scholarly journals CO<sub>2</sub> column-averaged volume mixing ratio derived over Tsukuba from measurements by commercial airlines

2010 ◽  
Vol 10 (2) ◽  
pp. 3401-3421 ◽  
Author(s):  
M. Araki ◽  
I. Morino ◽  
T. Machida ◽  
Y. Sawa ◽  
H. Matsueda ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Observational Data ◽  
Meteorological Data ◽  
Co2 Concentration ◽  
Concentration Data ◽  
Climatological Data ◽  
Mixing Ratios ◽  
Least Squares Fit ◽  
Small Uncertainty ◽  
Using Data

Abstract. Column-averaged volume mixing ratios of carbon dioxide (XCO2) during the period from January 2007 to May 2008 over Tsukuba, Japan, were derived by using CO2 concentration data observed by Japan Airlines Corporation (JAL) commercial airliners, based on the assumption that CO2 profiles over Tsukuba and Narita were the same. CO2 profile data for 493 flights on clear-sky days were analysed in order to calculate XCO2 with an ancillary dataset: Tsukuba observational data (by rawinsonde and a meteorological tower) or global meteorological data (NCEP and CIRA-86). The amplitude of seasonal variation of XCO2 (Tsukuba observational) from the Tsukuba observational data was determined by least-squares fit using a harmonic function to roughly evaluate the seasonal variation over Tsukuba. The highest and lowest values of the obtained fitted curve in 2007 for XCO2 (Tsukuba observational) were 386.4 and 381.7 ppm in May and September, respectively. The dependence of XCO2 on the type of ancillary dataset was evaluated. The average difference between XCO2 (global) from global climatological data and XCO2 (Tsukuba observational), i.e., the bias of XCO2 (global) based on XCO2 (Tsukuba observational), was found to be -0.621 ppm with a standard deviation of 0.682 ppm. The uncertainty of XCO2 (global) based on XCO2 (Tsukuba observational) was estimated to be 0.922 ppm. This small uncertainty suggests that the present method of XCO2 calculation using data from airliners and global climatological data can be applied to the validation of GOSAT products for XCO2 over airports worldwide.

scholarly journals CO<sub>2</sub> column-averaged volume mixing ratio derived over Tsukuba from measurements by commercial airlines

2010 ◽  
Vol 10 (16) ◽  
pp. 7659-7667 ◽  
Author(s):  
M. Araki ◽  
I. Morino ◽  
T. Machida ◽  
Y. Sawa ◽  
H. Matsueda ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Standard Deviation ◽  
Ancillary Data ◽  
Climatological Data ◽  
Mixing Ratios ◽  
Clear Sky ◽  
Least Squares Fit ◽  
Small Uncertainty ◽  
Using Data ◽  
Co2 Profile

Abstract. Column-averaged volume mixing ratios of carbon dioxide (XCO2) during the period from January 2007 to May 2008 over Tsukuba, Japan, were derived using CO2 concentrations measured by Continuous CO2 Measuring Equipment (CME). The CMEs were installed on Japan Airlines Corporation (JAL) commercial airliners, which frequently fly to and from Narita Airport. It was assumed that CO2 profiles over Tsukuba and Narita are the same. CO2 profile data for 493 flights on clear-sky days were analyzed in order to calculate XCO2 with one of two ancillary datasets: "Tsukuba observational" data (rawinsonde and meteorological tower), or "global" forecast/reanalysis and climatological data (NCEP and CIRA-86). The amplitude of the seasonal variation of XCO2 using the ancillary data measured in Tsukuba (XCO2 (Tsukuba observational)) was determined by a least squares fit using a harmonic function to roughly evaluate the seasonal variation over Tsukuba. The highest and lowest values of the obtained fitted curve in 2007 for XCO2 (Tsukuba observational) were 386.4 ± 1.0 and 381.7 ± 1.0 ppm in May and September, respectively, where the errors represent 1 standard deviation of the fit residuals. The dependence of XCO2 on the type of ancillary dataset was evaluated. The average difference between XCO2 from global climatological data, XCO2 (global), and XCO2 (Tsukuba observational), i.e., the bias of XCO2 (global) based on XCO2 (Tsukuba observational), was found to be −0.621 ppm with a standard deviation of 0.682 ppm. The uncertainty of XCO2 (global) based on XCO2 (Tsukuba observational) was estimated to be 0.922 ppm. This small uncertainty relative to the GOSAT precision suggests that calculating XCO2 using data from airliners and global climatological data can be applied to the validation of GOSAT products for XCO2 over airports worldwide.

