The depletion of fossil fuel reserves and increased environmental concerns related to fossil fuel production and combustion has forced the global communities to search for renewable fuels. In this regard, microalgae-based biodiesel has been considered as one of the interesting alternatives. Biodiesel production from the cultivation of microalgae is eco-friendly and sustainable. Moreover, microalgae have several advantages over other bioenergy sources, including their good photosynthetic capacity and faster growth rates. The productivity of microalgae per unit land area is also significantly higher than that of terrestrial plants. The produced microalgae biomass is rich with high quality lipids, which can be converted into biodiesel by transesterification reactions. Generally, the transesterification reactions are carried out in the presence of a homogeneous or heterogeneous catalyst. The homogeneous catalysts have many disadvantages, including their single use, slow reaction rate and saponification issues due to the presence of fatty acids in the feedstock. The acidic nature of the homogeneous catalysts also causes equipment corrosion. On the other hand, the heterogeneous catalysts offer several advantages, including their reusability, higher reaction rate and selectivity, easy product/catalyst separation and low cost. Due to these facts, the development of solid phase transesterification catalysts have been receiving growing interest. The present review is focused on the use of heterogeneous catalysts for biodiesel production from microalgal oil as a reliable feedstock with a comparison to other available feedstocks. It also highlights optimal reaction conditions for maximum biodiesel yields, reusability of the solid catalysts, cost, and environmental impact. The superior lipid content of microalgae and the efficient concurrent esterification and transesterification of the solid acid−base catalysts can offer new advancements in biodiesel production.

Background: Pure water is an absolutely necessary component of the earth not only for life but also for sustainable socio-economic development of today's civilization. The aim of this study was to analysis the quality of water resources and to investigate the influences of mining activities on water quality around the Maddhapara Granite Mining area, Dinajpur, Bangladesh.Result: 31 samples from surface and groundwater were collected from this selected area to assess their hydrochemistry, suitability, and their possible sources of contamination. Consequently, in the case of physico-chemical analysis, the 24 parameters such as P H , Electrical Conductivity, TDS, TH, Caetc. were analyzed in laboratory. Multivariate statistical methods were adopted for determining the water quality and their sources of contamination. The Gibbs ratio plot suggests that most of the samples fall in the rock dominance fields and some are in the precipitation dominance field. Cluster analysis confirms that three main groups of water samples where cluster I includes 70.97% of water samples, cluster II and cluster III includes rest 22.58 and 6.45% of the water samples respectively. Factor Analysis/Principal Component Analysis (FA/PCA) illustrates five factors extracted which explain 75.89% of the total variance. Conclusion:The collective results of multivariate analysis and Water Quality Index implies that most of the areas around the mining area are dominated by the good to excellent quality water for different purposes. In addition, the results of this research will then be helpful to estimate the major sources of contamination in different areas within the framework of activities intending to improve the quality of water.

Injecting drug users (IDU) in Bangladesh are at the early stages of an HIV epidemic. To understand the dynamics of the HIV epidemic, male IDU (n = 561) were recruited from the needle/syringe exchange program in Dhaka in 2002, who underwent a risk-behavior survey and were tested for HIV, syphilis, hepatitis C, and hepatitis B. Correlates of HIV infection were determined by conducting bivariate and multiple regression analyses. The median age of the IDU was 35 years, 39.6% had no formal education, approximately half were married and/or living with their regular sex partner and 26% were currently homeless. The median age at first injection was 29 years. HIV was detected in 5.9% of the IDU and homelessness was the only factor independently associated with HIV (OR = 5.5). Urgent measures must be undertaken to prevent escalation of the HIV epidemic. The study's limitations are noted.

This study investigates the use of microalgae as a biosorbent to eliminate heavy metals ions from wastewater. The Chlorella kessleri microalgae species was employed to biosorb heavy metals from synthetic wastewater specimens. FTIR, and SEM/XRD analyses were utilized to characterize the microalgal biomass (the adsorbent). The experiments were conducted with several process parameters, including initial solution pH, temperature, and microalgae biomass dose. In order to secure the best experimental conditions, the optimum parameters were estimated using an integrated response surface methodology (RSM), desirability function (DF), and crow search algorithm (CSA) modeling approach. A maximum lead(II) removal efficiency of 99.54% was identified by the RSM–DF platform with the following optimal set of parameters: pH of 6.34, temperature of 27.71 °C, and biomass dosage of 1.5 g L−1. The hybrid RSM–CSA approach provided a globally optimal solution that was similar to the results obtained by the RSM–DF approach. The consistency of the model-predicted optimum conditions was confirmed by conducting experiments under those conditions. It was found that the experimental removal efficiency (97.1%) under optimum conditions was very close (less than a 5% error) to the model-predicted value. The lead(II) biosorption process was better demonstrated by the pseudo-second order kinetic model. Finally, simultaneous removal of metals from wastewater samples containing a mixture of multiple heavy metals was investigated. The removal efficiency of each heavy metal was found to be in the following order: Pb(II) > Co(II) > Cu(II) > Cd(II) > Cr(II).