Pollution In Cities Research Articles

Heterogeneous influences of urban compactness on air pollution: evidence from 285 prefecture-level cities in China

Fine particulate matter (PM2.5) pollution can harm the climate, the environment, and human health. With sustainability initiatives receiving increasing attention, whether compact urban development can yield green environmental benefits has become an essential research proposition among urban planners. The compact city theory advocates energy efficiency enhancement through the mutual superposition of urban functions. First, a theoretical analysis framework for the effect of urban compactness on environmental quality was constructed. Second, on the basis of panel data from 285 prefecture-level cities in China from 2010 to 2021, exploratory spatial data analysis (ESDA) was applied to reveal the spatiotemporal nonstationary distribution of urban PM2.5 pollution. Finally, the heterogeneous influence mechanisms of compact urban factors on PM2.5 pollution were empirically studied through the geographical and temporal weighted regression (GTWR) model. The main findings are as follows: (1) From 2010 to 2021, the proportion of cities in China with good air quality significantly increased. The cities with high–high pollution clustering are currently located mainly in Beijing–Tianjin–Hebei, Shandong, and Henan. (2) Economic and transportation compactness factors mitigated PM2.5 pollution in most cities, whereas the long-term combined effects of the population and land compactness factors may have exacerbated urban PM2.5 pollution. (3) There was significant spatial and temporal heterogeneity in the effects of urban compactness factors on air pollution. Cities where land-use compactness exerts a pollution mitigation effect were located east of the Hu line. Cities in Northeastern China had the strongest pollution mitigation effect from transportation compactness, followed by the cities in Southwestern China.

Analysis of COVID-19 Lockdown to Understand Air Pollution Processes and Their Impacts on Health: A Case Study in the Western Balkans

The effect of COVID-19 lockdown (LD) on many ambient air pollutants (NO, NO2, PM2.5, PM10, O3 and SO2) was assessed for the first time in the Western Balkans with an innovative approach that evaluates a variety of factors including the stringency of the LD measures, the type of location, the pollution sources, the correlation with traffic fluxes and the meteorology. To that end, observations from 10 urban sites were compared with historical time series. The time window 1 February–30 May 2020 was classified in sub-periods on the basis of the stringency of the circulation restrictions. NO2 and O3 are the pollutants most affected by restrictions to population circulation due to lockdown during the first phase of the COVID-19 pandemic, and are well correlated with traffic fluxes. A reduction in fine particulate matter (PM2.5 and PM10) concentrations is observed in all sites only during the full LD periods, while the relation between SO2 average and maximum hourly concentrations and LD periods in industrial and traffic sites vary from site to site. The reduction in NO2 concentrations during the LD resulted in a reduction in mortality associated with air pollution in the largest cities, while the interpretation of the changes in O3 and particulate matter is less clear.

Investigating the factors contributing to the maintenance of high levels of warm season nocturnal ozone in a basin city.

In recent years, ozone (O3) pollution in many Chinese cities has worsened. Several cities have also experienced incidents where nocturnal O3 concentrations did not decrease as expected, and instead remained at high levels (above 50 ppb). However, there have been few detailed studies on the causes of these events. The present study used air quality and meteorological monitoring data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory model, to investigate the causes contributing to nocturnal O3 remaining at high values (NORHV) in Linfen City, a typical basin city in the Fenhe River Basin, characterized by severe O3 pollution. These events are influenced by the individual or combined impacts of mountain-valley breezes, along with vertical and horizontal transport of O3-rich air masses. In addition, the influencing mechanisms of three NORHV events were identified as: 1) the alternating effect of the descent of upper-level air masses enriched with O3, CO, and SO2 and downslope mountain breezes carrying the O3-rich air mass; 2) the interaction between the transport of polluted air masses across mountains and downslope mountain breezes on both sides of the two mountains in urban areas; and 3) the O3-rich air mass from high-altitude regions descended into the urban area following two LLJ events. These conclusions were supported by vertical distributions of O3 concentrations in the three cases simulated using the WRF-CMAQ model. In addition, the NORHV events resulted in higher initial O3 concentrations on the following day compared to normal days, exacerbating O3 pollution. The results of this study demonstrate the significant contributions of complex synergistic effects, such as mountain-valley breezes and vertical transport, to the occurrence of NORHV events in basin cities. These findings may be applicable to other basin cities around the world.

