Economical and environmental implications of solid waste compost applications to agricultural fields in Punjab,Pakistan

Application of municipal solid waste compost (MSWC) to agricultural soils is becoming an increasingly important global practice to enhance and sustain soil organic matter (SOM) and fertility levels. Potential risks associated with heavy metals and phosphorus accumulations in surface soils may be minimized with integrated nutrient management strategies that utilize MSWC together with mineral fertilizers. To explore the economic feasibility of MSWC applications, nutrient management plans were developed for rice–wheat and cotton–wheat cropping systems within the Punjab region of Pakistan. Three-year field trials were conducted to measure yields and to determine the economic benefits using three management strategies and two nutrient doses. Management strategies included the application of mineral fertilizers as the sole nutrient source and application of mineral fertilizers in combination with MSWC with and without pesticide/herbicide treatments. Fertilizer doses were either based on standard N, P and K recommendations or on measured site-specific soil plant available phosphorus (PAP) levels. It was found that combining MSWC and mineral fertilizer applications based on site-specific PAP levels with the use of pesticides and herbicides is an economically and environmentally viable management strategy. Results show that incorporation of MSWC improved soil physical properties such as bulk density and penetration resistance. The PAP levels in the surface layer increased by the end of the trials relative to the initial status. No potential risks of heavy metal (Zn, Cd, Cr, Pb and Ni) accumulation were observed. Treatments comprised of MSWC and mineral fertilizer adjusted to site-specific PAP levels and with common pest management showed highest cumulative yields. A basic economic analysis revealed a significantly higher cumulative net profit and value-to-cost ratio (VCR) for all site-specific doses.     

A review of recent developments in the application of machine learning in solar thermal collector modelling

Environmental Science and Pollution Research - Over the past few decades, the popularity of solar thermal collectors has increased dramatically because of many significant advantages like being a...     

Content of potentially toxic elements (PTEs) in various animal meats: a meta-analysis study,systematic review,and health risk assessment

Environmental Science and Pollution Research - Meat comprises the main part of the diet in many countries around the world. The present study aimed to assess potentially toxic elements (PTEs) lead...     

Support vector machine based prediction of photovoltaic module and power station parameters

ABSTRACT

The uncertainty in the output power of the photovoltaic (PV) power generation station due to variation in meteorological parameters is of serious concern. An accurate output power prediction of a PV system helps in better design and planning. The present study is carried out for the prediction of output power of PV generating station by using Support Vector Machines. Two cases are considered in the present study for prediction. Case-I deals with the prediction of PV module parameters such as V_oc, I_sh, R_s, R_sh, I_max, V_max, P_max, and case-II deals with the prediction of power generation parameters such as P_DC, P_AC, and system efficiency. Historical data of PV power station with an installed capacity of 10 MW and weather information are used as input to develop four different seasons-based SVM models for all parameters. The performance results of the models are presented in terms of Mean Relative Error (MRE) and Root Mean Square Error (RMSE). Additionally, the performance results obtained with polynomial and Radial Based Function kernel are also compared to show that which kernel has better prediction accuracy, and practicability. The result shows that the minimum average RMSE and MRE for case-I with Radial Based Function kernel are 0.034%, 0.055%, 0.002%, 1.726%, 0.044%, 0.047%, 2.342%, and 0.005%, 0.014%, 0.079%, 0.885%, 0.005%, 0.007%, 0.013%, and for case-II with poly kernel are 0.014%, 0.016%, 0.149% and 0.011%, 0.0175, 1.03%, respectively. The present study will be helpful to provide technical guidance to the prediction of the PV power System.     

An alternative approach for delineating eco-sensitive zones around a wildlife sanctuary applying geospatial techniques

The dynamics, degradation, and conservation of forest ecosystems are matters of prime concerns worldwide at the present. Proper planning and management of a forest area are essentially needed to protect it from the grasp of burgeoning pressure of urban-industrial sprawl. Establishment of eco-sensitive zones (ESZs), which act as buffer areas around the core forests, is one of the key approaches towards achieving this goal. This paper deals with the applicability of geospatial techniques to identify the ESZ around an Indian wildlife sanctuary following the different rules and acts prescribed by the Government of India. Gumti Wildlife Sanctuary, located in the northeastern state of Tripura in India, has been selected here as a case study. Collected pieces of information on the distribution of biodiversity and human population in the area were also used to make the approach more holistic. As inferred from this study, remote sensing and geographical information systems were found to be easily implementable and time as well as cost-effective tools for this purpose with a distinct advantage of spatial as well as temporal accuracy in identifying the existing land use and land cover patterns in pilot assessments. However, the results indicated that only appropriate hybridization of field-based information on the biodiversity and ecological aspects of the forest as well as patterns of human interferences with the remote sensing and GIS-based data could make this approach more relevant in actual implementations.     

