Time Series Plots Research Articles

Distinguish Potential Source Areas of PM2.5 and PM10 by Statistical Data Analysis

Power plant from biomass in community area has been complained as emission source of many pollutants such as wastewater, noise, odor and polluted air. For air pollution, total suspended particulate (TSP) and particulate matter less than 10 micron (PM10) from rice husk energy process is quite large. Roi Et is a province located in Northeast of Thailand which obtains power plants using rice husk as fuel in the process. According to the land-use pattern of the community area of Roi Et, it revealed that there are many sources of air pollution. This study selected the statistical techniques to distinguish the potential source area influencing the high concentrations of particulate matter less than 2.5 micron (PM2.5), particulate matter 2.5 to 10 micron (PMcoarse), and PM10 which caused the health effects of people around the rice husk power plants. Hourly observed PM10 and PM2.5 data were analysed by time series and bivariate polar plot. Results showed that increasing the concentration of PM10 and PM2.5 were observed in the duration of low wind speed. However, daily average of PM10 was not more than National Ambient Air Quality Standard (NAAQS) which is 120 μg/m3. In contrast, results showed that daily average of PM2.5 performed over NAAQS of 50 μg/m3 in the 20th -22nd December 2018 that high pressure cover Thailand. While sources of these particulate matters could be distinguished into 3 potential areas based on the mobile air quality monitoring station location which were 1) east side and 2) north-west side which regarded as sub-urban and traffic areas, respectively and 3) north-east side which regarded as power plants area. Results revealed that PMcoarse was the major pollutant dispersed from the Northeast direction that the power plants were the major source. Whereas traffic and community were the main sources of PM2.5 coming from the East and the Northwest direction of the mobile air quality monitoring station point. Therefore, discrimination of each point source could be done first before implementing to control and manage the air pollution effectively.

Web-Based Visualization of Scientific Research Findings: National-Scale Distribution of Air Pollution in South Korea.

Background: As scientific findings of air pollution and subsequent health effects have been accumulating, public interest has also been growing. Accordingly, web visualization is suggested as an effective tool to facilitate public understanding in scientific evidence and to promote communication between the public and academia. We aimed to introduce an example of easy and effective web-based visualization of research findings, relying on predicted concentrations of particulate matter ≤ 10 µg/m3 (PM10) and nitrogen dioxide (NO2) obtained from our previous study in South Korea and Tableau software. Our visualization focuses on nationwide spatial patterns and temporal trends over 14 years, which would not have been accessible without our research results. Methods: Using predicted annual average concentrations of PM10 and NO2 across approximately 250 districts and maps of administrative divisions in South Korea during 2001–2014, we demonstrate data preprocessing and design procedures in the Tableau dashboard, comprising maps, time-series plots, and bar charts. Results: Our visualization allows one to identify high concentration areas, a long-term temporal trend, and the contrast between two pollutants. The application of easy tools for user-interactive options in Tableau suggests possible easy access to the scientific knowledge of non-experts. Conclusion: Our example contributes to future studies that develop the visualization of research findings in further intuitive designs.

Development of multiple linear regression model for biochemical oxygen demand (BOD) removal efficiency of different sewage treatment technologies in Delhi, India

Among the various modeling techniques applied to dataset, multiple linear regression (MLR) analysis is the most efficient way to figure out the relationship between the response variable and the predictive variables. This study emphasizes on establishment of multiple linear regression models to analyze Biochemical Oxygen Demand (BOD) removal efficiency for technologies, namely Densadeck, Extended Aeration and Activated Sludge Process. Assumptions of multiple linear regression like linear relationship, multivariate normality, multicollinearity and Homoscedasticity were examined. The data that verify the assumptions were analyzed with multiple linear regression. Time series plots indicate drastic decline in BOD removal efficiency in the month of Feb and March during the years 2012 and 2013. This study was significant as it gives the technology having the best-fit regression equation based upon multiple correlation coefficient (R), coefficient of determination (R2), standard error, residual and F-ratio value. Societal benefits include enhancement in the performance of sewage treatment plants.