PROSPECTS FOR OBTAINING GREEN HYDROGEN AT MINI-HYDROELECTRIC POWER PLANTS FOR TRANSPORT

The use of hydrogen transport is an important step towards carbon neutrality. Unlike traditional transport running on hydrocarbon fuel, hydrogen cars do not hurt the environment during their operation. New green technologies for public transport deserve special attention since they are a key source of pollution in large cities. Achieving carbon neutrality is impossible without using new hydrogen technologies in all industries. Hydrogen, in its final form, should become the main fuel for transport (cars, buses, trains, and river vessels), which, unlike electric transport, is not limited by the speed of refueling.Hydrogen energy in the process of its development faces a large number of tasks and challenges, such as the production of "environmentally friendly" hydrogen, namely "green", which is in greatest demand during the transition to a carbon-free economy, as a result of the problems arising during the storage of hydrogen.Russia has huge water resources and one of the promising, energy-efficient and economically inexpensive technologies is the production of hydrogen fuel at hydroelectric power plants. This study presents a scheme for producing green hydrogen at a mini-hydroelectric power station with subsequent use for public transport purposes. In the Russian Federation, various domestic companies are developing and designing hydrogen-fueled transport. In the near future, hydrogen public transport may become a good alternative to diesel buses.

Spread of harmful substances in the atmosphere of industrial cities of Kazakhstan: modeling and data refinement

In Kazakhstan, air pollution in industrial cities poses a significant challenge that requires urgent attention. This study investigates the dispersion of harmful pollutants in the air across nine prominent industrial cities in Kazakhstan. The research involves modeling the emissions from major pollution sources for each city, which provides a comprehensive view of how these substances spread through the atmosphere. The study also examines the distribution patterns of these pollutants to gauge their concentration levels in each urban area. Additionally, it addresses the inverse problem of data assimilation from automated monitoring stations (AMS), aiming to refine the information on pollution sources. By utilizing the conjugate equations method, the study successfully converged to an accurate solution. Detailed visualizations for Almaty, Ust-Kamenogorsk, and Pavlodar illustrate the pollution dynamics and pinpoint the most affected regions. These findings are crucial for formulating strategies to mitigate the adverse effects of industrial emissions on both the environment and public health.

Forecasting system for urban air quality with automatic correction and web service for public dissemination

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

Forecasting of monthly air quality index and understanding the air pollution in the urban city, India based on machine learning models and cross-validation

Forecasting of monthly air quality index and understanding the air pollution in the urban city, India based on machine learning models and cross-validation

TOSHKENT SHAHRI HAVOSINING 2019-2023-YILLAR DAVOMIDA PM2,5 VA PM10 MAYDA DISPERS ZARRALAR BILAN IFLOSLANISH HOLATINING TAHLILI

Tashkent city is the capital of our country and a rapidly developing large city. Along with the growth of the city’s economy and population, the level of air pollution is also increasing. PM2.5 and PM10 particulate matter are indicators of air pollution. This article analyzes the level of pollution in Tashkent city from PM2.5 and PM10, examines its causes and negative effects. The dynamics of change was statistically analyzed based on the Mann-Kendal Test. According to the trend test results, significant changes were found for PM2.5 in 2019 (Z=2.21), in 2020 (Z=2.11), in 2021 (Z=3.63), in 2022 (Z=2.31), and in 2023 (Z=3.21), with similar results for PM10 (Z=2.02) and NO2 (Z=2.26). Based on these results, necessary conclusions were drawn for future atmospheric air monitoring.

Enhanced PM2.5 prediction in Delhi using a novel optimized STL-CNN-BILSTM-AM hybrid model

Accurate air pollution predictions in urban areas facilitate the implementation of efficient actions to control air pollution and the formulation of strategies to mitigate contamination. This includes establishing an early warning system to notify the public. Creating precise estimates for PM2.5 air pollutants in large cities is a challenging task because of the numerous relevant factors and quick fluctuations. This study introduces a novel hybrid model named STL-CNN-BILSTM-AM. It combines the seasonal-trend decomposition method with LOESS (STL) to simplify learning tasks and increase prediction accuracy for complex, nonlinear time-series data. Convolutional neural networks (CNNs) extract features from decomposed components of PM2.5 and other feature variables, such as pollutants and meteorological variables. Bidirectional long-short-term memory (BILSTM) uses these features to extract temporal relationships, enabling the forecasting of daily PM2.5 levels at four locations in Delhi. This hybrid model uses attention mechanisms to extract the most significant information, as well as Bayesian optimization to tune the hyperparameters. The suggested model greatly improved performance in all four regions used in this study, as evidenced by the findings. We compared it with the CNN-BILSTM, BILSTM, LSTM, and CNN models, and the suggested model outperformed the state-of-the-art models by utilizing STL decomposition components and other features. The overall results show that the STL-CNN-BILSTM-AM is better at predicting air quality, especially the concentration of PM2.5 in cities when the data has a high seasonal trend and is complex.Graphical

Baseline concentrations and quantitative health risk assessment of polycyclic aromatic hydrocarbons (PAHs) in relation to particle grain size in street dust of Warsaw Poland.

