Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network.

With the quick development of society and industry, air quality has become a grim and global environmental concern. Predicting and rating air quality for many cities remains a significant challenge. Consequently, machine learning algorithms have garnered considerable attention for their potential to address these issues effectively. In this paper, firstly, based on daily air quality data from July 1, 2022 to June 30, 2023 in Lanzhou city of China, five machine learning models, including Bayes Model Averaging (BMA), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are developed to predict the Air Quality Index (AQI) via six major air pollutants (PM2.5, PM10, SO2, NO2, O3 and CO). Secondly, we integrate Bootstrap algorithm into the optimal model, leading to the proposal of the LSTM-Bootstrap algorithm for deriving the standard errors and confidence intervals of the predicted AQI. Thirdly, a cumulative logit model is employed to evaluate and forecast AQI rating. The analysis results indicate that AQI rating is significantly affected by PM10, CO and O3. Additionally, to validate the efficacy of the suggested methods, a similar analysis is conducted on air quality data from Chengdu city for the same period. The findings provide valuable insights for future environmental policies and air quality management strategies.

The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality prediction helps us to undertake practical action plans for controlling air pollution. The Air Quality Index (AQI) reflects the degree of concentration of pollutants in a locality. The average AQI was calculated for the various cities in China to understand the annual trends. Furthermore, the air quality index has been predicted for ten major cities across China using five different deep learning techniques, namely, Recurrent Neural Network (RNN), Bidirectional Gated Recurrent unit (Bi-GRU), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network BiLSTM (CNN-BiLSTM), and Convolutional BiLSTM (Conv1D-BiLSTM). The performance of these models has been compared with a machine learning model, eXtreme Gradient Boosting (XGBoost) to discover the most efficient deep learning model. The results suggest that the machine learning model, XGBoost, outperforms the deep learning models. While Conv1D-BiLSTM and CNN-BiLSTM perform well among the deep learning models in the estimation of the air quality index (AQI), RNN and Bi-GRU are the least performing ones. Thus, both XGBoost and neural network models are capable of capturing the non-linearity present in the dataset with reliable accuracy.

Clean air is essential for maintaining a healthy lifestyle, yet poor air quality remains a major contributor to numerous respiratory ailments.Air pollutants such as PM2.5, NOx, COx, and SOx significantly impact human health, emphasizing the need for accurate forecasting models.However, predicting air quality is challenging due to the dynamic interplay of various factors, including meteorological conditions, vehicular emissions, and industrial discharges.This study proposes a novel hybrid forecasting model, Hyb_LSTM+KCNN, which combines Long Short-Term Memory (LSTM) networks and Kernel Convolutional Neural Networks (KCNN) for time-series prediction of the Air Quality Index (AQI).The LSTM component captures temporal dependencies in air quality data, enabling effective modeling of sequential patterns over time.Meanwhile, the KCNN module enhances feature extraction by leveraging convolutional kernels to identify local data patterns.By integrating these complementary strengths, the hybrid architecture provides a more robust and accurate AQI forecasting model.The proposed model was evaluated on comprehensive datasets, incorporating various pollutant levels and meteorological parameters.Experimental results demonstrated that the Hyb_LSTM+KCNN model consistently outperforms traditional forecasting techniques, achieving higher prediction accuracy and generalization capabilities.The model effectively captures correlations between pollutants and environmental factors, leading to precise AQI forecasts even under dynamic and complex conditions.This study presents a promising solution for air quality prediction, with practical implications for environmental monitoring and public health management.Future research will explore further optimization of the model for real-time applications and investigate its performance when integrating additional data sources, such as satellite and hyperspectral imagery.

Air quality index forecast in Beijing based on CNN-LSTM multi-model

Since most of the existing air quality index (AQI) predicting models focused on prediction of the time series data of a single target monitoring station, they failed to consider the correlation and mutual influence among the air quality monitoring station sites and the spatio-temporal characteristics of air quality. And this will lead to a certain one-sidedness during air quality prediction of a particular site. Aimed at this problem, a short-term air quality prediction model based on K-nearest neighbor (KNN) and Long Short-Term Memory (LSTM) was proposed. The model firstly used KNN algorithm to select the time and space-related monitoring stations, then the air quality index sequences of these stations were constructed into data sets, followed by training and testing processes in the LSTM model, and finally the model was verified with real data. It is suggested that the prediction accuracy of the hybrid prediction model constructed in this paper is acceptable in terms of the space-time correlation, and it could be an alternative for further application of air quality prediction.

