
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39223178
71269
10.1038/s41598-024-71269-7
Article
Transforming air pollution management in India with AI and machine learning technologies
Rautela Kuldeep Singh
Goyal Manish Kumar mkgoyal@iiti.ac.in

https://ror.org/01hhf7w52 grid.450280.b 0000 0004 1769 7721 Department of Civil Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552 Madhya Pradesh India
2 9 2024
2 9 2024
2024
14 2041211 3 2024
26 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

Keywords

Air pollution
Technological innovation
Regulatory frameworks
Societal engagement
Global collaboration
Subject terms

Environmental chemistry
Engineering
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Air pollution has emerged as a critical global environmental health issue, with 92% of the world's population exposed to pollutant levels exceeding air quality guidelines1,2. This widespread exposure poses significant health risks, including increased incidence of respiratory diseases, cardiovascular problems, and premature mortality3,4. In India specifically, ambient particulate matter (PM) exposure has been linked to an estimated 1.1 million premature deaths annually, with air pollution becoming the fourth leading cause of mortality nationwide5,6. The economic impact is also substantial, with the World Bank estimating that air pollution costs India 3–8% of its GDP due to healthcare expenses, reduced productivity, and premature deaths7.

Atmospheric aerosols, particularly black carbon, organic carbon, dust, sea salt, and sulfates, have been extensively researched in South and Southeast Asia over the past two decades8. However, the magnitude of these impacts is largely influenced by spatio-temporal variability and the composition of these aerosols9. Aerosols, including, are significant constituents of atmospheric PM and account for approximately 30–70% of the fine aerosol mass over urban areas in India5,10. However, in recent decades, this concern has increased notably, primarily attributed to the rapid surge in population, unplanned urban development, and the expansion of industries11,12. India, home to the world's largest population share at 17.76%, faces a significant environmental challenge, with many of its cities (eg; Delhi, Mumbai, Kolkata) ranking among the most polluted on the global scale13,14. An investigation based on World Health Organization (WHO) data from 2008–2013 brought attention to India's status among the most polluted nations15. India has faced alarming and extensive air pollution incidents in the last twenty years, prompting substantial concern among regulatory authorities. The Indo-Gangetic Plain (IGP) is highly susceptible to severe pollution incidents, notably prevalent in the post-monsoon and winter period16. Similarly, in many metropolitan cities across India, such as Delhi, air quality has deteriorated to hazardous levels. Concentrations of particulate matter (PM2.5 and PM10) have surged beyond 500 µg/m3, while nitrogen oxides (NO2) have exceeded 10 µg/m3. Additionally, ozone (O3) and sulfur dioxide (SO2) levels have surpassed 5 µg/m3, alongside other pollutants17. The concentration of these pollutants, often surpassing 500 µg/m3, far exceeds WHO's safe annual limit of 10 µg/m3 and India’s national ambient air quality standards (NAAQS) of 40 µg/m3 during winters18. According to the Economic Times, 12.25 million vehicles are registered in Delhi, growing at a rate of 7% per annum, and they account for 67% of the total pollution19,20. Additionally, Coal-based thermal power plants and small-scale industries each contribute 12% to the pollution, including emissions from various industrial units followed by the agricultural and biomass burning in Delhi and surrounding areas20. This increased pollution level has raised considerable concern among authorities and stakeholders, prompting focused efforts towards addressing this critical issue9. The urgency of addressing air pollution in India is evident through compelling data illustrating its significant impact across various sectors.

AI & ML have become pivotal in addressing air pollution by harnessing big data analytics, utilising advanced computing systems, scalable storage, and parallel processing technologies21–23. These innovations enable comprehensive management and mitigation strategies for various air pollutants, bridging the gap between atmospheric and climate sciences through sophisticated data-driven approaches. Previous studies have proposed various AI&ML-based models as pivotal components for air pollution and aerosol transport5,8,24–26. Initially, researchers have introduced succinct and efficient statistical models for practical applications. These statistical models primarily encompass multiple linear regression (MLR)27 and autoregression moving average (ARMA)28 methods. The predominant use of linear hypotheses in developing statistical models contrasts with the inherent nonlinear properties exhibited by pollutant concentrations. Consequently, researchers have advocated for integrating data mining methods29 and machine learning models30–32 designed to accommodate nonlinear predictions in studying air pollutants. However, the notably nonlinear and non-stationary nature of pollutants poses challenges for achieving high prediction accuracy with these models. As a result, several studies have turned to various deep learning techniques8,33–36 to enhance the prediction of air pollutant levels.

Despite numerous efforts to forecast concentration of major pollutants, comprehending the complex relationship among diverse influencing factors remains a persistently challenging task. Studies exploring the relevance of these factors in predicting pollutants have been scarce and constrained in scope37,38. Typically, researchers tend to utilize all accessible features and input them into prediction models. While it holds true that AI&ML models exhibit superior performance in scenarios with abundant data availability, the effectiveness of these models in pollutant prediction hinges on understanding and incorporating the most influential factors. Figure 1 illustrates the comprehensive AI/ML model development workflow for environmental or traffic-related predictions. The process includes data collection across various domains, preprocessing, algorithm selection, model development, training, testing, and validation. The process completes with prediction, incorporating a feedback loop for model refinement if needed, ensuring adaptability and continuous improvement in predictive accuracy.Fig. 1 Charting the sequential steps of AI and ML involvement in predicting air pollution concentrations.

Previous studies have conducted comparative analyses between AI&ML-based methodologies for forecasting concentrations of various pollutants. Initially, Mc Kendry39 evaluated Artificial Neural Networks (ANN) with MLR for simulating the concentrations of PM2.5 and PM10. Similarly, Dutta and Jinsart40 compared the performances of decision tree and ANN algorithms in estimating PM10 concentrations. Other comparisons include Turias et al.41 pitting back-propagation based ANN against ARIMA for predicting the Sulfur Dioxide (SO2), concentrations of Carbon Monoxide (CO) and Suspended Particulate Matter (SPM), over an industrialized region. Shang and He42 formulated an innovative prediction method by coupling of ANN and Random forest (RF) to forecast hourly PM2.5 concentrations. Bozdağ et al.43 presented a comprehensive analysis for the simulation of PM10 concentrations by comparing various modelling approaches—ANN, KNN (K-Nearest Neighbour Algorithm), SVM (Support Vector Machine) , LASSO (Least Absolute Shrinkage and Selection Operator), RF, and xGBoost.

This study systematically explores the consequences of severe air pollution in India, focusing on contributors like PM, Organic Aerosols (OAs), BC, Water-Soluble Brown Carbon (WS-BrC), and Volatile Organic Compounds (VOCs). Remediation techniques, including legislation, NAAQS, and an Air Quality Index (AQI), are inspected alongside the evolution of emission load studies and management strategies. Additionally, the study investigates the integration of AI&ML in mitigating and predicting air pollution. It details the application of AI&ML models and underscores the potential of deep learning algorithms, exemplified through a case study predicting PM2.5 concentrations over India. Identifying challenges like technological barriers, regulatory hurdles, public awareness gaps, agricultural practices, urbanization impacts, cross-border pollution, climate change interlinkages, and socio-economic disparities, the study emphasizes the urgency of comprehensive solutions. Looking forward, the study discusses prospects involving emerging technologies and global collaborations. The study emphasizes the imperative to address air pollution in India holistically, leveraging AI&ML advancements, global cooperation, and technological innovations to formulate effective strategies for combatting the multifaceted challenges posed by air pollution in the region.

Results and discussion

Consequences of air pollution in India

Air pollution in India specially in metropolitan cities has dire consequences for public health, stemming from increased levels of particulate matter, nitrogen oxides, and various pollutants. This increase pollution level is consistently linked to increased respiratory diseases, particularly asthma, chronic obstructive pulmonary disease (COPD), and bronchitis7,44. Children, with developing respiratory systems, are particularly vulnerable to irreversible health issues upon prolonged exposure, while the elderly, with compromised immune systems, face pre-eminent risks, including deep lung penetration, inflammation, and enduring damage caused by PM2.5. Beyond respiratory implications, air pollution has severe cardiovascular consequences, with nitrogen oxides significantly contributing to an increased risk of heart attacks and strokes, leading to heightened cardiovascular mortality with prolonged exposur7. The significant study conducted by the CPCB in Delhi highlighted robust correlations between air quality levels and negative health effects. Comparative analysis against a rural control population in West Bengal indicated a 1.7-fold higher occurrence of respiratory symptoms in Delhi, emphasizing the direct impact of air quality on public health20,45–47. Odds ratios for upper and lower respiratory symptoms were 1.59 and 1.67, respectively, emphasizing the profound impact of air pollution. The study also highlighted a significantly higher prevalence of current and physician-diagnosed asthma in Delhi, with lung function notably reduced in 40.3% of Delhi's participants compared to 20.1% in the control group20.

In addition to respiratory effects, non-respiratory impacts were observed in the cities as compared to rural controls. The prevalence of hypertension was notably higher in cities (36% vs. 9.5% in controls), correlating positively with respirable suspended particulate matter (PM10) levels in ambient air48. Chronic headaches, eye irritation, and skin irritation were significantly more pronounced in most of the cities. Community-based studies consistently affirm the association between air pollution and respiratory morbidity. Studies focusing on indoor air pollution reveal similar correlations with respiratory morbidity, extending to conditions such as attention-deficit hyperactivity disorder in children, increased blood levels of lead, and decreased serum concentration of vitamin D metabolites49. Beyond health impacts, the environmental consequences of air pollution are profound. Pollutants harm plants and animals, disrupt ecosystems, and lead to biodiversity loss50. The issue extends beyond health and the environment, impacting economics and society, straining healthcare, productivity, and social equity, demanding holistic strategies spanning economic, social, and environmental facets making it imperative, in this crisis, to understand the existing and potential remediation techniques51.

The economic and social ramifications are substantial, with healthcare costs soaring as the incidence of pollution-related illnesses rises7. Treating respiratory and cardiovascular diseases places a significant burden on the healthcare system, affecting both public and private healthcare expenditures44. Air pollution in India incurred an estimated economic toll of $95 billion in 2019, amounting to 3% of the country's GDP, attributable to decreased productivity, increased work absences, and premature fatalities52. The economic implications of air pollution extend beyond direct healthcare costs, affecting labor markets and overall productivity53. Social disparities are accentuated by air pollution, with vulnerable communities facing disproportionate exposure to pollutants. Factors such as socio-economic status, access to healthcare, and geographic location contribute to disparities in exposure and health outcomes54. Addressing these social dimensions is crucial for devising equitable solutions that prioritize environmental justice. As India grapples with the immediate consequences of air pollution, emerging challenges require attention. Also, climate change exacerbates existing issues, influencing weather patterns and contributing to the persistence of stagnant air masses that trap pollutants and their transportation mechanism8. The increasing frequency of extreme weather events further complicates pollution dynamics55. Moreover, the complex interplay of indoor and outdoor air pollution adds another layer of complexity, with indoor air pollution often stemming from household activities such as cooking with solid fuels, compounding the overall burden on public health49. However, government policies and initiatives take center stage in this exploration, with regulatory measures, such as emission standards and vehicle restrictions, scrutinized for their effectiveness and implementation challenges12. Sustainable urban planning, including the creation of green spaces and transportation planning for pollution reduction, is examined as a proactive approach to mitigate pollution at its source56. Technological solutions, ranging from air purifiers to pollution monitoring devices, are also evaluated57. The challenges of scalability, accessibility, and integration into existing infrastructure are dissected to discern the practicality and potential impact of these technologies. Emerging technologies and global collaborations are explored as potential catalysts for change57,58.

Contributors to air pollution in India

Air pollution in India is a complex issue with multiple sources and contributors, as highlighted by various studies conducted by Lalchandani et al.59, Tobler et al.60, Rai et al.61, Talukdar et al.62 and Wang et al.63. The sources and contributors to air pollution can be broadly categorized into particulate matter (PM2.5 and PM10), organic aerosols (OAs) including black carbon (BC), water-soluble brown carbon (WS-BrC), and volatile organic compounds (VOCs). Each of these components plays a signifsicant role in the overall air quality of the region.

Particulate matter (PM)

Particulate matter is a key component of air pollution, and Lalchandani et al.59 conducted studies using the Positive Matrix Factorization (PMF) model to identify and apportion different sources of PM. The sources identified included traffic-related emissions, dust transportation, solid-fuel burning emissions, and secondary factors62,64. Traffic-related emissions in metropolitan cities were found to be the significant contributor to the total concentration of PM, for example, at the IIT Delhi site, emphasizing the impact of vehicular activities on air quality. Additionally, solid fuel burning emissions, often associated with residential cooking and heating, were identified as a major contributor to PM, particularly at night62. Rai et al.61 conducted source apportionment of elements in PM10 and PM2.5, identifying nine source profiles/factors, including dust, non-exhaust sources, solid fuel combustion, and industrial/combustion aerosol plume events. The contribution of anthropogenic sources to elements associated with health risks, such as carcinogenic elements. The geographical origins of these sources were also determined, emphasizing the regional and local influences on element concentrations in atmosphere65.