Total concentrations, toxicity, and health risks of 16 polycyclic aromatic hydrocarbons (PAH) in street dust from Warsaw (Poland) in 6 granulometric fractions were investigated. Street dust was collected from 149 sampling points distributed among Area 1 (central districts, left bank of the Vistula River, mostly traffic-related pollution) and Area 2&3 (suburb area, mostly residential, right bank of the river). Street dust was investigated before ("all") and after separating into 5 size-dependent samples: (1-0.8mm) "0.8", (0.8-0.6mm) "0.6", (0.6-0.4mm) "0.4", (0.4-0.2mm) "0.2", and (below 0.2mm) " < 0.2". ΣPAH mean concentration was 3.21mg/kg for Area 1 and 0.89mg/kg for Area 2&3. ∑BaPTPE values calculated collectively for Area 1&2&3 were observed to be 318.3, 83.5, 131.1, 81.4, 164.3, and 339.7ng/g for "all", "0.6", "0.4", "0.2", and " < 0.2", respectively. Significant differences in ∑BaPTPE values were observed between fractions and specific areas. The cancer risk levels for children and adults, for all particulate size fractions, were comparable for dermal contact and by ingestion and ranged from 10-5 to 10-4, whereas the cancer risk levels via inhalation always ranged from 10-10 to 10-8. Therefore, inhalation of resuspended street dust is almost negligible compared to other pathways. Environmental implication. Street dust pollution in cities is one of the most important issues in the world and it negatively affects the quality of the environment and people's health. This study contributed to filling the gap in knowledge about the characteristics of PAH contaminants in the subject of the grain size of street dust from Poland and assessing the potential health risks. Therefore, our work has provided new significant information on PAH pollution, methods of measuring PAHs content and assessing the risk to human health, which may be useful to the scientific community, policymakers, and the general public.

Optimal machine learning techniques for meteorological modeling of $${\textrm{PM}}_{2.5}$$ concentration in five major polluted cities of South-East Asia

Optimal machine learning techniques for meteorological modeling of $${\textrm{PM}}_{2.5}$$ concentration in five major polluted cities of South-East Asia

Energy Aware Smart Car Parking System Based Internet of Spatial Things

With the advent of smart and green cities, the development of energy-efficient smart parking systems has attracted researchers to reduce environmental pollution in smart cities due to reducing traffic congestion as well as waste of time and fuel consumption. This article investigates how to integrate the Internet of Spatial Things (IoST) with workload balancing and image processing in fog computing to build an energy-efficient smart parking system. The suggested system applies Q-Learning, a reinforcement learning method, to achieve workload balancing and then deploys image processing to detect vacant parking slots. The proposed method’s main objective of load balancing is to reduce energy consumption. The evaluation of the proposed system is done by comparing two case studies, fog-based and cloud-based IoST implementations, in the iFogSim simulator for various scenarios and scales. Moreover, it evaluates and compares the energy consumption of various devices. Experimental findings show that the devised system in fog-based IoST greatly reduces energy consumption with improves parking space availability in smart parking in contrast to the cloud-based deployment of smart car parking.

Can low-carbon pilot policies reduce air pollution levels?

This paper explores the impact of China’s low-carbon pilot policy on air pollution levels. Employing a difference-in-difference approach based on propensity score matching, we find that the policy significantly reduced air pollution in the pilot cities. However, we also uncover a negative spillover effect, with neighboring cities experiencing increased pollution levels. After controlling for this spillover, the policy’s impact on air pollution decreases. Heterogeneity analysis reveals that the policy’s effectiveness varies across time and location, with more pronounced effects in coastal and southern regions and on non-statutory holidays. These findings highlight the complexity of environmental policy impacts and underscore the need for comprehensive, regionally tailored approaches to air pollution control.