scholarly journals A Precise Benchmark for Cluster Scaling Relations: Fundamental Plane, Mass Plane and IMF in the Coma Cluster from Dynamical Models

2020 ◽  
Author(s):  
Shravan Shetty ◽  
Michele Cappellari ◽  
Richard M McDermid ◽  
Davor Krajnović ◽  
P T de Zeeuw ◽  
...  
Keyword(s):  
Galaxy Formation ◽  
Scaling Relations ◽  
Coma Cluster ◽  
Dynamical Models ◽  
Fundamental Plane ◽  
Cluster Galaxies ◽  
Dynamical Scaling ◽  
Small Uncertainty ◽  
Cluster Distance ◽  
Using Data

Abstract We study a sample of 148 early-type galaxies in the Coma cluster using SDSS photometry and spectra, and calibrate our results using detailed dynamical models for a subset of these galaxies, to create a precise benchmark for dynamical scaling relations in high-density environments. For these galaxies, we successfully measured global galaxy properties, modeled stellar populations, and created dynamical models, and support the results using detailed dynamical models of 16 galaxies, including the two most massive cluster galaxies, using data taken with the SAURON IFU. By design, the study provides minimal scatter in derived scaling relations due to the small uncertainty in the relative distances of galaxies compared to the cluster distance. Our results demonstrate low (≤55% for 90th percentile) dark matter fractions in the inner 1Re of galaxies. Owing to the study design, we produce the tightest, to our knowledge, IMF-σe relation of galaxies, with a slope consistent with that seen in local galaxies. Leveraging our dynamical models, we transform the classical Fundamental Plane of the galaxies to the Mass Plane. We find that the coefficients of the mass plane are close to predictions from the virial theorem, and have significantly lower scatter compared to the Fundamental plane. We show that Coma galaxies occupy similar locations in the (M* - Re) and (M* - σe) relations as local field galaxies but are older. This, and the fact we find only three slow rotators in the cluster, is consistent with the scenario of hierarchical galaxy formation and expectations of the kinematic morphology-density relation.

scholarly journals Seasonal variations of triple oxygen isotopic compositions of atmospheric sulfate, nitrate and ozone at Dumont d'Urville, coastal Antarctica

2016 ◽  
Author(s):  
Sakiko Ishino ◽  
Shohei Hattori ◽  
Joel Savarino ◽  
Bruno Jourdain ◽  
Susanne Preunkert ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Ice Cores ◽  
Oxidative Capacity ◽  
So2 Oxidation ◽  
Mixing Ratios ◽  
Isotopic Compositions ◽  
Oxygen Isotopic ◽  
Low Sensitivity ◽  
Complex Chemistry ◽  
Oxidation Chemistry

Abstract. Reconstruction of the oxidative capacity of the atmosphere is of great importance to understanding climate change, because of its key role in determining the life times of trace gases. Triple oxygen isotopic compositions (Δ17O = δ17O − 0.52 × δ18O) of atmospheric sulfate (SO42−) and nitrate (NO3−) in the Antarctic ice cores have shown potential as stable proxies, because they reflect the oxidation chemistry involved in their formation processes. However, observations of Δ17O values of SO42−, NO3− and ozone in the present-day Antarctic atmosphere are very limited, and their complex chemistry is not fully understood in this region. We present the first simultaneous measurement of Δ17O values of atmospheric sulfate, nitrate, and ozone collected at Dumont d'Urville station (66°40' S, 140°01' E) throughout 2011. Δ17O values of sulfate and nitrate exhibited seasonal variation characterized by summer minima and winter maxima, within the ranges of 0.9–3.4 ‰ and 23.0–41.9 ‰, respectively. In contrast, Δ17O values of ozone showed no significant seasonal variation, with values of 26 ± 1 ‰ through the year. These contrasting seasonal trends suggest that Δ17O(O3) is not the major factor determining seasonal changes in Δ17O(SO42−) and Δ17O(NO3−) values. The summer/winter trends for Δ17O(SO42−) and Δ17O(NO3−) values are caused by sunlight-driven changes in O3/ROX ratios, which decrease in summer through ozone destruction and photo-oxidants production, resulting in co-variation between ozone mixing ratios and Δ17O(SO42−) and Δ17O(NO3−) values. However, despite similar ranges of ozone mixing ratios in spring (September to November) and fall (March to May), Δ17O(SO42−) values observed in spring were lower than in fall. The relatively low sensitivity of Δ17O(SO42−) values to the ozone mixing ratio in spring is possibly explained by (i) lower O3/ROX ratios caused by NOX emission from snowpack and/or (ii) SO2 oxidation by hypohalous acids (HOX = HOCl + HOBr) in the aqueous phase.

