponic tables canada and particulate nitrate were measured.”Table 22 reproduces the annual and summertime mean concentrations of particle strong acidity. The grand average across all 24 sampling sites was 28 nmol/m3 for the annual period and 44 nmol/m3 for the summer.

The studied sites were more highly concentrated in areas expected to have elevated acidity. The different averages among the geographic clusters illustrate large-scale spatial variability, with annual averages of 41 for the “sulfate belt,” in the “transport region,” for the “West coast” sites, and 6 for the “background” sites. The data reported in Table 22 were acquired using a sampling system developed by Koutrakis et al.For determining particle strong acidity, particles larger than 2.1 µm in diameter are excluded by means of an inlet impactor. The sampled air then passes through two denuders in series to remove acidic gases and ammonia. Fine particles, collected on a Teflon filter downstream of the denuders, are extracted after sampling and analyzed for strong-acid pH.From the pH measurement result and the sampling conditions, particle strong acidity is determined, in units of nmol of H+ per m3 of air sampled. Two essential features of the measurement method should be highlighted. First, removing coarse particles from the sampling stream allows the acidity of fine particles to be isolated from the potential neutralizing contributions of basic minerals associated with coarse airborne particles. Lipfert et al. cautioned that, “an aerosol sampling device that combines small acidic particles with larger basic particles … may yield misleading information with respect to biological responses.”Second, the use of denuders avoids artifact generation that would result from acidic and basic gases interacting with previously collected particles or with the filter material itself. That particle pH varies with size and that small particles tend to be more acidic has been demonstrated in several studies. For example, using a cascade impactor, Ludwig and Klemm determined the size-dependent acidity of aerosol particles at three locations in Bavaria, Germany. They reported that, “the in situ pH’s were calculated as pH 1 … 2 for these [fine] particles at all sampling sites.

Coarse particles were only slightly acidic, with a mean in situ pH 5.5 … 6.5.” Fang et al. assessed the pH of size-segregated aerosol particles sampled from roadside and urban sites in Atlanta, GA.Their assessment used a thermodynamic model applied to measured ionic species. Quoting a key result, “sulfate was spatially uniform and found mainly in the fine mode, whereas toxic metals and mineral dust cations were highest at the road-side site and in the coarse mode, resulting in fine mode pH < 2 and near neutral coarse mode.”The large-scale pattern of aerosol strong acidity is mainly controlled by the respective spatial distribution of the key precursors. On a regional scale, atmospheric sulfate concentrations are relatively uniform owing to the combined effects of numerous emission sources of SO2 , atmospheric mixing prior to secondary atmospheric production of sulfate from the oxidation of SO2, and relatively slow removal of sulfate from the atmosphere. Being a primary pollutant, ammonia exhibits a spatial pattern more closely associated with the pattern of source emissions, which tend to be more concentrated in urban areas and in rural areas with intensive agricultural activity as contrasted with more remote rural environments. Brook et al. describe the “Canadian Acid Aerosol Measurement Program,” with sampling conducted over three years at 10 sites. They reported that “acidities were lower in areas where the fine particle acidity experienced greater neutralization from reaction with ammonia. This included the major urban centres and areas with greater amounts of agricultural activity, as in rural southern Ontario.”Suh et al. studied the spatial variability of aerosol strong acidity in and around Philadelphia. They reported that, “outdoor sulfate concentrations were uniform within metropolitan Philadelphia; however, aerosol strong acidity concentrations varied spatially. This variation … was related to local factors, such as the NH3 concentration.”Interpreting results from a measurement campaign conducted in three sites in Pennsylvania during the summer of 1990, Liu et al. reported that “aerosol acidity was found to be lower in the urban area than the semi-rural areas.

Ammonia levels were higher at the urban site than in the semi-rural environments, probably due to the higher population density at the urban site.”The respective balances between atmospheric sulfate and ammonia levels is believed to be responsible for the observation that fine particles in the air in and around Beijing, China, are much less acidic than in North America and Europe. Liu et al. studied the pH of fine particles in Beijing during selected haze episodes occurring during late 2015 and 2016. Using a thermodynamic model to interpret measurements of particle-phase ions and precursor gases, they reported that, “Fine particles were moderately acidic, with a pH range of 3.0-4.9 and an average of 4.2 … Excess NH3 and high aerosol water content are responsible for the relatively lower aerosol acidity.” They reported remarkably high levels of aerosol water content during the haze episodes, up to several hundred µg m-3 . Ding et al. describe a more extensive investigation of the pH associated with PM2.5 particles in Beijing. Among the features that would support such an emphasis would be the lower aerosol water content levels during cold weather. A year-long study in Detroit, MI, using a sampler with an open-faced filter, showed seasonal variation in aerosol strong acidity with highest values during the summer: 39 nmol/m3 , 15 nmol/m3 , 13 nmol/m3 , and 3 nmol/m3 . An intensive study of aerosol strong acidity during summer months in Toronto separately assessed concentrations during daytime and overnight . Averaging first across the monitoring sites and then across the three years,microgreen rack for sale the levels were somewhat higher during daytime hours than overnight . With regional variation, the overall global trend has been a decrease in anthropogenic SO2 emissions over the period 1990-2015. As a result, one might expect substantial shifts in aerosol strong acidity. However, while there is agreement that SO2 emissions and atmospheric sulfate levels are decreasing, there isn’t a consensus about the consequences for acidity. Using a modeling approach, Murphy et al. report that, “steep increases in pH and the gas fraction of NHx are found as NHx:SO4 varies from below 1 to above 2.”They state that, “regions of the world where the ratio of NH3:SO2 emissions is beginning to exceed 2 on a molar basis may be experiencing rapid increases in aerosol pH of 1-3 pH units.” On the other hand, focusing on a rural area in the southeastern US, and combining experimental observations with modeling interpretation, Weber et al. conclude that, “the reductions in aerosol acidity widely anticipated from sulfur reductions, and expected acidity-related health and climate benefits, are unlikely to occur until atmospheric sulfate concentrations reach near pre-anthropogenic levels.”During the period of most intensive study of aerosol strong acidity, which centered on the decade 1985-1995, several investigations reported indoor conditions and/or personal exposures. Key findings are presented here, in approximate chronological order. Spengler et al. provided one of the earliest reports substantially concerned with acidic aerosols indoors and associated exposures.

They stated that “acidic aerosols occurring indoors are assumed to originate from outdoors.” They also reported that, “indoor gaseous ammonia concentration is expected to be higher compared to outdoors since it is produced by people, pets, and household products.” In considering exposures, they stressed the importance of micro-environmental conditions and time-activity patterns, highlighting, for example, that “children are more likely to be outdoors during the day, particularly in the summer.” They combined micro-environmental measured and modeled concentrations with time-activity patterns to estimate means and percentiles of the distribution of exposures to aerosol strong acidity for children. For Portage, Wisconsin, the annual average exposure concentration for aerosol H+ so determined was 7.6 nmol/m3 , with variation among averages between 1.2 nmol/m3 for winter and 18 nmol/m3 for summer daytime conditions. Corresponding results for Steubenville, Ohio, were 24 nmol/m3 for the annual average, 5.1 nmol/m3 for winter average, and 55 nmol/m3 for summer daytime average. This report highlighted the finding that atmospheric acidic aerosols could be elevated episodically: “measurements made in Kingston, TN, and Steubenville, OH, resulted in 24-h H+ ion concentrations exceeding 100 nmol/m3 more than 10 times during summer months.”An important conclusion from their investigation is that, “children engaged in summertime outdoor activities can experience H+ doses comparable to effects levels reported in human clinical studies.” Brauer et al.undertook the first direct experimental study of personal exposure to particle strong acids. Sampling was carried out in the Boston metropolitan area for 24 days during the summer of 1988. Two volunteers were each outfitted with two personal sampling systems similar to that described in Koutrakis et al.In each case, one sampler was operated continuously to collect a 24-h total exposure. For one subject, the second sampler was turned on only when outdoors; for the other subject, the second sampler was turned on only when indoors. Separate stationary samplers were used to measure aerosol strong acidity at a central monitoring site outdoors and overnight in three residences. The authors reported that “personal exposures to aerosol strong H+ were slightly lower than concentrations measured at the stationary site due to the neutralization of acidic particles and their incomplete penetration into indoor environments.”Using the same type of sampling system, indoor and outdoor concentrations of aerosol strong acidity were sampled in 11 homes in the Boston area during late winter and summer .In this study, the indoor/outdoor ratio of fine-particle strong acidity had geometric mean values of 0.48 in the summer and 0.36 in the winter. The mean ± standard deviation indoor H+ concentration was 2.4 ± 1.8 nmol/m3 in the winter and 8.8 ± 4.8 nmol/m3 in the summer. The authors reported that, “Indoors, we found a large available excess of NH3, which apparently coexisted at times with particle acidity.”Liang and Waldman measured indoor aerosol strong acidity at three institutional sites in New Jersey: a child care facility, a nursing home, and a home for the elderly. Simultaneously, outdoor sampling was conducted at a nearby central station and at the home for the elderly. Sampling was conducted during a six-week period, June-July 1989. The sampling train included a particle impactor that excluded particles larger than 2.5 µm and a denuder to remove gaseous ammonia. Sampling was conducted for 12-h daytime periods at all three indoor and both outdoor sites. Nighttime samples were also collected indoors at the elderly home and at both outdoor sites. The number of samples collected varied between 28 and 41 for each combination of conditions. Table 23 reproduces the mean and 90th percentile values of H+ concentrations reported by Liang and Waldman. The authors concluded that, “75% of the daily dose of aerosol acidity for the elderly was due to indoor exposures” and that “these data suggest that indoor settings are protective, but children may still be at risk from summertime acidic aerosol exposure, depending on their activities outdoors.”Suh et al. studied indoor, outdoor, and personal exposure to aerosol strong acidity in Uniontown, PA130 and in State College, PA. The Uniontown study focused on 24 children with monitoring conducted during summer 1990.

