Papers by Sandhya Patidar
Energy and Buildings
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Energy and Buildings, Nov 1, 2012
ABSTRACT The Low Carbon Futures project, funded by the Adaptation and Resilience in a Changing Cl... more ABSTRACT The Low Carbon Futures project, funded by the Adaptation and Resilience in a Changing Climate (ARCC) Programme, has the objective of using the latest UK climate projections (UKCP’09) to assess overheating in a range of domestic and non-domestic buildings. As these climate projections are probabilistic in nature, and dynamic building simulation is being used by the project to assess building performance, the information produced is vast. To understand how to filter this data into a useable tool that can interact with current building practices, the project has commissioned a range of focus groups to obtain practitioner feedback. These focus groups provide guidance on how buildings are currently designed with respect to overheating but also how future overheating risk assessments, incorporating probabilistic climate projections, might be carried out. This paper describes the assimilation of all this research into a coherent building simulation methodology that could be used by building practitioners to assess future overheating risks of a range of buildings, and provide guidance for applying adaptation solutions to prevent defined comfort thresholds being exceeded.
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New Journal of Physics, Jul 2, 2009
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Expert Systems with Applications
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Building Simulation Conference Proceedings
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EGU General Assembly Conference Abstracts, Apr 1, 2019
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Geosciences, Jun 13, 2021
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River Research and Applications, Jul 25, 2018
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Water Resources Research
Modeling hydrological processes for managing the available water resources effectively is often c... more Modeling hydrological processes for managing the available water resources effectively is often complex due to the existence of high nonlinearity, and the associated prediction uncertainty mainly arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance. This paper presents a novel modeling framework for minimizing the prediction uncertainty in the streamflow simulation of the conceptual hydrological model (HBV) by integrating with the Bayesian‐based Particle Filter technique (PF) and machine learning algorithm (Random Forest algorithm, RF). Initially, the streamflow prediction interval (PI) is derived from the stochastically estimated parameters of the HBV model through the PF technique (HBV‐PF model). As the HBV‐PF model quantifies only parametric uncertainty, the RF algorithm was employed (HBV‐PF‐RF model) for fur...
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Frontiers in soil science, Dec 6, 2022
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Frontiers in Soil Science
The rapid growth of urban development, industrialization, mining, farming, and biological activit... more The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil ...
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Journal of South American Earth Sciences, 2022
The condition of the soil environment is critical for human health and agricultural sustainabilit... more The condition of the soil environment is critical for human health and agricultural sustainability. As a result, the environmental and ecological issues impacting the soils throughout the world are receiving more attention. This research focuses on local site-specific studies in Cerrito Blanco, Matehuala municipality, San Luis Potosi, Mexico, and describes different types of GIS interpolation techniques, multivariate statistical analysis, and various contamination indices to investigate the relationship between predictive accuracy, levels of contamination risk, and soil toxic metal elements variation. Inductively coupled plasma optical emission spectroscopy (ICP-EOS) used to test 39 digested surface soil samples for significant toxic metals (Ag, Cd, Co, Cr, Li, and Ni) after suitable dilution with deionised water. According to the results, we found that only the mean value of cadmium (Cd) exceeded the permissible standard value. After evaluating the four types of interpolation techniques, the Inverse Distance Weighting (IDW) was determined to be the optimal interpolation model for assessing the spatial distribution patterns of toxic metal concentration in the research area. The calculated contamination risk indices showed no significant high contamination risk due to soil-borne toxic metals. These results provide a comprehensive analysis of the impact of past mining activities on toxic metal concentrations in non-cultivated surface soil.
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IECG 2022
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Proceedings MDPI, 2023
In recent decades, heavy metal contamination in soils has caused global concern. Quantitative app... more In recent decades, heavy metal contamination in soils has caused global concern. Quantitative apportionment of heavy metal sources in the surface soil is a complex task. This study indicated a receptor model to evaluate the heavy metal concentrations of various sources for the soil and the related contamination impacts. In this study, the surface soil at the Cerrito Blanco in San Luis Potosi, Mexico was chosen as the case study location to reveal the potential pollution sources of heavy metals. The research suggested the combined use of the positive matrix factorization (PMF) model for the quantitative assessment of contamination sources as well as the spatial distribution techniques for the estimation of the pollution sources. This approach forms the basis for later soil contamination control and treatment. Throughout the study region, a total of thirty-nine samples of surface soil were collected. However, the mean concentration levels of Co, Cr, Cu, Ni, and Pb in the soils were lower than the permissible standards. It was observed that As and Cd were higher than their permissible standard values by around 5.43 and 1.19 times, respectively. The PMF findings demonstrate that the three main diverse sources of heavy metals in this study area’s soils were natural, past mining activities, and industrialisation, as well as groundwater. The concentrations of heavy metals in surface soils were considerably influenced by natural sources, which were generally the main contributing factor. The spatial distribution of soil contamination for heavy metals was also mapped using the geographic information system (GIS) technique.
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Frontiers in Soil Science, 2022
The rapid growth of urban development, industrialization, mining, farming, and biological activit... more The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil samples. After implementing PMF to analyze the soils, it was possible to differentiate the variations in factors linked to the contaminants, farming impacts, and the reference soil geochemistry. The soil in the two studied locations included high concentrations of As, Ca, Mg, K, Sr, Mn, and Fe, including variations in their spatial compositions, which were caused by direct mining activities, the movement and deposition of smelting waste, and the extensive use of irrigated contaminated groundwater for irrigation. The four possible factors were identified for soil pollution including industrial, transportation, agricultural, and naturogenic based on the PMF and geostatistical analysis. The spatial distribution of metal concentrations in the soil was also presented using a geographical information system (GIS) interpolation technique. The identification of metal sources and contamination risk mapping presents a significant role in minimizing pollution sources, and it may be performed in regions with high levels of soil contamination risk.
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Papers by Sandhya Patidar