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Sea salt aerosols contribute significantly to the mass loading of ambient aerosol, which may serve as cloud condensation nuclei and can contribute to light scattering in the atmosphere. Two major chemical components commonly found in sea salts are ammonium sulfate (AS) and sodium chloride (NaCl). It has been shown that alkylamines, derivatives of ammonia, can react with ammonium salts in the particle-phase to displace ammonia and likely change the particle properties. This study investigated the effects of atmospheric alkylamines on the composition and properties of sea salt aerosols using a chemical system of methylamine (MA, as a proxy of alkylamines), AS and NaCl (as a proxy of sea salt aerosol). The concentrations of ammonia and MA in aqueous/gas phases at the thermodynamic equilibrium were determined using the Extended Aerosols and Inorganics Model (E-AIM) under varying initial inputs, along with the deliquescence relative humidity (DRH) and the corresponding particle water content. Our findings indicated a notable negative relationship between MA concentration and the DRH for both AS and NaCl while the effect of MA on NaCl is smaller than that on AS. The salt of MA in the particle phase may absorb water vapor and may lead to the displacement reaction between AS and NaCl due to the low solubility of sodium sulfate. The acidity in the particle phase also played a significant role in affecting the DRH of sea salt aerosols. Since both sea salt aerosol and alkylamines are emitted into the atmosphere from the ocean in large quantities, our study suggested the potential impact of alkylamines on the environment and the climate via the modification of sea salt aerosol properties. 

Recent research in atmospheric chemistry suggested that gaseous amines may rapidly react with the acidic components in the aerosol to be incorporated in the particle phase. However, laboratory experiments suggested that these heterogeneous processes may be sensitive to the reaction conditions, such as relative humidity (RH), the initial aerosol acidity and the initial concentration of gaseous ammonia which is ubiquitous in the atmosphere. We studied the heterogenous reactions between several amines and ammonium sulfate using a series of thermodynamic simulations under varying initial conditions, including RH, particle-phase acidity and gaseous amine and ammonia concentrations. Several distinctively different trends in the particle-phase ammonium, amines and water content were observed, depending significantly on the particle-phase acidity and the initial amine to ammonia mole ratio. One notable observation was that alkylamines may facilitate the water uptake of ammonium sulfate even in the presence of 1000 times more ammonia gas. Such change in aerosol water content may alter the surface tension, uptake coefficient and could formation properties of aerosol and influence the radiative forcing of the particles. 

Data transformations (e.g. rotations, reflections,and cropping) play an important role in self supervised learning. Typically, images are transformed into different views, and neural networks trained on tasks involving these views produce useful feature representations for downstream tasks, including anomaly detection. However, for anomaly detection beyond image data, it is often unclear which transformations to use. Here we present a simple end-to-end procedure for anomaly detection with learnable transformations. The key idea is to embed the transformed data into a semantic space such that the transformed data still resemble their untransformed form, while different transformations are easily distinguishable. Extensive experiments on time series show that our proposed method outperforms existing approaches in the one-vs.-rest setting and is competitive in the more challenging n-vs.-rest anomaly detection task. On medical and cyber-security tabular data, our method learns domain-specific transformations and detects anomalies more accurately than previous work.