Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude –0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude –0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude –0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude –0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at –0.3769.     

The use of vegetation,bees, and snails as important tools for the biomonitoring of atmospheric pollution—a review

The continuous discharge of diverse chemical products in the environment is nowadays of great concern to the whole world as some of them persist in the environment leading to serious diseases. Several sampling techniques have been used for the characterization of this chemical pollution, although biomonitoring using natural samplers has recently become the technique of choice in this field due to its efficiency, specificity, and low cost. In fact, several living organisms known as biomonitors could accumulate the well-known persistent environmental pollutants allowing their monitoring in the environment. In this work, a review on environmental biomonitoring is presented. The main sampling techniques used for monitoring environmental pollutants are first reported, followed by an overview on well-known natural species used as passive samplers and known as biomonitors. These species include conifer needles, lichen, mosses, bees and their byproducts, and snails, and were widely used in recent research as reliable monitors for environmental pollution.

     

New application of Orchis mascula as a biocarrier for immobilization of mixed cells for biodegradation and detoxification of reactive azo dyes

Environmental Science and Pollution Research - In this study, the application of novel biocarrier Orchis mascula plant for immobilization of non-adapted mixed cells biodegrade reactive azo dyes in...     

Topiramate potential neurotoxicity and mitigating role of ginger oil in mice brain

Environmental Science and Pollution Research - Topiramate has multiple pharmacological mechanisms that are efficient in treating epilepsy and migraine. Ginger has been established to have gingerols...     

Identification of sources of Phoenix aerosol by positive matrix factorization

Chemical composition data for fine and coarse particles collected in Phoenix, AZ, were analyzed using positive matrix factorization (PMF). The objective was to identify the possible aerosol sources at the sampling site. PMF uses estimates of the error in the data to provide optimum data point scaling and permits a better treatment of missing and below-detection-limit values. It also applies nonnegativity constraints to the factors. Two sets of fine particle samples were collected by different samplers. Each of the resulting fine particle data sets was analyzed separately. For each fine particle data set, eight factors were obtained, identified as (1) biomass burning characterized by high concentrations of organic carbon (OC), elemental carbon (EC), and K; (2) wood burning with high concentrations of Na, K, OC, and EC; (3) motor vehicles with high concentrations of OC and EC; (4) nonferrous smelting process characterized by Cu, Zn, As, and Pb; (5) heavy-duty diesel characterized by high EC, OC, and Mn; (6) sea-salt factor dominated by Na and Cl; (7) soil with high values for Al, Si, Ca, Ti, and Fe; and (8) secondary aerosol with SO4(-2) and OC that may represent coal-fired power plant emissions. For the coarse particle samples, a five-factor model gave source profiles that are attributed to be (1) sea salt, (2) soil, (3) Fe source/motor vehicle, (4) construction (high Ca), and (5) coal-fired power plant. Regression of the PM mass against the factor scores was performed to estimate the mass contributions of the resolved sources. The major sources for the fine particles were motor vehicles, vegetation burning factors (biomass and wood burning), and coal-fired power plants. These sources contributed most of the fine aerosol mass by emitting carbonaceous particles, and they have higher contributions in winter. For the coarse particles, the major source contributions were soil and construction (high Ca). These sources also peaked in winter.     

A comprehensive review of domestic-open waste burning: recent trends,methodology comparison,and factors assessment

Journal of Material Cycles and Waste Management - Open burning is a waste management practice performed by many people worldwide, especially in developing countries. Lack of detailed data of open...     

Enhancing the anti-ageing,antimicrobial activity and mechanical properties of surface-coated paper by Ag@TiO2-modified nanopigments

Environmental Science and Pollution Research - In this work, the effect of using Ag-doped TiO2 nanopigments on optical, mechanical and antimicrobial properties of coated paper was explored....