Urban air quality index forecasting using multivariate convolutional neural network based customized stacked long short-term memory model

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper.

/ A simple air quality index (AQI) was introduced for the Helsinki Metropolitan Area in 1993, in order to inform the public about the current air quality in an easily understood way. The pollutants included in the AQI are CO (1 and 8 h), NO2 (1 and 24 h), SO2 (1 and 24 h), O3 (1 h) and PM10 (24 h). The AQI is linked to the new air quality guidelines in Finland. The AQI is based on acute health effects, but long-term effects on nature and materials are also taken into consideration. Subindices are calculated hourly for all pollutants and for a given hour the highest subindex becomes the AQI. AQI values are currently calculated for the center of Helsinki and for typical suburban areas.KEY WORDS: Air pollution; Air quality index; Guidelines; Helsinki; Finland

Background: Emerging evidence suggests that air pollution is a significant but under recognized contributor to cardiovascular disease (CVD) mortality, even in middle age adults. This study evaluates the association between long term statewise air quality, as measured by median Air Quality Index (AQI) and cardiovascular mortality rates among adults aged 35–44 years across the United States over a 25-year period. Methods: We linked annual statewise median AQI data from EPA AQS (2000–2024) with CDC WONDER mortality records for non-infectious CVD in the 35–44 age group. States were ranked by mean AQI to define “Highest 10” and “Lowest 10” AQI cohorts. Mean annual cardiovascular crude death rates were compared between these groups using t-tests, and trends were visualized year-by-year. Correlation and linear regression analyses assessed the association between AQI and mortality across all state years as well. Results: States with the highest long-term AQI (poor air quality) had significantly higher mean cardiovascular mortality rates (mean 46.99 per 100,000) than states with the lowest AQI (good air quality) (mean 30.35 per 100,000; p = 0.01). Yearwise analysis showed a persistent gap in mortality rates with the highest AQI states consistently exhibiting greater CVD mortality across the study period. Linear regression confirmed a significant positive association between AQI and mortality (slope = 0.62, p < 0.001), although AQI alone explained only a modest portion of the variation (R 2 = 0.095). Conclusions: Higher long term air pollution exposure, as measured by AQI, is associated with increased cardiovascular mortality among U.S. adults aged 35–44 years. This relationship is robust at the extremes of air quality and persists over time. This highlights the need for public health policies to improve air quality as a means to reduce early onset CVD mortality. Further studies should explore mechanisms and policy interventions to mitigate this risk.

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting.

Air Qualities (AQ) are developed to be a serious environmental and health concern as a result of worldwide population growth. A predictive model for air quality is essential for the timely prevention and management of air pollution. This study proposes a novel Self Tuning Deep Regression Neural Network (STDRNN) for identifying air pollution in the environment using deep neural network. The Air Quality Index (AQI) dataset is employed as the primary source of input data. The methodology begins with a data pre processing phase where the input data finds the missing data and shows the duplicate AQ values using data imputation and remove outlier method. This is followed by feature construction where the each AQ label is converted into an integer AQ value and scaling the features to a similar range using feature encoding and feature scaling method. The AQI data are then given to the STDRNN model for training and testing, enabling accurate prediction. The performance of the proposed model is evaluated using standard metrics such as RMSE, MSE and R2 coefficient. Using the python software AQI dataset shows the comparative analysis with existing methods on similar datasets demonstrates the superiority of the proposed approach in terms of selection performance. This work highlights the potential of STDRNN in advancing the accuracy of the model as well as its promising potential applications.

Air pollution has posed a serious public health issue in China. In the study, we aimed to examine the burden of air pollution and its association with climate factors and total mortality. City-level daily air quality index (AQI) data in 161 cities of China in 2014, and meteorological factors, socioeconomic status and total morality were obtained from China environmental, meteor-ology and healthcare agencies. Linear regression, spatial autocorrelation analysis and panel fixed models were applied in data analysis. Among 161 cities, monthly average AQI was significantly different by seasons and regions. The highest average AQI was in winter, and the lowest in summer. A significant clustering distribution of AQI by cities was observed, with the highest AQI in north China (22 cities, mean = 117.36). Among the 161 cities, 5 cities (3%) had AQI > 150 (e.g., moderate polluted reference value), and 50 cities (31.1%) had AQI between 100 and 150 (slightly polluted value). Heat index, precipitation and sunshine hours were negatively and significantly, but air pressure was positively correlated with AQI. Cities with higher AQI concentrations had higher total mortality than those with lower AQI. This AQI-mortality association remained significant after adjustment for socioeconomic status. In conclusion, the study highlights the burden and seasonal, regional and areas variations in air pollution across the nation. Air pollution is estimated to account for more than 4% of the urban health inequality in total mortality in China.