Internet data traffic analysis for identifying usage trends on each day of the week in a university

<p><em>Internet data traffic monitoring and management are important requirements for ensuring top notch quality of service in a network. Data traffic logs contain useful hidden information that can be harnessed and interpreted as a resource for making informed network management decisions. In this study, logged internet data traffic for both the upload and download traffic in a university for one year was analysed using statistics and partial least squares approach to structural equation modelling (PLS-SEM). Time series plots, statistical properties and trends for each day of the week over a 51-week period were developed. The result shows that the most data was downloaded on Thursdays while the most upload occurred on Mondays. A path model was developed using Smart PLS3, and the performance of the model was evaluated using the construct reliability and validity of the model. The results reveal that the weekly variance is majorly accounted for by usage variations on Tuesdays, Fridays and Saturdays. An overall model R-square value of 0.876 was observed.</em></p>

Dynamical behavior of nonlinear wave solutions of the generalized Newell–Whitehead–Segel equation

Dynamical behavior of nonlinear wave solutions of the perturbed and unperturbed generalized Newell–Whitehead–Segel (GNWS) equation is studied via analytical and computational approaches for the first time in the literature. Bifurcation of phase portraits of the unperturbed GNWS equation is dispensed using phase plane analysis through symbolic computation and it shows stable oscillation of the traveling waves. Chaotic behavior of the perturbed GNWS equation is obtained by applying different computational tools, like phase plot, time series plot, Poincare section, bifurcation diagram and Lyapunov exponent. A period-doubling bifurcation behavior to chaotic behavior is shown for the perturbed GNWS equation and again it shows chaotic to periodic motion through inverse period-doubling bifurcation. The perturbed GNWS equation also shows chaotic motion through a sequence of periodic motions (period-1, period-3 and period-5) depending on the variation of the parameter of linear coefficient. Thus, the parameter of linear coefficient plays the role of a controlling parameter in the chaotic dynamics of the perturbed GNWS equation.

On the observation of the transient behavior of gas turbine combustion instability using the entropy analysis of dynamic pressure

In the present study, two new assessment methods for combustion instability (CI) based on the concept of entropy are proposed and their performances are evaluated. These two methods are (1) the entropy of energy (EoE) method, which utilizes the probability distribution of the squared value of dynamic pressure (DP) and (2) the entropy of energy amplified by pressure magnitude (EoEPM) method, which considers both the averaged pressure magnitude and the energy distribution in the time domain to compensate for the limitation of the EoE method. In these methods, the entropy was defined as the energy distribution of the DP. Therefore, the assessment for CI can be performed by using the entropy characteristics: the unstable combustion status shows the relatively wider energy distribution than the stable one. The performances of these methods were validated using the DP data obtained from a gas turbine combustion experiment and were compared with those of the conventional methods, namely, the temporal kurtosis (TK) method, the root-mean-square method, and the continuous wavelet transform method. The EoE method showed a comparable performance with the TK method in terms of detection time and accuracy of CI detection in terms of time series plot trends. The EoEPM showed the best performance by precisely detecting the transition from the unstable to the stable combustion status, and its detection time was faster by 0.06–0.71 s than that of the other methods. This new assessment method is expected to contribute to the safe and reliable operation of combustion engines, such as gas turbines and rockets, by rapidly detecting the onset of CI and by preventing fatal accidents before a dynamic pressure fluctuation worsens.

Multistability and dynamical properties of ion-acoustic wave for the nonlinear Schrödinger equation in an electron–ion quantum plasma

Multistability and dynamical properties of quantum ion-acoustic (QIA) waves are examined in a quantum plasma consisting of warm ions and inertialess electrons. Using phase plane analysis, QIA periodic, a pair of solitonic and superperiodic wave solutions are reported. The physical parameters viz., ion–electron Fermi temperature ratio (ρ), quantum parameter (H), travelling wave velocity (V) and β affect significantly on these wave solutions. Considering an external periodic perturbation various types of QIA coexisting hidden attractors (periodic, quasiperiodic and chaotic attractors) are observed in parametric space through phase plots, time series plots and Lyapunov exponents. Coexisting QIA hidden attractors are reported for the first time in electron–ion quantum plasmas.