Organic aerosols (OAs)

Organic aerosols are another crucial component of air pollution, and studies by Tobler et al.60 and Lalchandani et al.62 revealed three main components of OAs: solid fuel combustion OAs (SFC OAs), hydrocarbon-like OA (HOAs) from vehicular emissions, and oxygenated OAs (OOAs). Lalchandani et al.65 further categorized these components into sub-factors, providing a detailed understanding of the OA composition. Emissions stemming from traffic emerged as the primary contributor to the overall OA mass, underscoring the profound influence of vehicular pollution59.

Black carbon (BC)

BC, a product of incomplete combustion, was studied by Using the Absorption Ångström Exponent (AAE) method, contributions from biomass burning and vehicular emissions were apportioned66. Vehicular emissions were found to be a dominant source of BC, contributing around 67.5% 62,67. The distinction between BC and brown carbon (BrC), which absorbs light in the near-UV to visible region, was also discussed, highlighting the need to consider multiple light-absorbing aerosols in air quality assessments.

Water-soluble brown carbon (WS-BrC)

Rastogi et al.68 performed a PMF analysis of WS-BrC spectra, identifying six factors representing specific sources of BrC. The study revealed diurnal variability in BrC absorption, with factors associated with different emission sources. The presence of secondary BrC was indicated, suggesting the importance of atmospheric processes in the formation of brown carbon. This finding adds another layer of complexity to the sources of light-absorbing aerosols in the atmosphere69.

Volatile organic compounds (VOCs)

Wang et al.63 investigated the characteristics and sources of VOCs, identifying six factors related to traffic, solid fuel combustion, and secondary sources. Traffic-related emissions were found to be the dominant source of VOCs at the urban site, while at the suburban site (MRIIRS), contributions from secondary formation and solid fuel combustion were more significant. The study highlighted the major role of anthropogenic sources in VOC pollution70.

Current remediation techniques

India has faced escalating challenges in managing air pollution over the years, necessitating the implementation of diverse remediation techniques. Figure 2 illustrates the legislative evolution of air quality management in India across three eras: Pre-Internet (1905–89), Transition (1990–99), and Internet Era (2000 onwards). This timeline showcases key acts and regulations implemented over time to address air pollution. The bottom timeline highlights the progression of NAAQS in India, from monitoring just 3 pollutants in 1982 to 7 in 1994, and 12 in 2009. The latest phase (2019–24) involves a comprehensive review of air quality standards under the National Clean Air Programme (NCAP) in 2019, demonstrating India's ongoing commitment to improving air quality management.Fig. 2 Legalisation and Evaluation of NAAQS in India12.

Legislation and regulatory measures

India's legislative landscape has evolved significantly to address air pollution. The introduction of key acts such as the Air (Prevention and Control of Air Pollution) Act in 1981 and subsequent amendments empowered central and state pollution control boards to handle severe air pollution emergencies71. The Environment (Protection) Act of 1986 served as an umbrella act for environmental protection, while the Motor Vehicles Act has been periodically amended to regulate vehicular pollution72. Recent developments include the Motor Vehicles (Amendment) Bill of 2019, allowing the government to recall vehicles causing environmental harm73. The establishment of institutions like the National Green Tribunal (NGT) and the National Environment Tribunal reflects a commitment to environmental accountability74.

National ambient air quality standards (NAAQS) and air quality index (AQI)

The formulation and periodic revision of National Ambient Air Quality Standards (NAAQS) have been pivotal in regulating air quality18. Beginning in 1982, the Central Pollution Control Board (CPCB) introduced NAAQS, initially covering SO2, NO2, and SPM47. Subsequent amendments expanded the list to include RSPM, Pb, NH3, and CO75. The National Air Quality Index (NAQI) was introduced to enhance public awareness, categorizing air quality into six levels from 'Good' to 'Severe'76. This index, based on the concentration of eight pollutants, guides interventions for improved air quality.

Air pollution monitoring network

India's air quality monitoring network has witnessed substantial growth. The initiation of the National Ambient Air Quality Monitoring (NAAQM) Network in 1984, expanded to the National Air Quality Monitoring Programme (NAMP), marked a critical step77. The network, comprising both manual and Continuous Ambient Air Quality Monitoring System (CAAQMS) stations, now stands at 1082 locations78,79. Real-time monitoring, as exemplified by CAAQMS, provides valuable data for prompt decision-making. The introduction of the System of Air Quality and Weather Forecasting and Research (SAFAR) further enhances forecasting capabilities80.

Evolution of studies on emission load

Emission inventories, critical for formulating air pollution control policies, have evolved over time. Initiatives by CSIR-NEERI and CPCB in the late twentieth century laid the foundation12. Emission inventory data, collected through GIS, has become integral in mapping pollution sources and understanding spatial distribution81. The Air Pollution Knowledge Assessments (APnA) city program and organizations like TERI contribute to city-specific inventories82. The emphasis on utilizing secondary data streamlines the process, enabling the creation of comprehensive databases for national and urban pollution inventories. The secondary data refers to datasets that include emission loads from various sources such as vehicular emissions, industrial outputs, construction activities, residential heating, and biomass burning83.

Management strategies and control policies

India's air pollution management strategies encompass a multifaceted approach, with a blend of judicial interventions and executive actions.

Judicial interventions

The judiciary, particularly through petitions filed by M.C. Mehta, has been instrumental in setting guidelines and policies84. For instance, interventions in the Taj Trapezium Zone and the oversight of air quality management plans for non-attainment cities by the National Green Tribunal (NGT) are notable74. The judiciary has played a significant role in shaping policies for better governance and legislation.

Executive actions

Several executive measures contribute to air pollution control. The Auto Fuel Policy, initiated in 2003 and updated in 2014, addresses vehicular emissions85. Emphasis on alternative fuels, as seen in the National Auto Fuel Policy and the Pradhan Mantri Ujwala Yojana (PMUY) for subsidized LPG connections, aligns with cleaner fuel initiatives86. Stricter emission standards for thermal power plants and the push for Hybrid and Electric Vehicles (EVs) under schemes like Faster Adoption and Manufacturing of Hybrid & Electric Vehicles (FAMHE) contribute to pollution reductions87.

AI&ML Techniques for addressing and forecasting air pollution

Overview of AI&ML models

Various AI&ML techniques, such as ANN, Fuzzy logic (FL), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Recurrence Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Autoencoder (CA) etc., are commonly used in previous studies to predict and forecast earth and atmospheric variables8,25,88–91 (Table 1). AI&ML models have become pivotal in processing and simulating non-linear information, with a notable focus on ANNs92. ANNs emulate the human nervous system, comprising interconnected neurons that collectively address a spectrum of challenges, from function approximation to clustering and optimization93. The three-stage process involved in ANN modelling, encompassing design, training, and validation, underscores its versatility92. During the design phase, crucial parameters such as architecture, layers, neurons, and learning algorithms are thoroughly chosen94. Training involves iterative adjustments of synaptic weights to minimize errors, while validation gauges the network's generalization performance for unknown data. Table 1 Different AI&ML models with target pollutants.

S. No	Authors	AI&ML Model	Target Pollutants	Network Type	Outcomes	
1	Mlakar et al.95	MLP-ANN	SO2	Feed-forward, three layered, nonlinear MLP trained by backpropagation	Successfully predicted SO2 concentrations at a thermal power plant in Sostanj	
2	Arena et al.96	MLP	SO2	Explained MLP efficacy in predicting SO2 concentrations across varied climates	Highlighted model accuracy in diverse climatic conditions	
3	Sohn et al.97	ANN	Multiple pollutants	Extended ANN approach for multiple pollutants, achieving reasonable accuracy within a limited range	Highlighted the need for optimization by incorporating additional weather-related parameters	
4	Slini et al.98, Kandya99	ANN	Gaseous pollutants	Highlighted the significance of optimizing input parameters to achieve enhanced accuracy	Consistent preference for ANNs in modelling gaseous pollutants	
5	Chaloulakou et al.100 Mishra and Goyal101	MLR, ANN	Ozone (O3), nitrogen dioxide (NO2)	ANN showed better accuracy than MLR in predicting O3 and NO2 concentrations	Highlighted better accuracy of ANN in predictions of O3 and NO2	
6	Fernando et al.102, Grivas and Chaloulakou103	ANN	PM10	Successfully predicted PM10 levels with consistent accuracy, even in noisy datasets	Showcased ANN versatility in predicting roadside contributions to PM10 concentrations	
7	Paschalidou et al.106, Zhang et al.105, Suleiman et al.104	BP-ANN, MLP-NN, MLP	PM10	Used BP-ANN to predict PM10 levels, surpassing other models in accuracy	Showed MLP-based NN's superiority in achieving accurate PM10 levels predictions	
8	Liu et al.109	Ensemble model (WPD, PSO, BPNN)	PM2.5	Ensembled WPD-PSO-BPNN for forecasting PM2.5 concentrations	Showed better accuracy compared to separate models	
9	Chen et al.110, Jain and Khare111	Fuzzy logic (FL) and neuro-fuzzy time series	O3, CO	Introduced enhanced FL and neuro-fuzzy models for precise pollutant estimation	Exhibited precise forecasts of intricate urban CO concentrations; focused on enhanced O3 prediction	
10	Carbajal-Hernández et al.112, Al-Shammari113	FL	Air quality in Mexico City	Used FL with signal processing and autoregression for air quality predictions	Compared models, favouring FL for estimating daily maximum ozone concentrations during extreme pollution events	
11	Song et al. 115 , Bougoudis et al.114	FIE, adaptive neuro-fuzzy interface system (ANFIS)	Air pollution forecasting	Implemented FIE for precise pollution forecasts, emphasized ANFIS	Emphasized the role of density functions in tackling uncertainty in upcoming PM trends	
12	Wang et al.116, Arbabsiar et al.118	FL and ANFIS	NO2, PM	Developed hybrid models combining FL and ANFIS with uncertainty analysis for pollutant prediction	Shown accuracy in predicting NO2 and PM concentrations using hybrid techniques	
13	Feng et al.119, Yeganeh et al.120	SVM, hybrid (SVM and PLS)	O3, CO	Compared SVM with other models for ozone predictions; showcased stable performance	Evaluated SVM-PLS for accurate CO predictions, indicating favourable results	
14	Nieto et al.121, Luna et al.122	SVM, PCA with ANN and SVM	PM10	Compared models for PM10predictions; SVM was most accurate and robust	Applied PCA with ANN and SVM for ozone forecasts, considering meteorological impacts on concentrations	
15	Wang et al.123, Behal and Singh117	Hybrid adaptive models (SVM and ANN), FL	PM10, SO2	Proposed superior hybrid models combining SVM and ANN for accurate predictions	Utilized fuzzy logic for benzene monitoring, displaying acceptable statistical performance	
16	Freeman et al.124, Wang and Song125	RNN, LSTM, ensemble LSTM with FL-c-means (FcM) clustering	O3	Used RNN, LSTM, and LSTM with FcM clustering to predict O3 concentrations, focusing on refinement	Established an ensemble method combining LSTM and FcM for air quality forecasting, achieving better performance than individual models	
17	Li et al.131, Zhou et al.126	LSTM, DL algorithms	PM2.5, PM10, NOx	Used LSTM and DL for multi-step forecasting, resulting in excellent performance	Used different DL algorithms for spatiotemporal modelling of pollutants, demonstrating high accuracy	
18	Soh et al.127, Qi et al.128	Spatio-Temporal (ST)-DNN (ANN, CNN, LSTM), GC-LSTM	PM2.5	Advanced ST- DNN for air quality, showcasing short-term consistency	Suggested improvements for long-term PM2.5 predictions in spatial–temporal modelling	
19	Fan et al.91, Li et al.129, Zhang et al.132	CNN, LSTM, deep–RNN	PM2.5	Developed an LSTM-based deep RNN for PM2.5 prediction, outperforming baseline models	Used large-scale graphical datasets with CNN to enhance air pollution estimation	
20	Akhtar et al.32	MLP, SVM, Naïve Bayes	PM10	Used multiple ML models for PM10 predictions, improving forecast accuracy	Enhanced predictions using different statistical metrics	
21	Qiu et al.133	DL framework, Pollution-Predicting Net with WRF-Chem	PM2.5	Used encoder-decoder setup for PM2.5

predictions, demonstrating improved performance in severe events

	Highlighted significant meteorological parameter impact on prediction accuracy	
22	Rautela and Goyal

(Present Study)

	Convolutional autoencoders	PM2.5	Used encoder-decoder architecture for PM2.5 predictions via key aerosols	Enhanced forecasting performance with fine spatio-temporal resolutions using SSIM, PSNR, RMSE, and MSE	

Multilayer Perceptron (MLPs), a prominent type of ANN, have proven effective in predicting atmospheric pollution events. Typically featuring input, hidden, and output layers, MLPs can adapt to complex patterns by incorporating multiple hidden layers92. Configuring neurons in the hidden layers is of utmost importance, as an incorrect count can lead to over-fitting or under-fitting. Techniques like thumb rule and trial and error, network reduction offer solutions to optimize neuron numbers. FL, another AI technique, operates on a different paradigm by assigning truth values in a range. Developed from fuzzy set theory, it accommodates linguistic variables, making it adept at handling uncertainty in natural language statements. Fuzzy logic's three main phases—fuzzification, inference, and defuzzification—form a robust modelling system capable of addressing nuanced problems. SVM are popular for supervised learning, excelling in classification, prediction, density estimation, and pattern recognition. SVM seeks an optimal hyperplane to segregate data into predefined classes, with kernel functions playing a pivotal role in introducing non-linearity.

