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The article compares the Lagrangian Stochastic Particle Dispersion Model (LSPDM) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) as used in assessment of source identification from seven regions. In 2002, analysis was done in four 7-day summertime events and residence times in the modeling domain was computed using the inverse LSPDM and HYSPLIT-simulated backward trajectories. Between the two, LSPDM gave a higher overall correlation coefficient compared to HYSPLIT.

Critical loads (CLs) of atmospheric deposition for nitrogen (N) and sulfur (S) are used to support decision making related to air regulation and land management. Frequently, CLs are calculated using empirical methods, and the certainty of the results depends on accurate representation of underlying ecological processes. Machine learning (ML) models perform well in empirical modeling of processes with non-linear characteristics and significant variable interactions. We used bootstrap ensemble ML methods to develop CL estimates and assess uncertainties of CLs for the growth and survival of 108 tree species in the conterminous United States. We trained ML models to predict tree growth and survival and characterize the relationship between deposition and tree species response. Using four statistical methods, we quantified the uncertainty of CLs in 95 % confidence intervals (CI). At the lower bound of the CL uncertainty estimate, 80 % or more of tree species have been impacted by nitrogen deposition exceeding a CL for tree survival over >50 % of the species range, while at the upper bound the percentage is much lower (<20 % of tree species impacted across >60 % of the species range). Our analysis shows that bootstrap ensemble ML can be effectively used to quantify critical loads and their uncertainties. The range of the uncertainty we calculated is sufficiently large to warrant consideration in management and regulatory decision making with respect to atmospheric deposition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Analgesic trials pose unique scientific, ethical, and practical challenges in pediatrics. Participants in a scientific workshop sponsored by the US Food and Drug Administration developed consensus on aspects of pediatric analgesic clinical trial design. The standard parallel-placebo analgesic trial design commonly used for adults has ethical and practical difficulties in pediatrics, due to the likelihood of subjects experiencing pain for extended periods of time. Immediate-rescue designs using opioid-sparing, rather than pain scores, as a primary outcome measure have been successfully used in pediatric analgesic efficacy trials. These designs maintain some of the scientific benefits of blinding, with some ethical and practical advantages over traditional designs. Preferred outcome measures were recommended for each age group. Acute pain trials are feasible for children undergoing surgery. Pharmacodynamic responses to opioids, local anesthetics, acetaminophen, and nonsteroidal antiinflammatory drugs appear substantially mature by age 2 years. There is currently no clear evidence for analgesic efficacy of acetaminophen or nonsteroidal antiinflammatory drugs in neonates or infants younger than 3 months of age. Small sample designs, including cross-over trials and N of 1 trials, for particular pediatric chronic pain conditions and for studies of pain and irritability in pediatric palliative care should be considered. Pediatric analgesic trials can be improved by using innovative study designs and outcome measures specific for children. Multicenter consortia will help to facilitate adequately powered pediatric analgesic trials.