平水期和丰水期殷村港污染物浓度时空变异比较研究

殷村港是太湖的重要入湖河流之一.本研究在2010年4月(平水期)和6月(丰水期)对其从上游滆湖到下游太湖入口进行了2次全程高密度水质监测,并以化学需氧量(COD)和总氮(TN)为例,采用基于河流水系距离的地统计方法分析殷村港沿程污染物浓度的时空变化特征.结果表明:①COD和TN在平水期和丰水期呈现显著的时空差异.6月COD浓度显著低于4月,TN浓度变化截然相反.②2次监测期间,COD入河负荷较为稳定.4月和6月的COD浓度半变异函数皆呈指数型空间相关结构.该时期COD浓度变化主要受水文环境影响.③2次监测期间,源于农业的氮素负荷显著增加.大量无序的个体农户施肥行为使得6月TN浓度无显著空间相关性.6月TN浓度变化受农业施肥和水文环境的双重影响.鉴于太湖流域水情复杂,污染严重,地统计方法是研究该区域河流污染物浓度分布特征和预测污染物浓度的有力工具.

Explore the Spatial and Temporal Patterns of Water Pollution in the Yincungang Canal of the Lake Taihu Basin, China

Two high-density snap-shot samplings were conducted along the Yincungang canal, one important tributary of the Lake Tai, in April (low flow period) and June (high flow period) of 2010. Geostatistical analysis based on the river network distance was used to analyze the spatial and temporal patterns of the pollutant concentrations along the canal with an emphasis on chemical oxygen demand (COD) and total nitrogen (TN). Study results have indicated: 1 COD and TN concentrations display distinctly different spatial and temporal patterns between the low and high flow periods. COD concentration in June is lower than that in April, while TN concentration has the contrary trend. 2 COD load is relatively constant during the period between the two monitoring periods. The spatial correlation structure of COD is exponential for both April and June,and the change of COD concentration is mainly influenced by hydrological conditions. 3 Nitrogen load from agriculture increased significantly during the period between the two monitoring periods. Large amount of chaotic fertilizing by individual farmers has led to the loss of the spatial correlation among the observed TN concentrations. Hence, changes of TN concentration in June are under the dual influence of agricultural fertilizing and hydrological conditions. In the view of the complex hydrological conditions and serious water pollution in the Lake Taihu region, geostatistical analysis is potentially a useful tool for studying the characteristics of pollutant distribution and making predictions in the region.