Application of dynamic models to estimate greenhouse gas emission by wastewater treatment plants of the pulp and paper industry

Greenhouse gas (GHG) emission in wastewater treatment plants of the pulp-and-paper industry was estimated by using a dynamic mathematical model. Significant variations were shown in the magnitude of GHG generation in response to variations in operating parameters, demonstrating the limited capacity of steady-state models in predicting the time-dependent emissions of these harmful gases. The examined treatment systems used aerobic, anaerobic, and hybrid—anaerobic/aerobic—biological processes along with chemical coagulation/flocculation, anaerobic digester, nitrification and denitrification processes, and biogas recovery. The pertinent operating parameters included the influent substrate concentration, influent flow rate, and temperature. Although the average predictions by the dynamic model were only 10 % different from those of steady-state model during 140 days of operation of the examined systems, the daily variations of GHG emissions were different up to ±30, ±19, and ±17 % in the aerobic, anaerobic, and hybrid systems, respectively. The variations of process variables caused fluctuations in energy generation from biogas recovery by ±16, ±17, and ±14 % in the three examined systems, respectively. The lowest variations were observed in the hybrid system, showing the stability of this particular process design.     

Optimal redesign of groundwater quality monitoring networks: a case study

Assessment and redesign of water quality monitoring networks is an important task in water quality management. This paper presents a new methodology for optimal redesign of groundwater quality monitoring networks. The measure of transinformation in discrete entropy theory and the transinformation–distance (T–D) curves are used to quantify the efficiency of sampling locations and sampling frequencies in a monitoring network. The existing uncertainties in the T–D curves are taken in to account using the fuzzy set theory. The C-means clustering method is also used to classify the study area to some homogenous zones. The fuzzy T–D curve of the zones is then used in a multi-objective hybrid genetic algorithm-based optimization model. The proposed methodology is utilized for optimal redesign of monitoring network of the Tehran aquifer in the Tehran metropolitan area, Iran.     

Sources of lead and zinc associated with metal smelting activities in the Trail area,British Columbia,Canada

The spatial distribution and deposition of lead and zinc emitted from the Trail smelter, British Columbia, Canada, was studied by strategically locating moss bags in the area surrounding the smelter and monitoring the deposition of elements every three months. A combined diffusion/distribution model was applied to estimate the relative contribution of stack-emitted material and material emitted from the secondary sources (e.g., wind-blown dust from ore/slag storage piles, uncovered transportation/trucking of ore, and historical dust). The results indicate that secondary sources are the major contributor of lead and zinc deposited within a short distance from the smelter. Gradually, the stack emissions become the main source of Pb and Zn at greater distances from the smelter. Typical material originating from each source was characterized by SEM/EDX, which indicated a marked difference in their morphology and chemical composition.     

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

The long-term effect of exposure to respirable particulate matter on the incidence of myocardial infarction: a systematic review and meta-analysis study

Environmental Science and Pollution Research - Although several studies have investigated the long-term association of respirable particulate matter (PM?≤?10 µm)...     

Speciation of nickel in Canadian subbituminous and bituminous feed coals, and their ash by-products

The nickel species in the feed coals and ash by-products from seven Canadian power plants (including one with a fluidized bed combuster) burning local subbituminous and bituminous coals with sulfur contents ranging from 0.22 to 3.6% have been examined using nickel XANES spectroscopy. XANES spectroscopy of Ni in coal and coal derived ash is complicated by a poor signal : noise ratio due to fluorescence of the much more abundant iron in the coal. Nevertheless, it has proved possible to show that the Ni environment in coals varies from largely oxidic species to mixtures of Ni-containing oxide and sulfide species. The nickel in one oxidized coal appears to be present as nickel sulfate. Nickel in all bottom and fly ash samples examined appears also to be present largely in oxygen anion environments. With the exception of one fly ash sample, for which the Ni exhibited spectral features similar to those for Ni(2+) in spinel or oxide phases, the nickel in the bottom and fly ash samples appears to exist largely as Ni(2+) in environments similar to those reported for Ni in silicate glasses. The data obtained indicate that the presence of potentially carcinogenic nickel sulfides in ash by-products from combustion of these coals is unlikely.     