Deep Neural Networks (DNNs) represent an advanced version of ANNs, characterized by structural depth and scalability8. DNNs, with more than three layers, can automatically extract features from raw inputs, known as feature learning. Notable architectures within DNNs, such as CA, LSTM, CNNs and RNNs have demonstrated superior performance, especially in air pollution forecasting. The training of DNNs demands significant computational power, leading to advancements in processing capabilities and the development of sophisticated algorithms. Overcoming challenges like vanishing gradient and overfitting has prompted the application of advanced algorithms like SVM, RF, Greedy layer-wise, and Dropout. The application of these models extends across various domains due to their versatility and robust performance. The modelling of complex atmospheric variables such as air pollution forecasting, LSTM, CA, and CNNs emerge as particularly effective and popular architectures.

Application of AI&ML in addressing and forecasting air pollution

The application of AI&ML models, particularly ANNs, FL, SVM and DL models, have emerged as a crucial tool in addressing and forecasting air pollution. ANNs have helped in a transformative era in air pollution forecasting, with a diverse range of applications capturing the attention of researchers. Numerous studies attest to the success of ANNs in predicting both particulate and gaseous pollutants with desired accuracy over various spatio-temporal resolution. The early forays into air pollution forecasting by Mlakar et al.95 marked a significant milestone, employing a trained nonlinear three-layered back propagation feed forward network. This model successfully predicted the concentration of SO2 over a thermal power plant, showcasing the potential of ANNs. Subsequent research expanded the scope and sophistication of ANN applications. Similarly, Arena et al.96 demonstrated the efficacy of multi-layer perceptron in predicting concentration of SO2 over an industrial area, emphasizing the model's accuracy across diverse weather conditions. Sohn et al.97 extended the ANN approach to model multiple pollutants, including NO, SO2, NO2, CO, O3, CH4 and total hydrocarbons. The results indicated reasonable accuracy within a limited prediction range, highlighting the need for further optimization by incorporating additional weather-related input parameters. The application of ANNs in gaseous pollutants forecasting continued with studies by Slini et al.98 and Kandya99 both emphasizing the importance of optimizing input parameters for improved accuracy. Comparative assessments with other forecasting techniques consistently positioned ANNs as superior for gaseous pollutants. Chaloulakou et al.100 found that ANN outperformed Multiple Linear Regression (MLR) in predicting ozone concentrations, showcasing the model's superior accuracy. Similar findings were reported by Mishra and Goyal101, compared Principal Component Analysis (PCA)-based ANN model with MLR for estimating the concentrations of NO2. In the realm of particulate matter forecasting, ANNs have proven equally effective. Fernando et al.102 successfully used multi-layered MLP to predict PM10 concentrations, considering parameters such as hourly meteorological data, particulate, matter with statistical indicators. Grivas and Chaloulakou103 employed an ANN model for hourly PM10 predictions, showcasing consistent accuracy even in the presence of noisy datasets. The versatility of ANNs extends to predicting roadside contributions to PM10 concentrations, as demonstrated by Suleiman et al.104. Comparative studies with other models have affirmed the efficacy of ANNs in particulate matter forecasting. Zhang et al.105 utilized BPANN to forecast the concentrations of PM10 and found BPANN outperforming other models in predictive accuracy. Paschalidou et al.106 evaluated the multi-layer perceptron-based ANN those models provided superior results compared to Radial Basis Function models, establishing the former's dominance in terms of forecasting capability. Contrasting trends were observed in certain studies, such as those by Mishra et al.107 and Moisan et al.108, where alternative models outperformed ANN during extreme events. This highlights the nuanced nature of model performance, with specific conditions favouring different approaches. However, recent progress has witnessed researchers utilizing ensemble methods to improve both the stability and accuracy of ANN models. Liu et al.109 combined Wavelet Packet Decomposition (WPD), Particle Swarm Optimization (PSO), and BPNN to create an ensemble model for PM2.5 forecasting, demonstrating superior precision compared to individual models.

FL, renowned for its capacity to manage uncertainty, enhanced fault tolerance, and adeptness in handling highly complex nonlinear functions, has garnered extensive adoption in the realm of air pollution prediction. The advantages of FL are exemplified in various studies. For example, Chen et al.110 innovatively introduced a novel fuzzy time series model specifically for O3 prediction, showcasing its superior performance when compared to traditional fuzzy time series models. Jain and Khare111 applied a neuro-fuzzy model to predicts the concentration of CO in Delhi, achieving accurate estimates at complex urban levels. Carbajal-Hernández et al.112 predicts air quality in Mexico City by utilising FL model alongside autoregression model and signal processing. The introduction of a novel algorithm, the "Sigma operator," allowed for precise evaluation of air quality variables, showcasing the effectiveness of fuzzy-based models. Moreover, Al-Shammari et al.113, evaluates stochastic and FL-driven models to estimate the daily maximum concentrations of O3. The findings indicated that the FL-based model exhibited a marginal superiority over the statistical model particularly in instances of severe pollution events. Innovative approaches like the Fuzzy Inference Ensemble (FIE), as proposed by Bougoudis et al.114, demonstrated high accuracy in air pollution forecasting for Athens. Another significant application was presented by Song et al.115, where different probability density functions were employed to enhance particulate matter (PM) forecasting. They developed an adaptive neuro-fuzzy model, emphasizing the importance of density functions in addressing uncertainty associated with future PM trends. Furthermore, Wang et al.116 presented a hybrid model for forecasting air pollution. This model merges uncertainty analysis with fuzzy time series, demonstrating precision in predicting PM and NO2 concentrations. Behal and Singh117 leveraged FL within an intelligent IoT sensor framework to monitor and simulate benzene, demonstrating satisfactory statistical efficacy in recent advancements. The versatility of fuzzy logic extends to unconventional pollutants as demonstrated by Arbabsiar et al.118, who modelled the leakage of CH4 and H2S using a fuzzy inference technique. The suggested model demonstrated satisfactory performance when evaluating these contaminants.

Support Vector Machines (SVM), when combined with other machine learning algorithms, have been helpful in forecasting diverse types of pollutants. Feng et al.119 compared SVM with other models for forecasting daily maximum concentrations of O3 in Beijing, highlighting its stable and accurate performance. Yeganeh et al.120 assessed the efficacy of a forecasting model utilizing SVM integrated with Partial Least Squares (PLS) for the prediction of CO concentrations, demonstrating positive outcomes. García Nieto et al.121 conducted a comparative analysis of various prediction models for PM10 concentrations, determining that the SVM method exhibited superior accuracy and robustness. Luna et al.122 utilized Principal PCA in combination with SVM and ANN for the prediction of O3 levels in Rio de Janeiro. Their study specifically investigated the influence of meteorological parameters on the concentrations of O3. Wang et al.123 proposed hybrid adaptive forecasting models combining SVM and ANN for predicting PM10 and SO2, demonstrating superior performance compared to individual models. FL and SVM in the forecasting air pollution levels have proven to be highly effective in addressing the complexities and uncertainties associated with predicting pollutant concentrations.

While still in its early stages, the potential of DNNs in this domain is evident from a review of various applications such as forecasting of variables in earth and atmospheric sciences. Early on, Freeman et al. (2018) employed a combination of Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to predict ozone concentrations in an urban area. While showing strong predictability in 8 h average ozone concentrations, various model runs revealed overfitting concerns, underscoring the necessity for further refinement. Wang and Song125 introduced an ensemble method using a deep LSTM network with fuzzy c-means clustering for air quality forecasting. This ensemble approach outperformed individual models, showcasing its efficacy in both short-term and long-term predictions. Zhou et al.126 explored the application of LSTM and deep learning algorithms for multi-step ahead forecasting of PM2.5, PM10, and NOx. Their deep learning architecture, integrating dropout neurons and L2 regularization, demonstrated exceptional capabilities in capturing variations in the processes of air pollutant generation. Recent research highlights the growing preference for employing deep neural networks to capture dynamic spatiotemporal features from historical air quality and climatological datasets. Fan et al.91 introduced stacked LSTM (LSTME), spatiotemporal deep learning (STDL), time delay neural network (TDNN), autoregressive moving average (ARMA), and support vector regression (SVR) for modelling of air pollutants over different spatiotemporal resolutions. The inclusion of auxiliary inputs resulted in a model with exceptional performance, outshining other machine learning techniques. Soh et al.127 proposed a STDL integrating ANN, CNN, and LSTM for PM2.5 prediction. The model exhibited stability over extended time periods, with noise reduction achieved through Airbox sensor source models, further enhancing prediction accuracy. Qi et al.128 presented a novel forecasting approach employing a fusion of Graph Convolutional and LSTM (GC-LSTM) neural networks, aiming to investigate spatial interdependence within air quality data. The spatial correlation modelling highlighted the consistency of the GC-LSTM model for short-term forecasting, suggesting potential improvements for long-term predictions with enhanced spatiotemporal considerations. Fan et al.91 developed a LSTM-based deep–RNN for predicting PM2.5 for different spatiotemporal frames showcasing superior specificity measures compared to baseline models. In a novel approach, Li et al.129 and Zhang et al.130 incorporated large-scale datasets of graphical images for air pollution estimation, utilizing CNN. The models, trained on images capturing various atmospheric conditions, demonstrated improved prediction accuracy, emphasizing the adaptability of deep learning to diverse data types. These models offer robust solutions, demonstrating superior performance in various studies and showcasing their potential to contribute significantly to the field of environmental monitoring and public health.

Performance analysis

The evaluation is based on the comparison of their performances using statistical measures such as RMSE and R2, widely accepted metrics in air pollution forecasting studies. Previous research, utilizing a range of datasets, has yielded disparate results134. While certain studies advocate for ensemble methods, others find negligible disparities in the overall accuracy of the outcomes. The efficacy of AI and ML-driven methodologies relies heavily on the precise curation of influential parameters, especially when addressing various pollutants such as PM, O3, NO2, SO2, and CO29. For example, for PM forecasting, critical elements such as precipitation, pressure, humidity, land utilization, wind speed and direction, traffic flow on roads, and population density exert significant influence. Similarly, different influential parameters are identified for SO2, NO2, O3, and CO, emphasizing the importance of tailoring models to specific pollutants. The precision of the methods is notably impacted by the direct correlation between these factors and forecasted levels of pollutants. Additionally, the efficacy of AI&ML models hinges upon variables including network structure, intricacy, learning algorithms, correspondence between input and output information, and the presence of data interference. A comprehensive analysis shows the varying performances of DNN, SVM, ANN, and Fuzzy techniques across different pollutants. DNNs emerge as particularly effective in forecasting PM concentrations, outperforming other techniques with R2 and mean RMSE values of 0.96 and 7.27 μg/m3, respectively91,126,133. In O3 prediction, SVM, FL and DNN exhibit superior accuracy, with DNNs once again leading with R2 and mean RMSE values of 0.92 and 3.51 μg/m3, respectively119,120. SVM excels in forecasting NO2 concentrations, although Fuzzy and DNN techniques also demonstrate reasonable accuracy116,118,131. Notably, the DNN approach consistently stands out, showcasing the best statistical performance for O3 and CO categories. For CO, DNN achieves an exceptional RMSE of 0.69 × 10–5 ppm and an R2 of 0.95119,120,124,125. The overall analysis represents the superiority of DNN across all pollutants, with the lowest overall RMSE score of 5.68. However, despite DNN's dominance, it is crucial to note the underdeveloped application of ensemble methodologies based on DL models for the forecasting of air pollution131,135,136. These approaches, involving multiscale spatiotemporal predictions, have untapped potential to further advance the field, incorporating more explanatory variables to represent air pollution episodes with robust dynamical forcing. The DNN emerges as the leading AI&ML system for the forecasting and prediction of air pollution based on statistical evidence, the exploration of ensemble approaches presents an avenue for future developments in enhancing predictive accuracy.

