ABSTRACT
This study aims to identify the effect of seasonal land surface temperature variation on the COVID-19 infection rate. The study area of this research is Bangladesh and its 8 divisions. The Google Earth Engine (GEE) platform has been used to extract the land surface temperature (LST) values from MODIS satellite imagery from May 2020 to July 2021. The per-day new COVID-19 cases data has also been collected for the same date range. Descriptive and statistical results show that after experiencing a high LST season, the new COVID-19 cases rise. On the other hand, the COVID-19 infection rate decreases when the LST falls in the winter. Also, rapid ups and downs in LST cause a high number of new cases. Mobility, social interaction, and unexpected weather change may be the main factors behind this relationship between LST and COVID-19 infection rates.
ABSTRACT
Remediation of colored textile wastewaters is a matter of interest. In this study, 49 bacteria were isolated from the textile wastewater and tested for their ability to decolorize reactive yellow-2 (RY2) dye. The most efficient isolate, RN34, was identified through amplification, sequencing, and phylogenetic analysis of its 16S rDNA and was designated as Serratia sp. RN34. This bacterium was also found capable of decolorizing other related reactive azo-dyes, including reactive black-5, reactive red-120, and reactive orange-16 but at varying rates. The optimum pH for decolorization of RY2 by the strain RN34 was 7.5 using yeast extract as cosubstrate under static incubation at 30 °C. The strain RN34 also showed potential to decolorize RY2 in the presence of considerable amounts of hexavalent chromium and sodium chloride. A phytotoxicity study demonstrated relatively reduced toxicity of RY2 decolorized products on Vigna radiata plant as compared to the uninoculated RY2 solution.