Springtail (Hexapoda: Collembola) functional group composition varies between different biotopes in Russian rice growing systems

2020 ◽  
Vol 99 ◽  
pp. 103208 ◽  
Author(s):  
Ruslan A. Saifutdinov ◽  
Rushan M. Sabirov ◽  
Andrey S. Zaitsev
Minerals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 239
Author(s):  
Wei Wang ◽  
Long Liang ◽  
Yaoli Peng ◽  
Maria Holuszko

Micro-Fourier transform infrared (micro-FTIR) spectroscopy was used to correlate the surface chemistry of low rank coal with hydrophobicity. Six square areas without mineral impurities on low rank coal surfaces were selected as testing areas. A specially-designed methodology was applied to conduct micro-FTIR measurements and contact angle tests on the same testing area. A series of semi-quantitative functional group ratios derived from micro-FTIR spectra were correlated with contact angles, and the determination coefficients of linear regression were calculated and compared in order to identify the structure of the functional group ratios. Finally, two semi-quantitative ratios composed of aliphatic carbon hydrogen, aromatic carbon hydrogen and two different types of carbonyl groups were proposed as indicators of low rank coal hydrophobicity. This work provided a rapid way to predict low rank coal hydrophobicity through its functional group composition and helped us understand the hydrophobicity heterogeneity of low rank coal from the perspective of its surface chemistry.


PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e74852 ◽  
Author(s):  
Justus P. Deikumah ◽  
Clive A. McAlpine ◽  
Martine Maron

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0125678 ◽  
Author(s):  
Tanja Strecker ◽  
Romain L. Barnard ◽  
Pascal A. Niklaus ◽  
Michael Scherer-Lorenzen ◽  
Alexandra Weigelt ◽  
...  

2014 ◽  
Vol 14 (4) ◽  
pp. 4787-4826 ◽  
Author(s):  
S. Gilardoni ◽  
P. Massoli ◽  
L. Giulianelli ◽  
M. Rinaldi ◽  
M. Paglione ◽  
...  

Abstract. The interaction of aerosol with atmospheric water affects the processing and wet removal of atmospheric particles. Understanding such interaction is mandatory to improve model description of aerosol lifetime and ageing. We analyzed the aerosol-water interaction at high relative humidity during fog events in the Po Valley, in the framework of the ARPA-ER Supersite project. For the first time in this area, the changes in particle chemical composition caused by fog are discussed along with changes in particle microphysics. During the experiment, 14 fog events were observed. The average mass scavenging efficiency was 70% for nitrate, 68% for ammonium, 61% for sulfate, 50% for organics, and 39% for black carbon. After fog formation, the interstitial aerosol was dominated by particles smaller than 200 nm Dva (vacuum aerodynamic diameter) and enriched in carbonaceous aerosol, mainly black carbon and water insoluble organic aerosol (WIOA). For each fog event, the size segregated scavenging efficiency of nitrate and organic aerosol (OA) was calculated by comparing chemical species size distribution before and after fog formation. For both nitrate and OA, the size segregated scavenging efficiency followed a sigmoidal curve, with values close to zero below 100 nm Dva and close to 1 above 700 nm Dva. OA was able to affect scavenging efficiency of nitrate in particles smaller than 300 nm Dva. A linear correlation between nitrate scavenging and particle hygroscopicity (κ) was observed, indicating that 44–51% of the variability of nitrate scavenging in smaller particles (below 300 nm Dva) was explained by changes in particle chemical composition. The size segregated scavenging curves of OA followed those of nitrate, suggesting that organic scavenging was controlled by mixing with water-soluble species. In particular, functional group composition and OA elemental analysis indicated that more oxidized OA was scavenged more efficiently than less oxidized OA. Nevertheless, the small variability of organic functional group composition during the experiment did not allow us to discriminate the effect of different organic functionalities on OA scavenging.


PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e57027 ◽  
Author(s):  
Ellen L. Fry ◽  
Pete Manning ◽  
David G. P. Allen ◽  
Alex Hurst ◽  
Georg Everwand ◽  
...  

2009 ◽  
Vol 16 (8) ◽  
pp. 969-976 ◽  
Author(s):  
Jin Lu ◽  
Bing Niu ◽  
Liang Liu ◽  
Wen-Cong Lu ◽  
Yu-Dong Cai

2015 ◽  
Vol 8 (3) ◽  
pp. 1097-1109 ◽  
Author(s):  
A. M. Dillner ◽  
S. Takahama

Abstract. Organic carbon (OC) can constitute 50% or more of the mass of atmospheric particulate matter. Typically, organic carbon is measured from a quartz fiber filter that has been exposed to a volume of ambient air and analyzed using thermal methods such as thermal-optical reflectance (TOR). Here, methods are presented that show the feasibility of using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE or Teflon) filters to accurately predict TOR OC. This work marks an initial step in proposing a method that can reduce the operating costs of large air quality monitoring networks with an inexpensive, non-destructive analysis technique using routinely collected PTFE filter samples which, in addition to OC concentrations, can concurrently provide information regarding the composition of organic aerosol. This feasibility study suggests that the minimum detection limit and errors (or uncertainty) of FT-IR predictions are on par with TOR OC such that evaluation of long-term trends and epidemiological studies would not be significantly impacted. To develop and test the method, FT-IR absorbance spectra are obtained from 794 samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011. Partial least-squares regression is used to calibrate sample FT-IR absorbance spectra to TOR OC. The FTIR spectra are divided into calibration and test sets by sampling site and date. The calibration produces precise and accurate TOR OC predictions of the test set samples by FT-IR as indicated by high coefficient of variation (R2; 0.96), low bias (0.02 μg m−3, the nominal IMPROVE sample volume is 32.8 m3), low error (0.08 μg m−3) and low normalized error (11%). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision to collocated TOR measurements. FT-IR spectra are also divided into calibration and test sets by OC mass and by OM / OC ratio, which reflects the organic composition of the particulate matter and is obtained from organic functional group composition; these divisions also leads to precise and accurate OC predictions. Low OC concentrations have higher bias and normalized error due to TOR analytical errors and artifact-correction errors, not due to the range of OC mass of the samples in the calibration set. However, samples with low OC mass can be used to predict samples with high OC mass, indicating that the calibration is linear. Using samples in the calibration set that have different OM / OC or ammonium / OC distributions than the test set leads to only a modest increase in bias and normalized error in the predicted samples. We conclude that FT-IR analysis with partial least-squares regression is a robust method for accurately predicting TOR OC in IMPROVE network samples – providing complementary information to the organic functional group composition and organic aerosol mass estimated previously from the same set of sample spectra (Ruthenburg et al., 2014).


2016 ◽  
Vol 90 (4) ◽  
pp. 470-483 ◽  
Author(s):  
Lalita M. Calabria ◽  
Kate Petersen ◽  
Sarah T. Hamman ◽  
Robert J. Smith

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