Molecular approaches for the detection and monitoring of microbial communities in bioaerosols: A review

Bioaerosols significantly affect atmospheric processes while they undergo long-range vertical and horizontal transport and influence atmospheric chemistry and physics and climate change. Accumulating evidence suggests that exposure to bioaerosols may cause adverse health effects, including severe disease. Studies of bioaerosols have primarily focused on their chemical composition and largely neglected their biological composition and the negative effects of biological composition on ecosystems and human health. Here, current molecular methods for the identification, quantification, and distribution of bioaerosol agents are reviewed. Modern developments in environmental microbiology technology would be favorable in elucidation of microbial temporal and spatial distribution in the atmosphere at high resolution. In addition, these provide additional supports for growing evidence that microbial diversity or composition in the bioaerosol is an indispensable environmental aspect linking with public health.     

Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability

For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.     

Decision-Tree-based data mining and rule induction for predicting and mapping soil bacterial diversity

Soilmicrobial ecology plays a significant role in global ecosystems. Nevertheless, methods of model prediction and mapping have yet to be established for soil microbial ecology. The present study was undertaken to develop an artificial-intelligence- and geographical information system (GIS)-integrated framework for predicting and mapping soil bacterial diversity using pre-existing environmental geospatial database information, and to further evaluate the applicability of soil bacterial diversity mapping for planning construction of eco-friendly roads. Using a stratified random sampling, soil bacterial diversity was measured in 196 soil samples in a forest area where construction of an eco-friendly road was planned. Model accuracy, coherence analyses, and tree analysis were systematically performed, and four-class discretized decision tree (DT) with ordinary pair-wise partitioning (OPP) was selected as the optimal model among tested five DT model variants. GIS-based simulations of the optimal DT model with varying weights assigned to soil ecological quality showed that the inclusion of soil ecology in environmental components, which are considered in environmental impact assessment, significantly affects the spatial distributions of overall environmental quality values as well as the determination of an environmentally optimized road route. This work suggests a guideline to use systematic accuracy, coherence, and tree analyses in selecting an optimal DT model from multiple candidate model variants, and demonstrates the applicability of the OPP-improved DT integrated with GIS in rule induction for mapping bacterial diversity. These findings also provide implication on the significance of soil microbial ecology in environmental impact assessment and eco-friendly construction planning.     

A comparison study of the potential risks induced in arable land and forest soils by carcass-derived pollutants

Environmental Geochemistry and Health - Improper decisions concerning animal carcass disposal sites pose grave threats to environmental biosecurity. However, only a few studies have focused on the...