Air Pollution Management Research Articles

Incentivizing emission controls toward clean air and carbon neutrality in China: Perspectives from a risk-based approach for air quality management.

Air Quality Standards (AQSs) worldwide have continued to employ concentration-based approaches since their first implementation under the 1970 Clean Air Act in the United States. The primary objective of establishing these AQSs is widely recognized as protecting public health. With the significantly improved understanding of the health risks associated with air pollutants today, it is an opportune time to reassess air pollution management from a fundamental risk perspective. This study applied a risk-based AQS system to assess the overall mortality risks associated with both long- and short-term exposure to air pollution in China between 2015 and 2022. The analyses revealed that the health benefits resulting from reductions in concentrations of non-ozone (O3) pollutants exceeded the health risks associated with increasing O3 concentrations. As a result, the overall mortality risk across China showed a significant descending trend. During the initial phase of decarbonization, emission reductions may unintentionally lead to increased O3 concentrations in China due to the non-linear response of O3 to its precursors. Nevertheless, the overall health benefits derived from emission controls incentivize governments to implement stringent measures toward achieving carbon neutrality. These findings highlight the substantial benefits of applying the risk-based AQS system for synergistic health and carbon management.

Air Pollution Management and Policy in Sub-Saharan Africa: A Systematic Review

Air Pollution Management and Policy in Sub-Saharan Africa: A Systematic Review

Air Quality Legislation in Australia and Canada—A Review

Air pollution is a pervasive global issue affecting human health, ecosystems, and the environment. This paper reviews the evolution and implementation of air quality legislation in Australia and Canada, providing a comparative analysis of their approaches to managing air pollution. Both countries have established robust legal frameworks to address air quality issues, involving governmental policies, regulatory mechanisms, and collaborative efforts among stakeholders. Australia began its air quality regulation at the state level in the 1950s, evolving into a national framework with the National Environment Protection Measure for Ambient Air Quality in 1998. In contrast, Canada centralized its efforts with the Clean Air Act in 1971, complemented by the National Air Pollution Surveillance program. Key findings reveal that, while both nations have achieved significant improvements in air quality, challenges remain in addressing the broader impacts of air pollution, such as climate change and health-related economic burdens. Australia’s air quality standards are generally more stringent than Canada’s for particulate matter but more relaxed for sulfur dioxide, nitrogen dioxide, and ozone. Both countries employ advanced air quality monitoring and reporting systems, with Australia’s Air Quality Index and Canada’s Air Quality Health Index providing critical public health information. The study highlights the need for continuous improvement and a more integrated approach to air quality management. By examining the legislative and regulatory landscapes of Australia and Canada, this paper offers valuable insights for other countries striving to enhance their air quality governance and mitigate the adverse effects of air pollution on a global scale.

Mapping environmental perceptions in Romania: A mixed-methods research

The present study investigates environmental perceptions in Romania, emphasizing their role in shaping individual and collective responses to ecological challenges. By exploring how people understand and interact with their natural environment, the research aims to explore values, needs, behaviors, and motivations that drive pro-environmental actions. We used a mixed-methodology approach, combining qualitative and quantitative analyses to capture a comprehensive view of these perceptions. An innovative empathy map was developed through one-on-one interviews to visualize and analyze participants' perceptions of the environment. Participants were characterized by a biocentric-anthropocentric value orientation, balancing respect for nature with human needs. They considered that the top three environmental problems in Romania were: water and air pollution, deforestation, and poor waste management. Complementing this, a bibliometric analysis was conducted to examine the co-occurrence of keywords related to environmental perceptions in the academic literature, providing a quantitative perspective that aligned with the themes from the qualitative analysis. The strong presence of terms like “intention” and “energy consumption” in the analysis of keywords co-occurrence supported the idea that personal and social norms around energy use were significant themes in the environmental literature dedicated to Romania, which were also central to the participants’ perceptions. The findings contribute to a deeper understanding of environmental perceptions in Romania, offering useful insights for policymakers and environmental managers to promote sustainable development and pro-environmental behaviors in the region.

