Air Quality Management Research Articles

Air Pollution Effects on Mental Health Relationships: Scoping Review on Historically Used Methodologies to Analyze Adult Populations

Air pollution’s effects on physical health, especially cardiovascular and respiratory, are well known. Exposure to air pollution may damage every organ and cell in the human body. New evidence is emerging showing that air pollution adversely affects human mental health. Current research suggests that high air pollution levels have long-term mental health effects, such as reduced mental capacity and increased cognitive decline, leading to increased stress, anxiety, and depression. Objectives: This scoping review aims to provide a comprehensive overview of the methods used in epidemiological literature to ascertain the existence of links between outdoor particulate matter (PM) and multiple adverse mental health (MH) effects (depression, anxiety, and/or stress). A better understanding of the practical research methodologies could lead to improved air quality (AQ) management and enhanced well-being strategies. Methods: This paper undertakes a scoping review. PubMed and EMBASE databases from 2010 to 2024 were searched for English-language human cohort observational studies stating methodologies used in analyzing the link between outdoor particulate matter (ultrafine (UFT) (<0.1 μm), fine (<2.5 μm), and course (<10 μm)) and mental health outcomes (depression, anxiety, and stress) in adults (>18 years), excluding vulnerable populations (i.e., elderly, children, and pregnant women). The study focuses on urban, suburban areas, and rural areas. Results: From an initial search of 3889 records, 29 studies met the inclusion criteria and were included in the review. These studies spanned various countries and employed robust quantitative methodologies to assess AQ and MH. All included studies investigated the impact of PM on mental health, with some (n = 19/65.52%) also examining nitrogen oxides (NOx), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Depression was the most frequently studied outcome (n = 10/34.48%), followed by anxiety and depression (n = 6/20.69%), and anxiety, stress, and depression, and stress (n = 4/13.79%, each). Depression, anxiety, and stress together were examined in a single study (n = 1/3.45%). Standardized questionnaires involving psychological scales such as Patient Health Questionnaire (PHQ) (n = 7/24.14%) and The Center for Epidemiological Studies-Depression (CES-D) (n = 3/10.34%) for depression and Generalized Anxiety Disorder Questionnaire (GAD) (n = 2/6.90%) for anxiety were commonly used MH tools. 27 out of 29 studies found a significant negative impact of air pollution on mental health, demonstrating a solid consensus in the literature. Two studies did not find a significant correlation. The results consistently indicated that higher levels of air pollution were associated with increased symptoms of depression, anxiety, and stress. Conclusion: Of the 3889 identified studies, 29 were suitable for inclusion in the scoping review per inclusion criteria. The results show the most preferred methods in assessing air quality and mental health in relevant studies, providing a detailed account of each method’s strengths and limitations used in studies. This scoping review was conducted to assist future research and relieve the decision-making process for researchers aiming to find a correlation between air quality and mental health. While the inclusion criteria were strict and thus resulted in few studies, the review found a gap in the literature concerning the general adult population, as most studies focused on vulnerable populations. Further exploration of the methodologies used to find the relationship between air quality and mental health is needed, as reporting on these outcomes was limited.

Optimal air quality management using novel dual Mamdani and neuro fuzzy inference system for real-time accurate prediction

Optimal air quality management using novel dual Mamdani and neuro fuzzy inference system for real-time accurate prediction

PMFORECAST: leveraging temporal LSTM todeliver in situairqualitypredictions.

The physical and chemical properties of atmospheric aerosol particles are crucial in influencing global climate and ecosystem processes. Given the numerous studies highlighting adverse health effects from exposure to aerosol particulates, particularly Particulate Matter (PM), effective air quality management strategies are under consideration (Annesi-Maesano et al. Eur Respir Soc 29(3):428-431. 2007). Herein, we introduce a predictive model-PMFORECAST-employing a self-adaptive long short-term memory (LSTM) architecture to predict PM values in the real atmosphere. Specifically, we explore adopting a LSTM model to better benefit from temporal dimensions. PMFORECAST is strategically designed with four key phases: preprocessing, temporal attention, prediction horizon, and LSTM layers. By leveraging LSTM's significant predictive ability in time-series data, the inclusion of temporal attention enhances the model's specificity. Temporal dynamics modeling entails generating insights over time, utilizing temporal attention to extract essential characteristics from historical air pollutant concentrations, with the flexibility to adjust the historical data according to the forecasting period. To assess PMFORECAST, we consider measurements collected from the QAMELEO network, a sparse network of air-quality micro-stations deployed in Dijon, France. The self-adaptive capabilities of PMFORECAST allow the model to be dynamically updated, evaluating its performance and continuously tuning hyper-parameters based on the latest data trends. Our empirical evaluation reports that PMFORECAST outperforms the state of the art, achieving notable accuracy in both short-term and long-term predictions. The PMFORECAST deployment at scale can serve as a valuable tool for proactive decision-making and targeted interventions to mitigate the health risks associated with air pollution.