scholarly journals Seasonal Variation Analysis Method of GHG at Municipal Solid Waste Incinerator

2020 ◽  
Vol 12 (18) ◽  
pp. 7425
Author(s):  
Seongmin Kang ◽  
Joonyoung Roh ◽  
Eui-chan Jeon
Keyword(s):  
Seasonal Variation ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Ghg Emissions ◽  
Co2 Concentration ◽  
Waste Incineration ◽  
Seasonal Effect ◽  
Waste Incinerator ◽  
Waste Composition

The greenhouse gas emissions of the waste incineration sector account for approximately 43% of the total GHG emissions and represent the majority of the CO2 emissions from waste in Korea. Improving the reliability of the GHG inventory of the waste incineration sector is an important aspect for the examination of global GHG emission management according to the Paris Agreement. In this study, we introduced a statistical approach to analyze seasonal changes through analysis of waste composition and CO2 concentration in Municipal Solid Waste incinerators and applied the methodology to one case study facility. The analysis results in the case study showed that there was no seasonal variation in waste composition and CO2 concentrations, except for wood. Wood is classified as biomass, and the GHG emissions caused by biomass incineration are reported separately, indicating that the effect of an MSW incinerator on GHG emissions is not significant. Therefore, the seasonal effect of CO2 concentration or waste composition may not be an impact when calculating GHG emissions from case study facilities’ MSW incinerators. This study proposed an approach for analyzing factors that affect the GHG inventory reliability by analyzing seasonal characteristics and variation through the statistical analysis, which are used for the calculation of the GHG emissions of an MSW incinerator.

scholarly journals Seasonal Variation And Preliminary Risk Assessment Of Trace Element Pollution In Surface Water From Langat River, Malaysia

2019 ◽  
Author(s):  
Lubna Alam ◽  
Md. Mahmudul Alam ◽  
Mazlin Bin Mokhtar ◽  
Azizul Bar ◽  
Nicholas Kathijotes ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Seasonal Variation ◽  
River Water ◽  
Critical Index ◽  
Principal Component ◽  
Dermal Absorption ◽  
Potential Threat ◽  
Concentration Data ◽  
Trace Element Pollution ◽  
Langat River

Heavy metals are widely used in various industries and became a great concern all over the world due to environmental contamination. This study provides an assessment of seasonal variability and risks to human health associated with the exposure to heavy metals concentrated in Langat river water of Malaysia. The Department of Environment (DOE) Malaysia kindly provided the heavy metal concentration data in water for this study. Several multivariate estimation such as an independent t test, box-and-whisker plot and Principal component analysis were carried out to evaluate the seasonal variation of metals concentration in water. The average value of ten analyzed metals was 250.81 µg/l and followed in order of abundance by August &gt; Jun &gt; February &gt; October &gt; April &gt; December &gt; March &gt; May &gt; September &gt; January &gt; July &gt; November. The calculated HPI was 123.42, which is far above the critical index value of 100, indicating pollution with respect to heavy metals. Estimates of health risks associated with river water were summarized according to non-carcinogenic and carcinogenic health effects. No potential threat was detected for noncarcinogenic risk as the HI values calculated were &lt;1. Potential carcinogenic risks associated with the ingestion and dermal absorption of heavy metals in water were evaluated probabilistically by performing 10,000 trails for Monte Carlo simulation where potential carcinogenic risk exists in case of Cd and As.It is necessary to take proper steps to reduce the pollution of heavy metals in Langat River.

Merits and caveats of propensity scores to adjust for confounding

2018 ◽  
Vol 34 (10) ◽  
pp. 1629-1635 ◽  
Author(s):  
Edouard L Fu ◽  
Rolf H H Groenwold ◽  
Carmine Zoccali ◽  
Kitty J Jager ◽  
Merel van Diepen ◽  
...  
Keyword(s):  
Risk Factors ◽  
The Netherlands ◽  
Health Outcomes ◽  
Observational Data ◽  
Propensity Scores ◽  
Cooperative Study ◽  
Dialysis Patients ◽  
Proper Understanding ◽  
The Past ◽  
Using Data

Abstract Proper adjustment for confounding is essential when estimating the effects of treatments or risk factors on health outcomes in observational data. To this end, various statistical methods have been developed. In the past couple of years, the use of propensity scores (PSs) to control for confounding has increased. Proper understanding of this method is necessary to critically appraise research in which it is applied. In this article, we provide an overview of PS methods, explaining their concept, advantages and possible disadvantages. Furthermore, the use of PS matching, PS adjustment and PS weighting is illustrated using data from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) cohort of dialysis patients.

