CN110110934A - A method of pollutant plant-atmosphere distribution coefficient is predicted based on plant growth difference factor - Google Patents
A method of pollutant plant-atmosphere distribution coefficient is predicted based on plant growth difference factor Download PDFInfo
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- 150000003071 polychlorinated biphenyls Chemical class 0.000 description 17
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- 235000008331 Pinus X rigitaeda Nutrition 0.000 description 8
- 235000011613 Pinus brutia Nutrition 0.000 description 8
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- BZTYNSQSZHARAZ-UHFFFAOYSA-N 2,4-dichloro-1-(4-chlorophenyl)benzene Chemical compound C1=CC(Cl)=CC=C1C1=CC=C(Cl)C=C1Cl BZTYNSQSZHARAZ-UHFFFAOYSA-N 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 3
- 239000007789 gas Substances 0.000 description 3
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- QVWUJLANSDKRAH-UHFFFAOYSA-N 1,2,4-trichloro-3-(2,3-dichlorophenyl)benzene Chemical compound ClC1=CC=CC(C=2C(=C(Cl)C=CC=2Cl)Cl)=C1Cl QVWUJLANSDKRAH-UHFFFAOYSA-N 0.000 description 1
- NGQQUXXTDZADNX-UHFFFAOYSA-N 2,3,4,5-tetrachlorofuran Chemical compound ClC=1OC(Cl)=C(Cl)C=1Cl NGQQUXXTDZADNX-UHFFFAOYSA-N 0.000 description 1
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- CKAPSXZOOQJIBF-UHFFFAOYSA-N hexachlorobenzene Chemical compound ClC1=C(Cl)C(Cl)=C(Cl)C(Cl)=C1Cl CKAPSXZOOQJIBF-UHFFFAOYSA-N 0.000 description 1
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Abstract
Pollutant plant-atmosphere distribution coefficient method is predicted based on plant growth difference factor the present invention relates to a kind of, for reducing the monitoring cost of persistence organic pollutant in monitoring atmosphere.Gaseous organic pollutant distributes the correlation of behavior to the present invention by research under the conditions of bionical and in actual environment, it is proposed appliable plant growth differences factor-alpha, realize a kind of method for capableing of precise calibration organic matter distribution coefficient in plant-air system between different species, to eliminate different pollutants, plant and environmental difference, the uncertainty of gaseous organic pollutant level of pollution prediction is reduced, the accuracy of pollutant risk assessment is improved.Also the environmental risk and safety evaluation of volatile chemical and toxic and harmful gas, the related management of timely implement general plan chemicals are helped to realize.
Description
Technical Field
The invention belongs to the technical field of predicting correlation between enrichment of persistent organic matters in plants and the level of persistent organic matters in the atmosphere, and particularly relates to a method for predicting a pollutant plant-atmosphere distribution coefficient based on a plant growth difference factor, which is used for reducing the monitoring cost of monitoring persistent organic pollutants in the atmosphere.
Background
In recent years, with the development of human society and global industry, the phenomenon of air pollution has become more serious, and although the content of pollutants in the air is still low, the concentration of air pollutants, especially organic pollutants, tends to increase year by year, and has attracted attention of people widely. Therefore, the exploration of sensitive and efficient sampling methods and detection techniques for gaseous pollutants has been an active research direction in the field of environmental engineering. The sampling method of the atmospheric pollutants mainly comprises active sampling and passive sampling, but the large-volume active sampler has complicated equipment, needs to consume electric power, has a sampling result greatly influenced by environmental factors such as wind power, wind direction, weather, sampling position and the like, cannot truly reflect the atmospheric pollution condition, and is not suitable for monitoring the atmospheric pollutants in remote areas and at low concentration; the passive sampling technology of plants has the advantages of simple sampling equipment, stable long-term sampling, high accuracy and the like, and is continuously used for biological detection of volatile and semi-volatile organic compounds in the air in recent years.
The lipophilic substances of the plant leaves can adsorb and accumulate organic pollutants in the atmosphere, and are used for passive sampling of volatile organic pollutants. As a biological passive sampler of atmospheric pollutants, the distribution behavior of the pollutants in plants and the atmosphere is much more complicated than that of a common sampling method. The pollutant has plant-air distribution coefficient (K) in different plants, different seasons, and different growth stages of plantsPA) Different, the method brings huge workload and obstacles for monitoring the pollutants, and becomes a bottleneck difficult to overcome by passive sampling analysis and ecological risk evaluation of the gaseous organic pollutants.