Prediction of PM2.5 concentrations

The study used a convolutional autoencoder (CA) for analysing PM2.5 concentrations. The dataset was divided into training (70%), testing (20%), and validation (10%) sets, trained over 30 epochs (Fig. 3). This PM2.5-focused CA processes sequences of ten consecutive images, using acquired features to reconstruct subsequent images. The visual representation of the model's capabilities includes sequences of 10 input images, their corresponding 11th ground truth, and the model's predictions (Fig. 4). The model demonstrates promising performance in predicting PM2.5 concentration patterns across India. Comparing the actual 11th image with the predicted one reveals that the model successfully captures the broad spatial distribution of PM2.5 concentrations. Key findings show that the model accurately predicts high concentration areas in the northern regions, particularly in the IGP (Fig. 4). It also effectively represents lower concentrations in southern and eastern coastal areas. The model captures the general gradient from northwest to southeast quite effectively. The prediction tends to slightly overestimate PM2.5 levels in the northwestern region. Additionally, some localized high-concentration areas in central India are not fully captured in the prediction. Furthermore, the model's prediction shows a smoother distribution compared to the more granular actual data. (Fig. 4). Performance evaluation employed established image quality metrics: Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) (Fig. 5). SSIM, which assesses image similarity, predominantly ranged from 0.50 to 0.70 during training, slightly lowering to 0.45 to 0.55 during testing, and stabilizing at 0.50 to 0.60 in validation. PSNR peaked at 25 to 30 dB during training, followed by 24 to 28 dB in testing, and 28 to 30 dB in validation. Lower MSE values (10 to 15 µg/m3 in training, 10 to 20 µg/m3 in testing, and 8 to 11 µg/m3 in validation) signify improved accuracy at the pixel level.Fig. 3 RMSE loss during the training, testing and validation phase.

Fig. 4 Example set for predicting the 11th image of PM2.5 by providing a batch of 10 images of concentration and comparing with the 11th actual image. The maps were generated using Python in a Jupyter Notebook with Matplotlib (v3.3.4) and Basemap (v1.2.2) libraries (https://matplotlib.org/ and https://matplotlib.org/basemap/).

Fig. 5 Model evaluation parameters used for prediction the PM2.5 concentrations.

These metrics offer insights into image quality, indicating some variation between training, testing, and validation, yet within acceptable ranges. Consistently higher SSIM and PSNR values and lower MSE values highlight the model's exceptional precision compared to benchmarks. The model's excellence traces back to its ability to capture complex spatio-temporal features through Autoencoder-based models and strategic integration of Conv2d, Batch Normalization, and Upsampling layers. The model outperforms prior methodologies in predicting PM2.5 concentrations, achieving precise and high-quality predictions across phases. Attempting to forecast PM2.5 levels for the next 4 days led to efficiency parameter decreases (SSIM, PSNR, MSE) with increased time frames, suggesting the need for more parameters for model efficiency improvement (Fig. 6). Predicting PM2.5 concentrations remains challenging due to intricate spatiotemporal features, where DL models offer promise. Leveraging deep learning architectures and transfer learning, this study fine-tuned models, achieving promising PM2.5 prediction results. Despite ongoing challenges in precise location predictions due to PM2.5's dynamic nature, the model demonstrated spatial distribution prediction abilities, evident in visual comparisons between predicted and actual PM2.5 concentration maps.Fig. 6 Example set of predictions of PM2.5 for next 4 days compared with their actual images. The maps were generated using Python in a Jupyter Notebook with Matplotlib (v3.3.4) and Basemap (v1.2.2) libraries (https://matplotlib.org/ and https://matplotlib.org/basemap/).

Challenges and limitations

Technological barriers

One of the primary challenges lies in overcoming technological barriers. While advanced pollution control technologies exist, their widespread adoption is hindered by factors such as high costs and limited access to cutting-edge solutions. Many regions, particularly in rural areas, lack the infrastructure necessary to deploy and maintain sophisticated air quality monitoring and purification systems. Bridging this technological divide is essential for comprehensive pollution control.

Regulatory and enforcement challenges

India grapples with the challenge of implementing and enforcing air quality regulations consistently. While the country has established regulatory frameworks to curb emissions from industries, vehicles, and other pollution sources, enforcement remains uneven. This inconsistency is often compounded by resource constraints, bureaucratic hurdles, and the need for stronger mechanisms to penalize non-compliance. Strengthening regulatory frameworks and enhancing enforcement mechanisms are critical steps in addressing this challenge.

Public awareness and participation

Creating widespread awareness and fostering public participation are essential components of any successful pollution control strategy. However, there is a considerable gap in public awareness regarding the causes and consequences of air pollution. Engaging citizens in proactive measures, such as adopting sustainable practices and reducing individual carbon footprints, requires comprehensive educational campaigns and community involvement. Overcoming societal inertia and instigating behavioral change are significant challenges in this regard.

Agricultural practices and crop burning

Agricultural practices, particularly the prevalent practice of crop burning, contribute significantly to air pollution. The burning of crop residues releases substantial amounts of particulate matter and pollutants into the air. Farmers resort to this practice due to a lack of viable alternatives and time constraints between harvest seasons. Developing and promoting sustainable agricultural practices, coupled with providing farmers with effective alternatives to crop burning, is a complex challenge that requires a holistic approach.

Urbanization and infrastructure development

Rapid urbanization and infrastructure development, while essential for economic growth, often contribute to increased pollution levels. The construction industry, in particular, releases pollutants into the air. Balancing the need for development with sustainable and environmentally conscious practices poses a significant challenge. Implementing green building technologies, stringent emission norms for construction activities, and incorporating urban planning strategies that prioritize air quality are vital steps in addressing this challenge.

Cross-border pollution

Air pollution knows no boundaries, and India contends with the impact of cross-border pollution. Transboundary movement of pollutants, especially during crop burning seasons, contributes to elevated pollution levels in various regions. Collaborative efforts with neighbouring countries are necessary to address this challenge effectively. Developing joint strategies, sharing data, and fostering regional cooperation are imperative for tackling the transboundary dimension of air pollution.

Climate change interlinkages

The interlinkages between air pollution and climate change present a complex challenge. Mitigating air pollution often aligns with climate action goals, but there are trade-offs and synergies that need careful consideration. Striking a balance between addressing immediate air quality concerns and contributing to long-term climate resilience requires integrated policies and strategic planning.

Socio-economic disparities

Air pollution disproportionately affects vulnerable communities, exacerbating existing socio-economic disparities. The challenge lies in designing interventions that address environmental concerns and promote social equity. Ensuring that pollution control measures do not inadvertently burden marginalized communities and providing equitable access to clean technologies are critical to overcoming this challenge.

Future prospects

India stands at the cusp of a pivotal moment in its battle against air pollution, with promising avenues emerging on both technological and collaborative fronts.

Emerging technoloagies

The integration of cutting-edge technologies offers hope for India's future in pollution control. Advancements in AI&ML, when coupled with sophisticated numerical weather prediction models, present a potent toolset for predicting and managing air pollution. These technologies can enhance real-time monitoring, improve predictive capabilities, and facilitate data-driven decision-making, allowing for more precise and targeted interventions. Additionally, the fusion of AI&ML with numerical weather prediction (NWP) models can refine pollution control strategies by providing a deeper understanding of atmospheric dynamics and pollutant dispersion patterns. Furthermore, exploring potential breakthroughs in sustainable energy sources offers a transformative pathway. Shifting from traditional, pollutant-intensive energy sources to sustainable alternatives is crucial for reducing the overall carbon footprint. Investments in research and development, coupled with policy incentives, can accelerate the adoption of clean and renewable energy solutions, fostering a paradigm shift in India's energy landscape.

Global collaborations

Recognizing that air pollution transcends national boundaries, India looks toward global collaborations as a key driver for progress. International efforts in combating air pollution gain significance as countries join forces to address shared challenges. Collaborative platforms provide opportunities for knowledge sharing, exchange of best practices, and collective research initiatives. India's participation in these global endeavours not only enriches its own understanding of air pollution dynamics but also contributes to the global pool of knowledge. By fostering partnerships with other nations, India can access expertise, technologies, and resources that augment its capacity to implement effective pollution control measures. Knowledge sharing and collaborative research initiatives form the cornerstone of global efforts. Platforms that facilitate the exchange of data, research findings, and innovative solutions enable nations to collectively tackle the intricate and interconnected challenges of air pollution. As India engages in these collaborative endeavours, it not only benefits from the collective wisdom of the global community but also contributes its unique insights and experiences, enriching the collective understanding of air pollution dynamics.

India's strategic focus on emerging technologies and global collaborations holds immense promise in navigating the future. By harnessing the power of advanced technologies and participating in international initiatives, India can chart a course toward a cleaner, more sustainable future where the skies are clear, and the air is a testament to the collective commitment to environmental well-being.

Materials and methods

Maintaining fresh air quality is a complex undertaking influenced by various factors over time. These elements encompass air pollutant emissions, deposition, weather patterns, traffic dynamics, and human activities, among others8,64. The complexity of these interrelated factors makes it challenging for traditional shallow models to offer precise portrayals of air quality attributes. Based on the above review, deep learning algorithms were found most suitable for predicting air quality variables without needing prior knowledge. This capability enhances the potential for more accurate predictions regarding air quality, signifying a valuable contribution to addressing the intricacies associated with sustaining optimal air quality levels.

Dataset

The case study utilized MERRA-2 reanalysis data from the NASA GESDISC DATA ARCHIVE application137,138. This dataset, spanning from January 1, 2015, to December 31, 2022, features a spatial resolution of 0.5° × 0.625° and a temporal resolution of 1 h (Fig. 7). It includes five key variables: black carbon surface mass concentration (BCSMASS), dust surface mass concentration—PM2.5 (DUSMASS25), organic carbon surface mass concentration (OCSMASS), sea salt surface mass concentration—PM2.5 (SSSMASS25), and SO4 surface mass concentration (SO4SMASS). These variables are analysed across three dimensions: latitude, longitude, and time. The concentration of the PM2.5 (µg/m3) for each grid cell was computed as139,140:Fig. 7 Surface PM2.5 concentration over India during (a) Winters and (b) Summers; Maps were generated using R Studio (v4.3.3, https://www.rstudio.com/).

PM2.5=(BCSMASS+DUSMASS25+OCSMASS+SSSMASS25+1.375×SO4SMASS)

Convolutional Autoencoder model

Air quality monitoring and predicting PM2.5 concentrations accurately stands crucial for public health and environmental management8. The case study explores an innovative approach employing an Autoencoder-based DL model for forecasting PM2.5 concentrations from spatiotemporal data over India. The study begins by complexly handling the datasets, leveraging PyTorch's Dataset and data loader classes. The ATMriver Dataset class is crafted to capture the dataset, enabling sequential data handling9. The data, formatted into tensors and split into training, testing, and validation subsets in a ratio of 70, 20 and 10, respectively, undergoes a custom transformation via the tensor class, ensuring compatibility with the neural network model8,141. The core of this methodology lies in the architecture of the Autoencoder, a neural network comprising convolutional and transposed convolutional layers. Specifically, the model comprises convolutional layers (conv1, conv2, conv3) responsible for feature extraction and transposed convolutional layers (conv1_d, conv2_d, conv3_d) for data reconstruction (Fig. 8). Each convolutional layer is paired with batch normalization and dropout (set at 25%) to regularize the network and prevent overfitting. The use of five layers in this Autoencoder architecture allows for hierarchical feature extraction and reconstruction, enhancing the model's ability to learn complex representations. The learning rate, a critical hyperparameter governing the magnitude of parameter updates during optimization, is set to 0.0025 for the Adam optimizer. This value influences the convergence speed and stability of the training process. A higher learning rate might lead to faster convergence but risks overshooting the optimal parameters, while a lower rate might result in slower convergence. The chosen learning rate balances the trade-off between convergence speed and stability, aiming to facilitate efficient model training while preventing divergence or oscillation in the optimization process.Fig. 8 Convolution autoencoder architecture for PM2.5 data processing with model features an encoding phase with three autoencoder stages, followed by a decoding phase with two transpose convolution stages; structure enables dimensionality reduction and subsequent reconstruction of PM2.5 concentration maps.

To train the Autoencoder, a custom root mean squared error (RMSE) loss function is defined. This loss function quantifies the disparity between predicted and actual PM2.5 concentrations, guiding the model toward more accurate predictions. The training process iterates through the dataset multiple times (epochs), optimizing the model parameters using the Adam optimizer. The evaluation phase of the model involves assessing its predictive capabilities on separate testing and validation sets. The model's outputs are compared against the original images PM2.5 concentrations, and the RMSE loss is computed. The best-performing model, based on its performance on the testing set, is identified and saved for the prediction. Further the records and reports the losses incurred during training, testing, and validation across epochs, providing insights into the model's loss curve and performance stability. Additionally, the best model's loss metric is highlighted, signifying its capability to accurately predict PM2.5 concentrations. The evaluation of the trained model's predictive capability in this study primarily relied on two widely accepted image quality metrics: Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). The Structural Similarity Index (SSIM) serves as a measure to assess the similarity between the predicted and actual images142. SSIM evaluates the perceived change in structural information, including luminance, contrast, and structure, between the predicted and actual images. A higher SSIM score, closer to 1, indicates a greater similarity between the two images, implying better predictive performance of the model. Peak Signal-to-Noise Ratio (PSNR) is another commonly used metric for quantifying the quality of reconstructed or predicted images. PSNR measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR values signify lower image distortion or higher image fidelity, implying better prediction accuracy in capturing the details of the actual images.