Performance evaluation of a continuous flow photocatalytic reactor for wastewater treatment

A novel photocatalytic reactor for wastewater treatment was designed and constructed. The main part of the reactor was an aluminum tube in which 12 stainless steel circular baffles and four quartz tube were placed inside of the reactor like shell and tube heat exchangers. Four UV–C lamps were housed within the space of the quartz tubes. Surface of the baffles was coated with TiO₂. A simple method was employed for TiO₂ immobilization, while the characterization of the supported photocatalyst was based on the results obtained through performing some common analytical methods such as X-ray diffraction (XRD), scanning electron microscope (SEM), and BET. Phenol was selected as a model pollutant. A solution of a known initial concentration (20, 60, and 100 ppmv) was introduced to the reactor. The reactor also has a recycle flow to make turbulent flow inside of the reactor. The selected recycle flow rate was 7?×?10^?5 m³.s^?1, while the flow rate of feed was 2.53?×?10^?7, 7.56?×?10^?7, and 1.26?×?10^?6 m³.s^?1, respectively. To evaluate performance of the reactor, response surface methodology was employed. A four-factor three-level Box–Behnken design was developed to evaluate the reactor performance for degradation of phenol. Effects of phenol inlet concentration (20–100 ppmv), pH (3–9), liquid flow rate (2.53?×?10^?7?1.26?×?10^?6 m³.s^?1), and TiO₂ loading (8.8–17.6 g.m^?2) were analyzed with this method. The adjusted R ² value (0.9936) was in close agreement with that of corresponding R ² value (0.9961). The maximum predicted degradation of phenol was 75.50 % at the optimum processing conditions (initial phenol concentration of 20 ppmv, pH?～?6.41, and flow rate of 2.53?×?10^?7 m³.s^?1 and catalyst loading of 17.6 g.m^?2). Experimental degradation of phenol determined at the optimum conditions was 73.7 %. XRD patterns and SEM images at the optimum conditions revealed that crystal size is approximately 25 nm and TiO₂ nanoparticles with visible agglomerates distribute densely and uniformly over the surface of stainless steel substrate. BET specific surface area of immobilized TiO₂ was 47.2 and 45.8 m² g^?1 before and after the experiments, respectively. Reduction in TOC content, after steady state condition, showed that maximum phenol decomposition occurred at neutral condition (pH?～?6). Figure

The schematic view of the experimental set-up     

Pasture diversification to combat climate change impacts on grazing dairy production

Among livestock systems, grazing is likely to be most impacted by climate change because of its dependency to feed quality and availability. In order to reduce the impact of climate change on grazing livestock systems, adaptation measures should be implemented. The goal of this study is to identify the best pasture composition for a representative grazing dairy farm in Michigan in order to reduce the impacts of climate change on production. In order to achieve the goal of this study, three objectives were sought: (1) identify the best pasture composition, (2) assess economic and resource use impacts of pasture compositions under future climate scenarios, and (3) evaluate the resiliency of pasture compositions. A representative farm was developed based on a livestock practices survey and incorporated into the Integrated Farm System Model (IFSM). For the pasture compositions, four cool-season grass species and two legumes were evaluated under both current and future climate scenarios. The effectiveness of adaptation measures based on economic and resource use criteria was evaluated. Overall, the pasture composition with 50% perennial ryegrass (Lolium multiflorum) and 50% red clover (Trifolium pratense) was identified as the best. In addition, the increase in precipitation and temperature of the most intensive climate scenario could significantly improve farm net return per cow (Bos taurus) and whole farm profit while no significant impact was observed on resource use criteria. Finally, the overall sensitivity assessment showed that the most resilient pasture composition under future climate scenarios was ryegrass with red clover and the least resilient was orchardgrass (Dactylis glomerata) with white clover (Trifolium repens).     

The use of some plant-derived products as effective alternatives to antibiotic growth promoters in organic poultry production: a review

Environmental Science and Pollution Research - Improving poultry production, increasing poultry immunity, and reducing the disease spreading can be achieved by adding various potentially valuable...