The release of chloroform from indoor use of chlorinated drinking water along with associated exposures has been extensively studied. For example, Weisel et al. measured chloroform in exhaled breath following showering among 49 female subjects throughout New Jersey. They found systematically and substantially higher chloroform exhaled from subjects who had elevated water levels of THMs. Nuckols et al. noted particularly that “epidemiology studies concerning THMs need to consider hot water use activities as important exposure events.” Wallace provided a thorough review of the state of knowledge regarding human exposure to chloroform in the US, concluding that “the major source of exposure to chloroform is chlorination of water supplies.” He also concluded that each of the three main exposure routes — ingestion, inhalation, and dermal absorption — “appear to be potentially substantial contributors to total exposure.” Disinfection byproducts other than THMs can be formed in drinking water treatment. Another category of regulatory concern is the haloacetic acids , including chloroacetic acid , dichloroacetic acid , and trichloroacetic acid . These HAAs have very high Henry’s law constants, so any inhalation exposure associated with indoor water use would likely be associated with inhaled particles rather than with gaseous species. Xu and Weisel261 conducted experiments to assess the rate of shower-generated particulate HAA and associated exposure. They reported that “the dose from inhalation exposure of disinfection byproducts in the particulate phase [would] represent less than 1% of the ingestion dose.” There also is a substantial literature on reactive chemistry and associated exposures and health risks from the use of hypochlorous acid for swimming pool disinfection. A highlighted concern in this case is the formation of chloramines, trimming tray arising from reactions of hypochlorous acid with ammonia and related reduced-nitrogen compounds produced by the swimmers.

Among the chloramines, the greatest attention focuses on nitrogen trichloride , also known as trichloramine, which is considerably more volatile in the presence of water than the other chloramines. Measured concentrations of gas-phase NCl3 in the air of indoor swimming pool facilities are high, with reported mean values of ~ 0.5 mg/m3 .The chemistry of chloramine formation in swimming pool environments is nicely summarized by Schmalz et al.;more generally, Zwiener et al. provide a thorough overview of the issues associated with disinfection byproduct formation in chlorinated swimming pools.Epidemiologically, clear evidence has emerged to document associations between time spent in indoor environments of chlorinated swimming pools and asthma risk. Bernard et al. stated that “regular attendance at chlorinated pools by young children is associated with an … increase in the risk of developing asthma.” Bernard et al. later reported that “use of indoor chlorinated pools especially by young children interacts with atopic status to promote the development of childhood asthma.” Jacobs et al. found “an excess risk for respiratory symptoms indicative of asthma … in swimming pool employees.” Organic acids are a vast class. Even considering only the species that are potentially relevant to indoor environmental concerns leaves a daunting challenge. On the other hand, only a few of the many organic acid species have been extensively studied indoors. The most prominent of these are formic acid and acetic acid, the simplest homologues of n-alkanoic carboxylic acids. This section reviews the state of knowledge regarding formic acid and acetic acid, in particular, plus other noteworthy examples from the broader class of carboxylic acids.The carbon in the functional group is double bonded to an oxygen atom and single bonded to the hydroxy moiety . For the broadest interpretation of carboxylic acids, R can represent any organic component. We will restrict our attention here to n-alkanoic carboxylic acids, for which R is a saturated, straight chain hydrocarbon .

Table 14 presents information on some of the more prominent of these acids. The compounds with carbon number C1-C6 will be substantially gaseous when airborne. Among these species, there is an overall tendency for the gaseous abundance to decrease with increasing carbon number. The tendency is not monotonic, however: acetic acid is typically more abundant than formic acid in indoor air. The three highest MW species in Table 14 are likely to be substantially in the particle phase if airborne. value is about 11, where Koa is the octanol-air partition coefficient. Interest in carboxylic acids indoors arises from several considerations. Acetic and formic acid are among the more abundant organic compounds found indoors, and so, provided there are no analytical barriers in sampling and analysis, broad surveys of indoor volatile organic compounds will include these species.If present at sufficiently high levels, carboxylic acids can contribute to odor and irritancy.Formic and acetic acids are among the most prominent and potent corrosive agents in air, so their abundance poses preservation threats for cultural artifacts.Conservation challenges are amplified because hardwoods, such as oak, which might otherwise be favored for storage cabinets and display cases, can be strong emission sources.Formic acid is an important oxidation product of atmospheric chemistry; comparing modeled to measured concentrations can help test and refine understanding of oxidative transformation processes.Particle-phase carboxylic acids are noteworthy tracers for cooking as an air-pollutant emission source.Considering measured indoor concentrations, even at the low ends of the reported ranges, formic and acetic acids could have substantial influence on the pH of indoor water. For example, the equilibrium pH of water exposed only to 800 ppm of CO2 would be 5.46. If that level of CO2 were combined with 1 ppb of formic acid and 4 ppb of acetic acid, the equilibrium pH of exposed water would decline by more than a full pH unit, to 4.36.

Larger, but realistic concentrations of carboxylic acids could cause substantial further pH decline. Specifically, a combination of 800 ppm CO2, 30 ppb formic acid, and 70 ppb acetic acid would yield an equilibrium pH for exposed water of 3.64. Starting with 800 ppm of CO2 and 20 ppb of NH3, the equilibrium pH of exposed water, in the absence of carboxylic acids, would be 7.12. Adding the lower levels of 1 ppb formic acid plus 4 ppb acetic acid to this mix would decrease the equilibrium pH to 6.02. At the higher carboxylic acid levels of 30 ppb formic acid plus 70 ppb acetic acid, the equilibrium pH would further decline to 5.30. In sum, ordinarily encountered levels of these carboxylic acids in indoor environments have the potential to contribute to notable shifts in the acidity of exposed water. In several studies, formic and acetic acid have been measured in special types of indoor environments or under special conditions. In museums and archives, these acids pose an unusual concern that arises, in part, because degradation of cellulosic and lignin materials may contribute substantially to indoor emissions, and, in part, because these acids pose corrosive damage risks to certain artifact materials. An extended monitoring campaign was undertaken in the Baroque Library Hall of the National Library, Prague. Over a nine-month period,trim tray pollen the median monthly average indoor concentration of acetic acid was 215 µg/m3 . The peak, which occurred during summer, was 417 µg/m3 . The corresponding monthly median and monthly peak values for formic acid were 24 µg/m3 and 102 µg/m3 . Gibson et al. reported on the concentrations of acetic acid and formic acid measured by passive sampling over 28-day exposure periods for three locations in each of eight museums and archives in the UK. The average ± standard deviation results for the 24 reported measurements for each species are 145 ± 91 µg/m3 for acetic acid and 63 ± 61 µg/m3 for formic acid. Hodgson et al. measured acetic acid concentrations in manufactured houses and site built houses in the eastern and southeastern United States. These were sampled shortly after construction under unoccupied and unfurnished conditions. The geometric mean of the measured concentrations were 117 ppb for the manufactured houses and 54 ppb for the site-built homes. Maddalena et al. sampled acetic acid concentrations in trailers intended to provide emergency shelter in the aftermath of hurricanes in the southern United States. The trailers were sampled for two 1-h periods under unoccupied and closed conditions. The average ± standard deviation acetic acid concentration for the four trailers was 1090 ± 340 µg/m3 . Formic and acetic acid have been measured in a simulated aircraft cabin. Here, in addition to primary emissions from furnishing materials and from the passengers, there is the possibility of secondary production of the acids as byproducts of ozone reaction with skin oils and other unsaturated organic molecules.