Unraveling the Complexities of Ozone and PM2.5 Pollution in the Pearl River Delta region: impacts of Precursors Emissions and Meteorological factors, and Effective Mitigation Strategies

Ozone (O3) and fine particulate matter (PM2.5) are known to be interconnected due to shared precursor compounds. While numerous studies have examined the impact of precursors and meteorological factors on compound pollution events, few have proposed effective mitigation strategies tailored to specific regions. In this study, we conducted simulations of two types of O3 and PM2.5 pollution events in the Pearl River Delta (PRD) region during 2018 using the GEOS-Chem model. We applied a multiple linear regression model to quantify and distinguish the contributions of precursor emissions and meteorological factors to these events. Our findings highlight the predominant role of precursor emission factors in driving these pollution events. Notably, reducing NOx emissions in the Pearl River Estuary (PRE) region was found to exacerbate O3 pollution during specific periods, while reducing emissions of C4 alkanes (ALK4), lumped C3 alkenes (PRPE) and NH3 in proportion to their respective contributions emerged as an effective strategy to mitigate combined O3 and PM2.5 pollution. This research elucidates the mechanisms underlying O3 and PM2.5 compound pollution in the PRD region and presents a practical and significant approach to managing air pollution in this area.

The AI-assisted Traditional Design Methods for the Construction Sustainability: A Case Study of the Lisu Ethnic Minority Village

In recent decades, economic and technical advancements have surged tremendously, accompanied by non-regulatory and unsustainable use of non-renewable assets. The ecological field of toxicology has garnered considerable focus on elucidating the impact of harmful substances on people's wellness. Environmental hazardous chemicals induce many illnesses, posing a heightened danger to youngsters, pregnant women, the elderly, and anyone with preexisting health conditions. Air pollution adversely impacts human health, leading to heightened morbidity and death, necessitating intensified toxicological investigations on industrial air pollution affecting the general populace. It is necessary to develop a computerized Ecological Toxicology-based Air Pollution Management System. Constructive social development is driven by cultural education. It can guarantee a sustainable future, protect national history, and unleash community potential. This study examines Chinese architects' perspectives and understanding of sustainability in village revitalization and conservation. It incorporates traditional Lisu building techniques and materials in Hunan, China. To close research gaps, this study looks at the contributions made by architects to traditional practice integration and environmentally conscious architectural design. This study proposes an IoT-enabled Environmental Toxicology platform for air pollution surveillance, utilizing artificial intelligence (AI) techniques to mitigate the limits of standard monitoring systems and lower total costs, enhancing human wellness. The research examined the intricate interaction between architects' understanding of sustainability, Lisu building techniques and materials, and sustainable architectural designs in village preservation and revitalization programs using path analysis on data from 326 architects in Hunan Province. Lisu building practices and materials strongly correlate with architects' awareness of and attitudes toward sustainability. According to the research, these characteristics are necessary for sustainable architectural solutions that support village revival and preservation. This study has significant theoretical and practical ramifications for academics, lawmakers, and architects. To evaluate the air quality monitoring efficacy of the suggested approach, a comprehensive series of simulation analyses is conducted, and the results are assessed at durations of 5, 15, 30, and 60 minutes. The experimental results demonstrate the superior efficacy of the suggested model compared to current methodologies. These findings could lead to the creation of sustainable design solutions that advance rural development. A thorough overview of upcoming studies on sustainable design approaches in Chinese contexts rounds out this talk.

Spatial and temporal patterns of urban air pollution in tehran with a focus on PM2.5 and associated pollutants

Understanding the spatial and temporal dynamics of air pollutants is crucial for effective urban air pollution management. This study focuses on the temporal dynamics of air quality monitoring stations (AQMSs) and the association among air pollutants, particularly PM2.5, in Tehran, Iran. Using time series clustering and the Copula model, we analyzed data from 2019 to 2022. We found that the levels and dynamics of O3 and SO2 were similar across most AQMSs and unrelated to geographical positions. CO levels and dynamics were consistent among urban and border AQMSs, with higher concentrations in urban stations. NO2 levels and dynamics varied significantly among northern AQMSs with no relationship geographical positions. PM10 levels and dynamics had a relationship with geographical positions, with western clusters having the highest and northern clusters the lowest concentrations. The dynamics of PM2.5 showed significant relationship among AQMSs in the eastern, southern, and western regions, but not in the north. We also observed that PM10 and O3 levels were higher in warm seasons, whereas CO, SO2, NO2, and PM2.5 levels were higher in cold seasons. Most pollutants, except O3, peaked during traffic hours. Notably, the significant increase in PM2.5 since spring 2021 was primarily due to PM10. Policymakers should focus on these spatial and temporal variations to improve urban air quality and public health outcomes through targeted interventions.

