Measurement of Opacity and Particulate Emissions with an On-Stack Transmissometer

An on-stack transmissometer system which is designed to provide a precision measurement of the opacity of visible emissions is described. The sources of error in opacity measurements with regard to recent EPA emission monitoring requirements and planned specifications are discussed. Sources of error are voltage changes, temperature changes, light source and detector aging and effects of ambient light. Other major operational errors are caused by alignment drift and soiling drift. The methods employed to minimize these errors achieve an accuracy of ±3% of span and a maintenance free operational period of 3 months. The relationships between optical density, opacity and transmittance are described. The instrument measurement can be correlated with dust loading provided the particle size distribution is constant. Examples are given of correlations obtained between optical density and particulate concentration in the gas on various types of emission sources and the observed error margins are summarized.     

Determination of biocides and pesticides by on-line solid phase extraction coupled with mass spectrometry and their behaviour in wastewater and surface water

This study focused on the input of hydrophilic biocides into the aquatic environment and on the efficiency of their removal in conventional wastewater treatment by a mass flux analysis. A fully automated method consisting of on-line solid phase extraction coupled to LC-ESI-MS/MS was developed and validated for the simultaneous trace determination of different biocidal compounds (1,2-benzisothiazoline-3-one (BIT), 3-Iodo-2-propynylbutyl-carbamate (IPBC), irgarol 1051 and 2-N-octyl-4-isothiazolinone (octhilinone, OIT), carbendazim, diazinon, diuron, isoproturon, mecoprop, terbutryn and terbutylazine) and pharmaceuticals (diclofenac and sulfamethoxazole) in wastewater and surface water. In the tertiary effluent, the highest average concentrations were determined for mecoprop (1010 ng/L) which was at comparable levels as the pharmaceuticals diclofenac (690 ng/L) and sulfamethoxazole (140 ng/L) but 1-2 orders of magnitude higher than the other biocidal compounds. Average eliminations for all compounds were usually below 50%. During rain events, increased residual amounts of biocidal contaminants are discharged to receiving surface waters.     

Effect of vegetation in pilot-scale horizontal subsurface flow constructed wetlands treating sulphate rich groundwater contaminated with a low and high chlorinated hydrocarbon

In order to characterize the effect of vegetation on performance of constructed wetlands (CWs) treating low and high chlorinated hydrocarbon, two pilot-scale horizontal subsurface flow (HSSF) CWs (planted with Phragmites australis and unplanted) treating sulphate rich groundwater contaminated with MCB (monochlorobenzene, as a low chlorinated hydrocarbon), (about 10 mg L⁻¹), and PCE (perchloroethylene, as a high chlorinated hydrocarbon), (about 2 mg L⁻¹), were examined. With mean MCB inflow load of 299 mg m⁻² d⁻¹, the removal rate was 58 and 208 mg m⁻² d⁻¹ in the unplanted and planted wetland, respectively, after 4 m from the inlet. PCE was almost completely removed in both wetlands with mean inflow load of 49 mg m⁻² d⁻¹. However, toxic metabolites cis-1,2-DCE (dichloroethene) and VC (vinyl chloride) accumulated in the unplanted wetland; up to 70% and 25% of PCE was dechlorinated to cis-1,2-DCE and VC after 4 m from the inlet, respectively. Because of high sulphate concentration (around 850 mg L⁻¹) in the groundwater, the plant derived organic carbon caused sulphide formation (up to 15 mg L⁻¹) in the planted wetland, which impaired the MCB removal but not statistically significant. The results showed significant enhancement of vegetation on the removal of the low chlorinated hydrocarbon MCB, which is probably due to the fact that aerobic MCB degraders are benefited from the oxygen released by plant roots. Vegetation also stimulated completely dechlorination of PCE due to plant derived organic carbon, which is potentially to provide electron donor for dechlorination process. The plant derived organic carbon also stimulated dissimilatory sulphate reduction, which subsequently have negative effect on MCB removal.     