These experiments utilized a 2 ´ 2 matrix design, with low and high ventilation rates combined with low and high ozone levels . For formic acid, cabin air concentrations ranged from a low of 0.8 ppb for the low ozone – high ventilation condition to 5.3 ppb when the high ozone level was combined with the lower ventilation rate. The analogous results for acetic acid were 3.1 ppb for low ozone – high ventilation and 10.6 ppb for high ozone – low ventilation. In the past few years, instruments that can measure carboxylic acids with high sensitivity and fast response times have begun to be employed in indoor air studies. The first few of these studies have already revealed important new information about factors influencing the abundance and dynamic behavior of carboxylic acids indoors. Tang et al. used proton-transfer-reaction time-of-flight mass spectrometry to make time-resolved measurements of a broad suite of volatile organic compounds in a university classroom during normal use. From the data generated, they apportioned the source of individual VOCs among three major categories: outdoor air, indoor building materials and furnishings, and the occupants. They determined occupant associated emission rates to be 48.5 µg h-1 person-1 for formic acid and 329 µg h-1 person-1 for acetic acid. On a mass-weighted basis, among the quantified occupant-associated VOC emissions, acetic acid ranked 3rd and formic acid 10th. Both compounds were among those “whose source was ~ 1/3 or more from human occupants.” Liu et al.13 also studied the organic gas composition of a university classroom, applying a high resolution time-of-flight chemical ionization mass spectrometer. Carboxylic acids were prominently featured in their study. Overall, the average indoor concentration of total carboxylic gases was 6.8 ppb whereas the average outdoor level was only 1.0 ppb. The timeaveraged indoor concentrations of n-alkanoic carboxylic acids reported in this study were 1.2 ppb for formic acid, 38 ppt for propionic acid, 110 ppt for butyric acid, and 54 ppt for valeric acid. Acetic acid could not be measured with their analytical method. Duncan et al.32 used iodide reagent ion chemistry in high-resolution time-of-flight chemical ionization mass spectrometry to study time-resolved concentrations of water-soluble organic gases, including acetic and formic acid, in a North Carolina residence over several days. Measured concentrations were in the range 30-130 µg/m3 for acetic acid and 15-53 µg/m3 for formic acid. A striking feature was the rhythmic and substantial decline of indoor acetic and formic acid concentrations associated with air conditioner cycling. The authors suggested that “these highly water-soluble compounds … are taken up by water condensed on the AC surfaces and/or in the AC condensate.” Liu et al. conducted an extensive monitoring campaign in a single-family house in northern California. They utilized PTR-ToF-MS to analyze indoor air VOC composition with high time resolution over two multi-week sampling campaigns. Among the species quantified were the series of n-alkanoic carboxylic acids extending from formic acid through undecanoic acid9COOH. Table 16 shows that the time-averaged concentrations tended to decrease with increasing carbon number . The average indoor air concentrations and the effective emission rates were also consistently higher in the summer than in the winter. Considering the sum of continuously emitted compounds that they were able to measure with PTR-ToF-MS, the authors reported that “acetic acid alone accounted for half of the summed VOC emission rate.” They also observed a systematic temperature dependence of emissions, stating that “comparing 23 °C to 16 °C, an overall doubling of building-associated VOC emission rate was observed.” Another important inference was that “high abundance of acetic acid and furfural in both the attic and in the living zone … is consistent with the hypothesis of wood decomposition being their major source.” It is worthwhile to highlight a comparison of emissions data from the classroom study of Tang et al. and the residence study of Liu et al. 

Although the above cited studies have discussed the product of gaseous nitric acid and ammonia concentrations as a test of equilibrium between these gas-phase species and particulate NH4NO3, in indoor environments the mass of NH4NO3 accumulated on surfaces is anticipated to be much larger than the mass associated with indoor airborne particles. The complicating features of this additional compartment for ammonium and nitrate have not been fully incorporated into efforts to predict or interpret indoor ammonia behavior. Nevertheless, the rapid loss of nitric acid to indoor surfaces, which is not matched by ammonia loss, is likely to result in products of the gas-phase species that are smaller than would be predicted for equilibrium with aerosol NH4NO3. Additional measurements of ammonium concentrations on indoor surfaces, coupled with measurements of gaseous nitric acid and ammonia, would contribute to better understanding of indoor ammonia chemistry.By raising the pH of surface-associated aqueous films, ammonia has the potential to influence the partitioning of various species between the gas phase and indoor surfaces. In chamber experiments , Webb et al. found that elevated ammonia levels promoted the desorption of nicotine from nylon carpet, but not from painted gypsum board. In a more detailed subsequent series of studies, Ongwandee and colleagues examined the impact of NH3 and CO2 on the sorption of N-containing organics to mineral and real-world surfaces. In the experiments most directly relevant to acids and bases in ordinary indoor environments,roll bench the investigators found that the sorption of nicotine to polyester curtain and to carpet increased as the RH increased.

Ammonia at high levels suppressed the sorption of nicotine to carpet at 50% and 90% RH but not at 0% RH. We stress that the ammonia concentrations in these studies were much larger than those routinely observed indoors . This point is particularly relevant for situations where NH3 appears to be competing with N-containing basic organics for surface sites: the behavior at high concentrations may not be directly predictive of effects at much lower levels. Nonetheless, these studies illustrate different mechanisms, including pH modification, through which NH3 can influence the sorption of N-containing organics to real room surfaces. During the HOMEChem campaign, an ammonia cleaner was used on surfaces in the living room and kitchen of the test house either before or after mopping with a vinegar solution.A rapid decrease was observed in the gas-phase concentrations of acidic species when ammonia surface cleaning preceded vinegar mopping. A final note concerns the potential for ammonia to contribute to discoloration of interior surfaces. Updyke et al. demonstrated that when filter samples of secondary organic aerosol generated from both O3-initiated and OH reactions with biogenic and anthropogenic precursors were exposed to 100 ppb of NH3 in humid air, the samples changed from initially white to a red brown color. The extent to which this browning occurred varied with the SOA precursors and ranged from no color change for SOA from isoprene to a strong color change for SOA derived from limonene. In the latter case, the light absorption coefficients for wavelengths 300-700 nm were comparable to values measured for brown carbon from biomass burning. The authors hypothesize that “browning” begins when NH3 reacts with a carbonyl group in SOA constituents forming hemiaminals that subsequently dehydrate into primary imines. Not only is such chemistry anticipated to occur indoors, but it may also occur on indoor surfaces soiled with SOA formed from reactions between ozone and terpenoids or sesquiterpenes.

Such SOA may be close to colorless when first deposited on indoor surfaces, but over time, in the presence of NH3, the chemicals could become “brown,” contributing to discoloration of lightly colored indoor surfaces.Sulfur dioxide and sulfate are prominent contributors to atmospheric acidity. Sulfur, originating as a minor constituent of coal, is oxidized to SO2 when the fuel is burned. In the atmosphere, sulfur can be oxidized from +IV to +VI . That oxidation process is an important factor in the acidifying influence of atmospheric sulfur for two reasons. First, whereas SO2 is moderately soluble in water, sulfuric acid is highly soluble and – in the atmosphere – is almost entirely found in the condensed phase. Second, although both acids are diprotic, sulfuric acid is a much stronger acid than is sulfurous acid . Consequently, atom for atom, the conversion of S to S substantially increases the acidic potency of airborne S. Sulfur dioxide and sulfate play important roles in the acid-base properties of indoor environments, too. In this subsection, we’ll first consider SO2 and its related S species and then discuss sulfate and associated S compounds. We explore sources, dynamic behavior, and fates, especially considering the role affecting the pH of condensed-phase water indoors. The role of sulfate contributing to aerosol strong acidity is further considered in §3.8.Over the past few decades, atmospheric levels of sulfur dioxide have declined in the United States and in Europe owing in large part to reduced sulfur emissions from coal combustion. In 2018, the US national average SO2 concentration, as measured across a network of 287 outdoor air monitoring stations, was 14 ppb . In India, SO2 emissions trended upwards between 1996 and 2010. In China, the temporal patterns of emission rates and concentrations have exhibited variability, with an overall decreasing trend emerging during the past several years.

Over the past three decades, a slight reduction of ambient SO2 has been reported from a monitoring station in South Korea, with an overall mean SO2 abundance of 5.5 ppb for 14 y of sampling during the period 1987-2013. Global anthropogenic SO2 emissions are estimated to have increased between 2000 and 2006 with a declining trend subsequently, through 2011. The presence of SO2 in outdoor air constitutes a major source for SO2 in buildings, it being transported indoors along with ventilation air. In the absence of indoor emission sources, SO2 concentrations in buildings are observed to be lower than the corresponding outdoor concentrations. Table 8 presents a summary from one major US study, in 1977-1978, of measured SO2 levels in residences and outdoors in circumstances in which outdoor air was thought to be the most important indoor SO2 source. If an average I/O ratio of 0.4 is assumed to prevail,drying rack cannabis then the average indoor SO2 concentration in the US in 2018 is estimated to have been approximately 6 ppb for homes with no indoor sources of SO2. Sulfur dioxide is emitted indoors when sulfur-containing fuels are burned and the combustion byproducts are released directly into the indoor space. One potentially important indoor emission source is unvented kerosene space heaters. Even though household-grade kerosene is low in sulfur , unvented combustion of kerosene for space heating can have a discernible impact on indoor SO2 levels. For example, Leaderer et al. measured 24-h average SO2 levels in homes in Virginia and Connecticut during summer and winter . For the wintertime measurements, the average indoor SO2 level in kerosene-heater homes was 16 ppb, 20´ higher than the average level of 0.8 ppb inside homes without kerosene heaters and 4´ higher than the concurrently measured average outdoor level. The maximum 24- h average indoor level in a kerosene-heater home in that study was 107 ppb. Coal used for space heating and cooking can also contribute to elevated indoor SO2 levels. The potential is even greater than with kerosene space heaters because coal has a higher sulfur content than kerosene. Empirical data are not abundant; however, Seow et al. reported a median 24-h average indoor SO2 concentration of 907 µg/m3 for 42 households that used “smokeless” coal as a residential fuel in Yunnan Province, China. Sulfur dioxide interacts with indoor surfaces. In the absence of indoor sources, the associated net loss to surfaces causes concentrations indoors to be lower than corresponding outdoor levels. Indoor surface reactions would also diminish the contributions of indoor emission sources to indoor concentrations. Although not the subject of many systematic recent investigations, studies of SO2 interactions with indoor surface materials were widely undertaken in the 1960s and 1970s.One exception to the historical pattern is the more recent work by Grøntoft and Raychaudhuri, who assessed the humidity dependence of SO2 uptake on a variety of indoor surface materials.

