The removal of pesticides in the environment mainly depends on natural degradation, especially on microbial degradation. Biodegradation has many advantages, such as complete degradation, no secondary pollution, quick effect and wide spectrum. Based on the single-factor experiments and Box-Benhnken design, the effect of four factors on the degradation of chlorpyrifos by P. stutzeri ZH-1 was investigated. The four factors, including temperature (℃), oscillator speed (rpm), inoculum concentration (%) and pH, and their interactions on the degradation of chlorpyrifos were studied through the use of respons e surface analysis. The optimal conditions of chlorpyrifos-degrading were as follows: Temperature 36.7?C, oscillator speed 130.00 rpm, inoculum concentration 7%, pH 7. Under these conditions, the degradation rate of chlorpyrifos was 96.48%. Moreover, P. stutzeri ZH-1 could be used efficiently for remediation of contaminated soils.
In order to meet the growing demand for food, farmers grow high-yielding crop varieties all over the country in China. However, these high-yielding crop varieties are highly susceptible to various pests and diseases; thus, to protect their crops from pests and to improve their crop yields and quality of their products, farmers use pesticides [
Chlorpyrifos [O,O-diethyl O-(3,5,6-trichloro-2-pyridyl) phosphorothioate] is used worldwide as an agricultural insecticide [
Bioremediation is defined as the process that organic wastes are biologically degraded to an innocuous state or to the levels below concentration limits established by regulatory authorities under controlled conditions. Bioremediation, which involves the use of microbes to detoxify and degrade pollutants, has received increased attention as an effective biotechnological approach to clean up polluted environments [
Holden, Firestone and Vidali suggested that the success of microbial degradation depends on a number of factors, like pH, organic matter, moisture, temperature and nutrient status [
Chlorpyrifos (in the form of Hubei Sanonda Co. Ltd., China) was purchased from a local pesticide supplier (Linfen, China). High-performance liquid chromatography (HPLC) grade methanol was purchased from the Tianjin Guangfu Chemical Reagent Co., Ltd, China. All other reagents used in this study were analytical reagent grade [
A strain of P. stutzeri ZH-1 was separated from the sludge of the Fenhe River in Shanxi province of China. Based on the morphological, biochemical, the Bergey’s Manual of Determinative Bacterriology and 16S-rDNA gene sequence analysis, strain ZH-1 was recognized as a strain of P. stutzeri and was thereafter named P. stutzeri ZH-1. The NCBI accession number is DQ513513. It was cultivated on standard nutrient agar (NA) medium periodically at 37˚C for 24 h. Fresh slant cultures were used in every batch for inoculation. Nutrient broth (NB) was inoculated with a 24 h old culture and grow at 37˚C on a shaker at 140 rpm for 12 h.
The composition of the Mongina medium used for chlorpyrifos-degrading was as follows: glucose = 10 g/L, NH4Cl = 0.5 g/L, NaCl = 0.3g /L, KCl = 0.3 g/L, MgSO4 = 0.03 g/L, FeSO4 = 0.03 g/L, MnSO4 = 0.03 g/L, CaCl2 = 5 g/L [
For the investigation of the effect of the initial pH value on chlorpyrifos-degrading, the pH value of the medium was adjusted to3.0 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 or 12.0. For the sake of investigating the effect of the temperature on chlorpyrifos-degrading, the culture was incubated at 20˚C, 25˚C, 30˚C, 35˚C, 40˚C or 45˚C. In order to study the effect of the inoculum concentration (v/v)on chlorpyrifos-degrading, cells were inoculated at into cultures at 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9% or 10%. To explore the effect of the oscillator speed on chlorpyrifos-degrading, the oscillator speed was controlled at 0, 40, 80, 120, 160 or 200 rpm. For all experiments, cells were inoculated at 2% (v/v) into cultures of pH 7.0, then they were incubated at 37˚C on a shaker at 140 rpm for 5 days unless otherwise stated [
RSM was chosen to show the statistical significance of the effects pH, temperature, inoculum concentration and oscillator speed on the degradation of chlorpyrifos by P. stutzeri ZH-1. The RSM experiments were designed by using the Design-Expert 7.1.3. Calculations were done at 95% of confidence level. In order to optimize the incubation conditions and investigate effects of above independent variables on the degradation of chlorpyrifos, a central-composite rotary design with the variables at three levels was used in the experiments.
HPLC was used for detection of concentrations of insecticide. To extract chlorpyrifos from Mongina medium, 5 ml of liquid samples were centrifuged at 6000 rpm for 20 min at 4˚C. Chlorpyrifos in the supernatant fluid was extracted with an equal volume of dichloromethane, then oscillated for 10 minutes by the use of ultrasonic waves. The extract was dried over anhydrous Na2SO4 and dry using Termovap Sample Concentrator at room temperature. Residual was dissolved in an equal volume of methanol, then filter the membrane with 0.22 um. All samples were analyzed by HPLC (1260LC, Rheodyne 7750i manual injector and Variable Wavelength uv Detector; Agilent Technology Co.). Zorbax Eclipse XDB-C18 stationary phase was used in the separation column (4.6 mm internal diameter and 25 cm length). The mobile phase was methanol:water (80:20, v/v), and the flow rate was 1.0 ml/min. Chlorpyrifos was detected at uv wavelength of 290 nm.
