Evaluation of ionic pollutants of acid fog and rain using a factor analysis and back trajectories

Chemometrics and Related Fields in Python

The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

Discrimination of Ionic Pollutants except Condensation Nuclei of Acid Fog Using an Ultrasonic Humidifier

Fog droplets in the atmosphere are first produced by the activation of cloud condensation nuclei (CCN), which are originally some ionic compound. Subsequently, the nuclei grow by vapor diffusion. Fog droplets are polluted through the activation process and successive diffusion growth and residence (post activation). We cannot distinguish the effects of the two pollution processes of natural fog water samples. We found that fog droplets can be produced artificially without CCN using an ultrasonic humidifier. Because the artificial fog droplets are not polluted by CCN, the movement of the fog droplets in natural air will take up some pollutants in the air. Consequently, the two pollution processes of fog (the activation of CCN and the post activation process) can be discriminated using data from field experiments. This sampling analytical method is extremely important for further research regarding fog, clouds and environmental chemistry.

Effect of Long-range Transport of Air Mass on the Ionic Components in Radiation Fog in Northern Japan

Water samples from radiation and upslope types of fog were collected at Takanosu Basin and Hachimantai mountain range of Akita Prefecture in northern Japan, respectively. The effect of the long-range transport of pollutants from the Asian Continent to the basin and the mountain range on the chemical characteristics of two types of fog was studied using chemical analysis data for the water samples as well as the back-trajectories of the air mass. In particular, non-sea-salt (nss)-SO4(2-) of radiation fog provided a much higher concentration than that of upslope fog, which suggests that nss-SO4(2-) might be transported mainly from the Asian Continent. The transport and uptake mechanism of pollutants in the radiation fog water were explained based on their accumulation in the basin due to anticyclonic subsidence and by the long-lasting fog behavior.

Spectrofluorometric resolution of closely overlapping drug mixtures using chemometrics methods

A modified parallel factors analysis (PARAFAC) algorithm with a penalty diagonalization error (PDE), newly proposed by the present authors, was utilized to simultaneously resolve drug mixtures of propranolol (PRO), dipyridamole (DIP) and amiloride (AMI) without any loss of sensitivity. The analyses were performed in aqueous solution. The experimental results demonstrated that the profiles of the spectra and the concentrations could be accurately resolved using the PDE algorithm with a high sensitivity and stable repeatability. That is to say, the closely overlapping problem of the spectra could be easily solved. Furthermore, simultaneous determinations of three kinds of tablets, which contain PRO, AMI and DIP, respectively, were successfully performed with satisfactory results.