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Abstract This study investigates the new particle formation (NPF) events at an urban location in the Eastern Mediterranean. Particle size distribution, particulate chemical composition, and gaseous pollutants were monitored in Rehovot, Israel (31°53″N 34°48″E) during two campaigns: from April 29 to 3 May 2021 (Campaign 1) and from May 3 to 11 May 2023 (Campaign 2), coinciding with an intensive bonfire burning festival. The organic aerosols (OA) source apportionment identified two major factors—Hydrocarbon‐like OA and Biomass‐burning OA—as well as two secondary factors—MO‐OOA (more oxidized‐oxygenated OA) and LO‐OOA (low oxidized oxygenated OA). NPF events were frequently observed during the day (mostly well‐defined nucleation events) and at night (burst of ultrafine mode particles without any discernible growth). A condensation sink value of (9.4 ± 4.0) × 10⁻³ s⁻¹during Campaign 1 and (14.2 ± 6.0) × 10⁻³ s⁻¹during Campaign 2 was obtained. The daytime events were associated with enhanced sulfuric acid proxy concentrations of (2–12) × 10⁶molecules cm⁻³, suggesting the role of gas‐phase photochemistry in promoting NPF. A novel approach of hybrid positive matrix factorization analysis was used to deconvolve the chemical species responsible for the observed events. The results suggest the involvement of multiple components, including ammonium sulfate and MO‐OOA, in the nucleation; Nitrate, HOA and LO‐OOA participate in the subsequent particle growth for the daytime events. Nighttime events involve only semi‐volatile species (LO‐OOA, HOA and nitrate) along with ammonium sulfate. 

This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlatedGaussian errors due to confounding. First,we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a deconfounding adjustment to remove the confounding effects and a sequential procedure to estimate the causal order of variables. We implement DeFuSE via feedforward neural networks for scalable computation. Moreover, we establish the consistency of DeFuSE under an assumption called the strong causal minimality. In simulations, DeFuSE compares favorably against state of-the-art competitors that ignore confounding or nonlinearity. Finally, we demonstrate the utility and effectiveness of the proposed approach with an application to gene regulatory network analysis. The Python implementation is available at https://github.com/chunlinli/defuse. Supplementary materials for this article are available online. 

This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferential procedure. For causal discovery, we generalize the existing peeling algorithm to estimate the ancestral relations and candidate instruments in the presence of hidden confounders. Based on this, we propose a new procedure for instrumental variable estimation of each direct effect by separating it from any mediation effects. For inference, we develop a new likelihood ratio test of multiple causal effects that is able to account for the unmeasured confounders. Theoretically, we prove that the proposed method has desirable guarantees, including robustness to invalid instruments and uncertain interventions, estimation consistency, low-order polynomial time complexity, and validity of asymptotic inference. Numerically, GrIVET performs well and compares favorably against state-of-the-art competitors. Furthermore, we demonstrate the utility and effectiveness of the proposed method through an application inferring regulatory pathways from Alzheimer’s disease gene expression data. 

Atmospheric aging through diverse reaction pathways modifies redox potential and composition of organic aerosols, leading to varied dynamic behaviors of aerosols in the respiratory system and endpoint toxic results. 

Abstract Species functional traits can influence pathogen transmission processes, and consequently affect species' host status, pathogen diversity, and community‐level infection risk. We here investigated, for 143 European waterbird species, effects of functional traits on host status and pathogen diversity (subtype richness) for avian influenza virus at species level. We then explored the association between functional diversity and HPAI H5Nx occurrence at the community level for 2016/17 and 2021/22 epidemics in Europe. We found that both host status and subtype richness were shaped by several traits, such as diet guild and dispersal ability, and that the community‐weighted means of these traits were also correlated with community‐level risk of H5Nx occurrence. Moreover, functional divergence was negatively associated with H5Nx occurrence, indicating that functional diversity can reduce infection risk. Our findings highlight the value of integrating trait‐based ecology into the framework of diversity–disease relationship, and provide new insights for HPAI prediction and prevention.