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 Environ Eng Res > Volume 25(2); 2020 > Article
Ahari, Touze-Foltz, and Mazéas: Sorption of chlorophenols on geotextile of the geosynthetic clay liners

### Abstract

Knowledge of organic micropollutant transfers in barrier seal materials from waste storage facilities is limited to volatile organic compounds and phenolic compounds at ambient temperature. This study focused on the sorption of chlorophenols (CPs) from various geotextiles from clay geosynthetics under the influence of temperature. Also to study the impact of the polarity or the amount of CPs adsorbed on geotextiles with the partition coefficient. The effect of various parameters such as contact time, effect of temperature, initial CPs concentration and adsorbent dosage has been carried out in this study. The result obtained is non-linear and the data was calculated for affinity with Freundlich isotherm model. An important observation is that the amount of CPs sorbed on geotextiles increases with a growing number of chlorine atoms, ie increases with the partition coefficient (log Kow). During this study, a decrease in adsorbent properties was observed with the rise in temperature from 23°C to 55°C. The partitioning coefficients for CPs examined range are from 2.4 (R2 = 0.86) to 8.4 mL/g (R2 = 0.90). Among the CPs studied, the highest adsorbed quantity was observed for pentachlorophenol with 0.052 g/g at 23°C, this quantity will decrease with the increase in temperature.

### 1. Introduction

The intensive use of phenol and its derivatives in industrial processes in recent decades has led to widespread pollution of the environment by these toxic compounds [14]. Phenolic compounds are very toxic and are among the most dangerous pollutants for human health and the environment, also, most of them are known as potential carcinogens. The toxic and dangerous nature of phenols and their derivatives is well documented [59]. The majority of these compounds are known as potential carcinogens. Large-scale coal gasification and carbonization plants generate huge amounts of waste water with high phenolic content [810].
Environmental Protection Agency (EPA) regulations in the United States recommend reducing the phenol content in wastewater to less than 1 μg/L compared to the thousands of μg/L that are often present [11, 12]. Numerous studies of surface water, including seawater, have shown the presence of chlorophenols (CPs) in these waters at concentrations up to 10 ng/L [13, 14].
CPs has been widely used in industry, resulting in their widespread distribution in the aquatic environment [1517]. CPs is a group of 19 compounds with substituted chlorines in the phenolic ring formed by chlorination and hydrolysis of chlorobenzenes [18]. Also, have been used since 1920 in various applications, such as wood preservatives, flame retardants, pesticides, in the paper industry and solvents; Many of these compounds are by-products of industrial processes, including the production of pharmaceuticals, pesticides, paints and solvents, and the processing of wood, paper and pulp. These compounds are known to be widespread as components of industrial waste because they have been used in many industrial processes such as the manufacture of plastics, drugs and dyes [18].
Hazardous waste risk assessment of contaminated sites is generally conducted using a four-step process, including (1) hazard identification, (2) exposure assessment, (3) assessment of toxicity and (4) risk characterization [1920]. The toxicity of CPs depends in particular on the number of chlorines and also on the position of the chlorine atoms with respect to the (-OH) function of phenol. The possible association between CP-based pesticide production and carcinogenicity has been previously studied by some researchers [2123].
The transport of a chemical compound in the natural environment strictly depends on the value of the dissociation constant (Ka) and the partition coefficient (Kow) in the octanol-water system. The dissociation constant depends inter alia on the structure of a compound molecule on the number of chlorine atoms in the molecule. Sorption is a major process responsible for sharing toxic compounds between the solid phase of sediments and water [24].
Since our study is limited to the adsorption of CPs, some previously published papers have noted that environmental factors and sorbent characteristics play an important role in controlling the sorption characteristics of soil/sediment compounds. The adsorption of 4-chlorophenol on organophilic bentonite was studied as a function of the concentration and temperature of the solution. Recently, bentonite clay has been used successfully for the adsorption of metal ions and organic compounds [25, 26].
Geosynthetics clay liners (GCLs) are widely used in geoenvironmental applications. A number of studies regarding the use of GCLs in landfills focused on the chemical compatibility of bentonite with leachate or their performance in landfill covers following cation exchange [27]. There have been many recent studies related to the performance of GCLs [2833]. The adsorption behaviour was affected by several factors such as operating temperature, adsorption time, pH and pre-treatment of the clay [34]. Among the most cited methods to improve the adsorption capacity, we have the acid treatment and the pilling process [3536].
The objectives of this paper are to: (a) Evaluate CPs partitioning on the geotextile of GCLs in individual experiments; (b) Fitting two kinetic models to analyse the experimental data; (c) Polarity impact of CPs on adsorption; (d) Investigate the impact of temperature in a range from 23°C to 55°C on CPs sorption on geotextiles, in order to obtain representative transfer parameters. The parameters obtained for this study are very important later in order to model the potential transfer of CPs through different types of barriers of waste storage facilities on an underlying aquifer.

### 2.1. Presentation of Geotextiles Studied

Two geosynthetic clay coatings representative of the bottom coatings of municipal solid waste landfills were studied corresponding to GCL1 & GCL2 studied [37]. GCL1 and GCL2, obtained from same manufacturer consist of cover geotextile (GTX1) and carrier geotextile (GTX2). The two GTXs consist of a woven geotextile and nonwoven needled geotextile. The measured masses per unit area of the cover and the carrier geotextiles were ~250 g/m2 and ~110 g/m2, respectively.

### 2.2. Chlorophenols

Six CPs were evaluated 2-chlorophenol (2-CP), 4-chlorophenol (4-CP), 2,4-dichlorophenol (2,4-DCP), 2,4,6-trichlorophenol (2,4,6-TCP), 2,3,5,6-tetrachlorophenol (2,3,5,6-TeCP), and pentachlorophenol (PCP).
Some common chemical properties of the selected chlorophenolic compounds are given in Table 1 [38].
Except for 2-CP, which is a liquid at room temperature, all of the CPs are solids, with their melting points between 33 and 191°C. Generally, these compounds do not dissolve easily in water, but are very soluble in organic solvents. Although they are classified as acids, mainly those with a high degree of chlorination (pKa = 4.74), some of them have a high pKa. The dissociation constant of a compound increases (i.e. its pKa = −log Ka decreases), with the increasing number of chlorine atoms in a molecule, from 2.29 for 2-CP to 5.85 for PCP.
The octanol-water partition coefficient (Kow) of the pollutants under study in this paper strongly increases with the number of chlorine atoms whereas the water solubility (hydrophilicity) decreases. Furthermore, the degree of dissociation of CPs increases (indicated by decreasing pKa values) with increasing number of chlorine atoms. It is thus expected that the difference in properties of the various CPs results in differences in sorption behaviour on the various components of GCLs tested in this study.
The selection of those CPs was performed taking into account the following criteria: polarity, solubility in water, mobility of pollutants in soils, presence in leachate, and toxicity.
Concentrations of the contaminants (10 μg/L) were identical to the ones in the studies by Ahari et al. [37]. They were selected based on two important points, on the one hand a bibliographic research study to determine the minimum, maximum and average values of CPs concentrations in leachates [39]; and on the other hand on the values limit of detection (LOD) and the limit of quantification (LOQ).

### 2.3. Analytical Methods

A Varian gas chromatography/mass spectrometer GC-MS (trace GC and DSQ, Thermo fischer) equipped with a Combi PAL autosampler (CTC Analytics) allowing automatic SPME extraction. The GC split-splitless injector was operating in the splitless mode. The chromatographic column was a Zebron 5 MS column (5% phenyl-methyl polysiloxane, Phenomenex, 60 m length, 0.25 mm I.D., 0.25 μm film thickness). Chromatographic separation was performed using the following temperature programme: (a) 40°C for 5 min; (b) ramp one to 115°C at 15°C.min−1; (c) ramp two to 175°C at 3°C.min−1; (d) 175°C for 5 min; (e) an increase to 250°C at 30°C.min−1; (f) 250°C for 2 min; (g) an increase to 280°C at 30°C.min−1 and (h) finally 280°C for 5 min.
The quantification of CPs was performed using solid phase microextraction/gas chromatography-mass spectrometry (SMPE-GC-MS) using the analytical procedure developed in our laboratory by Limam et al. [40].
The procedure took place in two steps: a derivatization and a headspace extraction. CPs quantification was performed using the single ion monitoring mode. The ions m/z 162; 196; 202; 213; 266; 274 were used, respectively for the 2-CP; 4-CP; 2,4-DCP; 2,4,6-TCP; 2,3,5,6-TeCP and PCP. Then the headspace extraction of target compounds was performed with a 100 μm PDMS SPME fiber at 80°C for 30 min with agitation (500 rpm). Afterwards, the SPME fiber was desorbed in the injector at 250°C for 5 min. GC/MS analysis was then performed as previously described.
The performance of the GC/MS was checked each time samples were tested by running duplicates, a lab blank, and a known standard sample and was found to be within the 90% of the target value.

