A Model of Fuel and Energy Sector Contribution to Economic Growth

The study examined the impact of foreign direct investment (FDI) in the fuel and energy sector and related industries on economic growth in response to the debates on FDI's impact on economic growth being positive (government officials and policymakers) or negative (the World Bank, some researchers). The hypothesis that a significant relationship is present between the Russian Federation GDP and gross FDI in Fuel and Energy Sector (fuels and non-fuels fossils mining, coke and petrochemicals production, rubber and plastic production, and energy supply) is introduced and validated by using a regression model. The derived model tests changes of regression results patterns of the Russian GDP against FDI in energy-related industries in different periods 1998-2004 and 2010-2017. GDP is assessed in five different measures: current US dollars, international US dollars (purchasing power parity), growth rates of the former and the latter, and physical growth index. It was concluded that, to a greater extent, economic growth is influenced by foreign investment in energy supply and petrochemical production in the both periods. Increased investment in power generation also contributes to economic growth, while other constituents of the sector, including mining, have a statistically insignificant or even retarding effect on economic growth, thus evidencing in favor of the World Bank's criticism towards FDI. Policy implications of the findings prove the necessity to introduce structural changes intended to redirect capital flows from oil and gas to prevent from economic growth deterioration in the long-term perspective. Keywords: Economic growth; Foreign Direct Investment; Fuel and energy sector JEL Classifications: C3, O4, Q43 DOI: https://doi.org/10.32479/ijeep.784

Retrieval of Physical Properties of Particulate Emission from Animal Feeding Operations Using Three-Wavelength Elastic Lidar Measurements

Agricultural operations produce a variety of particulates and gases that influence ambient air quality. Lidar (LIght Detection And Ranging) technology provides a means to derive quantitative information of particulate spatial distribution and optical/physical properties over remote distances. A three-wavelength scanning lidar system built at the Space Dynamic Laboratory (SDL) is used to extract optical parameters of particulate matter and to convert these optical properties to physical parameters of particles. This particulate emission includes background aerosols, emissions from the agricultural feeding operations, and fugitive dust from the road. Aerosol optical parameters are retrieved using the widely accepted solution proposed by Klett. The inversion algorithm takes advantage of measurements taken simultaneously at three lidar wavelengths (355, 532, and 1064 nm) and allows us to estimate the particle size distribution. A bimodal lognormal particle size distribution is assumed and mode radius, width of the distribution, and total number density are estimated, minimizing the difference between calculated and measured extinction coefficients at the three lidar wavelengths. The results of these retrievals are then compared with simultaneous point measurements at the feeding operation site, taken with standard equipment including optical particle counters, portable PM10 and PM2.5 ambient air samplers, multistage impactors, and an aerosol mass spectrometer

Integrating Lidar and Atmospheric Boundary Layer Measurements to Determine Fluxes and Dynamics of Particulate Emissions from an Agriculture Facility

Lidar technology offers the ability to quantify concentrations of small particulates in the atmosphere in certain ranges of time and space. While this is a valuable tool to visualize the behavior of plumes emitted from the surface, the actual flux of particles cannot be estimated from such data alone. To determine the mass flux of particles, the concentrations must be properly integrated with wind and turbulence properties. The goal of this study is to utilize a model that uses wind and particle density information to calculate the flux of particles from an animal facility near Ames, Iowa. The model is a simplified conservation equation for particle density in the atmosphere. This approach essentially quantifies fluxes in and out of a box centered over the facility and estimates the surface source by assuming conservation of mass. In addition, we hypothesize that distinct turbulence structures will sometimes interact with the intermittency of the surface emission from the buildings, resulting in episodic changes in emission fluxes from the site. A second objective involves documenting how intermittent the emission plumes are and how they are connected to periodic large scale turbulence events. Lidar data of particle size and density in the vicinity of the site were collected during an intensive field campaign lasting nearly 2 weeks. In addition to the lidar data, turbulence data were measured at several levels on each of three towers, located upwind, inside and downwind of the source area. The model requires measurements of the vertical profiles of both concentrations of particulates and the mean horizontal wind. The concentrations were measured using the lidar, while winds were measured using a combination of cup anemometers and sonic anemometers. This allows the emission fluxes to be calculated during 15 to 30 minute periods when winds are consistent. Flux calculations await the final calibration of the lidar returns using measured particle densities. Flux estimates will be made when distinct plumes are observed under steady-state wind conditions. Current results are presented showing evidence of episodic plumes of CO2 in response to intermittent vertical motions of turbulences

