Comparison of growth performance of Lolium perenne L., Dactylis glomerata L. and Agropyron elongatum (Host.) P. Beauv. for erosion control in Turkey

This study was carried out in plastic containers to compare growth performances of perennial ryegrass, orchardgrass and tall wheatgrass to be given priority in revegetation studies in Turkey. Three pre-germinated seeds of each grass species were planted separately into the soil in the black plastic containers. Seedlings were harvested 2, 4 and 6 months after planting pre-germinated seeds and measured for percentage of seedling emergence, rooting depth, height growth, leaf and tiller development and shoot and root weights. Germination percentage was 97.8% for perennial ryegrass, 64.1% for orchardgrass and 11.6% for tall wheatgrass and perennial ryegrass had the greatest whereas tall wheatgrass had the lowest seedling emergence. Two months old rooting depth was 25.66 cm for perennial ryegrass, 20.56 cm for orchardgrass and 30.10 cm for tall wheatgrass. At the end of the study, perennial ryegrass developed about 104 tillers per plant while they were 21.4 and 36.6 tillers per plant for orchardgrass and tall wheatgrass, respectively. Orchardgrass produced greater shoot and root biomasses than tall wheatgrass and similar to perennial ryegrass. All these meant that perennial ryegrass had a better growth performance than orchardgrass and tall wheatgrass to be used for erosion control.     

An online air pollution forecasting system using neural networks

In this work, an online air pollution forecasting system for Greater Istanbul Area is developed. The system predicts three air pollution indicator (SO(2), PM(10) and CO) levels for the next three days (+1, +2, and +3 days) using neural networks. AirPolTool, a user-friendly website (http://airpol.fatih.edu.tr), publishes +1, +2, and +3 days predictions of air pollutants updated twice a day. Experiments presented in this paper show that quite accurate predictions of air pollutant indicator levels are possible with a simple neural network. It is shown that further optimizations of the model can be achieved using different input parameters and different experimental setups. Firstly, +1, +2, and +3 days' pollution levels are predicted independently using same training data, then +2 and +3 days are predicted cumulatively using previously days predicted values. Better prediction results are obtained in the cumulative method. Secondly, the size of training data base used in the model is optimized. The best modeling performance with minimum error rate is achieved using 3-15 past days in the training data set. Finally, the effect of the day of week as an input parameter is investigated. Better forecasts with higher accuracy are observed using the day of week as an input parameter.     

Effect of meteorological parameters on fine and coarse particulate matter mass concentration in a coal-mining area in Zonguldak, Turkey

In this work, the effect of meteorological parameters and local topography on mass concentrations of fine (PM2.5) and coarse (PM2.5-10) particles and their seasonal behavior was investigated. A total of 236 pairs of samplers were collected using an Anderson Dichotomous sampler between December 2004 and October 2005. The average mass concentrations of PM2.5, PM2.5-10, and particulate matter less than 10 microm in aerodynamic diameter (PM10) were found to be 29.38, 23.85, and 53.23 microg/m3, respectively. The concentrations of PM2.5 and PM10 were found to be higher in heating seasons (December to May) than in summer. The increase of relative humidity, cloudiness, and lower temperature was found to be highly related to the increase of particulate matter (PM) episodic events. During non-rainy days, the episodic events for PM2.5 and PM10 were increased by 30 and 10.7%, respectively. This is a result of the extensive use of fuel during winter for heating purposes and also because of stagnant air masses formed because of low temperature and low wind speed over the study area.     

Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey

Inhalable particulate matter (PM10) has been monitored at several stations by Istanbul Municipality. On the other hand, information about fine fraction aerosols (PM2.5) in Istanbul atmosphere was not reported. In this study, 86 daily aerosol samples were collected between July 2002 and July 2003. The PM10 annual arithmetic mean value of 47.1 microg m(-3), was lower than the Turkish air quality standard of 60 microg m(-3). On the other hand, this value was found higher than the annual European Union air quality PM(10) standard of 40 microg m(-3). Furthermore, the annual mean concentration of PM2.5 20.8 microg m(-3) was found higher than The United States EPA standard of 15 microg m(-3). The statistics and relationships of fine, coarse, and inhalable particles were studied. Cyclic behavior of the monthly average concentrations of PM10 and PM2.5 data were investigated. Several frequency distribution functions were used to fit the measured data. According to Chi-squared and Kolmogorov-Smirnov tests, the frequency distributions of PM2.5 and PM10 data were found to fit Log-logistic functions.     

Assessment of potential benefits of traffic and urban mobility reductions during COVID-19 lockdowns: dose-response calculations for material corrosions on built cultural heritage

Environmental Science and Pollution Research - Air pollution, particularly in urban areas, puts human health in danger and has adverse impacts on the built environment. It can accelerate the...