### 2.4. Adsorption Procedure

Duplicate geotextile (GTX) partitioning tests were performed together with one control test (CPs in DDW and no GTX) and one blank test (GTX in DDW and no CPs) at room temperature.
The adsorption tests of CPs on the geotextiles GTX-1 and GTX-2 were carried out in order to quantify the adsorption coefficients Kd of the phenolic compounds. The test consists of completely immersing 2 g of each type of geotextile cut into pieces in solutions with five different concentrations, in 100 mL glass bottles.
The solid/liquid ratio adopted for our experiments is 1/40, ie 2 g of geotextile for 80 mL of the solution containing the pollutants. The solid/liquid ratio adopted in the experiments, is consistent with the ones adopted by Rowe et al. [39] and Ganne et al. [41] in works they performed regarding the sorption of volatile organic compounds (VOCs) on geotextiles.
The experiments were carried out in two stages, first of all determining the time of attaining equilibrium on two geotextiles and two concentrations. First, a kinetic study was done to determine the time to reach equilibrium. Once the equilibrium time was determined, the adsorption tests of different geotextiles were carried out in different solutions at four different concentrations for the determination of isotherms (equilibrium sorption). Indeed all the tests are duplicated. Samples were taken at t = 0 to determine the initial concentration of phenols in the solution, followed by sampling at the end of the tests at t = 72 h. All the experiments were carried out in the geosynthetic test laboratory whose temperature is close to 23°C.

### 3.1. Effect of Time of Adsorption

To determine the time needed to reach equilibrium, a kinetic study was carried out with GTX1 and GTX2. The solutions were immediately sampled after introduction in narrow-neck bottles to determine initial concentrations of CPs in the reaction medium. Subsequently, samples were taken after 2, 4, 6, 24, 48, 72 and 144 h of contact between the solutions and geotextiles. Samples were taken using a syringe 5 mL samples were taken from each vial of 80 mL at the beginning and the end of the tests and 2 mL samples were taken from each bottle at intermediate sampling times.
Fig. 1 shows the results obtained. Equilibrium was reached for CPs after 72 h. This time was the testing time adopted in the following experiments performed in order to determine the adsorption isotherms for the two geotextiles, following the procedure previously described in section 2.4. This period is consistent with equilibrium times obtained by Rowe et al. [39] and Ganne et al. [41] regarding the sorption of VOCs on geotextiles.
All the tests, for each geotextile and each concentration were realized in duplicate, in the laboratory where the temperature is 23°C.

### 3.2. Adsorption Isotherm

Once the equilibrium time was determined, the adsorption tests of the two geotextiles were carried out in different solutions at four different concentrations for the determination of isotherms (equilibrium sorption), using 32 vials in total. Indeed all the tests are duplicated. Samples were taken immediately after injection to determine the initial concentration of CPs in the solution, followed by sampling at the end of the tests at t = 72 h.
The experimental results obtained for the determination of the adsorption parameters concerning the geotextiles are presented in the form of isotherms in Fig. 2 for GTX1. Sorption isotherms are non-linear contrarily to what was observed in the case of VOCs [39, 41].
The linear and Freundlich model described by Eq. (1) and Eq. (2), respectively, therefore were used to determine the sorption coefficient:
##### (1)
$qe=KFCe1/n\$
##### (2)
$qe=KdCe\$
Where: Kd is defined as the distribution coefficient in the linear isotherm. qe and Ceq are the concentration in the solid in μg/g and the equilibrium solute concentration in μg/mL, respectively, KF represents the sorption capacity, and 1/n is related to the energy distribution of the sorption sites.
The amount of each phenolic compound adsorbed at equilibrium, qe, was calculated using Eq. (3).
##### (3)
$qe=(C0-Ceq)×V/W$
Where: C0 and Ceq (μg/L) are the liquid-phase concentrations of each organic compound at initial and equilibrium, respectively, V is the volume of the solution (L), and W is the masse of dry adsorbent used (g).
The linear form of the Freundlich isotherm model is given by the following Eq. (4):
##### (4)
$Log qe=KF+(1/n) Log Ce$
The values of KF and 1/n can be obtained from the intercept and slope, respectively, of the linear plot of experimental data of Log qe versus Log Ce as shown in Fig. 3.
Synthesis of sorption coefficient Kd in our case CPs allowed us on the one hand to have a comparison with the VOC sorption expressed in Kd in different literatures, and on the other hand to have a simpler evaluation synthesis of the sorption as a function of the temperature (section 3.4).
Thus, the synthesis of the adsorption parameters of the different CPs calculated for the various geotextiles. The results obtained for linear and Freundlich approaches are presented in Table 2. The best correlations were obtained for the Freundlich equation.
The coefficient of determination, R2 values, which are a measure of goodness-of-fit, show that both the Freundlich isotherm models can sufficiently, describes the adsorption data well for all six CPs studied. The concentrations measured during these experiments were not corrected, since the blanks tests did not reveal any sorption of the CPs on the glass.
When the Freundlich model is applied, we find KF and 1/n values ranging from 7.62 to 97.53 mg1-1/nmL1/ng−1 and from 1.28 to 1.74, respectively. In this case, it is observed that there is a considerable difference between the woven geotextiles and the needled geotextiles for certain CPs. It should be noted that the coefficients of determination show that the adjustment of Freundlich is the most reliable. Indeed, the evolution of the linear partition coefficients with the contaminants always has the same tendency for the two geotextiles; it is not obvious for the Freundlich settings. It should also be noted that PCP has the highest adsorption tendency vis-a-vis the other pollutants studied.
Kd expresses the proportionality between the mass of contaminant removed from solution and the concentration in solution. The values of Kd partition coefficients of different phenols for different geotextiles are 2.7 to 8.9 mL/g and there is a slight difference between woven geotextiles and needled geotextiles. For comparison, these values remain low compared to VOCs. As an example, the adsorption coefficient (Kd in mL/g) for benzene and toluene are: 32.2 and 97.4 from Rowe et al. [39], 41 and 135 from Ganne et al. [41].

### 3.3. Effect of Hydrophobicity on Adsorption

An important observation is that the amount of CPs sorbed on geotextiles increases with an increasing number of chlorine atoms. Fig. 4 shows the relationship between the partition coefficient (log Kow) of CPs and amount sorbed at equilibrium (qe) by the two different geotextiles. In the case of CPs polarity appears to be connected with the differences in adsorption.
It does thus seem that the partitioning coefficient decreases as the hydroxyl group, hydrophilic (due to its hydrogen bonds) tends to make the molecule soluble. Indeed, while the solubility of benzene is only 1.78 g/L at 20°C the one of phenol is 90 g/L.
For example for GTX1, the values of the partition coefficients Kd of the CPs increase with the increase of chlorine number on the phenol of 2.90, 3.00, 4.00, 4.50, 6.40 to 8.40 for 2-CP, 4-CP, 2,4-DCP, 2,4,6-TCP, 2,3,5,6-TeCP and PCP, respectively (Table 2).
Also in this figure, two different trends can be seen, one for the nonwoven needle-punched geotextile GTX1, and the other for the woven geotextile GTX2.