Conclusion

Addressing the complex challenges of air pollution in India necessitates a multifaceted and technically informed approach. The existing impediments, including technological barriers and limited access to advanced pollution control technologies, underline the urgency of bridging the technological divide, particularly in rural areas. While regulatory frameworks are in place, inconsistent enforcement due to resource constraints and bureaucratic hurdles requires strategic strengthening. Public awareness and participation, integral components of effective pollution control, demand targeted educational campaigns to instigate behavioural change. Agricultural practices, notably crop burning, pose a significant challenge, and resolving this requires not only viable alternatives but a holistic approach that integrates sustainable agricultural practices. Rapid urbanization and infrastructure development, while essential for economic growth, necessitate the incorporation of green building technologies, stringent emission norms, and urban planning strategies prioritizing air quality. Cross-border pollution adds a transboundary dimension, demanding collaborative efforts with neighbouring countries. The intricate interlinkages between air pollution and climate change underscore the need for carefully balanced policies that address immediate air quality concerns while contributing to long-term climate resilience. Moreover, the disproportionate impact of air pollution on vulnerable communities emphasizes the importance of interventions that promote social equity alongside environmental considerations. Looking towards the future, the convergence of emerging technologies offers a beacon of hope. The integration of AI&ML with numerical weather prediction models presents a potent toolset for real-time monitoring, precise predictive capabilities, and data-driven decision-making. This amalgamation not only enhances our understanding of atmospheric dynamics and pollutant dispersion patterns but also refines pollution control strategies. Exploring breakthroughs in sustainable energy sources becomes imperative for reducing the overall carbon footprint. Shifting from traditional, pollutant-intensive energy sources to clean and renewable alternatives require concerted efforts through research, development, and policy incentives.

Furthermore, global collaborations stand out as a key driver for progress, given the transboundary nature of air pollution. Participating in international efforts fosters knowledge sharing, exchange of best practices, and collective research initiatives. By engaging in these collaborative activities, India not only enriches its understanding of air pollution dynamics but contributes to the global pool of knowledge. Platforms facilitating data exchange, research findings, and innovative solutions enable nations to collectively tackle the complex challenges of air pollution. In navigating the future, India's strategic focus on emerging technologies and global collaborations holds immense promise. The careful harnessing of advanced technologies and participation in international initiatives can chart a course toward a cleaner, more sustainable future. The fusion of AI&ML with numerical weather prediction (NWP) models positions India to proactively manage air quality, with the skies serving as a testament to the collective commitment to environmental well-being. As India progresses, the synergy of technological advancements and global cooperation emerges as the cornerstone for effective, informed, and sustainable solutions to combat air pollution.

Acknowledgements

We would like to express our sincere gratitude to the Department of Civil Engineering, Indian Institute of Technology, Indore for their support and resources, which have been instrumental in the successful completion of the present study.

Author contributions

Kuldeep Singh Rautela: Conception and design, material preparation, data collection and analysis, writing original draft -review and editing. Manish Kumar Goyal: Conception and design, provided critical feedback, writing original draft -review and editing, supervision.