Remediation of heavy metal polluted sediment by suspension and solid-bed leaching: estimate of metal removal efficiency

Remediation of heavy metal polluted sediment by extracting the metals with sulfuric acid can be performed as follows: abiotic suspension leaching, microbial suspension leaching, abiotic solid-bed leaching, and microbial solid-bed leaching. Abiotic leaching means that the acid is directly added, while microbial leaching means that the acid is generated from sulfur by microbes (bioleaching). These four principles were compared to each other with special emphasis on the effectiveness of metal solubilization and metal removal by subsequent washing. Abiotic suspension leaching was fastest, but suspending the solids exhibits some disadvantages (low solid content, costly reactors, permanent input of energy, high water consumption, special equipment required for solid separation, large amounts of waste water, sediment properties hinder reuse), which prevent suspension leaching in practice. Abiotic solid-bed leaching implies the supply of acid by percolating water which proceeds slowly due to a limited bed permeability. Microbial solid-bed leaching means the generation of acid within the bed and has been proven to be the only principle applicable to practice. Metal removal from leached sediment requires washing with water. Washing of solid beds was much more effective than washing of suspended sediment. The kinetics of metal removal from solid beds 0.3, 0.6 or 1.2m in height were similar; when using a percolation flow of 20lm(-2)h(-1), the removal of 98% of the mobile metals lasted 57-61h and required 8.5, 4.2 or 2.3lkg(-1) water. This means, the higher the solid bed, the lower the sediment-mass-specific demand for time and water.     

Modeling interactions of Hg(II) and bauxitic soils

The adsorptive interactions of Hg(II) with gibbsite-rich soils (hereafter SOIL-g) were modeled by 1-pK surface complexation theory using charge distribution multi-site ion competition model (CD MUSIC) incorporating basic Stern layer model (BSM) to account for electrostatic effects. The model calibrations were performed for the experimental data of synthetic gibbsite-Hg(II) adsorption. When [NaNO(3)] > or = 0.01M, the Hg(II) adsorption density values, of gibbsite, Gamma(Hg(II)), showed a negligible variation with ionic strength. However, Gamma(Hg(II)) values show a marked variation with the [Cl(-)]. When [Cl(-)] > or = 0.01M, the Gamma(Hg(II)) values showed a significant reduction with the pH. The Hg(II) adsorption behavior in NaNO(3) was modeled assuming homogeneous solid surface. The introduction of high affinity sites, i.e., >Al(s)OH at a low concentration (typically about 0.045 sites nm(-2)) is required to model Hg(II) adsorption in NaCl. According to IR spectroscopic data, the bauxitic soil (SOIL-g) is characterized by gibbsite and bayerite. These mineral phases were not treated discretely in modeling of Hg(II) and soil interactions. The CD MUSIC/BSM model combination can be used to model Hg(II) adsorption on bauxitic soil. The role of organic matter seems to play a role on Hg(II) binding when pH>8. The Hg(II) adsorption in the presence of excess Cl(-) ions required the selection of high affinity sites in modeling.     

Retrospective monitoring of synthetic musk compounds in aquatic biota from German rivers and coastal areas

The polycyclic musk compounds HHCB (Galaxolide) and AHTN (Tonalide) are commonly used as synthetic fragrances in personal care products and household cleaners. These and other synthetic musk fragrances were quantified in different aquatic samples from the German Environmental Specimen Bank (ESB). While HHCB and AHTN were found in almost all samples, most of the other musk fragrances were detected only in a few samples and mostly at lower concentration levels. Blue mussels from the North Sea showed varying levels of 0.5-1.7 ng g(-1) ww for HHCB and 0.4-2.5 ng g(-1) ww for AHTN (ww, wet weight) in the period from 1986 to 2000, while blue mussels from the Baltic Sea were only slightly contaminated with synthetic musk fragrances. Lipid weight-related concentrations of synthetic musk compounds in blue mussels were higher than in eelpout muscles, bladder wrack and herring gull eggs. In comparison to the marine specimens, muscles of bream from German rivers had higher concentrations of HHCB and AHTN. The ranges of HHCB and AHTN concentrations in bream from the Elbe River were 545-6400 ng g(-1) lw and 48-2130 ng g(-1) lw, respectively (lw, lipid weight; five sampling sites, period 1993-2003). In the Rhine River, HHCB and AHTN levels of bream muscles were highest at the Iffezheim site (up to 9750 ng g(-1) lw HHCB, 1998). Even higher synthetic musk levels were detected in bream from the rivers Saale and Saar. In recent years, levels of both compounds determined in bream from most sampling sites have decreased from maximum values in the 1990s. As the concentrations of AHTN have decreased faster, the ratio of HHCB to AHTN increased from 2-4 in the 1990s to 10-20 in recent years.     

