A comparative analysis of trace metal (Cu, Pb, Fe, Mn, Zn, Cd, Ni and Co) concentration and physical parameters (pH, EC, TDS and DO) in rainwater samples collected from two major coastal cities in Malaysian Borneo (Sarawak state) were determined in the present research. Cumulative monthly rainwater samples were collected from the Limbang city and Miri city during October 2016–September 2017. Rainwater collected from the Limbang city shows slightly alkaline nature with a mean pH≥6.07 whereas the rainwater in Miri city is acidic(mean pH = 5.35). Trace metal concentration in rainwater collected from both locations shows slight variation. Mean concentration of trace metals in rainwater samples follows the decreasing order of Fe>Ni>Pb>Mn> Co>Cu>Zn>Cd and Fe>Ni>Pb>Mn>Zn>Co>Cu>Cd in Limbang city and Miri city respectively. Among the trace metals, Fe (1.09 and 0.98 mg/L) and Ni (0.15 and 0.13 mg/L) shows the highest mean concentration in rainwater samples collected from both locations and maximum concentration of trace metals are observed in rainwater samples collected from the Limbang city. Pearson’s correlation test explained the inter-relationship between the parameters whereas the factor analysis confirmed the contributing sources of trace metals (anthropogenic activities such as pollution from vehicles, petrochemical industries, forest biomass burning and dust particles from exposed land area) and its variation in the rainwater samples by showing a total variance of 80.18% with three factor components in the Limbang city and a variance of 93.11% with four factor components in Miri city. High Pb/Zn ratio also indicates the strong influence of anthropogenic activities present in the region. Backward air mass trajectory analysis supports the findings by indicating a contribution from combined marine and crustal sources of air mass trajectories reaching the sampling locations and is heavily controlled by prevailing monsoon characteristics of the region. Overall, it can be concluded that, the major source of trace metals in rainwater in this region is contributed by anthropogenic processes operated in the region.
Performance and sensitivity of freely available equal resolution space-borne digital elevation model derivatives in landslide susceptibility analysis were carried out in a selected part of the Western Ghats, India. ASTER and SRTM digital elevation models having a 30-m resolution were used to derive the terrain variables such as slope, aspect, relative relief, slope length and steepness, curvature, landform and stream networks. Most of the variables showed spatial variability in distribution pattern, which affects the results of geo-environmental processes analysed. Sensitivity and performance of each variable derived from the digital elevation models were assessed by preparing landslide susceptibility index (LSI) maps using the Information Value (InfoVal) technique and were validated through receiver operator characteristics (ROC) curve analysis. LSI maps generated point towards the capability of the SRTM digital elevation model to correctly generate the terrain variables than the ASTER elevation surface, by giving the accuracy of LSI maps greater than those produced using the ASTER-derived parameters (0.77 and 0.72 for SRTM; 0.67 and 0.65 for ASTER). The results of the present study suggest that the SRTM digital elevation data are more sensitive and suitable for terrain analysis and earth surface process modelling than the ASTER elevation data sets, although both possess equal resolutions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.