楊蕾，羅翔，王雅，張亞南，陳景文

工業(yè)生態(tài)與環(huán)境工程教育部重點實驗室，大連理工大學環(huán)境學院，大連 116024

有機污染物的被動采樣材料-水分配系數(shù)的QSAR研究

楊蕾，羅翔，王雅，張亞南，陳景文*

工業(yè)生態(tài)與環(huán)境工程教育部重點實驗室，大連理工大學環(huán)境學院，大連 116024

被動采樣材料-水分配系數(shù)；聚乙烯；聚丙烯酸酯；硅橡膠；定量構效關系

Received 15 April 2016 accepted 26 May 2016

近年來，被動采樣技術在水中痕量有機污染物的監(jiān)測領域得到了廣泛應用。半透膜采樣裝置(SPMDs)[1]、聚乙烯被動采樣器(PE)[2]和梯度擴散薄膜技術(DGT)[3]等被動采樣技術被用于測定污染物的自由溶解態(tài)的濃度，對于生物有效性評價具有重要意義。在被動采樣過程中，被動采樣器通過吸附作用將水中的污染物富集到采樣材料上，從而得到污染物的時間加權平均濃度[4]。有機污染物的被動采樣材料-水分配系數(shù)(KPW)，是衡量被動采樣器性能和進行優(yōu)化的一個重要指標[5]。目前，大部分污染物的KPW值都是通過實驗測定獲得，但實驗方法費時費力[6]，難以滿足數(shù)量龐大且與日俱增的有機污染物的監(jiān)測需求，需要發(fā)展預測方法來獲得有機污染物的KPW值。

定量構效關系(QSAR)是一種可以有效預測有機污染物理化性質、環(huán)境行為和毒理學效應參數(shù)的方法[7]。許多研究曾采用有機化合物的正辛醇-水分配系數(shù)(logKOW)、正十六烷-水分配系數(shù)(logKHW)或水溶解度(logSW)對其KPW進行預測[8-16]。Lohmann[8]分別用logKHW和logSW預測了100種化合物的聚乙烯-水分配系數(shù)，相關系數(shù)(R2)分別為0.86和0.92；用logKOW預測79種化合物的聚乙烯-水分配系數(shù)，R2達到0.91。Hale等[9]分別用logKHW和logKOW預測了34種化合物的聚乙烯-水分配系數(shù)，R2分別為0.85和0.53。另外，有些研究基于線性溶解能關系(LSER)預測KPW。Endo等[5]構建了79種化合物的聚丙烯酸酯-水分配系數(shù)的LSER模型，R2達到0.97。然而這些模型所采用的預測變量通常也是需要實驗測定的經(jīng)驗性參數(shù)，導致模型的適用范圍有限。本研究針對3類常用的被動采樣材料，即聚乙烯(PE)、聚丙烯酸酯(PA)和硅橡膠(SR)，遵循經(jīng)濟合作與發(fā)展組織(OECD)發(fā)布的QSAR構建與驗證導則[17]，構建KPW的QSAR預測模型，并對模型進行表征和機理解釋。

1 材料與方法(Materials and methods)

1.1 數(shù)據(jù)來源

考慮7種被動采樣材料(聚乙烯、聚丙烯和5種不同的硅橡膠)，有機污染物在19 ～ 26 ℃下的KPW實測數(shù)據(jù)均來自文獻報道[2,5-6,8-9,18-27]，包括：215種有機物的聚乙烯-水分配系數(shù)(此處為區(qū)分不同采樣材料記為logKPE)，數(shù)值范圍為2.3 ～ 7.8；107種有機物的聚丙烯酸酯-水分配系數(shù)(logKPA)，數(shù)值范圍為0.0 ～ 6.0；67種有機物的Silastic A型硅橡膠-水分配系數(shù)(logKSR1)，數(shù)值范圍為3.0 ～ 7.6；67種有機物的SR batch 0型硅橡膠-水分配系數(shù)(logKSR2)，數(shù)值范圍為2.8 ～ 7.4；93種有機物的AlteSil型硅橡膠-水分配系數(shù)(logKSR3)，數(shù)值范圍為3.0 ～ 7.8；67種有機物的SR-RED型硅橡膠-水分配系數(shù)(logKSR4)，數(shù)值范圍為3.0 ～ 7.6；67種有機物的SR-TF型硅橡膠-水分配系數(shù)(logKSR5)，數(shù)值范圍為2.9 ～ 7.4。有機物涵蓋烷烴、烯烴、芳香類、醇類、酮類、酯類、醚類等多種類別。

