High Time-Resolution Monitoring of Volatile Organic Compounds During Multi-Surface Disinfection Activities in Buildings with PTR-TOF-MS

Increased usage of chemical disinfectants during the COVID-19 pandemic may impact the chemical composition of indoor air in residential and commercial buildings. This study characterized gas-phase concentrations of volatile organic compounds (VOCs) during multi-surface disinfection activities in a tiny house research facility. This unique facility provided a controlled, yet realistic environment for simulating whole-building disinfection events. VOCs were measured in real-time (1 Hz) in the bulk air of the tiny house with a proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS). In addition, particle number (PN) size distributions were measured with a high-resolution electrical low-pressure impactor (HR-ELPI+). PTR-TOF-MS measurements demonstrate that chemical disinfectant spray products applied to multiple surfaces can substantially increase indoor VOC concentrations. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Deep Learning Based NLP Embedding Approach for Biosequence Classification

Biological sequence analysis involves the study of structural characteristics and chemical composition of a sequence. From a computational perspective, the goal is to represent sequences using vectors which bring out the essential features of the virus and enable efficient classification. Methods such as one-hot encoding, Word2Vec models, etc. have been explored for embedding sequences into the Euclidean plane. But these methods either fail to capture similarity information between k-mers or face the challenge of handling Out-of-Vocabulary (OOV) k-mers. In order to overcome these challenges, in this paper we aim explore the possibility of embedding Biosequences of MERS, SARS and SARS-CoV-2 using Global Vectors (GloVe) model and FastText n-gram representation. We conduct an extensive study to evaluate their performance using classical Machine Learning algorithms and Deep Learning methods. We compare our results with dna2vec, which is an existing Word2Vec approach. Experimental results show that FastText n-gram based sequence embeddings enable deeper insights into understanding the composition of each virus and thus give a classification accuracy close to 1. We also provide a study regarding the patterns in the viruses and support our results using various visualization techniques. © 2022, Springer Nature Switzerland AG.

Heavy haze pollution during the COVID-19 lockdown in the Beijing-Tianjin-Hebei region, China.

To investigate the characteristics of particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) and its chemical compositions in the Beijing-Tianjin-Hebei (BTH) region of China during the novel coronavirus disease (COVID-19) lockdown, the ground-based data of PM2.5, trace gases, water-soluble inorganic ions, and organic and elemental carbon were analyzed in three typical cities (Beijing, Tianjin, and Baoding) in the BTH region of China from 5-15 February 2020. The PM2.5 source apportionment was established by combining the weather research and forecasting model and comprehensive air quality model with extensions (WRF-CAMx). The results showed that the maximum daily PM2.5 concentration reached the heavy pollution level (>150 µg/m3) in the above three cities. The sum concentration of SO42-, NO3- and NH4+ played a dominant position in PM2.5 chemical compositions of Beijing, Tianjin, and Baoding; secondary transformation of gaseous pollutants contributed significantly to PM2.5 generation, and the secondary transformation was enhanced as the increased PM2.5 concentrations. The results of WRF-CAMx showed obviously inter-transport of PM2.5 in the BTH region; the contribution of transportation source decreased significantly than previous reports in Beijing, Tianjin, and Baoding during the COVID-19 lockdown; but the contribution of industrial and residential emission sources increased significantly with the increase of PM2.5 concentration, and industry emission sources contributed the most to PM2.5 concentrations. Therefore, control policies should be devoted to reducing industrial emissions and regional joint control strategies to mitigate haze pollution.

Characteristics and source apportionment of PM 2.5 under the dual influence of the Spring Festival and the COVID-19 pandemic in Yuncheng city

Based on the online and membrane sampling data of Yuncheng from January 1st to February 12th, 2020, the formation mechanism of haze under the dual influence of Spring Festival and COVID-19 (Corona Virus Disease) was analyzed. Atmospheric capacity, chemical composition, secondary transformation, source apportionment, backward trajectory, pollution space and enterprise distribution were studied. Low wind speed, high humidity and small atmospheric capacity inhibited the diffusion of air pollutants. Four severe pollution processes occurred during the period, and the pollution degree was the highest around the Spring Festival. In light, medium and heavy pollution periods, the proportion of SNA (SO 4 2 ???, NO 3 ??? and NH 4 + ) was 59.6%, 56.0% and 54.9%, respectively, which was the largest components of PM 2.5 ;the [NO 3 ???]/[SO 4 2 ???] ratio was 2.1, 1.5 and 1.7, respectively, indicating that coal source had a great influence;the changes of NOR (nitrogen oxidation ratio, 0.44, 0.45, 0.61) and SOR (sulphur oxidation ratio, 0.40, 0.49, 0.65) indicated the accumulation of secondary aerosols with increasing pollution. The coal combustion, motor vehicle, secondary inorganic sources and industrial sources contributed 36.8%, 26.59%, 11.84% and 8.02% to PM 2.5 masses, respectively. Backward trajectory showed that the influence from the east was greater during the Spring Festival, and the pollutants from the eastern air mass were higher, which would aggravate the pollution. Meteorological and Spring Festival had a great impact on heavy pollution weather. Although some work could not operate due to the impact of the COVID-19 epidemic, the emission of pollutants did not reduce much. ?? 2022 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Exploring Machine Learning Pipelines for Raman Spectral Classification of COVID-19 Samples

Raman Spectroscopy can analyze and identify the chemical compositions of samples. This study aims to develop a computational method based on machine learning algorithms to classify Raman spectra of serum samples from COVID-19 infected and non-infected human subjects. The method can potentially serve as a tool for rapid and accurate classification of COVID-19 versus non-COVID-19 patients and toward a direction for biomarker discoveries in research. Different machine learning classifiers were compared using pipelines with different dimensionality reduction and scaler techniques. The performance of each pipeline was investigated by varying the associate parameters. Assessment of dimensionality reduction application suggests that the pipelines generally performed better when the number of components does not exceed 50. The LightGBM model with ICA and MMScaler applied, yielded the highest test accuracy of 98.38% for pipelines with dimensionality reduction while the SVM model with MMScaler applied yielded the highest test accuracy of 96.77% for pipelines without dimensionality reduction. This study shows the effectiveness of Raman spectroscopy to classify COVID-19-induced characteristics in serum samples. © 2022 IEEE.