Impacts of tropical cyclone–heat wave compound events on surface ozone in eastern China: comparison between the Yangtze River and Pearl River deltas

Abstract. China has implemented some air pollution management measures in recent years, yet severe ozone pollution remains a significant issue. The southeastern coast of China (SECC) is often influenced by hot extremes and tropical cyclones (TCs), and the two can occur simultaneously (TC–HDs). The compound TC–HDs show a rising trend in the summers of 2014–2019, potentially affecting ozone pollution. Here, we found that surface ozone concentrations over the SECC are more elevated during extremely hot days than the summer climatology. However, compared to extremely hot days alone (AHDs), the maximum 8 h average ozone (MDA8 O3) concentration increases by an average of 6.8 µg m−3 in the Pearl River Delta (PRD) and decreases by 13.2 µg m−3 in the Yangtze River Delta (YRD) during the compound TC–HDs. The meteorological conditions during AHDs favor the chemical production of ozone over the SECC, exhibiting increased temperature and solar radiation and decreased relative humidity. Relative to AHDs, strong northeasterly winds prevail in the SECC during TC–HDs, suggesting the potential of ozone cross-regional transport between YRD and PRD. The process analysis in the chemical transport model (GEOS-Chem) suggests that relative to AHDs, the chemical production of ozone is enhanced in YRD during TC–HDs, while horizontal transport alleviates ozone pollution in YRD but worsens it in PRD through cross-regional transport. The results highlight the significant effects of cross-regional transport in modulating ozone pollution in the two megacity clusters during hot extremes accompanied by TC activities, giving insight into future ozone control measures over the SECC under global warming.

Large eddy simulation of the dispersion of short duration emissions: Implications for the metrological evaluation of remote sensing devices for on-road emissions monitoring

Remote sensing techniques have emerged as valuable tools for characterizing pollutant emissions from large vehicle fleets and identifying high emitter single vehicles in real driving conditions. Nevertheless, the use of these systems for official emission control purposes by public administrations is an issue because the remote sensing devices must obtain official metrological certification, which currently lacks an international technical standard. The fluid dynamic study that we present demonstrates the promising potential of using pulsed synthetic reference plumes of known chemical composition in order to simulate exhaust emissions produced by combustion engine vehicles in a repetitive and controlled way. This scheme would facilitate the implementation of these complex metrological certification tests and drastically reduce the potential costs associated to these certifications and the emission of gases. In this paper, the atmospheric dispersion of the synthetic puff-like plumes after being released from a vehicle has been studied through fluid dynamic simulations, in order to identify their optimal usage conditions as reference materials. The simulations have allowed to study the evolution of two types of reference short plumes (puffs generated at 2 and 6 bars) from a vehicle at static and dynamic conditions. Results show that, in spite of the fast dispersion of these puffs, it is possible to accurately determine their chemical composition by optical techniques, for instance, by differential absorption spectroscopy. This opens the way for designing advanced and robust metrological evaluation procedures that could be the basis of a future technical standard for the certification of optical remote sensors of traffic emissions. This would allow future deployment of those certificated remote sensors on roads, contributing to a sustainable mobility and effective air pollution management strategies.

Multi-scale modeling of aerosol transport in a mouth-to-truncated bronchial tree system

Computational fluid particle dynamics (CFPD) is widely employed to predict aerosol transport in a truncated bronchial tree model on account of its capacity to reveal details of flow field and particle movement. However, setting a physiologically consistent boundary condition in the CFPD for the idealized or image-based truncated bronchial tree model is still a challenge. This paper proposes a multi-scale modeling method, which contains an Extend-Bronchial tree-Network (EBN) boundary condition for a mouth-to-truncated bronchi system. The comparison between EBN boundary condition and a commonly used uniform pressure (UP) boundary condition is conducted. Subsequently, EBN method is used to study the nano-micron (100 nm–10 μm) particles transport in the mouth-to-truncated bronchi model at different inhalation volume rates (15, 60, 90 L/min). Results show that EBN method is more physiologically rational and two methods differ in flow distribution in lobes, vortex structure, and particle transport. The maximum difference in flow rate distribution in lobes between two methods is about 20 %, while the maximum relative disparity of particle penetration fraction from lobes and deposition fraction in the TLB is about 93 % and 30 %, respectively. Meanwhile, this paper reveals the variation of deposition fraction and penetration fraction with the changes in particle diameter and inhalation volume. Deposition efficiency, deposition hotspots and deposition mechanism are also analyzed with inlet Stokes number (Stk) and Reynolds number (Re). This research establishes a foundation for the simulation of aerosol transport in a whole respiratory tract and provides references for inhalation drug delivery and air pollutant management.

