Traffic noise estimation models are useful in evaluation of the noise pollution in current circumstances. They are helpful tools for design and planning new roads and highways. Measurement of average traffic noise level is possible when traffic speed and volume are known. The objective of this study was to devise a model for prediction of highway traffic noise levels based on current traffic variables in Iran. The design of this model was to take the impact of traffic congestion into consideration and to be field tested. This study is a library research augmented by field study conducted on Saeedi Highway located south west of Tehran. The period for the field study lasted 5 days from 7-12 February, 2013. This study examined liner and non-liner methods in formulation of its model. Liner method without a fixed coefficient was the best fit for the intended model. The proposed model can serve as a decision making tool to estimate the impact of key influential factors on sound pressure levels in urban areas in Iran.

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

High amounts of air pollution in crowded urban areas are always considered as one of the major environmental challenges especially in developing countries. Despite the errors in air pollution prediction, the forecasting of future data helps air quality management make decisions promptly and properly. We studied the air quality of the Aqdasiyeh location in Tehran using factor analysis and the Box-Jenkins time series methods. The Air Quality Control Company (AQCC) of the Municipality of Tehran monitors seven daily air quality parameters, including carbon monoxide (CO), Nitrogen Monoxide (NO), Nitrogen dioxide ($NO_2$), $NO_x$, ozone ($O_3$), particulate matter ($PM_{10}$) and sulfur dioxide ($SO_2$). We applied the AQCC data for our study. According to the results of the factor analysis, the air quality parameters were divided into two factors. The first factor included CO, $NO_2$, NO, $NO_x$, and $O_3$, and the second was $SO_2$ and $PM_{10}$. Subsequently, the Box- Jenkins time series was applied to the two mentioned factors. The results of the statistical testing and comparison of the factor data with the predicted data indicated Auto Regressive Integrated Moving Average (0, 0, 1) was appropriate for the first factor, and ARIMA (1, 0, 1) was proper for the second one. The coefficient of determination between the factor data and the predicted data for both models were 0.98 and 0.983 which may indicate the accuracy of the models. The application of these methods could be beneficial for the reduction of developing numbers of mathematical modeling.

The few lignin biomarker studies conducted in tropical environments are hampered by having to use references signatures established for plants and soils characteristic of the temperate zone. This study presents a lignin biomarker analysis (vanillyls (V), p-hydroxyls (P), syringyls (S), cinnamyls (C)) of the dominant plant species and soil horizons as well as an analysis of the interrelated terrigenous organic matter (TOM) dynamics between vegetation and soil of the $Tapaj{\acute{o}}s$ river region, an active colonization front in the Brazilian Amazon. We collected and analyzed samples from 17 fresh dominant plant species and 48 soil cores at three depths (0-5 cm, 20-25 cm, 50-55 cm) from primary rainforest, fallow forest, subsistence agriculture fields and pastures. Lignin signatures in tropical plants clearly distinguish from temperate ones with high ratios of Acid/aldehyde of vanillyls ((Ad/Al)v) and P/V+S. Contrary to temperate environments, similarly high ratios in tropical soils are not related to TOM degradation along with pedogenesis but to direct influence of plants growing on them. Lignin signatures of both plants and soils of primary rainforest and fallow forest clearly distinguish from those of non-forested areas, i.e., agriculture fields and pastures. Attalea speciosa Palm trees, an invasive species in all perturbed landscapes of the Amazon, exhibit lignin signatures clearly distinct from other dominant plant species. The study of lignin signatures in tropical areas thus represents a powerful tool to evaluate the impact of primary rainforest clearing on TOM dynamics in tropical areas.

In this study, the effect of initial nitrate loading on nitrate removal and byproduct selectivity was evaluated in a continuous system. Nitrate removal decreased from 100% to 25% with the increase in nitrate loading from 10 to $300mg/L\;NO_3-N$. Ammonium selectivity decreased and nitrite selectivity increased, while nitrogen selectivity showed a peak shape in the same range of nitrate loading. The nitrate removal was enhanced at low catalyst to nitrate ratios and 100% nitrate removal was achieved at catalyst to nitrate ratio of ${\geq}33mg\;catalyst/mg\;NO_3-N$. Maximum nitrogen selectivity (47%) was observed at $66mg\;catalyst/mg\;NO_3-N$, showing that continuous Cu-Pd-NZVI system has a maximum removal capacity of 37 mg $NO_3{^-}-N/g_{catalyst}/h$. The results from this study emphasize that nitrate reduction in a bimetallic catalytic system could be sensitive to changes in optimized regimes.