scholarly journals GROUND AND SATELLITE MONITORING OF POLLUTION PROCESSES ISKITIM-LINEVSK INDUSTRIAL ZONE

2021 ◽  
Vol 4 (1) ◽  
pp. 60-65
Author(s):  
Ruslana A. Amikishieva ◽  
Vladimir F. Raputa ◽  
Irina A. Solov ‘eva

The results of a numerical analysis of atmospheric pollution in the vicinity of the industrial site of the Chernorechensky cement plant (CCP) and the territory of Iskitim are presented. The research material was the results of sampling melted snow for 2019-20. The snow index (NDSI), calculated from high-resolution images from the Landsat and Sentinel satellites, was used as satellite data. Statistical relationships between ground-based and satellite observations are given. The general dynamics of changes in the impurity concentration in the snow and NDSI values are revealed. The concentration is calculated on the basis of low-parameter reconstruction models using ground-based measurements. For calculations and visualization, the means of the geographic information system, which was developed earlier, were used. These studies represent the basis for the development of a methodology for a comprehensive analysis of the process of atmospheric pollution using ground-based and satellite observations.

2021 ◽  
Author(s):  
R.A. Amikishieva ◽  
V.F. Raputa ◽  
A.A. Lezhenin

The results of the analysis of atmospheric pollution processes in the vicinity of the Chernorechensky cement plant and the Iskitim city were presented. Snow cover samples and high-resolution satellite images were used as research materials. The reconstruction of the fields of impurity concentration was carried out on the basis of low-parameter models. Statistical relationships were identified between ground-based and satellite observations.


2015 ◽  
Vol 7 (2) ◽  
pp. 275-287 ◽  
Author(s):  
C. Funk ◽  
A. Verdin ◽  
J. Michaelsen ◽  
P. Peterson ◽  
D. Pedreros ◽  
...  

Abstract. Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, doi:10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.


2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Daniel-Eduard Constantin ◽  
Mirela Voiculescu ◽  
Lucian Georgescu

Satellite-based measurements of atmospheric trace gases loading give a realistic image of atmospheric pollution at global, regional, and urban level. The aim of this paper is to investigate the trend of atmospheric NO2content over Romania for the period 1996–2010 for several regions which are generally characterized by different pollutant loadings, resulting from GOME-1, SCIAMACHY, OMI, and GOME-2 instruments. Satellite results are then compared with ground-based in situ measurements made in industrial and relatively clean areas of one major city in Romania. This twofold approach will help in estimating whether the trend of NO2obtained by means of data satellite retrievals can be connected with the evolution of national industry and transportation.


Author(s):  
Leonid V. Katkovsky

At present, air pollution is one of the most serious environmental problems. To monitor the air condition, sampling methods are used at stationary and automatic stations, lidar ground measurements and satellite monitoring. In Belarus, the atmosphere is monitored at 66 stationary stations of the National Environmental Monitoring System of the Republic of Belarus. The main disadvantage of the sampling method at stations is its localization and inoperability. The method implemented in the work overcomes these shortcomings. Estimates of the atmospheric pollution of megacities in units of maximum permissible concentrations (MPC) are carried out using the space reflection spectrum and the image in red region of the visible spectrum. Using data from the EO-1, Landsat, Sentinel-2 satellites, as well as calculations, the integral pollution levels (in MPC) over the city of Minsk were obtained in 2015 and 2016. Estimated pollution values averaged over the quarter were compared with data from the annual of the status of atmospheric air of the National Environmental Monitoring System of the Republic of Belarus. The method showed good agreement between the data. However, the conditions of application and the accuracy of this method require further research and evaluation, the advantages and disadvantages of the method are analyzed in the work, and ways to overcome the identified shortcomings are outlined.