Comparative Study of Air Pollution with Ayurveda and its Management

Background Environmental pollution is increasing day by day and imposing severe and irreversible damage to the world. Air pollution is one of the biggest threats for the environment and affecting humans, animals, crops, cities, forests, aquatic ecosystems. Environmental Toxicology is the science concerned with study of harmful effects of various chemical, biological and physical agents on living organisms. In Ayurveda Charakacharya has briefly described the environmental health in chapter named Janpadodhvansaniyavimanam. Ayurveda has a dedicated branch called Agadatantra which deals with the toxicological aspects of living entities of environment. Aims and objectives We aim to relate and validate this knowledge pertaining to the air pollution. Material and method the references available regarding the air pollution in classical texts were studied and compared with the available information in the contemporary medical literatures. Signs of air pollution given in the text compared and studied with signs of Air pollution. Discussion Ayurveda terms of Vikrut and Prakrut Vayu described in Granthas are found to be relatable with contemporary findings. Conclusion Air pollution described in Ayurveda are very precise and explanatory. Management of air pollution with this ancient science are the new open windows for healthy air breathing for every living organism.

Development of Machine Learning and Deep Learning Prediction Models for PM2.5 in Ho Chi Minh City, Vietnam

The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution sources and trends, ultimately contributing to more effective and targeted environmental protection strategies. Ho Chi Minh City (HCMC), a major metropolitan area in southern Vietnam, has experienced a significant rise in air pollution levels, particularly PM2.5, in recent years, creating substantial risks to both public health and the environment. Given the challenges posed by air quality issues, it is essential to develop robust methodologies for predicting PM2.5 concentrations in HCMC. This study seeks to develop and evaluate multiple machine learning and deep learning models for predicting PM2.5 concentrations in HCMC, Vietnam, utilizing PM2.5 and meteorological data over 911 days, from 1 January 2021 to 30 June 2023. Six algorithms were applied: random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), artificial neural network (ANN), generalized regression neural network (GRNN), and convolutional neural network (CNN). The results indicated that the ANN is the most effective algorithm for predicting PM2.5 concentrations, with an index of agreement (IOA) value of 0.736 and the lowest prediction errors during the testing phase. These findings imply that the ANN algorithm could serve as an effective tool for predicting PM2.5 concentrations in urban environments, particularly in HCMC. This study provides valuable insights into the factors that affect PM2.5 concentrations in HCMC and emphasizes the capacity of AI methodologies in reducing atmospheric pollution. Additionally, it offers valuable insights for policymakers and health officials to implement targeted interventions aimed at reducing air pollution and improving public health.

Does administrative division adjustment reduce air pollution? Evidence from city-county mergers in China

Administrative division adjustment plays a crucial role in transforming the spatial layout of cities and effectively contributes to green transformation and high-quality regional economic development. We empirically investigate the impact of administrative division adjustment, exemplified by city-county mergers (CCM), on air pollution using the difference-in-differences method. Our results demonstrate that CCM significantly reduces air pollution, which passes a series of robustness tests including heterogeneous treatment effect tests. Mechanism analysis reveals that CCM reduces air pollution by optimizing industrial structure, promoting market allocation, and constraining government size; geo-climatic conditions significantly moderate the haze reduction effect of CCM. Further analyses indicate that the haze reduction effect of CCM is more pronounced in the eastern and western regions, the southeast regions of the Hu Line, and non-resource-based regions. Moreover, CCM not only alleviates air pollution in border areas and mitigates the pollution boundary effect but also yields the social welfare effect. This paper provides valuable insights for maximizing the haze reduction potential of administrative division adjustment, understanding the role of Chinese-style decentralization in regional air pollution management, especially in border areas, and supporting the achievement of carbon emission reduction and sustainable development goals.

Monitoring the temporal variations of plant stress using the air pollution tolerance index in the Sejzi industrial area (Isfahan, Iran).

