scholarly journals APPLICATION OF OPEN AIR MODEL (R PACKAGE) TO ANALYZE AIR POLLUTION DATA

2017 ◽  
Vol 1 (1) ◽  
pp. 94 ◽  
Author(s):  
Intan Agustine ◽  
Hernani Yulinawati ◽  
Endro Suswantoro ◽  
Dodo Gunawan
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Air Quality ◽  
Meteorological Data ◽  
Ambient Air ◽  
Quality Data ◽  
Fluctuation Analysis ◽  
Ambient Air Quality ◽  
Long Time ◽  
Air Quality Data

Air pollution problem is faced by many countries in the world. Ambient air quality studies and monitoring need a long time period of data to cover various atmospheric conditions, which create big data. A tool is needed to make easier and more effective to analyze big data. <strong>Aims: </strong>This study aims to analyze various application of <em>openair</em> model, which is available in open-source, for analyzing urban air quality data. <strong>Methodology and results: </strong>Each pollutant and meteorological data were collected through their sampling-analysis methods (active, passive or real-time) from a certain period of time. The data processed and imported in the <em>openair</em> model were presented in <em>comma separated value</em> (csv) format. The input data must consist of date-time, pollutant, and meteorological data. The analysis is done by selecting six functions: <em>theilSen</em> for trend analysis, <em>timeVariation</em> for temporal variations, <em>scatterPlot</em> for linear correlation analysis,<em> timePlot</em> for fluctuation analysis, <em>windRose</em> for wind rose creation, and <em>polarPlot</em> for creating pollution rose. Results from these functions are discussed. <strong>Conclusion, significance and impact study: </strong><em>Openair</em> model is capable of analyzing a long time air quality data. Application of <em>openair</em> model is possible to cities in Indonesia that already monitor ambient air quality but have not analyzed the data yet

Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City

2008 ◽  
Vol 157 (1-4) ◽  
pp. 105-112 ◽  
Author(s):  
Pragati Sharma ◽  
Avinash Chandra ◽  
S. C. Kaushik
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Air Quality Data

Particular matter prediction using synergy of multiple source urban big data in smart cities

2021 ◽  
pp. 1-15
Author(s):  
Ali Reza Honarvar ◽  
Ashkan Sami
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Air Quality ◽  
Smart Cities ◽  
Data Sources ◽  
Quality Data ◽  
Data Set ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Air Quality Data

At present, the issue of air quality in populated urban areas is recognized as an environmental crisis. Air pollution affects the sustainability of the city. In controlling air pollution and protecting its hazards from humans, air quality data are very important. However, the costs of constructing and maintaining air quality registration infrastructure are very expensive and high, and air quality data recording at one point will not be generalizable to even a few kilometers. Some of the gains come from the integration of multiple data sources, which can never be achieved through independent single-source processing. Urban organizations in each city independently produce and record data relevant to the organization’s goals and objectives. These issues create separate data silos associated with an urban system. These data are varied in model and structure, and the integration of such data provides an appropriate opportunity to discover knowledge that can be useful in urban planning and decision making. This paper aims to show the generality of our previous research, which proposed a novel model to predict Particulate Matter (PM) as the main factor of air quality in the regions of the cities where air quality sensors are not available through urban big data resources integration, by extending the model and experiments with various configuration for different settings in smart cities. This work extends the evaluation scenarios of the model with the extended dataset of city of Aarhus, in Denmark, and compare the model performance against various specified baselines. Details of removing the heterogeneity of multiple data sources in the Multiple Data Set Aggregator & Heterogeneity Remover (MDA&HR) and improving the operation of Train Data Splitter (TDS) part of the model by focusing on the finding more similar pattern of air quality also are presented in this paper. The acceptable accuracy of the results shows the generality of the model.

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