Examining Resource Dependency and Socioeconomic Disparities: A Case Study of Sustaining Rural Livelihoods in India

2025-09-16T10:36:33+08:00

Rural communities in developing countries often struggle with resource dependency, economic challenges, and poor infrastructure, and villages in Uttarakhand, India, are no exception. This study aims to examine the socioeconomic factors influencing forest conservation, assess livelihood dependency on forest resources, and evaluate how socioeconomic status shapes sustainable forest management in Shishambara and Buddhi villages in Dehradun. The study employed purposive and random sampling covering 10% of households, using structured surveys, interviews, field observations, market surveys, and focus group discussions. The survey reveals an agriculture-dominated livelihood, engaging 60% in Buddhi and 65% in Shishambara, alongside private-sector jobs and daily wage labour. Literacy rates differ significantly, with Buddhi at 72% and Shishambara at 58%. Despite accessibility to LPG connections, traditional cooking fuels like fuelwood and cow dung remain predominant, utilized by 70% of households in Buddhi and 75% in Shishambara. Most homes are Pakka, yet only 18.8% in Buddhi and 22% in Shishambara have toilets. Public transport is scarce, leaving villagers reliant on private vehicles. These findings underscore the need for policies that address resource management, improve basic services, and support sustainable development, offering a road map for uplifting rural livelihoods and bridging infrastructure gaps.

Response Surface Methodology and Deep Learning Modelling for the Removal of Pb2+ ions from Wastewater Using Apple Pomace

2025-08-07T17:33:24+08:00

Recent studies have demonstrated a growing global interest in utilising agricultural waste to remediate wastewater. This stems from growing apprehensions about high levels of heavy metals, especially Pb²⁺ ions, in wastewater produced by industrial processes such as mining, paint production, oil refining, smelting, and electroplating. This study examined apple pomace's Pb²⁺ ions adsorption from wastewater. Response Surface Methodology (RSM) was employed, utilising the central composite face-centred design (CCFD) with three variables: initial concentration (1–50 mg/L), adsorbent dosage (0.1–1 g), and particle size (75–425 µm) to formulate a mathematical model for the biosorption of Pb²⁺ ions on apple pomace. An artificial neural network (ANN) was developed using data generated from the RSM design. The CCFD and ANN models showed considerable efficacy in the adsorption process, exhibiting correlation coefficient values of 0.9921 and 0.9999, respectively. The isotherm and kinetic studies were performed, and the Freundlich Isotherm model best fitted the equilibrium data, with a correlation coefficient of 0.972 and a q_e of 5.145 mg/g. Additionally, the pseudo-second-order model proved to be the most appropriate for the kinetic data, with an R² of 0.9996. These results confirm that apple pomace functions as an effective, low-cost, and environmentally and sustainably biosorbent for the removal of Pb²⁺ ions from wastewater. Both RSM and ANN models exhibited high predictive capability for the biosorption process. While ANN provides more flexibility in modelling complex non-linear relationships, it is prone to overfitting, particularly with limited datasets, and this was addressed through a 5-fold cross-validation technique.

Journal of Environmental & Earth Sciences

Examining Resource Dependency and Socioeconomic Disparities: A Case Study of Sustaining Rural Livelihoods in India

Response Surface Methodology and Deep Learning Modelling for the Removal of Pb2+ ions from Wastewater Using Apple Pomace