Exploring time-series of selected air pollution elements in Castelporziano, Rome: the impact on soil and forest ecosystem

2015 ◽  
Vol 26 (S3) ◽  
pp. 499-505
Author(s):  
Rita Aromolo ◽  
Valerio Moretti ◽  
Luca Salvati
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Forest Ecosystem ◽  
The Impact

scholarly journals Time Series Modeling of Air Pollution and Its Association with Season and Climate Variables in Istanbul, Turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Wind Speed ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using NARX method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

scholarly journals On the Estimation of Trends in Annual Rainfall Using Paired Gauge Observations

2008 ◽  
Vol 47 (6) ◽  
pp. 1814-1818 ◽  
Author(s):  
Nathan Paldor
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Opposite Sign ◽  
Annual Rainfall ◽  
Trend Line ◽  
Rainfall Variation ◽  
Natural Rainfall ◽  
High Degree ◽  
The Impact ◽  
Best Fit

Abstract A method was recently proposed for evaluating the impact of a perturbation, such as air pollution or urbanization, on the precipitation at a location by calculating the ratio between the precipitation at the perturbed location and that at a location believed to be unperturbed. However, this method may be inappropriate because of the high degree of variability of precipitation at each of the stations. To explore the validity of this approach, noisy annual rainfall records are generated numerically in an upwind, unperturbed station and in a downwind, perturbed station, and the time series of ratio between the annual rainfalls in the two stations is analyzed. The noisy rainfall records are 50 yr long, and the imposed trend for the downwind, perturbed station is −2 mm yr−1 while at the upwind station the variations in annual rainfall are purely noisy. Many pairs of noisy rainfall records are numerically generated (each pair constitutes an experiment), and in every experiment the slope of the linear best fit to the rainfall ratio yields an estimate of the trend of rainfall at the perturbed station. In the absence of noise, the trend of the rainfall ratio is explicitly related to the trend of rainfall at the perturbed station, but the natural rainfall variation at the stations completely masks this explicit relationship. The results show that in some experiments the trend line of the rainfall ratio has the opposite sign to the imposed trend and that in only about one-half of the experiments does the ratio’s trend line lie within ±75% of the imposed trend. Trend estimates within ±25% of the imposed trend are obtained in less than one-quarter of the experiments. This result casts doubt on the generality and validity of using trends of rainfall ratio between two stations to estimate trends of precipitation in one of these stations.

scholarly journals Effect of socioeconomic status on the association between air pollution and mortality in Bogota, Colombia

2014 ◽  
Vol 56 (4) ◽  
pp. 371 ◽  
Author(s):  
Luis Camilo Blanco-Becerra ◽  
Víctor Miranda-Soberanis ◽  
Albino Barraza-Villarreal ◽  
Washington Junger ◽  
Magali Hurtado-Díaz ◽  
...  
Keyword(s):  
Air Pollution ◽  
Socioeconomic Status ◽  
Time Series ◽  
Particulate Matter ◽  
Ecological Study ◽  
Aerodynamic Diameter ◽  
Components Analysis ◽  
The Impact ◽  
The Relationship ◽  
Respiratory Causes

Objective. To evaluate the modification effect of socioeconomic status (SES) on the association between acute exposure to particulate matter less than 10 microns in aerodynamic diameter (PM10) and mortality in Bogota, Colombia. Materials and methods. A time-series ecological study was conducted (1998-2006). The localities of the cities were stratified using principal components analysis, creating three levels of aggregation that allowed for the evaluation of the impact of SES on the relationship between mortality and air pollution. Results. For all ages, the change in the mortality risk for all causes was 0.76% (95%CI 0.27-1.26) for SES I (low), 0.58% (95%CI 0.16-1.00) for SES II (mid) and -0.29% (95%CI -1.16-0.57) for SES III (high) per 10µg/m3 increment in the daily average of PM10 on day of death. Conclusions. The results suggest that SES significantly modifies the effect of environmental exposure to PM10 on mortality from all causes and respiratory causes.

scholarly journals Time series modeling of air pollution and its association with season and climate variables in istanbul, turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact ◽  
Nonlinear Autoregressive

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using Nonlinear AutoregRessive network with eXogenous inputs (NARX) method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

Premature births in Spain: Measuring the impact of air pollution using time series analyses

2019 ◽  
Vol 660 ◽  
pp. 105-114 ◽  
Author(s):  
Virginia Arroyo ◽  
Cristina Linares ◽  
Julio Díaz
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Premature Births ◽  
Time Series Analyses ◽  
The Impact

scholarly journals Time series modeling of air pollution and its association with season and climate variables in istanbul, turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact ◽  
Nonlinear Autoregressive

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using Nonlinear AutoregRessive network with eXogenous inputs (NARX) method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

scholarly journals Time series modeling of air pollution and its association with season and climate variables in istanbul, turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact ◽  
Nonlinear Autoregressive

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using Nonlinear AutoregRessive network with eXogenous inputs (NARX) method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

Short-Term Air Pollution as a Risk for Stroke Admission: A Time-Series Analysis

2020 ◽  
Vol 49 (4) ◽  
pp. 404-411
Author(s):  
Colm Patrick Byrne ◽  
Kathleen E. Bennett ◽  
Anne Hickey ◽  
Paul Kavanagh ◽  
Brian Broderick ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Meta Analysis ◽  
Air Pollutant ◽  
Pollution Level ◽  
Short Term ◽  
Urban Centre ◽  
Stroke Incidence ◽  
Relative Risks ◽  
The Impact

Background: The harmful effects of outdoor air pollution on stroke incidence are becoming increasingly recognised. We examined the impact of different air pollutants (PM2.5, PM10, NO2, ozone, and SO2) on admission for all strokes in two Irish urban centres from 2013 to 2017. Methods: Using an ecological time series design with Poisson regression models, we analysed daily hospitalisation for all strokes and is­chaemic stroke by residence in Dublin or Cork, with air pollution level monitoring data with a lag of 0–2 days from exposure. Splines of temperature, relative humidity, day of the week, and time were included as confounders. Analysis was also performed across all four seasons. Data are presented as relative risks (RRs) and 95% confidence intervals (95% CI) per interquartile range (IQR) increase in each pollutant. Results: There was no significant association between all stroke admission and any individual air pollutant. On seasonal analysis, during winter in the larger urban centre (Dublin), we found an association between all stroke cases and an IQR increase in NO2 (RR 1.035, 95% CI: 1.003–1.069), PM10 (RR 1.032, 95% CI: 1.007–1.057), PM2.5 (RR 1.024, 95% CI: 1.011–1.039), and SO2 (RR 1.035, 95% CI: 1.001–1.071). There was no significant association found in the smaller urban area of Cork. On meta-analysis, there remained a significant association between NO2 (RR 1.013, 95% CI: 1.001–1.024) and PM2.5 (1.009, 95% CI 1.004–1.014) per IQR increase in each. Discussion: Short-term air pollution in winter was found to be associated with hospitalisation for all strokes in a large urban centre in Ireland. As Ireland has relatively low air pollution internationally, this highlights the need to introduce policy changes to reduce air pollution in all countries.

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