### 3.4. Effect of Temperature on CPs Adsorption

The objective of this study is to investigate the impact of temperature in a range from 23°C to 55°C on the sorption of CPs on geotextiles, in order to obtain representative transfer parameters. The first experiment was carried out at the geosynthetic test laboratory whose temperature is close to 23°C, while the 2nd and 3rd experiments were carried out in the laboratory in an oven at 35°C and 55°C, respectively. The experimental results obtained for the determination of the adsorption parameters of CPs calculated for the two geotextiles at 35°C and 55°C are shown in Table S1.
It is noted that in a linear approximation of the experimental results, the values of the partition coefficients Kd of CPs are 2.4 and 8.4 mL/g for GTX1 and of 2.3 and 7.6 for GTX2 at 35°C and 55°C, respectively. We observed a slight decrease in Kd when the temperature increases which is consistent with the results presented in the literature for VOCs [4243].
When the Freundlich model is applied, we find KF and 1/n values at 35°C ranging from 5.62 to 117.77 and from 1.25 to 1.59, respectively and at 55°C KF values ranging from 7.71 to 83.37 and 1/n values from 1.47 to 1.54.
Fig. 5 clearly shows the impact of the temperature on the adsorption of chlorophenols on the geotextils. The evolution of equilibrium absorption of chlorophenols by both geotextil was affected by temperature. It has indeed been observed, a decrease in adsorbent properties with the increase in temperature from 23°C to 55°C. This effect, of course, would have inverse effects on the diffusive transfer through the geosynthetic clay liners, ie, the temperature increases the diffusive transfers. The decrease of adsorption capacity with the increased in temperature indicated that the adsorption of CPs onto geotextil is exothermic in nature.
A similar trend was also observed by Bilgili with 4-chlorophenol adsorption by XAD-4 resin [44] and by Hameed on Trichlorophenol adsorption onto activated clay [42]. Rowe et al. [39] also demonstrated that for VOCs, temperature had an impact on both diffusion coefficient and sorption in geosynthetic clay liners at temperatures between 7 and 22°C. Indeed, a decrease of the absorption coefficients with the increase of the temperature is observed for the VOCs. The decrease in adsorption when the temperature increases, can be explained by the weakening of the forces between the adsorbent and the adsorbed compounds [42, 43].
For example, in the this study, in the case of GTX1, an adsorption towards 2,4-dichlorophenol of the order of 0.0344, 0.0317 and 0.0274 μg/g is obtained at temperatures of 23, 35 and 55°C, respectively, confirming the exothermic nature of the adsorption process. The following Table S2 represents the equilibrium adsorbed quantity for the rest of the CPs, qe, by the two geotextiles. In this study, it was also observed that the amount of CPs sorbed on geotextiles increased with increasing numbers of chlorine atoms at either 35°C or 55°C. At the present time, the sorption structure and sorption mechanism of CPs onto geotextiles are not elucidated. Décány [4548], Park [4950] and other researchers [7, 51] have widely studied the sorption properties of clay minerals for organic chemicals and surfactants. They have the effect of hydrophobic properties of surface, and developed the sorption model. The loaded surfactants created the organic solvent-like hydrophobic phase and the interaction can be described as partition mechanism, resulting in a strong attraction of CPs molecules with the organoclays through Van der Waals interaction. In addition, there are several possible mechanisms for the adsorption of CPs, including electrostatic interaction, donor-acceptor complex formation, and hydrogen bonding.

### 4. Conclusions

Results of sorption tests of various CPs (2-CP, 4-CP, 2,4-dichlorophenol, 2,4,6-TCP, 2,3,5,6-TeCP, PCP) on constitutive geotextiles of GCLs were presented in this paper. The first result obtained is that the sorption isotherms obtained are non-linear. The sorbed amounts of CPs increase with increase in contact time, reaching a maximum value after 72 h. The Freundlich isotherm was demonstrated to provide the best correlation for the adsorption of CPs.
In terms of hydrophobicity, an important observation is that the amount of CPs sorbed on geotextiles increases with a growing number of chlorine atoms, ie increases with the partition coefficient (log Kow). For example for GTX1, the values of the partition coefficients Kd of the CPs increase with the increase of chlorine number on the phenol of 2.90 for 2-CP to 8.40 for PCP. Tending to show that the partitioning coefficient decreases as the hydroxyl group tends to make the molecule more soluble. As far as the Freundlich constants are concerned, alight difference was observed between nonwoven needle-punched geotextiles and woven geotextiles. This latest result is logical as the less polar the contaminant is, the less it is soluble in water. This results in a potential for a larger adsorption on the geotextile.
During this study, a decrease in adsorbent properties was observed with the increase in temperature from 23°C to 55°C. It is noted that in a linear approximation of the experimental results, the values of the partition coefficients Kd of 2,4-CP for example are 4.0; 3.7 and 2.7 mL/g for GTX1 at 23°C; 35°C and 55°C, respectively. The decrease of adsorption capacity with the increased in temperature indicated that the adsorption of CPs onto geotextile is exothermic in nature.
In this study, it was also observed that the amount of CPs sorbed on geotextiles increased with increasing numbers of chlorine atoms at either 23°C or 55°C. If we take the same case of 2,4-DCP, we observe that an adsorption towards of the order of 0.0344, 0.0317 and 0.0274 μg/g is obtained at temperatures of 23, 35 and 55°C, respectively. This effect obviously would have inverse effects on the diffusive transfer through the GCLs.

### Acknowledgments

The study presented in this paper was funded RGC&U (C2D2) for the Duragéos research program labelled by Axelera and Advancity which partners were IFSTTAR, IRSTEA, LTHE, ENTPE, PIMM Arts et Métiers ParisTech, Suez-Environment and Veolia Propreté.

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42. Hameed BHEquilibrium and kinetics studies of 2,4,6-trichlorophenol adsorption onto activated clay. Colloid Surf A Physicochem Eng Asp. 2007;307:45–52.

43. Zheng H, Wang Y, Zheng Y, Zhang H, Liang S, Long MEquilibrium, kinetic and thermodynamic studies on the sorption of 4-hydroxyphenol on Cr-bentonite. Chem Eng J. 2008;143:117–123.

44. Bilgili MSAdsorption of 4-chlorophenol from aqueous solutions by xad-4 resin: isotherm, kinetic, and thermodynamic analysis. J Hazard Mater. 2006;137:157–164.

45. Dékány I, Szánto E, Nagy LGSorption and immersional wetting on clay minerals having modified surface. Part I. Surface properties of nonswelling clay mineral organocomplexes. J Colloid Interface Sci. 1985;103:321–331.

46. Dékány I, Szánto F, Nagy LGSorption and immersional wetting on clay minerals having modified surface. Part II. Interlamellar sorption and wetting on organic montmorillonites. J Colloid Interface Sci. 1986;109:376–384.

47. Dékány I, Szánto F, Weiss A, Lagaly GInteractions of hydrophobic layer silicates with alcohol-benzene mixtures I. Adsorption isotherms. Ber Bunsenges Phys Chem. 1986;90:422–427.

48. Dékány I, Szánto F, Weiss A, Lagaly GInteractions of hydrophobic layer silicates with alcohol-benzene mixtures II. Structure and composition of the adsorption layer. Ber Bunsenges Phys Chem. 1986;90:427–431.

49. Park Y, Ayoko GA, Horváth E, Kurdi R, Kristof J, Frost RLStructural characterisation and environmental application of organoclays for the removal of phenolic compounds. J Colloid Interface Sci. 2013;393:319–334.

50. Park Y, Sun Z, Ayoko AG, Frosta RLBisphenol A sorption by organo-montmorillonite: Implications for the removal of organic contaminants from water. Chemosphere. 2014;107:249–256.

51. Nagasaki S, Nakagawa Y, Tanaka SSorption of nonylphenol on Na-Montmorillonite. Colloid Surf A Physicochem Eng Asp. 2004;230:131–139.

##### Fig. 1
Adsorption of chlorophenols by GTX1.
##### Fig. 2
Results of batch adsorption tests for GTX1 at 23°C.
##### Fig. 3
Freundlich isotherms for chlorophenols adsorption on GTX1 at temperature of 23°C.
##### Fig. 4
Relationship between the partition coefficient (log Kow) of chlorophenols and amount adsorbed at equilibrium by the two geotextiles.
##### Fig. 5
Effect of temperature on chlorophenols adsorption onto GTX1.
##### Table 1
Selected Properties of Chlorophenols Tested
Contaminants 2-CP 4-CP 2,4-DCP 2,4,6-TCP 2,3,5,6-TeCP PCP
Structure
Formula C6H5ClO C6H5ClO C6H4Cl2O C6H3Cl3O C6H2Cl4O C6Cl5OH
Molecular weight (g/mol) 128.56 128.56 163.00 197.45 231.89 266.34
Solubility at 20°C (g/L) 27 28 4.5 0.434 0.100 0.014
pKa 9.37 8.52 7.90 6.00 5.02 4.74
Log Kow 2.29 2.53 3.20 3.67 5.02 5.85
Melting point°C 9.3 43.5 45 69 115 190
Boiling point°C 175 220 210 264 No data 310
##### Table 2
Synthesis of Partitioning Coefficients for the Geotextiles of the Chlorophenols under Study at 23°C
GTX1 GTX2

Freundlich Linear Freundlich Linear

KF 1/n R2 Kd R2 KF 1/n R2 Kd R2
2-CP 20.03 1.47 0.98 2.90 0.88 13.59 1.41 0.96 2.70 0.86
4-CP 30.59 1.55 0.98 3.00 0.87 90.32 1.78 0.97 2.80 0.83
2,4-DCP 26.59 1.45 0.99 4.00 0.90 7.62 1.24 0.97 3.00 0.86
2,4,6-TCP 26.15 1.40 0.99 4.50 0.92 12.13 1.28 0.97 3.50 0.89
2,3,5,6-TeCP 60.40 1.52 0.99 6.40 0.90 47.57 1.48 0.99 5.90 0.91
PCP 97.53 1.54 0.99 8.40 0.90 61.79 1.47 0.99 7.60 0.92
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# RSVSim insertions from chr1 to chr2

I tried using the BioConductor forums for asking this question Question on BioConductor. However I do not seem to get an answer there and after some help with other tools on this website, i thought I could also ask the question here, in the hope that someone knows something about it.