The acidic properties of sulfur dioxide parallel those of carbon dioxide. Sulfur dioxide is moderately soluble ;aqueous SO2 forms diprotic sulfurous acid upon combining with a water molecule. Dissociation liberates the H+ ion and forms bisulfite from a first dissociation reaction, and then sulfite from a second. The respective acid dissociation constants are pKa = 1.86 for H2SO3 and 7.17 for HSO3 – . Compared with carbon dioxide, SO2 has a larger Henry’s law constant; and sulfurous acid is much stronger than carbonic acid. The net consequences are that SO2 can contribute to more acidification of condensed water than does CO2, even with much smaller gas-phase abundance. This point is illustrated in Figure 6, which shows the equilibrium pH for water exposed to SO2 with and without the simultaneous presence of CO2. In this situation, for SO2 levels below about 3 ppb, the pH is a function of both the CO2 and the SO2 levels. For indoor CO2 levels in the range 400-1000 ppm, and with SO2 at or below 5 ppb, the pH spans a range 5.0-5.6. For higher SO2 levels, as might occur from unvented indoor combustion of kerosene, the pH becomes largely independent of the CO2 level and declines to approximately 4.4 at 100 ppb SO2. An interesting feature regarding the fate of indoor SO2 is suggested from a detail in the empirical study of Spengler et al.As displayed in Table 8, the indoor/outdoor SO2 ratio in Kingston, TN , was markedly lower than in any of the other five cities studied . The authors noted that all the homes in Kingston were air conditioned. Keeping windows closed rather than open would have the tendency to decrease I/O ratios simply by reducing the air-exchange rate, a, as is evident from equation . However, it is also plausible that condensing water on air conditioner coils could constitute an additional sink for SO2. For example, water in equilibrium with 1 ppb of SO2 and 800 ppm CO2 would, at a pH of 5.26, contain 3.3 µM of bisulfite. If the air conditioner were to condense and drain 1 L per hour of liquid water equilibrated with 1 ppb of SO2 at pH 5.26, that would correspond to a removal rate of 210 µg/h of SO2. That amount is equivalent to the removal of SO2 by means of 80 m3 h-1 of ventilation, a ventilation rate that could occur in a closed home with air conditioner use. Consequently, aqueous removal of S could constitute a meaningful loss mechanism associated with air conditioning in humid climates. As yet, there are no experimental data with which to directly test this inference.In the atmosphere, over a time scale of hours to days, the sulfur in SO2 is oxidized from S to S, as in sulfate. The oxidation process can occur in the gas phase, initiated by the hydroxyl radical, or in the aqueous phase, e.g. in cloud droplets. Atmospheric sulfate is an important contributor to urban and regional air pollution, prominently featured in issues as seemingly disparate as acid deposition, visibility impairment, and cardiovascular health risk. A major attribute of inorganic S is that it is essentially nonvolatile. It is found in the atmosphere concentrated in condensed-phase components, e.g., in cloud water, rain drops, and particulate matter. When aerosol sulfate is formed from gaseous SO2, most of the sulfur mass is concentrated in particles with diameters in the range 0.1-1 µm. Because particles in this size range penetrate and persist well indoors, and because indoor sources of sulfate are uncommon , fine-mode aerosol sulfate has been used as an indicator of outdoor air’s influence on indoor fine-particle concentrations183 and on personal exposures to fine particle mass. Oxidation of sulfur from S to S strongly influences acidification. Whereas SO2 is moderately soluble and so is substantially gaseous, to a good approximation all of the highly soluble sulfuric acid formed in the atmosphere will be transferred to the condensed phase. In addition, sulfurous acid and bisulfite are considerably weaker acids than their respective counterparts, sulfuric acid and bisulfate .

Rowen and Blaine50 reported apparent surface areas on such a comparative basis. The specific surface areas for the two sorbates were comparable for titanium dioxide. However, for nylon and for wool specific surface areas were much larger for sorbing water as compared to N2. With dynamically changing indoor humidity conditions, an important consideration for sorptive partitioning of water is the accessibility of fabrics. As one example, the magnitude of clothing worn by a person is ~ 1 kg. Closets and clothing cabinets contain tens to hundreds of kg of clothing. While being worn, clothing fibers are readily accessible to moisture and to gaseousacids and bases. While stored, however, limited rates of mass transfer might extend the equilibration time scale such that gaseous interactions are not responsive to rapid dynamic changes. The influence of long sorptive time scales associated with abundant fibrous materials has not been studied even with regards to water vapor buffering.Another key consideration is the thermodynamic properties of the sorbed water. In particular, to what extent do volatile and semivolatile species partition into water that is sorbed to fibrous materials? Do the Henry’s law constants developed for partitioning to bulk water have meaning, even as estimates, for the expected partitioning between gaseous and sorbed water? Can sorbed water serve as a proton acceptor for the case of an acid or as a proton donor for the case of a base? Are the respective pKa values, e.g. for acids in bulk water, reasonably predictive of acid behavior in sorbed water? We don’t know the answers to these questions. Given the expected abundance of sorbed water indoors,ebb flow research is warranted to answer them. Water may be sorbed at any of the interfaces between solid materials and indoor air. In relation to the forms of condensed water already discussed, this surface-sorbed water has small abundance.

However, because the surface-sorbed water occurs in thin films, the equilibration time scales are rapid for species partitioning between the water film and air. We acknowledge an overlap in classifying water in aqueous surface films as compared with sorbed water in indoor materials. Compare water for an impervious surface such as window glass to a porous sorbent such as gypsum board. Water molecules are not expected to penetrate materially into window glass; instead, condensed water is present as part of an invisible surface film along with other deposited materials, such as inorganic ions and organic compounds. 56 For gypsum board, at equilibrium, sorbed water will be present throughout the bulk of the material . Mass-transfer limitations influence the degree to which water is present throughout the bulk of a porous material such as gypsum board, but would not strongly affect water’s abundance in surface films. The distinction in this comparison between surface-sorbed water and bulk-sorbed water should be clear. But what then about sorptive water partitioning to textiles indoors, such as carpet, furniture fabrics, and window coverings? In this review, we have categorized such water as bulk-sorbed, in relation to the bulk properties of the fabrics. For materials like painted gypsum board or wood furniture, contributions to total condensed-phase water abundance occur from both the bulk sorption throughout the material and from surface sorption in a film. Bulk sorption in such cases was discussed in the previous section. One should consider the possibility of additional water being present in surface films on such permeable and porous materials. To date, the relevant literature about surface films, itself limited in scope and extent, focuses on impervious surfaces.

Important lessons about the adsorption of water on glass can be found in an early investigation by Razouk and Salem. They measured the sorption of water on glass wool, glass powder, and glass microspheres. For water-washed glass, they found that the “real surface is about two to three times greater than the geometric surface.” They also found that “the amounts adsorbed at 0.20 and 0.80 relative pressures [i.e., at RH = 20% and 80%, respectively] correspond closely to one and two molecular layers .” Their experimental evidence was interpreted to support the view that, “water adsorbed by glass is made up of two parts: one part being readily removed by pumping and thus loosely held, the other part being held firmly and driven off only by heating at higher temperatures.” Water sorbed on a surface can be quantified in terms of monolayer equivalents. As suggested by the name, a monolayer corresponds to the quantity of water just sufficient to completely cover the surface at a thickness of a single molecule. A few clarifying points are needed. First, the idea of a monolayer is conceptually valuable, but not real. Sorption occurs in a patchy manner, with multiple layers of sorbed molecules occurring before a first layer is full. Second,isotherm data are often based on measurements of the mass of sorbed water per mass of sorbent. To convert to equivalent monolayers, basic information is needed about the geometry of sorbed water and about the specific surface area of sorbents. For sorbed water a nominal linear dimension can be obtained as the cube root of the effective volume occupied by a single water molecule. From this perspective, water at 18 g/mol and 1.0 g cm-3 has a molecular-specific volume of 3.0 ´ 10-23 cm3 molecule-1, and the corresponding linear scale is the cube root, i.e. 0.31 nm. Based on their literature review, McClellan and Harnsberger recommended 12.5 Å2 as the effective surface area occupied by adsorbed water molecules. The corresponding monolayer thickness to produce the proper molecular-specific volume would be 0.24 nm.