Over the last three decades, air pollution has become a major environmental challenge in many of the fast growing cities in China, including Beijing. Given that any long-term exposure to high-levels of air pollution has devastating health consequences, accurately monitoring and reporting air pollution information to the public is critical for ensuring public health and safety and facilitating rigorous air pollution and health-related scientific research. Recent statistical research examining China’s air quality data has posed questions regarding data accuracy, especially data reported during the Blue Sky Day (BSD) period (2000 – 2012), though the accuracy of publicly available air quality data in China has improved gradually over the recent years (2013 – 2017). To the best of our understanding, no attempt has been made to re-estimate the air quality data during the BSD period. In this paper, we put forward a machine-learning model to re-estimate the official air quality data during the BSD period of 2008 – 2012, based on the PM2.5 data of the Beijing US Embassy, and the proxy data covering Aerosol Optical Depth (AOD) and meteorology. Results have shown that the average re-estimated daily air quality values are respectively 64% and 61% higher than the official values, for air quality index (AQI) and AQI equivalent PM2.5, during the BSD period of 2008 to 2012. Moreover, the re-estimated BSD air quality data exhibit reduced statistical discontinuity and irregularity, based on our validation tests. The results suggest that the proposed data re-estimation methodology has the potential to provide more justifiable historical air quality data for evidence-based environmental decision-making in China.

Air pollution in urban areas has significantly increased over the last few years due to industrialization and overpopulation. Therefore, accurate predictions are needed to minimize their impact. This paper presents a neural network-based approach for Air Quality Index (AQI) forecasting, applying two different models: a variable depth neural network (NN) called slideNN, inspired by the capabilities of a Recurrent Neural Network (RNN) and a Gated Recurrent Unit (GRU)-based model. Both models use past particulate matter measurements alongside local meteorological data as inputs. Then, this research explores the practical application of multi-depth and recurrent neural networks in air quality forecasting, providing a hands-on case study and model evaluation for the city of Ioannina, Greece, targeting the appliance of such models either on edge devices or as cloud services. SlideNN variable-depth architecture consists of multiple independent neural sub-model strands varying in size, enhancing feature extraction and predictive accuracy. At the same time, the GRU model is examined on its ability to capture temporal dependencies in the data. Finally, both models are combined to offer a cloud-based high-precision hybrid. Experimental results show that the GRU model consistently outperforms the variable-depth architecture regarding forecasting losses. In contrast, complex hybrid GRU-NN models outperform both, delivering additional localized information that can be exploited by established particle concentration map monitoring services.

The air quality is quite poor due to the high quantities of particulate matter in the surrounding environment. Incomplete coal combustion, automobile emissions, and particulate matter (PM2.5) emissions are all potential causes of air pollution that may have an impact on human health. Carbon Monoxide (CO) is another gas produced mostly by incomplete combustion of fuel. Vehicle engines are responsible for 80% of carbon monoxide emissions in urban areas. Smog is an inescapable component of city life, especially in big metropolitan areas. This paper introduces an IoT and deep learning-based architecture for air quality monitoring and forecasting. In this framework, the input data set is created by gathering air quality-related data from various sensors and IoT devices. The raw input data set is then reduced into features using the principal component analysis approach. This strategy is also useful for coping with noisy and missing data in the data collection. The classification model is created utilising convolution, neural network, recurrent neural network, and LSTM deep learning approaches. The classification model is trained and tested on a preprocessed data set. Performance of various models is evaluated using metrics such as R2, RMSE, and MSE. The air quality index is also anticipated in the conclusion. Major Findings: The finding shows that the CNN consistently outperforms the other models, achieving the lowest MSE and RMSE values and the highest R² scores. This implies that CNN is the most effective model for predicting air pollution levels.