Implementation of a geometrically informed and energetically constrained mesoscale eddy parameterization in an ocean circulation model

The global stratification and circulation of the ocean and their sensitivities to changes in forcing depend crucially on the representation of the mesoscale eddy field. Here, a geometrically informed and energetically constrained parameterization framework for mesoscale eddies --- termed GEOMETRIC --- is proposed and implemented in three-dimensional primitive equation channel and sector models. The GEOMETRIC framework closes mesoscale eddy fluxes according to the standard Gent--McWilliams scheme, but with the eddy transfer coefficient constrained by the depth-integrated eddy energy field, provided through a prognostic eddy energy budget evolving with the mean state. It is found that coarse resolution calculations employing GEOMETRIC broadly reproduce model sensitivities of the eddy permitting reference calculations in the emergent circumpolar transport, meridional overturning circulation profile and the depth-integrated eddy energy signature; in particular, eddy saturation emerges in the sector configuration. Some differences arise, attributed here to the simple prognostic eddy energy budget employed, to be improved upon in future investigations. The GEOMETRIC framework thus proposes a shift in paradigm, from a focus on how to close for eddy fluxes, to focusing on the representation of eddy energetics.Comment: 19 pages, 9 figures, submitted to Journal of Physical Oceanography; comments welcome. (Copyright statement: see section 7a of https://www.ametsoc.org/ams/index.cfm/publications/ethical-guidelines-and-ams-policies/ams-copyright-policy/

Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions

This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM2.5) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM2.5 concentrations during June 2015-May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM2.5 concentration was approximately 30mugm(-3) (geometric standard deviation: approximately 1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60mugm(-3)) during 2-5% of observation days. Average concentrations were approximately 25mugm(-3) higher during winter than during monsoon and approximately 8mugm(-3) higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM2.5 concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM2.5 in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations

Nonmedical Prescription Opioid Use in Childhood and Early Adolescence Predicts Transitions to Heroin Use in Young Adulthood: A National Study

Objectives: To examine the relationship between nonmedical use of prescription opioids and heroin initiation from childhood to young adulthood, and to test whether certain ages, racial/ethnic, and income groups were at higher risk for this transition. Study design: Among a nationally representative sample of US adolescents assessed in the 2004-2011 National Surveys on Drug Use and Health cross-sectional surveys (n = 223 534 respondents aged 12-21 years), discrete-time hazard models were used to estimate the age-specific hazards of heroin initiation associated with prior history of nonmedical use of prescription opioids. Interactions were estimated between prior history of nonmedical use of prescription opioids and age of nonmedical use of prescription opioid initiation, race/ethnicity, and income. Results: A prior history of nonmedical use of prescription opioids was strongly associated with heroin initiation (hazard ratio 13.12, 95% CI 10.73, 16.04). Those initiating nonmedical use of prescription opioids at ages 10-12 years had the highest risk of transitioning to heroin use; the association did not vary by race/ethnicity or income group. Conclusions: Prior use of nonmedical use of prescription opioids is a strong predictor of heroin use onset in adolescence and young adulthood, regardless of the user's race/ethnicity or income group. Primary prevention of nonmedical use of prescription opioids in late childhood may prevent the onset of more severe types of drug use such as heroin at later ages. Moreover, because the peak period of heroin initiation occurs at ages 17-18 years, secondary efforts to prevent heroin use may be most effective if they focus on young adolescents who already initiated nonmedical use of prescription opioids

Racial/ethnic differences in trends in heroin use and heroin-related risk behaviors among nonmedical prescription opioid users

Background: This study examines changing patterns of past-year heroin use and heroin-related risk behaviors among individuals with nonmedical use of prescription opioids (NMUPO) by racial/ethnic groups in the United States. Methods: We used data from the National Survey on Drug Use and Health (NSDUH) from 2002 to 2005 and 2008 to 2011, resulting in a total sample of N = 448,597. Results: Past-year heroin use increased among individuals with NMUPO and increases varied by frequency of past year NMUPO and race/ethnicity. Those with NMUPO in the 2008–2011 period had almost twice the odds of heroin use as those with NMUPO in the 2002–2005 period (OR = 1.89, 95%CI: 1.50, 2.39), with higher increases in non-Hispanic (NH) Whites and Hispanics. In 2008–2011, the risk of past year heroin use, ever injecting heroin, past-year heroin abuse or dependence, and the perception of availability of heroin increased as the frequency of NMUPO increased across respondents of all race/ethnicities. Conclusion: Individuals with NMUPO, particularly non-Hispanic Whites, are at high risk of heroin use and heroin-related risk behaviors. These results suggest that frequent nonmedical users of prescription opioids, regardless of race/ethnicity, should be the focus of novel public health efforts to prevent and mitigate the harms of heroin use