This finding is consistent with direct experimental measurements of water film thickness. We will adopt the following practice. When original sources quoted here report results in units of monolayers, then we will cite those results without amendment. . When original sources report isotherm data in other terms, such as mass of water sorbed per mass of sorbent, we then convert these data to monolayer equivalents using a monolayer thickness of 0.24 nm, based on the McClellan and Harnsberger recommendation.Another important detail is that the true surface area onto which sorption occurs often exceeds the nominal surface area, even for seemingly impervious materials such as glass. In cases in which we have computed monolayer equivalents, we use the specific surface areas measured by the BET method as reported in the original source . Studies of water sorption on the surfaces of different types of materials reveal two important points. First, at the ordinary relative humidity levels encountered indoors, it is common for the abundance of sorbed water to exceed a monolayer. Table 5 records the numbers of equivalent monolayers of sorbed water onto different types of surface materials,pot drying typically either pure minerals or mixtures of mineral origin. Among the entries, the median are as follows: at 30% RH — 1.9 monolayers; at 50% RH — 2.3 monolayers; at 70% RH — 3.8 monolayers. It is noteworthy that that the results exhibit a fair degree of homogeneity despite the diverse minerals tested. These results also are in broad agreement with the information presented by Leygraf et al. concerning metal surfaces: “a number of metals are seen to be covered by water equivalent to 2-10 monolayers for relative humidities exceeding 40% at normal room temperatures.” They also state, “The first layer of water has a high degree of ordering relative to the substrate because of its proximity to the solid surface. The second and third layers are more mobile with a higher degree of random orientation. Aqueous films thicker than three monolayers possess properties that are close to those of bulk water.” Although the properties of surface water approach those of bulk water as the film thickness grows, the water film may also have properties that differ from bulk water so as to coexist at equilibrium with water vapor at relative humidities less than 100%. A second point is that the specific surface areas of these materials commonly are much greater than the superficial or apparent surface areas. For example, a sample of sand studied by Lin et al.had a specific surface area based on the BET test with N2 of 0.4 m2 g-1 , a value 60´ as large as the nominal surface area of equivalently sized solid spheres. Most of the additional surface area was associated with internal pores, even though the within-grain porosity was determined to be only 1.4%. With the high internal surface area, at 50% RH, this sand sample had a sorbed water content of 1.1 mg per g of sand.The existence of a much higher internal surface area than the apparent or nominal surface poses difficulties for translating the isotherm-based information of the type displayed in Table 5 into information about the expected contribution of surface-sorbed water to L*. Fundamentally, we lack adequate information about the effective true surface areas of mineraltype materials exposed indoors. Another complication in applying the data reported in Table 5 is that interior surfaces are commonly coated with films of organic materials that may alter the nature of water-surface interactions . Liu et al. were among the first to characterize such films on indoor window surfaces, and many subsequent studies support an inference that organic films are ubiquitous on indoor impervious surfaces. Wu et al. demonstrated that airborne exposure to “kitchen grime” caused different surfaces to exhibit comparable thermodynamic properties with respect to sorptive partitioning of phthalates. Weschler and Nazaroff56 and Eichler et al. have modeled film formation and growth. Schwartz Narbonne and Donaldson72 exposed initially clean, gold-coated quartz crystals for periods of approximately two months in two occupied homes in Toronto.

These crystals were oriented horizontally during the exposure period, so they would accumulate both settling dust as well as organic vapors. After the exposure period, the crystals were exposed to conditions in which the relative humidity could be controlled. The humidity was systematically varied “from 5% to 85% at a rate of 1% per minute.” By measuring the change in oscillation frequency, the mass change associated with water uptake could be evaluated as a function of relative humidity. Unexposed crystals were also analyzed and these results were used for blank correction, so that only the uptake of water into the surface film was assessed. The detailed results, displayed in Figures 2- 4 of the cited reference, show an irregular feature: the water mass associated with several exposed crystals did not rise monotonically with increasing RH. Nevertheless, among 12 samples for which results were reported, at RH = 30%, 50%, and 70%, the mean ± standard deviation of water surface densities was 0.15 ± 0.09, 0.42 ± 0.26, and 0.90 ± 0.56 µg cm-2 , respectively. The corresponding effective mean monolayer thicknesses of sorbed water would be 6, 18, and 38, respectively. There was not a clear pattern of differences between pairs of rooms sampled at each site or between study sites. For an effective monolayer thickness of 0.24 nm, 18 monolayers would correspond to an equivalent thickness of 4 nm. By comparison, the experimental data on window film growth summarized in Weschler and Nazaroff show median and mean growth rates of 0.15 nm/d, so that an expected representative thickness of organic constituents after an exposure period of two months would be about 9 nm, or about 2´ the associated contribution from sorbed water at 50% RH. An important point to highlight is that the Schwartz-Narbonne and Donaldson results are based on the nominal or superficial surface area of the sorbing substrate, in this case the goldcoated quartz crystal covered with an indoor-exposure acquired surface film. If the mean water surface density of 0.42 µg cm-2 for RH = 50% applied for the average exposed surface in an indoor environment with an overall surface-to-volume ratio23-25 of 3.5 m2 m-3, then this water could contribute 1.5 ´ 10-5 L m-3 to the indoor liquid water ratio, L*. Recent studies have investigated the properties of surface water in relation to the behavior of acids. Using silica as the substrate, Fang et al.showed that a surface-bound acid can deprotonate in the presence of sorbed water and that the degree of deprotonation is greater for a strong acid than for a weak acid . Wellen et al.investigated the behavior of octanoic , nonanoic , and decanoic acids. They found a reduction in the acidity of the species at the water-air interface. In contrast to the water/air interface, at the substrate/water interface Parashar et al.used a modeling approach to “show how the acidity of pyruvic acid at the quartz/water interface is increased by almost two units” .

In an initial safety and efficacy trial, ibudilast improved mood resilience following stress exposure and reduced tonic levels of craving . Mood resilience was defined as a faster recovery of positive mood to baseline levels in the treatment condition following exposure to a stressful personal narrative. However, as noted above, ibudilast did not robustly alter subjective response during an alcohol administration paradigm. Yet, this study had a relatively small sample size , and findings could not be extended to subjective effects of alcohol in real-world settings, as participants were required to maintain abstinence during the trial for safety reasons. Extending medications development to naturalistic settings, particularly for novel pharmacotherapies like ibudilast, is needed, as it enables researchers to assess medication effects with far greater ecological validity and to examine dynamic within-person processes through repeated assessments. Electronic real-world data capture is a cost-effective way to collect numerous occasions of alcohol self administration and related subjective effects in participants’ natural environment . As such, work testing ibudilast’s ability to modulate subjective response in naturalistic drinking settings has the potential to further our understanding of its bio-behavioral mechanisms, particularly in the context of powerful natural reinforcers and cues. For this reason, the present study will extend findings published from a two-week clinical trial of ibudilast in our laboratory, which utilized daily diary methods . DDAs of subjective alcohol response were collected during this trial to identify bio-behavioral mechanisms of ibudilast,mobile vertical rack but had yet to be analyzed. The present study sought to test the effect of neuroimmune modulation by ibudilast on subjective response to alcohol in the naturalistic environment.

This secondary analysis leveraged DDAs from a two-week experimental medication RCT of ibudilast, stratified on sex and withdrawal-related dysphoria, that enrolled non-treatment seeking participants with AUD. The DDAs included reports of alcohol use and subjective response measures of stimulation, sedation, mood, and craving. Each morning, participants retrospectively reported on their mood and craving levels both before and during the previous day’s drinking episodes, as well as stimulation and sedation levels during the previous day’s drinking episodes. We hypothesized that ibudilast would significantly reduce average levels of alcohol-related stimulation and increase average levels of alcohol-related sedation compared with placebo during participant naturalistic drinking episodes. Second, we hypothesized that ibudilast would significantly attenuate daily alcohol-induced changes in craving and mood compared with placebo. Two sets of exploratory analyses were also undertaken in which we tested if ibudilast moderated the effect of alcohol-related stimulation and sedation on same-day number of drinks consumed and if the presence of withdrawal-related dysphoria moderated ibudilast’s effects on daily alcohol-induced changes in mood and craving.The current study is a secondary analysis of data collected during a two-week clinical trial of ibudilast for heavy drinking reduction and negative mood improvement in a sample of non-treatment seeking individuals with AUD .Fifty-two eligible participants were randomized to either ibudilast or matched placebo. Randomized participants were asked to attend in-person study visits on Day 1 , Day 8 , and Day 15 , and complete electronic DDAs to report on previous day craving, mood, and alcohol and cigarette use. When participants endorsed previous day alcohol consumption, they also reported on levels of stimulation and sedation. Participants completed a neuroimaging scan at study midpoint.

The clinical trial was approved by the University of California, Los Angeles Institutional Review Board [UCLA IRB#17–001741]. Prior to completing study procedures, all participants provided written informed consent after receiving a full study explanation. A community-based sample of individuals with current DSM-5 AUD was recruited for the trial through social media and mass transit advertisements in the greater Los Angeles area. Study inclusion criteria were: between 21 and 50 years of age; meet current DSM-5 diagnostic criteria for mild-to-severe AUD ; and report heavy drinking levels 30 days prior to their screening visit, as defined by the National Institute on Alcohol Abuse and Alcoholism as >14 drinks per week for men and >7 drinks per week for women. Exclusion criteria were: currently receiving or seeking treatment for AUD; current DSM-5 diagnosis of another substance use disorder ; lifetime DSM-5 diagnosis of bipolar disorder or any psychotic disorder; current use of psychoactive drugs, other than cannabis, as verified by a urine toxicology screen; if female: pregnancy, nursing, or decision to not use a reliable method of birth control; presence of non-removable ferromagnetic objects, claustrophobia, serious head injury, or prolonged period of unconsciousness ; medical condition that could interfere with safe study participation; and recent use of medications contraindicated with ibudilast treatment . Participants were also required to have reliable internet access to complete electronic DDAs. A total of 190 individuals consented to participate in the initial in-person screening visit. Of those, 81 individuals were deemed clinically eligible and were invited to complete a physical screening to determine medical eligibility. A total of 52 participants were randomized to study medication or placebo . Included in the present analyses are 50 participants who completed at least one daily diary report after randomization. Participants were compensated up to $250 for their participation in the study and received an additional $100 bonus if all study visits and ≥80% of DDAs were completed. The clinical trial was conducted at an outpatient research clinic in an academic medical center. Interested individuals completed an initial telephone-screening interview and eligible callers were then invited to the laboratory for an in-person behavioral screening visit.

