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.     

Impact of industrial wastewater disposal on surface water bodies in Mostord area north Greater Cairo

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Dynamical coupling of PBPK/PD and AUC-based toxicity models for arsenic in tilapia Oreochromis mossambicus from blackfoot disease area in Taiwan

A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models were developed for arsenic (As) in tilapia Oreochromis mossambicus from blackfoot disease area in Taiwan. The PBPK/PD model structure consisted of muscle, gill, gut wall, alimentary canal, and liver, which were interconnected by blood circulation. We integrate the target organ concentrations and dynamic response describing uptake, metabolism, and disposition of As and the associated area-under-curve (AUC)-based toxicological dynamics following an acute exposure. The model validations were compared against the field observations from real tilapia farms and previously published uptake/depuration experimental data, indicating that predicted and measured As concentrations in major organs of tilapia were in good agreement. The model was utilized to reasonably simulate and construct a dose-dependent dynamic response between mortality effect and equilibrium target organ concentrations. Model simulations suggest that tilapia gills may serve as a surrogate sensitive biomarker of short-term exposure to As. This integrated As PBPK/PD/AUC model quantitatively estimates target organ concentration and dynamic response in tilapia and is a strong framework for future waterborne metal model development and for refining a biologically-based risk assessment for exposure of aquatic species to waterborne metals under a variety of scenarios.     

Temporary natural resource cartels

We analyze the behavior of a nonrenewable resource cartel that anticipates being forced, at some date in the future, to break up into an oligopolistic market in which its members will then have to compete as rivals. Under reasonable assumptions about the value function of the individual firms in the oligopolistic equilibrium that follows the breakup, we show that the cartel will then produce more over the same interval of time than it would if there were no threat of dissolution, and that its rate of extraction is a decreasing function of the cartel's life; that there are circumstances under which the cartel will attach a negative marginal value to the resource stocks, in which case the rate of depletion will be increasing over time during the cartel phase; that, for a given date of dissolution, the equilibrium stocks allocated to the post-cartel phase will increase as a function of the total initial stocks, whereas those allocated to the cartel phase will increase at first, but begin decreasing beyond some level of the total initial stocks.     

Population structure of the common sole (<Emphasis Type="Italic">Solea solea</Emphasis>) in the Northeastern Atlantic and the Mediterranean Sea: revisiting the divide with EPIC markers

Spatial and temporal population genetic structures of the common sole, Solea solea, were studied in Northeastern Atlantic and Mediterranean Sea populations, using three polymorphic exon-primed intron-crossing (EPIC) markers. Results demonstrated significant multilocus differentiation among Eastern Mediterranean and a group composed by Western Mediterranean and Atlantic populations (θ = 0.150, P < 0.001), but also suggested unrecorded genetic differentiation of the Adriatic Sea population. No pattern of isolation-by-distance was recorded across the range covered by sampling, from the Kattegat to the Aegean Sea. Conversely to genetically structured Mediterranean populations, Atlantic populations ranging from Denmark to Portugal could be considered as representative of the same panmictic unit (θ = 0.009, not significant). Results further demonstrated stability of multilocus genetic structure among temporarily replicated cohort samples [0+, 1+, subadults] from several coastal and estuarine locations from Bay of Biscay, excepted for the amylase locus Am2B3-2 at one location (Pertuis d’Antioche). Despite coherence of such observed patterns of multilocus differentiation with previous allozymic surveys in sole, and with patterns generally obtained for other marine fish species, single-locus results from EPICs indicated divergent coalescence schemes supporting a complex response to ecology and history of sole’s populations. Results stress the use of nuclear genes such as EPIC markers to investigate population structure, but also historical, demographic, and possibly selective processes in marine fishes.     

Stratospheric temperature monitoring using a vibrational Raman lidar. Part 1: aerosols and ozone interferences

Lidar measurements of temperature for the upper troposphere and lower stratosphere are commonly derived by the Raman technique. Lidar signals derived from vibrational Raman processes have been subjected to numerous simulation tests to examine their sensitivity to the presence of aerosols and ozone in the atmosphere. The influence of aerosols characteristics (wavelength dependence of aerosol extinction and particle phase function) and of ozone concentration on Raman temperature profiles is estimated. Simulations indicate large temperature deviations for post-volcanic conditions. For a Raman backscatter at 607 nm, bias is below 1 K for a total optical depth less than 9 x 10(-3) in the case of a stratospheric contamination and less than 6 x 10(-3) for a tropospheric contamination. The effect of aerosols depends on phase function and a few parameters such as altitude, optical depth and the shape of the high-altitude cloud. The wavelength dependence of aerosol extinction has some influence only for severe post-volcanic conditions (Scattering Ratio, SR >2). For a Raman backscatter at 387 nm, bias is larger and can be significant even in background aerosol conditions. Changes in the ozone density profile lead to significant Raman temperature deviations only for some specific conditions. Results suggest that both aerosol and ozone corrections are necessary to obtain an accuracy better than the 1 K requested for most atmospheric applications.     

Life cycle energy use,costs, and greenhouse gas emission of broiler farms in different production systems in Iran—a case study of Alborz province

In order to achieve sustainable development in agriculture, it is necessary to quantify and compare the energy, economic, and environmental aspects of products. This paper studied the energy, economic, and greenhouse gas (GHG) emission patterns in broiler chicken farms in the Alborz province of Iran. We studied the effect of the broiler farm size as different production systems on the energy, economic, and environmental indices. Energy use efficiency (EUE) and benefit-cost ratio (BCR) were 0.16 and 1.11, respectively. Diesel fuel and feed contributed the most in total energy inputs, while feed and chicks were the most important inputs in economic analysis. GHG emission calculations showed that production of 1000 birds produces 19.13 t CO_2-eq and feed had the highest share in total GHG emission. Total GHG emissions based on different functional units were 8.5 t CO_2-eq per t of carcass and 6.83 kg CO_2-eq per kg live weight. Results of farm size effect on EUE revealed that large farms had better energy management. For BCR, there was no significant difference between farms. Lower total GHG emissions were reported for large farms, caused by better management of inputs and fewer bird losses. Large farms with more investment had more efficient equipment, resulting in a decrease of the input consumption. In view of our study, it is recommended to support the small-scale broiler industry by providing subsidies to promote the use of high-efficiency equipment. To decrease the amount of energy usage and GHG emissions, replacing heaters (which use diesel fuel) with natural gas heaters can be considered. In addition to the above recommendations, the use of energy saving light bulbs may reduce broiler farm electricity consumption.     

Photocatalytic demulsification of oil/water emulsions containing nonionic surfactant

Environmental Science and Pollution Research - Separation of oil-water (OW) emulsions is investigated using a photocatalytic demulsification approach. Experiments were conducted using two types of...