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First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019-2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g-1) and summer (1.17 mg g-1) and lower in autumn (55 mg g-1) and winter (1.08 mg g-1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.

Predicting air pollution in cities has become an important tool for preventing its negative impacts. Therefore, citizens should be aware of air quality level, especially for individuals suffering from diseases caused by air pollutants. Collective efforts from researchers, environmental institutions, governments, industrial companies, and policy makers are shaping the future of the Air Quality Index (AQI) to effectively address severe air pollution in urban areas. Many air quality prediction models have been introduced in the literature; modern advances in deep learning techniques are promising more precise prediction results and data integration. The aim of this paper is to review methods, contributions, findings, limitations, and gaps in predicting air quality index and PM2.5 concentrations using a hybrid deep learning approach. A literature review led researchers to propose a Hybrid Deep Learning model with Particle Swarm Optimization (HDLP) which combines CNN, LSTM, and PSO. Algorithm, first Discrete Wavelet Transform (DWT) is used to solve the air pollution signal, then it is fed to CNN-LSTM neural network for PSO optimization to get the final prediction result. Optimized parameters input models are trained on the original data. In addition to the beneficial assessment cycle, to outperform current models. [ABSTRACT FROM AUTHOR]

In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality. We employ a sophisticated deep learning structure to develop an advanced system for ambient air quality prediction. We utilize three publicly available databases and real-world data to obtain accurate air quality measurements. These four datasets undergo a data cleaning to yield a consolidated, cleaned dataset. Subsequently, the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA)—a dual optimization method combining the Eurasian Oystercatcher Optimizer (EOO) and Pathfinder Algorithm (PFA)—is applied. This method aids in selecting weighted features, optimizing weights, and choosing the most relevant attributes for optimal results. These optimal features are then incorporated into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for the ambient Air Quality Prediction (AQP) process. The variables within MDS-ATCN are further refined using the proposed FEO-PFA to enhance predictive accuracy. An empirical analysis is performed to compare the efficacy of our proposed model with traditional methods, underscoring the superior effectiveness of our approach. The average cost function is reduced to 5.5%, the MAE to 28%, and the RMSE to 14% by the suggested method, according to the performance research conducted with regard to all datasets. [ABSTRACT FROM AUTHOR]

With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposes a surrogate sample entropy-based method to assess air quality index. • Air quality of most polluted state capital in the world, Delhi, is investigated. • Temporal data of the pollutants are subjected to time series analysis. • Unveils the dependence of PM2.5 concentration on humidity and rain. • Phase portrait's complexity and sample entropy increase with pollutants level. Pollution and its impacts on human health have become a crisis in regions with poor air quality index (AQI), which is an indicator of concentrations of pollutants, prompting the United Nations (UN) to set sustainable development goals (SDG). The present study proposes a surrogate sample entropy-based method in tune with UN's SDG, to assess AQI from the time series of any of the pollutants. New Delhi is one of the world's most polluted state capital, with a higher level of particulate matter (PM). The temporal data of the pollutants in New Delhi, recorded in the one-hour interval during the years 2016 and 2017, are subjected to time series analysis. The data collected from the Central Pollution Control Board of India are analyzed with special reference to PM and compared with the World Air Quality Report 2021 and University of Washington data. The dependence of PM2.5 concentration on humidity and rain is also studied. The study reveals the increase in complexity with the concentration of pollutants through the phase portrait. The sample entropy analysis of the nonlinear time series of the pollutants exhibits a linear relation with AQI suggesting the possibility of using sample entropy as a surrogate measure of AQI. [ABSTRACT FROM AUTHOR]

Air quality forecasting is a critical environmental challenge with significant implications for public health and urban planning. Conventional machine learning models, although quite effective, require data collection, which can be hampered by issues relating to privacy and data security. Federated Learning (FL) overcomes these limitations by enabling model training across decentralized data sources without compromising data privacy. This study describes a federated learning approach to predict the Air Quality Index (AQI) based on data from several Internet of Things (IoT) sensors located in different urban locations. The proposed approach trains a model using data from different sensors while preserving the privacy of each data source. The model uses local computational resources at the sensor level during the initial data processing and training, sharing only the model updates to the central location. The results show that the performance of the proposed FL model is comparable to a centralized model and ensures better data privacy with reduced data transmission requirements. This study opens new doors to real-time, scalable, and efficient air quality monitoring systems. The proposed method is quite significant for smart city initiatives and environmental monitoring, as it provides a solid framework for using IoT technology while preserving privacy. [ABSTRACT FROM AUTHOR]

