284 research outputs found
Performance Evaluation of a Subterranean Arsenic Removal (SAR) Community Water Treatment Plant: A Sustainable Long-Term Approach to Removing Arsenic from Drinking Water
No full textSubterranean arsenic removal (SAR) is a low-cost, zero-waste, and ‘easy to operate’ process that can remove arsenic and iron from groundwater without using any adsorption bed. The SAR plant creates an aerobic high Eh bed in the aquifer by recharging oxygenated groundwater, thereby supporting the growth of arsenic and iron oxidizing aerobic bacteria. The arsenic and iron are immobilized in the aquifer sand in the forms of As (V) and Fe(III), respectively. This work reports the key performance data of a SAR plant installed at Ghetugachi village in West Bengal, India under an increased demand three times above the designed production volume. The naturally occurring water has an As concentration of 154 ppb, which was decreased to 13 ppb for 3000 L per day (LPD) water during the period of 2009 to 2015. However, with an increase in demand of up to 10,000 LPD, due to an increase in local consumer population, the As in the SAR treated water increased to 30 ppb. In order to control the As and Fe levels, a novel HAIX-nano Fe resin bead media filter (Lehigh University) was installed in line with the SAR plant thereby reducing the As and Fe in delivery water to 6 ppb and 240 ppb, respectively. This resulted in low filter cost, no filter clogging over the past 6 months, and a lower maintenance cost of both the SAR plant and HAIX media filter. The combined SAR-HAIX plant has been able to maintain a favourable Eh–pH value of the water in order to immobilize the arsenic and iron consistently over the last 6 months of the study. Locally, ~600 people and two schools depend upon the safe water supplied by this plant and the operating cost comes to about $30.00 US dollars a month to produce 10,000 L of safe water per day
Source Apportionment of Heavy Metal(loid)s in the Surface Soils of Cerrito Blanco, Mexico: A Comparative Study of Three Receptor Models (APCS-MLR, PMF and UNMIX Model)
No full textIt is essential for the monitoring and conservation of soil environments to have a comprehensive awareness of the source characteristics including that of heavy metal (loid)s. This study focuses on a semi-arid mining area in Mexico where heavy metal (loid)s originated from past mining and rapid industrialisation activities, which have degraded the soil environment as well as the security of agricultural products. In this study, Cerrito Blanco, which is part of an abandoned mining region in Matehuala, Mexico was identified as the principal source of contaminants. It was also observed that the contributions of the contaminants to the overall pollution could be calculated by using a combination of multivariate statistics and receptor models. The three receptor models such as APCS-MLR, PMF, and UNMIX were used and mutually compared to improve the accuracy and quantitative assessment of source contributions. A total of eleven heavy metal(loid)s were selected for this study, out of which the mean concentrations level of As, Fe, Mn, and Zn exceeded their reference limit values. The spatial distribution mapping revealed the distribution patterns and significant effects on concentrations of heavy metal(loid)s in surface soil. APCS-MLR identified three potential sources with contribution rates of 18.16% (groundwater source), 57.33% (past mining and industrialisation), and 24.51% (natural source) respectively. Two models, namely, PMF and UNMIX were employed to establish the contributions from common pollution sources. The contributions from four common sources (groundwater, past mining and industrialisation, natural source, and human activity) contributed 15.57, 42.86, 36.06, and 5.51% according to the PMF model, but 14.73%, 45%, 31.91%, and 8.36% respectively by the UNMIX model. The results of three receptor models showed that heavy metal(loid)s concentrations were mostly influenced by past mining and industrialisation activities. As a result, the identification of the potential sources of heavy metal(loid)s performed better using the APCS-MLR model than PMF and UNMIX model due to its higher R2 value (0.90–1) and lower P/M error (1–1.15). To achieve more reliable and objective conclusions of source apportionment, it was recommended that multiple receptor models be applied
The Contamination of Water and Soil from the Dissolution of As-Bearing Mineral Waste in Matehuala, Mexico
No full textExtremely high concentrations of arsenic (As) in groundwater have been reported in central Mexico related to leachates from metallurgical wastes from an abandoned smelter. At the site, contaminated groundwater has been extensively used for maize cultivation for a long time resulting in very high soil pollution. However, the As-containing minerals’ identity, concentration, and solubility remain unresolved. In the present work, highly contaminated soil samples from the area were studied to determine total As concentrations in shallow (0–5 cm) and deepsoils (5–30 cm) and to identify and characterize As-bearing minerals and their solubility behavior. Results showed that soil samples contained up to 4.2% As and the mineralogy consisted mainly of calcite, gypsum, and quartz. Identified arsenic minerals included pharmacosiderite, bukovskýite, scorodite, beudantite, clinoclase, sodium arsenate, adamite, arsenolite, arsenopyrite, orpiment, and a mixture of calcium arsenates (guerinite, haidingerite and pharmacolite). Additionally, As was adsorbed on ferrihydrite. Soil fractionation analysis showed that up to 74% of total As was present in the most mobile fractions, e.g., soluble, exchangeable, phosphate absorbable, and slightly reducible. Furthermore, As solubility in water accounted for up to 60 mg/L at pH 7, explaining the high pollution observed in groundwater and highlighting the risk to humans and ecosystems. According to saturation indices calculations, As may derive from the dissolution of adamite, arsenolite, pharmacolite, and haidingerite, while clinoclase and ferrihydrite may precipitate, counteracting As solubility and mobilization. The results of this study increase knowledge on the identification and solubility of As-bearing minerals in calcicxerosols and semi-arid climatic conditions where considerable contamination is observed in groundwater from As-containing waste disposal