A multi-objective optimization study of berth scheduling considering shore side electricity supply

The maritime supply chain plays a crucial role in global trade. However, increasing maritime traffic exacerbates air pollution in port cities due to emissions from commonly used ship fuels. Efficient port operations and reduction of ship emissions are urgent priorities. Shore side electricity (SSE) is an effective means of reducing emissions in ports. And it is worthwhile to study how to optimize the berthing plan and SSE supply plan for ships. This study addresses the berth allocation problem (BAP) with a focus on integrating SSE to reduce emissions. We develop a multi-objective optimization model that to ensure port and ship benefits while improving efficiency and reducing emissions, considering key factors like SSE availability, SSE Voltage, fuel prices, and call schedules. Our improved NSGA-III algorithm, which combines fitness enhancements and local search operators, significantly outperforms classical heuristics, particularly in medium- and large-scale scenarios, with a roughly 30% improvement in effectiveness. Sensitivity analyses reveal the critical impact of matching SSE deployment on ships and berths, and the influence of electricity prices and retrofitting costs on SSE utilization. Our findings offer strategic insights for maritime stakeholders and policy guidance for SSE implementation.

Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India.

The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues to rise in global cities due to urbanization and climate change, air pollution monitoring and forecasting models for effective air quality monitoring that gather and forecast information about air pollution concentration are essential in every city. Air quality predictions have evolved to be more helpful for management. Recently, better performance and ability have developed due to the involvement of machine learning (ML) and artificial intelligence (AI) in forecasting air quality in urban cities in India. This paper focuses on air pollution as a significant ecological problem that directly impacts human health and the distribution of an environmental system in urban areas. Hence, we have developed advanced models for daily AQI forecasting to understand the air effluence level in the upcoming days. In this research, six data-driven models have been developed and implemented for daily AQI forecasting in the study area; it is crucial for understanding the future air pollution levels to plan and control air pollution in the entire city. The developed model is applied to air quality datasets. A comparison of the performance of ML models tested here indicates that the XGBoost algorithm achieves the highest coefficient of determination (R2) and root-mean-square deviation (RMSE) value of 0.99 and lower values value of 4.65 than other models in the testing phase. The results of the artificial neural network (ANN) algorithm are slightly lower than the extreme gradient boosting (XGBoost model); the ANN model results are as R2, mean squared error (MSE), and RMSE values of 0.99, 13.99, and 198.88, respectively. All the models were subjected to a ten-fold cross-validation model. However, the RF cross-validation model outperforms other models; the RF model result shows the R2, RMSE, and MSE values of 0.99, 3.64, and 4.12, respectively. This study also employed two interpretable models, namely feature importance analysis and Shapley additive explanation (SHAP), to evaluate both the global and local methods in a manner that is independent of specific ML models. The feature importance shows that particle matter (PM) 2.5, PM10, carbon monoxide (CO), and nitrogen oxides (NOx) were the most influential variables. The results determined that such novel DL and ML models may improve the accuracy of AQI forecasts and understanding of air pollution, particularly in metropolitan cities.

Optimal control strategy for a cutting-edge hybrid ventilation system in classrooms: Comparative analysis based on air pollution levels across cities

Natural ventilation has the potential to enhance indoor air quality in classrooms with elevated CO2 levels, although it may introduce outdoor pollutants. This study introduces a novel controller for automatic windows that simultaneously monitors outdoor air pollution and temperature, synchronizing window openings with mechanical ventilation system to create a comfortable, healthy, and energy-efficient indoor environment. The practicality of the proposed controller is assessed for a classroom in Delhi, Warsaw, and Stockholm, each with contrasting climates and outdoor pollution levels, specifically PM2.5 and NO2. The controller parameters are optimized for each city using a non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-off between thermal comfort, CO2 levels, and energy consumption. The results show that the controller successfully met the indoor air quality standards in all cities; however, its operation was significantly influenced by the climate and pollution levels. While natural ventilation was utilized for 44% and 31% of the year in Warsaw and Stockholm, respectively, it was used for only 11% of the year in Delhi, the most polluted city. The optimization process significantly reduced energy use across all cities while also successfully reducing indoor CO2 concentrations. Although thermal comfort decreased slightly, it remained within acceptable thermal comfort conditions.

