Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

Akdi, Yılmaz; Gölveren, Elif; Ünlü, Kamil Demirberk; Yücel, Mustafa Eray

doi:10.1007/s10661-021-09399-y

Cited by 14 publications

(4 citation statements)

References 79 publications

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“…Seasonal fluctuations of two years were identified as a result, where lower concentrations appear in warm days, while higher concentrations appear in cold days. Periodogram-based time series methodology was utilized to find the hidden periodic structure of monthly PM2.5 data, available for Paris between January 2000 and December 2019 [22].…”

Section: Introductionmentioning

confidence: 99%

Time Series Forecasting of Air Quality: A Case Study of Sofia City

2022

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Air pollution has a significant impact on human health and the environment, causing cardiovascular disease, respiratory infections, lung cancer and other diseases. Understanding the behavior of air pollutants is essential for adequate decisions that can lead to a better quality of life for citizens. Air quality forecasting is a reliable method for taking preventive and regulatory actions. Time series analysis produces forecasting models, which study the characteristics of the data points over time to extrapolate them in the future. This study explores the trends of air pollution at five air quality stations in Sofia, Bulgaria. The data collected between 2015 and 2019 is analyzed applying time series forecasting. Since the time series analysis works on complete data, imputation techniques are used to deal with missing values of pollutants. The data is aggregated by granularity periods of 3 h, 6 h, 12 h, 24 h (1 day). The AutoRegressive Integrated Moving Average (ARIMA) method is employed to create statistical analysis models for the prediction of pollutants’ levels at each air quality station and for each granularity, including carbon oxide (CO), nitrogen dioxide (NO2), ozone (O3) and fine particles (PM2.5). In addition, the method allows us to find out whether the pollutants’ levels exceed the limits prescribed by the World Health Organization (WHO), as well as to investigate the correlation between levels of a given pollutant measured in different air quality stations.

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Section: Introductionmentioning

confidence: 99%

Time Series Forecasting of Air Quality: A Case Study of Sofia City

2022

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“…The statistical forecast is to analyze data through mathematical modeling, using correlation analysis [7], multiple regression [8,9], principal component analysis [10], gray model [11], fuzzy comprehensive evaluation method [12], harmonic regression [13], and other methods to predict air quality. However, it is difficult to provide air quality data in a timely and rapid manner due to the long forecast period.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Forecast of Beijing’s Air Quality Index Based on ARIMA Model and Neural Network Model

Liu

You

2022

Atmosphere

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Based on Beijing’s Air Quality Index (AQI) and concentration changes of the six major pollutants from 2019 to 2021, the results are visualized through descriptive statistics, and the air pollution status and influencing factors of Beijing’s AQI are analyzed using the ARIMA model and neural network. A forecast system is built and the fitting effects of the two models are compared. The results show that PM2.5, PM10, and O3 of the six major pollutants have the greatest impact on AQI. Beijing’s air quality now shows a trend of improvement in recent years; however, there is obvious seasonal evidence that the summer pollution index has been high. Therefore, special attention should be paid to the treatment of ozone pollution in summer. Both models are useful for the forecast of AQI, but the forecast effect of the neural network model is better than that of the ARIMA model. Moreover, when using the additive seasonal model for the long-term forecast of monthly data, it is found that the Beijing AQI still shows seasonal cyclicality and has a slightly decreasing trend in the next two years. This research provides a basis for the forecast of air quality and policy enlightenment for environmental protection departments to deal with air pollution.

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“…Spectral analysis techniques [19] represent an optimal statistical approach for decomposing and identifying periodicities subjacent in data series. Those techniques were widely applied in the Earth sciences field and overall in solar periodicity related research [20][21][22], but also over air quality data series [23,24], both revealing important controls on climate. In regional studies, spectral analysis was also applied to climate records in order to diagnose the ENSO patterns [25] and to identify the regional structure of precipitation that matches seasonal patterns [26].…”

Section: Introductionmentioning

confidence: 99%

Climate Patterns and Their Influence in the Cordillera Blanca, Peru, Deduced from Spectral Analysis Techniques

Sánchez

Úbeda²,

Tanarro³

et al. 2022

Atmosphere

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Climate patterns are natural processes that drive climate variability in the short, medium, and long term. Characterizing the patterns behind climate variability is essential to understand the functioning of the regional atmospheric system. Since investigations typically reveal only the link and extent of the influence of climate patterns in specific regions, the magnitude of that influence in meteorological records usually remains unclear. The central Peruvian Andes are affected by most of the common climate patterns of tropical areas, such as Intertropical Convergence Zone (ITCZ), Sea Surface Temperature (SST), solar irradiance, Madden Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO), and El Niño Southern Oscillation (ENSO). They are also affected by regional processes that are exclusive from South America, such as the South American Low-Level Jet (SALLJ), South American Monsoon System (SAMS), Bolivian High (BH), and Humboldt Current. The aim of this research is to study the climate variability of precipitation, maximum and minimum temperature records over Cordillera Blanca (Peru), and its relationship with the intensity and periodicity of the common climate patterns that affect this region. To achieve this aim, a spectral analysis based on Lomb’s Periodogram was performed over meteorological records (1986–2019) and over different climate pattern indexes. Results show a coincidence in periodicity between MJO and SALLJ, with monthly cycles for precipitation and temperature (27-day, 56-day, and 90-day cycles). Moreover, the most intense periodicities, such as annual (365 days) and biannual (182 and 122 days) cycles in meteorological variables, possibly would be led by ITCZ and ENSO together, as well as a combination of the Humboldt Current and SALLJ. Additionally, interannual periodicities (3-year, 4.5-year, 5.6–7-year and 11-year cycles) would have coincidence with the ENSO–solar combination, while the longest cycles (16 years) could match PDO variability.

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Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

Cited by 14 publications

References 79 publications

Time Series Forecasting of Air Quality: A Case Study of Sofia City

Time Series Forecasting of Air Quality: A Case Study of Sofia City

Analysis and Forecast of Beijing’s Air Quality Index Based on ARIMA Model and Neural Network Model

Climate Patterns and Their Influence in the Cordillera Blanca, Peru, Deduced from Spectral Analysis Techniques

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