Predicted Ozone Concentrations Research Articles

Clustering-based spatial transfer learning for short-term ozone forecasting

Ground-level ozone is a critical atmospheric pollutant, and high concentrations of ozone can damage human health, affect plant growth and cause ecological harm. Traditional chemical transport models and popular machine learning models have difficulty in predicting ozone concentrations, especially in times with high concentrations. We proposes a clustering-based spatial transfer learning Multilayer Perceptron (SPTL-MLP) to predict ozone concentration at the target observation station for the next three days. We use k-means clustering algorithm to find similar stations and train them together to get a base model for spatial transfer learning. For practical applications, a weighted loss function has been designed with an extra emphasis on reducing prediction errors of high ozone concentrations. Evaluation using historical data of stations in Germany shows that our SPTL-MLP model has a smaller error (reduced by 9.13%) and higher prediction accuracies of ozone exceedances (improved by 8.21% and 16.9%) compared to MLP (without spatial transfer). The results demonstrate the effectiveness of the SPTL-MLP in the short-term ozone forecast. It can be used for timely warning of ozone exceedances and help governments to detect air quality.

Research Trends, Hotspots and Frontiers of Ozone Pollution from 1996 to 2021: A Review Based on a Bibliometric Visualization Analysis

A total of 2932 publications in the field of ozone pollution were obtained from the Web of Science and briefly reviewed using a bibliometric analysis and WOS-based citation reports. CiteSpace 5.7.R3 (64 bit) was used to perform a visualization of knowledge mapping by keywords co-words, burst analysis, co-cited reference analysis, and cooperation network analysis. Research topics in this field have mainly focused on three aspects: risk assessment of ozone pollution for both humans and plants under short-term and long-term exposure; ozone pollution characterization and modeling of ozone transport on different scales; and elucidating the mechanism of ozone formation and source apportionment. By clustering the co-cited references using the data from 2016 to 2021, the frontiers are found to be: (1) VOCs’ precursors and ozone transformation mechanism; (2) modeling of source apportionment and source-oriented chemical transport considering meteorological influence to predict ozone concentration at different spatial and temporal scales; and (3) premature mortality and health burden with relation to ozone exposure. It should be mentioned that an emerging research hotspot is the utilization of artificial intelligence (AI) tools (e.g., machine learning, deep learning, etc.) to facilitate the modeling of big data at different scales.

Correction to: A hybrid CNN-Transformer model for ozone concentration prediction

Correction to: A hybrid CNN-Transformer model for ozone concentration prediction

Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm

Ozone is one of the most important air pollutants, with significant impacts on human health, regional air quality and ecosystems. In this study, we use geographic information and environmental information of the monitoring site of 5577 regions in the world from 2010 to 2014 as feature input to predict the long-term average ozone concentration of the site. A Bayesian optimization-based XGBoost-RFE feature selection model BO-XGBoost-RFE is proposed, and a variety of machine learning algorithms are used to predict ozone concentration based on the optimal feature subset. Since the selection of the underlying model hyperparameters is involved in the recursive feature selection process, different hyperparameter combinations will lead to differences in the feature subsets selected by the model, so that the feature subsets obtained by the model may not be optimal solutions. We combine the Bayesian optimization algorithm to adjust the parameters of recursive feature elimination based on XGBoost to obtain the optimal parameter combination and the optimal feature subset under the parameter combination. Experiments on long-term ozone concentration prediction on a global scale show that the prediction accuracy of the model after Bayesian optimized XGBoost-RFE feature selection is higher than that based on all features and on feature selection with Pearson correlation. Among the four prediction models, random forest obtained the highest prediction accuracy. The XGBoost prediction model achieved the greatest improvement in accuracy.

Prediction of Ozone Hourly Concentrations Based on Machine Learning Technology

To optimize the accuracy of ozone (O3) concentration prediction, this paper proposes a combined prediction model of O3 hourly concentration, FC-LsOA-KELM, which integrates multiple machine learning methods. The model has three parts. The first part is the feature construction (FC), which is based on correlation analysis and incorporates time-delay effect analysis to provide a valuable feature set. The second part is the kernel extreme learning machine (KELM), which can establish a complex mapping relationship between feature set and prediction object. The third part is the lioness optimization algorithm (LsOA), which is purposed to find the optimal parameter combination of KELM. Then, we use air pollution data from 11 cities on Fenwei Plain in China from 2 January 2015 to 30 December 2019 to test the validity of FC-LsOA-KELM and compare it with other prediction methods. The experimental results show that FC-LsOA-KELM can obtain better prediction results and has a better performance.

