| 基于APCS-MLR模型的开封市公交站周边灰尘重金属源解析及健康风险评估 |
| 摘要点击 795 全文点击 182 投稿时间：2023-07-02 修订日期：2023-08-09 |
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| 中文关键词 灰尘重金属 污染评价 源解析 健康风险 公交车站 |
| 英文关键词 dust heavy metal pollution assessment source apportionment health risk bus stops |
| 作者 | 单位 | E-mail | | 段海静 | 河南大学地理与环境学院, 开封 475004
河南大学黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学土壤重金属污染控制与修复工程研究中心, 开封 475004 | jingjingd1983@126.com | | 申浩欣 | 河南大学地理与环境学院, 开封 475004 | | | 彭超月 | 河南大学地理与环境学院, 开封 475004 | | | 任翀 | 河南大学地理与环境学院, 开封 475004 | | | 王艳锋 | 河南大学地理与环境学院, 开封 475004 | | | 刘德新 | 河南大学地理与环境学院, 开封 475004
河南大学黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学土壤重金属污染控制与修复工程研究中心, 开封 475004 | | | 王玉龙 | 河南大学地理与环境学院, 开封 475004
河南大学黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学土壤重金属污染控制与修复工程研究中心, 开封 475004 | 10130133@vip.henu.edu.cn | | 郭瑞超 | 河南大学地理与环境学院, 开封 475004
河南大学黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学土壤重金属污染控制与修复工程研究中心, 开封 475004 | | | 马建华 | 河南大学地理与环境学院, 开封 475004
河南大学黄河中下游数字地理技术教育部重点实验室, 开封 475004
河南大学土壤重金属污染控制与修复工程研究中心, 开封 475004 | |
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| 中文摘要 |
| 为揭示城市交通系统对城市生态环境质量的影响,选择受交通影响扰动较强的环境指示物——公交站地表灰尘作为研究对象,利用电感耦合等离子体质谱仪（ICP-MS）和电感耦合等离子体发射光谱仪（ICP-ASE）分别测定灰尘中8种重金属（V、Cr、Co、Ni、Cu、Zn、Cd和Pb）含量,应用地累积指数和污染负荷指数法分析灰尘重金属的污染程度和空间分布特征,通过定性（相关性分析、主成分分析）和定量[绝对因子得分-多元线性回归模型（APCS-MLR）]相结合的方法开展公交站附近地表灰尘重金属来源探讨,应用克里格空间插值法明晰重金属不同来源的空间分布特征,利用美国环境保护署提出的健康风险评价模型来评价人体健康风险.结果表明,开封市公交站地表灰尘重金属ω（V）、ω（Cr）、ω（Co）、ω（Ni）、ω（Cu）、ω（Zn）、ω（Cd）和ω（Pb）平均值依次为:68.36、59.73、5.81、19.34、40.10、208.32、1.01和49.46 mg·kg-1,灰尘中重金属（Cd、Zn、Pb、Cu、Cr）含量平均值均高于开封市周边灰尘背景值,分别是背景值的3.37、2.70、2.01、1.95和1.28倍；8种重金属的地累积指数顺序为:Cd > Zn > Pb > Cu > Cr > V > Ni > Co,其中Cd、Zn、Cu和Pb属于轻度污染水平,其他元素为无污染；源解析结果显示,Cr、Co和Ni为自然源元素,Cu、Zn、Pb和Cd为交通源元素,V则来源于工业-自然混合源.APCS-MLR结果表明,4种来源的平均贡献率依次为自然源为34.17 %,交通源为29.84 %,工业-自然混合源为14.64 %,未知源为21.35 %,其中交通源贡献率空间分布规律与交通量及公交线路密集度分布趋势一致.由健康风险评价可知,儿童的总致癌风险指数和总非致癌风险指数均高于成人,Cr为主要的非致癌因子,Cd为主要的致癌因子,自然源和交通源分别对非致癌风险和致癌风险的贡献率最高. |
| 英文摘要 |
| In order to reveal the influence of urban transportation systems on the quality of urban ecological environment, this study selected surface dust from bus stops, which is strongly disturbed by transportation, as the research object. The contents of eight heavy metals (V, Cr, Co, Ni, Cu, Zn, Cd, and Pb) in the dust were determined through inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma atomic emission spectroscopy (ICP-ASE). The spatial distribution characteristics and pollution levels of the eight heavy metals in the dust were analyzed using the geo-accumulation index method. A combined qualitative (correlation analysis and principal component analysis) and quantitative (absolute principal component scores-multiple linear regression model (APCS-MLR)) method was used to explore the sources of heavy metals in surface dust near bus stops. The spatial distribution characteristics of heavy metals from different sources were elucidated using the Kriging interpolation method. The health risk assessment model proposed by the United States Environmental Protection Agency was used to evaluate the human health risks. The results showed that the average values of ω(V), ω(Cr), ω(Co), ω(Ni), ω(Cu), ω(Zn), ω(Cd), ω(Pb), and ω(As) in the bus stop surface dust were 68.36, 59.73, 5.81, 19.34, 40.10, 208.32, 1.01, and 49.46 mg·kg-1, respectively. The concentrations of heavy metals (Cd, Zn, Pb, Cu, and Cr) in the dust were all higher than the background values in the surrounding dust, exceeding them by 3.37, 2.70, 2.01, 1.95, and 1.28 times, respectively. The order of the geo-accumulation index for the eight heavy metals was Cd > Zn > Pb > Cu > Cr > V > Ni > Co, with Cd, Zn, Cu, and Pb in the dust indicating mild pollution levels and the others showing no pollution. The source analysis results showed that Cr, Co, and Ni were natural sources, whereas Cu, Zn, Pb, and Cd were traffic sources, and V was derived from a combination of industrial and natural sources. The APCS-MLR results indicated that the average contribution rates of the four sources were as follows:natural source (34.17 %), traffic source (29.84 %), industrial-natural mixed source (14.64 %), and unknown source (21.35 %). The spatial distribution map of the contribution rate of the traffic source was consistent with the trends of traffic volume and bus route density distribution. According to the health risk assessment, the cancer risk and non-cancer risk for children were both higher than those for adults. Cr was the main non-cancer factor, and Cd was the main cancer-causing factor. Natural and traffic sources contributed the most to non-cancer risk and cancer risk, respectively. |
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