The objective of this study was to screen air pollution-induced stress in some plant species in the Sejzi industrial region (Isfahan, Iran). An assessment of APTI and other physiological and biochemical features of significant species in the area was conducted across three seasons: spring, summer, and autumn. The physiological and biochemical factors of the following species were evaluated: Limonium persicum, Atriplex lentiformis, Nitraria schoberi, Haloxylon persicum, Tamarix hispida, Zygophyllum atriplicoides, Karelinia caspica, and Prosopis farcta. The physiological factors assessed included acidity and relative humidity content, while the biochemical factors assessed included proline, sugar, ascorbic acid, and total chlorophyll. Subsequently, a thorough evaluation was carried out on the species under investigation to ascertain their biomonitors' capabilities and APTI. The study findings indicated that the species P. farcta, N. schoberi, and K. caspica consistently had high APTI values during the spring, autumn, and summer seasons, classifying them as tolerant plant species. Conversely, the observed traits showed significant fluctuations across the seasons. The investigation's findings indicate that the species L. persicum, N. schoberi, and K. caspica exhibit higher annual averages of chlorophyll a, chlorophyll b, total chlorophyll, and carotenoid compared to other species. The examination of the annual variation in the tolerance levels of plant species to pollution ranked from highest to lowest was as follows: N. schoberi, P. farcta, K. caspica, Z. atriplicoides, H. persicum, T. hispida, L. persicum, and A. lentiformis. Moreover, based on the annual average, the primary determinants that impact the APTI in the species being studied include ascorbic acid (35%), leaf acidity (19%), total chlorophyll content (35%), and relative humidity content (69%). Furthermore, a distinct and significant correlation was found between proline and sugar levels and the annual APTI values. Additionally, the species P. farcta had the highest API compared to other species. The study revealed the high potential of some plant species against air pollution induced stress which can be used in air and dust pollution management in the region.

The impact of latent policy synergy between cities on air pollution: An empirical analysis from China's 12,764 air policies

Policy synergy plays a crucial role in managing regional air pollution. However, the current process of formulating collaborative policies often focuses too heavily on achieving optimal emission reduction outcomes, while neglecting the execution costs at the local level. This not only leads to a lack of coordination between existing governance measures and new policies but may also result in inefficient policy implementation and the wastage of administrative resources. To address this issue, we have conducted a thorough analysis of the latent interconnections in local governance and explored the possibility of formulating collaborative policies based on latent policy synergies. Utilizing a dataset of 12,764 policy documents from China spanning from 2000 to 2023, we employed unsupervised learning techniques and panel regression models to investigate the impacts of latent policy synergies among local governments. Our findings reveal that local governments initially relied on rigid command-and-control strategies to address air pollution, which were later enhanced with economic incentives like emissions trading and subsidies, thus broadening the scope of policy tools and improving compliance flexibility. Subsequently, as comprehensive monitoring networks were established, enabling real-time data acquisition and more accurate pollution assessments, the focus of policy efforts shifted towards a more integrated approach to regional air pollution management. This similar evolution facilitated latent policy synergy among local governments, leading to significant reductions in air pollution emissions. Notably, regions such as the Central Plains and coastal cities, characterized by their dense industrial activity and high energy consumption, have shown remarkable potential for collaborative efforts, achieving greater pollution control efficiencies. This study provides a valuable reference for the coordinated governance of air pollution policies.

Health Risk Assessment of Residents' Exposure to Air Pollutants around the Sinpyeong-Jangrim Industrial Complex in Busan.

In this study, we aimed to evaluate the health effects of heavy metals and volatile organic compounds (VOCs) in fine particulate matter (PM2.5) in the air around the Sinpyeong-Jangrim Industrial Complex, Busan, and the necessity of reduction measures. We measured the concentrations of heavy metals and VOCs in PM2.5 in five areas with dense population around the Sinpyeong-Jangrim Industrial Complex. A health risk assessment was conducted, and the spatial risk distribution for the entire Saha-gu area was predicted using inverse distance weighting (IDW). Our results indicated that the carcinogens benzene, As, and Cr6+, and the non-carcinogen m,p-xylene, have the potential to adversely affect health. In particular, As was found to have the potential to express health effects at all measurement points. Additionally, based on the IDW results, the minimum values of benzene, arsenic, and m,p-xylene exceeded the threshold level, potentially affecting the health of residents across the entire Saha-gu area. In contrast, Cr6+ could potentially impact health only in some parts of Saha-gu as only its maximum value exceeded the threshold level. We demonstrated the importance of reducing air pollutant emissions from general industrial complexes. Our results will be useful in establishing more systematic mitigation measures to protect the health of residents near the Sinpyeong-Jangrim Industrial Complex and developing air pollutant management systems.