Forecasting ground-level ozone concentration levels using machine learning

Ground-level ozone (GLO) has been widely recognized as a critical air pollutant that has the potential to induce various adverse environmental and health effects. To eliminate its hazardous impacts, the development of an accurate and effective approach to forecast the upcoming pollution concentration levels is in urgent need. Recent studies show that machine learning algorithms have excellent abilities in ground-level ozone concentration forecasting, however many of their forecasting models do not consider the contributing effects on meteorological measurements and traffic situations which were also identified as potential influencing factors to ground-level ozone concentration by some previous research. In the meantime, most of the existing models target short-term forecasting with rough temporal resolutions, such as daily and weekly scales. This paper aims to propose a methodology that can provide long-term GLO concentration forecasting with a high temporal resolution. To achieve this, a frequent pattern mining approach is utilized to analyze the local intercorrelation between GLO concentration and contributing factors such as meteorological parameters and transportation situations. Then, a series of machine learning algorithms were identified to forecast the ground-level ozone concentration levels using traffic and meteorological measurements data. A case study was conducted in the Houston region with 10 years of historical measurements, each of the historical ground-level ozone concentration records is associated with a series of meteorology and traffic situation parameters. Data from 2010 to 2019 was used to select and train the machine learning models, and data from 2020 was used to perform the final validation and evaluation. Results show that the extreme gradient boost (XGBoost) machine learning algorithm provides the most accurate prediction of the hourly ground-level ozone concentration on a yearly scale, which shows the annually forecasting ability and the robustness of the model built. The proposed approach could be applied to other similar regions and other critical air pollutants that are also influenced by transportation and meteorological factors.

A hybrid CNN-Transformer model for ozone concentration prediction

A hybrid CNN-Transformer model for ozone concentration prediction

Analysing the Pollutants Dispersion in Erbil City-Kurdistan with Support of Statistical Analysis.

Analysing the Pollutants Dispersion in Erbil City-Kurdistan with Support of Statistical Analysis.

Using Harris hawk optimization towards support vector regression to ozone prediction.

As an area experiencing air pollution, especially ozone concentrations that often exceed the threshold or are unhealthy, JABODETABEK (Jakarta, Bogor, Depok, Tangerang, and Bekasi) seeks to prevent and control pollution as well as restore air quality. Therefore, this study aims to build a predictive model of ozone concentration using Harris hawks optimization-support vector regression (HHO-SVR) in 14 sub-districts in JABODETABEK. This goal is achieved by collecting data on ozone concentration as a response variable and meteorological factors as predictor variables from the website that provides the data. Other predictor variables such as time and significant lag detected with partial autocorrelation function of ozone concentration were also used. Then the variables will be selected using the recursive feature elimination-support vector regression (RFE-SVR) to obtain a significant predictor variable that affects the ozone concentration. After that, the prediction model will be built using the HHO-SVR method, support vector regression (SVR) whose parameter values are optimized with the Harris hawks optimization (HHO) algorithm. When the model has been formed, several evaluation metrics used to determine the best model include mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), Coefficient of Determination (R2), Variance Ratio (VR), and Diebold–Mariano test. The results of this study indicate that lag 1, lag 2, air temperature, humidity, and UV index are significant predictor variables of the RFE-SVR results for most sub-districts. In general, the HHO process takes longer than other metaheuristic algorithms. On average, 7 of the 14 sub-districts using the HHO-SVR model yielded the best predictions with MAE below 10, RMSE and MAPE below 20, R2 around 0.97, and VR around 0.98. Then, the results of the Diebold–Mariano test also show that the accuracy of the prediction results and the stability of the performance of the HHO-SVR model is better, especially for the Ciputat and South Bekasi sub-districts. This shows that the two sub-districts are very suitable to use HHO-SVR in predicting ozone concentrations.

Evaluating the spatiotemporal ozone characteristics with high-resolution predictions in mainland China, 2013–2019