將各個數(shù)據(jù)集以4∶1的比例隨機拆分為訓練集和驗證集，其中，訓練集中的化合物用于構建模型，驗證集中的化合物用于模型驗證。

1.2 分子結構描述符的計算

采用ChemBio3D Ultra (Version 12.0)軟件中MOPAC 2012模塊的PM7算法[28]，對化合物結構進行優(yōu)化并獲得穩(wěn)定構型。同時，基于化合物優(yōu)化后的穩(wěn)定結構，由Dragon (Version 6.0)軟件計算得到分子結構描述符。

1.3 模型的建立

(1)

(2)

(3)

1.4 應用域表征

采用基于標準殘差(δ)對leverage值(hi)的Williams圖對模型的應用域進行表征[30]。δ和hi及其預警值(h*)的計算公式如下：

(4)

hi= xiT(XTX)-1xi

(5)

h*= 3(k + 1)/n

(6)

式中，k為自變量的個數(shù)，xi是第i個化合物的描述符矢量，X是描述符矩陣。將|δ| > 3的化合物視為離群點。

2 結果(Results)

2.1 最優(yōu)QSAR模型

得到7種被動采樣材料的最優(yōu)QSAR模型如下：

(1) 聚乙烯-水分配系數(shù)(logKPE)

logKPE= 0.015Vx- 0.034TPSA(NO) + 0.110nBM + 0.137nCl - 0.841

(2) 聚丙烯酸酯-水分配系數(shù)(logKPA)

logKPA= 0.010Vx+ 0.154nCl + 0.078nBM - 0.026TPSA(NO)+ 0.940NddsN - 0.778nROH + 0.701

(3) Silastic A型硅橡膠-水分配系數(shù)(logKSR1)

logKSR1= 0.024Vx- 0.117Rperim - 0.088

(4) SR batch 0型硅橡膠-水分配系數(shù)(logKSR2)

logKSR2= 0.024Vx- 0.139Rperim - 0.088

(5) AlteSil型硅橡膠-水分配系數(shù)(logKSR3)

logKSR3= 0.021Vx- 0.824

(6) SR-RED型硅橡膠-水分配系數(shù)(logKSR4)

logKSR4= 0.017Vx+ 0.140nCl

(7) SR-TF型硅橡膠-水分配系數(shù)(logKSR5)

logKSR5= 0.023Vx- 0.144Rperim + 0.327

2.2 模型的應用域表征

7個模型的應用域表征結果如圖2所示。訓練集和驗證集所有化合物的|δ| < 3，模型無離群點，表明訓練集化合物具有很好的代表性。logKPE模型中訓練集的4種化合物和logKPA模型中訓練集的1種化合物，hi> h*但|δ| < 3，說明這些化合物增加了模型的穩(wěn)定性和準確性。logKPE模型中驗證集的1種化合物，hi> h*但|δ| < 3，落在描述符域外，但其預測效果較好，說明模型適用于遠離描述符中心的化合物，進一步推論出模型具有一定的延展能力和外推性。因此，建立的模型可用于預測應用域內其他化合物的logKPW值。

圖1 logKPW的實測值與預測值擬合關系圖Fig. 1 Plot of the predicted versus experimental logKPW values

圖2 logKPW模型的Williams應用域表征圖Fig. 2 Williams plot of logKPW models

表1 本研究構建的預測模型中分子結構描述符的含義Table 1 Definitions of the molecular structural descriptors involved in the developed models

3 討論(Discussion)