I have a reference genome, and I need to put in some structural variations. It is really easy to do in RSVSim, and used it quite a lot already.

However, when processing INS, I found out that it just copies a DNA seq from one part, and puts it somewhere else in the same DNA seq (Bascially a DUP then), or if you specify another an extra DNAseq, to cut from, It randomly cuts from either chr1 or chr2 and puts it in the other. This creates deletions in the DNA seq I want to investigate, which is not what I want.

Does anyone know how to get this kind of result?

width seq                                                                                names
[1]    40 AAAAAAAAAAAAAAAAAAAATTTTTTTTTTTTTTTTTTTT    chr1
[2]    40 GGGGGGGGGGGGGGGGGGGGCCCCCCCCCCCCCCCCCCCC    chr2

to this:

width seq                                                                                names
[1]    32 AAAAAAAAAAAAAAAATTTTTTTTTTTTTTTT                    chr1
[2]    48 GGGGGGGGGGGGGGAAAATTTTGGGGGGCCCCCCCCCCCCCCCCCCCC    chr2

And do it 30 times, randomly in the genome? (so take a seq from chr1 and put an insertion in chr2.

I tried this and more already for days, and I just cannot figure it out.

    library(RSVSim)

format: plain


text:
## Materials

Material, abbreviated as MATL, is the matter an object is made of.  There are four categories of material: ceramic, composite, metal and polymer.

• Ceramic - inorganic non-metallic solid comprised of either metal or non-metal compounds made by heating together these materials.
• Composite - material mixture of two or more materials with different properties combined to produce a new material.
• Metal - chemical elements that are good conductors of heat and electricity.
• Polymer - a long chain of molecules linked together.

### Science Branches

Science
Applied Science
Engineering
Chemical Engineering
Corrosion Engineering

### Nomenclature & Symbols

• $$MATL$$  -  material
• $$ML$$  -  material list

Display #


text:
# Volume under the elliptic paraboloid

1. Nov 6, 2008

Greetings again,

this is another I'm stuck on...

Calculate the volume under the elliptic paraboloid z = x^{2} + 4y^{2} and over the rectangle R = [-1, 1] \times [-4, 4].

I'm not sure how to find the limits of z for the triple integral. Can I somehow integrate the function z, and only do a double integral? Perhaps going from -1, 0 and -4, 0 and then double it?

2. Nov 6, 2008


text:
Optical Spectra Caveats

There are several small caveats to watch out for in SDSS spectroscopic data. Some affect only a few spectra or a few data columns, while some have wider impacts. Some caveats in DR11/DR12 have been fixed with Data Release 13; those are listed on this page to allow for easier comparison between releases. This page contains a list of known caveats in SDSS Data Release 13 optical spectroscopic data.

Caveats that affect all spectra

Redshift status

The quality flags for the redshift fitting procedure is stored in the bit mask. Most redshift warnings indicate a likely substantial problem with the data, or an indication that the best-fit classification or redshift is not reliable (due, e.g., to low S/N, or the unusual nature of the spectrum). An exception is MANY_OUTLIERS, which flags when many pixels are poorly explained in a statistical sense by the best-fit redshift model. This bit is typically set for very high signal-to-noise ratio stars (where errors are small, so χ2 is high), or galaxies with broad lines (the redshift fitting model includes only narrow lines); in such cases, the redshift is usually fine. About 2% of non-sky spectra have some warning set other than MANY_OUTLIERS. The redshifts of the remainder are virtually always correct. Many of the spectra flagged with problems also have correct redshifts and classifications, but we recommend care before using them. Note that the ZWARNING flag bits in BOSS are similar, but not identical, to those used in SDSS-I/II.

Galactic extinction correction

The spectra released in DR10 have not been corrected for Galactic extinction, because the SDSS includes a substantial number of spectra of Milky Way stars whose extinction would differ from that given in the Galactic dust maps, as they don't lie beyond the full dust column. This policy has been the standard since DR2; in the EDR and DR1, the spectroscopic data were corrected for galactic extinction. The extinction is a relatively small effect over most of the survey area, since the median E(B-V) over the survey is around 0.04; however, for some SEGUE pointings the reddening can be substantially larger.

Night sky emission lines

The night sky emission lines at 5577Å, at 6300Å, 6363Å (when there is auroral activity), and in the OH forest in the red can be very strong, and leave significant residuals in the spectra whose amplitude is occasionally underestimated by the noise model. Be cautious about interpreting the reality of weak features close to these lines.

Sky Subtraction Bias (DR13: significantly improved!)

The sky spectrum estimates in BOSS (and in fact in SDSS) that are subtracted from each object are biased slightly low. This is due to the well-known bias associated with fitting an error-weighted model to data when the errors are estimated from the data itself (e.g. in the case of Poisson estimates of errors). These residuals can be detected by taking the average of the sky-subtracted sky fibers, which yield a slightly positive spectrum ranging from 7×10-20 erg/cm2/s/Å at around 8000 Angstroms to up to 10-18 erg/cm2/s/Å at the bluest and reddest end of the spectra.

The default BOSS spectra distributed in DR10 are coadded from several individual exposures. Each individual exposure has a slightly different relationship of pixel number to wavelength. Thus, errors in the coadded spectra have covariance between neighboring spectral bins; however, we do not calculate or track this covariance. As a result, there is a 10-20% "error-on-the-error" in the coadd noise model. If discrepancies at this level matter for your analysis, you should use the individual exposures, which have a much better accuracy in their noise model (1-2%).

Galaxy velocity dispersion measurements

We do not recommend using SDSS velocity dispersion measurements for:

• spectra with median per-pixel (S/N)2 < 10
• velocity dispersion estimates smaller than about 70 km s-1 given the typical S/N and the instrumental resolution of the SDSS spectra

Also note that the velocity dispersion measurements are not corrected to a standard aperture size. See the velocity dispersion algorithm for details.

Clipped Spectral Lines

Some emission lines are erroneously clipped because they were identified as cosmic rays. If an emission line is so bright that it is saturated in the individual 15-minute exposures of the spectrograph, it can suffer this effect. Unfortunately, such saturated pixels are not flagged as such, although usually that region of the spectrum has an inverse variance equal to zero. Luckily, objects with such strong emission lines are very rare, but the user should be aware of the possibility of objects with extremely strong emission lines and unphysical or unusual line ratios.

Spectrophotometric calibration induces artificial Balmer lines (DR12: corrected!)

Very occasionally, the spectrophotometric calibration procedure induces redshift-zero Balmer lines that are apparently due to mismatches between the calibration stars and the template library. This is noticeable in particular on some fibers in plate 274. This problem existed in previous data releases as well.

Known missing or corrupted spectra files on SAS

There are some spectra-related files on SAS which are known to be missing. These are documented in the "knownMissing.txt" files in each subdirectory. Most of these are logs and diagnostic plots, but a few spZline (redshift fits to individual lines) and spCFrame (calibrated individual exposures) files are missing. There are no known cases of missing coadded spectra. In addition, the individual spectrum exposure SPECTRO_REDUX/26/2639/spCFrame-b2-00042347.fits is missing HDU 6 (sky), but the other HDUs are fine.

Incorrectly-labeled Hydrogen Balmer Line

In SDSS spectra released in DR8 - DR13, the Balmer Series line Hζ (H-zeta, 3889.049 Å) was incorrectly labeled as Hε (H-epsilon, 3970.072 Å), and the real Hε was not included in the analysis of spectral lines. This affects line measurements tabulated in spZline files. These files are only available on the SAS. These measurements are not loaded into any version of the CAS.

