Evolution Mechanism of Atmospheric Pollution Based on Phase Reconstruction Theory and Time Series Data

2017 ◽  
Vol 8 (4) ◽  
pp. 43-52
Author(s):  
Guo-Feng Fan ◽  
Meng Han ◽  
Ya-Ting Wang ◽  
Jing-Ru Li
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Phase Space ◽  
Atmospheric Pollution ◽  
Time Series Data ◽  
Dimensional Space ◽  
Influencing Factor ◽  
Series Data ◽  
Phase Reconstruction ◽  
Evolution Mechanism

This article applies a delay method and recursive analysis to reconstruct the phase space to study the evolution mechanism of atmospheric pollution, i.e., air quality monitoring. Based on the theory of chaos, it is proven that there are chaotic characteristics of factors influencing air quality. In the meanwhile, the phase space reconstruction algorithm is employed to map the factors that affect the air quality into the high dimensional space, and then, gives its two-dimensional plane, the chaotic characteristics of each influencing factor are eventually proven. The results of the study not only analyze the evolution mechanism of air pollution in recent years, but also provide a theoretical support for the future of air pollution remediation.

scholarly journals PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD

2021 ◽  
Vol 7 (4) ◽  
pp. 81-88
Author(s):  
Chasandra Puspitasari ◽  
Nur Rokhman ◽  
Wahyono
Keyword(s):  
Air Quality ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Motor Vehicles ◽  
Primary Data ◽  
Series Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Air Quality Prediction

A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.

scholarly journals An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Forecasting Model ◽  
Model Based ◽  
Net Load

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

scholarly journals Quantifying the Opportunity Limits of Automatic Residential Electric Load Shaping

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3204
Author(s):  
Robert Cruickshank ◽  
Gregor Henze ◽  
Rajagopalan Balaji ◽  
Bri-Mathias Hodge ◽  
Anthony Florita
Keyword(s):  
Energy Use ◽  
Time Series Data ◽  
Supply And Demand ◽  
Electric Utility ◽  
Series Data ◽  
Stochastic Methods ◽  
Electric Load ◽  
Phase Reconstruction ◽  
Time Frequency ◽  
Reconstruction Methods

Electric utility residential demand response programs typically reduce load a few times a year during periods of peak energy use. In the future, utilities and consumers may monetarily and environmentally benefit from continuously shaping load by alternatively encouraging or discouraging the use of electricity. One way to shape load and introduce elasticity is to broadcast forecasts of dynamic electricity prices that orchestrate electricity supply and demand in order to maximize the efficiency of conventional generation and the use of renewable resources including wind and solar energy. A binary control algorithm that influences the on and off states of end uses was developed and applied to empirical time series data to estimate price-based instantaneous opportunities for shedding and adding electric load. To overcome the limitations of traditional stochastic methods in quantifying diverse, non-Gaussian, non-stationary distributions of observed appliance behaviour, recent developments in wavelet-based analysis were applied to capture and simulate time-frequency domain behaviour. The performance of autoregressive and spectral reconstruction methods was compared, with phase reconstruction providing the best simulation ensembles. Results show spatiotemporal differences in the amount of load that can be shed and added, which suggest further investigation is warranted in estimating the benefits anticipated from the wide-scale deployment of continuous automatic residential load shaping. Empirical data and documented software code are included to assist in reproducing and extending this work.

scholarly journals Impact of Poverty and Population Increase on Environmental Degradation: A Comparative Study of Pakistan and India

2020 ◽  
Vol 3 (2) ◽  
pp. 169-176
Author(s):  
Sher Ali ◽  
Bibi Aisha Sadiqa ◽  
Sajjad Ali ◽  
Shabana Parveen
Keyword(s):  
Air Pollution ◽  
Industrial Production ◽  
Co2 Emission ◽  
Time Series Data ◽  
Population Increase ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
Annual Time Series ◽  
The Impact

This study is devoted to elucidating the impact of poverty and population increase on air pollution (CO2-emission) in the two most populous countries of South Asia i.e. Pakistan and India. Annual time series data for the period of 1990-2018 are used to examine the said impact. To estimate the desired impact Autoregressive Distributed Lags (ARDL) technique is used. It is observed that CO2 emission is significantly determined by population increase and poverty in case of India. In the case of Pakistan population increase significantly affect CO2 emission in both the short run and long run, while poverty don not contributed significantly in the long run. Industrial production if found positive and statistically significant in both the runs. Stability of the model and other diagnostic tests are also employed not serious econometric problems are repowered. It is suggested on the bases of results that serious steps should be taken to reduce environmental pollution by reducing population increase and poverty. Industrial production also contributed to air pollution therefore industrial policies are also needed to be employed to reduce Air pollution.

scholarly journals Effect of COVID-19 pandemic induced lockdown (general holiday) on air quality of Dhaka City

2020 ◽  
Author(s):  
Md. Saiful Islam ◽  
Tahmid Anam Chowdhury
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Time Series Data ◽  
Urban Air Pollution ◽  
Motor Vehicles ◽  
Series Data ◽  
Dhaka City ◽  
Criteria Pollutants ◽  
Us Epa