Evaluating ozone levels at high resolutions and accuracy is crucial for understanding the spatiotemporal characteristics of ozone distribution and assessing ozone exposure levels in epidemiological studies. The national models with high spatiotemporal resolutions to predict ground ozone concentrations are limited in China so far. In this study, we aimed to develop a random forest model by combining ground ozone measurements from fixed stations, ozone simulations from the Community Multiscale Air Quality (CMAQ) modeling system, meteorological parameters, population density, road length, and elevation to predict ground maximum daily 8-h average (MDA8) ozone concentrations at a daily level and 1 km × 1 km spatial resolution. The model cross-validation R2 and root mean squared error (RMSE) were 0.80 and 20.93 μg/m3 at daily level in 2013–2019, respectively. CMAQ ozone simulations and near-surface temperature played vital roles in predicting ozone concentrations among all predictors. The population-weighted median concentrations of predicted MDA8 ozone were 89.34 μg/m3 in mainland China in 2013, and reached 100.96 μg/m3 in 2019. However, the long-term temporal variations among regions were heterogeneous. Central and Eastern China, as well as the Southeast Coastal Area, suffered higher ozone pollution and higher increased rates of ozone concentrations from 2013 to 2019. The seasonal pattern of ozone pollution varied spatially. The peak-season ozone pollution with the highest 6-month ozone concentrations occurred in different months among regions, with more than half domain in April–September. The predictions showed that not only the annual mean concentrations but also the percentages of grid-days with MDA8 ozone concentrations higher than 100/160 μg/m3 have been increasing in the past few years in China; meanwhile, majority areas in mainland China suffered peak-season ozone concentrations higher than the air quality guidelines launched by the World Health Organization in September 2021. The proposed model and ozone predictions with high spatiotemporal resolution and full coverage could provide health studies with flexible choices to evaluate ozone exposure levels at multiple spatiotemporal scales in the future.

A novel dual-scale ensemble learning paradigm with error correction for predicting daily ozone concentration based on multi-decomposition process and intelligent algorithm optimization, and its application in heavily polluted regions of China

Accurate prediction of daily ozone concentration is imperative to prevent and control photochemical pollution in China. A novel ensemble learning paradigm is proposed in this paper to perform high-precision ozone concentrations prediction, including model preprocess, optimized dual-scale prediction, and error correction. Firstly, the original ozone series is decomposed into two detailed sequences and two approximate sequences by wavelet packet decomposition (WPD). The detailed sequences are reconstructed into several new subsequences by complementary ensemble empirical mode decomposition (CEEMD) and fuzzy entropy (FE). Secondly, the extreme learning machine (ELM) and the support vector machine (SVM) are both optimized by the bald eagle search (BES) algorithm to carry out dual-scale parallel prediction. Finally, the error series is decomposed and predicted by the variational mode decomposition (VMD) and the optimized ELM to correct the previously predicted ozone and obtain the final ozone prediction. The actual ozone data from two typical cities in China are collected as the inputs of the model for empirical analysis under two scenarios, and one-day ahead prediction results show that: the RMSE values of the proposed model are 2.5319 and 3.2069 at Taiyuan and Shanghai sites when predicting low levels of ozone, respectively, while the RMSE values of the proposed model are 2.8451 and 3.8702 at two sites when predicting high levels of ozone, respectively; the proposed model outperforms the comparison models and four existing models under each scenario, demonstrating its robustness and serviceability. The results of this study can provide a valuable reference for analyzing the tendency of pollution.

Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction

Ozone (O3) is one of the common air pollutants. An increase in the ozone concentration can adversely affect public health and the environment such as vegetation and crops. Therefore, atmospheric air quality monitoring systems were found to monitor and predict ozone concentration. Due to complex formation of ozone influenced by precursors of ozone (O3) and meteorological conditions, there is a need to examine and evaluate various machine learning (ML) models for ozone concentration prediction. This study aims to utilize various ML models including Linear Regression (LR), Tree Regression (TR), Support Vector Regression (SVR), Ensemble Regression (ER), Gaussian Process Regression (GPR) and Artificial Neural Networks Models (ANN) to predict tropospheric (O3) using ozone concentration dataset. The dataset was created by observing hourly average data from air quality monitoring systems in 3 different stations including Putrajaya, Kelang, and KL in 3 sites in Peninsular Malaysia. The prediction models have been trained on this dataset and validated by optimizing their hyperparameters. Additionally, the performance of models was evaluated in terms of RMSE, MAE, R2, and training time. The results indicated that LR, SVR, GPR and ANN were able to give the highest R2 (83 % and 89 %) with specific hyperparameters in stations Kelang and KL, respectively. On the other hand, SVR and ER outweigh other models in terms of R2 (79 %) in Putrajaya station. Overall, regardless slightly performance differences, several developed models were able to learn patterns well and provide good prediction performance in terms of R2, RMSE and MAE. Ensemble regression models were found to balance between high prediction accuracy in terms of R2 and low training time and thus considered as a feasible solution for application of Ozone concentration prediction using the data in hourly scenario.

