Soil Contamination Patterns: A Spatial Analysis of Toxic Metals in Surface Soil

Spatial distribution based on optimal interpolation techniques and assessment of contamination risk for toxic metals in the surface soil

The health of our soil environment is fundamental for human well-being and agricultural sustainability. Consequently, there is a growing focus on understanding and addressing environmental issues affecting soils worldwide. This research delves into a local site-specific study conducted in Cerrito Blanco, Matehuala municipality, San Luis Potosi, Mexico, exploring various Geographic Information System (GIS) interpolation techniques, multivariate statistical analysis, and contamination indices to investigate the spatial distribution and contamination risk of toxic metals in surface soil.

Using inductively coupled plasma optical emission spectroscopy (ICP-EOS), we analyzed 39 digested surface soil samples for significant toxic metals, including Ag, Cd, Co, Cr, Li, and Ni. The results revealed that only the mean value of cadmium (Cd) exceeded permissible standards, raising concerns about potential health and environmental impacts. Subsequently, we evaluated four interpolation techniques and identified Inverse Distance Weighting (IDW) as the optimal model for assessing the spatial distribution patterns of toxic metal concentrations in the research area.

Furthermore, the calculated contamination risk indices indicated no significant high contamination risk associated with soil-borne toxic metals in the study area. These findings offer valuable insights into the impact of past mining activities on toxic metal concentrations in non-cultivated surface soil, highlighting the importance of proactive measures to mitigate environmental contamination.

Introduction:

Soil contamination poses significant risks to human health and ecosystem integrity, necessitating comprehensive assessments to understand and address potential hazards. In this study, we focus on Cerrito Blanco, Mexico, where concerns about toxic metal contamination have emerged due to historical mining activities. By employing advanced spatial analysis techniques and contamination indices, we aim to elucidate the spatial distribution and contamination risk of toxic metals in surface soil, providing critical insights for environmental management and remediation efforts.

Methodology:

Our methodology combines laboratory analysis using ICP-EOS to quantify toxic metal concentrations in surface soil samples with GIS-based spatial analysis techniques. Following sample collection and analysis, we apply multivariate statistical analysis to identify correlations and patterns among toxic metals. Subsequently, we employ various GIS interpolation techniques to spatially interpolate metal concentrations across the study area, facilitating the identification of contamination hotspots and spatial distribution patterns.

To assess contamination risk, we calculate contamination indices based on established guidelines and standards, providing a quantitative measure of potential environmental and human health risks associated with soil-borne toxic metals. Through rigorous data analysis and interpretation, we aim to provide a comprehensive understanding of soil contamination dynamics in the study area.

Results and Discussion:

Our results indicate that while cadmium (Cd) concentrations exceed permissible standards, other toxic metals remain within acceptable limits. The spatial distribution analysis reveals localized hotspots of contamination, particularly in areas with a history of mining activities. However, overall contamination risk levels are deemed low, suggesting limited immediate threats to human health and the environment.

Conclusion:

In conclusion, our study underscores the importance of spatial analysis and contamination risk assessment in understanding soil contamination dynamics. By combining laboratory analysis with GIS techniques, we offer valuable insights into the spatial distribution and contamination risk of toxic metals in surface soil. These findings can inform targeted environmental management strategies and guide future research efforts aimed at mitigating soil contamination and safeguarding human health.

Hydrological Dynamics: A Study on Flood Peak Estimation using Process-Based and Empirical Models

Hydrological Simulation using Process Based and Empirical Models for Flood Peak Estimation

Hydrological modelling plays a pivotal role in understanding and predicting flood events, particularly in regions susceptible to inundation. In this study, we explore the parameterization of hydrological models for the Asan and Song river basins within the Doon Valley. By employing three different models—SWAT, VIC, and HEC-HMS—we aim to generate flood peak estimates at predetermined locations and assess the impact of land use and land cover (LULC) changes on hydrological processes. Remote sensing data from Landsat and Google Earth imagery are utilized for land cover mapping, facilitating the observation of LULC change scenarios between 1995, 2005, and 2014.

Specific objectives include hydrological modelling for peak flow hydrograph generation, comparison and validation of simulated runoff using the three hydrological models, and analyzing the influence of meteorological, discharge, and sediment data on model performance. The VIC model demonstrates good performance, with simulated values closely aligning with observed data for the 2014 LULC map. Calibration of the SWAT model for the period 2006-2010 highlights the significance of the curve number parameter in determining total discharge.

Furthermore, our study underscores the importance of land use and vegetative cover in shaping watershed runoff and stream flow discharge patterns over time, particularly during peak flows. Rapid transitions in land cover due to increased human interventions have adversely impacted watershed processes and the hydrological cycle, emphasizing the need for sustainable land management practices..

Introduction

The occurrence of floods poses significant challenges to communities and ecosystems, necessitating effective flood risk management strategies. Hydrological modelling serves as a valuable tool for predicting flood events and understanding the underlying processes governing hydrological dynamics. In this study, we focus on the Asan and Song river basins within the Doon Valley, where floods are a recurrent phenomenon.

