PM<Subscript>2.5</Subscript> particulates and metallic elements (Ni,Cu, Zn,Cd and Pb) study in a mixed area of summer season in Shalu,Taiwan

PM_2.5 has become an important environmental issue in Taiwan during the past few years. Moreover, electricity increased significantly during the summertime and TTPP generated by coal burning base is the main electricity provider in central Taiwan. Therefore, summer season has become the main research target in this study. The ambient air concentrations of particulate matter PM_2.5 and PM₁₀ collected by using VAPS at a mixed characteristic sampling site were studied in central Taiwan. The results indicated that the average daytime PM_2.5 and PM₁₀ particulate concentrations were occurred in May and they were 44.75 and 57.77 µg/m³ in this study. The results also indicated that the average nighttime PM_2.5 and PM₁₀ particulate concentrations were occurred in June and they were 38.19 and 45.79 µg/m³ in this study. The average PM_2.5/PM₁₀ ratios were 0.7 for daytime, nighttime and 24-h sampling periods in the summer for this study. This value was ranked as the lowest ratios when compared to the other seasons in previous study. Noteworthy, the results further indicated that the metallic element Pb has the mean highest concentrations for 24-h, daytime and nighttime sampling periods when compared to those of the other metallic elements (Ni, Cu, Zn and Cd). The average mean highest metallic Pb concentrations in PM10 were 110.7, 203.0 and 207.2 ng/m³ for 24-h, daytime and nighttime sampling periods in this study. And there were 59.53, 105.2 and 106.6 ng/m³ for Pb in PM2.5 for 24-h, daytime and nighttime sampling periods, respectively. Moreover, the results further indicated that mean metallic element Pb concentrations on PM_2.5 and PM₁₀ were all higher than those of the other elements for 24 h, day and nighttime.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen (T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks (ANNs) and support vector machines (SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination (R²), Nash–Sutcliff efficiency (NSE), relative efficiency criteria (d_rel). Additionally, Latin-Hypercube one-factor-at-a-time (LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage. However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Iron isotopic fractionation in industrial emissions and urban aerosols

A study on tropospheric aerosols involving Fe particles with an industrial origin is tackled here. Aerosols were collected at the largest exhausts of a major European steel metallurgy plant and around its near urban environment. A combination of bulk and individual particle analysis performed by SEM–EDX provides the chemical composition of Fe-bearing aerosols emitted within the factory process (hematite, magnetite and agglomerates of these oxides with sylvite (KCl), calcite (CaCO₃) and graphite carbon). Fe isotopic compositions of those emissions fall within the range (0.08‰ < δ⁵⁶Fe < +0.80‰) of enriched ores processed by the manufacturer (−0.16‰ < δ⁵⁶Fe < +1.19‰). No significant evolution of Fe fractionation during steelworks processes is observed. At the industrial source, Fe is mainly present as oxide particles, to some extent in 3–4 μm aggregates. In the close urban area, 5 km away from the steel plant, individual particle analysis of collected aerosols presents, in addition to the industrial particle type, aluminosilicates and related natural particles (gypsum, quartz, calcite and reacted sea salt). The Fe isotopic composition (δ⁵⁶Fe = 0.14 ± 0.11‰) measured in the close urban environment of the steel metallurgy plant appears coherent with an external mixing of industrial and continental Fe-containing tropospheric aerosols, as evidenced by individual particle chemical analysis. Our isotopic data provide a first estimation of an anthropogenic source term as part of the study of photochemically promoted dissolution processes and related Fe fractionations in tropospheric aerosols.     

Laboratory investigation of flux reduction from dense non-aqueous phase liquid (DNAPL) partial source zone remediation by enhanced dissolution

