Dispersion Modeling of Particulate Matter in Different Size Ranges Releasing from a Biosolids Applied Agricultural Field Using Computational Fluid Dynamics

Nimmatoori, Praneeth; Kumar, Ashok

doi:10.4236/aces.2021.112012

Cited by 2 publications

(1 citation statement)

References 22 publications

(17 reference statements)

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“…Considering pollution source information and meteorological data to simulate air quality is widely used in most studies [54,55]. This study utilized air pollutant concentration and meteorological data from 2017 to 2020 in six Chinese urban agglomerations to simulate air quality conditions [56]. To predict air quality, this study employed seven single models and ensemble models of machine learning methods and constructed a hybrid LSTM-SVR model to predict air quality in the six urban agglomerations.…”

Section: Discussionmentioning

confidence: 99%

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Zhang,

Duan,

Sun

et al. 2023

Atmosphere

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Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control.

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Section: Discussionmentioning

confidence: 99%

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Zhang,

Duan,

Sun

et al. 2023

Atmosphere

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Particulate Matter Dispersion Modeling in Agricultural Applications: Investigation of a Transient Open Source Solver

et al. 2021

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Agriculture is a major emitter of particulate matter (PM), which causes health problems and can act as a carrier of the pathogen material that spreads diseases. The aim of this study was to investigate an open-source solver that simulates the transport and dispersion of PM for typical agricultural applications. We investigated a coupled Eulerian–Lagrangian solver within the open source software package OpenFOAM. The continuous phase was solved using transient large eddy simulations, where four different subgrid-scale turbulence models and an inflow turbulence generator were tested. The discrete phase was simulated using two different Lagrangian solvers. For the validation case of a turbulent flow of a street canyon, the flowfield could be recaptured very well, with errors of around 5% for the non-equilibrium turbulence models (WALE and dynamicKeq) in the main regions. The inflow turbulence generator could create a stable and accurate boundary layer for the mean vertical velocity and vertical profile of the turbulent Reynolds stresses R11. The validation of the Lagrangian solver showed mixed results, with partly good agreements (simulation results within the measurement uncertainty), and partly high deviations of up to 80% for the concentration of particles. The higher deviations were attributed to an insufficient turbulence regime of the used validation case, which was an experimental chamber. For the simulation case of PM dispersion from manure application on a field, the solver could capture the influence of features such as size and density on the dispersion. The investigated solver is especially useful for further investigations into time-dependent processes in the near-source area of PM sources.

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Dispersion Modeling of Particulate Matter in Different Size Ranges Releasing from a Biosolids Applied Agricultural Field Using Computational Fluid Dynamics

Cited by 2 publications

References 22 publications

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Particulate Matter Dispersion Modeling in Agricultural Applications: Investigation of a Transient Open Source Solver

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