A tiered procedure for assessing the formation of biotransformation products of pharmaceuticals and biocides during activated sludge treatment

Upon partial degradation of polar organic micropollutants during activated sludge treatment, transformation products (TPs) may be formed that enter the aquatic environment in the treated effluent. However, TPs are rarely considered in prospective environmental risk assessments of wastewater-relevant compound classes such as pharmaceuticals and biocides. Here, we suggest and evaluate a tiered procedure, which includes a fast initial screening step based on high resolution tandem mass spectrometry (HR-MS/MS) and a subsequent confirmatory quantitative analysis, that should facilitate consideration of TPs formed during activated sludge treatment in the exposure assessment of micropollutants. At the first tier, potential biotransformation product structures of seven pharmaceuticals (atenolol, bezafibrate, ketoprofen, metoprolol, ranitidine, valsartan, and venlafaxine) and one biocide (carbendazim) were assembled using computer-based biotransformation pathway prediction and known human metabolites. These target structures were screened for in sludge-seeded batch reactors using HR-MS/MS. The 12 TPs found to form in the batch experiments were then searched for in the effluents of two full-scale, municipal wastewater treatment plants (WWTPs) to confirm the environmental representativeness of this first tier. At the second tier, experiments with the same sludge-seeded batch reactors were carried out to acquire kinetic data for major TPs that were then used as input parameters into a cascaded steady-state completely-stirred tank reactor (CSTR) model for predicting TP effluent concentrations. Predicted effluent concentrations of four parent compounds and their three major TPs were corroborated by comparison to 3-day average influent and secondary effluent mass flows from one municipal WWTP. CSTR model-predicted secondary effluent mass flows agreed within a factor of two with measured mass flows and confidence intervals of predicted and measured mass flows overlapped in all cases. The observed agreement suggests that the combination of batch-determined transformation kinetics with a simple WWTP model may be suitable for estimating aquatic exposure to TPs formed during activated sludge treatment. Overall, we recommend the tiered procedure as a realistic and cost-effective approach to include consideration of TPs of wastewater-relevant compounds into exposure assessment in the context of prospective chemical risk assessment.     

Bioethanol from waste: Life cycle estimation of the greenhouse gas saving potential

This paper considers two alternative feedstocks for bioethanol production, both derived from household waste—Refuse Derived Fuel (RDF) and Biodegradable Municipal Waste (BMW). Life Cycle Assessment (LCA) has been carried out to estimate the GHG emissions from bioethanol using these two feedstocks. An integrated waste management system has been considered, taking into account recycling of materials and production of bioethanol in a combined gasification/bio-catalytic process. For the functional unit defined as the ‘total amount of waste treated in the integrated waste management system’, the best option is to produce bioethanol from RDF—this saves up to 196 kg CO₂ equiv. per tonne of MSW, compared to the current waste management practice in the UK.However, if the functional unit is defined as ‘MJ of fuel equiv.’ and bioethanol is compared with petrol on an equivalent energy basis, the results show that bioethanol from RDF offers no saving of GHG emissions compared to petrol. For example, for a typical biogenic carbon content in RDF of around 60%, the life cycle GHG emissions from bioethanol are 87 g CO₂ equiv./MJ while for petrol they are 85 g CO₂ equiv./MJ. On the other hand, bioethanol from BMW offers a significant GHG saving potential over petrol. For a biogenic carbon content of 95%, the life cycle GHG emissions from bioethanol are 6.1 g CO₂ equiv./MJ which represents a saving of 92.5% compared to petrol. In comparison, bioethanol from UK wheat saves 28% of GHG while that from Brazilian sugar cane – the best performing bioethanol with respect to GHG emissions – saves 70%. If the biogenic carbon of the BMW feedstock exceeds 97%, the bioethanol system becomes a carbon sequester. For instance, if waste paper with the biogenic carbon content of almost 100% and a calorific value of 18 MJ/kg is converted into bioethanol, a saving of 107% compared to petrol could be achieved. Compared to paper recycling, converting waste paper into bioethanol saves 460 kg CO₂ equiv./t waste paper or eight times more than recycling.