Machine learning models for predicting interactions between air pollutants in Tehran Megacity, Iran

Air pollution has significant detrimental impacts on the environmental compartments and human health. The present study investigates the interactions between selected air pollutants, including CO, O3, NO2, SO2, PM10, and PM2.5, in Tehran megacity, the capital of Iran, using statistical modeling and simulating approaches. Data on selected air pollutants have been extracted from 16 sensors across Tehran during 2013–2022. Using the development of three machine learning models, including XGBoost (XGB), LightGBM (LGBM) and Random Forest (RF), significant relationships between the air pollutants were observed. The comparison of the models demonstrated that the RF model has the highest level of optimality in forecasting the concentrations of CO, O3, NO2, and SO2 (R2CO = 0.63, R2O3 = 0.6, R2NO2 = 0.54, R2SO2 = 0.55). The LGBM model provided higher optimality for NO2 and PM2.5 (R2NO2 = 0.56, R2PM2.5 = 0.88), while the XGB model exhibited higher accuracy for the PM10 (R2PM10 = 0.88). Regarding the results obtained from the statistical models, it can be inferred that the RF model has superior performance in the forecasting of gaseous pollutants. Moreover, the XGB and LGBM models exhibited comparable performance and therefore may be regarded as appropriate options for PM prediction. The findings from the simulations indicated that a rise in an individual pollutant likely leads to an increase in other pollutants. Consequently, implementing air quality management strategies for a specific pollutant meaningfully, directly influences other pollutants, highlighting the significance of considering the chemical aspect of air pollutants interaction and enforcing air pollution management rules.

Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)

Abstract. Severe airborne particulate matter (PM, including PM2.5 and PM10) pollution in India has caused widespread concern. Accurate PM concentrations are fundamental for scientific policymaking and health impact assessment, while surface observations in India are limited due to scarce sites and uneven distribution. In this work, a simple structured, efficient, and robust model based on the Light Gradient-Boosting Machine (LightGBM) was developed to fuse multisource data and estimate long-term (1980–2022) historical daily ground PM concentrations in India (LongPMInd). The LightGBM model shows good accuracy with out-of-sample, out-of-site, and out-of-year cross-validation (CV) test R2 values of 0.77, 0.70, and 0.66, respectively. Small performance gaps between PM2.5 training and testing (delta RMSE of 1.06, 3.83, and 7.74 µg m−3) indicate low overfitting risks. With great generalization ability, the openly accessible, long-term, and high-quality daily PM2.5 and PM10 products were then reconstructed (10 km, 1980–2022). This showed that India has experienced severe PM pollution in the Indo-Gangetic Plain (IGP), especially in winter. PM concentrations have significantly increased (p<0.05) in most regions since 2000 (0.34 µgm-3yr-1). The turning point occurred in 2018 when the Indian government launched the National Clean Air Programme, and PM2.5 concentrations declined in most regions (−0.78 µgm-3yr-1) during 2018–2022. Severe PM2.5 pollution caused continuous increased attributable premature mortalities, from 0.73 (95 % confidence interval (CI) [0.65, 0.80]) million in 2000 to 1.22 (95 % CI [1.03, 1.41]) million in 2019, particularly in the IGP, where attributable mortality increased from 0.36 million to 0.60 million. LongPMInd has the potential to support multiple applications of air quality management, public health initiatives, and efforts to address climate change. The daily and monthly PM2.5 and PM10 concentrations are publicly accessible at https://doi.org/10.5281/zenodo.10073944 (Wang et al., 2023a).