Caveats that affect BOSS spectra

Target selection issues in early BOSS data(BOSS)

The details of the targeting algorithm and photometric pipeline have changed throughout the first year of BOSS observations. Particular care should be taken with the following:

• Chunks "boss1" and "boss2" (around 5% of the BOSS data in DR9): these used a different definition of the BOSS_TARGET1 flags. In particular, the GAL_CMASS and GAL_CMASS_SPARSE bits were used for internal tests and should not be used to select objects from these chunks. In order to select a CMASS or CMASS_SPARSE sample of objects, one should select objects based on the GAL_CMASS_COMM bit and sub-select objects that pass the final CMASS cuts (taking into account possible changes in photometry).
• Chunks "boss1" to "boss14" (around 70% of the BOSS data in DR9): the targeting photometry of a given object in these chunks may not correspond to its final photometry. This affects a tiny percentage of targets, and may mean that the final matched photometry of a target falls outside the color and flux limits. In these cases, such objects should still be considered as valid targets: the scatter across the boundaries simply reflects the stochastic element of targeting a sample from noisy data. To find the original targeting photometry for any galaxy use the targetObjID in specObj (either within CAS or within the flat files).
• Chunks "boss1" to "boss6" (around 40% of the LOWZ data): due to a bug in the target selection, LOWZ galaxies were incorrectly targeted during the initial stages of the survey. These chunks should be excluded from any LOWZ analysis. The simplest way to do so is to require tileID ≥ 10324.

Classification and redshift efficiency (BOSS/SEQUELS)

Classification and redshift efficiency depends mildly on fiber number and thus position on the focal plane. The spectrograph PSF gets worse at the CCD edges which results in lower-quality spectra with lower efficiency for determining classification and redshift. See Ross et al. (2012), Figure 3.

DR13: SEQUELS galaxy redshifting efficiency significantly reduces to 68% compared to BOSS CMASS galaxies (98%) due to lower signal-to-noise mostly. See details at the eBOSS overview paper (Dawson et al. 2016) .

NOQSO

A dominant source of bad classification/redshift fits to galaxy spectra is QSO templates with unphysical parameters, e.g. negative terms so that QSO emission lines "fit" galaxy absorption lines. To correct for this, galaxy spectra also have a ZWARNING_NOQSO mask, Z_NOQSO redshift, etc. which excludes QSO templates when performing classification/redshift fits. For studies with galaxy spectra, these *_NOQSO values should be used instead of the original ZWARNING mask, Z redshift, etc.

Bad CCD column results in bogus high-z quasars

Some unmasked intermittently bad CCD columns result in spurious identifications of spectra as z>5 quasars. These affect fibers 40, 59, 60, 833, 839, and 840. Treat any redshifts this large with caution, and especially these fibers.

QSO pipeline redshifts

The accuracy of the quasar redshift estimated by the BOSS pipeline depends on the quasar redshift. At low redshifts (z<1.6), the pipeline estimate is very accurate. At higher redshifts, when the CIV line enters in the BOSS wavelength range, the redshift estimate tends to be biased by the CIV emission line position. In the redshift range 2 to 2.5, where the MgII emission line still lies in the spectrum, about half of the redshifts are underestimated by about +0.005 in z.

QSO Flux Calibration is Wrong (BOSS)

BOSS QSO target fiber positions are purposefully offset in X and Y (position in the focal plane), and Z (vertical offset from focal plane) to optimize the S/N in the at 4000 Å for Lyman-alpha forest studies. Since the standard stars used for flux calibration are positioned for λ = 5400 Å (like the galaxies), and because (primarily) of chromatic differential refraction therefore affects the standards differently than the quasars, the derived flux solutions are not appropriate for quasars. This results in overall bluer quasar spectra, though the mis-calibration varies from exposure to exposure and position in the focal plane. This does not affect original SDSS quasar spectra which did not have the xyz hole offsets, or most of the ancillary spectra. The quantity LAMBDA_EFF stored for each spectrum reports what wavelength the fiber position was optimized for. See Dawson et al. (2012).

New in DR13: corrections for targets with focal plane offsets (mostly quasars) were computed. See Margala et al. 2016 for details. They are included in the DR13 individual spectrum files.

QSO Visual Scans (BOSS)

All QSO targets are visually inspected to verify their classification and redshift. In DR9, these results were included in the spAll and specObj catalogs as columns Z_PERSON, CLASS_PERSON, and Z_CONF_PERSON. For DR10 and beyond, these have been factored out into the DR10Q catalog be published separately as an update to the DR9Q SDSS QSO catalog.

QSO Z_ERR column left blank (BOSS)

In the SDSS quasar catalog, the Z_ERR column was left blank. The error in the redshift estimate from the SDSS quasar pipeline is in the column Z_PIPE_ERR; you should use that column instead for all studies.

For Z_PIPE_ERR, the majority of objects have error values listed. If Z_PIPE_ERR is negative, it instead acts as a flag indicating an invalid fit. Explanations of the flag values are:

Z_PIPE_ERR = -1: the best-fit is at the lowest or highest redshift tested for a template

Z_PIPE_ERR = -2: there are fewer than 3 good points in the spectrum

Z_PIPE_ERR = -4: the best-fit appears to be a local maximum rather than minimum of the chi^2 surface

Z_PIPE_ERR = -5: the chi^2 surface does not have a dynamic range of at least 1, meaning there are no fits that are formally 1-sigma worse

Z_PIPE_ERR = -6: the best-fit redshift is outside the fitting range

Z_PIPE_ERR = -7: all good data points in the spectrum have the same flux value

Z_PIPE_ERR = -8: MPFITPEAK fitting to a Gaussian of the chi^2 surface fails with a non-zero status

BOSS Flux Calibration

The flux calibration of individual exposures has an observing hour-angle and fiber dependence, especially below 4200 Angstroms. Analyses which rely upon accurate flux calibration of individual exposures should perform additional systematics cross checks for the consistency between different exposures of the same object, and avoid data observed at large hour-angles. This issue may also affect SDSS spectra but that has not been confirmed.

BOSS Stellar Classifications

BOSS object classifications are primarily focused on the identification of galaxy vs. quasar vs. star. Although sub-classifications are provided, they are not optimized for accuracy. In particular, the CV star templates have more degrees of freedom than other stellar templates, which can result in unphysical solutions where negative PCA components of the CV templates can fit absorption features of White Dwarfs. Fixing this has not been a high priority since the primary classification of "star"vs. "galaxy" or "qso" is still correct.

Artificial dichroic transitions at 6000 Å due to cross-talk from bright stars

A small number of spectra are affected by cross-talk from bright stars (generally spectrophotometric standards) in neighboring fibers. This is often manifested in a strong break feature at the dichroic transition around 6000 Å, resulting from different levels of cross-talk between the red and blue arms of the spectrograph. These effects appear to occur less frequently at later survey dates, which would be consistent with the improvements in the focus of the BOSS spectrograph cameras that have been achieved with routine operation. We intend to mitigate these effects in future BOSS data releases through improvements in the extraction codes, and to flag any spectra that remain compromised. No masking of this effect is implemented for BOSS DR10 data, however, except to the extent that it triggers a ZWARNING bit in certain instances.

Correcting for wavelength dependence of focal plane when observing quasars

In addition to the wavelength-dependent ADR offset, we also account for the wavelength dependence of the focal plane when observing the quasar targets. The focal plane for 4000 Ångstrom light differs from the focal plane for 5400 Å light by 0-300 microns, depending on the distance from the center of the plate. To account for this difference, small, sticky washers are inserted at the location of certain quasar targets. The washer causes the fiber tip to sit slightly behind the 5400 Å focus. No washers are used for holes within 1.02 degrees of the plate center. Between 1.02° and 1.34°, 175 μm washers are used; between 1.34° and 1.49°, 300 μm washers are used. Washers only became available after MJD 55441 (September 2, 2010), and were not consistently used until MJD 55474 (October 5, 2010). In the DR10 data model, the value of ZOFFSET is given in microns (μm). It can be found in the Science Archive Server in the plateDesign, spAll, and specObjAll files, and in SkyServer in the table and its associated views. Note that ZOFFSET is the intended washer usage, which may not match the actual washer usage. The exact washer usage for each observation during this transition period (including plates with repeat observations spanning this time-period) is documented in the file found at http://www.sdss3.org/svn/repo/idlspec2d/trunk/opfiles/washers.par. Observations prior to MJD 55441 did not have washers; Observations after 55474 have washers unless they are listed otherwise in washers.par. The discrepancy will be resolved with Data Release 10 in the summer of 2013. By optimizing the focal plane position, and thus the signal-to-noise ratio (SNR), for 4000 Å light, we are also perturbing the spectrophotometry relative to the standard stars as discussed in Dawson et al. (2012). Only the CORE, BONUS, and QSO_VAR_SDSS quasar targets are optimized in this way for 4000 Å focal plane and ADR offsets. Otherwise, the plate design remains the same as it was in the SDSS-I and SDSS-II surveys (Stoughton et al. 2002).

