Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

In recent years, the application of titanium dioxide (TiO₂) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NO_x concentration in the air as a function of traffic count (T_r) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO₂ on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO₂ solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NO_x measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.     

Reliable method for prediction of the flash point of various classes of amines on the basis of some molecular moieties for safety measures in industrial processes

This work presents a novel, reliable and simple method of estimating the flash point of various types of flammable amines, which are important for safety measures in industrial processes. Different amines include aliphatic amines such as primary, secondary, tertiary and cyclic amines as well as aromatic amines and hetero arenes containing nitrogen heteroatom. The proposed correlation is based on the contribution of some specific molecular moieties and functional groups, which can easily be used for any types of amines. Intermolecular forces are important in the new method, which are counted by two increasing and decreasing parameters. The root mean square (rms) deviation is 18 K for different classes of amines including 133 diverse compounds. The estimated flash points have been compared with one of the best available predictive methods, which gives much lower value of the rms deviation.     

Pollution,human health risk assessment and spatial distribution of toxic metals in urban soil of Yazd City,Iran

Environmental Geochemistry and Health - Heavy metal pollution significantly reduces the quality of the environment and threatens human health, especially in industrial cities. This study...     

Photocatalytic degradation of 2,4-DNT in simulated wastewater by magnetic CoFe2O4/SiO2/TiO2 nanoparticles

Environmental Science and Pollution Research - Discharge of 2,4-dinitrotoluene (2,4-DNT) into the environment leads to a serious soil and water sources pollution problem, due to toxicity and...     

Mobility and efficacy of 2,4-D herbicide from slow-release delivery systems based on organo-zeolite and organo-bentonite complexes

This research aimed to develop slow-release formulations (SRFs) of 2,4-dichlorophenoxyacetic acid (2,4-D) using zeolite and bentonite minerals modified with cetyltrimethylammonium (CTMA) surfactant. Adsorption–desorption, greenhouse bioassay and column experiments were carried out to assess the potential of the SRFs to control weeds while reducing the herbicide leaching losses to deep layers of soil. The results showed that only 6.5 mmol 2,4-D kg^?1 was retained by Na-bent, and the herbicide was not adsorbed by Na-zeol at all. The surface modification with CTMA surfactant, however, improved the 2,4-D adsorption capacity of the zeolite and bentonite up to 207.5 and 415.8 mmol kg^?1, respectively. The synthesized organo-minerals slowly released the retained 2,4-D discharging 22 to 64% of the adsorbed 2,4-D to the solution phase within 7 days. The SRFs significantly (P = 0.05) reduced the herbicide mobility within the soil columns keeping a great portion of the herbicide active ingredient in the upper 5 cm soil layer. The SRFs were significantly (P = 0.05) as effective as the free technical herbicide in weed control without harming the ryegrass as the main plant. Therefore, the synthesized SRFs could be considered as useful tools for weed control in sustainable agriculture.     

Assessment of the effect of welding fumes on welders’ cognitive failure and health-related quality of life

Introduction. This study examined whether cognitive symptoms and health-related quality of life can be affected by welding fume exposure. Method. Participants consisted of welders (n?=?40) and welder assistants (n?=?25) from welding units as the exposed group, and office workers (n?=?44) as the non-exposed group. All participants were studied using ambient air monitoring and two types of questionnaires: the Cognitive Failures Questionnaire (CFQ) and the 36-item Short Form Health Survey (SF-36). Results. Welders and welder assistants were exposed to higher concentrations of all airborne metals than office employees, except for aluminum and chromium (p?p?M (SD)?=?54.84 (17.88)). The relationships between total CFQ score and the measured concentration of nickel at peak work rate was significant for welders. Conclusion. Cognitive symptoms and health-related quality of life were not related to the measures of welding fume exposure and further research should be performed to find other influencing factors.     

Environmentally friendly synthesis of Fe2O3@SiO2 nanocomposite: characterization and application as an adsorbent to aniline removal from aqueous solution

Environmental Science and Pollution Research - Silica-based nanocomposite syntheses employ many harmful substances, which, in turn, demand the development of new synthetic environmental-friendly...     

Phosphorus leaching from a sandy soil in the presence of modified and un-modified adsorbents

Phosphorus (P) leaching from a sandy soil was investigated in the presence of modified and unmodified clay minerals and nanoparticles (NPs). Compared with control soil, amended soil with NPs had the highest percentage of P retention than amended soil with clay minerals. Among the adsorbents used, the highest percentage of P retention was produced by Al₂O₃-chitosan while the lowest percentage of P retention was by zeolite. Data measured for P leaching after using adsorbents were used to predict P leaching using transport model. PHREEQC model was able to model P leaching from control and amended soil. After leaching, P values in control and amended soil were fractionated by a sequential extraction procedure. Concentration of P in Ca-bound fraction (HCl–P) after application of modified and unmodified clay minerals and NPs (except TiO₂ and Al₂O₃) increased and decreased, respectively. Saturation indices (SIs) and P speciation were assessed using the Visual MINTEQ version 2.3 program. According to the SIs, leaching P from control and amended soil with different adsorbent was controlled by dissolution of hydroxyapatite. The results indicated that used adsorbents can reduce P leaching from the sandy soil. Thus, retention of P by amended soil reduced a risk in terms of groundwater contamination with P.     

Biosynthesis of iron nanoparticles and their application in removing phosphorus from aqueous solutions

This paper reports the biosynthesis of nanoscale zero-valent iron (nZVI) using the extracts of Shirazi thyme leaf (Th-nZVI) and pistachio green hulls (P-nZVI). Scanning electron microscopy verified the successful synthesis of the poorly crystalline nZVI with a spherical shape and diameter in the range of 40–70 nm. According to X-ray diffraction and Fourier transform infrared spectroscope analyses, the synthesised nZVI were composed of iron oxides nanoparticles and ployphenol obtained from Shirazi thyme leaf and pistachio green hulls extracts acting as both reducing and capping agents. The phosphorus removal efficiency of Th-nZVI and P-nZVI increased with time and reached equilibrium at about 4 and 2h, respectively. Sorption of phosphorus on both sorbents was observed to be pH-dependent with maximum phosphorus removal occurring in the pH range of 2–5. Langmuir, Freundlich, Redlich–Peterson, and Temkin models were used to describe phosphorus sorption at pH 5 and maximum sorption capacity for Th-nZVI and P-nZVI was about 40.52 and 29.33?mg?g^?1, respectively. Correlation coefficient (R²) and standard errors of estimate showed that the Elovich model was better than other models at describing the kinetic data. These results suggested that the synthesised nZVI with Shirazi thyme leaf and pistachio green hulls extracts could be employed as an efficient sorbent for the remediation of phosphorus from contaminated water sources.