Quantitative assessment of urban sustainability perceptions in Lurin, Peru

In the current context, urban centres in Latin America are facing fundamental challenges in their endeavour for Sustainable Development. The focus of this study is the meticulous assessment of the perception of urban sustainability within the Lurín District of Peru. It introduces a system based on urban sustainability indicators, derived from social surveys and implements this system through linear regression models to discern their interrelations. The objective of the research is to quantify and evaluate essential elements linked to the management of natural resources, air and water quality, the advancement of sustainable mobility, education and the well-being of urban residents. By delineating these mathematical and statistical correlations amongst variables pertinent to urban sustainability, this study provides a robust framework for quantitative decision-making in the urban sphere. A methodology for the development of univariable and multivariable models has been demonstrated. Amongst the most important findings, it has been discovered that the variable Environmental Education System (SEA) is perceived as the least important and even negligible within the multivariable models. However, we believe this effect occurs because the impacts of education are perceived in the long term. This article contributes significantly to the academic discourse by providing a more nuanced understanding of the social perception of urban sustainability and its influence on policy formulation and decision-making processes in Latin America.

Pozyx technology with SDN for advanced indoor air quality monitoring and management

Pozyx technology with SDN for advanced indoor air quality monitoring and management

Air pollution prediction using blind source separation with Greylag Goose Optimization algorithm

Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies.

Driving sustainability: assessing KPI effectiveness in the Saudi chemical industry

This study explores the relationship between Key Performance Indicators (KPIs) and environmental performance improvement within the Saudi chemical industry. Against the backdrop of global sustainability imperatives and Saudi Arabia’s Vision 2030, which promotes sustainability for economic diversification, this research aims to assess the effectiveness of KPIs in driving environmental sustainability practices. The motivation for this study stems from the identified gaps in the systematic implementation and utilisation of KPIs and the lack of awareness regarding certain aspects of environmental impact management within the industry in the Kingdom. The methodology involved a structured survey administered to a diverse range of chemical manufacturing companies, followed by rigorous data analysis using descriptive evaluation, Analysis of Variance (ANOVA), reliability analysis, and t-tests. The results revealed insights into pollution areas, KPI utilisation, methods for pollution assessment, alignment with strategic goals, and governance regulations. Descriptive analysis highlighted air quality management as a priority, with notable attention to water and land pollution, while quantitative analysis confirmed the significance of KPIs in driving environmental performance improvement in the area. However, it also unveiled the absence of a systematic approach to implementing and utilising KPIs effectively, coupled with a lack of awareness regarding certain aspects of environmental impact management, consequently leading to uncertainty. Overall, this study contributes to advancing sustainability efforts within the Saudi chemical sector, providing actionable insights for industry stakeholders and policymakers.

Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement

Human health risks from particles with a diameter of less than 2.5 µm (PM2.5) highlight the role of urban trees as bio-filters in air pollution control. However, whether the size and composition of particles captured by various tree species differ or not remain unclear. This study investigates how leaf attributes affect the capture of PM2.5, which can penetrate deep into the lungs and pose significant health risks. Using a self-developed particulate matter (PM) resuspension chamber and single-particle aerosol mass spectrometer, we measured the size distribution and mass spectra of particles captured by ten tree species. Notably, Cinnamomum camphora (L.) J.Presl and Osmanthus fragrans Lour. are more effective at capturing particles under 1 µm, which are most harmful because they can reach the alveoli, whereas Ginkgo biloba L. and Platanus × acerifolia (Aiton) Willd. tend to capture larger particles, up to 1.6 µm, which are prone to being trapped in the upper respiratory tract. Leaf physiological traits such as stomatal conductance and water potential significantly enhance the capture of larger particles. The Adaptive Resonance Theory neural network (ART-2a) algorithm classified a large number of single particles to determine their composition. Results indicate distinct inter-species variations in chemical composition of particles captured by leaves. Moreover, we identified how specific leaf wax compositions—beyond the known sticky nature of hydrophobic waxes—contribute to particle adhesion, particularly highlighting the roles of fatty acids and alkanes in adhering particles rich in organic carbon and heavy metals, respectively. This research advances our understanding by linking leaf physiological and wax characteristics to the selective capture of PM2.5, providing actionable insights for urban forestry management. The detailed exploration of particle size and composition, tied to specific tree species, enriches the current literature by quantifying how and why different species contribute variably to air quality improvement. This adds a crucial layer of specificity to the general knowledge that trees serve as bio-filters, offering a refined strategy for planting urban trees based on their particulate capture profiles.

