scholarly journals Time-series Variations in Nutrient Concentrations at Two Monitoring Stations in Tokyo Bay

2008 ◽  
Vol 31 (9) ◽  
pp. 559-564 ◽  
Author(s):  
Jota KANDA ◽  
Pachara CHOMTHAISON ◽  
Naho HORIMOTO ◽  
Yukuya YAMAGUCHI ◽  
Takashi ISHIMARU
Keyword(s):  
Time Series ◽  
Tokyo Bay ◽  
Nutrient Concentrations ◽  
Monitoring Stations

scholarly journals Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO<sub>2</sub> and CH<sub>4</sub>

2017 ◽  
Author(s):  
Abdelhadi El Yazidi ◽  
Michel Ramonet ◽  
Philippe Ciais ◽  
Gregoire Broquet ◽  
Isabelle Pison ◽  
...  
Keyword(s):  
Time Series ◽  
Detection Methods ◽  
Local Source ◽  
Observation System ◽  
High Mountain ◽  
Spike Detection ◽  
Automatic Data ◽  
Background Site ◽  
Local Emissions ◽  
Monitoring Stations

Abstract. This study deals with the problem of identifying atmospheric data that are influenced by local emissions which cause spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS), and standard deviation of the background (SD), to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the ICOS (Integrated Carbon Observation System) European Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in Eastern France (OPE); a high mountain observatory in the south-west of France (PDM); a regional marine background site in Crete (FKL); and a marine clean-air background site in the southern hemisphere in Amsterdam island (AMS). This panel allows addressing the spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analyzed. All the methods were found to be able to detect short-term spikes (lasting from a few seconds to few minutes) in the time series. Analysis of the results of each method leads us to exclude the use of the COV method because of its requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or under-estimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in the SD and the REBS methods after the best adjustment of parameter values. At PDM, measurements made by two analyzers 200 m from each other allow to confirm that the CH4 spikes identified in one of the time-series but not in the other correspond to a local source from a sewage treatment facility in one of the observatory buildings. From this experiment, we found that the REBS method underestimates the number of positive anomalies in the CH4 data caused by local sewage emissions. As a conclusion, we recommend the use of the SD method, which also appears as the easiest one to implement as automatic data processing, for the operational filtering of spikes in greenhouses gases time series at global and regional monitoring stations of networks like ICOS.

scholarly journals VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1096
Author(s):  
Edward Ming-Yang Wu ◽  
Shu-Lung Kuo
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Egarch Model ◽  
Southern Taiwan ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM10), ozone (O3) and nitrogen dioxide (NO2). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.

scholarly journals Spatial dissimilarities in seasonal cycle of PM10 particulate matters in Seoul, Korea

2019 ◽  
Author(s):  
Parth Bansal
Keyword(s):  
Time Series ◽  
Pm10 Concentration ◽  
Pollutant Concentration ◽  
Air Quality Monitoring ◽  
Comprehensive Understanding ◽  
Concentration Time ◽  
Spatial Differences ◽  
Concentration Time Series ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

The increase in spatial and temporal resolution of pollution data has advanced the understanding of trend in pollutant concentration through time and space. However, this has also made inspection of time series and the relation between observations from different monitoring stations difficult to comprehend. In this study, we decompose PM10 concentration time series (2010 - 2018) from 40 Air Quality Monitoring Stations (AQMs) in Seoul and then cluster the seasonal cyclic components using K-Means and K-Shape algorithms. Firstly, the influence of diurnal, weekly and annual cycle on PM10 concentration is shown. Secondly, the clustering results illustrate that both algorithms are useful in determining AQMs with similar cycles, and together provide a comprehensive understanding of spatial differences in pollutant concentration.

Changes in nutrient levels in some Eastern European rivers in response to large-scale changes in agriculture

2004 ◽  
Vol 49 (3) ◽  
pp. 29-36 ◽  
Author(s):  
P. Stålnacke ◽  
S.M. Vandsemb ◽  
A. Vassiljev ◽  
A. Grimvall ◽  
G. Jolankai
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Agricultural Land ◽  
Nutrient Concentrations ◽  
Similar Data ◽  
Eastern European ◽  
Nutrient Inputs ◽  
Nitrogen And Phosphorus ◽  
Catchment Areas ◽  
The Impact

Since the late 1980s, the use of commercial fertilisers in most Eastern European countries has decreased at an unprecedented rate. We examined the impact of this dramatic reduction in agricultural inputs on concentrations of nutrients in four rivers in Eastern Europe: the Emajogi and Õhnejogi (Estonia), the Daugava (Latvia), and the Tisza (Hungary). Time series of nitrate (NO3-N) and phosphate (PO4-P) concentrations and data on runoff were selected to represent catchments with substantial areas of agricultural land and available time series of sufficient length and frequency. The study period was 1987-1998. We detected downward trends in nitrate-N and phosphate-P in only two of the four rivers. Our results imply that the response to the extensive decrease in agricultural intensity since the late 1980s has been slow and limited in many rivers. Corresponding results in the literature are inconclusive and comprise several examples of both decreasing and non-decreasing nutrient concentrations. Our findings, along with similar data from other studies, indicate that large cuts in nutrient inputs do not necessarily induce an immediate response, particularly in medium-sized and large catchment areas. Moreover, the difference we noted between nitrogen and phosphorus suggests that factors other than reduced fertiliser application influenced the inertia of the water quality response.

scholarly journals ANALISIS DE CLASIFICACION DE SERIES TEMPORALES: EL CASO DE LA SALINIDAD EN LA CIENAGA GRANDE DE SANTA MARTA, COLOMBIA

2016 ◽  
Vol 24 ◽  
Author(s):  
Ramón Giraldo H, ◽  
Jorge Martínez C. ◽  
Luís H. Hurtado T. ◽  
Sven Zea ◽  
Eira R. Madera R.
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Water Salinity ◽  
Estuarine System ◽  
Numerical Classification ◽  
Arima Models ◽  
Autocorrelation Structure ◽  
First Time ◽  
Monitoring Stations

A numerical classification of 21 monitoring stations of the lagoonal - estuarine system comprised by the Ciénaga Grande de Santa Marta and Pajarales complex was carried out according to their similarity in water salinity behavior. Biweekly data from January 1987 to Octuber 1991 was used. Due to the autocorrelation structure of the data, it was necessary to use first time series ARIMA models followed by normal cluster analysis of the AR coefficients of infinite representation calculated for each station. The results were coherent with hypotheses concerning the distribution of the variable in the system.

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