scholarly journals Spatio-temporal variations of sulfur dioxide concentrations in industrial and urban area via a new statistical approach

2018 ◽  
Vol 11 (7) ◽  
pp. 801-813 ◽  
Author(s):  
A. A. Landim ◽  
E. C. Teixeira ◽  
D. Agudelo-Castañeda ◽  
I. Schneider ◽  
Luis F. O. Silva ◽  
...  
Keyword(s):  
Sulfur Dioxide ◽  
Urban Area ◽  
Statistical Approach ◽  
Temporal Variations ◽  
Spatio Temporal

scholarly journals Additions of Tropospheric Ozone (O3) in Regional Climates (A case study: Saudi Arabia)

2019 ◽  
Vol 01 (01) ◽  
Author(s):  
Syed Shehzad Hassan ◽  
Maham Mukhtar ◽  
Ehsan ul Haq ◽  
Muneeb Aamir ◽  
Hafiz M Rafique ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Saudi Arabia ◽  
Sulfur Dioxide ◽  
Nitrogen Dioxide ◽  
Optical Depth ◽  
Anthropogenic Activities ◽  
Temporal Variations ◽  
Vertical Column ◽  
Spatio Temporal ◽  
Carbon Dioxide Co2

Anthropogenic activities are responsible for enhancing the concentration of various toxic gases that produces bad Ozone in the troposphere which is harmful to human health. The specific objective of this research was to analyze the spatio-temporal variations in a vertical column of Ozone (O3) over Saudi Arabia during 2006-2016 using Atmospheric Infrared Sounder (AIRS) onboard AQUA platform and AErosol RObotic NETwork (AERONET) data. The results show that the optical depth of Ozone column varied from 252 Dobson Units (DU) to 264 DU. The main reason of this variation corresponds to the increase in O3 precursors including Carbon Dioxide (CO2), Nitrogen Dioxide (NO2) and Sulfur Dioxide (SO2). The concentration of CO2 varied between (379-401) Parts Per Million (PPM), SO2 varied (3.5x10-6 - 4x10-6kg m-2) kg m-2 and NO2 varies (2.25x1015 - 2.5x1015)1/cm2 during the investigated timeframe. The results confirm that NO2 and SO2 have contributed directly in O3 formation while CO2 just increased regional temperatures that enhanced the optical depth of O3. Keywords: AIRS, AERONET, Carbon dioxide, Nitrogen dioxide, Sulfur dioxide, Aerosol optical depth and Dopson Unit.

Spatio-temporal variations of winter wheat water requirement and climatic causes in Huang-Huai-Hai Farming Region

2012 ◽  
Vol 20 (3) ◽  
pp. 356-362 ◽  
Author(s):  
Xiao-Lin YANG ◽  
Zhen-Wei SONG ◽  
Hong WANG ◽  
Quan-Hong SHI ◽  
Fu CHEN ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Temporal Variations ◽  
Water Requirement ◽  
Spatio Temporal

ASSESSMENT OF THE SPATIO-TEMPORAL VARIATIONS IN GROUND WATER STORAGE USING DOWN-SCALED GRACE DATA

2018 ◽  
Author(s):  
Hossein Sahour ◽  
◽  
Mohamed Sultan ◽  
Karem Abdelmohsen ◽  
Sita Karki ◽  
...  
Keyword(s):  
Ground Water ◽  
Water Storage ◽  
Temporal Variations ◽  
Grace Data ◽  
Spatio Temporal

scholarly journals A robust machine learning approach to SDG data segmentation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kassim S. Mwitondi ◽  
Isaac Munyakazi ◽  
Barnabas N. Gatsheni
Keyword(s):  
Temporal Variations ◽  
Data Repository ◽  
The Sustainable Development ◽  
Complex Interactions ◽  
Data Segmentation ◽  
Technological Advances ◽  
Machine Learning Approach ◽  
Development Goals ◽  
Data Source ◽  
Spatio Temporal

Abstract In the light of the recent technological advances in computing and data explosion, the complex interactions of the Sustainable Development Goals (SDG) present both a challenge and an opportunity to researchers and decision makers across fields and sectors. The deep and wide socio-economic, cultural and technological variations across the globe entail a unified understanding of the SDG project. The complexity of SDGs interactions and the dynamics through their indicators align naturally to technical and application specifics that require interdisciplinary solutions. We present a consilient approach to expounding triggers of SDG indicators. Illustrated through data segmentation, it is designed to unify our understanding of the complex overlap of the SDGs by utilising data from different sources. The paper treats each SDG as a Big Data source node, with the potential to contribute towards a unified understanding of applications across the SDG spectrum. Data for five SDGs was extracted from the United Nations SDG indicators data repository and used to model spatio-temporal variations in search of robust and consilient scientific solutions. Based on a number of pre-determined assumptions on socio-economic and geo-political variations, the data is subjected to sequential analyses, exploring distributional behaviour, component extraction and clustering. All three methods exhibit pronounced variations across samples, with initial distributional and data segmentation patterns isolating South Africa from the remaining five countries. Data randomness is dealt with via a specially developed algorithm for sampling, measuring and assessing, based on repeated samples of different sizes. Results exhibit consistent variations across samples, based on socio-economic, cultural and geo-political variations entailing a unified understanding, across disciplines and sectors. The findings highlight novel paths towards attaining informative patterns for a unified understanding of the triggers of SDG indicators and open new paths to interdisciplinary research.

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