AQuamoAS: unmasking a wireless sensor-based ensemble for air quality monitor and alert system

The increased awareness by residents of their environment to maintain safe health states has consequently, birthed the integration of info tech to help resolve societal issues. These, and its adopted approaches have become critical and imperative in virtualization to help bridge the lapses in human mundane tasks and endeavors. Its positive impacts on society cannot be underestimated. Study advances a low-cost wireless sensor-based ensemble to effectively manage air quality tasks. Thus, we integrate an IoT framework to effectively monitors environment changes via microcontrollers, sensors, and blynk to assist users to monitor temperature, humidity, detect the presence of harmful gases in/out door environs. The blynk provides vital knowledge to the user. Our AQuaMoAS algorithm makes for an accurate and user-friendly mode using cloud services to ease monitor and data visualization. The system was tested at 3 different stages of rainy, sunny and heat with pollutant via alpha est method. For all functions at varying conditions, result revealed 70.7% humidity, 29.5OC, and 206 ppm on a sunny day. 51.5% humidity, 20.4OC and 198ppm on a rainy, and 43.1 humidity, 45.6OC, 199ppm air quality on heat and 66.5% humidity, 30.2 OC and 363 ppm air quality on application of air pollutant were observed

Optimized air quality management based on air quality index prediction and air pollutants identification in representative cities in China

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.

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Airborne particulate matter integral assessment in Magdalena department, Colombia: Patterns, health impact, and policy management

The relevance of atmospheric particulate matter (PM) to health and the environment is widely known. Long-term studies are necessary for understanding current and future trends in air quality management. This study aimed to assess the long-term PM concentration in the Magdalena department (Colombia). It focused on the following aspects: i) spatiotemporal patterns, ii) correlation with meteorology, iii) compliance with standards, iv) temporal trends over time, v) impact on health, and vi) impact of policy management. Fifteen stations from 2003 to 2021 were analyzed. Spearman-Rho and Mann-Kendall methods were used to correlate concentration with meteorology. The temporal and five-year moving trends were determined, and the trend magnitude was calculated using Teil-Sen. Acute respiratory infection odd ratios and risk of cancer associated with PM concentration were used to assess the impact on health. The study found that the maximum PM10 concentration was 194.5 μg/m3, and the minimum was 3 μg/m3. In all stations, a negative correlation was observed between PM10 and atmospheric water content, while the wind speed and temperature showed a positive correlation. The global trends indicated an increasing value, with five fluctuations in five-year moving trends, consistent with PM sources and socio-economic behavior. PM concentrations were found to comply with national standard; however, the results showed a potential impact on population health. The management regulation had a limited impact on increasing concentration. Considering that national regulations tend to converge towards WHO standards, the study area must create a management program to ensure compliance.

IMPORTANCE OF ENVIRONMENTAL PR IN ENSURING SUSTAINABLE DEVELOPMENT IN UZBEKISTAN

Environmental public relations (PR) plays a vital role in promoting sustainable development, particularly in countries undergoing rapid economic and environmental changes like Uzbekistan. This article examines the significance of environmental PR in fostering public awareness, corporate responsibility, and government policies aimed at achieving sustainability. Through case studies on water conservation, air quality, and waste management, the article highlights the effectiveness of environmental PR campaignsin addressing key environmental challenges in Uzbekistan. The findings suggest that while challenges remain, environmental PR is crucial for mobilizing support, influencing behavior, and ensuring the successful implementation of sustainable practices.

Spatial and temporal analysis of scooter-induced traffic patterns and their environmental implications

Scooters are a popular means of transportation in urban areas. However, studies examining their spatial and temporal features are lacking. This study examined traffic patterns in New Taipei City from September 2021 to June 2023 using real-time surveillance camera images and the YOLOv4 algorithm. The focus of the study was to investigate the role of scooters in urban transportation and their effect on air quality. The findings revealed that sedans and scooters accounted for approximately 90% of the total vehicles, and their usage exhibited significant spatial and temporal variations across the city. Sedans were more prevalent in rural areas, whereas scooters were predominant in urban and suburban regions. An examination of diurnal patterns revealed that peak traffic occurred during early morning and evening rush hours, with distinct usage patterns between weekdays and weekends. Through hierarchical clustering, the city's stations were categorized into three types based on the dominant vehicle usage: sedan-dominant, sedan-prevailing, and scooter-dominant. The analysis also established a correlation between the number of vehicles and air pollution, highlighting the significant role of sedans and scooters as primary sources of emissions, particularly in areas where scooters were dominant. This suggests that the current emission inventories may underestimate the impact of scooters and buses on air quality while overestimating the trucks' contribution. Consequently, this study emphasizes the significance of scooters in determining urban air quality and advocates for improved monitoring and image identification technologies to accurately assess the numbers and speeds of scooters. These measures are essential for improving the accuracy of emissions inventories and forecasts, which are crucial for effective urban air quality management.