The paper focuses on the measurement of PM and other meteorological parameters in a small region of central Europe—eastern Slovakia and northeastern Hungary. Due to the increasing availability of sensors measuring not only PM, but also temperature, humidity, pressure, VOC, NOx, and CO2, new possibilities arise in terms of comparing (mainly in terms of correlation) PM and the other measured parameters, thus generating a large amount of data for evaluation. The correlations found are typical for inland conditions, thus able to map other regions of the world. The presented measurements can also be used to predict the evolution of PM with alerts for people with respiratory diseases, or in virtual reality using a digital twin of a humanoid robot. [ABSTRACT FROM AUTHOR]

Lockdowns were implemented in nearly all countries in the world in order to reduce the spread of COVID-19. The majority of the production activities like industries, transportation, and construction were restricted completely. This unprecedented stagnation of resident's consumption and industrial production has efficiently reduced air pollution emissions, providing typical and natural test sites to estimate the effects of human activity controlling on air pollution control and reduction. Air pollutants impose higher risks on the health of human beings and also damage the ecosystem. Previous research has used machine learning (ML) and statistical modeling to categorize and predict air pollution. This study developed a binary spring search optimization with hybrid deep learning (BSSOHDL) for air pollution prediction and an air quality index (AQI) classification process during the pandemic. At the initial stage, the BSSO-HDL model pre-processes the actual air quality data and makes it compatible for further processing. In the presented BSSO-HDL model, an HDL-based air quality prediction and AQI classification model was applied in which the HDL was derived by the use of a convolutional neural network with an extreme learning machine (CNN-ELM) algorithm. To optimally modify the hyperparameter values of the BSSO-HDL model, the BSSO algorithm-based hyperparameter tuning procedure gets executed. The experimental outcome demonstrates the promising prediction classification performance of the BSSO-HDL model. This model, developed on the Python platform, was evaluated using the coefficient of determination R², the mean absolute error (MAE), and the root mean squared error (RMSE) error measures. With an R² of 0.922, RMSE of 15.422, and MAE of 10.029, the suggested BSSO-HDL technique outperforms established models such as XGBoost, support vector machines (SVM), random forest (RF), and the ensemble model (EM). This demonstrates its ability in providing precise and reliable AQI predictions. [ABSTRACT FROM AUTHOR]

An optimal solution for monitoring air pollution, the Internet of Things (IoT)-enabled system delivers real-time data and insights on the air quality within a specific location. Air pollution poses a substantial risk to human health worldwide, with pollutants like nitrogen dioxide, particulate matter, ozone, and sulfur dioxide contributing to a range of cardiovascular and respiratory ailments. Monitoring air pollution levels is critical to understand the effect on public health and the environment. Air Pollution Monitoring includes the systematic analysis and measurement of pollutant concentration in the air, through a network of monitoring stations equipped with instruments and sensors. This station provides real-time data on air quality, allowing authorities to evaluate issue warnings, and pollution levels, and implement strategies to alleviate its negative impact. Machine learning (ML) approaches are becoming more integrated into air pollution monitoring systems for enhancing efficiency and accuracy. By analyzing vast quantities of information gathered from satellite imagery, monitoring stations, and other sources, ML approaches could detect patterns, forecast pollution levels, and pinpoint sources of pollution. This study introduces Air Pollution Monitoring and Prediction using African Vulture Optimization Algorithm with Machine Learning (APMP-AVOAML) model in IoT environment. The drive of the APMP-AVOAML methodology is to recognize and classify the air quality levels in the IoT environment. In the APMP-AVOAML technique, a four stage process is encompassed. Firstly, min-max normalization is applied for scaling the input data. Secondly, a harmony search algorithm (HSA) based feature selection process is executed. Thirdly, the extreme gradient boosting (XGBoost) model is utilized for air pollution prediction. Finally, AVOA based parameter selection process is exploited for the XGBoost model. To illustrate the performance of the APMP-AVOAML algorithm, a brief experimental study is made. The resultant outcomes inferred that the APMP-AVOAML methodology has resulted in effectual outcome. [ABSTRACT FROM AUTHOR]

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Abstract In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality. We employ a sophisticated deep learning structure to develop an advanced system for ambient air quality prediction. We utilize three publicly available databases and real-world data to obtain accurate air quality measurements. These four datasets undergo a data cleaning to yield a consolidated, cleaned dataset. Subsequently, the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA)—a dual optimization method combining the Eurasian Oystercatcher Optimizer (EOO) and Pathfinder Algorithm (PFA)—is applied. This method aids in selecting weighted features, optimizing weights, and choosing the most relevant attributes for optimal results. These optimal features are then incorporated into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for the ambient Air Quality Prediction (AQP) process. The variables within MDS-ATCN are further refined using the proposed FEO-PFA to enhance predictive accuracy. An empirical analysis is performed to compare the efficacy of our proposed model with traditional methods, underscoring the superior effectiveness of our approach. The average cost function is reduced to 5.5%, the MAE to 28%, and the RMSE to 14% by the suggested method, according to the performance research conducted with regard to all datasets.

