紫外辐射增强对大豆影响的进一步分析

紫外辐射增强对大豆影响的进一步分析郑有飞简慰民李秀芬旦巴（南京气象学院应用气象系，南京２１００４４）关键词紫外辐射；大豆；影响．随着大气臭氧层的不断减薄，地表接受的紫外辐射量不断增加，预计未来六十多年内，地表紫外线辐射量将增加４％—２０％［１，２］...     

Using a social learning configuration to increase Vietnamese smallholder farmers’ adaptive capacity to respond to climate change

Social learning is crucial for local smallholder farmers in developing countries to improve their adaptive capacity and to adapt to the current and projected impacts of climate change. While it is widely acknowledged that social learning is a necessary condition for adaptation, few studies have systematically investigated under which conditions particular forms of social learning are most successful in improving adaptive capacity of the most vulnerable groups. This study aims to design, implement and evaluate a social learning configuration in a coastal community in Vietnam. We make use of various methods during four workshop-based interventions with local smallholder farmers: interviews with key farmers and commune leaders, farmer-to-farmer learning, participatory observations and focus group discussions. The methods for evaluation of social learning configuration include in-depth interviews, focus group discussions and structured survey interviews. Our findings show that the social learning configuration used in this study leads to an increased problem ownership, an enhanced knowledge-base with regard to climate change impacts and production adaptation options, improved ability to see connections and interdependencies and finally, strengthened relationships and social cohesion. The results suggest that increased social learning in the community leads to increase in adaptive capacity of smallholder farmers and improves both their economic and environmental sustainability. We discuss the key lessons for designing learning configurations that can successfully enhance adaptive capacity and smallholder farmers’ agency and responsiveness to the challenges posed by climate change impacts.     

Sensitivity Analysis of Aggregated Indices for Integrated Assessment with a Case Study of the Mid-Atlantic Region

Environmental indicators are often aggregated into a single index for various purposes in environmental studies. Aggregated indices derived from the same data set can differ, usually because the aggregated indices' sensitivities are not thoroughly analyzed. Furthermore, if a sensitivity analysis is carried out, it is not presented in a transparent fashion to policy decision-makers. This paper presents a method of generating various aggregated environmental indices and analyzing their sensitivities via the use of the fuzzy set concept. Results show that several insights into the environmental conditions of the study area (e.g., the distribution of good or bad values of indicators at a watershed and or across the region) can be revealed in the sensitivity analysis of aggregated indices.     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

     

Trace metals solubility in rainwater: evaluation of rainwater quality at a watershed area, Istanbul

In this study, 79 bulk precipitation samples were collected at two sampling sites near Büyükçekmece Lake, one of the important drinking water sources of Istanbul, for the period of October 2001 to July 2002. The study comprised the determination of trace and toxic metals concentrations in rain water. The concentrations of the metals in this study were found to be higher than those reported by other researchers around the world. The solubility of toxic metals was found in the order of Cd > Cu > V > Zn > Ni > Pb > Cr. Solubility of metals under acidic conditions (pH < 5.5) was approximately five times higher than those under neutral conditions with Cd as the most soluble metal (50% soluble). Statistical evaluations including seasonal variations, crustal enrichment factors, and correlation matrix were discussed to identify the possible sources of these pollutants. The study revealed that anthropogenic elements were highly enriched especially for Cd > Cu > Pb which were found to be highly enriched. Significant portion of Cu and Pb could be increased by the effect of local sources like cement industry in the area; however, the rest of the investigated trace metals could be brought to the sampling site by long-range transport to the Büyükçekmece Lake watershed area.     

Quantifying the Effects of Forest Management Strategies on the Production of Forest Values: Timber,Carbon, Oxygen,Water, and Soil

Forest management practices alter forest structure quantified with ecosystem characteristics and values. In this paper, we utilized a forest management simulation model to assess the effects of three forest management strategies focusing on timber production, carbon sequestration, oxygen production, soil erosion, and water production of a forest management unit in Turkey. A forest simulation model “ETÇAPSimülasyon” was developed and used to project forest ecosystem development over 100 years under three forest management policies of timber-oriented forest management (TFM), multipurpose forest management (MFM), and no intervention (NI). The results showed that TFM strategy produced more timber and its net present value than MFM and NI strategies did. The amount of carbon sequestration and oxygen production potential was also found to be the highest with TFM strategy than with the MFM and NI strategies. Compared with the other strategies, however, NI strategy produced the highest amount of water production and soil losses over the planning horizon. The effects of a forest management strategy depend mainly on the initial forest structure, the rate of development and the level of forest management interventions. Therefore, forest dynamics under various management strategies should be explained before the final management decision. Understanding long-term effects of any management strategies on forest structure will provide the basis for better reaching the management objectives.     

Utilization of high-volume mine tailing and by-products in composite binder production: hardened properties and sustainable development

Journal of Material Cycles and Waste Management - This study was designed to investigate the hardened performance of the paste specimens produced using a composite binder with high volumes of mine...