Spatiotemporal patterns of dengue outbreaks in Sri Lanka

Background: Dengue occurs epidemically in Sri Lanka and every year, when the monsoon season begins, health authorities warn on rising numbers of dengue cases. The popular belief is that dengue epidemics are driven by the two monsoons which feed different parts of the country over different time periods. We analysed the time series of weekly dengue cases in all districts of Sri Lanka from 2007 to 2019 to identify the spatiotemporal patterns of dengue outbreaks and to explain how they are associated with the climatic, geographic and demographic variations around the country.Methods: We used time-series plots, statistical measures such a community-wide synchrony and Kendall-W and a time-varying graph method to understand the spatiotemporal patterns and links.Results and conclusions: The southwest wet zone and surrounding areas which receive rainfall in all four seasons usually experience two epidemic waves per year. The northern and eastern coastal region in the dry zone which receives rainfall in only two seasons experiences one epidemic wave per year. The wet zone is a highly synchronised epidemic unit while the northern and eastern districts have more independent epidemic patterns. The epidemic synchrony in the wet zone may be associated with the frequent mobility of people in and out of the wet zone hot spot Colombo. The overall epidemic pattern in Sri Lanka is not a sole outcome of the two monsoons but the regional epidemic patterns are collectively shaped by monsoon an inter-monsoon rains, human mobility, geographical proximity and other climate and topographical factors.

Investigating size-segregated sources of elemental composition of particulate matter in the South China Sea during the 2011 <i>Vasco</i> cruise

Abstract. The South China Sea (SCS) is a receptor of numerous natural and anthropogenic aerosol species from throughout greater Asia. A combination of several developing countries, archipelagic and peninsular terrain, a strong Asian monsoon climate, and a host of multi-scale meteorological phenomena make the SCS one of the most complex aerosol–meteorological systems in the world. However, aside from the well-known biomass burning emissions from Indonesia and Borneo, the current understanding of aerosol sources is limited, especially in remote marine environments. In September 2011, a 2-week research cruise was conducted near Palawan, Philippines, to sample the remote SCS environment. Size-segregated aerosol data were collected using a Davis Rotating Uniform size-cut Monitor (DRUM) sampler and analyzed for concentrations of 28 elements measured via X-ray fluorescence (XRF). Positive matrix factorization (PMF) was performed separately on the coarse, fine, and ultrafine size ranges to determine possible sources and their contributions to the total elemental particulate matter mass. The PMF analysis resolved six sources across the three size ranges: biomass burning, oil combustion, soil dust, a crustal–marine mixed source, sea spray, and fly ash. Additionally, size distribution plots, time series plots, back trajectories and satellite data were used in interpreting factors. The multi-technique source apportionment revealed the presence of biogenic sources such as soil dust, sea spray, and a crustal–marine mixed source. Anthropogenic sources were also identified: biomass burning, oil combustion, and fly ash. Mass size distributions showed elevated aerosol concentrations towards the end of the sampling period, which coincided with a shift of air mass back trajectories to southern Kalimantan. Covariance between coarse-mode soil dust and fine-mode biomass burning aerosols were observed. Agreement between the PMF and the linear regression analyses indicates that the PMF solution is robust. While biomass burning is indeed a key source of aerosol, this study shows the presence of other important sources in the SCS. Identifying these sources is not only key for characterizing the chemical profile of the SCS but, by improving our picture of aerosol sources in the region, also a step forward in developing our understanding of aerosol–meteorology feedbacks in this complex environment.

Bifurcation analysis of quantum ion-acoustic kink, anti-kink and periodic waves of the Burgers equation in a dense quantum plasma

Bifurcations of quantum ion-acoustic kink, anti-kink and periodic waves of the Burgers equation are examined in a dense quantum (DQ) plasma comprising of positive ions, electrons and positrons utilizing reductive perturbation technique (RPT) and the concept of dynamical system for the first time. Using RPT and travelling wave transformation, the model equations are converted to a dynamical system. Existence of kink, anti-kink and periodic wave solutions is shown through phase plot and time series plot based on suitable parametric conditions on coefficient of viscosity ($$\eta $$), density ratio of electrons and ions ($$\mu $$) and wave velocity (V). Depending on different parameters, variation of anti-kink, kink and periodic waves is depicted through numerical simulation. The periodic wave of the Burgers equation due to the periodic orbit is offered for the first time in plasmas through Jacobi elliptic function. It is seen that physical parameters $$\eta ,~\mu $$ and V significantly influence amplitude and smoothness of the kink, anti-kink and periodic wave profiles. The output of this contribution may be utilized to interpret the bifurcation behaviour of periodic, kink and anti-kink waves in DQ plasmas with viscosity effect.