Lidar Based Emissions Measurement at the Whole Facility Scale: Method and Error Analysis

Particulate emissions from agricultural sources vary from dust created by operations and animal movement to the fine secondary particulates generated from ammonia and other emitted gases. The development of reliable facility emission data using point sampling methods designed to characterize regional, well-mixed aerosols are challenged by changing wind directions, disrupted flow fields caused by structures, varied surface temperatures, and the episodic nature of the sources found at these facilities. We describe a three-wavelength lidar-based method, which, when added to a standard point sampler array, provides unambiguous measurement and characterization of the particulate emissions from agricultural production operations in near real time. Point-sampled data are used to provide the aerosol characterization needed for the particle concentration and size fraction calibration, while the lidar provides 3D mapping of particulate concentrations entering, around, and leaving the facility. Differences between downwind and upwind measurements provide an integrated aerosol concentration profile, which, when multiplied by the wind speed profile, produces the facility source flux. This approach assumes only conservation of mass, eliminating reliance on boundary layer theory. We describe the method, examine measurement error, and demonstrate the approach using data collected over a range of agricultural operations, including a swine grow-finish operation, an almond harvest, and a cotton gin emission study

Aglite Lidar: A Portable Elastic Lidar System for Investivating Aerosol and Wind Motions at or Around Agricultural Production Facilities

The Aglite Lidar is a portable scanning lidar that can be quickly deployed at agricultural and other air quality study sites. The purpose of Aglite is to map the concentration of PM10 and PM2.5 in aerosol plumes from agricultural and other sources. Aglite uses a high-repetition rate low-pulse energy 3-wavelength YAG laser with photon-counting detection together with a steerable pointing mirror to measure aerosol concentration with high spatial and temporal resolution. Aglite has been used in field campaigns in Iowa, Utah and California. The instrument is described, and performance and lidar sensitivity data are presented. The value of the lidar in aerosol plume mapping is demonstrated, as is the ability to extract wind-speed information from the lidar dat

Preflight Assessment of the Cross-track Infrared Sounder (CrIS) Performance

The Cross-track Infrared Sounder (CrIS) is a part of the Crosstrack Infrared and Microwave Sounding Suite (CrIMSS) that will be used to produce accurate temperature, water vapor, and pressure profiles on the NPOESS Preparatory Project (NPP) and upcoming Joint Polar Satellite System (JPSS) operational missions. The NPP CrIS flight model has completed sensor qualification, characterization, and calibration and is now integrated with the NPP spacecraft in preparation for the launch. This paper reviews the CrIS performance during thermal vacuum tests, including the spacecraft integration test, and provides a comparison to the AIRS and IASI heritage sensors that it builds upon. The CrIS system consists of the instrument itself and ground-based scientific algorithms. The data reported in this paper was processed with the latest version of the CrIS science sensor data record (SDR) algorithm and thus reflects the performance of the CrIS SDR system. This paper includes the key test results for Noise Equivalent Differential Noise (NEdN), Radiometric Performance, and Spectral Accuracy. The CrIS sensor performance is outstanding and will meet the mission needs for the NPP /JPSS mission. NEdN is one of the key performance tests for the CrIS sensor. The overall NEdN performance for the CrIS in the LWIR, MWIR and SWIR spectral bands is excellent and is comparable or exceeds NEdN performance of AIRS and IASI. Also discussed is the Principal Component Analysis (PCA) approach developed to estimate contribution of random and spectrally correlated noise components to the total NEDN

Characterization of Particulate Emission from Animal Feeding Operations with Three-wavelength Lidar Using Simultaneous In-Situ Point Measurements as Calibration Reference Sources