Data availability

Data will be made online on a reasonable request to the corresponding author.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Masood A Ahmad K A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance J. Clean. Prod. 2021 322 129072 10.1016/j.jclepro.2021.129072
Masood, A. & Ahmad, K. A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance. J. Clean. Prod. 322, 129072 (2021).10.1016/j.jclepro.2021.129072
2. WHO. Atlas on Children’s Health and the Environment. (2017).
3. Jiang XQ Mei XD Feng D Air pollution and chronic airway diseases: what should people know and do? J. Thorac. Dis. 2016 8 E31 E40 26904251
Jiang, X. Q., Mei, X. D. & Feng, D. Air pollution and chronic airway diseases: what should people know and do?. J. Thorac. Dis. 8, E31–E40 (2016).26904251
4. Hayes RB PM25 air pollution and cause-specific cardiovascular disease mortality Int. J. Epidemiol. 2020 49 25 35 10.1093/ije/dyz114 31289812
Hayes, R. B. et al. PM25 air pollution and cause-specific cardiovascular disease mortality. Int. J. Epidemiol. 49, 25–35 (2020).31289812 10.1093/ije/dyz114
5. Shakya D Deshpande V Goyal MK Agarwal M PM2.5 air pollution prediction through deep learning using meteorological, vehicular, and emission data: A case study of New Delhi India. J. Clean. Prod. 2023 427 139278 10.1016/j.jclepro.2023.139278
Shakya, D., Deshpande, V., Goyal, M. K. & Agarwal, M. PM2.5 air pollution prediction through deep learning using meteorological, vehicular, and emission data: A case study of New Delhi. India. J. Clean. Prod. 427, 139278 (2023).10.1016/j.jclepro.2023.139278
6. Brown PE Mortality Associated with Ambient PM2.5 Exposure in India: Results from the Million Death Study Environ. Health Perspect. 2022 130 097004 10.1289/EHP9538 36102642
Brown, P. E. et al. Mortality Associated with Ambient PM2.5 Exposure in India: Results from the Million Death Study. Environ. Health Perspect. 130, 097004 (2022).36102642 10.1289/EHP9538
7. Pandey A Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019 Lancet Planet. Heal. 2021 5 e25 e38 10.1016/S2542-5196(20)30298-9
Pandey, A. et al. Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019. Lancet Planet. Heal. 5, e25–e38 (2021).10.1016/S2542-5196(20)30298-9
8. Rautela KS Singh S Goyal MK Characterizing the spatio-temporal distribution, detection, and prediction of aerosol atmospheric rivers on a global scale J. Environ. Manage. 2024 351 119675 10.1016/j.jenvman.2023.119675 38048709
Rautela, K. S., Singh, S. & Goyal, M. K. Characterizing the spatio-temporal distribution, detection, and prediction of aerosol atmospheric rivers on a global scale. J. Environ. Manage. 351, 119675 (2024).38048709 10.1016/j.jenvman.2023.119675
9. Rautela KS Singh S Goyal MK Resilience to Air Pollution: A Novel Approach for Detecting and Predicting Aerosol Atmospheric Rivers within Earth System Boundaries Earth Syst. Environ. 2024 10.1007/s41748-024-00421-0
Rautela, K. S., Singh, S. & Goyal, M. K. Resilience to Air Pollution: A Novel Approach for Detecting and Predicting Aerosol Atmospheric Rivers within Earth System Boundaries. Earth Syst. Environ.10.1007/s41748-024-00421-0 (2024).10.1007/s41748-024-00421-0
10. Chakraborty, S. et al. Extending the Atmospheric River Concept to Aerosols: Climate and Air Quality Impacts. Geophys. Res. Lett. 48(9), e2020GL091827 (2021).
11. Kapoor M Managing Ambient Air Quality Using Ornamental Plants-An Alternative Approach Univers. J. Plant Sci. 2017 5 1 9 10.13189/ujps.2017.050101
Kapoor, M. Managing Ambient Air Quality Using Ornamental Plants-An Alternative Approach. Univers. J. Plant Sci. 5, 1–9 (2017).10.13189/ujps.2017.050101
12. Gulia S Evolution of air pollution management policies and related research in India Environ. Challenges 2022 6 100431 10.1016/j.envc.2021.100431
Gulia, S. et al. Evolution of air pollution management policies and related research in India. Environ. Challenges 6, 100431 (2022).10.1016/j.envc.2021.100431
13. UN. Two ‘Population Billionaires’, China and India, Face Divergent Demographic Futures. Dep. Econ. Soc. Affiars 1–10 (2023).
14. IQAir. (Report) World Air Quality Report. 2020 World Air Qual. Rep. 1–35 (2020).
15. WHO. World Health Statistics. (2014).
16. Kumari S Verma N Lakhani A Kumari KM Severe haze events in the Indo-Gangetic Plain during post-monsoon: Synergetic effect of synoptic meteorology and crop residue burning emission Sci. Total Environ. 2021 768 145479 10.1016/j.scitotenv.2021.145479 33736344
Kumari, S., Verma, N., Lakhani, A. & Kumari, K. M. Severe haze events in the Indo-Gangetic Plain during post-monsoon: Synergetic effect of synoptic meteorology and crop residue burning emission. Sci. Total Environ. 768, 145479 (2021).33736344 10.1016/j.scitotenv.2021.145479
17. Delhi Air Pollution: Real-time Air Quality Index. https://aqicn.org/city/delhi.
18. CPCB National Ambient Air Quality Status & Trends 2019 Cent. Pollut. Control Board 2020 53 1689 1699
CPCB. National Ambient Air Quality Status & Trends 2019. Cent. Pollut. Control Board 53, 1689–1699 (2020).
19. IITK. Comprehensive study on air pollution and green house Google Scholar. A Rep. Submitt. to Gov. NCT Delhi DPCC Delhi 1–334 (2016).
20. Rizwan S Nongkynrih B Gupta SK Air pollution in Delhi: Its Magnitude and Effects on Health Indian J. Community Med. 2013 38 4 10.4103/0970-0218.106617 23559696
Rizwan, S., Nongkynrih, B. & Gupta, S. K. Air pollution in Delhi: Its Magnitude and Effects on Health. Indian J. Community Med. 38, 4 (2013).23559696 10.4103/0970-0218.106617
21. Bai L Wang J Ma X Lu H Air Pollution Forecasts: An Overview Int. J. Environ. Res. Public Health 2018 15 780 10.3390/ijerph15040780 29673227
Bai, L., Wang, J., Ma, X. & Lu, H. Air Pollution Forecasts: An Overview. Int. J. Environ. Res. Public Health 15, 780 (2018).29673227 10.3390/ijerph15040780
22. Masood A Ahmad K A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches Procedia Comput. Sci. 2020 167 2101 2110 10.1016/j.procs.2020.03.258
Masood, A. & Ahmad, K. A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches. Procedia Comput. Sci. 167, 2101–2110 (2020).10.1016/j.procs.2020.03.258
23. Mo X Zhang L Li H Qu Z A Novel Air Quality Early-Warning System Based on Artificial Intelligence Int. J. Environ. Res. Public Health 2019 16 3505 10.3390/ijerph16193505 31547044
Mo, X., Zhang, L., Li, H. & Qu, Z. A Novel Air Quality Early-Warning System Based on Artificial Intelligence. Int. J. Environ. Res. Public Health 16, 3505 (2019).31547044 10.3390/ijerph16193505
24. Krishan M Air quality modelling using long short-term memory (LSTM) over NCT-Delhi India. Air Qual. Atmos. Heal. 2019 12 899 908 10.1007/s11869-019-00696-7
Krishan, M. et al. Air quality modelling using long short-term memory (LSTM) over NCT-Delhi. India. Air Qual. Atmos. Heal. 12, 899–908 (2019).10.1007/s11869-019-00696-7
25. Singh S Goyal MK An innovative approach to predict atmospheric rivers: Exploring convolutional autoencoder Atmos. Res. 2023 289 106754 10.1016/j.atmosres.2023.106754
Singh, S. & Goyal, M. K. An innovative approach to predict atmospheric rivers: Exploring convolutional autoencoder. Atmos. Res. 289, 106754 (2023).10.1016/j.atmosres.2023.106754
26. Singh S Goyal MK Enhancing climate resilience in businesses: The role of artificial intelligence J. Clean. Prod. 2023 418 138228 10.1016/j.jclepro.2023.138228
Singh, S. & Goyal, M. K. Enhancing climate resilience in businesses: The role of artificial intelligence. J. Clean. Prod. 418, 138228 (2023).10.1016/j.jclepro.2023.138228
27. Li C Hsu NC Tsay S-C A study on the potential applications of satellite data in air quality monitoring and forecasting Atmos. Environ. 2011 45 3663 3675 10.1016/j.atmosenv.2011.04.032
Li, C., Hsu, N. C. & Tsay, S.-C. A study on the potential applications of satellite data in air quality monitoring and forecasting. Atmos. Environ. 45, 3663–3675 (2011).10.1016/j.atmosenv.2011.04.032
28. Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. Journal of time series analysis book review time series analysis: forecasting and control, 5th edition, by. J. Time. Ser. Anal 37, 709–711 (2016).
29. Siwek K Osowski S Data mining methods for prediction of air pollution Int. J. Appl. Math. Comput. Sci. 2016 26 467 478 10.1515/amcs-2016-0033
Siwek, K. & Osowski, S. Data mining methods for prediction of air pollution. Int. J. Appl. Math. Comput. Sci. 26, 467–478 (2016).10.1515/amcs-2016-0033
30. Fu M Wang W Le Z Khorram MS Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model Neural Comput. Appl. 2015 26 1789 1797 10.1007/s00521-015-1853-8
Fu, M., Wang, W., Le, Z. & Khorram, M. S. Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model. Neural Comput. Appl. 26, 1789–1797 (2015).10.1007/s00521-015-1853-8
31. Sekar, C., Gurjar, B. R., Ojha, C. S. P. & Goyal, M. K. Potential Assessment of Neural Network and Decision Tree Algorithms for Forecasting Ambient PM2.5 and CO Concentrations: Case Study. J. Hazardous, Toxic, Radioact. Waste 20, (2016).
32. Akhtar A Masood S Gupta C Masood A Prediction and Analysis of Pollution Levels in Delhi Using Multilayer Perceptron 2018 Springer
Akhtar, A., Masood, S., Gupta, C. & Masood, A. Prediction and Analysis of Pollution Levels in Delhi Using Multilayer Perceptron (Springer, 2018).
33. Li X Peng L Hu Y Shao J Chi T Deep learning architecture for air quality predictions Environ. Sci. Pollut. Res. 2016 23 22408 22417 10.1007/s11356-016-7812-9
Li, X., Peng, L., Hu, Y., Shao, J. & Chi, T. Deep learning architecture for air quality predictions. Environ. Sci. Pollut. Res. 23, 22408–22417 (2016).10.1007/s11356-016-7812-9
34. Huang C-J Kuo P-H A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities Sensors 2018 18 2220 10.3390/s18072220 29996546
Huang, C.-J. & Kuo, P.-H. A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors 18, 2220 (2018).29996546 10.3390/s18072220
35. Ma J Ding Y Cheng JCP Jiang F Wan Z A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2.5 J. Clean. Prod. 2019 237 117729 10.1016/j.jclepro.2019.117729
Ma, J., Ding, Y., Cheng, J. C. P., Jiang, F. & Wan, Z. A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2.5. J. Clean. Prod. 237, 117729 (2019).10.1016/j.jclepro.2019.117729
36. Chang Y-S An LSTM-based aggregated model for air pollution forecasting Atmos. Pollut. Res. 2020 11 1451 1463 10.1016/j.apr.2020.05.015
Chang, Y.-S. et al. An LSTM-based aggregated model for air pollution forecasting. Atmos. Pollut. Res. 11, 1451–1463 (2020).10.1016/j.apr.2020.05.015
37. Biancofiore F Recursive neural network model for analysis and forecast of PM10 and PM2.5 Atmos. Pollut. Res. 2017 8 652 659 10.1016/j.apr.2016.12.014
Biancofiore, F. et al. Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmos. Pollut. Res. 8, 652–659 (2017).10.1016/j.apr.2016.12.014
38. Konovalov IB Beekmann M Meleux F Dutot A Foret G Combining deterministic and statistical approaches for PM10 forecasting in Europe Atmos. Environ. 2009 43 6425 6434 10.1016/j.atmosenv.2009.06.039
Konovalov, I. B., Beekmann, M., Meleux, F., Dutot, A. & Foret, G. Combining deterministic and statistical approaches for PM10 forecasting in Europe. Atmos. Environ. 43, 6425–6434 (2009).10.1016/j.atmosenv.2009.06.039
39. McKendry IG Evaluation of Artificial Neural Networks for Fine Particulate Pollution (PM 10 and PM 2.5) Forecasting J. Air Waste Manage. Assoc. 2002 52 1096 1101 10.1080/10473289.2002.10470836
McKendry, I. G. Evaluation of Artificial Neural Networks for Fine Particulate Pollution (PM 10 and PM 2.5) Forecasting. J. Air Waste Manage. Assoc. 52, 1096–1101 (2002).10.1080/10473289.2002.10470836
40. Dutta A Jinsart W Air Pollution in Indian Cities and Comparison of MLR, ANN and CART Models for Predicting PM10 Concentrations in Guwahati India. Asian J. Atmos. Environ. 2021 15 68 93
Dutta, A. & Jinsart, W. Air Pollution in Indian Cities and Comparison of MLR, ANN and CART Models for Predicting PM10 Concentrations in Guwahati. India. Asian J. Atmos. Environ. 15, 68–93 (2021).
41. Turias IJ González FJ Martin ML Galindo PL Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy Environ. Monit. Assess. 2008 143 131 146 10.1007/s10661-007-9963-0 17929183
Turias, I. J., González, F. J., Martin, M. L. & Galindo, P. L. Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy. Environ. Monit. Assess. 143, 131–146 (2008).17929183 10.1007/s10661-007-9963-0
42. Shang, Z. & He, J. Predicting Hourly <tex>$\mathbf{PM}_{2.5}$</tex> Concentrations Based on Random Forest and Ensemble Neural Network. in 2018 Chinese Automation Congress (CAC) 2341–2345 (IEEE, 2018). 10.1109/CAC.2018.8623175.
43. Bozdağ A Dokuz Y Gökçek ÖB Spatial prediction of PM10 concentration using machine learning algorithms in Ankara Turkey. Environ. Pollut. 2020 263 114635 10.1016/j.envpol.2020.114635 33618491
Bozdağ, A., Dokuz, Y. & Gökçek, Ö. B. Spatial prediction of PM10 concentration using machine learning algorithms in Ankara. Turkey. Environ. Pollut. 263, 114635 (2020).33618491 10.1016/j.envpol.2020.114635
44. Murray CJL Five insights from the Global Burden of Disease Study 2019 Lancet 2020 396 1135 1159 10.1016/S0140-6736(20)31404-5 33069324
Murray, C. J. L. et al. Five insights from the Global Burden of Disease Study 2019. Lancet 396, 1135–1159 (2020).33069324 10.1016/S0140-6736(20)31404-5
45. Haque M Singh R Air Pollution and Human Health in Kolkata, India: A Case Study Climate 2017 5 77 10.3390/cli5040077
Haque, M. & Singh, R. Air Pollution and Human Health in Kolkata, India: A Case Study. Climate 5, 77 (2017).10.3390/cli5040077
46. Rajak R Chattopadhyay A Short and Long Term Exposure to Ambient Air Pollution and Impact on Health in India: A Systematic Review Int. J. Environ. Health Res. 2020 30 593 617 10.1080/09603123.2019.1612042 31070475
Rajak, R. & Chattopadhyay, A. Short and Long Term Exposure to Ambient Air Pollution and Impact on Health in India: A Systematic Review. Int. J. Environ. Health Res. 30, 593–617 (2020).31070475 10.1080/09603123.2019.1612042
47. CPCB. Epidemiological Study on Effect of Air Pollution on Human Health (Adults) in Delhi CENTRAL POLLUTION CONTROL BOARD MINISTRY OF ENVIRONMENT & FORESTS. (2012).
48. â€˜India needs to address challenge of adult immunisationâ€TM - Elets eHealth. https://ehealth.eletsonline.com/2018/10/india-needs-to-address-challenge-of-adult-immunisation/.
49. Raju S Siddharthan T McCormack MC Indoor Air Pollution and Respiratory Health Clin. Chest Med. 2020 41 825 843 10.1016/j.ccm.2020.08.014 33153698