At the start of all in-person visits, participants were required to have a BrAC of 0.00g/dl and a urine toxicology test negative for all drugs excluding cannabis. Eligible participants were asked to complete an in-person physical screening visit consisting of laboratory tests and physical exam by a study physician. Participants meeting all study eligibility criteria who attended the in-person randomization visit were randomly assigned to receive either 50 mg BID of ibudilast or matched placebo. Randomization was stratified by sex and participant report of experiences with withdrawal-related dysphoria. This a-priori stratification variable was intended to capture the “dark side of addiction” , whereby individuals reporting withdrawal-related dysphoria were estimated to experience greater dysfunction of the immune system. MediciNova, Inc. supplied ibudilast and placebo for the trial but did not provide any financial support for the study. The UCLA Research pharmacy prepared and dispensed all study medication in blister packs. Research staff, participants,vertical grow rack and providers remained blind to medication condition during the trial. Participants were titrated on ibudilast as follows: 20 mg BID during days 1–2 and 50 mg BID during days 3–14. Target medication dose was selected based on safety considerations as well as preclinical and clinical data . Medication compliance was monitored through pill counts and self-report via DDA. Side effects were closely monitored and reviewed by study physicians. During the in-person screening visit, participants completed a set of assessments for individual differences and eligibility screening. Assessments included collection of demographic information , substance use characteristics and history, and psychological functioning and diagnoses. Surveys used to characterize the sample included the Beck Depression Inventory to assess levels of depression symptomatology, the Snaith-Hamilton Pleasure Scale to measure anhedonia , the Alcohol Use Disorders Identification Test to capture alcohol problem severity, Penn Alcohol Craving Scale to measure tonic craving levels, and the Reasons for Heavy Drinking Questionnaire to capture one’s motivations for heavy drinking. In addition, the RHDQ determined the presence of withdrawal-related dysphoria for randomization stratification as follows: raw scores ranging from 0 – 10 on the RHDQ question #6: “I drink because when I stop, I feel bad ”, were dichotomized into yes /no, based on a cut-off of 6+ points. Interviews used to determine eligibility criteria and determine baseline quantity and frequency of alcohol use were administered by clinical graduate students or trained research staff and included the Timeline Follow Back measuring alcohol, cigarette, and cannabis use over the previous 30 days , the Clinical Institute Withdrawal Assessment for Alcohol Scale – Revised assessing clinically significant alcohol withdrawal, and the Structured Clinical Interview for DSM-5 to determine current AUD diagnosis and severity and to screen for exclusionary psychiatric diagnoses.Each morning throughout the two-week trial, participants were asked to retrospectively report on their previous day experiences by completing an electronic DDA survey . Study staff provided instructions on DDA completion and participants practiced filling out the survey at their randomization visit. Daily text messages or emails containing links to DDAs were sent to participants at 8am each morning during their 14-day medication period. Additional telephone or text reminders were sent by study staff as needed.

At the start of each daily survey, participants were asked, “Did you drink any alcohol yesterday?” If participants endorsed alcohol use the previous day, they reported on drink type and quantity, and then completed two sets of items: 1) ratings of mood, craving, and urge before drinking, and 2) ratings of mood, craving, urge, stimulation, and sedation while drinking. For example, participants were asked: “Before you drank, how strong was your urge to drink alcohol yesterday?” and “While drinking, how strong was your urge to drink alcohol yesterday?” The current analyses focus primarily on drinking days, given our interest in medication-related changes in subjective response to alcohol. Mood states were assessed via the short form of the Profile of Mood States survey . POMS-SF is a standard, validated psychological rating scale that measures dimensions of transient mood states by asking subjects to indicate how well each item describes their mood on a 5-point Likert scale . To keep the survey brief and thus reduce the burden on participants, only select items from POMS-SF were chosen for DDAs . Reports of stimulation and sedation were assessed via the validated Brief Biphasic Alcohol Effects Scale . The B-BAES is a six-item measure on the acute stimulant and sedative effects of alcohol on an 11-point scale . Urge to drink was captured via the item, “How strong was your urge to drink alcohol yesterday” , in line with previously published reports . Phasic craving was assessed using the first and last items from the validated Alcohol Urge Questionnaire on a 7-point Likert scale . Participants reported the quantity of standard alcoholic drinks consumed according to established guidelines and provided details about non-standard drinks . Drink entries were reviewed and verified by study staff.DDA Item Scoring—All descriptive and statistical analyses were completed in SAS Version 9.4 on the sample of participants who completed at least one DDA. Select items from the POMS-SF tension and depression sub-scales , were summed to form a negative mood state score and select items from the vigor sub-scale were summed to form a positive mood state score for each time point, consistent with previous reports . The two AUQ items were summed to form a craving score . Stimulation and sedation sub-scales from B-BAES were calculated using standard methods . Multilevel Models—Models were fit in SAS using the MIXED procedure and a multilevel framework, unstructured covariance matrix, residual maximum likelihood estimation, and random intercepts with observations nested within subjects to account for clustering and to preserve suitable Type-1 error rates . Kenward-Rogers degrees of freedom were chosen to reduce bias and obtain more accurate p-value estimates. Main and simple effects were probed by recentering dichotomous variables and using the simple slopes approach. Daily alcohol use quantity, mood states, craving, and urge data from non-drinking days were treated as missing. Comparable three-level models were fit for variables having both before and during drinking observations , such that these observations were nested within day and days were nested within subjects. All models were tested with the following level-2 covariates: sex, AUD severity , and baseline drinks per drinking day . In addition, daily number of drinks consumed during the trial was included as a predictor with random effect in all subjective response models to account and control for potential day-level drink quantity effects on subjective response. To examine both between- and within-subject effects and interactions, covariates were centered at the grand mean and focal within-subject variables were centered within cluster . Specifically, to assess the effect of medication on the acute stimulant and sedative effects of alcohol, one model for each B-BAES sub-scale was estimated in which stimulation or sedation served as the outcome and medication condition served as the focal predictor.

Patients receiving methadone were required to attend a clinic daily to obtain medication following regulations regarding methadone dispensing and thus were more regularly in contact with the clinic personnel, which likely enhanced treatment engagement. Conversely, buprenorphine patients were not required to attend the clinic daily, given the nature of buprenorphine self administration without supervision. Another explanation is that methadone treatment was more accessible to this group of individuals who were largely impoverished. At the end of the follow-up, more than 5 years after baseline, participants with BPD had significantly more heroin and other opioid use in the past 30-days. This finding further supports the claim that some patients with OUD and comorbid psychiatric disorders may have higher rates of opioid use due to their greater psychiatric symptom severity . Consistent with previous studies , patients with OUD and comorbid psychiatric disorders reported poor functioning across multiple domains. Numerous significant group differences in components of ASI composite scores, BSI scale scores, SF-36 physical and mental component summary scores indicated higher problem severity across multiple problem areas in patients with OUD and different comorbid psychiatric disorders. Based on severity, participants with BPD had the poorest functional outcomes. Psychiatric treatment for patients with OUD can be combined with OUD pharmacotherapy and self-help groups. Since the 1970s and 80 s,cannabis grow set up a number of studies demonstrated that psychotherapy can be used effectively with individuals with SUDs . To reduce healthcare costs, however, support was reduced for these psychiatrically focused treatments.

These findings point to an unmet need for medication and psychosocial therapies for patients with OUD and psychiatric comorbidity. This study has several limitations. First, we assessed the type of psychiatric disorders at follow-up Visit 2. Although the question about the history of psychiatric disorders was included at treatment entry , the pre-existing diagnosis patterns according to objective measures and the temporal relationship between OUD and psychiatric disorders are unknown. Second, attrition analysis showed that female participants had a higher follow-up rate, which might be over represented in this study, but the rates of treatment engagement in the present study were similar to an 11-year follow-up of the Australian Treatment Outcome Study . Third, results are based on a sample of individuals treated for OUD in community-based, federally regulated OTP clinics, and thus findings may have limited applicability to patients treated in primary care clinics or other settings. Fourth, we did not include sedative use , which is common in individuals with OUD and did not collect information about participants’ treatment for mental health disorders, both of which could have impacted treatment outcomes. Finally, substance use and treatment participation were self-reported and may be subject to recall bias. As for study strengths, this secondary analysis was conducted with a relatively large sample derived from a multi-site clinical trial and a follow-up prospective longitudinal study with a long duration to assess associations between OUD pharmacotherapy treatment outcomes and co-occurring psychiatric conditions. Our study sample has a similar rate of psychiatric disorders as has been reported in nationally representative data . Electronic -cigarettes are drug delivery devices primarily used for the inhalation of nicotine and marijuana, in the form of tetracannabinoids . The modern e-cigarette was invented in 2003, entered the global market in 2007, and has rapidly become popular across the world. There are many types of e-cigarettes, from cig-a-likes to vape pens and box Mods to pod-devices, but they all involve heating and aerosolization of e-liquids .