Based on the peak area of control group and test group respectively to calculate chlorpyrifos concentration in the sample through the regression equation of reference standard curve, the corresponding chlorpyrifos degradation rate can be obtained.
Degradationrate = ( 1 − C 1 / C 0 ) × 1 00 % . (1)
where, C1 (mg/L) was the residual concentration of the test group after the degradation of bacteria, while C0 (mg/L) was the residual concentration of chlorpyrifos in the control group.
In previous studies, the degradation rate of chlorpyrifos is strongly related to soil pH, and the degradation is microbial degradation and not due to abiotic hydrolysis [
In general, pesticide degradation in soil can be influenced by both biotic and
abiotic factors, which are linked and supplemented in series with each other in a micro environment. Environmental conditions play an important role in the survival and proliferation of microorganisms as well as the effect on chemical stability [
The inoculum age and density markedly influence the productivity and economics of bioprocesses [
P. stutzeri ZH-1 is a facultative aerobic bacterium, the dissolved oxygen content determines the growth of the cells, what determines the degradation of chlorpyrifos.
Appropriate degradation conditions have an important significance on the degradation of chlorpyrifos. According to the Box-Benhnken central combination experiment principle, the selection the temperature, oscillator speed, inoculum concentration, and the pH value carried on four factors three levels were displayed in the response surface analysis experiments.
Run | X1 (˚C) temperature | X2(rpm) Oscillator speed | X2 (%) inoculum concentration | X4 pH | degradation Rate (%) |
---|---|---|---|---|---|
1 | 0 (35) | 0 (120) | −1 (4) | −1 (6) | 79.89 |
2 | 0 | 0 | 1 (8) | 1 (8) | 85.70 |
3 | 0 | 1 (160) | 0 (6) | −1 | 84.26 |
4 | 1 (40) | 1 | 0 | 0 (7) | 89.89 |
5 | 0 | −1 (80) | 0 | 1 | 83.31 |
6 | 1 | 0 | 1 | 0 | 89.69 |
7 | −1 (30) | 0 | −1 | 0 | 79.59 |
8 | −1 | 0 | 0 | −1 | 81.99 |
9 | 0 | 0 | 0 | 0 | 94.09 |
10 | 0 | 0 | 0 | 0 | 95.80 |
11 | −1 | −1 | 0 | 0 | 78.39 |
12 | 0 | −1 | 0 | −1 | 81.78 |
13 | 1 | 0 | −1 | 0 | 89.85 |
14 | −1 | 1 | 0 | 0 | 75.56 |
15 | 0 | 1 | 0 | 1 | 93.45 |
16 | 0 | 1 | 1 | 0 | 90.53 |
17 | 1 | 0 | 0 | 1 | 77.88 |
18 | 0 | 0 | 0 | 0 | 95.39 |
19 | 0 | 0 | 0 | 0 | 96.60 |
20 | 1 | −1 | 0 | 0 | 82.09 |
21 | 0 | −1 | 1 | 0 | 82.60 |
22 | 0 | 0 | 1 | −1 | 92.51 |
23 | 0 | 0 | −1 | 1 | 84.96 |
24 | 0 | 0 | 0 | 0 | 95.72 |
25 | −1 | 0 | 1 | 0 | 86.63 |
26 | 1 | 0 | 0 | 1 | 88.15 |
27 | 0 | 1 | −1 | 0 | 82.19 |
28 | 1 | 0 | 0 | −1 | 89.03 |
29 | 0 | −1 | −1 | 0 | 84.44 |
Y = β 0 + ∑ i = 1 4 β i X i + ∑ i = 1 4 β i i X i 2 + ∑ i , j = 1 4 β i j X i X j + ε (2)
where Y is predicted response, XiXj are input variables which influence the response variable Y; β0 is the offset term; βi is the ith linear coefficient; βii the ith quadratic coefficient and βij is the ijth interaction coefficient. The term ε allows for uncertainties or discrepancies between what the model predicts and what was actually measured and stands for residual [
Multiple regression analysis was used to analyse the data and thus a polynomial equation was derived from regression analysis as follows [
Y = − 533.66400 + 17.34780 * X 1 + 0.036129 * X 2 + 26.03217 * X 3 + 63.95650 * X 4 + 0.013288 * X 1 * X 2 − 0.18000 * X 1 * X 3 + 0.16150 * X 1 * X 4 + 0.031812 * X 2 * X 3 + 0.047875 * X 2 * X 4 − 1.48500 * X 3 * X 4 − 0.25974 * X 1 2 − 4.07797 E − 003 * X 2 2 − 100338 * X 3 2 − 4.72225 * X 4 2 (3)
The adequacy of the model was checked using analysis of variance (ANOVA) which was tested using Fisher’s statistical analysis and the results are showed in
