CROSS-SPECTRUM ANALYSIS APPLIED TO AIR POLLUTION TIME SERIES FROM SEVERAL URBAN AREAS OF ROMANIA

2013 ◽  
Vol 12 (4) ◽  
pp. 677-684 ◽  
Author(s):  
Stefania Iordache ◽  
Daniel Dunea
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Spectrum Analysis ◽  
Urban Areas ◽  
Cross Spectrum

scholarly journals Potential health impact of transport traffic restriction in the center of Baku, the capital of Azerbaijan

2020 ◽  
Vol 2 (3) ◽  
pp. 28-32
Author(s):  
Samir Mehtiyev
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Urban Areas ◽  
Health Impact ◽  
Health Priorities ◽  
Local Data ◽  
Pollution Effects ◽  
Potential Health ◽  
Pollution Levels ◽  
Air Pollution Effects

The transport traffic is known to be the major reason for air pollution in urban areas. Two pollutants should be considered with attention: the particles with diameter of less than 10 microns (PM10) and carbon monoxide. The paper discusses application of time-series and geographical studies to the investigation of air pollution health effects as well as attempts to estimate potential health impact of restriction traffic in the center of Baku by 25% based on local data published in 2012(4) and 2013(5). Time-series studies investigate association between short-term variations in air pollution levels and health events counts. Confounding factors that change slowly over the time do not introduce much distortion for the association in question as population is used as its own control. Geographical studies aimed at investigation of association between long-term exposure to air pollution and chronic health outcomes. They are known to be prone to confounding because they compare populations from different locations. The evidence of air pollution effects from time-series and geographical studies is complementary. The problem of traffic air pollution is being intensified with each year and becomes one of the main public health priorities in Baku. Assuming that the results of six cities study (3) can be generalized to Baku the total number of preventable annual deaths should be around 419 in case of restriction traffic in the center of Baku by 25%. For the low border of 95%CI the result is as much as 153. Despite uncertainties in assumptions the produced evidence fully justifies the proposed intervention.

TIME SERIES ANALYSIS IN AGROMETEOROLOGICAL PROBLEMS WITH EMPHASIS ON SPECTRUM ANALYSIS

1967 ◽  
Vol 47 (5) ◽  
pp. 477-491
Author(s):  
R. L. Desjardins
Keyword(s):  
Time Series ◽  
Environmental Factors ◽  
Time Series Analysis ◽  
Multiple Regression ◽  
Spectrum Analysis ◽  
Meteorological Parameters ◽  
Regression Technique ◽  
Cross Spectrum ◽  
Time Series Studies ◽  
Regression Techniques

This paper reviews the principles and use of spectrum analysis and discusses the effectiveness of combining multiple regression techniques with cross-spectrum analysis in time series studies. This method is useful in comparing the variations of an empirical index or the response of different instruments to environmental factors as a function of time. The variability of daily latent-evaporation estimates from a regression technique is examined and compared with evaporation readings from atmometers. A comparison is made of the variation in evaporation from two types of small atmometers and two types of tank evaporimeters. All four instruments appear to respond to meteorological parameters similarly at Swift Current and Ottawa.

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

2017 ◽  
Vol 68 (4) ◽  
pp. 841-846
Author(s):  
Hai-Ying Liu ◽  
Daniel Dunea ◽  
Mihaela Oprea ◽  
Tom Savu ◽  
Stefania Iordache
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Urban Areas ◽  
Behavioural Change ◽  
Health Assessment ◽  
Warning System ◽  
Personal Exposure ◽  
Mobile Technologies ◽  
Quality Maps

This paper presents the approach used to develop the information chain required to reach the objectives of the EEA Grants� RokidAIR project in two Romanian cities i.e., Targoviste and Ploiesti. It describes the PM2.5 monitoring infrastructure and architecture to the web-based GIS platform, the early warning system and the decision support system, and finally, the linking of air pollution to health effects in children. In addition, it shows the analysis performance of the designed system to process the collected time series from various data sources using the benzene concentrations monitored in Ploiesti. Moreover, this paper suggests that biomarkers, mobile technologies, and Citizens� Observatories are potential perspectives to improve data coverage by the provision of near-real-time air quality maps, and provide personal exposure and health assessment results, enabling the citizens� engagement and behavioural change. This paper also addresses new fields in nature-based solutions to improve air quality, and studies on air pollution and its mental health effects in the urban areas of Romania.

Estimating time series of aerosol particle number concentrations in the five HEAPSS cities on the basis of measured air pollution and meteorological variables

2005 ◽  
Vol 39 (12) ◽  
pp. 2261-2273 ◽  
Author(s):  
Pentti Paatero ◽  
Pasi Aalto ◽  
Sally Picciotto ◽  
Tom Bellander ◽  
Gemma Castaño ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Aerosol Particle ◽  
Particle Number ◽  
Meteorological Variables

scholarly journals Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

2021 ◽  
Vol 13 (9) ◽  
pp. 4933
Author(s):  
Saimar Pervez ◽  
Ryuta Maruyama ◽  
Ayesha Riaz ◽  
Satoshi Nakai
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Urban Areas ◽  
Industrial Development ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Land Use Classification ◽  
Land Use Regression ◽  
Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

scholarly journals Development of a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear near the Ground under Stable Atmospheric Conditions

2020 ◽  
Vol 4 (1) ◽  
pp. 17
Author(s):  
Saisantosh Vamshi Harsha Madiraju ◽  
Ashok Kumar
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Wind Shear ◽  
Dispersion Model ◽  
Near Field ◽  
Ground Level ◽  
Gaseous Pollutants ◽  
Air Pollution Control ◽  
Mobile Sources ◽  
Input Variables

Transportation sources are a major contributor to air pollution in urban areas. The role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model based on the solution of the convective-diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The model input includes emission rate, wind speed, wind direction, turbulence, and terrain features. The dispersion coefficients are based on the near field parameterization. The sensitivity of the model to compute ground level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables. However, the model equations should be re-examined for three input variables (wind velocity at the reference height and two variables related to the vertical spread of the plume) to make sure that that the model is valid for computing ground level concentrations.

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