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Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images were used to assess the urban expansion dynamics and the corresponding thermal characteristics in Shenyang City, China. Unsupervised classification (ISODATA) and a hierarchy decision tree were applied to eight scenes of the Landsat images to derive the land use/land cover (LULC) around the Shenyang metropolitan region from 1986 to 2007. Landsat TM/ETM+ thermal infrared (TIR) images (band 6) were used to investigate the urban surface thermal patterns by retrieving land surface temperature (LST) using a mono-window algorithm. Results reveal that the built-up area has doubled from 1986 (20.2 %) to 2007 (42.3 %), most of which is converted from croplands around the urban fringe area. The built-up area has close association with the population increase (R 2 = 0.89), the gross domestic production (R 2 = 0.94), and fixed asset investments (R 2 = 0.95). These illustrate the contributions of socioeconomic factors to the rapid urban expansion in Shenyang. Three urban heat island (UHI) indices [i.e., heat effect contribution index (H i ), weighted heat unit index (D 1), and regional weighted heat unit index (D 2)] were used to characterize the urban thermal patterns for removing the phenological effects and to confirm the linkage between UHI and urban expansion. Results show that urban areas have an obvious daytime heating effect (heat source) that is strongly correlated with urban expansion, wherein a higher percentage of an impervious surface is usually associated with a higher surface temperature. Further analyses indicate that urban expansion is fairly correlated to H i ' (R 2 = 0.63) but strongly to D 2 (R 2 = 0.91). Additional research is needed to further quantify the inner urban area to gain a better understanding of UHI resulting from various heat fluxes and urban components.

This article describes a simultaneously autoregressive model applied to the erosion data collected at 17 natural lake watersheds in Greece. The methodology considers spatially correlated random area effects taking into account the information provided by neighbouring torrents/streams. The article discusses the gain obtained from modelling the spatial correlation among small area random effects useful in representing the unexplained variation of the small area target quantities.

Five two-parameter (sorptivity, S, and saturated hydraulic conductivity, Ks) vertical one-dimensional infiltration equations are compared quantitatively over the whole time range from zero to very long times. These equations are Green-Ampt (GA), Talsma-Parlange (TP), Brutsaert (B), Swartzendruber (SW), and Valiantzas (V). The results showed that the cumulative infiltration i (t) predictions of equations B, SW, and V are between the two limit soils characterized by GA and TP equations. Compared with the TP equation, the equations of B, SW, and V showed a maximum discrepancy of 3.8%, 5.6%, and 9.88%, respectively, while compared with the GA equation showed a maximum discrepancy of 14.8%, 11.4%, and 7.8%, respectively. However, the differences among SW, B, and V are lower. The study of cumulative infiltration data of four porous media showed that the S/Ks ratio may be a criterion for the appropriate infiltration equation selection. The S/Ks values for the porous media studied ranged from 2.6 to 136 min0.5. The V equation showed very good prediction of the results in the case of low S/Ks values (coarse-textured soils), while the SW equation in the case of high values (fine-textured soils). Also, the linear form of the V equation was applied for direct estimation of the two parameters, S and Ks, using vertical infiltration data of four porous media with relatively good results (0.980 < R2 < 0.999).

This study demonstrates that prior to Typhoon Morakot, the index of heavy metals such as Cd, Pb, and Cr was above moderate pollution levels in Dapeng Bay and the three neighboring rivers. During January 2007, the content of Zn metal in Dapeng Bay and Tungkang River was also above moderate pollution levels, while after the Typhoon Morakot event, all metals were at levels below the criteria for low pollution. This work has demonstrated that the samples collected from Dapeng Bay and the three neighboring river systems displayed individual crowd–distribution phenomena, indicating variability between the heavy metal content of sediments collected from Dapeng Bay and the three neighboring rivers. Understanding the spatial and temporal variability of heavy metal pollutants in the sediments of Dapeng Bay, along with pollution sources from three neighboring rivers, provides useful information in the fields of disaster management, habitat recovery, operative management, as well as ecotourism specification.

Air pollution models are often used to support decisions in air quality management. Due to the complexity of the forecasting system and difficulty in acquiring precise enough input data, an environmental prognosis of air quality with an analytical model of the air pollution dispersion is burdened with a substantial share of uncertainty, especially as regards urban areas. To ignore the uncertainty in the modeling would lead to incorrect policy decisions, with further negative environmental and health consequences. This paper presents a case study which shows how emission uncertainty of air pollutants generated by the industry, traffic, and the municipal sector relates to concentrations measured at receptor points. The computational experiment was implemented in the Warsaw metropolitan area, Poland. The main source of this adverse environmental impact is the transportation system, including the transit traffic. The Monte Carlo technique was used for assessing the key uncertainty factors. Several types of pollution species that are characteristic for the urban atmospheric environment (e.g., PM10, PM2.5, NO x , SO2, Pb) were included in the analysis. The results show significant spatial variability of the modeled uncertainty. The reason of this variability is discussed in detail. It depends not only on the category of the emission source but also on the contributing emission sources and their quantity.

This paper describes a new methodology for modeling contaminant transport in ground water for (1) better quantifying the magnitude of exposure in a contaminated aquifer, (2) characterizing the spatial and temporal variation of exposure in a heterogeneous aquifer, and (3) providing more tools for characterizing population exposure. This methodology is also applied to a semi-hypothetical case study.

This study looks at the role of biophysical sectors and world markets on an integrated economic assessment of climate change impacts on Egypt. Using the outcome from Global Circulation Models (GCMs) and a global model of world food trade (the Basic Linked System; BLS) — changes in crop yields; crop water demands; water and land resource availability; and world market prices were applied to a dynamic, computable general equilibrium model of Egypt (the Standard National Model of the BLS). Modeling results consistently showed that the net effect of climate change on the macro indicator of per capita GDP was not great — regardless of the level of integration. This outcome was only realized through autonomous economic adjustments which implied significant socio-economic and structural change. Including or excluding certain biophysical sectors and world markets influenced the development path and the nature of the autonomous adjustments. The importance of a particular biophysical sector or of world markets was also dependent upon the specific climate change scenario.