A Review of Air Quality Modeling

Karroum, Khaoula; Lin, Yijun; Chiang, Yao‐Yi; Maissa, Yann Ben; Haziti, Mohamed El; Sokolov, Anton; Delbarre, Hervé

doi:10.1007/s12647-020-00371-8

Cited by 23 publications

(15 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most dispersion models use computer programs to simulate the movement of air pollutants in the atmosphere and to estimate the pollutant concentrations in a geographic location. There are different types of models based on the nature of sources (such as point, line, area, and volume sources) [5]. "Line source models" are used to calculate and predict the concentration of pollutants that are continuously emitted from automobiles/trucks on highways.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Madiraju¹,

Kumar²

2021

Atmosphere

View full text Add to dashboard Cite

Transportation sources are a major contributor to air pollution in urban areas, and 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 (SLINE 1.0) based on the solution of the convective–diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The dispersion coefficients for stable and unstable atmospheric conditions are based on the near-field parameterization. Initial vertical dispersion coefficient due to the wake effect of mobile sources is incorporated based on a literature review. The model inputs include emission factor, wind speed, wind direction, turbulence parameters, and terrain features. The model is evaluated based on the Idaho Falls field study (2008). The performance of the model is evaluated using several statistical parameters. Results indicate that the model performs well against this dataset in predicting concentrations under both the stable and unstable atmospheric conditions. 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 using the ASTM (American Society for Testing and Materials) method. However, some recalibration of the model constants is needed using several field datasets to make sure that the model is acceptable for computing ground-level concentrations in engineering applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Madiraju¹,

Kumar²

2021

Atmosphere

View full text Add to dashboard Cite

show abstract

“…(1) We used the Root Mean Squared Error (RMSE) and R-Squared (R 2 ) scores to evaluate the model performance. (2) We quantified the relationship between the variation in the fine-scale PM 2.5 predictions and the distance to critical environmental features, e.g., comparing the average predictions near highways within 500m and 1,000m, and evaluated the results with findings in the literature.…”

Section: Discussionmentioning

confidence: 99%

“…Fine-scale spatiotemporal prediction is an important scientific problem applicable to diverse phenomena, such as air quality, ambient noise, traffic conditions, and meteorology. 1 One of the primary motivations for spatiotemporal prediction is that the observed data are only available at a few unevenly distributed measurement locations (e.g., ground-based sensors) [1,2]. As an example, there is only a handful of Federal monitoring sites and a few hundred low-cost sensors reporting air quality in Los Angeles, an area that covers nearly 5,000 square miles and 15 million people.…”

Section: Introductionmentioning

confidence: 99%

“…Expert-based air quality prediction methods, such as atmospheric dispersion models, utilize mathematical equations to simulate how pollutants travel in the physical environment. They require significant expert knowledge and computational resources (e.g., modeling chemical transformations in the atmosphere [2,6]). In contrast, data-driven methods aim to characterize spatiotemporal patterns in the available measurement data and leverage the patterns for prediction.…”

Section: Introductionmentioning

confidence: 99%

“…The main challenge is how to automatically discover meaningful spatial and temporal relationships between rich contextual data and sparse measurement data in an area. For example, land-use regression (LUR) models generate explainable air quality predictions by including expert-selected land-use predictors (e.g., traffic indi- 2 For example, fine particles with an aerodynamic diameter smaller than 2.5µm, i.e., PM 2.5 . arXiv:2112.05313v1 [cs.LG] 10 Dec 2021 cators, industrial facilities, and population density) [8], which can vary from one geographic region to another [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction

Lin

Chiang

Franklin

et al. 2020

2020 IEEE International Conference on Data Mining (ICDM)

Self Cite

View full text Add to dashboard Cite

Many scientific prediction problems have spatiotemporal data-and modeling-related challenges in handling complex variations in space and time using only sparse and unevenly distributed observations. This paper presents a novel deep learning architecture, Deep learning predictions for LocATion-dependent Time-sEries data (DeepLATTE), that explicitly incorporates theories of spatial statistics into neural networks to addresses these challenges. In addition to a feature selection module and a spatiotemporal learning module, DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends of the predictions in the learned spatiotemporal embedding space to be consistent with the observed data, overcoming the limitation of sparse and unevenly distributed observations. During the training process, both supervised and semi-supervised losses guide the updates of the entire network to: 1) prevent overfitting, 2) refine feature selection, 3) learn useful spatiotemporal representations, and 4) improve overall prediction. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction, in a well-studied, complex physical environment -Los Angeles. The experiment demonstrates that the proposed approach provides accurate fine-spatial-scale air quality predictions and reveals the critical environmental factors affecting the results.

show abstract

Geostatistical Methods and Framework for Pollution Modelling

Khan,

Jehangir

2023

Geospatial Analytics for Environmental Pollution Modeling

View full text Add to dashboard Cite

A Review of Air Quality Modeling

Cited by 23 publications

References 67 publications

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction

Geostatistical Methods and Framework for Pollution Modelling

Contact Info

Product

Resources

About