Design of an optofluidic biosensor using the slow-light effect in photonic crystal structures.

The authors propose a biosensor architecture based on the selective infiltration of photonic crystal (PhC) structures. The proposed sensor consists of a ring cavity coupled to an optofluidic slow-light waveguide in a PhC platform. A high potential sensitivity of 293 nm/refractive index unit is numerically demonstrated, while maintaining an ultracompact footprint.

Analysis and assessment of ground-level ozone measured at two stations in Tehran.

In this study, variations of ambient ozone level are thoroughly analysed according to the monitored data in a mixed residential, commercial and industrial city, Tehran, based on considering the meteorological factors. Ozone as a pollutant shows typical annual, weekly and diurnal cycles. This analysis has shown that the ozone level concentrations were below the WHO guidelines in Tehran during 2000-2003. The relation between ozone level at two different stations (Aghdasieh and Fatemi) is found (r = 0.51). The ozone level response to meteorological parameters is investigated. The results suggest that the ozone level is affected (positively or negatively) by meteorological conditions, e.g. relative humidity, solar radiation, air temperature, wind speed and wind direction.

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.