Monte Carlo Simulation of Crystal Size Distribution with Attrition Effects in a Mixed Suspension Crystallizer
No full textAnalysis of Transient Crystal Size Distribution in a Continuous Sodium Chloride Crystallizer
No full textThis work deals with the transient analysis of crystal size distribution (CSD) in a continuous sodium chloride crystallizer. The crystallization is assumed to take place under diffusion controlled conditions and the crystal growth models reported by Sen Gupta and Dutta elsewhere have been used. Monte Carlo (MC) scheme has been employed for simulation purposes. The simulation results have been compared with the available experimental data at steady state.</p
Effect of dispersions of CSD in continuous MSMPR crystallizers
No full textA general model is proposed to predict the crystal size distribution from multistage MSMPR Crystallizers taking into account shape factor, birth size and growth rate dispersions. Two cases, namely nucleation in the first crystallizer and the same process in all crystallizers have been considered. The developed equations can be solved easily by the Monte Carlo technique. The model represents an extension of the earlier work of Sen Gupta and Dutta.</p
Monte Carlo Simulation of Transient CSD in a Continuous Crystallizer under Stochastic Dispersion Effects
No full textMonte Carlo simulation is a very powerful tool for simulation of transient and steady state crystal size distribution (CSD) in a continuous crystallizer under stochastic dispersion effects. In the present work, transient CSD in a continuous crystallizer has been reported when shape factor and growth rate dispersions conform to normal distribution. For the steady state run, the algorithm reported by Sen Gupta and Dutta elsewhere has been used to validate the results obtained in the present work when the steady state is reached.</p
Monte carlo simulation of transient CSD in a continuous crystallizer
No full textA Monte Carlo simulation scheme is proposed for transient crystal size distribution in a continuous crystallizer. The suggested scheme can taken into account dispersion effects of growth rate, shape factor and birth size on crystal size distribution. This method is simple and more versatile than solution of the number balance equation or the finite‐state Markov chain model. The proposed algorithm of the process has a very simple structure and can be easily implemented on a personal computer. The present contribution is extension of an earlier work of Sen Gupta and Dutta.</p
A multivariate statistical and GIS approach to estimate heavy metal(loid)s in contaminated surface soils
Full text linkIn recent decades, there has been a growing concern over the escalating pollution of soil
with heavy metal(loid)s, which poses an immediate threat to human health, food safety,
and the overall soil environment. This research aimed to assess the extent of
contamination, spatial distribution, sources of contamination, potential ecological risks,
and health hazards associated with heavy metal(loid)s (specifically As, Cd, Co, Cr, Cu,
Fe, Mn, Ni, Pb, Sr, and Zn) by collecting soil samples from the surface soils in the
mining region of Cerrito Blanco and Matehuala, San Luis Potosi in central Mexico. In
addition to this, another study was conducted on rare trace metal(loid)s (B, Ba, Sb, Sn,
and V) and other trace metals (Ca, Mg, Na, and K) in this selected region, which shows
a level of contamination for those metals. The contamination levels of these heavy
metal(loid)s were determined using various indices such as Igeo (geo-accumulation
index), Cf (contamination factor), PLI (pollution load index), Cd (degree of
contamination), mCd (modified degree of contamination), PIN (nemerow pollution
index), EF (enrichment factor), and PERI (potential ecological risk index). Multivariate
statistical techniques, such as principal component analysis (PCA), cluster analysis, or
factor analysis, were used to identification of patterns and correlations among different
heavy metal(loid)s and soil parameters. The findings indicated a significant degree of
contamination in the surface soil due to heavy metal(loid)s. The integrated
contamination indices and the potential ecological risk index revealed high levels of
contamination and substantial ecological risks in the study areas, with particular
emphasis on the need to control As in the surface soils surrounding Matehuala. Source
identification of heavy metal(loid)s were performed using the APCS-MLR, PMF, and
UNMIX receptor models, which detected three potential sources: mining and smelting
activities, natural sources, and anthropogenic activities. The APCS-MLR model
appeared to be more suitable for identifying complex contamination sources,
demonstrating a better R2
coefficient and P/M (predicted/measured) ratio than the other
models. Mining and smelting activities were identified as the primary factors
influencing the distribution of heavy metal(loid)s in the surface soils. The most effective
GIS interpolation technique was selected to analyse the spatial distribution patterns of
heavy metal(loid) content, comparing five different GIS interpolation approaches such
as Inverse Distance Weighting (IDW), Local Polynomial (LP), Ordinary Kriging (OK),
Empirical Bayesian Kriging (EBK), and Radial Basis Functions (RBF). The results
indicated regions of significant concentrations for all heavy metal(loid)s, with the northern, western, and central parts of the study area exhibiting particularly elevated
levels. Ecological risk assessment based on PERI revealed considerable risk for As and
moderate risk for the remaining metals. Moreover, a probabilistic evaluation of health
risks indicated minimal non-carcinogenic risks (HI) for humans but significant
carcinogenic risks (CR) for both adults and children. Notably, children were found to be
more vulnerable to the health risks associated with exposure to these heavy metals
compared to adults. Consequently, enhanced monitoring efforts are necessary to address
the issue of heavy metal(loid)s contamination in the rapidly developing Matehuala
regions.James Watt Scholarshi
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