The Beautiful City of Jakarta, Indonesia

This research aims to analyze environmental conditions in Jakarta and identify practical solutions to air pollution, water pollution, and waste management. The research methods include air and water quality monitoring with modern sensors, analysis of emissions data, and interviews with stakeholders. Simulation models evaluate the impact of various interventions, such as public transportation improvements and pollution control technologies. The research results show that Jakarta faces significant challenges, with pollutant concentrations often exceeding safe limits. Simulation models estimate that reducing vehicle emissions and using renewable energy could reduce air pollution by up to 30% within five years. Pilot projects such as sensor-based air quality monitoring systems and household waste separation programs are progressing positively. However, budget constraints and resistance to change are still obstacles. The conclusions of this research emphasize the importance of integrating modern technology, strengthening environmental policies, and active community involvement. This research also offers a model that can be implemented by other cities in developing countries. Suggestions include policy adjustments, increased financial support, and community education programs to increase awareness and participation in environmental improvement.

Seasonal biogenic volatile organic compound emission factors in temperate tree species: Implications for emission estimation and ozone formation

Variability in biogenic volatile organic compound (BVOC) emissions across species and seasons poses challenges for accurate regional emission estimates and effective ozone (O3) control policies. To address this issue, we conducted in-situ measurements of emission factors for six dominant tree species in Beijing across four seasons. Subsequently, we developed monthly dynamic standard emission factors (SER-MDs) to model monthly BVOC emissions and their impacts on O3 formation at citywide and district levels. Our observations revealed pronounced seasonal differences in the BVOC composition and emission rates, as well as their responsiveness to monthly average temperature. By introducing the SER-MDs, we estimated BVOC emissions from the dominant tree species in Beijing to be 38.2 Gg yr−1, with monoterpenes and isoprene contributing 49% and 11%, respectively. This calculation reduced the overestimation associated with constant standard emission factors by 31%–38% at district level. The estimates also revealed regional differences in plant compositions rather than simple feedback from regional temperature and photosynthetically active radiation periods. Under these conditions, the maximum monthly BVOC-induced O3 concentration occurred in August and ranged from 4 to 17 μg m−3 across districts, with isoprene being the dominant contributor. Quercus mongolica and Populus tomentosa played significant roles in the formation of BVOC-induced O3 due to their strong isoprene emitting potential in July–August. These results indicate the necessity of introducing species-specific rhythms of BVOC emissions from dominant species in the development of urban BVOC emission inventories. This approach could inform the development of air pollution management policies that are consistent with the local vegetation composition and O3 pollution characteristics. For Beijing and other similar northern cities, reducing the use of tree species emitting substantial amounts of isoprene during periods of regional peak ambient O3 concentrations could constitute an effective nature-based solution for improving urban air quality in the future.

Transforming air pollution management in India with AI and machine learning technologies

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.

Spatiotemporal Changes in Air Pollution within the Studied Road Segment

Environmental protection is a pivotal element of sustainable development, both essential and indispensable in the construction of smart, green cities. Road transport contributes significantly to atmospheric pollution, accounting for as much as 25% of annual emissions within the European Union (EU). To combat the adverse effects of road transport, the EU has set targets to reduce greenhouse gas emissions from both passenger and commercial vehicles. Consequently, sustainable air pollution management has become a focal point for numerous researchers. This study continues the investigation into the distribution of air pollutants along a specific highway segment in Poland. The article addresses two primary research questions: first, the temporal and spatial variations in air pollution adjacent to a major highway in Poland, and second, whether emission levels exhibit significant annual differences and if there is a correlation between pollutant concentrations and the distance from the roadway. The findings offer valuable data on one of the principal substances polluting the air along EU transportation routes. Moreover, the analysis provides recommendations for future road infrastructure renovation projects and strategies to protect the public from harmful traffic-related pollutants, thereby supporting the development of green cities in accordance with sustainable development principles.