Abstract With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi’an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi’an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values ​​and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757.

This article describes the forecasting system urbanAQF, which incorporates several developments to deal with the complexities of air pollution in cities, including the adaptation of high-resolution numerical weather prediction data to the urban canopy, the coupling with regional forecast data, and an interactive web service for public dissemination of urban air quality information. The system applies a unique bias correction algorithm that adjusts boundary conditions and traffic emissions to observations of the previous days. An evaluation of the air quality forecasts during 2021 for Hamburg, Germany, against a comprehensive dataset of the administrative monitoring network and meteorological data, demonstrated the system’s capability to describe space and time variations of NO2 and PM10. At traffic sites, the high number of missed alerts in relation to exceedance of the daily mean limit for NO2 indicates the need to improve the simulation of traffic emissions. The forecast of PM2.5 alerts was affected by the time lag of the automatic correction, leading to a low number of correct alerts. The overall performance for O3 was very good, despite frequent false alarms connected to the prediction of unstable atmospheric conditions. The urbanAQF system empowers policymakers to implement effective measures for improving air quality in cities.

Abstract Air pollution poses a major threat to both the environment and public health. The air quality index (AQI), aggregate AQI, new health risk–based air quality index (NHAQI), and NHAQI-WHO were employed to quantitatively evaluate the characterization of air pollution and the associated health risk in Gansu Province before (P-I) and after (P-II) COVID-19 pandemic. The results indicated that AQI system undervalued the comprehensive health risk impact of the six criteria pollutants compared with the other three indices. The stringent lockdown measures contributed to a considerable reduction in SO2, CO, PM2.5, NO2 and PM10; these concentrations were 43.4%, 34.6%, 21.4%, 17.4%, and 14.2% lower in P-II than P-I, respectively. But the concentration of O3 had no obvious improvement. The higher sandstorm frequency in P-II led to no significant decrease in the ERtotal and even resulted in an increase in the average ERtotal in cities located in northwestern Gansu from 0.78% in P-I to 1.0% in P-II. The cumulative distribution of NHAQI-based population-weighted exposure revealed that 24% of the total population was still exposed to light pollution in spring during P-II, while the air quality in other three seasons had significant improvements and all people were under healthy air quality level.

Abstract Industrial developments and consumption of massive amount of fossil fuels, vehicle pollution, and other calamities upsurges the AQI (Air Quality Index) of major cities in a drastic manner. Owing to these factors, it is important to take proactive measures for reducing the air pollution in order to avoid life- threatening consequence. Therefore, prediction of air quality is significant for improving the health of living beings as highly polluted regions have a higher concentration of pollutants mixed in the air, affecting the respiratory system and reducing the lifetime. To control pollution, AQI is used as a measure for estimating the pollutant content in the air. Even though many existing techniques have predicted AQI, enhancement is required in prediction algorithms with minimized loss. To address the challenges in traditional algorithms, the proposed smart cities-based AQI prediction intends to utilize the proposed regression algorithm in the dataset, namely Air- Quality-Data, which collected harmful pollutants on an hourly and daily basis from multiple cities in India between 2015 to 2020. To achieve prediction efficiency with reduced loss, pre-processing of input data is being performed using Deep GAN (Generative Adversarial Network). It performs the imputation of data in place of missing values to improve accurate prediction. Additionally, feature scaling normalizes independent real-data features to a fixed scale. With the processed data, regression is done through modified Stacked Attention GRU with KL divergence, which predicts Ernakulam, Chennai and Ahmedabad cities with higher, medium, and low levels of AQI in India. The performance of the proposed regression algorithm is measured using metrics such as MAE (Mean Absolute Error), MSE (Mean Square Error), R2 (Coefficient of determination), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) and better MAE, MSE, R2, MAPE and RMSE obtained by the model is 0.1013, 0.0134, 0.9479, 0.1152 and 0.1156. Internal assessment and comparative analysis performed with existing regression algorithms exhibit lower loss values obtained from the present research, which determines the efficacy of the proposed model.

Abstract Air pollution poses a significant threat to the health of the environment and human well-being. The air quality index (AQI) is an important measure of air pollution that describes the degree of air pollution and its impact on health. Therefore, accurate and reliable prediction of the AQI is critical but challenging due to the non-linearity and stochastic nature of air particles. This research aims to propose an AQI prediction hybrid deep learning model based on the Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) and XGBoost modelling techniques. Daily air quality data were collected from the official Seoul Air registry for the period 2021 to 2022. The data were first preprocessed through the ARIMA model to capture and fit the linear part of the data and followed by a hybrid deep learning architecture developed in the pretraining–finetuning framework for the non-linear part of the data. This hybrid model first used convolution to extract the deep features of the original air quality data, and then used the QPSO to optimize the hyperparameter for LSTM network for mining the long-terms time series features, and the XGBoost model was adopted to fine-tune the final AQI prediction model. The robustness and reliability of the resulting model were assessed and compared with other widely used models and across meteorological stations. Our proposed model achieves up to 31.13% reduction in MSE, 19.03% reduction in MAE and 2% improvement in R-squared compared to the best appropriate conventional model, indicating a much stronger magnitude of relationships between predicted and actual values. The overall results show that the attentive hybrid deep Quantum inspired Particle Swarm Optimization model is more feasible and efficient in predicting air quality index at both city-wide and station-specific levels.

