Advection-diffusion model for the simulation of air pollution distribution from a point source emission

Ulfah, Syafika; Awalludin, Subhan Ajiz; Wahidin,

doi:10.1088/1742-6596/948/1/012067

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…Air Pollution Visualization an Observed Area Surrounded by Mountains Udomsak RAKWONGWAN et al http://wjst.wu.ac.th Many studies on air pollution modeling have employed FEM to solve the problems with a single generator [21][22][23][24] or multiple generators [9,25]. [26] visualized the distribution of the pollutants in a street area through obstacles, whereas the simulation on the regional scale was carried out using online-coupled Weather Research and Forecasting with Chemistry (WRF-Chem) model developed collaboratively by several agencies which simultaneously simulates the emission, mixing, transport, trace gases and aerosols [27].…”

Section: Discussionmentioning

confidence: 99%

A Visualization of Air Pollution Distribution over an Observed Area Surrounded by Mountains: A Computational Approach

Rakwongwan

Tangmanussukum

Rujivan

2021

Walailak J Sci & Tech

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We study the propagation of pollutants emitted from a single generator such as a factory chimney located between 2 mountains as well as its effects on an observed area such as a village or agricultural land. The problem is formulated as a system of partial differential equations, composed of Navier-Stokes equations and an advection-diffusion equation, and is solved by the finite element method. We visualize the propagation of the pollutants for several variants of the problem depending on the heights of the mountains and investigate their negative effects on the observed area by computing an average concentration of the pollutants over the observed area. We found that the observed area between the two mountains experienced a long-term negative effect compared with those located on flat land. This is because the mountain on the side, where the wind is blowing, obstructs the wind resulting in air recirculation. In contrast, the other mountain bounces some pollutants back to the observed area, preventing them from leaving the domain. The higher the mountains, the longer the time the pollutants remain in the observed area. If the heights of the mountains encircling the observed area are not equal, the residual remains in the area longer if the taller mountain is on the windward side. HIGHLIGHTS Air Visualization of air pollution between two mountains Air pollution propagation modeling A system of partial differential equations for air pollution modeling with FEM GRAPHICAL ABSTRACT

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Section: Discussionmentioning

confidence: 99%

A Visualization of Air Pollution Distribution over an Observed Area Surrounded by Mountains: A Computational Approach

Rakwongwan

Tangmanussukum

Rujivan

2021

Walailak J Sci & Tech

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“…For a grid cell, the continuity equation simulates the temporal evolution of the concentration of air pollutants, taking into accounts the pollutant sources, transport, sinks as well as meteorological, topological and geographic effects. Using Reynolds decomposition (30,31), advection and diffusion processes were summarized based on direction velocities, the advection partial derivatives, diffusion coe cients and the eddy diffusion terms (Eq. 2).…”

Section: Graph Convolutions For Ne-scale Dynamics Of Air Pollutantsmentioning

confidence: 99%

“…1 uses horizontal advection and diffusion ( and directions); vertical convection ( direction) is not explicitly considered, but proxy variables are used to account for variation of vertical convection. Advection and diffusion can be simpli ed (30,31): 2 where ( ) is the Reynolds velocity of the air pollutant of the ( ) direction of the grid, (…”

Section: Modeling Architecturementioning

confidence: 99%

Improving air quality assessment using physics-inspired deep graph learning

Wang

Franklin³

et al. 2022

Preprint

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Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pollution, potentially leading to physically inconsistent or implausible results. Here, we report a hybrid multilevel graph neural network that encodes fluid physics to capture spatiotemporally dynamic characteristics of air pollutants. On a multi-air pollutant test in China, our method consistently improved extrapolation accuracy by an average of 11–22% compared to representative machine learning methods, and generated physically consistent spatiotemporal trends of air pollutants at regional and fine scales.

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“…These models can take into consideration the impacts of wind, turbulence, and other meteorological conditions that affect the movement of air particles. By inputting data on the location and timing of air pollution emissions, as well as meteorological data such as wind speed and direction, these models may simulate the movement of air pollutants and provide insights into their dispersion patterns [16][17][18][19][20][21][22][23][24][25][26][27][28]. One of the most extensively used lagrangian trajectory models is the HYSPLIT.…”

Section: Introductionmentioning

confidence: 99%

Short Path Wind-Field Distance-Based Lagrangian Trajectory Model for Enhancing Atmospheric Dispersion Prediction Accuracy

R’Bigui,

Cho

2023

IEEE Access

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Air pollution is a major global issue that not only threatens the safety of our planet but also poses risks to global health. Weather plays a crucial role in the rapid dispersion of air pollution. Various models have been used to predict air pollution; however, atmospheric pollution dispersion remains unpredictable, especially in relation to meteorological conditions. Our research scope focuses on developing an Air Diffusion Model using Future Wind and Pollutant sensing data to enhance prediction accuracy. In this paper, we present a new approach based on a mathematical model named the Short Path Distance based Lagrangian Trajectory Model (SPD-LTM). This model utilizes a trajectory approach and short path wind-field distance optimization to predict future air dispersion using pollutant sensing data. The framework developed in this work aims to model changes in Particulate Matter (PM2.5) and predict its concentration based on short path distance and time dependencies. The Lagrangian trajectory and concentration calculations are performed using the Hybrid Single-Particulate Lagrangian Integrated Trajectory algorithm (HYSPLIT). Then, we apply the short path distance algorithm using the Dijkstra algorithm. The obtained results demonstrate that the SPD-LTM outperforms the usual LTM and provides better accuracy to our predictive model.

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Advection-diffusion model for the simulation of air pollution distribution from a point source emission

Cited by 10 publications

References 6 publications

A Visualization of Air Pollution Distribution over an Observed Area Surrounded by Mountains: A Computational Approach

A Visualization of Air Pollution Distribution over an Observed Area Surrounded by Mountains: A Computational Approach

Improving air quality assessment using physics-inspired deep graph learning

Short Path Wind-Field Distance-Based Lagrangian Trajectory Model for Enhancing Atmospheric Dispersion Prediction Accuracy

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