This study presents a meticulously edited article that assesses the uncertainty associated with contamination dispersion in a porous aquifer environment. The article employs a conceptual model incorporating finite difference flow modeling and particle tracking methods grounded in particle movement theory within groundwater flow. The groundwater flow model utilizes an automatic calibration technique, PES, and independent verification, to minimize statistical discrepancies in the optimized parameters. The statistical summary of the model output reveals the average pollution concentration in the study area, specifically the landfill aquifer in northern Iran. The results indicate that the initial contamination value at the boundaries is zero, whereas 91,802 units are discharged into the sea. Moreover, approximately 3,317,290,003 pollution units have been extracted from pumping wells along the contamination plume over 10 years. Furthermore, the drainage network releases 1,778,680,001 pollution units, resulting in a net outflow of 5,082,740,007 units from the same drainage network. Additionally, 9,912,180,003 units were discharged from the leaky boundaries. It should be noted that these statistics can be reversed depending on the specified period. The difference between the input and output pollution values in the study area indicates a net accumulation of 8192 units. To analyze the sensitivity of contamination dispersion in the porous aquifer environment and the variations in coastal drainage patterns obtained from the finite difference simulation, the explicit and implicit methods, as well as the random walk particle tracking method, were investigated in a portion of the basin area using the South Side Implicit method. One of the main objectives of this sensitivity analysis is to evaluate the results of the uncalibrated qualitative model and examine the particle dispersion in the adjacent area of the contaminated landfill region to assess the error of the proposed model by developing a limited test. The calculated error or computational difference between the explicit and implicit methods and the applet model is considered negligible. For example, the calculated error between the explicit and backward methods and the applet model ranges from 0.086 to 0.372 units for a concentration interval of 0.512 units and 0.680 units for a time interval 0.537. Therefore, the computational differences are not significant. Furthermore, based on theoretical sensitivity analysis of contamination dispersion, the laboratory model demonstrates that the flow direction in this modeling is from west to east, as expected.

这项研究提出了一篇精心编辑的文章，评估了多孔含水层环境中污染物扩散的不确定性。本文采用的概念模型结合了有限差分流建模和基于地下水流中粒子运动理论的粒子跟踪方法。地下水流模型采用自动校准技术、PES 和独立验证，以最大限度地减少优化参数的统计差异。模型输出的统计摘要揭示了研究区域的平均污染浓度，特别是伊朗北部垃圾填埋场含水层。结果表明，边界处的初始污染值为零，而排入海中的数量为 91,802 单位。此外，10 年来，沿污染羽流从抽水井中提取了约 3,317,290,003 个污染单位。此外，排水管网排放污染物1,778,680,001个单位，导致该排水管网净流出5,082,740,007个单位。此外，还有9,912,180,003个单位从渗漏边界排出。应该注意的是，这些统计数据可以根据指定的时间段进行反转。研究区输入和输出污染值之差表明净累积量为8192个单位。为了分析多孔含水层环境中污染物扩散的敏感性以及通过有限差分模拟获得的沿海排水模式的变化，在流域的一部分中研究了显式和隐式方法以及随机游走粒子跟踪方法使用 South Side 隐式方法的区域。该敏感性分析的主要目标之一是评估未校准定性模型的结果，并检查受污染垃圾填埋场区域邻近区域的颗粒分散情况，以通过开发有限测试来评估所提出模型的误差。显式和隐式方法与小程序模型之间的计算误差或计算差异被认为可以忽略不计。例如，对于浓度间隔为 0.512 单位的显式方法和后向方法与小程序模型之间的计算误差范围为 0.086 到 0.372 单位，对于时间间隔 0.537 的范围为 0.680 单位。因此，计算差异并不显着。此外，基于污染物扩散的理论敏感性分析，实验室模型表明该模型中的流向是从西向东，正如预期的那样。