Evaluation of a Scalar Eddy Transport Coefficient Based on Geometric Constraints

A suite of idealized models is used to evaluate and compare several previously proposed scalings for the eddy transport coefficient in downgradient mesoscale eddy closures. Of special interest in this comparison is a scaling introduced as part of the eddy parameterization framework of Marshall et al. (2012), which is derived using the inherent geometry of the Eliassen–Palm eddy flux tensor. The primary advantage of using this coefficient in a downgradient closure is that all dimensional terms are explicitly specified and the only uncertainty is a nondimensional parameter, α, which is bounded by one in magnitude. In each model a set of passive tracers is initialized, whose flux statistics are used to invert for the eddy- induced tracer transport. Unlike previous work, where this technique has been employed to diagnose the tensor coefficient of a linear flux-gradient relationship, the idealization of these models allows the lateral eddy transport to be described by a scalar coefficient. The skill of the extant scalings is then measured by comparing their predicted values against the coefficients diagnosed using this method. The Marshall et al. (2012) scaling is shown to scale most closely with the diagnosed coefficients across all simulations. It is shown that the skill of this scaling is due to its functional dependence on the total eddy energy, and that this scaling provides an excellent match to the diagnosed fluxes even in the limit of constant α. Possible extensions to this work, including how to incorporate the resultant transport coefficient into the Gent and McWilliams parameterization, are discussed

Concentrations of criteria pollutants in the contiguous U.S., 1979 – 2015: Role of model parsimony in integrated empirical geographic regression

BACKGROUND: National- or regional-scale prediction models that estimate individual-level air pollution concentrations commonly include hundreds of geographic variables. However, these many variables may not be necessary and parsimonious approach including small numbers of variables may achieve sufficient prediction ability. This parsimonious approach can also be applied to most criteria pollutants. This approach will be powerful when generating publicly available datasets of model predictions that support research in environmental health and other fields. OBJECTIVES: We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants, for all years with regulatory monitoring data during 1979 – 2015; (2) explore the impact of model parsimony on model performance by comparing the model performance depending on the numbers or variables offered into a model; and (3) provide publicly available model predictions. METHODS: We compute annual-average concentrations from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979-2015. We also compute ~900 geographic characteristics at each location including measures of traffic, land use, and satellite-based estimates of air pollution and landcover. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of independent variables. For all pollutants and years, we compare three approaches for choosing variables to include in the model: (1) no variables (kriging only), (2) a limited number of variables chosen by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional randomly-selected and spatially-clustered test data. RESULTS: Models using 3 to 30 variables generally have the best performance across all pollutants and years (median R2 conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Using the best models mostly including 3-30 variables, we predicted annual-average concentrations of six criteria pollutants for all Census Blocks in the contiguous U.S. DISCUSSION: Our findings suggest that national prediction models can be built on only a small number (30 or fewer) of important variables and provide robust concentration estimates. Model estimates are freely available online

Predictors of Daily Mobility of Adults in Peri-Urban South India

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women's activity spaces were smaller and more circular than men's. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health

Development of land-use regression models for fine particles and black carbon in peri-urban South India

Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e.g., roads, industries, non-residential places) for a peri-urban area outside Hyderabad, India. As part of the CHAI (Cardiovascular Health effects of Air pollution in Telangana, India) project, concentrations of fine particulate matter (PM2.5) and black carbon were measured over two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2) mug/m(3) for PM2.5 and 2.7 (0.5) mug/m(3) for black carbon. The LUR model for annual black carbon explained 78% of total variance and included both local-scale (energy supply places) and regional-scale (roads) predictors. Explained variance was 58% for annual PM2.5 and the included predictors were only regional (urbanicity, vegetation). During leave-one-out cross-validation and cross-holdout validation, only the black carbon model showed consistent performance. The LUR model for black carbon explained a substantial proportion of the spatial variability that could not be captured by simpler interpolation technique (ordinary kriging). This is the first study to develop a LUR model for ambient concentrations of PM2.5 and black carbon in a non-urban area of LMICs, supporting the applicability of the LUR approach in such settings. Our results provide insights on the added value of manually-collected built-environment data to improve the performance of LUR models in settings with limited data availability. For both pollutants, LUR models predicted substantial within-village variability, an important feature for future epidemiological studies