Seasonal characteristics and sources of carbonaceous components and elements of PM10 (2010–2019) in Delhi, India

2021 ◽  
Author(s):  
Sudhir Kumar Sharma ◽  
Rubiya Banoo ◽  
Tuhin Kumar Mandal
Keyword(s):  
Seasonal Characteristics

Seasonal characteristics of microplastics ingested by copepods in Jiaozhou Bay, the Yellow Sea

2021 ◽  
Vol 776 ◽  
pp. 145936
Author(s):  
Shan Zheng ◽  
Yongfang Zhao ◽  
Tao Liu ◽  
Junhua Liang ◽  
Mingliang Zhu ◽  
...  
Keyword(s):  
Yellow Sea ◽  
Jiaozhou Bay ◽  
The Yellow Sea ◽  
Seasonal Characteristics

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Seasonal characteristics of boundary layer over a high-altitude rural site in Western India: implications on dispersal of particulate matter

2021 ◽  
Author(s):  
Mohammed Yusuff Aslam ◽  
Subrata Mukherjee ◽  
Vasudevan Anil Kumar ◽  
Rohit Dilip Patil ◽  
Sachin Suresh Patil ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Particulate Matter ◽  
High Altitude ◽  
Rural Site ◽  
Western India ◽  
Seasonal Characteristics

scholarly journals Predicting the Performance of Solar Power Generation Using Deep Learning Methods

2021 ◽  
Vol 11 (15) ◽  
pp. 6887
Author(s):  
Chung-Hong Lee ◽  
Hsin-Chang Yang ◽  
Guan-Bo Ye
Keyword(s):  
Power Generation ◽  
Solar Energy ◽  
Power Output ◽  
Power Plants ◽  
Solar Power ◽  
Photovoltaic Cell ◽  
Power Prediction ◽  
Seasonal Characteristics ◽  
Variable Nature ◽  
Solar Power Plants

In recent years, many countries have provided promotion policies related to renewable energy in order to take advantage of the environmental factors of sufficient sunlight. However, the application of solar energy in the power grid also has disadvantages. The most obvious is the variability of power output, which will put pressure on the system. As more grid reserves are needed to compensate for fluctuations in power output, the variable nature of solar power may hinder further deployment. Besides, one of the main issues surrounding solar energy is the variability and unpredictability of sunlight. If it is cloudy or covered by clouds during the day, the photovoltaic cell cannot produce satisfactory electricity. How to collect relevant factors (variables) and data to make predictions so that the solar system can increase the power generation of solar power plants is an important topic that every solar supplier is constantly thinking about. The view is taken, therefore, in this work, we utilized the historical monitoring data collected by the ground-connected solar power plants to predict the power generation, using daily characteristics (24 h) to replace the usual seasonal characteristics (365 days) as the experimental basis. Further, we implemented daily numerical prediction of the whole-point power generation. The preliminary experimental evaluations demonstrate that our developed method is sensible, allowing for exploring the performance of solar power prediction.

scholarly journals Seasonal characteristics of fine particulate matter (PM) based on high-resolution time-of-flight aerosol mass spectrometric (HR-ToF-AMS) measurements at the HKUST Supersite in Hong Kong

2015 ◽  
Vol 15 (1) ◽  
pp. 37-53 ◽  
Author(s):  
Y. J. Li ◽  
B. P. Lee ◽  
L. Su ◽  
J. C. H. Fung ◽  
C.K. Chan
Keyword(s):  
Hong Kong ◽  
Organic Aerosols ◽  
Organic Aerosol ◽  
Resolution Time ◽  
Time Resolved ◽  
Air Mass ◽  
Aerosol Mass ◽  
Seasonal Characteristics ◽  
Autumn And Winter ◽  
Air Mass Origin