The Air Quality Index is a numerical tool used to quantify air pollution levels and classify pollution severity. It plays a vital role in ensuring healthcare system stability by understanding air pollution's dynamic behavior and shifts in pollution intensity. To analyze the probabilistic transition between pollution severity levels, a Markov Chain model was utilized. This study examined six air pollution states (Clean, Healthy, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous) across 12 stations in Tehran's northern, southern, eastern, western, and central regions from 2018 to 2022. Results revealed that the western and southern areas exhibited the highest pollution levels, with over 44% and 34% of instances corresponding to unhealthy indices, respectively. In contrast, northern Tehran consistently maintained cleaner air quality. Probability transition matrices highlighted the greatest stability continuity in healthy air quality across all regions. Transitioning between clean/healthy air to very unhealthy/hazardous air was minimal, with hazardous air quality almost absent in most stations, except for the west and south (25% stability). The probability of continued unhealthy air quality in these areas reached 60%, indicating heightened pollution. The findings of transition probability matrices indicated that the western and southern regions had the highest likelihood of sustained pollution, while the northern region consistently maintained cleaner air. The probability of continuous clean air in the west was below 30%, while transitioning from very unhealthy/hazardous air to unhealthy air reached 50%. Conversely, the northern Tehran region exhibited over 40% stability for unhealthy air quality and over 50% for clean and healthy air.

Air pollution poses a major threat to both the environment and public health. The air quality index (AQI), aggregate AQI, new health risk–based air quality index (NHAQI), and NHAQI-WHO were employed to quantitatively evaluate the characterization of air pollution and the associated health risk in Gansu Province before (P-I) and after (P-II) COVID-19 pandemic. The results indicated that AQI system undervalued the comprehensive health risk impact of the six criteria pollutants compared with the other three indices. The stringent lockdown measures contributed to a considerable reduction in SO2, CO, PM2.5, NO2 and PM10; these concentrations were 43.4%, 34.6%, 21.4%, 17.4%, and 14.2% lower in P-II than P-I, respectively. But the concentration of O3 had no obvious improvement. The higher sandstorm frequency in P-II led to no significant decrease in the ERtotal and even resulted in an increase in the average ERtotal in cities located in northwestern Gansu from 0.78% in P-I to 1.0% in P-II. The cumulative distribution of NHAQI-based population-weighted exposure revealed that 24% of the total population was still exposed to light pollution in spring during P-II, while the air quality in other three seasons had significant improvements and all people were under healthy air quality level. [ABSTRACT FROM AUTHOR]

The present investigation proposes a Bayesian Regularized Artificial Neural Network (BRANN) based multi input single output (MISO) soft sensor. It predicts the value of air quality index (AQI) by receiving measured inlet concentration of different air pollutants such as particulate matter-2.5 (PM2.5), particulate matter-10 (PM10), SO2, NO2, O3, and CO. The performance of BRANN is also evaluated comparing with other two soft sensors namely, scaled conjugate gradient trained artificial neural network (SCGANN) and Levenberg-Marquardt artificial neural network (LMANN). The Bayesian Regularization (trainbr), trainscg, and Levenberg-Marquardt (LM) feed-forward backpropagation algorithms are used to train BRANN, SCGANN and LMANN, respectively. Hence, for their training and validation, two sets of 24 hours average of real time data released by the Central Pollution Control Board (CPCB) of India for Talcher coalfield and its surrounding area in Odisha from Jan-2019 to Dec-2020 are used. The training performances of all three soft sensors are reasonable (average R2 values of 0.975452, 0.995533 and 0.998718 for SCGANN, LMANN and BRANN, respectively). Obviously, the LMANN exhibits its superiority over SCGANN (corresponding average R2 values of 0.811955 and 0.760921). Here, the BRANN overperforms (average R2 values of 0.891167) other two soft sensors in predicting the AQI. [ABSTRACT FROM AUTHOR]