2021 ◽  
Author(s):  
Boguslaw Usowicz ◽  
Jerzy Lipiec

<p>The dynamic processes of mass and energy exchange on the soil surface are mainly influenced by plant cover, soil physical quantities and meteorological conditions. The aims of the research were: (a) to identify spatial and temporal changes in soil moisture (SM) obtained from satellite observations and ground measurements at the regional scale and (b) to determine the temporal variability of soil moisture in the soil profile with and bare soil (reference). The study area included 9 sites in the eastern part of Poland. Agro-meteorological stations in each site allowed monitoring soil moisture (SM). Satellite SM data (time series) for the years 2010–2016 (every week) obtained from the Soil Moisture and Ocean Salinity satellite (SMOS L2 v. 650 datasets) were gridded using the discrete global grid (DGG) with the nodes spaced at 15 km. Seven DGG pixels per each site were considered in a way that the central one (named S0) containing the agrometeorological station was bordered with 6 others (S1÷S6). The measurements of SM were performed at depths of 0.05, 0.1, 0.2, 0.3, 0.4, 0.5 and 0.8 m once a day in April-July in plots of spring barley, rye and bare soil. The temporal dependence of the SMOS surface soil moisture was observed in S0÷S6 with the radius of autocorrelation time from 8.1 to 25.2 weeks. The smallest autocorrelation time (3 weeks ) was found in pixels with dominance of arable lands and the largest one - with dominance of wetlands (16.8 weeks) and forests (from 12 to 15.6 weeks). The autocorrelation times in S0 were much greater for ground-based SM data (11.1 to 43.1 weeks) than those for SMOS SM data. The autocorrelations enabled satisfactory predicting changes in SM forwards and backwards using the kriging method and filling gaps in the SM time series. As to ground measurements the highest autocorrelation times were in the soil below the plough layer under rye (170 days) and the lowest in the surface soil under barley and bare soil (18 and 19 days). In the plot of rye with the highest soil density the autocorrelation radius was over 1.5 months. The fractal dimensions (D0) indicated a large randomness of the surface SMOS SM distribution (D0 1.86–1.95) and the ground SM measurements (D0 1.82–1.92). The D0 values clearly decreased with the depth (from 1.7 to 1.15) in plant-covered soil while in the bare soil they did not change much throughout the profile (D0 1.7–1.8). The D0 values indicated that the temporal distribution of SM in the soil profile was more random in bare than plant-covered soil. The results help to understanding autocorrelation time ranges in surface and deeper soil and spatial changes in soil moisture depending on plant cover.</p><p>Acknowledgements. Research was conducted under the project "Water in soil – satellite monitoring and improving the retention using biochar" no. BIOSTRATEG3/345940/7/NCBR/2017 which was financed by Polish National Centre for Research and Development in the framework of “Environment, agriculture and forestry" – BIOSTRATEG strategic R&D programme.</p>


2015 ◽  
Vol 8 (1) ◽  
pp. 401-425 ◽  
Author(s):  
C. Funk ◽  
A. Verdin ◽  
J. Michaelsen ◽  
P. Peterson ◽  
D. Pedreros ◽  
...  

Abstract. Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high resolution (0.05°) global precipitation climatologies that perform reasonably well in data sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, http://dx.doi.org/10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.


2020 ◽  
Vol 4 (1) ◽  
pp. 36-41
Author(s):  
Ruslana A. Amikishieva ◽  
Vladimir F. Raputa ◽  
Tatyana V. Yaroslavtseva

Technologies for analyzing atmospheric pollution processes based ground measurements and high-resolution multispectral images were developed. Physico-chemical characteristics of snow samples and calculated snow index (NDSI) are the original data of the analysis. Functional relationships based on light and monodisperse impurities models of the spread in the atmosphere between ground observation data and NDSI were found. A GIS system that implements these methods was developed in Python. GIS was tested at objects of the industrial zone of Iskitim district.


1994 ◽  
Vol 144 ◽  
pp. 541-547
Author(s):  
J. Sýkora ◽  
J. Rybák ◽  
P. Ambrož

AbstractHigh resolution images, obtained during July 11, 1991 total solar eclipse, allowed us to estimate the degree of solar corona polarization in the light of FeXIV 530.3 nm emission line and in the white light, as well. Very preliminary analysis reveals remarkable differences in the degree of polarization for both sets of data, particularly as for level of polarization and its distribution around the Sun’s limb.


1975 ◽  
Vol 26 ◽  
pp. 461-468
Author(s):  
S. Takagi

In this article, we intended to see whether we can obtain the same pole motion from two kinds of telescopes: the floating zenith telescope (PZT) and the ILS zenith telescope (VZT). The observations with the PZT have been pursued since 1967.0 with a star list whose star places are taken from the PK4 and its supplement. We revised the method of reduction of the observations with the PZT by adopting a variable scale value for the photographic plate (Takagi et al., 1974).


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