Position errors in some early plates in the Low-Mass Binary Stars ancillary target program

There was an error in correcting the positions of the target for their proper motions in the first year of the ancillary target program, affecting targets in plates numbered less than 3879 or between 3965-3987.

Caveats that affect SEGUE spectra

Duplicate Spectra

Some objects have multiple spectroscopic observations, either from being an intentional repeat, as a QA target, or as part of a different program or survey, or, finally, from being on a plate with multiple observations. Thus, while each object in the CAS has only one bestobjid, associated with the photometry, it may have multiple specobjid, one for each spectroscopic observation. SpecObjAll has a number of parameters that signify whether or not an observation is the best (defined as the highest S/N) available:

segue1primary
Best observation of a target in SEGUE-1
segue2primary
Best observation of a target in SEGUE-2
segueprimary
Best observation of a target in all of SEGUE, also in the sppParams table

For example, imagine a star that we have three observations of. Two of these observations are on SEGUE-1 plates. Of these two, the one with the highest S/N will have segue1primary set to 1. Imagine that the third observation is on a SEGUE-2 plate, and that this has the highest S/N overall. This third observation will have segue2primary set to 1, as it is the best observation of an object in SEGUE-2. It will also have segueprimary=1. Thus, even though one of the two SEGUE-1 observations is better than the other, and has segue1primary, set to 1, it will not have segueprimary=1. To make sure that any query returns one and only one spectroscopic observation of any object, and that it is the best observation of that object, use the segueprimary parameter in either sppParams or SpecObjAll. To extract the best observation from exclusively SEGUE-1 plates, use segue1primary. Finally, to pull out the best observation from SEGUE-2, use segue2primary. The criteria for an observation to be sciencePrimary and more general information is available at the SDSS Spectroscopic Catalogs page. Adding the following clause to a query will ensure that it returns a unique set of SEGUE objects:

SELECT ... FROM SpecObjAll as sp WHERE sp.seguePrimary = 1 AND ....

This same criteria will work for the sppParams table. If, for any particular reason, you do not want to use the seguePrimary parameter to eliminate duplicates, you can examine the number of times a particular target appears in CASJobs output by using the count function. This can be a useful way to verify that your queries are working appropriately. For example, to examine the number of times each bestobjid value appears in a particular sample, one would use the following query:

SELECT sp.bestobjid, count(sp.bestobjid) as count FROM SpecObjAll as sp group by sp.bestobjid

The query above lists every bestobjid in SpecObjAll and the number of times it appears. If you want to avoid any target that is observed multiple times, which will severely limit any sample, you can use the following query:

SELECT sp.bestobjid, count(sp.bestobjid) as count FROM SpecObjAll as sp group by sp.bestobjid having count(sp.bestobjid) = 1

Similarly, altering the query above to read having count(sp.bestobjid) > 1 would list every target that is observed multiple times. It is critical to identify and account for duplicates in your sample, both from the perspective of avoiding repeats and to ensure a complete sample. They can also be very useful for testing different aspects of the SSPP and other estimates of stellar properties. Duplicate SEGUE Observations lists the stars with multiple observations.

Quality Cuts

There are a number of CAS parameters that allow you to avoid poor quality SEGUE data. These are detailed in the SEGUE SQL Cookbook.

Observational Biases in SEGUE

The survey design and target selection algorithm of SEGUE will give rise to a number of different observational biases. It is imperative to constrain and correct for these biases when extracting a SEGUE sample representative of the underlying Milky Way properties. Schlesinger et al. 2012 determined and corrected for the effect of SEGUE target selection on cool dwarf stars using a series of scaling weights. These weights, and a brief description of how they were determined, is available in the Target Selection Weights value-added catalog. Although DR10 contains corrections for only the G- and K-dwarf SEGUE-1 samples, many of the techniques are applicable, with modification, to other SEGUE stellar categories.

SSPP: Caveats that affect fitted parameters from SSPP

Correlation Coefficient

The correlation coefficient quantifies how well an observed SEGUE spectrum matches a synthetic spectrum generated with its adopted Teff, log g, and [Fe/H]. These measurements are listed in the SSPP as CCCAHK, which compares the spectra from 3850-4250 Å, and CCMGH, from 4500-5500 Å. The correlation coefficient ranges from 0 to 1, with 1 indicating an excellent match between the two. However, due to an error in the treatment of the inverse variance flux error array in the methods of NGS1, NGS2, and CaIIK1, there are some stars with very incorrect parameters. There are 8280 stars are affected by this bug in the SSPP. This is less than 2% of the set of SDSS, SEGUE-1, and SEGUE-2 stellar spectra with valid g-r and S/N limits that the SSPP is able to estimate parameters for. These stars can be removed by requiring CCMGH and CCCAHK in the SSPP parameter table to be greater than zero, that is, CCMGH > 0 and CCCAHK > 0. Similarly, if more than 5% of a wavelength region used by a particular parameter estimation method is missing pixels for an individual star (e.g., has the inverse variance of the spectrum flux array set to 0), the SSPP does not report the estimated value from this technique. This improves the reliability of the parameter estimates, especially at very low metallicity.

Signal-to-Noise Constraints

The SSPP only provides stellar parameter estimates for stars where the measured S/N per 1Å pixel is 10 or greater (sppParams.snr). Below this limit, the spectra are too noisy for reliable estimates. There are around 373,300 stars in SEGUE-1 and SEGUE-2. Around 67,200 of these spectroscopic observations have S/N<10, approximately 18%. Thus, S/N constraints affect a significant portion of the sample.

Effective Temperature Scale

The DR9 and beyond versions of the SSPP adopts a much improved (g-i)-temperature relation, the InfraRed Flux Method (IRFM) (Casagrande et al. 2010). Each SSPP temperature estimate is re-scaled to match the IRFM estimate. In particular, this improves the estimates of Teff for cool stars (<5000 K).

Surface Gravity Determinations

The SSPP for DR7 and DR8 used 10 different methods to estimate surface gravity. However, the gravity estimates from MgH, CaI2, and k24, have been removed starting with DR9. Comparison with high-resolution observations of SEGUE targets found that these techniques deviated significantly from the expected log g. Although removing these techniques from the pipeline have improved the surface gravity estimates, there are still known problems. Specifically, the DR9+ SSPP gravity estimate tends to overestimate log g by up to 1.0 dex for very cool giant stars. This issue was also in the DR8 SSPP. Although the SSPP continues to improve its log g techniques, the SSPP surface gravity estimates have large uncertainties. They are meant to help distinguish between the evolutionary states of different stars but are not meant to be used as a precise log g value.

The standard redshift z from idlspec2d is available unaltered in the specObj and sppParams tables. These redshifts, primarily for galaxy work, have no offsets or corrections applied. For stars, a better redshift to use is the ELODIE-matched template redshifts, stored as elodie_z in the specObj file and the specObj table in CAS. The CAS also records this as the quantity elodierv in the sppParams table, but with a correction term:

elodierv = c*elodie_z+7.3 km/s

The 7.3 km/s is an empirically derived offset putting the elodierv of all stars on a system consistent with that of other literature measures of known radial velocity standards.

SSPP Flags

The DR10 SSPP has a flag 'B', which indicates that the measured Hα strength is different than that predicted from the Hδ line. The relation used to predict Hα strength breaks down for stars below 5800 K, as their Hδ lines are too weak. Therefore, the 'B' flag should not be used for stars below this temperature.

Incomplete spptarget information in DR8 CAS

This issue only affects 6 plates (2053/2073, 2054/2074, 2055/2075, 2056/2076, 2057/2077), and is caused by a missing camcol1 (in some cases also camcol2 is missing). This translates directly to having a smaller area covered than should be by objects in the spptargets table for those plate pairs. Spectroscopic objects on those plates are therefore sometimes matched to objects on adjacent plates instead, or are unmatched at all.

Photometric-Spectroscopic Matching Caveats

Caveats that affect matching between photometric and spectroscopic data.

Missing matches in DR10 photoObjAll table (fixed)

At the time of the DR10 release, the photoObjAll table values of specObjID were incompletely updated at first. This meant that a JOIN ON photoObjAll.specObjID = specObjAll.specObjID would yield incorrect results (although JOIN ON photoObjAll.objID = specObjAll.bestObjID would yield correct results). This problem was corrected as of August 10, 2013.