Abstract A worldwide pandemic of COVID-19 has forced to implement a lockdown during April-May 2020 by restricting people's movement, the shutdown of industries and motor vehicles in Dhaka, Bangladesh, to contain the virus. This type of strict measures returned an outcome of the reduction of urban air pollution around the world. The present study aims to investigate the reduction of the concentration of pollutants in the air of Dhaka City and the reduction of the Air Quality Index (AQI). Necessary time-series data of the concentration of PM2.5, NO2, SO2, and CO have been collected from the archive of the U.S. Environmental Protection Agency (US EPA) and Sentinel-5P. The time-series data have been analyzed by descriptive statistics, and AQI is calculated following an appropriate formula suggested by US EPA based on the criteria pollutants. The study found that the concentrations of PM2.5, NO2, SO2, and CO have been reduced by 23, 30, 07, and 07% during April-May 2020, respectively, compared with the preceding year's concentration. Moreover, the AQI has also been reduced by up to 35% than the previous year in April-May 2020. However, the magnitude of pollution reduction in Dhaka is lower than other cities and countries globally, including Delhi, Sao Paulo, Wuhan, Spain, Italy, USA, etc. The main reason includes the poor implementation of lockdown, especially in the first week of April and the second fortnight of May. The findings will be useful for policymakers to find a way to control the pollution sources to enhance Dhaka's air quality.

scholarly journals Brief Communication: Breeding vectors in the phase space reconstructed from time series data

2015 ◽  
Vol 2 (4) ◽  
pp. 1301-1315
Author(s):  
E. Lynch ◽  
D. Kaufman ◽  
A. S. Sharma ◽  
E. Kalnay ◽  
K. Ide
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Evolution Equations ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Series Data ◽  
Nonlinear Instability ◽  
Bred Vectors ◽  
Dynamical Properties ◽  
A New Technique

Abstract. Bred vectors characterize the nonlinear instability of dynamical systems and so far have been computed only for systems with known evolution equations. In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the standard and nearest-neighbor breeding are shown to be similar, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.

scholarly journals Understanding Thermal Impact of Roads on Permafrost Using Normalized Spectral Entropy

2019 ◽  
Vol 11 (24) ◽  
pp. 7177 ◽  
Author(s):  
Chi Zhang ◽  
Hong Zhang ◽  
Fuqiang Zhao ◽  
Jing Sun
Keyword(s):  
Thermal Stability ◽  
Time Series Data ◽  
Influencing Factor ◽  
Road Construction ◽  
Tibet Plateau ◽  
Series Data ◽  
Road Maintenance ◽  
Spectral Entropy ◽  
Thermal Dynamics ◽  
Permafrost Regions

Permafrost is characterized by low temperature, and its thermal stability is key to geohydrological cycles, energy exchange, and climate regulation. Increasing engineering activities, i.e., road construction and operations, are affecting the thermal stability in permafrost regions and have already led to the degradation of permafrost and caused environmental problems. To understand the spatiotemporal influence of road construction and operations on the thermal dynamics in permafrost regions, we conducted a study in the Ela Mountain Pass where multiple roads intersect on the Qinghai–Tibet Plateau (QTP) and calculated the thermal dynamics from 2000 to 2017 using normalized spectral entropy (measuring the disorderliness of time-series data). Our results indicate that road level is a significant influencing factor, where high-level roads (expressways) exhibit stronger thermal impacts than low-level roads (province- and county-level roads). Our results also indicate that duration of operation is the most significant factor that determines the thermal impacts of roads on permafrost: the thermal impacts of the newly paved expressway are positively related to elevation, while the thermal impacts of the old expressway are positively related to less vegetated areas. The study provides an excellent method for understanding the spatiotemporal impacts of engineering activities on the temperature dynamics in permafrost regions, thereby helping policymakers in China and other countries to better plan their infrastructure projects to avoid environmentally vulnerable regions. The study also calls for advanced techniques in road maintenance, which can reduce the accumulated disturbance of road operations on permafrost regions.

scholarly journals On the Limit to the Accuracy of Regional-Scale Air Quality Models

2019 ◽  
Author(s):  
S. Trivikrama Rao ◽  
Huiying Luo ◽  
Marina Astitha ◽  
Christian Hogrefe ◽  
Valerie Garcia ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Input Data ◽  
Regional Scale ◽  
Atmospheric Dynamics ◽  
Series Data ◽  
Quality Models ◽  
Averaged Model ◽  
Air Pollution Models

Abstract. Regional-scale air pollution models are routinely being used world-wide for research, forecasting air quality, and regulatory purposes. It is well known that there are both reducible and irreducible uncertainties in the meteorology-atmospheric chemistry modeling systems. Inherent or irreducible uncertainties stem from our inability to properly characterize stochastic variations in atmospheric dynamics and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations in atmospheric dynamics and emissions forcing influencing the air pollutant concentrations are difficult to explicitly simulate, one can expect to find a percentile value from the distribution of measured concentrations to have much greater variability than that of the model. This paper presents an observation-based methodology to determine the expected errors from regional air quality models even when the model design, physics, chemistry, and numerical analysis techniques as well as its input data were perfect. To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8-hr ozone time series are separated from the longer-term forcings using a simple recursive moving average filter. The inherent variability attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States. The results reveal that the expected root mean square error at the median and 95th percentile is about 2 ppb and 5 ppb, respectively, even for perfect air quality models driven with perfect input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state-of-the-science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements.

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