It is important to understand and predict the accumulation of contaminants in plants, the exchange of contaminants in the atmosphere with plants. On one hand, the plants are an important way for removing toxic and harmful pollutants in the environment, and can effectively remove organic pollutants in the atmospheric environment; on the other hand, plants act as the starting point of the food chain, plant accumulation of pollutants is the first step in the entry of organic pollutants into the food network, and the absorption of volatile pollutants by agricultural plants constitutes a direct threat to the exposure of these compounds to humans. By monitoring the concentration of organic matter in the atmosphere and analyzing the accumulation of organic matter in the plant, the level of pollutants in the atmosphere can be predicted, the environmental risk of pollutants can be evaluated, and the influence on human beings and the ecological system can be predicted. However, at present, there is very little research on the inter-species variation rule of the plant absorption of gaseous pollutants, and the comprehensive and effective analysis method and evaluation mechanism provided for the plant passive sampling technology. K for accurately predicting volatile organic compounds along with more serious global air pollution problemPAHas important significance and value for estimating the concentration of the atmospheric pollutants by the concentration of the organic matters enriched in the plants, so how to overcome the interspecific difference of the plants and provide a simple and feasible method for predicting the K of different plantsPAThe value method has very important significance for air quality evaluation and ecological risk assessment, and can simplify air pollutant monitoring.
Disclosure of Invention
Based on the pollutant n-octanol-air partition coefficient (K)OA) And its different plants KPAThe invention provides a concept of a plant growth difference factor α, analyzes and researches K of the environment temperature, the plant growth difference factor and the like on volatile organic compoundsPAThe influence of the value. Provides a method for estimating K of different plants more accurately and effectivelyPAThe method of the value has important significance for predicting the concentration of the volatile organic pollutants in the atmosphere.
A method for predicting pollutant plant-atmosphere distribution coefficient based on plant growth difference factors comprises the following steps:
s1, selecting any pollutant, and determining or looking up K of the pollutant on a certain plant by adopting an instrumentPA、 KOAA numerical value;
wherein: kPAIs the plant-air distribution coefficient of the contaminants on the plant at equilibrium distribution;
KOAis the n-octanol-air partition coefficient of a contaminant at a given temperature;
CPconcentration of contaminants in the plant at equilibrium, mg/L;
CAis the concentration of the contaminant in the gas phase, mg/L, at equilibrium for distribution;
COis the concentration of the contaminant in the n-octanol phase at partition equilibrium, mg/L;
s2. K on specific plants according to the pollutantsPACalculating and selecting KOAK 'of the contaminant at the same temperature'PA；
K which influences the contamination due to temperature changesPAAnd KOAAnd the influence degrees are different, in order to reduce the prediction error and make the prediction and the evaluation of the atmospheric pollution level more real and effective, K needs to be used when necessaryPAConversion of the measured value of (A) to K at a prescribed temperaturePAAnd (4) predicting. In general, KPAThe relationship with temperature can be expressed by equation (3):
wherein, K'PA: plant-air distribution coefficient of contaminants on plants at equilibrium of distribution at a given temperature;
t: ambient temperature, K;
TR: reference temperature, K;
ΔHPA: enthalpy of phase change of the contaminant between the plant and the air at a reference temperature, kJ/mol;
r: the ideal gas constant, often 8.314.
Since 25 ℃ is the most common ambient temperature in experiments and research, the present invention is primarily concerned with TRIs K at 25 DEG CPAAnd KOAAnd (4) and performing prediction.
S3, calculating plant growth difference factor α
Given that n-octanol can act as a pseudobiotic, it is found in the K of a particular plant when the contaminant reaches a partition equilibrium between the gaseous and organic phasesPAWith K at this temperatureOARatio of (A) to (B), i.e. Log (K)PA/KOA) Therefore, based on the thinking of establishing a more convenient and accurate prediction method, deeply analyzing the distribution condition of organic pollutants in the atmosphere in different plants, reducing the complexity of the prediction process, providing an effective means for risk evaluation of the atmospheric pollutants and the like, the invention provides a method for researching the distribution relation and the distribution rule of volatile and semi-volatile organic pollutants in the atmosphere on different plants by applying the plant growth difference factor α.