Lidar (LIght Detection And Ranging) provides the means to quantitatively evaluate the spatial and temporal variability of particulate emissions from agricultural activities, including animal feeding operations. A three-wavelength portable scanning Lidar system built at the Space Dynamic Laboratory (SDL) is used to extract optical properties of the particulate matter from the return Lidar signal and to convert these optical properties to physical parameters including the spatial distribution of particulate concentration around the agricultural facility and its temporal variations. The inversion algorithm developed to retrieve physical parameters of the particulate matter takes advantage of measurements taken simultaneously at three different wavelengths (355, 532, and 1064 nm) and allows us to estimate the particle size distribution in the emitted plume as well; however, quantitative evaluation of particulate optical and physical properties from the Lidar signal is complicated by the complexity of particles composition, particle size distribution, and environmental conditions such as the ambient humidity. Additional independent measurements of particulate physical and chemical properties are needed to unambiguously calibrate and validate the particulate physical properties retrieved from the Lidar measurements. In this paper we present results of the particulate emission characterization obtained by simultaneous remote measurements with Lidar and point measurements at the feeding operation site with standard equipment including optical particle counters, portable PM10 and PM2.5 ambient air samplers, multistage impactors, an aerosol mass spectrometer, and ion chromatography

SGLT2 Inhibitor Empagliflozin and DPP4 Inhibitor Linagliptin Reactivate Glomerular Autophagy in db/db Mice, a Model of Type 2 Diabetes

Recent data have indicated the emerging role of glomerular autophagy in diabetic kidney disease. We aimed to assess the effect of the SGLT2 inhibitor empagliflozin, the DPP4 inhibitor linagliptin, and their combination, on glomerular autophagy in a model of type 2 diabetes. Eight-week-old male db/db mice were randomly assigned to treatment with empagliflozin, linagliptin, empagliflozin–linagliptin or vehicle for 8 weeks. Age-matched non-diabetic db/+ mice acted as controls. To estimate glomerular autophagy, immunohistochemistry for beclin-1 and LAMP-1 was performed. Podocyte autophagy was assessed by counting the volume density (Vv) of autophagosomes, lysosomes and autolysosomes by transmission electron microscopy. LC3B and LAMP-1, autophagy markers, and caspase-3 and Bcl-2, apoptotic markers, were evaluated in renal cortex by western blot. Vehicle-treated db/db mice had weak glomerular staining for beclin-1 and LAMP-1 and reduced Vv of autophagosomes, autolysosomes and lysosomes in podocytes. Empagliflozin and linagliptin, both as monotherapy and in combination, enhanced the areas of glomerular staining for beclin-1 and LAMP-1 and increased Vv of autophagosomes and autolysosomes in podocytes. Renal LC3B and Bcl-2 were restored in actively treated animals. LAMP-1 expression was enhanced in the empagliflozin group; caspase-3 expression decreased in the empagliflozin–linagliptin group only. Mesangial expansion, podocyte foot process effacement and urinary albumin excretion were mitigated by both agents. The data provide further explanation for the mechanism of the renoprotective effect of SGLT2 inhibitors and DPP4 inhibitors in diabetes

Aglite Lidar: Calibration and Retrievals of Well Characterized Aerosols from Agricultural Operations Using a Three-Wavelength Elastic Lidar

Lidar (LIght Detection And Ranging) provides the means to quantitatively evaluate the spatial and temporal variability of particulate emissions from agricultural activities. AGLITE is a three-wavelength portable scanning lidar system built at the Space Dynamic Laboratory (SDL) to measure the spatial and temporal distribution of particulate concentrations around an agricultural facility. The retrieval algorithm takes advantage of measurements taken simultaneously at three laser wavelengths (355, 532, and 1064 nm) to extract particulate optical parameters, convert these parameters to volume concentration, and estimate the particulate mass concentration of a particulate plume. The quantitative evaluation of particulate optical and physical properties from the lidar signal is complicated by the complexity of particle composition, particle size distribution, and environmental conditions such as heterogeneity of the ambient air conditions and atmospheric aerosol loading. Additional independent measurements of particulate physical and chemical properties are needed to unambiguously calibrate and validate the particulate physical properties retrieved from the lidar measurements. The calibration procedure utilizes point measurements of the particle size distribution and mass concentration to characterize the aerosol and calculate the aerosol parameters. Once calibrated, the Aglite system is able to map the spatial distribution and temporal variation of the particulate mass concentrations of aerosol fractions such as TSP, PM10, PM2.5, and PM1. This ability is of particular importance in the characterization of agricultural operations being evaluated to minimize emissions and improve efficiency, especially for mobile source activities