The P values denotes the significance of the coefficients and also of importance in understanding the pattern of the mutual interactions between the variables. The regression analysis of the optimization study indicated that the model terms, X1, X3, X 1 2 , X 2 2 , X 3 2 and X 4 2 were very significant (P < 0.01); X2, X1 * X2, X3 * X4 were significant (P < 0.05). The variable X4, X1 * X3, X1 * X4, X2 * X3, X2 * X4 was not significant (P > 0.05). However,
Source | Sum of squares | df | Mean square | F value | p-value |
---|---|---|---|---|---|
Prob > F | |||||
Model | 951.28 | 14 | 67.95 | 11.87 | <0.0001 |
X1 | 197.32 | 1 | 197.32 | 34.46 | <0.0001 |
X2 | 45.12 | 1 | 45.12 | 7.88 | 0.0140 |
X3 | 59.59 | 1 | 59.59 | 10.41 | 0.0061 |
X4 | 1.33 | 1 | 1.33 | 0.23 | 0.6377 |
X1 * X2 | 28.25 | 1 | 28.25 | 4.93 | 0.0433 |
X1 * X3 | 12.96 | 1 | 12.96 | 2.26 | 0.1547 |
X1 * X4 | 2.61 | 1 | 2.61 | 0.46 | 0.5107 |
X2 * X3 | 25.91 | 1 | 25.91 | 4.52 | 0.0517 |
X2 * X4 | 14.67 | 1 | 14.67 | 2.56 | 0.1318 |
X3 * X4 | 35.28 | 1 | 35.28 | 6.16 | 0.0264 |
X 1 2 | 273.51 | 1 | 273.51 | 47.77 | <0.0001 |
X 2 2 | 276.15 | 1 | 276.15 | 48.23 | <0.0001 |
X 3 2 | 104.49 | 1 | 104.49 | 18.25 | 0.0008 |
X 4 2 | 144.65 | 1 | 144.65 | 25.26 | 0.0002 |
Residual | 80.16 | 14 | 5.73 | ||
Lack of Fit | 78.23 | 10 | 7.82 | 16.15 | 0.0082 |
Pure Error | 1.94 | 4 | 0.48 | ||
Cor Total | 1031.44 | 28 |
The design expert presented the optimal conditions as following: temperature 36.66˚C, oscillator speed 131.00 rpm, inoculum concentration 6.65%, pH 7.03. Under these conditions, P. stutzeri ZH-1 optimal degradation conditions of correction for temperature 36.7˚C, oscillator speed 130.00 rpm, inoculation concentration 7% and pH 7.0, the degradation rate of chlorpyrifos actually measured is 96.48. As the regression model to predict the theoretical value is up to 96.90, the actual measured value is lower 0.43% than the theoretical value. Consequently, this result demonstrated the mathematical model can predict the relationship between the factors and degradation of chlorpyrifos effectively.
Success or failure of bioremediation depends on several factors, such as the competitive ability of the bioremedial agents, bioavailability of pollutants and abiotic factors such as soil moisture, pH, and temperature. Successful removal of pesticides by the addition of bacteria has been previously reported for many compounds including, parathion, coumaphos, ethoprophos and atrazine [
This work showed that the P. stutzeri ZH-1 had a high degradation of chlorpyrifos. In general, the degradation rate of chlorpyrifos increased with the increase of bacterial content. The optimal conditions of chlorpyrifos-degrading were as follows: Temperature 36.7˚C, oscillator speed 130.00 rpm, inoculum concentration 7%, pH 7. The degradation rate of chlorpyrifos predicted was 96.60% by this model and the test value under optimal condition was 96.48%.
It was investigated that the degradation capacity of P. stutzeri ZH-1 was changeable under different environmental conditions and the environmental factors had great influence on degradation process. The acquiring of degradation bacteria’s optimum degradation conditions are able to provide data support and reference for the application of this bacteria. In addition, this also develops one new resource for pesticide degradation bacteria.
This work was supported by grants from Student's Platform for Innovation and Entrepreneurship Training Program of Shanxi Province, China (2017574), and the Natural Science Foundation of Shanxi Normal University program (No. ZR1514).
He, F., Zhang, M.M., Zhang, L.H. and Hu, Q.P. (2018) Response Surface Methodology for the Optimization of Chlorpyrifos-Degrading Conditions by Pseudomonas stutzeri ZH-1. Open Access Library Journal, 5: e4405. https://doi.org/10.4236/oalib.1104405