Raju, S., Siddharthan, T. & McCormack, M. C. Indoor Air Pollution and Respiratory Health. Clin. Chest Med. 41, 825–843 (2020).33153698 10.1016/j.ccm.2020.08.014
50. Manisalidis I Stavropoulou E Stavropoulos A Bezirtzoglou E Environmental and Health Impacts of Air Pollution: A Review Front. Public Heal. 2020 8 14 10.3389/fpubh.2020.00014
Manisalidis, I., Stavropoulou, E., Stavropoulos, A. & Bezirtzoglou, E. Environmental and Health Impacts of Air Pollution: A Review. Front. Public Heal. 8, 14 (2020).10.3389/fpubh.2020.00014
51. Fang Z Wu P-Y Lin Y-N Chang T-H Chiu Y Air Pollution’s Impact on the Economic, Social, Medical, and Industrial Injury Environments in China Healthcare 2021 9 261 10.3390/healthcare9030261 33804567
Fang, Z., Wu, P.-Y., Lin, Y.-N., Chang, T.-H. & Chiu, Y. Air Pollution’s Impact on the Economic, Social, Medical, and Industrial Injury Environments in China. Healthcare 9, 261 (2021).33804567 10.3390/healthcare9030261
52. Economy and air pollution - Clean Air Fund. https://www.cleanairfund.org/theme/economics/.
53. OECD. Climate-resilient Infrastructure. Policy Perspectives. OECD Environ. Policy Pap. 1–46 (2018).
54. EPA Research: Environmental Justice and Air Pollution | US EPA. https://www.epa.gov/ej-research/epa-research-environmental-justice-and-air-pollution.
55. Chakraborty S Fu R Massie ST Stephens G Relative influence of meteorological conditions and aerosols on the lifetime of mesoscale convective systems Proc. Natl. Acad. Sci. 2016 113 7426 7431 10.1073/pnas.1601935113 27313203
Chakraborty, S., Fu, R., Massie, S. T. & Stephens, G. Relative influence of meteorological conditions and aerosols on the lifetime of mesoscale convective systems. Proc. Natl. Acad. Sci. 113, 7426–7431 (2016).27313203 10.1073/pnas.1601935113
56. Sturiale & Scuderi The Role of Green Infrastructures in Urban Planning for Climate Change Adaptation Climate 2019 7 119 10.3390/cli7100119
Sturiale & Scuderi. The Role of Green Infrastructures in Urban Planning for Climate Change Adaptation. Climate 7, 119 (2019).10.3390/cli7100119
57. Gulia S Performance evaluation of air pollution control device at traffic intersections in Delhi Int. J. Environ. Sci. Technol. 2022 19 785 796 10.1007/s13762-021-03641-3
Gulia, S. et al. Performance evaluation of air pollution control device at traffic intersections in Delhi. Int. J. Environ. Sci. Technol. 19, 785–796 (2022).10.1007/s13762-021-03641-3
58. Allioui H Mourdi Y Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey Sensors 2023 23 8015 10.3390/s23198015 37836845
Allioui, H. & Mourdi, Y. Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey. Sensors 23, 8015 (2023).37836845 10.3390/s23198015
59. Lalchandani V Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter Sci. Total Environ. 2021 770 145324 10.1016/j.scitotenv.2021.145324 33736388
Lalchandani, V. et al. Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter. Sci. Total Environ. 770, 145324 (2021).33736388 10.1016/j.scitotenv.2021.145324
60. Tobler A Chemical characterization of PM2.5 and source apportionment of organic aerosol in New Delhi India. Sci. Total Environ. 2020 745 140924 10.1016/j.scitotenv.2020.140924 32738681
Tobler, A. et al. Chemical characterization of PM2.5 and source apportionment of organic aerosol in New Delhi. India. Sci. Total Environ. 745, 140924 (2020).32738681 10.1016/j.scitotenv.2020.140924
61. Rai P Real-time measurement and source apportionment of elements in Delhi’s atmosphere Sci. Total Environ. 2020 742 140332 10.1016/j.scitotenv.2020.140332 33167294
Rai, P. et al. Real-time measurement and source apportionment of elements in Delhi’s atmosphere. Sci. Total Environ. 742, 140332 (2020).33167294 10.1016/j.scitotenv.2020.140332
62. Talukdar S Air Pollution in New Delhi during Late Winter: An Overview of a Group of Campaign Studies Focusing on Composition and Sources Atmosphere (Basel). 2021 12 1432 10.3390/atmos12111432
Talukdar, S. et al. Air Pollution in New Delhi during Late Winter: An Overview of a Group of Campaign Studies Focusing on Composition and Sources. Atmosphere (Basel). 12, 1432 (2021).10.3390/atmos12111432
63. Wang T Wei K Ma J Atmospheric Rivers and Mei-yu Rainfall in China: A Case Study of Summer 2020 Adv. Atmos. Sci. 2020 10.1007/s00376-021-1096-9
Wang, T., Wei, K. & Ma, J. Atmospheric Rivers and Mei-yu Rainfall in China: A Case Study of Summer 2020. Adv. Atmos. Sci.10.1007/s00376-021-1096-9 (2020).10.1007/s00376-021-1096-9
64. Sarkar S Chauhan A Kumar R Singh RP Impact of Deadly Dust Storms (May 2018) on Air Quality, Meteorological, and Atmospheric Parameters Over the Northern Parts of India GeoHealth 2019 3 67 80 10.1029/2018GH000170 32159032
Sarkar, S., Chauhan, A., Kumar, R. & Singh, R. P. Impact of Deadly Dust Storms (May 2018) on Air Quality, Meteorological, and Atmospheric Parameters Over the Northern Parts of India. GeoHealth 3, 67–80 (2019).32159032 10.1029/2018GH000170
65. Wei W Comprehensive Assessment of Pollution Sources and Health Impacts in Suburban Area of Shanghai Toxics 2023 11 552 10.3390/toxics11070552 37505518
Wei, W. et al. Comprehensive Assessment of Pollution Sources and Health Impacts in Suburban Area of Shanghai. Toxics 11, 552 (2023).37505518 10.3390/toxics11070552
66. Blanco-Donado EP Source identification and global implications of black carbon Geosci. Front. 2022 13 101149 10.1016/j.gsf.2021.101149
Blanco-Donado, E. P. et al. Source identification and global implications of black carbon. Geosci. Front. 13, 101149 (2022).10.1016/j.gsf.2021.101149
67. Mangaraj P Sahu SK Beig G Yadav R A comprehensive high-resolution gridded emission inventory of anthropogenic sources of air pollutants in Indian megacity Kolkata SN Appl. Sci. 2022 4 117 10.1007/s42452-022-05001-3
Mangaraj, P., Sahu, S. K., Beig, G. & Yadav, R. A comprehensive high-resolution gridded emission inventory of anthropogenic sources of air pollutants in Indian megacity Kolkata. SN Appl. Sci. 4, 117 (2022).10.1007/s42452-022-05001-3
68. Rastogi N Diurnal variability in the spectral characteristics and sources of water-soluble brown carbon aerosols over Delhi Sci. Total Environ. 2021 794 148589 10.1016/j.scitotenv.2021.148589 34214816
Rastogi, N. et al. Diurnal variability in the spectral characteristics and sources of water-soluble brown carbon aerosols over Delhi. Sci. Total Environ. 794, 148589 (2021).34214816 10.1016/j.scitotenv.2021.148589
69. Mukherjee A Sources and atmospheric processing of brown carbon and HULIS in the Indo-Gangetic Plain: Insights from compositional analysis Environ. Pollut. 2020 267 115440 10.1016/j.envpol.2020.115440 32858437
Mukherjee, A. et al. Sources and atmospheric processing of brown carbon and HULIS in the Indo-Gangetic Plain: Insights from compositional analysis. Environ. Pollut. 267, 115440 (2020).32858437 10.1016/j.envpol.2020.115440
70. Tripathi N Characteristics of VOC Composition at Urban and Suburban Sites of New Delhi, India in Winter J. Geophys. Res. Atmos. 2022 10.1029/2021JD035342 35602912
Tripathi, N. et al. Characteristics of VOC Composition at Urban and Suburban Sites of New Delhi, India in Winter. J. Geophys. Res. Atmos.10.1029/2021JD035342 (2022).35602912 10.1029/2021JD035342
71. Act, A. (Prevention and C. of A. P. Air_Act_1981. (1981).
72. Environment (Protection) Act. The Environment (Protection) Act, 1986 Act No. 29 OF 1986. 1–9 (1986).
73. Bill MV Amendment THE GAZETTE OF INDIA EXTRAORDINARY. 2019 1988 4 6
Bill, M. V. Amendment. THE GAZETTE OF INDIA EXTRAORDINARY. 1988, 4–6 (2019).
74. Rengarajan, S., Palaniyappan, D., Ramachandran, P. & Ramachandran, R. National Green Tribunal of India—an observation from environmental judgements. Environ. Sci. Pollut. Res. 25, 11313–11318 (2018).
75. CPCB. Pollution Control Acts, Rules & Notifications Issued Thereunder. Central Pollution Control Board, Ministry of Environment, Forest and Climate Change, Government of India. https://cpcb.nic.in/7thEditionPollutionControlLawSeries2021.pdf (2021).
76. CPCB. National Air Quality Index. Cent. Pollut. Control Board 1–58. https://app.cpcbccr.com/ccr_docs/About_AQI.pdf (2014).
77. National Ambient Air Quality Monitoring. Air Quality Trends and Action For Plan. Naaqms 5. http://cpcb.nic.in/upload/NewItems/NewItem_104_airquality17cities-package-.pdf (2006).
78. Roychowdhury, A. & Somvanshi, A. Breathing Space: How to track and report air pollution under the National Clean Air Programme. Cent. Sci. Environ. (New Delhi, 2020).
79. Roychowdhury, A., Somvanshi, A. & Kaur, S. Urban Lab-Centre for Science and Environment Analysis Status of air quality monitoring in India: Spatial spread, population coverage and data completeness. https://www.cseindia.org/Note-AQM-Network-analysis.pdf (2023).
80. Yadav, R. et al. COVID-19 lockdown and air quality of SAFAR-India metro cities. Urban Clim. 34, 100729 (2020).
81. Lestari P Arrohman MK Damayanti S Klimont Z Emissions and spatial distribution of air pollutants from anthropogenic sources in Jakarta Atmos. Pollut. Res. 2022 13 101521 10.1016/j.apr.2022.101521
Lestari, P., Arrohman, M. K., Damayanti, S. & Klimont, Z. Emissions and spatial distribution of air pollutants from anthropogenic sources in Jakarta. Atmos. Pollut. Res. 13, 101521 (2022).10.1016/j.apr.2022.101521
82. Guttikunda SK Nishadh KA Jawahar P Air pollution knowledge assessments (APnA) for 20 Indian cities Urban Clim. 2019 27 124 141 10.1016/j.uclim.2018.11.005
Guttikunda, S. K., Nishadh, K. A. & Jawahar, P. Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Clim. 27, 124–141 (2019).10.1016/j.uclim.2018.11.005
83. Gargava P Rajagopalan V Source apportionment studies in six Indian cities—drawing broad inferences for urban PM10 reductions Air Qual. Atmos. Heal. 2016 9 471 481 10.1007/s11869-015-0353-4
Gargava, P. & Rajagopalan, V. Source apportionment studies in six Indian cities—drawing broad inferences for urban PM10 reductions. Air Qual. Atmos. Heal. 9, 471–481 (2016).10.1007/s11869-015-0353-4
84. M.C. Mehta And Anr vs Union Of India & Ors on 20 December, 1986. https://indiankanoon.org/doc/1486949/.
85. AFVP 2025. Report of the Expert Committee on Auto Fuel Vision & Policy 2025. Press Inf. Bur. 221, 174. https://cdn.climatepolicyradar.org/navigator/IND/2014/national-auto-fuel-policy-and-auto-fuel-vision-and-policy-2025_c53488e9acdfd8095d576abd64e15892.pdf (2014).
86. Sahu, V. et al. Assessment of a clean cooking fuel distribution scheme in rural households of India – “Pradhan Mantri Ujjwala Yojana (PMUY)”. Energy Sustain. Dev. 81, 101492 (2024).
87. Das, P. K. & Bhat, M. Y. Global electric vehicle adoption: implementation and policy implications for India. Environ. Sci. Pollut. Res. 29, 40612–40622 (2022).
88. Gimeno, L. et al. Major Mechanisms of Atmospheric Moisture Transport and Their Role in Extreme Precipitation Events. Annu. Rev. Environ. Resour. 41, 117–141. 10.1146/annurev-environ-110615-085558 (2016).
89. Thayyib PV State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary Sustainability 2023 15 4026 10.3390/su15054026
Thayyib, P. V. et al. State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary. Sustainability 15, 4026 (2023).10.3390/su15054026
90. Alzubaidi L Review of deep learning: concepts, CNN architectures, challenges, applications, future directions J. Big Data 2021 8 53 10.1186/s40537-021-00444-8 33816053
Alzubaidi, L. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 53 (2021).33816053 10.1186/s40537-021-00444-8
91. Fan, J. et al. A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. IV-4/W2, 15–22 (2017).
92. Rautela KS Kumar D Gandhi BGR Kumar A Dubey AK Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya Uttarakhand. RBRH 2022 10.1590/2318-0331.272220220045
Rautela, K. S., Kumar, D., Gandhi, B. G. R., Kumar, A. & Dubey, A. K. Application of ANNs for the modeling of streamflow, sediment transport, and erosion rate of a high-altitude river system in Western Himalaya. Uttarakhand. RBRH10.1590/2318-0331.272220220045 (2022).10.1590/2318-0331.272220220045
93. Sofi MS Modeling the hydrological response of a snow-fed river in the Kashmir Himalayas through SWAT and Artificial Neural Network Int. J. Environ. Sci. Technol. 2023 10.1007/s13762-023-05170-7
Sofi, M. S. et al. Modeling the hydrological response of a snow-fed river in the Kashmir Himalayas through SWAT and Artificial Neural Network. Int. J. Environ. Sci. Technol.10.1007/s13762-023-05170-7 (2023).10.1007/s13762-023-05170-7
94. Karagulian F Contributions to cities’ ambient particulate matter (PM): A systematic review of local source contributions at global level Atmos. Environ. 2015 120 475 483 10.1016/j.atmosenv.2015.08.087
Karagulian, F. et al. Contributions to cities’ ambient particulate matter (PM): A systematic review of local source contributions at global level. Atmos. Environ. 120, 475–483 (2015).10.1016/j.atmosenv.2015.08.087
95. Mlakar P Božnar M Lesjak M Millán MM Neural Networks Predict Pollution Air Pollution Modeling and Its Application X 1994 Springer
Mlakar, P., Božnar, M. & Lesjak, M. Neural Networks Predict Pollution. In Air Pollution Modeling and Its Application X (ed. Millán, M. M.) (Springer, 1994).
96. Arena P Fortuna L Gallo A Nunnari G Xibilia MG Air pollution estimation via neural networks IFAC Proc. 1995 28 787 792
Arena, P., Fortuna, L., Gallo, A., Nunnari, G. & Xibilia, M. G. Air pollution estimation via neural networks. IFAC Proc. 28, 787–792 (1995).
97. Sohn SH Oh SC Yeo Y-K Prediction of air pollutants by using an artificial neural network Korean J. Chem. Eng. 1999 16 382 387 10.1007/BF02707129
Sohn, S. H., Oh, S. C. & Yeo, Y.-K. Prediction of air pollutants by using an artificial neural network. Korean J. Chem. Eng. 16, 382–387 (1999).10.1007/BF02707129
98. Slini T Karatzas K Moussiopoulos N Correlation of air pollution and meteorological data using neural networks Int. J. Environ. Pollut. 2003 20 218 10.1504/IJEP.2003.004279
Slini, T., Karatzas, K. & Moussiopoulos, N. Correlation of air pollution and meteorological data using neural networks. Int. J. Environ. Pollut. 20, 218 (2003).10.1504/IJEP.2003.004279
99. Kandya A Forecasting the Tropospheric Ozone using Artificial Neural Network Modelling Approach: A Case Study of Megacity Madras India. J. Civ. Environ. Eng. 2013 01 2
Kandya, A. Forecasting the Tropospheric Ozone using Artificial Neural Network Modelling Approach: A Case Study of Megacity Madras. India. J. Civ. Environ. Eng. 01, 2 (2013).
100. Chaloulakou A Saisana M Spyrellis N Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens Sci. Total Environ. 2003 313 1 13 10.1016/S0048-9697(03)00335-8 12922056
