Using the polychaete Arenicola marina to determine toxicity and bioaccumulation of PAHS bound to sediments

Transportation system has contributed significantly to the development of human civilization; on the other hand it has an enormous impact on the ambient air quality in several ways. In this paper the air and noise pollution at selected sites along three sections of National Highway was monitored. Pakistan National Highway Authority has started a Highway Improvement program for rehabilitations and maintenance of National highways to improve the traffic flows, and would ultimately improve the air quality along highways. The ambient air quality and noise level was monitored at nine different locations along these sections of highways to quantify the air pollution. The duration of monitoring at individual location was 72 h. The most of the sampling points were near the urban or village population, schools or hospitals, in order to quantify the air pollution at most affected locations along these roads. A database consisting of information regarding the source of emission, local metrology and air quality may be created to assess the profile of air quality in the area.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

Integration of Remote Sensed and In-situ Data in an Analysis of the Air Pollution Effects on Terrestrial Ecosystems in the Border Areas between Norway and Russia

Transboundary air pollution from industries in Nikel and Zapolyarnij has caused severe damage to the environment in Southern-Varanger in Norway and in Pechenga municipality in Russia. The work presented in this paper focuses on the integration of in-situ air pollution data with remote sensing based land cover maps. Land cover maps have been utilised to detect changes in the major land cover types within the area. The major change in the environment was the decrease of the sensitive lichen-dominated land cover types, and the increase of bilberry-dominated land cover types and finally the increase of the land cover types with the greatest air pollution stress (industrial barren, barren, and partly damaged vegetation, defoliated forests, lichen removal). A GIS based method for assessing the relationship of the remotely sensed land cover maps with the environmental condition parameters was developed and applied. By comparing the results from this analysis we observed that the land cover types with the greatest stress had the largest concentrations of SO₂ in the ground air layer, while the land cover types with minor damage (the remaining lichen-dominated vegetation) had rather low concentrations of sulphur dioxide in the ground air layer. The area of the land cover types with the greatest stress (industrial barren, barren and partly damaged vegetation) has increased in the period 1973–1988, and the degradation is carried out in a such manner that sensitive mountain and lichen vegetation formations have been transformed into a more barren-like environment. The increase in the emissions has also transferred the natural barrens which also consisted of some sparse vegetation into a complete barren with little vegetation left. Also the epilitic lichens and mosses on bare rocks and stones were also removed by the high concentrations of SO₂. The land cover types with minor damage (with the remaining lichen-dominated vegetation) had rather low concentrations of the contaminants (SO₂, Ni and S), while the partly damaged and damaged land cover types had the highest concentrations of the contaminants. An exception was the Ni and S concentrations found in class 11 Industrial barrens which were lower than expected. Associations between the degradation and the SO₂ concentration in the air were also documented. The conclusion from this analysis is that the in-situ data support the observations of damaged vegetation and industrial barrens imaged by the Landsat satellites, especially in the surroundings of Nikel and Zapolyarnij.