Exposure to ambient fine particulate matter (PM2.5) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM2.5 sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM2.5 to interpret globally dispersed PM2.5 mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Measured site mean PM2.5 composition varies substantially for secondary inorganic aerosols (2.4-19.7 mu g/m(3)), mineral dust (1.9-14.7 mu g/m(3)), residual/organic matter (2.1-40.2 mu g/m(3)), and black carbon (1.0-7.3 mu g/m(3)). Interpretation of these measurements with the GEOS-Chem model yields insight into sources affecting each site. Globally, combustion sectors such as residential energy use (7.9 mu g/m(3)), industry (6.5 mu g/m(3)), and power generation (5.6 g/m(3)) are leading sources of outdoor global population-weighted PM2.5 concentrations. Global population-weighted organic mass is driven by the residential energy sector (64%) whereas population-weighted secondary inorganic concentrations arise primarily from industry (33%) and power generation (32%). Simulation-measurement biases for ammonium nitrate and dust identify uncertainty in agricultural and crustal sources. Interpretation of initial PM2.5 mass and composition measurements from SPARTAN with the GEOS-Chem model constrained by satellite-based PM2.5 provides insight into sources and processes that influence the global spatial variation in PM2.5 composition.

AbstractThe Visible Infrared Imaging Radiometer Suite (VIIRS) is the next‐generation polar‐orbiting operational environmental sensor with a capability for global aerosol observations. The VIIRS aerosol Environmental Data Record (EDR) is expected to continue the decade‐long successful multispectral aerosol retrieval from the NASA's Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) for scientific research and applications. Since the launch of the Suomi National Polar‐orbiting Partnership (S‐NPP), the VIIRS aerosol calibration/validation team has been continuously monitoring, evaluating, and improving the performance of VIIRS aerosol retrievals. In this study, the VIIRS aerosol optical thickness (AOT) at 550 nm EDR at current Provisional maturity level is evaluated by comparing it with MODIS retrievals and measurements from the Aerosol Robotic Network (AERONET) and the Maritime Aerosol Network (MAN). The VIIRS global mean AOT at 550 nm differs from that of MODIS by approximately −0.01 over ocean and 0.03 over land (0.00 and −0.01 for the collocated retrievals) but shows larger regional biases. Global validation with AERONET and with MAN measurements shows biases of 0.01 over ocean and −0.01 over land, with about 64% and 71% of retrievals falling within the expected uncertainty range established by MODIS over ocean (±(0.03 + 0.05AOT)) and over land (±(0.05 + 0.15AOT)), respectively. The VIIRS retrievals over land exhibit slight overestimation over vegetated surfaces and underestimation over soil‐dominated surfaces. These results show that the VIIRS AOT at 550 nm product provides a solid global data set for quantitative scientific investigations and environmental monitoring.

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the Suomi National Polar‐orbiting Partnership (S‐NPP) spacecraft was launched in October 2011. The instrument has 22 spectral channels with band centers from 412 nm to 12,050 nm. The VIIRS aerosol data products are derived primarily from the radiometric channels covering the visible through the short‐wave infrared spectral regions (412 nm to 2250 nm). The major components of the VIIRS aerosol retrieval process are data screening, land inversion, ocean inversion, suspended matter typing, and aggregation. The primary data product produced is the aerosol optical thickness (AOT) environmental data record. A higher resolution AOT intermediate product is also produced. These AOT products and their corresponding retrieval algorithms are described in detail, including theoretical basis, retrieval limitations, and data quality flagging. Preliminary evaluation of the data products has been undertaken by the VIIRS aerosol calibration/validation team using Aerosol Robotic Network ground‐based observations to show that the performance of AOT retrievals meets the requirements specified in the Joint Polar Satellite System Level 1 requirements.

The sensitivity to polarized radiance input of the VIIRS instrument (Flight Unit 1 on NPP) has been extensively characterized and shown to be compliant with the sensor specifications [1]. We have rigorously modeled VIIRS measurements of polarized, top-of-atmosphere radiances, using Global Synthetic Data in open ocean regions and a VIIRS sensor model incorporating the polarization sensitivity and other sensor characterization data from thermal vacuum chamber tests. An atmospheric correction over ocean (ACO) algorithm is employed to retrieve the water-leaving radiances, Lw. Analysis of Lw retrieval performance indicates that the impact of polarization on relative errors is significant before any correction and is greatly reduced after the demonstrated correction for effects of polarization. The uncertainty in characterization of VIIRS polarization sensitivity induces errors in retrieved Lw in open oceans that are much smaller than the other errors, which are attributable to the inherent ACO algorithm errors and other sensor errors.