Chaloulakou, A., Saisana, M. & Spyrellis, N. Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Sci. Total Environ. 313, 1–13 (2003).12922056 10.1016/S0048-9697(03)00335-8
101. Mishra D Goyal P Development of artificial intelligence based NO 2 forecasting models at Taj Mahal Agra. Atmos. Pollut. Res. 2015 6 99 106 10.5094/APR.2015.012
Mishra, D. & Goyal, P. Development of artificial intelligence based NO 2 forecasting models at Taj Mahal. Agra. Atmos. Pollut. Res. 6, 99–106 (2015).10.5094/APR.2015.012
102. Fernando HJS Forecasting PM10 in metropolitan areas: Efficacy of neural networks Environ. Pollut. 2012 163 62 67 10.1016/j.envpol.2011.12.018 22325432
Fernando, H. J. S. et al. Forecasting PM10 in metropolitan areas: Efficacy of neural networks. Environ. Pollut. 163, 62–67 (2012).22325432 10.1016/j.envpol.2011.12.018
103. Grivas G Chaloulakou A Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens Greece. Atmos. Environ. 2006 40 1216 1229 10.1016/j.atmosenv.2005.10.036
Grivas, G. & Chaloulakou, A. Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens. Greece. Atmos. Environ. 40, 1216–1229 (2006).10.1016/j.atmosenv.2005.10.036
104. Suleiman A Tight MR Quinn AD Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter Environ. Model. Assess. 2016 21 731 750 10.1007/s10666-016-9507-5
Suleiman, A., Tight, M. R. & Quinn, A. D. Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter. Environ. Model. Assess. 21, 731–750 (2016).10.1007/s10666-016-9507-5
105. Zhang H Liu Y Shi R Yao Q Evaluation of PM 10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City China. J. Air Waste Manage. Assoc. 2013 63 755 763 10.1080/10962247.2012.755940
Zhang, H., Liu, Y., Shi, R. & Yao, Q. Evaluation of PM 10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City. China. J. Air Waste Manage. Assoc. 63, 755–763 (2013).10.1080/10962247.2012.755940
106. Paschalidou AK Karakitsios S Kleanthous S Kassomenos PA Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: Implications to local environmental management Environ. Sci. Pollut. Res. 2011 18 316 327 10.1007/s11356-010-0375-2
Paschalidou, A. K., Karakitsios, S., Kleanthous, S. & Kassomenos, P. A. Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: Implications to local environmental management. Environ. Sci. Pollut. Res. 18, 316–327 (2011).10.1007/s11356-010-0375-2
107. Mishra D Goyal P Upadhyay A Artificial intelligence based approach to forecast PM 2.5 during haze episodes: A case study of Delhi India Atmos. Environ. 2015 102 239 248 10.1016/j.atmosenv.2014.11.050
Mishra, D., Goyal, P. & Upadhyay, A. Artificial intelligence based approach to forecast PM 2.5 during haze episodes: A case study of Delhi India. Atmos. Environ. 102, 239–248 (2015).10.1016/j.atmosenv.2014.11.050
108. Moisan, S., Herrera, R. & Clements, A. A dynamic multiple equation approach for forecasting PM 2.5 pollution in Santiago. Chile. Int. J. Forecast. 34, 566–581 (2018).
109. Liu H Jin K Duan Z Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China Atmos. Pollut. Res. 2019 10 1588 1600 10.1016/j.apr.2019.05.007
Liu, H., Jin, K. & Duan, Z. Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China. Atmos. Pollut. Res. 10, 1588–1600 (2019).10.1016/j.apr.2019.05.007
110. Chen J A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide Environ. Int. 2019 130 104934 10.1016/j.envint.2019.104934 31229871
Chen, J. et al. A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environ. Int. 130, 104934 (2019).31229871 10.1016/j.envint.2019.104934
111. Jain S Khare M Adaptive neuro-fuzzy modeling for prediction of ambient CO concentration at urban intersections and roadways Air Qual. Atmos. Heal. 2010 3 203 212 10.1007/s11869-010-0073-8
Jain, S. & Khare, M. Adaptive neuro-fuzzy modeling for prediction of ambient CO concentration at urban intersections and roadways. Air Qual. Atmos. Heal. 3, 203–212 (2010).10.1007/s11869-010-0073-8
112. Carbajal-Hernández JJ Sánchez-Fernández LP Carrasco-Ochoa JA Martínez-Trinidad JF Assessment and prediction of air quality using fuzzy logic and autoregressive models Atmos. Environ. 2012 60 37 50 10.1016/j.atmosenv.2012.06.004
Carbajal-Hernández, J. J., Sánchez-Fernández, L. P., Carrasco-Ochoa, J. A. & Martínez-Trinidad, J. F. Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmos. Environ. 60, 37–50 (2012).10.1016/j.atmosenv.2012.06.004
113. Al-Shammari ET Public warning systems for forecasting ambient ozone pollution in Kuwait Environ. Syst. Res. 2013 2 2 10.1186/2193-2697-2-2
Al-Shammari, E. T. Public warning systems for forecasting ambient ozone pollution in Kuwait. Environ. Syst. Res. 2, 2 (2013).10.1186/2193-2697-2-2
114. Bougoudis I Demertzis K Iliadis L HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens Neural Comput. Appl. 2016 27 1191 1206 10.1007/s00521-015-1927-7
Bougoudis, I., Demertzis, K. & Iliadis, L. HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens. Neural Comput. Appl. 27, 1191–1206 (2016).10.1007/s00521-015-1927-7
115. Song Y Qin S Qu J Liu F The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region Atmos. Environ. 2015 118 58 69 10.1016/j.atmosenv.2015.06.032
Song, Y., Qin, S., Qu, J. & Liu, F. The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region. Atmos. Environ. 118, 58–69 (2015).10.1016/j.atmosenv.2015.06.032
116. Wang J Li H Lu H Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China Appl. Soft Comput. 2018 71 783 799 10.1016/j.asoc.2018.07.030
Wang, J., Li, H. & Lu, H. Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China. Appl. Soft Comput. 71, 783–799 (2018).10.1016/j.asoc.2018.07.030
117. Behal V Singh R Personalised healthcare model for monitoring and prediction of airpollution: machine learning approach J. Exp. Theor. Artif. Intell. 2021 33 425 449 10.1080/0952813X.2020.1744197
Behal, V. & Singh, R. Personalised healthcare model for monitoring and prediction of airpollution: machine learning approach. J. Exp. Theor. Artif. Intell. 33, 425–449 (2021).10.1080/0952813X.2020.1744197
118. Arbabsiar MH Ebrahimi Farsangi MA Mansouri H Fuzzy logic modelling to predict the level of geotechnical risks in rock tunnel boring machine (TBM) tunnelling Rud. Zb. 2020 35 1 14
Arbabsiar, M. H., Ebrahimi Farsangi, M. A. & Mansouri, H. Fuzzy logic modelling to predict the level of geotechnical risks in rock tunnel boring machine (TBM) tunnelling. Rud. Zb. 35, 1–14 (2020).
119. Feng Y Zhang W Sun D Zhang L Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification Atmos. Environ. 2011 45 1979 1985 10.1016/j.atmosenv.2011.01.022
Feng, Y., Zhang, W., Sun, D. & Zhang, L. Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmos. Environ. 45, 1979–1985 (2011).10.1016/j.atmosenv.2011.01.022
120. Yeganeh B Motlagh MSP Rashidi Y Kamalan H Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model Atmos. Environ. 2012 55 357 365 10.1016/j.atmosenv.2012.02.092
Yeganeh, B., Motlagh, M. S. P., Rashidi, Y. & Kamalan, H. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmos. Environ. 55, 357–365 (2012).10.1016/j.atmosenv.2012.02.092
121. García Nieto PJ Combarro EF del Coz Díaz JJ Montañés E A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study Appl. Math. Comput. 2013 219 8923 8937
García Nieto, P. J., Combarro, E. F., del Coz Díaz, J. J. & Montañés, E. A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study. Appl. Math. Comput. 219, 8923–8937 (2013).
122. Luna AS Paredes MLL de Oliveira GCG Corrêa SM Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro Brazil. Atmos. Environ. 2014 98 98 104 10.1016/j.atmosenv.2014.08.060
Luna, A. S., Paredes, M. L. L., de Oliveira, G. C. G. & Corrêa, S. M. Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro. Brazil. Atmos. Environ. 98, 98–104 (2014).10.1016/j.atmosenv.2014.08.060
123. Wang P Liu Y Qin Z Zhang G A novel hybrid forecasting model for PM10 and SO2 daily concentrations Sci. Total Environ. 2015 505 1202 1212 10.1016/j.scitotenv.2014.10.078 25461118
Wang, P., Liu, Y., Qin, Z. & Zhang, G. A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Sci. Total Environ. 505, 1202–1212 (2015).25461118 10.1016/j.scitotenv.2014.10.078
124. Freeman BS Taylor G Gharabaghi B Thé J Forecasting air quality time series using deep learning J. Air Waste Manage. Assoc. 2018 68 866 886 10.1080/10962247.2018.1459956
Freeman, B. S., Taylor, G., Gharabaghi, B. & Thé, J. Forecasting air quality time series using deep learning. J. Air Waste Manage. Assoc. 68, 866–886 (2018).10.1080/10962247.2018.1459956
125. Wang J Song G A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction Neurocomputing 2018 314 198 206 10.1016/j.neucom.2018.06.049
Wang, J. & Song, G. A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction. Neurocomputing 314, 198–206 (2018).10.1016/j.neucom.2018.06.049
126. Zhou Y Chang F-J Chang L-C Kao I-F Wang Y-S Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts J. Clean. Prod. 2019 209 134 145 10.1016/j.jclepro.2018.10.243
Zhou, Y., Chang, F.-J., Chang, L.-C., Kao, I.-F. & Wang, Y.-S. Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J. Clean. Prod. 209, 134–145 (2019).10.1016/j.jclepro.2018.10.243
127. Soh P-W Chang J-W Huang J-W Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations IEEE Access 2018 6 38186 38199 10.1109/ACCESS.2018.2849820
Soh, P.-W., Chang, J.-W. & Huang, J.-W. Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations. IEEE Access 6, 38186–38199 (2018).10.1109/ACCESS.2018.2849820
128. Qi Y Li Q Karimian H Liu D A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory Sci. Total Environ. 2019 664 1 10 10.1016/j.scitotenv.2019.01.333 30743109
Qi, Y., Li, Q., Karimian, H. & Liu, D. A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Sci. Total Environ. 664, 1–10 (2019).30743109 10.1016/j.scitotenv.2019.01.333
129. Li, Y., Huang, J. & Luo, J. Using user generated online photos to estimate and monitor air pollution in major cities. Proceedings of the 7th International Conference on Internet Multimedia Computing and Service 1–5 (ACM, New York, NY, USA). 10.1145/2808492.2808564. (2015).
130. Zhang L Nan Z Xu Y Li S Hydrological impacts of land use change and climate variability in the headwater region of the Heihe River Basin, northwest China PLoS One 2016 11 1 25
Zhang, L., Nan, Z., Xu, Y. & Li, S. Hydrological impacts of land use change and climate variability in the headwater region of the Heihe River Basin, northwest China. PLoS One 11, 1–25 (2016).
131. Li X Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation Environ. Pollut. 2017 231 997 1004 10.1016/j.envpol.2017.08.114 28898956
Li, X. et al. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environ. Pollut. 231, 997–1004 (2017).28898956 10.1016/j.envpol.2017.08.114
132. Zhang, C. et al. On Estimating Air Pollution from Photos Using Convolutional Neural Network. in Proceedings of the 24th ACM international conference on Multimedia 297–301 (ACM, New York, NY, USA). 10.1145/2964284.2967230. (2016).
133. Qiu Y Regional aerosol forecasts based on deep learning and numerical weather prediction Npj Clim. Atmos. Sci. 2023 6 71 10.1038/s41612-023-00397-0
Qiu, Y. et al. Regional aerosol forecasts based on deep learning and numerical weather prediction. Npj Clim. Atmos. Sci. 6, 71 (2023).10.1038/s41612-023-00397-0
134. Goyal MK Rautela KS Aerosol Atmospheric Rivers: Detection and Spatio-Temporal Patterns. 2024 10.1007/978-3-031-66758-9_2
Goyal, M. K. & Rautela, K. S. Aerosol Atmospheric Rivers: Detection and Spatio-Temporal Patterns.10.1007/978-3-031-66758-9_2 (2024).10.1007/978-3-031-66758-9_2
135. Du S Li T Yang Y Horng S-J Deep Air Quality Forecasting Using Hybrid Deep Learning Framework IEEE Trans. Knowl. Data Eng. 2021 33 2412 2424 10.1109/TKDE.2019.2954510
Du, S., Li, T., Yang, Y. & Horng, S.-J. Deep Air Quality Forecasting Using Hybrid Deep Learning Framework. IEEE Trans. Knowl. Data Eng. 33, 2412–2424 (2021).10.1109/TKDE.2019.2954510
136. Araujo LN Belotti JT Alves TA de Tadano YS Siqueira H Ensemble method based on Artificial Neural Networks to estimate air pollution health risks Environ. Model. Softw. 2020 123 104567 10.1016/j.envsoft.2019.104567
Araujo, L. N., Belotti, J. T., Alves, T. A., de Tadano, Y. S. & Siqueira, H. Ensemble method based on Artificial Neural Networks to estimate air pollution health risks. Environ. Model. Softw. 123, 104567 (2020).10.1016/j.envsoft.2019.104567
137. Randles CA The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation J. Clim. 2017 30 6823 6850 10.1175/JCLI-D-16-0609.1 29576684
Randles, C. A. et al. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation. J. Clim. 30, 6823–6850 (2017).29576684 10.1175/JCLI-D-16-0609.1
138. Rautela KS Singh S Goyal MK Aerosol atmospheric rivers: patterns, impacts, and societal insights Environ. Sci. Pollut. Res. 2024 10.1007/s11356-024-34625-8
Rautela, K. S., Singh, S. & Goyal, M. K. Aerosol atmospheric rivers: patterns, impacts, and societal insights. Environ. Sci. Pollut. Res.10.1007/s11356-024-34625-8 (2024).10.1007/s11356-024-34625-8
139. Buchard V Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States Atmos. Environ. 2016 125 100 111 10.1016/j.atmosenv.2015.11.004
Buchard, V. et al. Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States. Atmos. Environ. 125, 100–111 (2016).10.1016/j.atmosenv.2015.11.004
140. Provençal S Buchard V da Silva AM Leduc R Barrette N Evaluation of PM surface concentrations simulated by Version 1 of NASA’s MERRA Aerosol Reanalysis over Europe Atmos. Pollut. Res. 2017 8 374 382 10.1016/j.apr.2016.10.009 29628782
Provençal, S., Buchard, V., da Silva, A. M., Leduc, R. & Barrette, N. Evaluation of PM surface concentrations simulated by Version 1 of NASA’s MERRA Aerosol Reanalysis over Europe. Atmos. Pollut. Res. 8, 374–382 (2017).29628782 10.1016/j.apr.2016.10.009
141. Singh S Goyal MK Jha S Role of large-scale climate oscillations in precipitation extremes associated with atmospheric rivers: nonstationary framework Hydrol. Sci. J. 2023 10.1080/02626667.2022.2159412
Singh, S., Goyal, M. K. & Jha, S. Role of large-scale climate oscillations in precipitation extremes associated with atmospheric rivers: nonstationary framework. Hydrol. Sci. J.10.1080/02626667.2022.2159412 (2023).10.1080/02626667.2022.2159412
142. Cheggoju N Satpute VR Blind quality scalable video compression algorithm for low bit-rate coding Multimed. Tools Appl. 2022 81 33715 33730 10.1007/s11042-022-12061-5
Cheggoju, N. & Satpute, V. R. Blind quality scalable video compression algorithm for low bit-rate coding. Multimed. Tools Appl. 81, 33715–33730 (2022).10.1007/s11042-022-12061-5
