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數據品質目標-重金屬污染土壤整治後如何驗證的案例評估

Data Quality Objectives - An Example Presented for Evaluating the Attainment of Cleanup of Heavy Metal Polluted Agricultural Soils

摘要


數據品質目標總共含有七個步驟,用於環境採樣計畫中的數據收集,而藉由所收集的資料判斷是否支持所作的決定。其以系統性的計畫步驟去收集數據,包括樣品數目、何時、何處及如何採集樣品,而後利用統計假設測試區分成兩個明確且相對的假設,雖然步驟內容是敘述性的,但能明確說明整個計畫的流程規劃。而這些步驟在台灣的環境數據收集計畫中不曾被採用過,本文以彰化市為例說明如何運用數據品質目標。由於台灣受重金屬污染的農地必須在2004年以前,採用翻轉稀釋或酸淋洗法加以整治,因此強烈推薦利用此有系統且具邏輯性的數據品質目標步驟,以使參與驗證計畫團隊成員能更有效的規劃採樣設計及所需做成的決定。根據預算金額、土地利用型態及整治方法,建議採用平均值參數、簡單逢機系統採樣設計、序列區間深度及一公頃面積的整治單位,做為評估彰化市受污染農業土壤經整治後是否達成整治目標。

並列摘要


The Data Quality Objectives (DQO) is a seven-step approach to develop environmental sampling design for data collection activities that support decision making. This process, widely used in U.S. EPA projects, employs systematic planning for data collecting which includes the numbers to collect, and when, where, and how to collect samples, and subsequently differentiate between two clear alternatives by using statistical hypothesis test. DQO is an iterative but powerful systematic planning process for wholly clarifying the sampling project. However, this process has never been used in Taiwan environmental data collecting programs. One example taken from Chunghua City is presented to illustrate how to implement the procedure of data quality objectives. Since the agricultural soils contaminated by heavy metals in Taiwan are required to be renovated by using acid-leaching or turnover-diluted method by 2004, the systematic and logic process of DQO s strongly recommended for it will help members of planning team to organize their sampling design and decision making proposals. In so far as the amounts of budget, types of land use, and methods of remediation are concerned, mean statistical parameters, simple systematic sampling design, sequential interval depth, and the limitation of remediation units of about one hectare are strongly recommended as methods for verifying the attainment of cleanup for polluted agricultural soil in Chunghua City.

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