Monitoring spatiotemporal variations of diel radon concentrations in peatland and forest ecosystems based on neural network and regression models

Concentrations of outdoor radon-222 (²²²Rn) in temperate grazed peatland and deciduous forest in northwestern Turkey were measured, compared, and modeled using artificial neural networks (ANNs) and multiple nonlinear regression (MNLR) models. The best-performing multilayer perceptron model selected out of 28 ANNs considerably enhanced accuracy metrics in emulating ²²²Rn concentrations relative to the MNLR model. The two ecosystems had similar diel patterns with the lowest ²²²Rn concentrations in the afternoon and the highest ones near dawn. Mean level (5.1?+?2.5 Bq?m^?3 h^?1) of ²²²Rn in the forest was three times smaller than that (15.8?+?9.7 Bq?m^?3) of ²²²Rn in the peatland. Mean ²²²Rn level had negative and positive relationships with air temperature and relative humidity, respectively.     

Removal of copper (II) from aqueous solution by adsorption onto low-cost adsorbents

The use of low-cost adsorbents was investigated as a replacement for current costly methods of removing metals from aqueous solution. Removal of copper (II) from aqueous solution by different adsorbents such as shells of lentil (LS), wheat (WS), and rice (RS) was investigated. The equilibrium adsorption level was determined as a function of the solution pH, temperature, contact time, initial adsorbate concentration and adsorbent doses. Adsorption isotherms of Cu (II) on adsorbents were determined and correlated with common isotherm equations such as Langmuir and Freundlich models. The maximum adsorption capacities for Cu (II) on LS, WS and RS adsorbents at 293, 313 and 333 K temperature were found to be 8.977, 9.510, and 9.588; 7.391, 16.077, and 17.422; 1.854, 2.314, and 2.954 mg g(-1), respectively. The thermodynamic parameters such as free energy (delta G0), enthalpy (delta H0) and entropy changes (delta S0) for the adsorption of Cu (II) were computed to predict the nature of adsorption process. The kinetics and the factors controlling the adsorption process were also studied. Locally available adsorbents were found to be low-cost and promising for the removal of Cu (II) from aqueous solution.     

Relationships between Aerosol Extinction Coefficients Derived from Airport Visual Range Observations and Alternative Measures of Airborne Particle Mass

Epidemiological assessments of population exposures to airborne particles are often hampered by the scarcity of available fine particle mass measurements. In an attempt to overcome this serious problem, we analyze In this paper methods for predicting fine particle (M ₁) and Inhalable particle (IP) mass concentrations using relative humidity corrected light extinction coefficient (b _ext) estimated from airport visual range (V _r) observations. The analyses presented are based on theoretical determinations as well as statistical investigations utilizing EPA's NASN and Inhalable Particle Monitoring Network (IPMN) data bases and routine airport visual range observations In twelve large U.S. cities. Our results Indicate that, after controlling for certain limitations of airport visual range data, most of the regression models developed in this paper can be applied satisfactorily to predict M _t and IP. Furthermore, our findings Indicate that a more representative formula than the commonly used meteorological range formula to predict atmospheric b _ext values in urban areas may be b _ext = (1.8 ± 0.04)/V _r. Because of known local or regional influences, however, we do suggest calibration of any predictive model which utilizes airport visibility data against site-specific aerometric data on particle mass concentrations or scattering coefficient measurements.