Abstract. Atmospheric particulate matter (PM) remains poorly understood due to the lack of comprehensive measurements at high time resolution for tracking its dynamic features and the lack of long-term observation for tracking its seasonal variability. Here, we present highly time-resolved and seasonal compositions and characteristics of non-refractory components in PM with a diameter less than 1 μm (NR-PM1) at a suburban site in Hong Kong. The measurements were made with an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) at the Hong Kong University of Science and Technology (HKUST) Air Quality Research Supersite for 4 months, with one in each season of the year. The average NR-PM1 concentration of ~ 15 μg m−3 is higher than those AMS measurements made in South Korea and Japan, but lower than those in North China, the Yangtze River Delta and the nearby Pearl River Delta. The seasonal dependence of the total NR-PM1 monthly averaged concentrations was small, but that of the fractions of the species in NR-PM1 was significant. Site characteristic plays an important role in the relative fractions of species in NR-PM1 and our results are generally consistent with measurements at other non-urban sites in this regard. Detailed analyses were conducted on the AMS data in the aspects of (1) species concentrations, (2) size distributions, (3) degree of oxygenation of organics, and (4) positive matrix factorization (PMF)-resolved organic factors in a seasonal context, as well as with air mass origin from back-trajectory analysis. Sulfate had the highest fraction in NR-PM1 (> 40%), and the surrogates of secondary organic species – semi-volatile oxygenated organic aerosol (SVOOA) and low-volatility oxygenated organic aerosol (LVOOA) – prevailed (~ 80%) in the organic portion of NR-PM1. Local contributions to the organic portion of NR-PM1 at this suburban site was strongly dependent on season. The hydrocarbon-like organic aerosol (HOA) factor related to local traffic emissions contributed > 10% to organic aerosols in spring and summer but only 6–7% in autumn and winter. The cooking organic aerosol (COA) factor contributed > 10% to organic aerosols in winter. With the aid of highly time-resolved data, diurnal patterns of the degree of oxygenation of organic aerosols were used to determine the sources and formation processes of the least understood organic portion of PM. The oxygen-to-carbon atomic ratio (O : C) and average carbon oxidation state OS C) showed little variation in autumn and winter, when the long-range transport of oxidized organics dominated, whereas they peaked in the afternoon in spring and summer, when locally produced secondary organic aerosol prevailed. Air mass origin, in contrast, had a strong influence on both NR-PM1 concentrations and the fractions of species in NR-PM1. The findings of the current study provide a better understanding of the role of air mass origin in the seasonal characteristics of the PM composition and the relative importance of local vs. transported organic aerosols in this region.

scholarly journals SEASONAL CHARACTERISTICS OF LONG-PERIOD CURRENTS FLUCTUATION IN ISHIKARI BAY

1999 ◽  
Vol 15 ◽  
pp. 403-408 ◽  
Author(s):  
Tadashi FUKUMOTO ◽  
Tsuyoshi HASHIMOTO ◽  
Masaki NIIYAMA ◽  
Hideki HOSHI ◽  
Toshihiko YAMASHITA
Keyword(s):  
Long Period ◽  
Seasonal Characteristics

RETROSPECTIVE ASSESSMENT OF UNREGULATED LATERAL INFLOWS OF THE KUIBYSHEVSKY (NOW ZHIGULYOVSKAYA HYDROPOWER PLANT) HYDRAULIC UNIT OF THE VOLGA RIVER BASIN

2021 ◽  
pp. 87-99
Author(s):  
G. KH. ISMAIYLOV ◽  
◽  
N. V. MURASCHENKOVA ◽  
I. G. ISMAIYLOVA
Keyword(s):  
River Basin ◽  
Air Temperature ◽  
Atmospheric Precipitation ◽  
Total Precipitation ◽  
Volga River ◽  
Hydraulic Unit ◽  
Seasonal Characteristics ◽  
Lateral Inflow ◽  
Increasing Trend

The results of the analysis and assessment of changes in annual and seasonal characteristics of hydrometeorological processes in a private catchment area of the Kuibyshev hydroelectric complex of the Volga river are presented. To analyze the temporal dynamics of the variability of the annual and seasonal characteristics of the hydrometeorological processes in the considered territory of the river basin we used more than 100 years of observations of annual and seasonal fluctuations of lateral inflow, total atmospheric precipitation and air temperature regimes on the Volgariver. Relationship equations for annual and seasonal changes in hydrometeorological characteristics in time are obtained. It was found that long-term fluctuations of hydrometeorological processes (lateral inflow, total atmospheric precipitation and air temperature) are characterized by tendencies (trends). The analysis of these trends showed that the non-standard climatic situation, starting from the 70s of the last century, had a very significant impact on the distribution of annual and especially on the seasonal (low-water and winter) characteristics of hydrometeorological processes. It has been established that non-standard unidirectional changes are found in the fluctuations in the total atmospheric precipitation. If the winter total precipitation is characterized over the 100-year period in question by a continuously decreasing trend,the summer-autumn period is an increasing trend. This leads to the fact that long-term fluctuations in total precipitation during the period of low water are formed as a stationary process. At the same time, the total precipitation of the spring flood and inflowing to the Kuibyshev hydroelectric unit is characterized by a constantly increasing trend.

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