Mismatches between spectra and photometric data

There are occasional "mismatches" between the spectra and the photometry, both due to problems on the spectroscopic side in identifying the location associated with every fiber, and due to problems on the photometric side in finding an associated photometric object given a location. With some frequency, the fiber mapping failed which identifies which fiber has been plugged into which hole. There are around 7200 such cases in DR10, which are marked as UNPLUGGED in the   bitmask. The vast majority of these cases occur because the fiber was actually not plugged or was broken (in such cases, essentially no signal is detected in the fiber, and snMedian is reported as zero). In around 200 cases, there is measurable signal down the fiber. In cases where there is more than one such fiber on plate, there is a possibility that the fiber location associated with the spectrum is incorrect (and thus that the photometric and spectroscopic information is mismatched). This problem occurs for around 70 objects in the survey. Other mismatches can occur due to problems in the photometry. Errors in the deblending algorithm in the target reductions caused spectroscopy to be carried out occasionally on non-existent objects (e.g., diffraction spikes of bright stars or satellite trails). Many of these objects no longer exist in the current imaging reductions, with its improvements to the deblender over the years. We have in fact tried to mitigate this problem in this data release, as described in the spectroscopic-photometric matching documentation.

Missing SEGUE Photometry

The latest photometry is available for nearly all SEGUE objects; however, for a small fraction of fields (about 0.5%), the photo pipeline timed out before it finished cataloging and deblending objects. This is usually because there is a bright star in the field with scattered light wings that cause the deblender to work especially hard, as mentioned above. It also occurs for some of the lines-of-sight that include an open or globular cluster, where the deblender has difficulty separating stars from one another in the crowded field. Finally, it also occurs for SEGUE "SKY" spectra, which are pointed at a blank piece of sky, with no star or other imaging object underneath, for calibration purposes. For all of these spectroscopic observations, bestobjid is set to 0. There are about 12,500 stellar spectra that have no matching photometry.

One can still find the photometry for these objects by looking in the DR7 database and doing a position match. This requires a two stage query, as follows:

1. To extract spectra of objects with no photometry, search for targets with sppparams.bestobjid = 0 and sppparams.elodiervfinalerr > 0, while rejecting sky spectra by excluding objects with sp.sourcetype ='SKY' or sp.sectarget != 16:
SELECT s.plate,s.mjd,s.fiberid,sp.ra,sp.dec,s.elodiervfinal,s.elodiervfinalerr, s.fehadop,s.loggadop,sp.sourcetype INTO mydb.orphandr9spectra FROM sppparams s JOIN specobjall sp on s.specobjid=sp.specobjid WHERE s.bestobjid = 0 AND s.scienceprimary =1 AND elodiervfinalerr > 0 AND sp.sourcetype != 'SKY'AND sp.sectarget != 16
2. The PhotoObjDR7 and SpecDR7 tables match the objid from DR7 to those from later data releases. We can use these to extract DR7 photometry from PhotoObjAll:
SELECT top 10 poa7.run,poa7.rerun,poa7.camcol,poa7.field,poa7.obj, poa7.ra as pra,poa7.dec as pdec,poa7.psfmag_g,poa7.psfmag_r,m.* FROM mydb.orphandr9spectra_newquery as m JOIN specdr7 as sdr7 on m.specobjid=sdr7.specobjid JOIN dr7.photoobjall as poa7 on sdr7.dr7objid=poa7.objid

Not all of the "orphan" spectra have matching DR7 photometry, only around 5,300 do. Many of the lines of sight missing photometry come from crowded fields, such as the segcluster pointings.

Targeting photometry vs. matched photometry (BOSS)

The SDSS photometry version used when selecting targets for spectroscopy can be different than the DR8 version of the photometry used for matching observed spectra with photometric objects. The extreme case is ancillary programs, which may not have used SDSS photometry at all for their target selection.

plugMap information
in spPlate HDU 5, tracks the photometry used for targeting.
photoPos information
in photoPosPlate*.fits, tracks the match of the spectroscopic (RA, dec) with an object from DR8 photometry.

If the matching process identifies a different object from what was originally targeted, the following fields may disagree between the plugMap and the photoPos: RUN, RERUN, CAMCOL, FIELD, ID, RA, DEC, and plugMap.MAG may not match photoPos.FIBER2MAG. If the matching process fails to identify an object, then photoPos.THING_ID = -1, which is also the same THING_ID used for sky fibers.

BOSS Photometric Mismatches

There are main survey targets with THING_ID = -1 due to a mismatch between targetting on pre-DR8 photometry followed by matching to DR8 photometry.

Caveats that affect specific plates

SAS-only plates

If one browses the directory trees containing all of the spectra (see the spectroscopic data access page) one will find files associated with a certain number of plates not listed in the DR10 list of plates and not loaded into CAS. In essentially all cases, it is best to ignore such files and plates. We went through some effort to include all reasonably good plate observations; any plate observations found on SAS but not in CAS are likely to be disastrously bad.

A small number of plates suffered from a variety of problems, some more serious than others. For plates that we deem that the data is unreliable, they have had their platequality set to bad, and some terse comments put into the qualityComments status.

• Plates with comments about collimation problems refer to hardware problem causing a mismatch between the flatfields and the science exposure instrumental profile shapes, in both the spatial and wavelength directions. This problem caused the optimal extraction process to reject an excessive number of pixels. This problem was fixed in software, and comparing overlapping objects from adjacent plates confirms that the redshifts from these problematic plates are unbiased. However, the spectra themselves should not be used for precision work or spectrophotometry.
• Plates in the apbias program used multiple, very slightly offset pointings, but the reductions do not properly combine them. They should have valid redshifts in these spectra, but the spectrophotometry will be very inaccurate.
• For some plates the software had issues with rejecting cosmic rays, because there was only a single exposure to work with. These are all marked as bad plates (though again in many cases the redshifts and spectrophotometry are fine, except for the cosmic rays).
• Plates located in regions with extended diffuse Galactic emission (like in Orion or Taurus) often have sky-subtraction errors and issues, because there is no truly blank sky available. In these cases, the emission lines from the nebula are partially, but not wholly, subtracted and hard to interpret. Similar problems can occasionally happen if there is auroral activity while the spectrum was taken. If you suspect such problems, examine the spectra associated with the sky fibers.
• Because of time-variability in the dichroic throughput, occasionally the spectrophotometry has "kinks" at the transition between the red and blue spectrographs; we have identified some, though perhaps not all, of the worst cases of these.
• Occasionally the second spectrograph electronics caused serious issues for fibers 321 through 640 in SDSS.
• One plate had substantial contamination from Pollux because of light scattered through clouds.
• A number of other plates are simply low signal-to-noise ratio for a variety of reasons, but because they were special plates, needed to have their quality values set by hand. That is, they targeted deeper than we normally do, and so would have passed the survey's signal-to-noise criteria at the standard fiducial magnitudes.

Uncertain ZOFFSETs for some QSO targets (BOSS)

BOSS QSO targets at plate radius > 1.02 degrees generally have washers to offset their fibers in Z to optimize the signal-to-noise at 4000 Angstroms. ZOFFSET records the intended z-offset in microns, not the actual offset.

• Prior to MJD 55442, washers were not used.
• 55442 <= MJD <= 55474 was a transition period where washers were only sometimes used.
• After MJD 55474 washers were regularly used for new plates.
• Plates observed both before and after MJD 55474 may or may not have had washers for the later observations.

The actual washer state of a given plate/mjd is recorded in the yanny parameter file idlspec2d/opfiles/washers.par. Analyses which use ZOFFSET should consult that file to confirm the washer state or restrict themselves to plates which were first observed after MJD 55474.

Bad Sky Measurements for Some Plates

Plate 3770 MJD 55234 has bad sky measurements for fibers ≤ 500, due to being taken in marginal conditions.

Corrupt File Header for Plate 6138 MJD 56598 (BOSS)

Although all its spectra were correctly observed, the FITS file for Plate 6138 MJD 56598 has a corrupted header. The workaround is to delete the keyword EXPID upon reading in the FITS header.

Second night of data missing for Plates 389 and 2338 (SDSS-I)

For plates 389 and 2338, data from their second night of observations was not included in the reductions. This was caused by a bug in the planning of reductions (not in the reductions themselves).

This causes the file spPlate-0389-51795.fits to contain exactly the same data as spPlate-0389-51794.fits, and the file spPlate-2338-53679.fits to contain exactly the same data as spPlate-2338-53676.fits.

