Determination and Quantification of Heavy Metals in Sediments through Laser-Induced Breakdown Spectroscopy and Partial Least Squares Regression

Conventional analysis techniques and sample preprocessing methods for identifying trace metals in soil and sediment samples are costly and time-consuming. This study investigated the determination and quantification of heavy metals in sediments by using a Laser-Induced Breakdown Spectroscopy (LIBS) system and multivariate chemometric analysis. Principle Component Analysis (PCA) was conducted on the LIBS spectra at the emission lines of 11 selected elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si). The results showed apparent clustering of four types of sediment samples, suggesting the possibility of application of the LIBS technique for distinguishing different types of sediments. Mainly, the Cd, Cr, and Pb concentrations in the sediments were analyzed. A data-smoothing method—namely, the Savitzky–Golay (SG) derivative—was used to enhance the performance of the Partial Least Squares Regression (PLSR) model. The performance of the PLSR model was evaluated in terms of the coefficient of determination (R2), Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV). The results obtained using the PLSR with the SG derivative were improved in terms of the R2 and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrated that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples and can play a significant role in controlling and managing the water quality of rivers.

Mineralogical and chemical properties inversed from 21-lunar-day VNIS observations taken during the Chang'E-4 mission

We report on the mineralogical and chemical properties of materials investigated by the lunar rover Yutu-2, which landed on the Von Kármán crater in the pre-Nectarian South Pole-Aitken (SPA) basin. Yutu-2 carried several scientific payloads, including the Visible and Near-infrared Imaging Spectrometer (VNIS), which is used for mineral identification, offering insights into lunar evolution. We used 86 valid VNIS data for 21 lunar days, with mineral abundance obtained using the Hapke radiative transfer model and sparse unmixing algorithm and chemical compositions empirically estimated. The mineralogical properties of the materials at the Chang'E-4 (CE-4) site referred to as norite/gabbro, based on findings of mineral abundance, indicate that they may be SPA impact melt components excavated by a surrounding impact crater. We find that CE-4 materials are dominated by plagioclase and pyroxene and feature little olivine, with 50 of 86 observations showing higher LCP than HCP in pyroxene. In view of the effects of space weathering, olivine content may be underestimated, with FeO and TiO2 content estimated using the maturity-corrected method. Estimates of chemical content are 7.42-18.82 wt% FeO and 1.48-2.1 wt% TiO2, with a low-medium Mg number (Mg # ~ 55). Olivine-rich materials are not present at the CE-4 landing site, based on the low-medium Mg #. Multi-origin materials at the CE-4 landing site were analyzed with regard to concentrations of FeO and TiO2 content, supporting our conclusion that the materials at CE-4 do not have a single source but rather are likely a mixture of SPA impact melt components excavated by surrounding impact crater and volcanic product ejecta.

New Approaches to Identifying and Reducing the Global Burden of Disease From Pollution

Pollution from multiple sources causes significant disease and death worldwide. Some sources are legacy, such as heavy metals accumulated in soils, and some are current, such as particulate matter. Because the global burden of disease from pollution is so high, it is important to identify legacy and current sources and to develop and implement effective techniques to reduce human exposure. But many limitations exist in our understanding of the distribution and transport processes of pollutants themselves, as well as the complicated overprint of human behavior and susceptibility. New approaches are being developed to identify and eliminate pollution in multiple environments. Community-scale detection of geogenic arsenic and fluoride in Bangladesh is helping to map the distribution of these harmful elements in drinking water. Biosensors such as bees and their honey are being used to measure heavy metal contamination in cities such as Vancouver and Sydney. Drone-based remote sensors are being used to map metal hot spots in soils from former mining regions in Zambia and Mozambique. The explosion of low-cost air monitors has allowed researchers to build dense air quality sensing networks to capture ephemeral and local releases of harmful materials, building on other developments in personal exposure sensing. And citizen science is helping communities without adequate resources measure their own environments and in this way gain agency in controlling local pollution exposure sources and/or alerting authorities to environmental hazards. The future of GeoHealth will depend on building on these developments and others to protect a growing population from multiple pollution exposure risks.

Assessment of contribution of Australia's energy production to CO2 emissions and environmental degradation using statistical dynamic approach

Energy production remains the major emitter of atmospheric emissions, thus, in accordance with Australia's Emissions Projections by 2030, this study analyzed the impact of Australia's energy portfolio on environmental degradation and CO₂ emissions using locally compiled data on disaggregate energy production, energy imports and exports spanning from 1974 to 2013. This study employed the fully modified ordinary least squares, dynamic ordinary least squares, and canonical cointegrating regression estimators; statistically inspired modification of partial least squares regression analysis with a subsequent sustainability sensitivity analysis. The validity of the environmental Kuznets curve hypothesis proposes a paradigm shift from energy-intensive and carbon-intensive industries to less-energy-intensive and green energy industries and its related services, leading to a structural change in the economy. Thus, decoupling energy services provide better interpretation of the role of the energy sector portfolio in environmental degradation and CO₂ emissions assessment. The sensitivity analysis revealed that nonrenewable energy production above 10% and energy imports above 5% will dampen the goals for the 2030 emission reduction target. Increasing the share of renewable energy penetration in the energy portfolio decreases the level of CO₂ emissions, while increasing the share of non-renewable energy sources in the energy mix increases the level of atmospheric emissions, thus increasing climate change and their impacts.