Plant growth difference factor α ═ logK'PA-logKOA(4)
Wherein α is a plant growth differential factor;
K'PAis the plant-air distribution coefficient of the contaminants on the plant at a given temperature at equilibrium of distribution;
KOAis the n-octanol-air partition coefficient of a contaminant at a given temperature;
s4, predicting K of other pollutants on the plant by using plant growth difference factor αPAPrediction value
According to equation (4), K can be calculated by the following equationPAAnd (3) predicting:
wherein,is that at a given temperature, the compound or contaminant to be predicted is on the plant at equilibrium of distributionPlant-air distribution coefficient of (a);
KOAis the n-octanol-air partition coefficient of the pollutant to be predicted at a specified temperature;
s5, according to K at the specified temperaturePA predictionCalculating K at ambient temperature by equation (3)T PA prediction
Wherein,the plant-air distribution coefficient of the pollutant to be predicted is obtained when the distribution is balanced at the ambient temperature;
KPA(R)is the equilibrium plant-air distribution coefficient of the pollutant to be predicted at the reference temperature;
s6, according toAnd known as CPThe atmospheric concentration of the contaminant is predicted by equation (2), i.e.:
CP＝CA×KT prediction。
Has the advantages that:
according to the invention, by researching the correlation of the distribution behavior of the gaseous organic pollutants in the bionic condition and the actual environment, the application of the plant growth difference factor α is provided, so that a method capable of accurately correcting the distribution coefficient of organic matters in plant-air systems among different species is realized, the differences among different pollutants, plants and environments are eliminated, the uncertainty of the prediction of the pollution level of the gaseous organic pollutants is reduced, the accuracy of the risk evaluation of the pollutants is improved, the environmental risk and safety evaluation of volatile chemicals and toxic and harmful gases and the standardization of the risk evaluation of food chain health are facilitated, and the application and management of the chemicals are facilitated.
Drawings
FIG. 1K of PCBs on pine needles in high-altitude areasPAThe predicted situation of (2);
FIG. 2 different Compounds KPAThe measured value and the predicted value;
FIG. 3 different Compounds KPAThe measured value and the predicted value of (c).
Detailed Description
The invention is described in more detail below with reference to specific examples, without limiting the scope of the invention.
Example 1 acquisition and predictive evaluation of the ryegrass growth Difference factor α
The distribution of the experimentally obtained polychlorinated biphenyls (PCBs) between air and ryegrass at 25 ℃ (condition where the distribution of contaminants in the plant-air system has reached equilibrium), Log K for the corresponding PCBs at 25 ℃OAValues, and K for other PCBs using the plant growth difference factor α of PCBs 84 and 101PAThe prediction was performed as shown in Table 1.
Table 1 uses plant growth index α vs. KPAPredicted situation of
Note: the numerical sequence numbers of the PCBs column in Table 1 are numbers for compounds according to IUPAC nomenclature.
Due to the K of the PCB in this caseOAAnd KPAAt the same temperature, so thatIn case there is no need for KPAThe temperature of (2) was corrected, and the plant growth factor α was directly obtained.
S1, taking PCB 84 and 101 as examples, the plant growth factor α is LogKPA－LogKOA＝6.45－8.8＝－2.6
S2, predicting other PCBs on ryegrass by using plant growth difference factor α
Using an ease meter, when the plant uptake rate of a contaminant equals the volatilization rate, indicating that equilibrium of distribution is reached, the following calculation can be made according to equation (4) when equilibrium of distribution is reached:
as can be seen from table 1, with a small error (0 to 0.22) in the prediction results, the difference in the molecular structure and physicochemical properties of the compound may be a major cause of the occurrence of such a small error.
Example 2 predictive selection of pine needle-to-atmospheric distribution coefficients for PCBs at different temperatures
In the embodiment, the balance distribution coefficient K of PCBs on pine needles in high-altitude areas is mainly researchedPA、 KOAAnd phase change enthalpy delta H from various PCBs to air from pine needlesPASpecific data on ambient temperature are shown in Table 2. Wherein, KOAIs the n-octanol-air partition coefficient value at 25 ℃.
TABLE 2 distribution parameters and temperatures of PCBs on pine needles in high-altitude areas
S1, according to a formula (3) and K at various temperaturesPAValue calculation pine needle-air distribution coefficient, K ', at 25 deg.C (298.15K) for various PCBs'PA。
For example, PCB28, which is K 'at 25 ℃'PAComprises the following steps:
s2, calculating a plant growth difference factor α.