An approach is presented to distinguish between clouds and heavy aerosols in sun-glint regions with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. The approach extends the applicability of an algorithm that has already been applied successfully in areas outside the geometric and wind-induced sun-glint areas of the earth over both land and water surfaces. The successful application of this approach to include sun-glint regions requires an accurate cloud phase analysis, which can be degraded, especially in regions of sun glint, because of poorly calibrated radiances of the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Consequently, procedures have been developed to replace bad MODIS level 1B (L1B) data, which may result from saturation, dead/noisy detectors, or data dropouts, with radiometrically reliable values to create the Visible Infrared Imager Radiometer Suite (VIIRS) proxy sensor data records (SDRs). Cloud phase analyses produced by the NPOESS VIIRS cloud mask (VCM) algorithm using these modified VIIRS proxy SDRs show excellent agreement with features observed in color composites of MODIS imagery. In addition, the improved logic in the VCM algorithm provides a new capability to differentiate between clouds and heavy aerosols within the sun-glint cone. This ability to differentiate between clouds and heavy aerosols in strong sunglint regions is demonstrated using MODIS data collected during the recent fires that burned extensive areas in southern Australia. Comparisons between heavy aerosols identified by the VCM algorithm with imagery and heritage data products show the effectiveness of the new procedures using the modified VIIRS proxy SDRs. It is concluded that it is feasible to accurately detect clouds, identify cloud phase, and distinguish between clouds and heavy aerosol using a single cloud mask algorithm, even in extensive sun-glint regions.

A geometry-based approach is presented to identify cloud shadows using an automated cloud classification algorithm developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit both the cloud confidence and cloud phase intermediate products generated by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The procedures have been tested and found to accurately detect cloud shadows in global datasets collected by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are applied over both land and ocean background conditions. These new procedures represent a marked departure from those used in the heritage MODIS cloud mask algorithm, which utilizes spectral signatures in an attempt to identify cloud shadows. However, they more closely follow those developed to identify cloud shadows in the MODIS Surface Reflectance (MOD09) data product. Significant differences were necessary in the implementation of the MOD09 procedures to meet NPOESS latency requirements in the VCM algorithm. In this paper, the geometry-based approach used to predict cloud shadows is presented, differences are highlighted between the heritage MOD09 algorithm and new VIIRS cloud shadow algorithm, and results are shown for both these algorithms plus cloud shadows generated by the spectral-based approach. The comparisons show that the geometry-based procedures produce cloud shadows far superior to those predicted with the spectral procedures. In addition, the new VCM procedures predict cloud shadows that agree well with those found in the MOD09 product while significantly reducing the execution time as required to meet the operational time constraints of the NPOESS system.

A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-mu m bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.

The algorithmic approach and expected performance for the VIIRS cloud mask, sea surface temperature and land surface temperature is presented.

A method is proposed to enhance the performance of automated cloud detection algorithms in the vicinity of desert regions. The approach uses data in MODIS Channel 8, which has a bandpass of 405-420 nanometers (nm) where a strong contrast exists between the more highly reflective clouds and lower reflective cloud-free desert regions. Special processing is required to exploit cloud signatures since the MODIS high (880) signal-to-noise- ratio (SNR) requirement in this band causes saturation. The value of 412 nm data is demonstrated in the analysis of a scene that contains clouds over the western part of the Sahara Desert and has airborne sand and dust extending over the eastern Atlantic Ocean.

Measurements have been made of the rates of vibrational relaxation of CF4 and of CH4 in the temperature range 400–700 K using a shock tube.Below 600 K the equilibrium population of CO in the (v= 1) state is very small and it is possible to measure the rates of the (VT) excitation of CF4 and of CH4 by CO. Rate constants have been determined for these processes in the temperature range 400–450 K.Above temperatures of 750 K it is possible to measure the rates for the vibrational excitation of CO by vibrationally excited CF4, or by vibrationally excited CH4. Data are given for these processes in the temperature range 750–1500 K and comparisons are made with laser fluorescence data at lower temperatures on the vibrational deactivation of CO by CF4 and by CH4.

Using a quantitative approach, the submitted paper explores research based on the contrastive analysis of three sources of authentic materials: academic, literary and journalistic texts. The hypothesis - based on the assumption that authentic materials enhance language acquisition and cultural awareness more significantly than pedagogically modified materials - is discussed in the paper. Literary and journalistic materials as effective teaching materials for providing cultural and linguistic input will be analyzed and academic authentic texts will be compared with pedagogically modified materials. The findings indicate that authentic materials are more appropriate for advanced L2 readers than pedagogically modified texts. Practical recommendations for language teachers will be provided in the conclusion.

A simple monitor for heart rate, utilizing a modified phase-shift acceptor amplifier, is described. This device records the QRS component of the electrocardiogram as a single pip, written out by means of a modified relay, and permits constant monitoring of EKG-related artifacts without the loss of an EEG channel.