Time series of mathematics education program, FKIP university of jember enthusiast through exponential smoothing method

The purpose of this research was to discuss the implementation of exponential smoothing methods in predicting the number of Mathematics Education Study Program enthusiasts, FKIP University of Jember based on entrance exam. The methodology of the exponential smoothing forecasting method were sketching the time series plot, identifying the model, selecting the best model and forecasting. The accuracy of the estimation results could be seen from the smallest MAPE value. The results of this research showed that the forecasting results were close to the actual data because the MAPE value obtained in this research respectively are close to criteria good or quite good. As a result it can be concluded that forecasting with the exponential smoothing method in predicting the number of Mathematics Education Study Program enthusiasts, FKIP University of Jember had good accuracy since MAPE on ten percent to twenty percent, and had quite good accuracy since MAPE on twenty percent to fifty percent.

Air Pollutant Index Calendar-Based Graphics for Visualizing Trends Profiling and Analysis

Detection of air quality abnormality is important as an early warning system for air quality control and management. The information can raise citizens’ awareness towards current air quality status. By using time series plot, the data pattern can be identified but not able to exactly determine the abnormality due to overcrowded plot. Therefore, visualization data profiling was presented in this study by using seven years Malaysia daily air pollutant index to improve the detection. Result shown, the developed approach can simply identify the poor air quality across the month and year. Malaysia air quality was good and consistent between November and May. However, upward trend existed between June and October due to the forest fire happened in Sumatra. This visualization approach improved air pollution detection profiling and it is useful for related agencies to guide the control actions to be taken. This approach can be applied to any countries and data set to give more competent information.

Picturing beach erosion and deposition trends using PSInSAR: an example from the non-barred southern west coast of India

Coastal dynamics can rapidly alter beach morphology. In some places, such as the non-barred southern west coast of India, studying changes to beach morphology is a relatively arduous task. Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), a remote sensing technique that utilizes stacks of radar images for accurate long-term monitoring of ground features, allows for detailed observations of coastal morphodynamics. Twenty-two single look complex (SLC) synthetic aperture radar (SAR) Sentinel-1A images, acquired from 4 March 2016 to 3 June 2017, are used to monitor geomorphological processes such as coastal erosion and deposition. Ground deformation measurements from PSInSAR processing shows the coast stretching ~ 70 km between Thaickal and Munambam is highly dynamic, characterized by phases of erosion and deposition. The highest negative displacement of − 24.9 mm at Thaickal versus the + 7.6 mm at Chellanam in the north show the co-existing milieus of erosion and deposition. PSInSAR results concur with corresponding Google Earth images. In addition, beach sediment texture and scanning electron microscope grain micro-texture in the beach segment further corroborate temporal phases of erosion and deposition. Two locations are identified as typical erosional sites, while one location typified deposition. Erosion and deposition or rebuilding of beaches, usually correspond respectively with the onset and offset of SW monsoon wave climate in the Arabian Sea. When zones of deposition are mainly located in the proximity of river and/or lake inlets, stretches characterized by erosion are distal to inlets. Linear regression analyses of displacement–time series plots were used to identify general erosion or depositional regimes along beach segments. Results from this study illustrate how PSInSAR is a capable and reliable processing tool for monitoring temporal phases of coastal morpho-dynamics.

Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model

Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid “ARCH-DDM” models outperformed standalone models in predicting monthly streamflow.

Sulfur dioxide: Behaviour and trends at the industrial city of Araucaria/Brazil

Introduction: It is known that the levels of sulfur dioxide have a close rela- tionship to the industrial emissions and they are also related to the climate, but it’s dispersion its mainly caused by the wind dynamics. The goal of this paper is to analyze the behaviour of this compound in two stations at Araucária city, in the state of Paraná, Southern Brazil, the city which is located in the indus- trial zone of Curitiba, the capital of the state. Materials and methods: Measurements of SO2 were done in two monitoring stations in different regions of the city; one was located in the industrial zone and the other one was a residential zone. A basic statistical treatment was per- formed the R, which includes the construction of time series plots, histograms and analysis of SO2 levels and wind direction. The Theil-Sen trend estimator was applied on both time series in order to understand the past and present behaviour of the pollutant. Results: The analysis of wind and SO2 levels revealed a straight relationship between the location of the industrial zone and air pollution. The trend calcu- lation revealed for both stations, similar situations, in which the sulfur dioxide levels are decreasing. Conclusion: During this research, it was possible to notice that there is an environmental problem on the suburbs of the city with a direct result from urban growth and the lack of planning for the suburban areas. This study reinforces the importance of urban planning policies as well as monitoring and fiscalization

Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics.

Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques used to illustrate these effects are currently limited to single- or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure. Whilst these methods each have their own strength and weaknesses, they are only able to show a subset of the data and are suboptimal at articulating one or both of the space-time components.Here, we propose Porthole and Stormcloud, a set of data visualisation tools which can automatically generate context appropriate graphics for both print and screen with the following graphical capabilities: Animated two-dimensional scalp maps with dynamic timeline annotation and optional user interaction; Three-dimensional construction of discrete clusters within sparse spatiotemporal volumes, rendered with 'cloud-like' appearance and augmented by cross-sectional scalp maps indicating local maxima. These publicly available tools were designed specifically for visualisation of M/EEG spatiotemporal statisticalparametric maps, however, we also demonstrate alternate use cases of posterior probability maps and weight maps produced by machine learning classifiers. In principle, the methods employed here are transferrable to visualisation of any spatiotemporal image.

Visually Communicating Future Climate in a Web Environment

AbstractWhile there is growing demand for use of climate model projections to understand the potential impacts of future climate on resources, there is a lack of effective visuals that convey the range of possible climates across spatial scales and with uncertainties that potential users need to inform their impact assessments and studies. We use usability testing including eye tracking to explore how a group of resource professionals (foresters) interpret and understand a series of graphical representations of future climate change, housed within a web-based decision support system (DSS), that address limitations identified in other tools. We find that a three-map layout effectively communicates the spread of future climate projections spatially, that location-specific information is effectively communicated if depicted both spatially on a map and temporally on a time series plot, and that model error metrics may be useful for communicating uncertainty and in demonstrating the utility of these future climate datasets.

On the Fourier Residual Modification of Arima Models in Modeling Malaria Incidence Rates among Pregnant Women

This work provides a general overview and consideration of Box-Jenkins models for temporal data and its extension known as Fourier residual autoregressive moving average models. We examined the modeling and forecasting of malaria incidence rate during pregnancy at Bishop Shannahan Hospital, Nsukka using Autoregressive Integrated Moving Average (ARIMA) Models propounded by Box and Jenkins. We adoptted the Box-Jenkins methodology to build ARIMA model for malaria incidences during pregnancy for a period of 10 years spanning from January 2006 to December 2016. Among the candidate models considered, ARIMA (3,1,1) was identified to be the most robust based on some model performance measures. The model was further improved upon by incorporating Fourier residual modification on the fitted ARIMA model. The Fourier Residual Autoregressive Moving Average (FARIMA) model obtained yielded improved result. Besides, model evaluation criterion such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Mean Bias Error (MBE), Mean Absolute Scaled Error (MASE), were used to access the models. FARIMA Model out performed ARIMA Model. Several time series plots and tests like augmented dickey fuller test, correlogram, Ljung-Box test for serial correlation of the residuals, etc were carried out in this study to test for stationarity, identify the order of ARIMA model and serial correlation residual respectively.

Experimental study on fault analysis of a needle roller bearing with wear evaluation

In this paper, an experimental setup has been developed to find out the wear of needle roller bearing. The signal processing method is used to extract the wear evaluation progress. Results are obtained in terms of the time series and FFT plots. The five events of the wear evaluation are analysed such as Misalignment fault, Indentation event, Smoothened dent, Defects and Damage growth state. The test is carried out for 480 hours of run of test bearing; the temperature varies from 68 to 78 °C. It is found that the needle roller bearing is damaged due to the high wear rate at the end of 480 hours of run of test bearing and the amplitude of acceleration in frequency domain is 32.51 m/s 2 at fifth event. It is also found that the amplitude of acceleration in frequency domain is more than the critical value of 30 m/s 2 . Therefore, the state at the end of 480 hours is the damage growth state of the needle roller bearing.

Bifurcation analysis of the propagation of femtosecond pulses for the Triki-Biswas equation in monomode optical fibers

Bifurcation analysis of the propagation of femtosecond pulses for the Triki–Biswas (TB) equation in monomode optical fibers is reported for the first time. Bifurcation of phase plots of the dynamical system is dispensed using phase plane analysis through symbolic computation. It is observed that the TB equation supports femtosecond solitary pulse, periodic pulse, superperiodic pulse, kink and anti-kink pulses, which are presented through time series plot numerically. Analytical forms of the femtosecond solitary pulses are obtained. This contribution may be applicable to interpret the dynamical behavior of various femtosecond pulses in monomode optical fibers beyond the Kerr limit.