SkyServer returns "response buffer limit exceeded"

SkyServer returns "response buffer limit exceeded"

A SQL Search query on SkyServer might, on very rare occasions, return the following error:  SQL returned the following error message: 006~ASP 0251~Response Buffer Limit Exceeded~Execution of the ASP page caused the Response Buffer to exceed its configured limit. Your SQL command was:   This message is due to a default behavior of Microsoft Internet Information Services (IIS) version 6.0 and higher - documented in the Microsoft Knowledge Base - in which responses to actions of Active Server Pages (.asp) are limited to 4 MB file size. This error is extremely rare, but there is a simple workaround: simply run the query in CasJobs instead.


text:
## Electronic Journal of Probability

### Integrability of solutions of the Skorokhod embedding problem for diffusions

David Hobson

#### Abstract

Suppose $X$ is a time-homogeneous diffusion on an interval $I^X \subseteq {\mathbb R}$ and let $\mu$ be a probability measure on $I^X$. Then $\tau$ is a solution of the Skorokhod embedding problem (SEP) for $\mu$ in $X$ if $\tau$ is a stopping time and $X_\tau \sim \mu$. There are well-known conditions which determine whether there exists a solution of the SEP for $\mu$ in $X$. We give necessary and sufficient conditions for there to exist an integrable solution. Further, if there exists a solution ofthe SEP then there exists a minimal solution. We show that every minimal solution of the SEP has the same first moment. When $X$ is Brownian motion, there exists an integrable embedding of $\mu$ if and only if $\mu$ is centred and in $L^2$. Further,every integrable embedding is minimal. When $X$ is a general time-homogeneous diffusion the situation is more subtle. The case with drift can be reduced to the local martingale case by a change of scale. If $Y$ is a diffusion in natural scale, and if the target law is centred, then as in the Brownian case, there is an integrable embedding if the target law satisfies an integral condition. However, unlike in the Brownian case, there exist integrable embeddings of target laws which are not centred. Further, there exist integrable embeddings which are not minimal. Instead, if there exists an integrable embedding, then the set of minimal embeddings is the set of embeddings such that the mean equals a certain quantity, which we identify.

#### Article information

Source
Electron. J. Probab., Volume 20 (2015), paper no. 83, 26 pp.

Dates
Accepted: 10 August 2015
First available in Project Euclid: 4 June 2016

https://projecteuclid.org/euclid.ejp/1465067188

Digital Object Identifier
doi:10.1214/EJP.v20-4121

Mathematical Reviews number (MathSciNet)
MR3383567

Zentralblatt MATH identifier
1328.60104

Rights

#### Citation

Hobson, David. Integrability of solutions of the Skorokhod embedding problem for diffusions. Electron. J. Probab. 20 (2015), paper no. 83, 26 pp. doi:10.1214/EJP.v20-4121. https://projecteuclid.org/euclid.ejp/1465067188

#### References

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• Azema, Jacques; Yor, Marc. Le probleme de Skorokhod: complements d' "Une solution simple au probleme de Skorokhod”. (French) Seminaire de Probabilites, XIII (Univ. Strasbourg, Strasbourg, 1977/78), pp. 625–633, Lecture Notes in Math., 721, Springer, Berlin, 1979.
• Azema, Jacques; Yor, Marc. Une solution simple au probleme de Skorokhod. (French) Seminaire de Probabilites, XIII (Univ. Strasbourg, Strasbourg, 1977/78), pp. 90–115, Lecture Notes in Math., 721, Springer, Berlin, 1979.
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• Borodin, Andrei N.; Salminen, Paavo. Handbook of Brownian motion-facts and formulae. Second edition. Probability and its Applications. Birkhäuser Verlag, Basel, 2002. xvi+672 pp. ISBN: 3-7643-6705-9.
• Chacon, R. V.; Walsh, J. B. One-dimensional potential embedding. Seminaire de Probabilites, X (Premiere partie, Univ. Strasbourg, Strasbourg, annee universitaire 1974/1975), pp. 19–23. Lecture Notes in Math., Vol. 511, Springer, Berlin, 1976.
• Cox, A. M. G.; Hobson, D. G. An optimal Skorokhod embedding for diffusions. Stochastic Process. Appl. 111 (2004), no. 1, 17–39.
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text:
Design and Performance Evaluation of M×M MIMO Transmission in ESPAR Antenna

Title & Authors
Design and Performance Evaluation of M×M MIMO Transmission in ESPAR Antenna
Bok, Junyeong; Lee, Seung Hwan; Ryu, Heung-Gyoon;

Abstract
In this paper, we propose a $\small{M{\times}M}$ beam-space multiple input multiple output (BS-MIMO) system using electronically steerable parasitic array radiator (ESPAR) antenna. Conventional MIMO method required multiple RF chains because it map the transmission signals onto multiple antennas. So, conventional MIMO system has high cost for design and high energy consumption at RF circuit. Also, It is difficult to use MIMO system in battery based mobile terminals with limited physical area. In order to solve these problems, BS-MIMO technique which map the data signal onto bases in beam space domain was proposed using ESPAR antenna with single RF chain. This paper, we design and analyze the performance of extended $\small{M{\times}M}$ BS-MIMO technique. Simulation results show that the proposed BS-MIMO system has similar BER performance compare to conventional MIMO scheme. Therefore, BS-MIMO system with single RF chain will has a low RF power consumption and low cost for RF hardware design as compared with conventional MIMO technique with multiple RF chains.
Keywords
Beam space MIMO;ESPAR;pattern modulation;orthogonal basis;beam domain;
Language
Korean
Cited by
References
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A. Kalis, A. G. Kanatas, and C. B. Papadias, "A novel approach to MIMO transmission using a single RF front end," IEEE J. Sel. Areas Commun., vol. 26, no. 6, pp. 972-980, Aug. 2008.

2.
O. N. Alrabadi, C. B. Papadias, A. Kalis, and R. Prasad, "A universal encoding scheme for MIMO transmission using a single active element for PSK modulation schemes," IEEE Trans. Wireless Commun., vol. 8, no. 10, pp. 5133-5142, Oct. 2009.

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5.
J. Kotecha, "LTE:MIMO techniques in 3GPP-LTE," Freescale Semiconductor, Inc., June 2008, [Online]. Available:http://www.lteportal.com/Files/MarketSpace/Download/130_LTEMIMOTechniquesFreescaleNov52008.pdf.

6.
K. Gyoda and T. Ohira, "Design of electronically steerable passive array radiator (ESPAR) antenna," in Proc. IEEE Antenna Progag. Soc. Int. Symp., vol. 2, pp. 922-955, Salt Lake city, U.S.A., July 2000.

7.
J. Cheng, Y. Kamiya, and T. Ohira, "Adaptive beamforming of ESAPR antenna based on steepest descent gradient algorithm," IEICE Trans. Commun., vol. E84-B, no. 7, pp. 1790-1800, July 2001.

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M. R. Islam and M. Ali, "Elevation plane beam scanning of a novel parasitic array radiator antenna for 1900 MHz mobile handheld terminals," IEEE Trans. Antennas Propag., vol. 58, no. 10, pp. 3344-3352. Oct. 2010.

9.
M. Yamamoto, M. Taromaru, H. Sadamichi, and A. Shimizu, "Performance of angle switch diversity using ESPAR antenna for mobile reception of terrestrial digital TV," in Proc. IEEE 64th Veh. Technol. Conf. (VTC-Fall 2006), pp. 1-5, Montreal, Canada, Sep. 2006.

10.
A. Kalis, A. G. Kanatas, and C. Papadias, "An ESPAR antenna for beamspace-MIMO systems using PSK modulation schemes," IEEE Int. Conf. Commun. (ICC '07), pp. 5348-5353, Glasgow, U.K., June 2007.

11.
O. N. Alrabadi, C. B. Papadias, A. Kalis, N. Marchetti, and R. Prasad, "MIMO transmission and reception techniques using three-element ESPAR antennas," IEEE Commun. Lett., vol. 13, no. 4, pp. 236-238, Apr. 2009.

12.
V. Barousis and A. G. Kanatas, "Aerial degrees of freedom of parasitic arrays for single RF front-end MIMO transceivers," Progress in Electromagn. Research. B (PIER B), vol. 35, pp. 287-306, 2011.

13.
O. N. Alrabadi, J. Perruisseau-Carrier, and A. Kalis, "MIMO transmission using a single RF Source: Theory and antenna design," IEEE Trans. Antennas Propag., vol. 60, no. 2, pp. 654-64, Feb. 2012.