Multifarious revolutionary aspects of microbial keratinases: an efficient green technology for future generation with prospective applications

Environmental Science and Pollution Research - Since the dawn of century, tons of keratin bio-waste is generated by the poultry industry annually, and they end up causing environmental havoc....     

Designing a sustainable reverse logistics channel: the 18 generic structures framework

Remanufacturing end-of-life products requires setting up an economically and environmentally viable reverse logistics channel for supplying reusable used modules to the production chain. This paper introduces a framework to allow generating and assessing different reverse logistics channel structures. The framework is then applied to a product remanufacturing case. We analyze the current reverse logistics channel structure and propose alternative structures with less environmental impact and higher economic benefits.     

Effects of catholyte conditioning on electrokinetic extraction of copper from mine tailings

Effect of electrokinetic treatment on copper partitioning and distribution in mine tailings were studied. In particular the effects of catholyte enhancement by HAc-NaAc, HCl, HAc-NaAc+EDTA and lactic acid+NaOH were evaluated. The results show that conditioning the catholyte plays a very important role in improving Cu removal. When HAc-NaAc is used in the catholyte, the removal percentage of total Cu from the mine tailings sample reached 12.3% under 40 V in 15 days of treatment. The removal percentage of Cu increased to 31.2% when EDTA was used together with HAc-NaAc in the catholyte. At the same time, increasing the applied voltage and treatment time result in an increase in the Cu removal from the mine tailings. Compared with HAc-NaAc (pH=3.52), the use of lactic acid+NaOH (pH=3.15) in the catholyte resulted in better performance in Cu removal from the mine tailings. HCl treatment resulted in removal of about 17.5% of Cu from the mine tailings; however, it resulted in production of significant amounts of toxic chlorine gas. Copper partitioning in the mine tailings was analyzed before and after the electrokinetic treatments. The analysis was conducted using 0.25 mol/l MgCl2 and 0.5 mol/l HCl as extractants, consequently, to assess the mobility of Cu after treatment. The results showed that lowering the pH of the mine tailings increased the exchangeable Cu fraction (or the portion extracted by MgCl2). Accordingly, further acidification results in an increased mobility of Cu and increase in the environmental risk of mine tailings.     

Modeling nonlinearly transient zero-order bioreduction and transport of Cr(VI) in groundwater

Cr(VI) contamination of soil and groundwater is considered a major environmental concern. Bioreduction of Cr(VI) to Cr(III) can be considered a potentially effective technology in remediating Cr(VI) contaminated sites. Shewanella oneidensis MR-1 (MR-1) is one of the bacteria capable of reducing Cr(VI) to Cr(III) under anaerobic conditions. The kinetics of Cr(VI) reduction by MR-1 is defined by the dual-enzyme kinetic model which is nonlinear, transient, and zero-order. Existing transport models are not designed to simulate such reaction kinetics. The objective of this paper is to present a Petrov–Galerkin finite element model (PGFEM) to simulate transport and bioreduction of Cr(VI), by MR-1, in groundwater. The model developed is unconditionally stable and provides oscillation free accurate results for a wide range of Peclet number (Pn) and Courant number (Cn).     

Mapping pesticide contamination potential

Groundwater contamination by agricultural chemicals is a major environmental pollution issue nation-wide. The regulatory agencies of towns and counties face the problem of finding a methodology for assessing the ground-water contamination potential of a large number of agricultural pesticides. Because of the spatial nature of the problem and the limited data availability for comprehensive pesticide movement models, a contamination potential index was employed for preliminary assessment. A specially designed geographic information system was used to create ground-water contamination likelihood maps for a 1500 km² area. The results suggest that this methodology can be used successfully for evaluating the relative contamination potential of a large number of pesticides over large areas with limited input data. A tentative approach for using this method for monitoring and registration of pesticides is also discussed.