Methodology

We employ three hydrological models—SWAT, VIC, and HEC-HMS—to simulate flood peak generation and assess the impact of LULC changes on hydrological processes. Remote sensing data from Landsat and Google Earth imagery are utilized for land cover mapping, enabling the observation of LULC change scenarios over a span of two decades. Various input parameters, including meteorological, discharge, and sediment data, are processed to facilitate model calibration and validation. Results and Discussion

Our findings highlight the importance of accurately parameterizing hydrological models for flood peak estimation. The VIC model demonstrates robust performance, capturing the dynamics of peak flows with high accuracy. Calibration of the SWAT model emphasizes the influence of the curve number parameter on total discharge, underscoring the significance of land cover characteristics in modulating hydrological processes.

Furthermore, our study elucidates the impact of LULC changes on watershed dynamics, with human interventions leading to rapid transitions in land cover and consequent alterations in hydrological patterns. While land cover changes exert a pronounced effect during low flows, their influence diminishes during high-flow events, emphasizing the complex interplay between land use, vegetation cover, and hydrological processes.

Conclusion

In conclusion, our study sheds light on the intricate relationship between land cover dynamics and hydrological processes, particularly in the context of flood peak estimation. By integrating process-based and empirical hydrological models, we gain valuable insights into watershed behaviour and the influence of human activities on hydrological dynamics. Moving forward, sustainable land management practices are essential for mitigating the adverse impacts of land cover changes on watershed processes and ensuring the resilience of ecosystems to flood events.

Unraveling Snow Cover with Precision: Exploring Mixed Pixel Mapping Techniques in Hyperspectral Imagery

Mixed Pixel Snow Cover Mapping of Hyperspectral Imagery using Linear Spectral Unmixing Method

Traditionally, snow cover mapping has been a binary affair, categorizing pixels as either snow or non-snow. However, this approach oversimplifies the complexity of snow-covered landscapes, where mixed pixels—combinations of snow, vegetation, and soil—abound. In this study, conducted in the lower Indian Himalayan region during February 2017, we delve into the intricacies of snow reflectance and mixed pixels using Hyperion hyperspectral imagery. By employing the Linear Spectral Unmixing (LSU) method, we aim to enhance the accuracy and efficiency of snow cover estimation at a finer, mixed pixel level.

Field experiments utilizing an ASD Spectroradiometer (350-2500nm) were conducted in Dhundi, Himachal Pradesh, India, to capture ground truth data. Snow, vegetation, and soil reflectance spectra were meticulously collected, forming the basis for end-member spectra identification. Our approach involves predicting these end-member spectra for each pixel, allowing for the unmixing of mixed pixels using a linear mixture model.

In addition to examining the effects of snow reflectance and mixed pixels, this study encompasses comprehensive data processing steps. Dimensionality reduction techniques and corrections for atmospheric and topographic distortions in Hyperion hyperspectral data are analyzed, ensuring the accuracy of our results.

Preliminary findings suggest that our novel method not only improves accuracy but also reduces computation time. By accurately estimating the actual snow cover region at the mixed pixel level, our approach offers a valuable tool for researchers and policymakers involved in snow monitoring and management.

Introduction

Snow cover mapping is crucial for various applications, including climate modelling, hydrological assessments, and disaster management. Conventional methods typically classify pixels as either snow-covered or not, overlooking the intricate mixture of surface types present in many landscapes. This oversimplification can lead to inaccuracies, particularly in regions with heterogeneous terrain and vegetation.

Hyperspectral imagery, with its ability to capture a wide range of wavelengths, offers a promising solution for improving snow cover mapping. By leveraging the wealth of spectral information contained in hyperspectral data, researchers can better discriminate between snow, vegetation, and soil, even within mixed pixels.

Methodology

The Linear Spectral Unmixing (LSU) method forms the backbone of our approach. By identifying end-member spectra for snow, vegetation, and soil, we can effectively unmix mixed pixels using a linear combination model. Field experiments were conducted to collect ground truth data using an ASD Spectroradiometer, ensuring the accuracy of our end-member spectra.

Additionally, to mitigate atmospheric and topographic distortions inherent in hyperspectral imagery, we employed advanced data processing techniques. Dimensionality reduction methods such as Principal Component Analysis (PCA) were utilized to streamline the analysis, while corrections for atmospheric effects and terrain variations were applied to enhance the accuracy of our results. Results and Discussion

Preliminary results indicate that our method outperforms traditional snow cover mapping techniques in terms of accuracy and computational efficiency. By accounting for mixed pixels and leveraging hyperspectral data, we achieve higher precision in delineating snow-covered regions. Furthermore, our approach provides valuable insights into the spectral characteristics of snow, vegetation, and soil, enhancing our understanding of these complex landscapes. Conclusion

In conclusion, the application of Linear Spectral Unmixing (LSU) to hyperspectral imagery holds great promise for advancing snow cover mapping capabilities. By accurately estimating snow cover at the mixed pixel level, our method offers a more nuanced understanding of snow-covered landscapes. Moving forward, further refinements and validation efforts will be undertaken to ensure the reliability and applicability of our approach across diverse environments.