According to the formula (4), the plant growth difference factor α -8.38-7.92-0.46 of PCB28
S3, calculating K of pollutants on pine needles at 25 DEG CPAThe predicted value of (2).
K for PCBs 25, 101, 138, 153, 180 was calculated from the plant growth difference factor α of PCB28 at 0.46PAThe predicted value of (a) is determined,
s4, according to a formula 3, reducing to K at a corresponding temperatureT PA。
K for various PCB compounds according to equation 3 and 25 deg.CPAThe predicted value of (A) can be calculated to obtain K at the ambient temperaturePAThe predicted value of (2) and the prediction result are shown in FIG. 1.
FIG. 1 illustrates K of PCBs on pine needles in severe cold regionsPAThe measured and predicted conditions of (a). Numerals 28, 52, 101, 138, 153, 180 are IUPAC numbers for different PCBs, respectively. It can be seen that the factor pair K is differentiated by plant growthPAThe error generated by the result of prediction is smaller and is within the error allowable range, so that the prediction method provided by the invention has higher reliability and accuracy and has certain feasibility and practical significance.
Example 3 prediction of Lolium Perenne-air partition coefficient and atmospheric concentration of contaminants in a field Environment
The measurement environment of this case is a field environment whose average temperature is 18 deg.c (291.15K), and the ambient temperature is 7-32 deg.c due to day and night changes, but the change range is not large. Enthalpy of phase change Δ H of various compounds or pollutants in ryegrass-air systemPAK at 25 ℃OASpecific parameters for atmospheric concentration and plant concentration are listed in table 3.
TABLE 3 partitioning of Compounds between rye grass and air and parameters thereof
Since HCB is most widely used in the environment, the plant growth index α of HCB is taken as an example in the present example to predict K at 18 ℃ for other pollutantsPAAnd atmospheric concentration of contaminants. The specific implementation steps are as follows:
s1, calculating K of compound on ryegrass at ambient temperaturePA
From the formula (2) and the plant concentration and atmospheric concentration of the various compounds, K at ambient temperature can be calculatedPAIn the case of HCB, KPAComprises the following steps:
KPA＝CP/CA＝26÷220＝0.118
s2. K at 18 ℃ according to formula (3) and the compoundPACalculating and selecting KOAK 'of Compound at the same temperature (298.15K)'PA(HCB is taken as an example).
All the compound ringsLogk at ambient temperature and 25 ℃PAThe results of the calculations are shown in Table 4.
TABLE 4 for different temperatures KPAResult of calculation of (2)
And S3, calculating a plant growth difference factor α.
According to formula (4), plant growth difference factor α ═ logK'PA-logKOANamely:
α＝0.118-6.78≈-6.66
s4, predicting K at 25 DEG CPA
According to the prediction method provided by the invention, K of the compound at 25 DEG CPAThe predicted value of (c) can be expressed as: log KPA＝α+LogKOA
In the case of HCB, Log K at 25 ℃PA＝-6.66+6.78＝0.12
S5, according to K at 25 DEG CPAPredicted value, calculated according to equation (3) at ambient temperature (291.15K)(taking HCB as an example)
S6, according toKnown as CPAnd equation (2) predict contaminants
The comparison between the predicted result of the atmospheric concentration of the volatile pollutants in the field environment and the actual measured value by using the growth index α proposed by the invention is shown in fig. 2 and fig. 3.
FIG. 2 shows the environmental concentration C of the contaminant, as an example, the plant growth difference factor α of hexachlorobenzene, according to the prediction method of the present inventionAAnd carrying out predicted value and actual value conditions of prediction. As can be seen from the figure, the results of the predicted value and the true value are relatively close, and the error is extremely low. The prediction method provided by the invention has strong feasibility and practical significance.
FIG. 3 shows the predicted and measured values of atmospheric concentration of pollutants according to the prediction method of the present invention, using the plant growth difference factor α of HCB as an example, the predicted and measured values of other compounds except for tetrachlorofuran are close to the actual concentration, because it cannot be determined whether the precipitation process of the compound from air to plants is dominated by dry precipitation, but the error is (1)<0.1pg/m3) Still within the allowable range.