Multiple strategies for a novel hybrid forecasting algorithm of ozone based on data-driven models

Ground-level ozone is an air pollutant that has adverse impacts on human health and vegetation growth. The accurate prediction of ozone concentrations is essential for developing strategies for ozone mitigation. To obtain a better forecasting model to predict ozone, this study provides a detailed discussion of the application of three model optimization strategies (i.e., adding decomposition algorithms, adding data and adding factors) to benchmark models, including long short-term memory (LSTM) and support vector regression (SVR), to predict ozone concentrations in Shenzhen. The results showed that adding a decomposition strategy, particularly the wavelet decomposition (WD) algorithm, provided the greatest improvement to the prediction accuracy. Based on this, a novel hybrid forecasting model (WD-LSTMSVR) was further developed that first used the WD algorithm to convert the original data from one dimension to multiple dimensions. Subsequently, each layer of the data set was trained and forecast by the LSTM and SVR models, which involved parameters that were optimized by the autoregressive integrated moving average (ARIMA) partial algorithm and particle swarm optimization (PSO) algorithm. The hybrid forecasting model had the best prediction accuracy performance compared with the benchmark models and optimization models in this study. Our results indicate that the developed hybrid forecasting model is a good technique to provide accurate ozone concentration prediction results.

Improvement of summertime surface ozone prediction by assimilating Geostationary Operational Environmental Satellite cloud observations

Clouds play an important role in the Earth's climate system since they can affect various physical and chemical processes within the atmosphere. Misplacement of clouds is a major source of error in the numerical weather prediction (NWP) models, and it also impacts the accuracy of air quality simulations since the meteorology and air quality are directly coupled. In this study, a cloud assimilation technique was utilized to improve cloud placement within the Weather Research and Forecasting (WRF) model by assimilating Geostationary Operational Environmental Satellite (GOES)-derived cloud products. Meteorological outputs from the WRF model were then used as inputs for the Community Multiscale Air Quality (CMAQ) model. The impact of cloud assimilation on air quality was tested over the June–September 2016 period. The results indicated that, by modifying model clouds, cloud assimilation corrected surface solar radiation and photochemical reaction rates, altered light sensitive biogenic emissions, adjusted horizontal transport and vertical mixing, and finally improved the prediction of surface ozone concentration. Cloud assimilation improved daytime surface ozone prediction over most of the U.S. domain, with exceptions in California. On average, cloud assimilation improved the prediction of daytime peak ozone and reduced bias by 47% (∼1.5 ppb). The largest improvement was seen over the southeast U.S. region (∼2.6 ppb reduction in daytime peak ozone), where convective clouds are more frequent and transient and biogenic volatile organic compound (VOC) emissions are more intense than elsewhere.

해수 오존처리시 브로민 생성과 소독효과

Pathogenic and/or invasive organisms are problematic in ballast water, desalination plants and aquaculture systems for both ecological and economic reasons. In a desalination plant, the presence of microorganisms can lead to biofouling of the ultrafiltration and seawater reverse osmosis membrane equipment. Meanwhile, in the aquaculture industry, some pathogenic organisms cause serious fish diseases, which can result in mass die-offs. Various disinfection techniques such as ozonation, ultraviolet irradiation, electrolysis and chlorination, have been applied to remove pathogenic organisms from seawater environments. The purpose of this study was to develop a kinetic model for the prediction of residual ozone and bromine concentrations in seawater following ozonation, taking into consideration the ozone dose, water salinity, and pH. Ozone chemistry in saltwater is considerably different from that in fresh water because saltwater contains much higher concentrations of dissolved anions and cations (e.g., chloride, bromide, magnesium, sulfate) than fresh water. Salinity greatly affects the formation of residual ozone and bromine during saltwater ozonation. Unlike freshwater ozonation, there is no hydroxyl radical formation in seawater ozonation because ozone decomposition is considerably faster in water containing bromide. Therefore, seawater ozonation involves the initial unstable oxidant (ozone) and a secondary, more stable oxidant (bromine) for disinfection. This study also aimed to evaluate the performance of ozone and bromine disinfection using E. coli. The log inactivation of E. coli with ozone and bromine was 2.5 and 4 log, respectively. The log inactivation of E. coli with seawater ozonation was 5.8 log.