This study investigated the benefits of partial removal of dense nonaqueous phase liquid (DNAPL) source zones using enhanced dissolution in eight laboratory scale experiments. The benefits were assessed by characterizing the relationship between reductions in DNAPL mass and the corresponding reduction in contaminant mass flux. Four flushing agents were evaluated in eight controlled laboratory experiments to examine the effects of displacement fluid property contrasts and associated override and underride on contaminant flux reduction (R(j)) vs. mass reduction (R(m)) relationships (R(j)(R(m))): 1) 50% ethanol/50% water (less dense than water), 2) 40% ethyl-lactate/60% water (more dense than water), 3) 18% ethanol/26% ethyl-lactate/56% water (neutrally buoyant), and 4) 2% Tween-80 surfactant (also neutrally buoyant). For each DNAPL architecture evaluated, replicate experiments were conducted where source zone dissolution was conducted with a single flushing event to remove most of the DNAPL from the system, and with multiple shorter-duration floods to determine the path of the R(j)(R(m)) relationship. All of the single-flushing experiments exhibited similar R(j)(R(m)) relationships indicating that override and underride effects associated with cosolvents did not significantly affect the remediation performance of the agents. The R(j)(R(m)) relationship of the multiple injection experiments for the cosolvents with a density contrast with water tended to be less desirable in the sense that there was less R(j) for a given R(m). UTCHEM simulations supported the observations from the laboratory experiments and demonstrated the capability of this model to predict R(j)(R(m)) relationships for non-uniformly distributed NAPL sources.     

Inhalation of concentrated ambient particulate matter near a heavily trafficked road stimulates antigen-induced airway responses in mice

The goal of this study was to test the following hypotheses: (1) exposure to mobile emissions from mobile sources close to a heavily trafficked roadway will exacerbate airway inflammation and allergic airway responses in a sensitized mouse model, and (2) the magnitude of allergic airway disease responses will decrease with increasing distance from the roadway. A particle concentrator and a mobile exposure facility were used to expose ovalbumin (OVA)-sensitized BALB/c mice to purified air and concentrated fine and concentrated ultrafine ambient particles at 50 m and 150 m downwind from a roadway that was heavily impacted by emissions from heavy duty diesel-powered vehicles. After exposure, we assessed interleukin (IL)-5, IL-13, OVA-specific immunoglobulin E, OVA-specific immunoglobulin G1, and eosinophil influx as biomarkers of allergic responses and numbers of polymorphonuclear leukocytes as a marker of inflammation. The study was performed over a two-year period, and there were differences in the concentrations and compositions of ambient particulate matter across those years that could have influenced our results. However, averaged over the two-year period, exposure to concentrated ambient particles (CAPs) increased the biomarkers associated with airway allergies (IL-5, immunoglobulin E, immunoglobulin G1 and eosinophils). In addition, mice exposed to CAPs 50 m downwind of the roadway had, on the average, greater allergic responses and showed greater indications of inflammation than did mice exposed to CAPs 150 m downwind. This study is consistent with the hypothesis that exposure to CAPs close to a heavily trafficked roadway influenced allergic airway responses.     

A Comparative Validation of the RAM and Modified SAI Models for Short Term SO2 Concentrations in Detroit

The RAM model provided by the U.S. EPA has been applied to the metropolitan Detroit area for SO₂ concentrations and is compared to concentrations predicted by a numerical model and to field data obtained by the 14 station air sampling network maintained by the Wayne County Air Pollution Control Division. Great care was taken to develop the emission inventory. Based upon examination of the temporal and spatial correspondence of the respective model predictions and observed concentrations, the correlation coefficients for the 24-hour averaged data, the correlation coefficients for over 700 3-hour averaged observations, and the cumulative frequency distributions of the model output and observations, it is concluded that the numerical model provides a superior predictive tool to evaluate cause and effect relations, but that the RAM model, at far lower cost, predicts the correct magnitude of the worst events. Hence RAM might well be used in the Detroit Area for statistically based regulatory decisions.     

A hierarchical end-of-life decision model for determining the economic levels of remanufacturing and disassembly under environmental regulations

Remanufacturing, in contrast to material recycling and disposal, can reduce environmental impacts by retaining the geometrical form of the product, thereby regarded as a more eco-efficient approach. In this paper, an end-of-life (EOL) decision model for remanufacturing options is presented to facilitate remanufacturing. The proposed model, in order to maximize the economic value of remanufacturing options while meeting environmental regulations, takes an integrative approach to EOL-option decision-making. Also presented in this paper is a hierarchical approach that represents both the overall hierarchical structure of a product and the interconnections among components. Illustrative examples are provided to demonstrate the effectiveness of the model.