Complex studies of the features of the spatio-temporal distribution of aerosol-gas impurities of the atmosphere over Lake Baikal in summer period

2021 ◽  
Author(s):  
Sergei V. Nasonov ◽  
Ioganes Penner ◽  
Marina Klemasheva ◽  
Tamara Khodzher ◽  
Galina Zhamsueva
Keyword(s):  
Lake Baikal ◽  
Temporal Distribution ◽  
Summer Period ◽  
Spatio Temporal

scholarly journals Reasons and patterns of spatio-temporal variability of methane emission from the Mozhaysk reservoir in summer period

2020 ◽  
Vol 163 ◽  
pp. 03010
Author(s):  
Viktor Lomov ◽  
Maria Grechushnikova ◽  
Vladimir Kazantsev ◽  
Irina Repina
Keyword(s):  
Temporal Variability ◽  
Temporal Distribution ◽  
Clean Energy ◽  
Summer Period ◽  
Methane Budget ◽  
Synoptic Conditions ◽  
Methane Fluxes ◽  
Stormy Weather ◽  
Spatio Temporal ◽  
Work Done

Anthropogenic reservoirs are not only sources of clean energy, flow regulators, recreational and food resources, but also sources of greenhouse gases such as methane. The studies presented in this work, done on a well-studied low-flowing valley reservoir mainly in the summer, when the greatest variability of methane fluxes was observe. Depending on synoptic conditions, the temporal variability of methane fluxes varies greatly. Methane fluxes increase during the summer period and huge methane emissions observed before the autumn mixing stage. Emissions can occur during the destruction of temperature stratification, as a result of stormy weather. With such emissions, methane fluxes into the atmosphere can reach about 20 mgC*m-2*d-1, which is higher than the emission during convection. Revealing the patterns of spatio-temporal distribution of methane fluxes will help to determine the contribution of water bodies (in particular reservoirs) to the total methane budget of the atmosphere more accurately.

Influence of Planting Density on Root Spatio-Temporal Distribution of Different Types of Maize under High-Yielding Cultivation Conditions

2013 ◽  
Vol 38 (7) ◽  
pp. 1286-1294 ◽  
Author(s):  
Zong-Xin LI ◽  
Yuan-Quan CHEN ◽  
Qing-Cheng WANG ◽  
Kai-Chang LIU ◽  
Wang-Sheng GAO ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Planting Density ◽  
Cultivation Conditions ◽  
Different Types ◽  
Spatio Temporal

SPATIO-TEMPORAL DISTRIBUTION OF URANIUM CONCENTRATION IN SHALLOW AQUIFERS OF ARAVALLI TERRANE (NORTH GUJARAT), INDIA

2019 ◽  
Author(s):  
Rudra Mohan Pradhan ◽  
◽  
Karrie A. Weber ◽  
Karrie A. Weber ◽  
Daniel Snow ◽  
...  
Keyword(s):  
Uranium Concentration ◽  
Temporal Distribution ◽  
Shallow Aquifers ◽  
Spatio Temporal ◽  
North Gujarat

scholarly journals Variation Characteristics and Transportation of Aerosol, NO2, SO2, and HCHO in Coastal Cities of Eastern China: Dalian, Qingdao, and Shanghai

2021 ◽  
Vol 13 (5) ◽  
pp. 892
Author(s):  
Xiaomei Li ◽  
Pinhua Xie ◽  
Ang Li ◽  
Jin Xu ◽  
Zhaokun Hu ◽  
...  
Keyword(s):  
Eastern China ◽  
Temporal Distribution ◽  
Correlation Coefficients ◽  
Optical Absorption Spectroscopy ◽  
Near Surface ◽  
Mixing Ratios ◽  
Variation Characteristics ◽  
Spatio Temporal ◽  
Gas Volume ◽  
Transport Flux

This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM2.5 measured by the in situ instrument and the PM2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO2 and aerosols, but not for SO2 and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO2 in Shanghai and HCHO in Qingdao. PM2.5 presented the largest transport flux, followed by NO2 and SO2. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO2 and SO2 by CAMS is due to its overestimation of near-surface gas volume mixing ratios.

Sign in / Sign up

Export Citation Format

Share Document