Wang Minghao, Wang Yong, Duan Lijie, Liu Xiaoyang, Jia Haifeng, Zheng Binghui
Chinese Research Academy of Environmental Sciences, Beijing, 100012, People's Republic of China.
School of Environment, Tsinghua University, Beijing, 100084, People's Republic of China.
Environ Sci Pollut Res Int. 2022 Nov;29(51):77927-77944. doi: 10.1007/s11356-022-19696-9. Epub 2022 Jun 10.
The landscape analysis model establishes a quantitative relationship between landscape patterns and pollution processes. The spatial heterogeneity within and between landscapes affects the pollutant transmission process and originates from the superposition effect of terrestrial geographical and morphological characteristics. This study aimed to develop a new method to estimate the pollutant loss rate. From the perspective of the flow process of pollutants entering a water body, the interaction between each landscape unit and adjacent unit during pollutant migration was simulated along the pollutant migration flow path. The role of pollutants affected by external forces in the process of migration could be divided into "promoting" and "hindering." Four indices were proposed to simulate the pollutant loads entering the lake. The linear coefficients between the load of the pollutants chemical oxygen demand (COD), ammoniacal nitrogen (NH-N), total nitrogen (TN), and total phosphorus (TP) entering the lake and the pollutant load emission weighted by the upstream and downstream confluence ratio index were 0.930, 0.835, 0.925, and 0.795, respectively, and the non-linear variance explanation coefficients were 87.70%, 87.50%, 87.60%, and 84.70%, respectively. When the surface resistance was integrated into the index as a parameter, the linear and nonlinear correlation coefficients were significantly improved. The linear coefficients were 0.952, 0.897, 0.919, and 0.939, respectively, and the non-linear variance explanations were 99.00%, 97.30%, 95.10%, and 97.30%, respectively. The spatial distribution of landscape surface resistance reflects the spatial movement trend of pollutants from different sources. The indices characterizing the promoting and hindering effects could be integrated to calculate the loss rate of pollutant load entering the lake from landscape units at different locations in the basin space.
景观分析模型建立了景观格局与污染过程之间的定量关系。景观内部和之间的空间异质性影响污染物传输过程，其源于陆地地理和形态特征的叠加效应。本研究旨在开发一种估算污染物损失率的新方法。从污染物进入水体的流动过程角度，沿着污染物迁移流动路径模拟了各景观单元与相邻单元在污染物迁移过程中的相互作用。外力作用下污染物在迁移过程中的作用可分为“促进”和“阻碍”。提出了四个指标来模拟进入湖泊的污染物负荷。进入湖泊的污染物化学需氧量（COD）、氨氮（NH-N）、总氮（TN）和总磷（TP）负荷与上游和下游汇流比指数加权后的污染物负荷排放之间的线性系数分别为0.930、0.835、0.925和0.795，非线性方差解释系数分别为87.70%、87.50%、87.60%和84.70%。当将表面阻力作为参数纳入指标时，线性和非线性相关系数均显著提高。线性系数分别为0.952、0.897、0.919和0.939，非线性方差解释分别为99.00%、97.30%、95.10%和97.30%。景观表面阻力的空间分布反映了不同来源污染物的空间运动趋势。表征促进和阻碍作用的指标可综合起来计算流域空间中不同位置景观单元进入湖泊的污染物负荷损失率。