The significant air pollution issue in Nigeria has sparked severe widespread fear. This research is a part of the effort to eliminate or reduce this threat. The research aimed to examine the distributions of Air quality index (AQI), Particulate matter with less than 2.5 µm diameter (PM2.5), Relative humidity (RH), Wind speed, and Temperature (temp) in nineteen (19) Nigerian states, including the Federal Capital Territory (FCT). To accomplish this, satellite data obtained from IQAir's air quality monitoring platform for nineteen states and the Federal Capital Territory of Abuja between the 7th and 17th of October 2021 were used and statistically analyzed. The inverse distance weighting (IDW) interpolation method was applied to observe the spatial distribution of the data. The average AQI was 72.11, with minimum and maximum values of 26 and 17, respectively. The mean of PM2.5 was 28.71±13.7 µg/m³ . The following meteorological average values were recorded: RH (62.43%), wind speed (6.96 m/s), and temperature (28.71oC). The AQI was between good and unhealthy, the PM2.5 was more than 17% (annual) and 52% (24 h), which is higher than World Health Organisation (WHO) limits, and the correlations between AQI, PM2.5 and meteorological parameters were weak. According to the data, PM2.5 was not distributed uniformly across the regions but varied spatially and temporally. It is advised that people check their local daily air pollution forecasts, avoid exercising outside, avoid working out in crowded areas, and use less energy at home to prevent an increase in AQI. It is recommended to monitor and maintain the air quality and minimize harmful anthropogenic activities to mitigate the threat. [ABSTRACT FROM AUTHOR]

Objective : Acute pharyngitis is common worldwide. Meteorological changes contribute to respiratory infections. This study aims to explore the correlation between acute pharyngitis and meteorological parameters. Methods : We collected the monthly acute pharyngitis cases in our hospital and the meteorological data, including temperature, relative humidity, and air quality index (AQI) of Shanghai, China, from January, 2015, to December, 2017. Pearson correlation and multivariable regression analysis were used to explore the association between meteorological parameters and the monthly hospital visits. Results : A total of 8287 patients were included in this study, and the monthly number of hospital visits was 230.2 ± 39.0. The best air quality index was 46.4 and the worst was 113.2. Temperature ranged from 5.2°C to 32.0°C, and relative humidity ranged from 59.4% to 83.1%. The monthly hospital visits of acute pharyngitis were negatively correlated with temperature (r = −.558, 95% CI −.746, −.274) and relative humidity (r = −.480, 95% CI −.695, −.137). A decrease of 1°C could cause an increase in hospital visits by 1.9. No significant correlation was found were found between acute pharyngitis cases and AQI (P =.051, 95% CI −.005,.590). Multivariable linear regression analysis showed the temperature was the independent risk factor of acute pharyngitis (coefficient = −1.906, P =.022). Conclusion : Low temperature might cause an increased incidence of acute pharyngitis. [ABSTRACT FROM AUTHOR]

Air pollutants may aggravate atopic dermatitis (AD). However, the association between Air Quality Index (AQI) and incidence of AD remains unknown. To investigate association between AQI and incidence of AD, using the nationwide cohort in the Taiwan National Health Insurance Research Database (NHIRD). We included 21,278,938 participants from the NHIRD not diagnosed with AD before 2008. Long-term average AQI value, obtained from the Taiwan Air Quality Monitoring System Network, before AD diagnosis was calculated and linked for each participant. 199,205 incident cases of AD were identified from 2008 to 2018. Participants were classified into 4 quantiles (Q) by AQI value. With the lowest quantile, Q1, as reference, the AD risk increased significantly in the Q2 group (adjusted hazard ratio [aHR]: 1.29, 95% confidence interval [CI]: 1.04-1.65), Q3 group (aHR: 4.71, 95% CI: 3.78-6.04), and was highest in the Q4 group (aHR: 13.20, 95% CI: 10.86-16.60). As AQI treated as a continuous variable, an increase of 1 unit of AQI value added 7% of AD risk (aHR, 1.07, 95% CI: 1.07-1.08). The NHIRD lacks detailed information on individual subjects. The results demonstrated a significant positive association between AQI and incidence of AD with a clear dose-response relationship. [ABSTRACT FROM AUTHOR]

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment. [ABSTRACT FROM AUTHOR]

Everyone wishes to lead a healthy life, but the tremendous increase in air pollution affects our lives. The current tools can give updates on the Air Quality Index (AQI), but we can get further insights into air pollution by applying knowledge discovery and analytics techniques. Our research proposes a novel and integrated framework to predict AQI using pollutant concentration data and meteorological data. The framework comprises four modules for insights into air pollution. The first module (AQIfp) computes air quality by forecasting pollutant concentrations of particulate matter and harmful gases. The second module (AQIp) predicts air quality using pollutant data and historical air quality index data. The third module (AQIm) predicts AQI using meteorological features (temperature, surface pressure, cloud cover, humidity, wind speed, wind direction, and precipitation) and extra features (month, year, time, day of month, day of week). The fourth module (AQIc) computes AQI by combining the results of AQIp and AQIm. Various methods are used for forecasting pollutants and predicting AQI, viz artificial neural networks (ANN), random forest regression, XGradientboost, long-short term memory, vector autoregression, auto-regressive integrated moving average (ARIMA), decision tree regression, support vector regression, multiple linear regression, and K-nearest neighbour. The results show that different methods are best suited for various pollutants. However, ARIMA and ANN successfully predict almost all the pollutants. The results are promising, with Mean Absolute Error to predict AQI being 7.09 only when combining AQIp and AQIm. AQI can be predicted best with both pollutant and meteorological data. Since the modules are independent and if one data is missing, any module can be utilized to obtain an approximation of the AQI with minimal error. The framework will assist in providing decisions at the individual and administration levels to mitigate air pollution. [ABSTRACT FROM AUTHOR]