The base ingredients of e-liquids, nicotine, propylene glycol and glycerin, have an unappealing flavor on their own such that chemical flavorants are added to >99% of e-liquids to increase the appeal to users. Use patterns of electronic -cigarettes and vaping devices differ greatly across age groups. Adults most commonly pick up vaping in the setting of conventional cigarette smoking, either adding it into their smoking practice or switching to e-cigarettes as a means to stop smoking. While 3.2% of all adults use ecigarettes, the rates are much higher in young adults 18-24 years-old, of whom 7.6% vape, and higher still in high school students, of whom 27.5% have used a vaping device within the past month. Sadly, middle school students as young as age 11 also have high rates of e-cigarettes use. While adult e-cigarette users are most often active smokers or ex-smokers, 44.3% of adolescents and young adults were never smokers prior to e-cigarette use. Of concern, it has been shown that e-cigarette use in never smokers leads to higher initiation of cigarette smoking, up to four-fold. A great deal of research to date has been focused on comparing e-cigarette use to cigarette smoking to assess the potential benefit of switching from smoking to vaping as a form of harm reduction, while less focus has been on the health effects of vaping in non-smokers, for whom the rates of vaping continue to rise, particularly in the youth. E-cigarettes have been marketed as a form of harm reduction from traditional cigarette smoking, but neither the safety nor the efficacy of these devices has been established, and little is known about the short and long term pulmonary and systemic health effects. This review focuses on the known and unknown toxins contained in e-cigarette aerosols, lung diseases induced by vaping, and the predicted long-term consequences of e-cigarette use. Particular attention is given to the e-cigarette or vaping product use-associated lung injury epidemic that began in 2019 and is ongoing. E-cigarettes are devices composed of a power source, heating element, and liquid reservoir that heat and aerosolize e-liquids to make vapor that is inhaled into the lungs in a process known as vaping.

E-liquids are most often composed of 1) Addictive substances such as nicotine and/or tetrahydrocannabinol , 2) Flavorings, and 3) Solvents . There are many types of vaping devices, but the most frequently used include pod vapes, box mods, and vape pens . The pod devices were widely popularized by the company Juul, which developed its sleek device to look like a flash-drive that quickly became the most profitable e-cigarette by the end of 2017. The e-liquids in Juul pods contain high concentrations of the more rapidly absorbed nicotinic salts complexed with benzoic acid, compared to free based nicotine,outdoor cannabis grow thus increasing the addictive potential and toxicity. Given that nicotine exposure influences long-term molecular, biochemical, and functional changes in the adolescent brain, it is not surprising that teens who vape are at increased risk of subsequent use of traditional cigarettes, marijuana, opioids, and other illicit drugs with addictive potential. THC also induces alterations in reward networks in the adolescent brain, which increases risk for future drug use, and regular cannabis users of any age have poorer neurocognitive functioning and functional brain alterations relative to nonusers. Although vaping devices are not an approved nicotine replacement therapy, the Federal Drug Administration has allowed e-cigarette manufacturers to design e-liquids using components that have been ”generally recognized as safe” . Compounds that have GRAS status are only assessed as safe to ingest via the gastrointestinal tract, or put on the skin. Thus, the vast majority of compounds with this designation have not been tested for safety via the inhalation route. There are thousands of different flavoring ingredients used, and thermal decomposition of propylene glycol, glycerol, and flavoring agents result in the production of toxic aldehydes at levels that exceed occupational safety standards. Chemical flavorings such as diacetyl and 2,3-pentanedione, present in many e-liquids, have been found to induce transcriptomic changes that disrupt cilia function in human airway epithelium, impairing mucociliary clearance. Cumulative exposure to diacetyl is well known to be associated with the development of the irreversible airway fibrosing disorder bronchiolitis obliterans , with the term “popcorn lung” used when referring to BO described in microwave popcorn factory workers. Other toxins found in e-cigarette vapor with inhalant and systemic toxicities including terpenes, acrylonitrile, formaldehyde, crotonaldehyde, propylene oxide, acrylamide, and heavy metals. None of these products are currently regulated, but there is even greater cause for concern regarding the inhalant toxicity for the components in “black market” or counterfeit e-liquids and devices as well modified devices. Finally, microbial toxins may contaminate vaping devices even prior to use.

One study tested the leading U.S. pod vape and found that 81% of devices contained B-D-glucan, a fungal cell wall marker, and 23% contained endotoxin, found in the outer wall of gram-negative bacteria. Both of these microbial contaminants are associated with asthma and hypersensitivity pneumonitis. E-cigarettes have been marketed as a form of harm reduction from traditional cigarette smoking, but neither the safety nor the efficacy of these devices has been established, and little is known about the short and long term pulmonary and systemic health effects. There have been increasing reports in the literature of negative pulmonary effects with the recent epidemic of e-cigarette or vaping product use-associated lung injury being the most immediately concerning. Reports of the development of chronic respiratory symptoms, increased asthma morbidity, and the development of diffuse lung disease in both adolescents and adults highlight significant pulmonary toxicity and compel further research. E-cigarette users are more likely to report chronic respiratory symptoms and conditions in both adolescents and adults. In a large study from Hong Kong of 45,000 adolescents who vaped in the previous month reported chronic cough or phlegm production with increased odds. In a smaller study of 2,000 high school students in Southern California, past and current vaping was associated with a nearly two-fold increase in the risk of chronic bronchitis symptoms. In a longitudinal analysis of adults in the Population Assessment of Tobacco and Health Waves 1, 2, and 3 with data collected from 2013-2016, a significant association between former and current use at Wave 1 and incident respiratory disease at Waves 2 or 3 was demonstrated, controlling for combustible tobacco smoking and other demographic, and clinical variables. In this study, dual use of cigarettes and ecigarettes had increased odds of developing respiratory disease of 3.30 compared with a never smoker/vaper. Among asthmatic patients, primary e-cigarette use and secondhand exposure confers increased morbidity. Interestingly, vaping is more popular among asthmatic teenagers as compared to their non-asthmatic peers. The reason for this observation is unclear, but may be related to the commonly held belief that vaping is safer than smoking cigarettes. Among adult never cigarette smokers, current e-cigarette use was associated with 39% higher odds of self-reported asthma compared to never e-cigarette users. In South Korea, vaping in high school students was associated with increased odds of being diagnosed with asthma and more missed days of school secondary to asthma. The Florida Youth Tobacco Survey showed that past-30-day e-cigarette use was associated with having an asthma attack in the past 12 months among high school participants with asthma. This survey later revealed that 33% of 11- to 17-year-olds with asthma had secondhand ecigarette exposure, and this exposure was associated with increased risk of asthma exacerbation. There are also case reports of two adolescent asthmatic e-cigarette users who presented with life-threatening status asthmaticus requiring VV-ECMO. This may indicate increased risk for more severe exacerbations among asthmatic teens who are vaping. In addition, EVALI cases from both the Illinois and Wisconsin cohort and Rochester cohort reported higher- than expected rate of EVALI in asthmatics.There have been multiple case reports of different types of severe and life-threatening diffuse lung disease in patients using vaping products including hypersensitivity pneumonitis, eosinophilic pneumonitis, diffuse alveolar hemorrhage, lipoid pneumonia and bronchiolitis. These case reports demonstrate that there is undeniable harm associated with vaping even before decades of use. Among patients with EVALI, pathology results included findings consistent with acute lung injury including acute fibrinous pneumonitis, diffuse alveolar damage, and organizing pneumonia. Taken together these pathological findings indicate that severe lung injury in multiple different patterns can occur in the setting of vaping. Although the mechanism of injury in these patients is currently unknown, it is presumed that there are both product and host related factors contributing to lung injury. Given that multiple components of vaping products can cause pulmonary toxicity, it is unlikely that there is only one chemical component leading to these diverse patterns of toxic lung injury. Beyond the scope of this review are systemic toxicities as well as trauma due to explosions, thermal injuries and acute intoxications including ingestion of e-liquids.

