Design of an early alert system for PM2.5 through a stochastic method and machine learning models

Celis, Nathalia; Casallas, Alejandro; López-Barrera, Ellie Anne; Martinez, Hermes; Peña, Carlos; Arenas, Ricardo; Ferro, Camilo

doi:10.1016/j.envsci.2021.10.030

Cited by 15 publications

(16 citation statements)

References 49 publications

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“…Further sensitivity experiments are performed, by artificially increasing/decreasing the NO 2 by 5% and 10% of its mean (not shown); this indicates that the changes could be related to the NO 2 /NO ratio (hereafter NOratio), in which smaller values produce larger ozone inhibition (Seinfeld and Pandis 2016 ), so in the case of Bogotá when the NO is homogenized, NO 2 tend to produce a positive contribution to ozone formation (Betancourt-Odio et al 2021 ). It is possible that the addition of more pollutants (e.g., formaldehyde) to the X-matrix, the inclusion of more robust ML techniques (Celis et al 2022 ), the development of sensitivity experiments in physical models (Ballesteros-González et al 2020 ), or the combination of both techniques as in Sayeed et al ( 2021b ) could help to reduce the NO 2 uncertainties related to this experiment.…”

Section: Resultsmentioning

confidence: 99%

“…In the case that no background station has data from one variable, another type of urban station is selected to be part of the X-matrix. The missing values are imputed following Mogollon-Sotelo et al ( 2021 ), if the imputation is done with a neural network or a linear regression taking into account the four closest background stations, as made by Celis et al ( 2022 ), the results did not show considerable differences. Thus, we follow Mogollon-Sotelo et al ( 2021 ) as it needs fewer computational resources.…”

Section: Methodsmentioning

confidence: 99%

“…ML techniques have been used for air quality forecasting by many authors (e.g., Huang and Kao 2018 ; Sayeed et al 2021a ), and in Colombia, they produce high-precision forecasts (e.g., Casallas et al 2021b ; Celis et al 2022 ). This paper takes this idea further and is one of the first to use these techniques to quantify the contribution of the precursors, markers, and meteorological variables in the formation of ozone, an idea that is based on the surface fluxes’ decomposition approach of Tompkins and Semie ( 2021 ).…”

Section: Methodsmentioning

confidence: 99%

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Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Casallas

Castillo-Camacho

Sanchez

et al. 2023

Air Qual Atmos Health

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2020 presented the ideal conditions for studying the air quality response to several emission reductions due to the COVID-19 lockdowns. Numerous studies found that the tropospheric ozone increased even in lockdown conditions, but its reasons are not entirely understood. This research aims to better understand the ozone variations in Northern South America. Satellite and reanalysis data were used to analyze regional ozone variations. An analysis of two of the most polluted Colombian cities was performed by quantifying the changes of ozone and its precursors and by doing a machine learning decomposition to disentangle the contributions that precursors and meteorology made to form O 3 . The results indicated that regional ozone increased in most areas, especially where wildfires are present. Meteorology is associated with favorable conditions to promote wildfires in Colombia and Venezuela. Regarding the local analysis, the machine learning ensemble shows that the decreased titration process associated with the NO plummeting owing to mobility reduction is the main contributor to the O 3 increase (≈50%). These tools lead to conclude that (i) the increase in O 3 produced by the reduction of the titration process that would be associated with an improvement in mobile sources technology has to be considered in the new air quality policies, (ii) a boost in international cooperation is essential to control wildfires since an event that occurs in one country can affect others and (iii) a machine learning decomposition approach coupled with sensitivity experiments can help us explain and understand the physicochemical mechanism that drives ozone formation. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-023-01303-6.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Casallas

Castillo-Camacho

Sanchez

et al. 2023

Air Qual Atmos Health

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“…Bogotá has a large risk associated with PM2.5 on a yearly, daily and hourly time scale [51]. Different phenomena are related to these PM2.5 variations and their understanding helps to complete already published research.…”

Section: Discussionmentioning

confidence: 99%

Understanding high polluted events in a tropical megacity with air quality observations and ERA5-reanalysis data

Casallas

Córdoba²,

Sánchez-Cárdenas³

et al. 2022

Rev.Fac.Ing.Univ.Antioquia

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Already published studies found that 20% of the atmospheric pollution variations are linked to local meteorology. This relationship may be more important in places with dense populations such as Latin American cities since the topography and fast multiscale changes are part of the tropical climate. Even so, this possibility has not been addressed in previous studies. This research aims to characterize the relationship between tropical climate variables and PM2.5 levels during high pollution events. The relationship between wind field, Turbulent Kinetic Energy (TKE), radiation, temperature, relative humidity, boundary layer height, and atmospheric stability with PM2.5 concentration was investigated. Statistical correlations and the parcel method were used to analyze the relationship between vertical motions and PM2.5. Obtained results show that the stability, vertical velocity, and boundary layer height do not significantly affect pollution levels. We identified some signals that are strongly related to PM2.5 high concentrations: weaker than average horizontal wind speed and TKE throughout the day, easterly winds in the morning (associated with the transport of ashes from wildfires produced on the Eastern plains), combined with a higher than average radiation peak. These results lead to a better understanding of the PM2.5 variations, which can be applied for the improvement of air quality models and have the potential to be part of a novel policy to manage air quality risk.

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“…The model has a good effect on predicting PM 2.5 concentration. Celis [33] et al designed an air quality prevention and warning system for air pollution in Latin America. They compared three machine learning models (SVM, LSTM, and 1D-BDLM) and found that the 1D-BDLM model had the highest prediction accuracy and was able to effectively predict behavior, measurements, and air quality alerts.…”

Section: Introductionmentioning

confidence: 99%

A PM2.5 Concentration Prediction Model Based on CART–BLS

Wang

Chen

et al. 2022

Atmosphere

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With the development of urbanization, the hourly PM2.5 concentration in the air is constantly changing. In order to improve the accuracy of PM2.5 prediction, a prediction model based on the Classification and Regression Tree (CART) and Broad Learning System (BLS) was constructed. Firstly, the CART algorithm was used to segment the dataset in a hierarchical way to obtain a subset with similar characteristics. Secondly, the BLS model was trained by using the data of each subset, and the validation error of each model was minimized by adjusting the window number of the mapping layer in the BLS network. Finally, for each leaf in the tree, the global BLS model and the local BLS model on the path from the root node to the leaf node are compared, and the model with the smallest error is selected. The data collected in this paper come from the Chine Meteorological Historical Data website. We selected historical data from the Huaita monitoring station in Xuzhou city for experimental analysis, which included air pollutant content and meteorological data. Experimental results show that the prediction effect of the CART–BLS model is better than that of RF, V-SVR, and seasonal BLS models.

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Design of an early alert system for PM2.5 through a stochastic method and machine learning models

Cited by 15 publications

References 49 publications

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach

Understanding high polluted events in a tropical megacity with air quality observations and ERA5-reanalysis data

A PM2.5 Concentration Prediction Model Based on CART–BLS

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