Development of a high-performance machine learning model to predict ground ozone pollution in typical cities of China

High ozone concentrations have adverse effects on human health and ecosystems. In recent years, the ambient ozone concentration in China has shown an upward trend, and high-quality prediction of ozone concentrations has become critical to support effective policymaking. In this study, a novel hybrid model combining wavelet decomposition (WD), a gated recurrent unit (GRU) neural network and a support vector regression (SVR) model was developed to predict the daily maximum 8 h ozone. We used the ground ozone observation data in six representative megacities across China from Jan. 1, 2015 to Jun. 15, 2020 for model training, and we used data from Jun. 15 to Dec. 31, 2020 for model testing. The results show that the developed model performs very well for megacities; against observations, the model obtains an average cross-validated R2 (coefficient of determination) ranging from 0.90 for Shanghai to 0.97 for Chengdu in the one-step predictions, thereby indicating that the model outperformed any single algorithm or other hybrid algorithms reported. The developed model can also capture high ozone pollution episodes with an average accuracy of 92% for the next five days in inland cities. This study will be useful for the environmental health community to prevent high ozone exposure more efficiently in megacities in China and shows great potential for accurate ozone prediction using machine learning approaches.

One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level

With the rapid spread of urbanization, competent authorities become increasingly anxious from air pollution risks and effect on citizens especially those with respiratory diseases. In this work, performances of six machine learning methods were analyzed for prediction of maximum ozone (O_3) concentration for the next-day. The models make the prediction using concentrations of six atmospheric components (PM2.5, PM10, Ozone (O3), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO)). The utilized machine learning methods are multilayer perception (MLP), Support Vector Regression (SVM), k-Nearest Neighbor (K-NN), Random Forests (RF), Gradient Boosting (GB), and Elastic Net (EN). After the predictions made by these models, the predicted values were further processed to be classified into one of the six air quality levels defined by United States Environmental Protection Agency. The prediction performances of the models as well as their corresponding classification results were analyzed. It was shown that MLP model gives the lowest RMSE of 2246 for prediction step while SVR achieved the highest accuracy score of 0.790.

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

Adjusting prediction of ozone concentration based on CMAQ model and machine learning methods in Sichuan-Chongqing region, China

With increasing ozone pollution and deeper understanding of its harm to humans and climate, it is important to accurately forecast ozone. In this study, training and testing data sets were constructed with hourly numerical models forecasts and monitoring station observation for the year 2018 for Sichuan-Chongqing region, China. Three machine learning methods including Lasso, random forest and long short-term memory recurrent neural network (LSTM-RNN) coupled with CMAQ model were trained to forecast the ozone concentrations. The Lasso regression and random forest were used to realize feature optimization in four sub-regions separately. Coupled model with Lasso-random forest coupled feather selection schemes showed the best performance among different models. The main conclusions of adjusting results showed that deviations of hourly ozone prediction by CMAQ alone forecasts can be significantly reduced after machine learning coupled model adjusting, and correlation coefficients can be remarkably improved. Adjusting effects varied with different sub-regions and seasons. In three basin sub-regions, adjusting with random forest had the best performance, while in the plateau sub-region, adjusting with LSTM-RNN was most satisfactory, where root mean squared error decrease rate was 80.2% and correlation coefficient reached 91%. Machine learning methods performed better in summer and autumn for the three basin sub-regions, while in the plateau sub-region, adjusting was more significant in summer compared to other seasons.

Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review

The prediction of tropospheric ozone concentrations is vital due to ozone’s passive impacts on atmosphere, people’s health, flora and fauna. However, ozone prediction is a complex process and the wide range of traditional models is incapable to obtain an accurate prediction. “Artificial intelligence”, “machine learning” and “ozone prediction model” search terms in the title, abstract or keywords are involved. Inclusion criteria include subject area (engineering, computer science), English language and being published from 2015. This criterion obtained 156 articles, which were categorized into 4 areas of interest based on the machine learning technique applied. Recently as a result of the rapid development in the technology and the increase in the number of measured data, artificial intelligence techniques have been intensively used in predicting ozone concentration as an alternative to the traditional models. Therefore, the main objective of this study is to investigate the most developed techniques that have been used in predicting ozone concentrations as well as theoretic approaches such as information set approaches, fuzzy set approach and probabilistic set approaches. It is clearly stated that the standalone algorithms such as decision tree (DT) and support vector machine (SVM) outperformed multilayer perceptron (MLP); however, the latter is massively implemented by many researchers in the prediction of ozone concentrations. This review paper investigated artificial intelligence techniques integrated with optimization approaches. It can be concluded that hybrid algorithms have significantly improved the prediction accuracy. However, the majority of the proposed hybrid models have limitations; thus, there is a need to develop better hybrid algorithm that is able to tackle all the drawbacks of the improved algorithms and capable to capture the ozone concentration changes with a high level of accuracy.