scholarly journals Comparing high resolution WRF-VPRM simulations and two global CO<sub>2</sub> transport models with coastal tower measurements of CO<sub>2</sub>

2009 ◽  
Vol 6 (5) ◽  
pp. 807-817 ◽  
Author(s):  
R. Ahmadov ◽  
C. Gerbig ◽  
R. Kretschmer ◽  
S. Körner ◽  
C. Rödenbeck ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Fraction ◽  
Wrf Model ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Concentration Data ◽  
Modeling Tools ◽  
Global Models ◽  
Diurnal Variability ◽  
Resolution Model

Abstract. In order to better understand the effects that mesoscale transport has on atmospheric CO2 distributions, we have used the atmospheric WRF model coupled to the diagnostic biospheric model VPRM, which provides high resolution biospheric CO2 fluxes based on MODIS satellite indices. We have run WRF-VPRM for the period from 16 May to 15 June in 2005 covering the intensive period of the CERES experiment, using the CO2 fields from the global model LMDZ for initialization and lateral boundary conditions. The comparison of modeled CO2 concentration time series against observations at the Biscarosse tower and against output from two global models – LMDZ and TM3 – clearly reveals that WRF-VPRM can capture the measured CO2 signal much better than the global models with lower resolution. Also the diurnal variability of the atmospheric CO2 field caused by recirculation of nighttime respired CO2 is simulated by WRF-VRPM reasonably well. Analysis of the nighttime data indicates that with high resolution modeling tools such as WRF-VPRM a large fraction of the time periods that are impossible to utilize in global models, can be used quantitatively and may help to constrain respiratory fluxes. The paper concludes that we need to utilize a high-resolution model such as WRF-VPRM to use continental observations of CO2 concentration data with more spatial and temporal coverage and to link them to the global inversion models.

scholarly journals Estimating the weight of a snow cover using only meteorological factors

1993 ◽  
Vol 18 ◽  
pp. 190-192
Author(s):  
Kenji Shinojima ◽  
Hiroshi Harada
Keyword(s):  
Snow Cover ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Moving Average ◽  
Data Acquisition System ◽  
Degree Days ◽  
Forest Product Research Institute ◽  
Temperature And Precipitation ◽  
Using Data

We compute the weight of the snow cover as a function of the daily quantity of precipitation and daily melting using only data from the Automated Meteorological Data Acquisition System (AMeDAS), which is used widely in Japan. The correlation between long-term measurements and meteorological data in AMeDAS factors was computed by statistical methods from the Forestry and Forest Product Research Institute, Tokamachi Experiment Station, in Niigata Prefecture, using data for 11 winter seasons (1977–87). The daily quantity of melting is expressed with a three-day moving average of degree days. The coefficient of correlation between the daily groups of each value of the 1323 days during the 11 winter seasons was 0.986 with a standard deviation of ±590 Ν m−2. Thus, if air temperature and precipitation can be obtained for an area, the weight of the snow cover can be estimated with confidence.

scholarly journals The association of cellulitis incidence and meteorological factors in Taiwan

2019 ◽  
Vol 147 ◽  
Author(s):  
Ren-Jun Hsu ◽  
Chia-Cheng Chou ◽  
Jui-Ming Liu ◽  
See-Tong Pang ◽  
Chien-Yu Lin ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Incidence Rate ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Strongly Correlated ◽  
Total Rainfall ◽  
The Public ◽  
Increased Risk ◽  
Using Data ◽  
Central Weather Bureau

AbstractCellulitis is a common infection of the skin and soft tissue. Susceptibility to cellulitis is related to microorganism virulence, the host immunity status and environmental factors. This retrospective study from 2001 to 2013 investigated relationships between the monthly incidence rate of cellulitis and meteorological factors using data from the Taiwanese Health Insurance Dataset and the Taiwanese Central Weather Bureau. Meteorological data included temperature, hours of sunshine, relative humidity, total rainfall and total number of rainy days. In otal, 195 841 patients were diagnosed with cellulitis and the incidence rate was strongly correlated with temperature (γS = 0.84, P < 0.001), total sunshine hours (γS = 0.65, P < 0.001) and total rainfall (γS = 0.53, P < 0.001). The incidence rate of cellulitis increased by 3.47/100 000 cases for every 1° elevation in environmental temperature. Our results may assist clinicians in educating the public of the increased risk of cellulitis during warm seasons and possible predisposing environmental factors for infection.

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