Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations

Ren, Xiang; Mi, Zhongyuan; Cai, Ting; Nolte, Christopher G.; Georgopoulos, Panos G.

doi:10.1021/acs.est.1c04076

Cited by 31 publications

(17 citation statements)

References 47 publications

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“…Applications of ML on large datasets (e.g., over 100,000 samples) have been evaluated in environmental health modeling [ 41 , 42 ]. For small/moderate sample size (e.g., tens or hundreds of samples), earlier studies found no evidence of superior performance of ML over traditional statistical models [ 21 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

Ren

Georgopoulos

2023

J Expo Sci Environ Epidemiol

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Background Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales. Objective To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches. Methods We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches. Results We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models. Significance The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

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Section: Discussionmentioning

confidence: 99%

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

Ren

Georgopoulos

2023

J Expo Sci Environ Epidemiol

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“…MLAir provides a workflow framework for machine-learning-based atmospheric forecasts with easily extensible modules for data preprocessing, training, hyperparameter optimisation and evaluation. MLAir uses the TensorFlow (Abadi et al, 2015), dask (Rocklin, 2015) and xarray (Hoyer and Hamman, 2017) libraries. For each of the mentioned preprocessing methods (see Sect.…”

Section: Mlair Frameworkmentioning

confidence: 99%

“…One option to further enhance the approach presented in this study would be the use of Lagrangian particle modelling to derive the area of influence and chemical history for observation sites (see, for example, Yu et al, 2020). Furthermore, Sun et al, 2022;Ren et al, 2022).…”

Section: Alternative Neural Network Approachesmentioning

confidence: 99%

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework

et al. 2022

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Abstract. Tropospheric ozone is a secondary air pollutant that is harmful to living beings and crops. Predicting ozone concentrations at specific locations is thus important to initiate protection measures, i.e. emission reductions or warnings to the population. Ozone levels at specific locations result from emission and sink processes, mixing and chemical transformation along an air parcel's trajectory. Current ozone forecasting systems generally rely on computationally expensive chemistry transport models (CTMs). However, recently several studies have demonstrated the potential of deep learning for this task. While a few of these studies were trained on gridded model data, most efforts focus on forecasting time series from individual measurement locations. In this study, we present a hybrid approach which is based on time-series forecasting (up to 4 d) but uses spatially aggregated meteorological and chemical data from upstream wind sectors to represent some aspects of the chemical history of air parcels arriving at the measurement location. To demonstrate the value of this additional information, we extracted pseudo-observation data for Germany from a CTM to avoid extra complications with irregularly spaced and missing data. However, our method can be extended so that it can be applied to observational time series. Using one upstream sector alone improves the forecasts by 10 % during all 4 d, while the use of three sectors improves the mean squared error (MSE) skill score by 14 % during the first 2 d of the prediction but depends on the upstream wind direction. Our method shows its best performance in the northern half of Germany for the first 2 prediction days. Based on the data's seasonality and simulation period, we shed some light on our models' open challenges with (i) spatial structures in terms of decreasing skill scores from the northern German plain to the mountainous south and (ii) concept drifts related to an unusually cold winter season. Here we expect that the inclusion of explainable artificial intelligence methods could reveal additional insights in future versions of our model.

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“…Compared with the traditional control-of-variables strategy, the machine learning (ML) method, which is becoming more and more popular, can consider more related features and wider data ranges. − For example, ML helped achieve better fitting results when encountered with a large number of features with weak or nonlinear relationships. , This indicated that ML was suitable for predicting the CO 2 sequestration of steel slag with various interactive factors.…”

Section: Introductionmentioning

confidence: 99%

Interpretation and Prediction of the CO₂ Sequestration of Steel Slag by Machine Learning

He,

Zhu,

Cang

et al. 2023

Environ. Sci. Technol.

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The utilization of steel slag for CO 2 sequestration is an effective way to reduce carbon emissions. The reactivity of steel slag in CO 2 sequestration depends mainly on material and process parameters. However, there are many puzzles in regard to practical applications due to the different evaluations of process parameters and the lack of investigation of material parameters. In this study, 318 samples were collected to investigate the interactive influence of 12 factors on the carbonation reactivity of steel slag by machine learning with SHapley Additive exPlanations (SHAP). Multilayer perceptron (MLP), random forest, and support vector regression models were built to predict the slurryphase CO 2 sequestration of steel slag. The MLP model performed well in terms of prediction ability and generalization with comprehensive interpretability. The SHAP results showed that the impact of the process parameters was greater than that of the material parameters. Interestingly, the iron ore phase of steel slag was revealed to have a positive effect on steel slag carbonation by SHAP analysis. Combined with previous literature, the carbonation mechanism of steel slag was proposed. Quantitative analysis based on SHAP indicated that steel slag had good carbonation reactivity when the mass fractions of "CaO + MgO", "SiO 2 + Al 2 O 3 ", "Fe 2 O 3 ", and "MnO" varied from 50−55%, 10−15%, 30−35%, and <5%, respectively.

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Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations

Cited by 31 publications

References 47 publications

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework

Interpretation and Prediction of the CO₂ Sequestration of Steel Slag by Machine Learning

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Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations

Cited by 31 publications

References 47 publications

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework

Interpretation and Prediction of the CO2 Sequestration of Steel Slag by Machine Learning

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Product

Resources

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Interpretation and Prediction of the CO₂ Sequestration of Steel Slag by Machine Learning