Industrial developments and consumption of massive amount of fossil fuels, vehicle pollution, and other calamities upsurges the AQI (Air Quality Index) of major cities in a drastic manner. Owing to these factors, it is important to take proactive measures for reducing the air pollution in order to avoid life- threatening consequence. Therefore, prediction of air quality is significant for improving the health of living beings as highly polluted regions have a higher concentration of pollutants mixed in the air, affecting the respiratory system and reducing the lifetime. To control pollution, AQI is used as a measure for estimating the pollutant content in the air. Even though many existing techniques have predicted AQI, enhancement is required in prediction algorithms with minimized loss. To address the challenges in traditional algorithms, the proposed smart cities-based AQI prediction intends to utilize the proposed regression algorithm in the dataset, namely Air- Quality-Data, which collected harmful pollutants on an hourly and daily basis from multiple cities in India between 2015 to 2020. To achieve prediction efficiency with reduced loss, pre-processing of input data is being performed using Deep GAN (Generative Adversarial Network). It performs the imputation of data in place of missing values to improve accurate prediction. Additionally, feature scaling normalizes independent real-data features to a fixed scale. With the processed data, regression is done through modified Stacked Attention GRU with KL divergence, which predicts Ernakulam, Chennai and Ahmedabad cities with higher, medium, and low levels of AQI in India. The performance of the proposed regression algorithm is measured using metrics such as MAE (Mean Absolute Error), MSE (Mean Square Error), R2 (Coefficient of determination), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) and better MAE, MSE, R2, MAPE and RMSE obtained by the model is 0.1013, 0.0134, 0.9479, 0.1152 and 0.1156. Internal assessment and comparative analysis performed with existing regression algorithms exhibit lower loss values obtained from the present research, which determines the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Air pollution poses a significant threat to the health of the environment and human well-being. The air quality index (AQI) is an important measure of air pollution that describes the degree of air pollution and its impact on health. Therefore, accurate and reliable prediction of the AQI is critical but challenging due to the non-linearity and stochastic nature of air particles. This research aims to propose an AQI prediction hybrid deep learning model based on the Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) and XGBoost modelling techniques. Daily air quality data were collected from the official Seoul Air registry for the period 2021 to 2022. The data were first preprocessed through the ARIMA model to capture and fit the linear part of the data and followed by a hybrid deep learning architecture developed in the pretraining–finetuning framework for the non-linear part of the data. This hybrid model first used convolution to extract the deep features of the original air quality data, and then used the QPSO to optimize the hyperparameter for LSTM network for mining the long-terms time series features, and the XGBoost model was adopted to fine-tune the final AQI prediction model. The robustness and reliability of the resulting model were assessed and compared with other widely used models and across meteorological stations. Our proposed model achieves up to 31.13% reduction in MSE, 19.03% reduction in MAE and 2% improvement in R-squared compared to the best appropriate conventional model, indicating a much stronger magnitude of relationships between predicted and actual values. The overall results show that the attentive hybrid deep Quantum inspired Particle Swarm Optimization model is more feasible and efficient in predicting air quality index at both city-wide and station-specific levels. [ABSTRACT FROM AUTHOR]

The pollution index of any urban area is indicated by its air quality. It also shows a fine balance is maintained between the needs of the populace and the industrial ecosystem. To mitigate such pollution in real-time, smart cities have a significant role to play. It's common knowledge that air pollution in a city severely affects the health of its dependents. More alarmingly, human health damage and disease burden are caused by phenomena like acid rain, and global warming. More precisely, lung ailments, CPOD, heart problems and skin cancer are caused by polluted air in congested urban places. Amongst the worst air pollutants, CO, C6H6, SO2, NO2, O3, RSPM/PM10, and PM2.5 cause maximum havoc. The climatic variables like atmospheric wind velocity, direction, relative humidity, and temperature control air contaminants in the air. Lately, numerous techniques have been applied by researchers and environmentalists to determine the Air Quality Index over a place. However, not a single technique has found acceptance from all quarters as being effective in every situation or scenario. Here, the main aspect relates to achieving authentic prediction in AQI levels by applying Machine Learning algorithms so worst situations can be averted by timely action. To enhance the performance of Machine Learning methods study adopted imputation and feature selection methods. When feature selection is applied, the experimental outcomes indicate a more accurate prediction over other techniques, showing promise for the application of the model in smart cities by syncing data from different monitoring stations. [ABSTRACT FROM AUTHOR]