A new attention-based CNN_GRU model for spatial-temporal PM2.5 prediction.

Accurately predicting thespatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection.

Optimization of a PM2.5 automatic monitoring network based on the modeling approach – A case study in Long An province, Vietnam

Abstract Currently, fine particulate matter (PM2.5) pollution is one of the well-recognized serious environmental health risks. In particular, PM2.5 has remarkably impacted the human respiratory, cardiovascular, and circulatory systems. Vietnam has frequently ranked among the countries with the highest annual mean PM2.5 levels in Southeast Asia and worldwide. Thus, building a real-time continuous ambient PM2.5 monitoring network is an important environmental management tool to reduce health effects attributable to PM2.5 exposure. Nevertheless, measurement locations of an air quality monitoring (AQM) network depend strongly on meteorological conditions in the area and sensitivity to local primary emission sources, which significantly determines the AQM network’s operational efficiency. This study aims to estimate and optimize outdoor PM2.5 monitoring sites of an automatic air quality monitoring (AAQM) network using a system of low-cost sensors in Long An province, based on the spatio-temporal distribution assessment results of PM2.5 concentrations from WRF (Weather Research and Forecasting Model)/CMAQ (the Community Multiscale Air Quality) models. The prominent study outcomes have shown that the 2018 average PM2.5 concentration in Long An province exceeded the threshold of QCVN 05:2023/BTNMT (25 μg/m3) from 5.6 to 7.9 times. Moreover, this study has proposed four optimal monitoring sites (AQ1, AQ2, AQ3, and AQ4) for the automatically real-time PM2.5 observation in the areas of Tan An City, Can Giuoc District, the ring roads 3 and 4, and Kien Tuong Town. The study has provided an important scientific basis and support for local air quality management activities; furthermore, the newly proposed monitoring locations will complement the province’s periodic monitoring program towards 2025.

Exploring the tendency of Brownian agglomeration in polydispersed systems of solid atmospheric aerosols

Recent extensive research has focused on atmospheric aerosol systems, driven by their far-reaching implications for climate and public health, with a goal to identify optimal air quality management practices. This study employs Langevin dynamics to investigate the impact of size distribution and concentration of submicrometer solid aerosol particles on Brownian agglomeration tendencies within an isothermal, hydrostatically balanced atmosphere. To accomplish this, a Langevin dynamic model is devised, and parameters are carefully determined to ensure numerical integration precision, stability, and efficiency. Using this model, we numerically simulate the average agglomeration nucleus and overall agglomeration rate within the system while considering variations in the standard deviation of size distribution and volume fraction of aerosol particles.

Leveraging AI-Driven IoT Systems for Enhanced Air Quality Management in Nigeria: An Impact Examination towards Sustainable Development Goal

This paper conducts a comprehensive analysis of the impact of Artificial Intelligence (AI)-driven Internet of Things (IoT) systems on air quality management in Nigeria. The study examines the current state of air quality in Nigeria, highlighting prevailing challenges, health implications, and the environmental significance of escalating pollution levels. It delves into the existing conventional methods and technologies utilized for air quality management, emphasizing monitoring systems, regulatory frameworks, and pollution control measures. In addition, this review elucidates the integration of AI and IoT technologies globally in addressing environmental concerns, particularly in the realm of air quality management. It specifically zooms into the emergence and adoption of AI-driven IoT systems within Nigeria’s environmental landscape, spotlighting ongoing initiatives or projects. This review seeks to investigate the implementation and consequences of AI-powered Internet of Things (IoT) systems in air quality management within Nigeria context. It bridges existing knowledge gaps by scrutinizing the effectiveness of these advanced systems in mitigating air pollution and assesses the challenges encountered in practical implementation, encompassing technical, regulatory, and socio-economic barrier.