No abstract

No abstract

Airborne particulate matter with a diameter of 2.5 microns or less (PM2.5), as well as slightly bigger particles (PM10), arrive from the westerly direction and collect in the city centre of Tehran, the capital of Iran. The statistical characteristics and daily trend of the air quality index (AQI) in Theran were studied over an 11-year period (2002- 2012). Various statistical analyses were applied including descriptive statistics, correlation analysis, trend analysis and the sequential nonparametric Mann-Kendall test. The significance of the series was investigated by regression analysis and Kriging interpolation. It was found that Tehran's daily AQI increased by 11.8% over the study period, with the frequency distribution of days with good and average air quality showing a strongly declining trend. The AQI of Tehran was shown to contain a large part of PM10 and PM2.5, the latter having the largest contribution (coefficient=0.853).

This study was conducted to apply the enhanced differential interferometric process to interferometric data obtained from C-band Advanced Synthetic Aperture Radar (ASAR) and Phased Array type L-Band Synthetic Aperture Radar (PALSAR) systems, in the Marand plain, Iran. In advance, the capability of each sensor was examined with regard to the signal coherency for sensor-target interactions, which emphasized on the terrific excellency of PALSAR (L-band) to ASAR (C-band) measurements in study area. In the interferometric process, in addition to resolving the topography and baseline related errors (which is conventional in the standard D-InSAR process), subtle quantitative methods were outlined to minimize the secondary, but momentous errors (i.e., orbital and atmospheric) from differential phases. Subsequently, based on the outlined process, the mean deformation as well as three-dimensional displacement maps (using ascending and descending modes) were generated. The deformation maps significantly indicated the downward motion of the surface with the maximum rate of −0.5 millimetre per day for the period, i.e., from 2004 to 2010, with relative dominance in eastern and central to northern parts of the Marand plain. Finally, the results were validated and the source of deformation was inspected using field data such as seismic history; changes in piezometric levels and cross-checking the results from radar measurements itself.

Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of nonstationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current and future (2011-2030, 2045-2065, and 2080-2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011-2030, 2045-2065, and 2080-2099) during winter and summer in a range of + 4.21 and +4.7°C, and +3.62 and +3.55°C, respectively. HadCM3 analysis also revealed an increase in minimum (∼+ 3.03°C) and maximum (∼+3.3°C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann-Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods (p value=0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation (p value=0.0027), minimum (p value=0.000000029) and maximum (p value = 0.00016) temperatures. Based on nonstationary analysis and an upward trend in downscaled temperature extremes, climate change may control mangrove development in the future.