72 research outputs found
Consistency and trends of technological innovations: a network approach to the international patent classification data
Classifying patents by the technology areas they pertain is important to enable information search and facilitate policy analysis and socio-economic studies. Based on the OECD Triadic Patent Family database, this study constructs a cohort network based on the grouping of IPC subclasses in the same patent families, and a citation network based on citations between subclasses of patent families citing each other. This paper presents a systematic analysis approach which obtains naturally formed network clusters identified using a Lumped Markov Chain method, extracts community keys traceable over time, and investigates two important community characteristics: consistency and changing trends. The results are verified against several other methods, including a recent research measuring patent text similarity. The proposed method contributes to the literature a network-based approach to study the endogenous community properties of an exogenously devised classification system. The application of this method may improve accuracy and efficiency of the IPC search platform and help detect the emergence of new technologies
Improved Satellite Retrievals of NO2 and SO2 over the Canadian Oil Sands and Comparisons with Surface Measurements
Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO2 and SO2 average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO2 and SO2 (2005-2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of greater than 0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO2 instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals
Comparison of OMI ozone and UV irradiance data with ground-based measurements at two French sites
International audienceOzone Monitoring Instrument (OMI), launched in July 2004, is dedicated to the monitoring of the Earth's ozone, air quality and climate. OMI provides among other things the total column of ozone (TOC), the surface ultraviolet (UV) irradiance at several wavelengths, the erythemal dose rate and the erythemal daily dose. The main objective of this work is to validate OMI data with ground-based instruments in order to use OMI products (collection 2) for scientific studies. The Laboratoire d'Optique Atmosphérique (LOA) located in Villeneuve d'Ascq in the north of France performs solar UV measurements using a spectroradiometer and a broadband radiometer. The site of Briançon in the French Southern Alps is also equipped with a spectroradiometer operated by Interaction Rayonnement Solaire Atmosphère (IRSA). The instrument belongs to the Centre Européen Médical et Bioclimatologique de Recherche et d'Enseignement Supérieur. The comparison between the TOC retrieved with ground-based measurements and OMI TOC shows good agreement at both sites for all sky conditions. Comparisons of spectral UV on clear sky conditions are also satisfying whereas results of comparisons of the erythemal daily doses and erythemal dose rates for all sky conditions and for clear sky show that OMI overestimates significantly surface UV doses at both sites
Mapping Patent Classifications: Portfolio and Statistical Analysis, and the Comparison of Strengths and Weaknesses
The Cooperative Patent Classifications (CPC) jointly developed by the
European and US Patent Offices provide a new basis for mapping and portfolio
analysis. This update provides an occasion for rethinking the parameter
choices. The new maps are significantly different from previous ones, although
this may not always be obvious on visual inspection. Since these maps are
statistical constructs based on index terms, their quality--as different from
utility--can only be controlled discursively. We provide nested maps online and
a routine for portfolio overlays and further statistical analysis. We add a new
tool for "difference maps" which is illustrated by comparing the portfolios of
patents granted to Novartis and MSD in 2016.Comment: Scientometrics 112(3) (2017) 1573-1591;
http://link.springer.com/article/10.1007/s11192-017-2449-
Tropospheric emissions: Monitoring of pollution (TEMPO)
TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O), nitrogen dioxide (NO), sulfur dioxide (SO), formaldehyde (HCO), glyoxal (CHO), bromine monoxide (BrO), IO (iodine monoxide), water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O chemistry cycle. Multi-spectral observations provide sensitivity to O in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.Peer Reviewe
Aerosol optical depth retrieval using ATSR-2 and AVHRR data during TARFOX
Satellite retrieved aerosol optical properties are compared to aircraft
measurements for a case study during the Tropospheric Aerosol Radiative Forcing
Observational Experiment (TARFOX). Two satellite instruments are used: the
Along Track Scanning Radiometer 2 (ATSR-2) and the advanced very high
resolution radiometer (AVHRR). The aerosol optical depth in the mid-visible
(0.555 Jjm) retrieved from the ATSR-2 data agrees within 0.03 with colocated
sunphotometer measurements. Also, the spectral behavior of the aerosol optical
depth is retrieved accurately. Good correlation is found between aerosol optical
depths for AVHRR channel 1 (0.64 Jjm) and sunphotometer derived values, but
the satellite retrieved values are 0.05 to 0.15 lower. The Angstrom wavelength
exponent is determined both from the ATSR-2 and the AVHRR data. The ATSR-2
derived Angstrom exponents are in good agreement with the values computed from
the sunphotometer data. The Angstrom exponents determined from AVHRR data
show very large variations. Both the ATSR-2 and the AVHRR aerosol optical
depth images show a large gradient. Vertical profile data of temperature, relative
humidity, and particle scattering indicate that this gradient is probably caused by
changes in the dry aerosol properties, rather than a change in the relative humidity
Comparison of dust-layer heights from active and passive satellite sensors
Aerosol-layer height is essential for understanding the
impact of aerosols on the climate system. As part of the European Space
Agency Aerosol_cci project, aerosol-layer height as derived from passive
thermal and solar satellite sensors measurements have been compared with
aerosol-layer heights estimated from CALIOP measurements. The Aerosol_cci
project targeted dust-type aerosol for this study. This ensures relatively
unambiguous aerosol identification by the CALIOP processing chain. Dust-layer
height was estimated from thermal IASI measurements using four different
algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and
SCIAMACHY (IUP) measurements. Due to differences in overpass time of the
various satellites, a trajectory model was used to move the CALIOP-derived
dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height
pixels. It is not possible to construct a unique dust-layer height from the
CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative
extinction height defined as the height where the CALIOP extinction column is
half of the total extinction column, and the geometric mean height, which is
defined as the geometrical mean of the top and bottom heights of the dust
layer. In statistical average over all IASI data there is a general tendency
to a positive bias of 0.5–0.8 km against CALIOP extinction-weighted
height for three of the four algorithms assessed, while the fourth algorithm
has almost no bias. When comparing geometric mean height there is a shift of
−0.5 km for all algorithms (getting close to zero for the three
algorithms and turning negative for the fourth). The standard deviation of
all algorithms is quite similar and ranges between 1.0 and 1.3 km.
When looking at different conditions (day, night, land, ocean), there is more
detail in variabilities (e.g. all algorithms overestimate more at night than
during the day). For the solar sensors it is found that on average SCIAMACHY
data are lower by −1.097 km (−0.961 km) compared to the
CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are
lower by −1.393 km (−0.818 km)
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