Assessing long-term air quality trends helps evaluate the effectiveness of adopted air pollution control policies. A decade of SAFAR observations revealed that the trend of particulate matter (PM2.5 and PM10) in Delhi shows a reduction of 2.98 ± 0.53 µg/m³/y (4.91 ± 1.01 µg/m³/y) or overall 29% (23.7%) reduction between 2011 and 2021 while vehicles almost doubled but with the implementation of cleaner technologies and stricter industrial regulation. Seasonal negative trends of pre-monsoon (March-April-May; -3.43 ± 1.02 µg/m³/y) and postmonsoon (October-November; -4.51 ± 1.59 µg/m³/y) are relatively higher. The role of trends in dust storms, fire counts and annual rainy days are also discussed. The contribution of meteorology to the trend is estimated using WRF-Chem simulation of PM2.5 for October when maximum stubble burning occurs and gets transported to Delhi. The model is run with the meteorological initial conditions of 2018, 2015, and 2011 while keeping the emissions of 2018 with identical model configuration and found that meteorology contributed 9.8% in October, while the observed decline in PM2.5 is 35% (best fit) and 25% (value). The study identifies the governmental control measures at various levels and green initiatives as the significant contributors to air quality improvement during 2011-2021. [ABSTRACT FROM AUTHOR]

The primary objective of this investigation is to establish a clear correlation between air quality and the prevalence of respiratory conditions, this is done by employing deep learning (DL) methodologies. RNN was compared against alternative methods such as GNN, CNN, feed-forward technique (DL), k-nearest neighbours, linear regression, decision tree, and neural network. Performance evaluations were conducted employing an Octuple crossvalidation approach, with the root mean square error (RMSE) employed to perform comparative analysis. RMSE serves as the primary metric for evaluating regression models, additional criteria for model comparison include computational efficiency, scalability, assessing model performance with larger datasets; and generalization to new data. While RMSE's simplicity and sensitivity to large errors are strengths, limitations include sensitivity to outliers and overlooking distributional differences. The data was collected on two major features, the first being the Air quality dataset which contained various gases present in the atmosphere that affect the air quality, and the second being the hospital patient dataset which indicates the number of people suffering from respiratory diseases due to the air quality. The study acknowledges the superior performance of the Recurrent Neural Network (RNN) model but suggests the need for new learning methods handling small datasets and explores efficient, scalable approaches like distributed and federated learning. [ABSTRACT FROM AUTHOR]

Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images. [ABSTRACT FROM AUTHOR]

This paper aims to explore the relationship between the Air Quality Index (AQI), COVID-19 incidence rates, and population density within Malaysia's ten most populous cities from January 2018 to December 2021. Data were sourced from the Department of Statistics Malaysia, the World Air Quality Index Project, and Our World in Statistics. The methodology integrated population-based city classification and AQI assessment, cluster analysis through SPSS, and Generalized Additive Mixed Model (GAMM) analysis using R Studio despite encountering a data gap in AQI for five months in 2019. Cities were organized into three clusters based on their AQI: Cluster One included Ipoh, Penang, Kuala Lumpur, and Melaka, Cluster Two comprised Kuantan, Seremban, Johor Bahru, and Kota Bharu, Cluster Three featured Kota Kinabalu and Kuching. GAMM analysis revealed prediction accuracies for AQI variations of 58%, 60%, and 41% for the respective clusters, indicating a notable impact of population density on air quality. AQI variations remained unaffected by COVID-19, with a forecasted improvement in air quality across all clusters. The paper presents novel insights into the negligible impact of COVID-19 on AQI variations and underscores the predictive power of population dynamics on urban air quality, offering valuable perspectives for environmental and urban planning. [ABSTRACT FROM AUTHOR]

Air quality forecasting is a critical environmental challenge with significant implications for public health and urban planning. Conventional machine learning models, although quite effective, require data collection, which can be hampered by issues relating to privacy and data security. Federated Learning (FL) overcomes these limitations by enabling model training across decentralized data sources without compromising data privacy. This study describes a federated learning approach to predict the Air Quality Index (AQI) based on data from several Internet of Things (IoT) sensors located in different urban locations. The proposed approach trains a model using data from different sensors while preserving the privacy of each data source. The model uses local computational resources at the sensor level during the initial data processing and training, sharing only the model updates to the central location. The results show that the performance of the proposed FL model is comparable to a centralized model and ensures better data privacy with reduced data transmission requirements. This study opens new doors to real-time, scalable, and efficient air quality monitoring systems. The proposed method is quite significant for smart city initiatives and environmental monitoring, as it provides a solid framework for using IoT technology while preserving privacy.

