Spatio-temporal variations of PM2.5 and Respiratory Deposition Dose (RDD) before and during different COVID-19 lockdown phases at Delhi, India

2021 ◽  
Author(s):  
Sadaf Fatima ◽  
Ajit Ahlawat ◽  
Sumit Mishra ◽  
Vijay Soni ◽  
Randeep Guleria
Keyword(s):  
Fine Particles ◽  
Road Traffic ◽  
Virus Transmission ◽  
Particle Diameter ◽  
Temporal Variations ◽  
Percentage Decrease ◽  
The People ◽  
Respiratory Deposition ◽  
Deposition Dose ◽  
Spatio Temporal

<p>Present study explores pre-lockdown (1<sup>st</sup> January-24<sup>th</sup> March, 2020) and during lockdown (25<sup>th</sup> March-20<sup>th</sup> June, 2020) air quality changes in PM<sub>2.5 </sub>along with meteorological effects at megacity- Delhi (28.7041°N, 77.1025°E). Alipur (Rural), Okhla (Industrial) and Pusa Road (Traffic dominant area) experienced mean concentrations (S.D.) of PM<sub>2.5 </sub>as 87.56(±54.06), 124.45(±73.49) and 62.14(±58.64) µg/m<sup>3 </sup>before lockdown(BL; 1<sup>st</sup> January-24<sup>th</sup> March, 2020), while for Lockdown1(L1; 25<sup>th</sup> March-14<sup>th</sup> April, 2020), PM<sub>2.5</sub> decreased drastically as 39.26(±16.31), 38.01(±15.16) and 31.03(±12.79) µg/m<sup>3</sup> and gradually increased during Lockdown2(L2; 15<sup>th</sup> April-3<sup>rd</sup> May, 2020), Lockdown3(L3; : 4<sup>th</sup> May-17<sup>th</sup> May, 2020), Lockdown4(L4; 18<sup>th</sup> May-31<sup>st</sup> May, 2020), respectively. Percentage decrease in PM<sub>2.5 </sub>(-69.46%) correlated with outdoor activities of percentage decrease (-70 to -80%) in L1, from BL phase. Exposure assessment study showed, mean Respiratory Deposition Dose-RDD (S.D.) (µg/hr) for fine particles [Particle diameter (Dp) =0.5 µm] for walk and sit mode during BL, as 27.22(±13.53) and 9.90(±4.91) for Alipur, 30.55(±18.04) and 11.11(±6.56) for Okhla, and 28.67(±14.39) and 10.43(±5.23) for Pusa road, and decreased during L1 as 9.64(±4.00) and 3.50(±1.46) for Alipur, 9.33(±3.72) and 3.39(±1.35) for Okhla, and 7.62(±3.14) and 2.77(±1.14) for Pusa road, respectively. Delhiites were exposed to more fine RDD(walk/sit) before lockdown than during lockdown phases. People in sit mode found less exposed to fine RDD, in comparison to walk condition. The people living indoors were affected by outdoor RDD exposure with windows open condition, while exposed to different indoor pollution sources with windows closed condition during lockdown. Authors suggest avoid use of closed conditioned indoors and ACs; frequent opening of windows to lower the RDD and to minimize the COVID-19 virus transmission via particulates.</p><p>Keywords: PM<sub>2.5</sub>, RDD, COVID-19.</p>

scholarly journals Spatio-temporal variations in soil hydrology of a typical semiarid sand-meadow-desert landscape

2011 ◽  
Vol 8 (1) ◽  
pp. 1895-1928 ◽  
Author(s):  
L. Duan ◽  
T. Liu ◽  
X. Wang ◽  
G. Wang ◽  
L. Ma ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Physicochemical Properties ◽  
Fine Particles ◽  
Temporal Variations ◽  
P Value ◽  
Horqin Sandy Land ◽  
Sandy Land ◽  
Spatio Temporal ◽  
Semiarid Land

Abstract. A good understanding of the interrelations between land cover alteration and changes in hydrologic conditions (e.g., soil moisture) as well as soil physicochemical properties (e.g., fine particles and nutrients) is crucial for maintaining the fragile hydrologic and environmental conditions of semiarid land, such as the Horqin Sandy Land in China, but is lacking in existing literature. The objectives of this study were to examine: (1) spatio-temporal variations of soil moisture and physicochemical properties in semiarid land; and (2) how those variations are influenced by land cover alteration. Using the data collected in a 9.71 km2 well-instrumented area of the Horqin Sandy Land, this study examined by visual examination and statistical analyses the spatio-temporal variations of soil moisture and physicochemical properties. The results indicated that for the study area, the soil moisture and physicochemical properties were dependent on local topography, soil texture, vegetation density, and human activity. Long-term reclamation for agriculture was found to reduce soil moisture by over 23% and significantly (p-value < 0.05) lower the contents of soil organic matter, fine particles, and nutrients.

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.

Spatio-temporal variations of sea surface halocarbon concentrations and fluxes from southern Yellow Sea

2014 ◽  
Vol 121 (2) ◽  
pp. 369-388 ◽  
Author(s):  
Gui-Peng Yang ◽  
Bin Yang ◽  
Xiao-Lan Lu ◽  
Hai-Bing Ding ◽  
Zhen He
Keyword(s):  
Yellow Sea ◽  
Temporal Variations ◽  
Sea Surface ◽  
Southern Yellow Sea ◽  
Spatio Temporal
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