Although synthetic cannabinoids have been associated with sudden death in the USA and abroad, the exact physiological mechanisms for causation, or initiators to contributing factors, remain unclear. In Case 1, a possible anaphylactic etiology was ruled out by the forensic pathologist. Sudden onset cardiac dysrhythmias or seizure suggest plausible mechanisms. In contrast, Case 2 presented as a protracted, rapidly deteriorating clinical event over 24 h culminating in acute hepatic failure. Another possible biochemical etiology to these unfolding sequelae mayreside in a yet undetermined or unidentified metabolic intermediate of 5F-PB-22. It is noteworthy that a distinct feature of the quinolinyl carboxylate synthetic cannabinoids is an ester linkage. The earlier waves of compounds, such as JWH-018, AM-2201 and XLR-11, contained a ketone linkage between the indole moiety and the naphthoyl or tetramethylcyclopropyl groups. The ester bond may be susceptible to in vivo hydrolysis reactions catalyzed by carboxylesterase enzymes. This mechanism could cause the accumulation of a metabolite that is perhaps analogous to the toxic mechanism peculiar to paracetamol and the accumulation of a quinone metabolite and its reactions with hepatocellular proteins and nucleic acids. Because of the ease of degradation by in vivo hydrolysis reactions, it may be prudent to investigate the possible accumulation of probable metabolites in the blood. Clearly, further investigation is required with respect to the pharmacokinetics of 5F-PB-22 and other synthetic cannabinoids, their role in human toxidromes and their relevance to detection in postmortem casework. Important point sources for this information will continue to include the US National Network of Poison Information Centers,cannabis grow tray reporting emergency departments and urgent care centers and medical examiner/coroner systems with their attendant toxicology laboratories.

Alcohol use and binge drinking in youth aged 12 to 21 are frequent causes of accidents and injuries, preventable death, disease and psychosocial problems. Though past-month binge drinking1 and alcohol use among adolescents and young adults in the United States have declined over the past decade, rates remain high: 23 % report current alcohol use and 14 % binge drinking. Over the past several decades, there have been extensive efforts to address alcohol use among young people. Some interventions have focused on environmental factors while others have been individual or group level interventions aimed at improving knowledge and attitudes, and reducing alcohol use. These have been primarily face-to-face interventions delivered in structured school or community-based settings. The application of theory is widely recognized as a crucial component of behavior change interventions. Theories help explain the pathways that lead to or predict behavior and in doing so, provide guidance on how to influence or change behavior. Interventions, that clearly articulate their use of theories, can contribute to a greater understanding of not just what interventions work, but why they work. While the interventions targeting alcohol use among youth have resulted in mixed findings, this vast body of work has contributed to the evidence base for what constitutes effective interventions. Interventions that are grounded in established theories of behavior change, and include approaches that address social norms, build self-efficacy and enhance skills to resist pressure to use alcohol, have been found to be more effective than those lacking a theoretical framework. As the field of preventing/reducing alcohol use among adolescents and young adults is evolving, there has been growing attention to the development and use of computer-based modes of intervention delivery. Computer-based interventions have a number of advantages over traditional face-to-face interventions. They are more likely to be implemented with fidelity because they do not rely on the skills, motivation, or time of the facilitator; and they provide a standardized approach to delivering the intervention content. In addition, recent technology innovations enable CBIs to be interactive, provide individually tailored messages and simulate experiences where adolescents can learn and practice skills in convenient and private settings.

Additionally, computers have become widely accessible and are especially popular among adolescents and young adults. CBIs provide a promising approach to addressing alcohol use among adolescents and young adults. Over the last decade, there have been five literature reviews that have examined the nascent field of digital interventions for alcohol use prevention targeting adolescents and young adults. Overall, many of the CBIs have been shown to improve knowledge, attitudes, and reduce alcohol use in the short-term. Three of the five literature reviews examined interventions for college students. One review found that CBIs were more effective than no treatment and assessment-only controls, and approximately equivalent to various non-computerized interventions. Another review found that CBIs, when compared to non-CBIs, were more likely to reduce alcohol use. The third review found that CBIs reduced short term alcohol use compared to assessment-only controls, but not compared to face-to-face interventions. In addition to the reviews focused on alcohol use among young adults, there were two reviews of CBIs targeting younger adolescents. One demonstrated that CBIs delivered in middle or secondary schools effectively reduced alcohol, cannabis and/or tobacco use. The other review was a metanalysis focused on computer games to prevent alcohol and drug use among adolescents and concluded that the games improved knowledge, but it did not find sufficient evidence that these games changed substance use attitudes or behaviors. While these reviews suggest that CBIs have the potential to be efficacious, the mechanisms that contribute to improvements in attitudes and behaviors are not well understood. Use of a theoretical framework helps to explain the mechanisms of change by informing the causal pathways between specific intervention components and behavioral outcomes. Understanding these mechanisms improves our understanding of how and why a particular intervention works. There has been little attention as to how theoretical frameworks have informed the development of CBIs focused on alcohol use among adolescents and young adults.

Only two of the five aforementioned literature reviews covering CBIs for alcohol use in youth examined the underlying theoretical basis of the CBIs. In both of these reviews, the names of the theory and/or specific theoretical constructs were mentioned; however, there was little examination of how the theories were applied to the CBIs. In addition to the reviews focused specifically on adolescent and young adult substance use,vertical grow systems for sale there was an additional systematic review that examined the relationship between the use of theory and the effect sizes of internet-based interventions. This study found that extensive use of theory was associated with greater increases in the effect size of behavioral outcomes. They also found that interventions that utilized multiple techniques to change behavior change tended to have larger effect sizes compared to those using fewer techniques. This review builds on prior work demonstrating that health interventions grounded in established theory are more effective than those with no theoretical basis. However, this review did not exclusively focus on alcohol use or adolescents specifically. It is therefore important to build upon this knowledge base and focus on the application of theory in CBIs to address adolescent/young adult alcohol use. The primary goal of this study is to conduct a review of how theory is integrated into CBIs that target alcohol use among adolescents and young adults. Specifically, this study examines which CBIs are guided by a theoretical framework, the extent to which theory is applied in the CBIs and what if any measures associated with the theoretical framework are included in the CBI’s evaluation. A secondary goal is to provide an update of CBIs addressing alcohol use among youth in order to expand our understanding of their effectiveness.To be included in this review, the main component of the intervention was required to be delivered via computer, tablet or smartphone. Interventions could include a video game, computer program, or online module. In addition, the intervention needed to target alcohol use among adolescents and young adults between the ages of 12 to 21 years. While adolescence covers a wide range, we chose this age range because there is general consensus that it has begun by age 12, and we included youth up to age 21 since that is the legal drinking age in the U.S. Studies whose participants’ had a mean age between 12 and 21 years were included even when individual study’s participants’ ages extended outside this age range. Interventions intended to treat a substance use disorder were excluded. Non-English language articles, research protocols, and intervention studies that did not report outcomes were also excluded from analyses.Once eligible studies were identified, the characteristics of the intervention, the context of the intervention, the population targeted, intervention dosage, study author, year and outcomes were entered into a spreadsheet for analyses. Duplicate articles were deleted and journal articles which discussed the same intervention were grouped together. When there was more than one unique article for any given CBI, the CBI was counted only once. In some cases, a given CBI existed in several editions, was modified, or was applied to a different study population. These variations of the CBI were grouped together.

Painter et al.’s classification system was used to categorize the use of theory in each of the CBIs. Consistent with this system, first a CBI was examined to see if an established, broad theory was mentioned in any of the corresponding articles for a given CBI. If so, the CBI was classified as “mentioned”. Second, articles were reviewed to see if they provided any information about how the CBI used theory to inform the intervention. If any of the articles associated with a given CBI provided any information about the use of theory, the CBI was classified as “applied. For our third category, we used “measured” to classify CBIs if any associated article included at least one specific measure of a construct within the theoretical framework. This third category is a slight departure from Painter’s typology which classifies interventions as “tested” if over half of the constructs in the theory are measured in the evaluation of the intervention. We opted for “measured” because testing theories is a complex process and not a common practice of CBIs. We did not use Painter’s 4th category, “building or creating theory” because this was not applicable for any of these interventions. For all articles reporting on effects of the intervention on alcohol use, attitudes, or knowledge on an included CBI, the effectiveness of the CBI on these outcomes was also examined. Two senior health research scientists , with advanced training in theories of behavior change, oversaw the classifications system and addressed questions about the application of a theory/theoretical constructs. The review was conducted by a trained research associate with a master’s degree in public health. A spread sheet was created that included each classification, a description of how the theory was applied, and a list of relevant constructs that were measured.The search strategy yielded a total of 600 unique articles published between 1999 and 2014, including 15 articles identified through hand searches and reviews of previous literature reviews. Of these, 500 were excluded because they did not meet the study inclusion criteria. The final sample consisted of 100 articles of 42 unique CBIs. There were more articles than interventions because multiple articles were published on any one CBI intervention. See Fig. 1 for a more full explanation of the articles excluded and yielded during the search process. The list of the 42 interventions and corresponding articles associated with the intervention are provided in Tables 1 and 2. Of the interventions reviewed for this study, 50 % were not included in previous review articles. Of the 42 CBIs in this study, 33 were delivered in school settings and the remaining CBIs were administered in home or in clinic settings.Table 1 provides a list of the CBIs and how theory was used according to the classifications of “mentioned”, “applied” or “measured”. In addition, if the theory was applied to the intervention, a brief description of its application is provided. Similarly if it was classified as “measured” the measure of the theoretical construct was also listed. The CBIs in Table 1 all indicated use of a broad theoretical framework. Broad theories specify the relationship between a number of constructs and associated variables that explain or predict behaviors. Broad theories of behavior change take into account a number of complex contextual factors and inter-related sets of constructs that influence behaviors. CBIs that did not mention use of a broad theoretical framework are listed in Table 2.