This study analyses environmental sustainability indicators (ESIs) and explores their governance challenges in developing countries (Bangladesh and Thailand) and advances possible remedies in light of the practices of a developed country (Japan). A comparative analysis of countries' performance based on the ESIs could help identify useful practices from countries with high ESI to improve the poor ESI countries. While it is broadly understood that renewable energy and effective governance support environmental sustainability, our findings extend this knowledge by detailing how these factors interact specifically within the contexts of developed and developing nations. The analysis delineates the complex relationship between GDP growth, fossil fuel reliance, and sustainability efforts, offering a detailed examination of the variance in ESI performance across these countries. Beyond established notions, this study empirically validates the relationships between environmental sustainability (ES) and its influencing factors, providing a country-specific analysis that emphasizes the differential impact of renewable energy adoption, governance quality, and economic policies on environmental sustainability in Japan, Bangladesh, and Thailand. The results also revealed that Bangladesh's performance in terms of majority ESIs ranges from bad to worse, while Japan exhibits good performance in all its ESI indicators except for emissions. Thailand's ESI performance indicates its vulnerability to climate disasters and slow growth of renewable energy. The ESI measures of Thailand have shown its susceptibility to climate-related calamities and a slowdown in the rate of renewable energy implementation. A noticeable discrepancy in the execution of regulatory frameworks was noted between developing countries, such as Bangladesh, and industrialized ones, such as Japan. The outstanding results of Japan's ESI may be credited to the successful practices of its citizens and their strong devotion to the rule of law.

BackgroundAir quality health index (AQHI) is derived from exposure-response coefficients calculated from air pollution and morbidity/mortality time series, which helps to understand the overall short-term health impacts of air pollution. ObjectiveTo study the effects of common air pollutants on respiratory diseases in Urumqi and to develop an AQHI for the risk of respiratory diseases in the city. MethodsThe daily outpatient volume data of respiratory diseases from The First Affiliated Hospital of Xinjiang Medical University, meteorological data (daily mean temperature and daily mean relative humidity), and air pollutants [fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon dioxide (CO), and ozone (O3)] in Urumqi City, Xinjiang, China were collected from January 1, 2017 to December 31, 2021. A distributed lag nonlinear model based on quasi-Poisson distribution was constructed by time-stratified case crossover design. Adopting zero concentration of air pollutants as reference, the exposure-response coefficient (β value) was used to quantify the impact of included air pollutants on the risk of seeking medical treatment for respiratory diseases, and the AQHI was established. The association of between AQHI and the incidence of respiratory diseases and between air quality index (AQI) and the incidence of respiratory diseases was compared to evaluate the prediction effect of AQHI. ResultsEach 10 µg·m−3 increase in PM10, SO2, NO2, and O3 concentrations presented the highest excess risk of seeking outpatient services at 3 d cumulative lag (Lag03) and 2d cumulative lag (Lag02), with increased risks of morbidity of 0.687% (95%CI: 0.101%, 1.276%), 17.609% (95%CI: 3.253%, 33.961%), 13.344% (95%CI: 8.619%, 18.275%), and 4.921% (95%CI: 1.401%, 8.502%), respectively. There was no statistically significant PM2.5 or CO lag effect. An AQHI was constructed based on a model containing PM10, SO2, NO2, and O3, and the results showed that the excess risk of respiratory disease consultation for the whole population, different genders, ages, or seasons for each inter-quartile range increase in the AQHI was higher than the corresponding value of AQI. ConclusionPM10, SO2, NO2, and O3 impact the number of outpatient visits for respiratory diseases in Urumqi, and the constructed AQHI for the risk of respiratory diseases in Urumqi outperforms the AQI in predicting the effect of air pollution on respiratory health.

Abstract Background Dengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the occurrence of dengue fever in Taiwan have been proposed. However, to the best of our knowledge, no study has investigated the relationship between air quality indices (AQIs) and dengue fever in Taiwan. Results This study aimed to develop a dengue fever prediction model in which meteorological factors, a vector index, and AQIs were incorporated into different machine learning algorithms. A total of 805 meteorological records from 2013 to 2015 were collected from government open-source data after preprocessing. In addition to well-known dengue-related factors, we investigated the effects of novel variables, including particulate matter with an aerodynamic diameter 30 °C. The AQIs were not as important as temperature, but one was selected in the process of filtering the variables and showed a certain influence on the final results. Conclusions Our study is the first to demonstrate that AQI negatively affects dengue fever occurrence in Taiwan. The proposed prediction model can be used as an early warning system for public health to prevent dengue fever outbreaks.