Application of Regularized Logistic Regression and Artificial Neural Network Model for Ozone Classification across El Paso County, Texas, United States

This paper focuses on ozone prediction in the atmosphere using a machine learning approach. We utilize air pollutant and meteorological variable datasets from the El Paso area to classify ozone levels as high or low. The LR and ANN algorithms are employed to train the datasets. The models demonstrate a remarkably high classification accuracy of 89.3% in predicting ozone levels on a given day. Evaluation metrics reveal that both the ANN and LR models exhibit accuracies of 89.3% and 88.4%, respectively. Additionally, the AUC values for both models are comparable, with the ANN achieving 95.4% and the LR obtaining 95.2%. The lower the cross-entropy loss (log loss), the higher the model’s accuracy or performance. Our ANN model yields a log loss of 3.74, while the LR model shows a log loss of 6.03. The prediction time for the ANN model is approximately 0.00 seconds, whereas the LR model takes 0.02 seconds. Our odds ratio analysis indicates that features such as “Solar radiation”, “Std. Dev. Wind Direction”, “outdoor temperature”, “dew point temperature”, and “PM10” contribute to high ozone levels in El Paso, Texas. Based on metrics such as accuracy, error rate, log loss, and prediction time, the ANN model proves to be faster and more suitable for ozone classification in the El Paso, Texas area.

GAT-EGRU:A Deep Learning Prediction Model for PM2.5 Coupled with Empirical Modal Decomposition Algorithm

With the rapid development of the economy and industry and the improvement of pollution monitoring,how to accurately predict PM2.5 has become an issue of concern to the government and society.In the field of PM2.5 pollution forecasting,a series of results have emerged so far.However,in the existing research field of PM2.5 prediction,most studies tend to predict short-term temporal series.Existing studies tend to ignore the temporal and spatial characteristics of PM2.5 transport,which leads to its poor performance in long-term prediction.In this paper,by optimizing previous PM2.5 deep learning prediction models,we propose a model GAT-EGRU.First,we add a spatial modular Graph Attention Network(GAT)and couple an Empirical Modal Decomposition algorithm(EMD),considering the temporal and spatial properties of PM2.5.Then,we use Gated Recurrent Unit(GRU)to filter spatio-temporal features for iterative rolling PM2.5 prediction.The experimental results show that the GAT-EGRU model has more advantages in predicting PM2.5 concentrations,especially for long time steps.This proves that the GAT-EGRU model outperforms other models for PM2.5 forecasting.After that,we verify the effectiveness of each module by distillation experiments.The experimental results show that each model module has an essential role in the final PM2.5 prediction results.The new model improves the ability to predict PM2.5 after a long time accurately and can be used as a practical tool for predicting PM2.5 concentrations.

Large inter annual variation in air quality during the annual festival ‘Diwali' in an Indian megacity

A network of air quality and weather monitoring stations was established under the System of Air Quality Forecasting and Research（SAFAR） project in Delhi. We report observations of ozone（O_3）, nitrogen oxides（NO_x）, carbon monoxide（CO） and particulate matter（PM_2.5and PM_（10）） before, during and after the Diwali in two consecutive years, i.e., November 2010 and October 2011. The Diwali days are characterised by large firework displays throughout India. The observations show that the background concentrations of particulate matter are between 5 and 10 times the permissible limits in Europe and the United States. During the Diwali-2010, the highest observed PM_（10） and PM_2.5mass concentration is as high as2070 μg/m^3 and 1620 μg/m3, respectively（24 hr mean）, which was about 20 and 27 times to National Ambient Air Quality Standards（NAAQS）. For Diwali-2011, the increase in PM_（10） and PM_2.5mass concentrations was much less with their peaks of 600 and of 390 μg/m^3 respectively, as compared to the background concentrations. Contrary to previous reports,firework display was not found to strongly influence the NO_x, and O_3 mixing ratios, with the increase within the observed variability in the background. CO mixing ratios showed an increase. We show that the large difference in 2010 and 2011 pollutant concentrations is controlled by weather parameters.