Source apportionment of PAHs and n-alkanes in respirable particles in Tehran,Iran by wind sector and vertical profile

The vertical concentration profiles and source contributions of polycyclic aromatic hydrocarbons (PAHs) and n-alkanes in respirable particle samples (PM₄) collected at 10, 100, 200 and 300-m altitude from the Milad Tower of Tehran, Iran during fall and winter were investigated. The average concentrations of total PAHs and total n-alkanes were 16.7 and 591 ng/m³, respectively. The positive matrix factorization (PMF) model was applied to the chemical composition and wind data to apportion the contributing sources. The five PAH source factors identified were: ‘diesel’ (56.3 % of total PAHs on average), ‘gasoline’ (15.5 %), ‘wood combustion, and incineration’ (13 %), ‘industry’ (9.2 %), and ‘road soil particle’ (6.0 %). The four n-alkane source factors identified were: ‘petrogenic’ (65 % of total n-alkanes on average), ‘mixture of petrogenic and biomass burning’ (15 %), ‘mixture of biogenic and fossil fuel’ (11.5 %), and ‘biogenic’ (8.5 %). Source contributions by wind sector were also estimated based on the wind sector factor loadings from PMF analysis. Directional dependence of sources was investigated using the conditional probability function (CPF) and directional relative strength (DRS) methods. The calm wind period was found to contribute to 4.4 % of total PAHs and 5.0 % of total n-alkanes on average. Highest average concentrations of PAHs and n-alkanes were found in the 10 and 100 m samples, reflecting the importance of contributions from local sources. Higher average concentrations in the 300 m samples compared to those in the 200 m samples may indicate contributions from long-range transport. The vertical profiles of source factors indicate the gasoline and road soil particle-associated PAHs, and the mixture from biogenic and fossil fuel source-associated n-alkanes were mostly from local emissions. The smaller average contribution of diesel-associated PAHs in the lower altitude samples also indicates that the restriction of diesel-fueled vehicle use in the central area of Tehran has been effective in reducing the PAHs concentration.     

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     

Recovery of heavy metals from waste printed circuit boards: statistical optimization of leaching and residue characterization

Environmental Science and Pollution Research - Despite attempts to enhance the recycling of waste printed circuit boards (WPCBs), the simultaneous recovery of major metals of WPCBs using an...     

Phytoextraction of heavy metals from contaminated soil,water and atmosphere using ornamental plants: mechanisms and efficiency improvement strategies

Environmental Science and Pollution Research - Accumulation of heavy metals (HMs) in soil, water and air is one of the major environmental concerns worldwide, which mainly occurs due to...     

Who cares about climate change reporting in developing countries? The market response to,and corporate accountability for,climate change in Bangladesh

Undoubtedly, climate change is one of the greatest problems facing today’s world. Despite this, traditional research has ignored the market response to, and accountability for, climate change reporting in developing countries. Hence, this study critically examines climate change reporting practices in the most affected countries in the world, with specific reference to Bangladesh. In the study, 32 semi-structured interviews and 71 annual reports are evaluated. Using legitimacy theory, the study contributes to building an understanding of companies’ attitude toward stakeholder accountability regarding climate change. The study finds that Bangladeshi companies are reporting climate change information on an average of 2.23 %. More specifically, the study demonstrates that large companies are reporting on more climate change issues than others because of their legitimized positions in the market. Again, a lack of regulation and a culture of low social accountability among the companies contribute to a very low level of disclosure on climate change. Surprisingly, multinationals are not providing satisfactory disclosure. The study has policy implications in developing countries for both local policy makers (the government) and international policy makers (the Intergovernmental Panel on Climate Change, the European Union, the World Bank, the UN Environment Programme, the International Energy Agency and the World Economic Forum) as to how to engage local companies so that they become more socially accountable to climate change reporting.     

