| 联合PMF模型与稳定同位素的地下水污染溯源 |
| 摘要点击 5147 全文点击 763 投稿时间：2021-10-23 修订日期：2021-12-18 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 地下水污染 源解析 PMF模型 稳定同位素 土地利用 |
| 英文关键词 groundwater pollution source apportionment PMF model stable isotope land use |
|
| 中文摘要 |
| 基于传统水质监测与污染排放的污染源识别方法，存在监测频率与识别结果模糊等限制，难以实现污染源及迁移转化的准确量化.联合多元统计分析与稳定同位素技术，以成都平原典型混合用地区地下水污染为研究对象，提出利用正定矩阵因子分析（PMF）模型识别污染主控因子，减小环境因素对污染源判别的干扰，并基于水化学分析与土地利用构建贝叶斯稳定同位素混合模型，进一步量化不同污染源对地下水典型污染物硝酸盐氮（NO3-）的贡献率.结果表明，研究区地下水NO3-、NO2-、NH4+、Mn、Fe、SO42-和Cl-均存在不同程度超标，且具有空间异质性.地下水中"三氮"主要以NO3-为主，NO3-浓度在菜地的地下水中普遍偏高（平均值为9.29 mg·L-1），其次是在养殖场（平均值为7.66 mg·L-1）和耕地（平均值为7.09 mg·L-1），在工业区最低（平均值为2.20 mg·L-1）.研究区地下水水质受原生地质作用、农业活动、水文地球化学演化、生活污染和工业污染的复合影响，且农业活动是研究区地下水NO3-增长的主要原因.研究区内农业区地下水NO3-的主要来源贡献为化肥（32%）和土壤氮（25%）；工业区地下水NO3-的主要来源贡献为污水排放（28%）和大气降雨（27%）.通过多元统计与稳定同位素技术的有机结合，有效识别了地下水污染来源及其贡献率，可为地下水污染源头防控提供科学依据. |
| 英文摘要 |
| The pollution source identification methods based on traditional water quality monitoring and pollutant discharge loading typically require a high frequency of monitoring and generate a level of uncertainty in the identification results, owing to their limitations on the accurate and quantitative assessment of pollution source identification, migration, and transformation. This study combined multivariate statistical analysis and stable isotope technology to identify groundwater pollution sources in a typical multiple land-use area of the Chengdu Plain. A positive matrix factorization (PMF) model was adopted to reduce the interference of mass environmental factors on source identification and to determine the main factors influencing groundwater quality. Subsequently, a Bayesian stable isotope mixing model was developed to quantify the apportionment of each pollution source to groundwater nitrate (NO3-) with the consideration of hydro-chemical and land-use information. The results showed that the concentrations of NO3-, NO2-, NH4+, Mn, Fe, SO42-, and Cl- in groundwater of the study area exceeded the standard to different extents, presenting spatial variation. The main form of inorganic nitrogen in groundwater was NO3-. In general, concentrations of groundwater NO3- were the highest in vegetable fields (9.29 mg·L-1 on average), followed by livestock and poultry breeding farms (7.66 mg·L-1) and arable land (7.09 mg·L-1), whereas concentrations of groundwater NO3- in industrial areas were the lowest (2.20 mg·L-1). Groundwater quality in the study area was affected by geological processes, agricultural activities, hydrogeochemical evolution, and domestic and industrial discharges. Agricultural activities were the main contributor to the increase in groundwater NO3- in the study area. Chemical fertilizer (32%) and soil nitrogen (25%) contributed greatly to groundwater NO3- in agricultural areas, whereas sewage (28%) and atmospheric precipitation (27%) contributed most groundwater NO3- in industrial areas. Thus, the combination of multivariate statistical analysis and stable isotope technology could identify groundwater pollution sources and their apportionment effectively, providing scientific support for the prevention and control of groundwater pollution. |
|
|
|
