scholarly journals Balikpapan’s wind analysis to determine air quality monitoring point to support smart environment monitoring system

2021 ◽  
Vol 2106 (1) ◽  
pp. 012016
Author(s):  
M Faisal ◽  
B F Endrawati ◽  
C S Rahendaputri
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Green Plant ◽  
Google Earth ◽  
Wind Rose ◽  
Monitoring Point ◽  
Pollution Dispersion ◽  
Respiratory Problems ◽  
Air Pollutions

Abstract In this industrial era, air pollution become a concerning problem since it can cause some respiratory problems. One of the air pollutions was Sulphur dioxide which dilutes fast in atmospheric water vapor resulting in acid rain which can affect the organism. Thus, in this research, we study the probability of the receptor’s location according to wind direction, as the preliminary information on deciding monitoring point. The meteorological data were obtained from Balikpapan’s Agency for Meteorological, Climatological, and Geophysics. The wind data was then plotted using a wind rose plot program called WRPLOT. Afterward, the dominant wind speed and direction will then be analyzed using google earth to know which point will be affected by the pollution dispersion from the chimney in Balikpapan. The results show that wind in Balikpapan throughout 2020 mostly blew from Southwest to Northeast Direction. Thus, the most probable receptor locations were open green spaces with no settlement around. This will have less impact on human health. Nevertheless, further research can be conducted to know better the on-air dispersion model around the power plant, how the green plant will be suffered from this air pollution, and how this pollution will affect the workers around it.

A numerical air pollution dispersion model for central Stockholm

1974 ◽  
Vol 8 (2) ◽  
pp. 131-148 ◽  
Author(s):  
Björn Bringfelt ◽  
Thomas Hjorth ◽  
Sture Ring
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Pollution Dispersion ◽  
Air Pollution Dispersion

A Fuzzy Modified Gaussian Air Pollution Dispersion Model

2008 ◽  
Vol 2 (3) ◽  
pp. 156-169 ◽  
Author(s):  
M. Saeedi . ◽  
H. Fakhraee . ◽  
M. Rezaei Sadrabadi .
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Pollution Dispersion ◽  
Air Pollution Dispersion

Comparison of methods for assessment of children exposure to air pollution: dispersion model, ambient monitoring, and personal samplers

2021 ◽  
Author(s):  
Davi de Ferreyro Monticelli ◽  
Jane Meri Santos ◽  
Elisa Valentim Goulart ◽  
José Geraldo Mill ◽  
Jeferson da Silva Corrêa ◽  
...  
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Comparison Of Methods ◽  
Pollution Dispersion ◽  
Ambient Monitoring ◽  
Air Pollution Dispersion ◽  
Children Exposure

scholarly journals Air Pollution Dispersion Modelling Using Spatial Analyses

2018 ◽  
Vol 7 (12) ◽  
pp. 489 ◽  
Author(s):  
Jan Bitta ◽  
Irena Pavlíková ◽  
Vladislav Svozilík ◽  
Petr Jančík
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Hybrid Approach ◽  
Gaussian Model ◽  
Dispersion Modelling ◽  
Spatial Analyses ◽  
Pollution Dispersion ◽  
Determination Coefficient ◽  
Air Pollution Dispersion ◽  
Pollution Dispersion Modelling

Air pollution dispersion modelling via spatial analyses (Land Use Regression—LUR) is an alternative approach to the standard air pollution dispersion modelling techniques in air quality assessment. Its advantages are mainly a much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate more factors affecting pollutant’s concentration than standard dispersion models. The goal of the study was to model the PM10 particles dispersion via spatial analyses in the Czech–Polish border area of the Upper Silesian industrial agglomeration and compare the results with the results of the standard Gaussian dispersion model SYMOS’97. The results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover were included in the LUR model, the LUR model results improved significantly (65% determination coefficient) to a level comparable with the Gaussian model. A hybrid approach of combining the Gaussian model with the LUR gives superior quality of results (86% determination coefficient).

scholarly journals Air Pollution Dispersion Modelling Using Spatial Analyses

2018 ◽  
Author(s):  
Jan Bitta ◽  
Vladislav Svozilík ◽  
Irena Pavlíková ◽  
Petr Jančík
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Hybrid Approach ◽  
Gaussian Model ◽  
Dispersion Modelling ◽  
Spatial Analyses ◽  
Pollution Dispersion ◽  
Determination Coefficient ◽  
Air Pollution Dispersion ◽  
Pollution Dispersion Modelling

Abstract: The air pollution dispersion modelling via spatial analyses (Land Use Regression – LUR) is an alternative approach to the air quality assessment to the standard air pollution dispersion modelling techniques. Its advantages are mainly much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate other factors affecting pollutant’s concentration. The goal of the study was to model the PM10 particles dispersion modelling via spatial analyses v in Czech-Polish border area of Upper Silesian industrial agglomeration and compare results with results of the standard Gaussian dispersion model SYMOS’97. Results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover and were included into the LUR model, the LUR model results were significantly improved (65% determination coefficient) to the level comparable with Gaussian model. The hybrid approach combining the Gaussian model with the LUR gives superior quality of results (65% determination coefficient).

scholarly journals A field study to estimate the impact of noise barriers on mitigation of near road air pollution

2021 ◽  
Author(s):  
Ranga Rajan Thiruvenkatachari ◽  
Yifan Ding ◽  
David Pankratz ◽  
Akula Venkatram
Keyword(s):  
Air Pollution ◽  
Field Study ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Dispersion Models ◽  
Noise Barriers ◽  
Data Set ◽  
Wind Speeds ◽  
Sonic Anemometers ◽  
The Impact

AbstractAir pollution associated with vehicle emissions from roadways has been linked to a variety of adverse health effects. Wind tunnel and tracer studies show that noise barriers mitigate the impact of this pollution up to distances of 30 times the barrier height. Data from these studies have been used to formulate dispersion models that account for this mitigating effect. Before these models can be incorporated into Federal and State regulations, it is necessary to demonstrate their applicability under real-world conditions. This paper describes a comprehensive field study conducted in Riverside, CA, in 2019 to collect the data required to evaluate the performance of these models. Eight vehicles fitted with SF6 tracer release systems were driven in a loop on a 2-km stretch of Interstate 215 that had a 5-m tall noise barrier on the downwind side. The tracer, SF6, was sampled at over 40 locations at distances ranging from 5 to 200 m from the barrier. Meteorological data were measured with several 3-D sonic anemometers located upwind and downwind of the highway. The data set, corresponding to 10 h collected over 4 days, consists of information on emissions, tracer concentrations, and micrometeorological variables that can be used to evaluate barrier effects in dispersion models. An analysis of the data using a dispersion model indicates that current models are likely to overestimate concentrations, or underestimate the mitigation from barriers, at low wind speeds. We suggest an approach to correct this problem.

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