Health risk assessment due to heavy metal exposure from commonly consumed fish and vegetables

Contamination of heavy metals in fish and vegetables is regarded as a major crisis globally, with a large share in many developing countries. In Bogra District of Bangladesh, concentrations of six heavy metals, i.e., chromium (Cr), nickel (Ni), copper (Cu), arsenic (As), cadmium (Cd) and lead (Pb), were evaluated in the most consumed vegetables and fish species. The sampling was conducted during February–March 2012 and August–September 2013. The levels of metals varied between different fish and vegetable species. Elevated concentrations of As, Cd and Pb were observed in vegetable species (Solanum tuberosum, Allium cepa and Daucus carota), and fish species (Anabas testudineus and Heteropneustes fossilis) were higher than the FAO/WHO permissible limits, indicating these three metals might pose risk from the consumption of these vegetable and fish species. The higher concentration of heavy metals in these vegetable species might be due to the higher uptake from soil and sediment ingestion behavior in fish species. Multivariate principal component analysis (PCA) showed significant anthropogenic contributions of Cr, Ni, Cu and Pb in samples as the PCA axis scores were correlated with scores of anthropogenic activities. Target hazard quotients showed that the intakes of Cu, As and Pb through vegetables and fish were higher than the recommended health standards, indicated non-carcinogenic risk. Therefore, intakes of these elements via fish and vegetables for Bangladeshi people are a matter of concern.     

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Knowing the fraction of methane (CH₄) oxidized in landfill cover soils is an important step in estimating the total CH₄ emissions from any landfill. Predicting CH₄ oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH₄ oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O₂) concentration at a depth of 10 cm in cover soil, and CH₄ concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH₄ oxidation with a MSE of 0.0082, a coefficient of determination (R ²) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.

Implications:

To date, no attempts have been made to predict the percent of CH₄ oxidation within landfill cover soils using an ANN. This paper presents modeling of CH₄ oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH₄ oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH₄ oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH₄ emission and how much it contributes to global warming.     

River water quality assessment using environmentric techniques: case study of Jakara River Basin

Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH₄). Partial correlation analysis (r _p) results showed that there is a strong relationship between salinity and turbidity (r _p?=?0.930, p?=?0.001) and BOD₅ and COD (r _p?=?0.839, p?=?0.001) controlling for the linear effects of conductivity and NH₄, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD₅, COD, SS, NH₄, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Abundance of denitrification genes under different peizometer depths in four Irish agricultural groundwater sites

This study examined the relationship between the abundance of bacterial denitrifiers in groundwater at four sites, differing with respect to overlaying land management and peizometer depth. Groundwater was sourced from 36 multilevel piezometers, which were installed to target different groundwater zones: (1) subsoil, (2) subsoil to bedrock interface, and (3) bedrock. The gene copy concentrations (GCCs), as gene copies per liter, for bacterial 16S rRNA genes and the denitrifying functional genes, nirK, nirS, and nosZ, were determined using quantitative polymerase chain reaction assays. The results were related to gaseous nitrogen emissions and to the physicochemical properties of the four sites. Overall, nirK and nirS abundance appeared to show no significant correlation to N₂O production (P?=?0.9989; P?=?0.3188); and no significant correlation was observed between nosZ and excess N₂ concentrations (P?=?0.0793). In the majority of piezometers investigated, the variation of nirK and nirS gene copy concentrations was considered significant (P?<?0.0001). Dissolved organic carbon (DOC) decreased with aquifer depth and ranged from 1.0–4.0 mg l^?1, 0.9–2.4 mg l^?1, and 0.8–2.4 mg l^?1 within piezometers located in the subsoil, subsoil/bedrock interface, and bedrock depths, respectively. The availability of increasing DOC and the depth of the water table were positively correlated with increasing nir and nosZ GCCs (P?=?0.0012). A significant temporal correlation was noted between nirS and piezometer depth (P?<?0.001). Interestingly, the nirK, nirS, and nosZ GCCs varied between piezometer depths within specific sites, while GCCs remained relatively constant from site to site, thus indicating no direct impact of agricultural land management strategies investigated on denitrifier abundance.