Claims (5)
1. A method for predicting pollutant plant-atmosphere distribution coefficient based on plant growth difference factors comprises the following steps:
s1, selecting any pollutant in a certain class of volatile organic pollutants in the atmosphere, and measuring and calculating K of the pollutant on a certain plant by adopting an instrumentPAAnd KOA；
Wherein: kPAIs the plant-air distribution coefficient of the contaminants on the plant at equilibrium distribution;
KOAis the n-octanol-air partition coefficient of a contaminant at a given temperature;
CPconcentration of contaminants in the plant at equilibrium, mg/L;
CAis the concentration of the contaminant in the gas phase, mg/L, at equilibrium for distribution;
COis the concentration of the contaminant in the n-octanol phase at partition equilibrium, mg/L;
s2. K on specific plants according to the pollutantsPACalculating and selecting KOAK 'of the contaminant at the same temperature'PA；
Wherein: k'PA: plant-air distribution coefficient of contaminants on plants at equilibrium of distribution at a given temperature;
t: ambient temperature, K;
TR: reference temperature, K;
ΔHPA: enthalpy of phase change of the contaminant between the plant and the air at a reference temperature, kJ/mol;
r: ideal gas constant, often 8.314;
s3, calculating plant growth difference factor α
Plant growth difference factor α ═ logK'PA-logKOA(4)
S4, using plant growth difference factors α and KOAPredicting K of other contaminants on the plantPAPredicting a value;
s5, according to the specified temperatureCalculating the ambient temperature by equation (3)At the same time
S6, according toAnd known as CPThe atmospheric concentration of the contaminant is predicted by equation (2).
2. The method according to claim 1, wherein in step S2, a reference temperature T is usedRThe prediction was made at 25 ℃.
3. The method according to claim 1, wherein in step S4, the following calculation is obtained according to formula (1) and formula (4):
wherein:is the plant-air distribution coefficient of the compound or pollutant to be predicted on the plant at a given temperature at equilibrium of distribution;
KOAis the n-octanol-air partition coefficient of the pollutant to be predicted at a given temperature.
4. The method according to claim 1, wherein in step S5, the following calculation is obtained according to formula (3):
wherein,the plant-air distribution coefficient of the pollutant to be predicted is obtained when the distribution is balanced at the ambient temperature;
KPA(R)is the equilibrium plant-air distribution coefficient of the pollutant to be predicted at the reference temperature.
5. The method according to claim 1, wherein in step S6, the following calculation is obtained according to formula (2):
CP＝CA×KT prediction。
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101673321A (en) * | 2009-10-17 | 2010-03-17 | 大连理工大学 | Method for fast predicting organic pollutant n-caprylic alcohol/air distribution coefficient based on molecular structure |
| CN102999705A (en) * | 2012-11-30 | 2013-03-27 | 大连理工大学 | Method for predicting n-octyl alcohol air distribution coefficient (KOA) at different temperatures through quantitative structure-activity relationship and solvent model |
| CN103645124A (en) * | 2013-12-09 | 2014-03-19 | 宁波市环境监测中心 | Atmospheric persistent organic pollutant monitoring method based on particulate matter continuous monitor and gas-solid phase distribution model |
| CN104102826A (en) * | 2014-07-03 | 2014-10-15 | 同济大学 | Method for determining n-octanol / water partition coefficient of mixture |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101673321A (en) * | 2009-10-17 | 2010-03-17 | 大连理工大学 | Method for fast predicting organic pollutant n-caprylic alcohol/air distribution coefficient based on molecular structure |
| CN102999705A (en) * | 2012-11-30 | 2013-03-27 | 大连理工大学 | Method for predicting n-octyl alcohol air distribution coefficient (KOA) at different temperatures through quantitative structure-activity relationship and solvent model |
| CN103645124A (en) * | 2013-12-09 | 2014-03-19 | 宁波市环境监测中心 | Atmospheric persistent organic pollutant monitoring method based on particulate matter continuous monitor and gas-solid phase distribution model |
| CN104102826A (en) * | 2014-07-03 | 2014-10-15 | 同济大学 | Method for determining n-octanol / water partition coefficient of mixture |
Non-Patent Citations (3)
| Title |
|---|
| HARNER, T.: ""Octanol-air partition coefficient for describing particle/gas partition of Aromatic compounds in urban air"", 《ENVIRONMENTAL SCIENCE & TECHNOLOGY》 * |
| 刘兰: ""NKA-Ⅱ树脂吸附苯甲酸的热力学研究"", 《化工时刊》 * |
| 董玉瑛: ""固相微萃取及相关联用技术应用进展"", 《大连民族大学学报》 * |
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