Overview of Traditional and Environmental Factors Related to Bone Health

Environmental Science and Pollution Research - Bone mass in adulthood depends on growth and mineralization acquired during childhood and adolescence. It is well known that these stages of life are...     

Compositional analysis of topsoil metals and its associations with cancer mortality using spatial misaligned data

The presence of toxic metals in soil per se, and in soil impacted by mining, industry, agriculture and urbanisation in particular, is a major concern for both human health and ecotoxicology. The dual aim of this study was: to ascertain whether topsoil composition could influence the spatial distribution of mortality due to different types of cancer and to identify possible errors committed by epidemiological studies which analyse soil composition data as a closed number system. We conducted an ecological cancer mortality study, covering 861,440 cancer deaths (27 cancer sites) in 7917 Spanish mainland towns, from 1999 to 2008. Topsoil levels of Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn were determined by ICP-MS at 13,317 sampling points. We transformed the topsoil data in two ways, i.e. log transformation and centred logratio transformation. Principal factor analysis was performed to obtain independent latent factors for the transformed variables. To estimate the effect on mortality of topsoil factor loadings, we fitted Besag, York and Mollié models embedded in geostatistical-spatial models. This model included soil sample locations and town centroids (non-aligned data), fitted using the integrated nested Laplace approximation (INLA) as a tool for Bayesian inference and stochastic partial differential equations (SPDE). All results were adjusted for socio-demographic variables. The results indicated that soil composition could have an influence on the spatial distribution and mortality patterns of cancer. The analysis adjusted for socio-demographic variables showed excess male mortality due to digestive system tumours in areas with soils containing higher Cd, Pb, Zn, Mn and Cu concentrations, bladder cancer in areas with soils containing higher Cd concentrations, and brain cancer in areas with soils containing As. In both sexes, cancer of oesophagus was associated with soils containing a higher lead content, while lung cancer was associated with soils containing a higher copper content. Stress should be laid on the importance of taking into account the compositional nature of the data in this type of analysis.     

Evaluating pollution-related damage and restoration success in urban forests with participatory monitoring and digital tools

Peri-urban forest monitoring requires indicators of vegetation damage. An example is the sacred fir (Abies religiosa) forests surrounding Mexico City, which have been heavily exposed to tropospheric ozone, a harmful pollutant, for over 4 decades. We developed a participatory monitoring system with which local community members and scientists generated data on ozone tree damage. Santa Rosa Xochiac rangers (13) used the digital tool KoboToolBox to record ozone damage to trees, tree height, tree ages, tree condition, tree position, and whether the tree had been planted. Thirty-five percent of the trees (n = 1765) had ozone damage. Younger trees had a lower percentage of foliage damaged by ozone than older trees (p < 0.0001), and asymptomatic trees tended to be younger (p < 0.0001). Symptomatic trees were taller than asymptomatic trees of the same age (R²_c = 0.43, R²_m = 0.27). Involving local communities facilitated forest monitoring and using digital technology improved data quality. This participatory system can be used to monitor forest condition change over time and thus aids restoration efforts driven by government or local communities’ interests, facilitating local decision-making.     

Urban soil quality of Raipur,Chhattisgarh, India

Soil quality in urban areas in India is degraded due to multiple anthropogenic activities. The objectives of this work are to determine the concentration variations, toxicity, and sources of carbons, metals, and ions in the surface soil of Raipur, the industrialized capital city of Chhattisgarh state, India. High concentrations of Al, K, Ca, Ti, Fe, and elemental carbon (EC) were registered. Relatively lower concentrations of V, Cr, Mn, Ni, Cu, Zn, Sr, Ba, Pb, organic carbon (OC), and carbonate carbon (CC), as well as ions (viz. F^–, Cl^–, NO₃^–, SO₄^2–, Na⁺, K⁺, Mg²⁺, and Ca²⁺), were also recorded. EC was found to be one of the major pollutants, although enrichment factors pointed to high contamination with SO₄^2–, K⁺, Mg²⁺_, Cr, Mn, and Pb; and extreme contamination with NO₃^– and Ca²⁺. The spatial and temporal variations, enrichment factors, toxicity, and sources of the chemical species detected in the soil are discussed.     

Sustainability of recycling plastic waste as fibers for concrete: a review

Journal of Material Cycles and Waste Management - Plastic waste management is of global concern while construction industry keeps searching for innovations to become more sustainable. In this...     

Sediments: sink,archive, and source of contaminants

Environmental Science and Pollution Research -     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.