3.1 機理解釋

7個模型中，共包含7個描述符(如表1所示)，其中，Vx, nBM, nCl和NddsN與logKPW呈正相關；TPSA(NO), nROH和Rperim與logKPW呈負相關。在所有模型中，Vx的貢獻最大，是影響KPW的最主要因素。Vx是分子McGowan體積，表征空穴形成作用。由于水分子排列高度有序且凝聚性強[31]，在水中形成空穴所需能量遠大于其在被動采樣材料中所需能量，因此化合物分子更容易通過空穴形成作用分配到被動采樣材料相中。具有較大Vx值的化合物，其logKPW值越大。nCl是氯原子的個數(shù)。有研究表明，logKOW與鹵素原子個數(shù)有關[32]，可用化合物的鹵素原子數(shù)表征其疏水作用，nCl值越大的化合物疏水作用越強，因而越容易分配到采樣材料中。TPSA(NO)是由N, O極性貢獻的拓撲極性表面積，nROH是羥基的個數(shù)。這些親水結構中的氮和氧原子具有孤對電子，易形成氫鍵，增加了與水的氫鍵相互作用[33]，使化合物更不易被采樣材料吸附，從而具有更小的logKPW值。Rperim是環(huán)的周長，化合物的環(huán)周長越大，空間位阻越大，越難進入到被動采樣材料相中，從而具有更小的logKPW值。此外，nBM和NddsN表明logKPW還與化合物多重鍵和[-N(=)=] (硝基氮)原子的個數(shù)有關。

表2 本研究模型和前人相關模型的比較Table 2 Comparison of KPW prediction models from the current study and previous studies

注：N表示無應用域表征，Y表示有應用域表征；— 表示未報道。

Note: N, applicability domain was not characterized; Y, applicability domain was characterized; —, unreported.

3.2 模型比較

將本研究構建的模型與前人的一些代表性模型進行比較，見表2。本研究logKPE, logKPA和logKSR模型與前人模型相比，化合物種類更豐富且數(shù)量更多，而且所采用的分子結構參數(shù)均可通過計算獲得，不依賴于實驗測定。此外，本研究將數(shù)據(jù)集劃分為訓練集和驗證集，利用MLR方法建立模型，所有模型均進行了外部驗證和應用域的表征，并進行了機理解釋。

綜上，本研究遵循OECD關于QSAR模型構建和驗證的導則，構建了3類共7種被動采樣材料的KPW的QSAR預測模型。模型具有良好的擬合優(yōu)度、穩(wěn)健性和預測能力，能夠用于預測含有>C=C<, -OH, -O-, >C=O, -C=O(O), -C6H5, -NO2, -NH2, -NH-, -X(F, Cl, Br, I)等多種結構官能團的有機污染物的logKPW值，可為快速獲取有機污染物的KPW值以及為被動采樣器的應用提供基礎數(shù)據(jù)。

輔助信息：化合物logKPW實測值、預測值以及模型中包含的分子結構描述符值，需要者請和通訊作者聯(lián)系。

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QSAR Models for Predicting Partition Coefficients of Organic Pollutants between Passive Sampling Materials and Water

Yang Lei, Luo Xiang, Wang Ya, Zhang Ya’nan, Chen Jingwen*

Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China

passive sampling materials-water partition coefficients; polyethylene; polyacrylate; silicone rubber; quantitative structure-activity relationships

國家自然科學基金(21325729; 21661142001)

楊蕾(1990-)，女，碩士，研究方向為污染生態(tài)化學，E-mail: yanglei_dlut@mail.dlut.edu.cn；

*通訊作者(Corresponding author)， E-mail: jwchen@dlut.edu.cn

10.7524/AJE.1673-5897.20160415001

2016-04-15 錄用日期：2016-05-26

1673-5897(2016)6-053-08

X171.5

A

陳景文(1969-)，男，博士，教授，研究方向為污染生態(tài)化學、污染控制化學和環(huán)境生態(tài)技術。

楊蕾, 羅翔, 王雅, 等. 有機污染物的被動采樣材料-水分配系數(shù)的QSAR研究[J]. 生態(tài)毒理學報，2016, 11(6): 53-60

Yang L, Luo X, Wang Y, et al. QSAR models for predicting partition coefficients of organic pollutants between passive sampling materials and water [J]. Asian Journal of Ecotoxicology, 2016, 11(6): 53-60 (in Chinese)