Research on the impact of the digital economy on haze pollution: evidence from prefecture-level cities in China

Lately, the ongoing issue of haze pollution in multiple cities across China has had a profound impact on the residents’ wellbeing and overall health. The urgent necessity to address haze pollution is undeniable. Meanwhile, the rapidly evolving digital economy has sprung up as a pivotal driver for China’s economic growth, providing innovative approach to tackle haze pollution. This research explores the impact of the digital economy on haze pollution through utilizing the big data pilot zone as a quasi-natural experiment. Panel data from 280 prefecture-level cities in China is adopted, covering the period from 2011 through 2020. This analysis incorporates various models, namely, difference-in-differences model (DID), mediation effect model, and difference-in-differences spatial Durbin model. The findings highlight the substantial contributions of digital economy in alleviating haze pollution. Furthermore, these findings hold true even after a series of rigorous robustness checks. The constraint influence of the digital economy on haze pollution is particularly prominent in metropolitan areas, cities with elevated administrative levels, primary environmental protection cities, as well as economically developed cities. Financial development and technological innovation serve as important mechanisms through which the digital economy suppresses haze pollution. After accounting for spatial factors, the digital economy exhibits consequential spillover effects, leading to substantial inhibition of haze pollution in surrounding cities as well as locally. The novel aspects of this paper are as follows: Firstly, it regards the establishment of big data experimental zones as the exogenous policy shock of digital economy and employs a multi period DID model to evaluate the impact of the digital economy on haze pollution. Secondly, it explores the transmission pathways through which the digital economy affects haze pollution from the perspectives of financial development and technological innovation. Thirdly, it investigates the heterogeneous characteristics of the impact of the digital economy on haze pollution. Fourthly, it examines the spatial spillover effects of the digital economy on haze pollution from the perspective of spatial econometrics.

Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2.5) in Vietnam

This study aims to predict fine particulate matter (PM2.5) pollution in Ho Chi Minh City, Vietnam, using autoregressive integrated moving average (ARIMA), linear regression (LR), random forest (RF), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and convolutional neural network (CNN) combining Bi-LSTM (CNN+Bi-LSTM). Two experiments were set up: the first one used data from 2018–2020 and 2021 as training and test data, respectively. Data from 2018–2021 and 2022 were used as training and test data for the second experiment, respectively. Consequently, ARIMA showed the worst performance, while CNN+Bi-LSTM achieved the best accuracy, with an R² of 0.70 and MAE, MSE, RMSE, and MAPE of 5.37, 65.4, 8.08 µg/m³, and 29%, respectively. Additionally, predicted air quality indexes (AQIs) of PM2.5 were matched the observed ones up to 96%, reflecting the application of predicted concentrations for AQI computation. Our study highlights the effectiveness of machine learning model in monitoring of air pollution.

Observational study of ground-level ozone and climatic factors in Craiova, Romania, based on one-year high-resolution data.

Air pollution is a multifaceted issue affecting people's health, environment, and biodiversity. Gaining comprehension of the interactions between natural and anthropocentric pollutant concentrations and local climate is challenging. This study aims to address the following two questions: (1) What is the influential mechanism of climatic and anthropogenic factors on the ground-level ozone (O3) concentrations in an urban environment during different seasons? (2) Can the ozone weekend effect be observed in a medium-sized city like Craiova, and under which conditions? In order to answer these questions, ozone interactions with meteorological parameters (temperature, pressure, relative humidity) and pollutant concentrations (particulate matter, carbon dioxide, volatile organic compounds, formaldehyde, nitrogen dioxide, nitric oxide and carbon monoxide) is evaluated based on a one-year dataset given by a low-cost sensor and one-year dataset provided by the National Environment Agency. Using two statistical analysis programs, Python and SPSS, a good understanding of the correlations between these variables and ozone concentration is obtained. The SPSS analysis underscores the significant impact of three meteorological factors and nine other pollutants on the ozone level. A positive correlation is noticed in the summer when sunlight is intense and photochemical reactions are elevated. The relationship between temperature and ozone concentration is strong and positive, as confirmed by Spearman's rho correlation coefficient (r = 0.880). A significant negative correlation is found between relative humidity and ozone (r = -0.590). Moreover, the analysis shows that particulate matter concentrations exhibit a significant negative correlation with ozone (r ≈ -0.542), indicating that higher particulate matter concentrations reduce ozone levels. Volatile organic compounds show a significant negative correlation with ozone (r = -0.156). A negative relationship between ozone and carbon dioxide (r = -0.343), indicates that elevated carbon dioxide levels might also suppress ozone concentrations. A significant positive correlation between nitrogen dioxide and ozone (r = 0.060), highlights the role of nitrogen dioxide in the production of ozone through photochemical reactions. However, nitric oxide shows a negative correlation with ozone (r = -0.055) due to its role in ozone formation. Carbon monoxide has no statistically significant effect on ozone concentration. To observe the differences between weekdays and weekends, T-Test was used. Even though significant differences were observed in temperature, humidity, carbon dioxide, volatile organic compounds, nitrogen dioxide, nitric oxide and carbon monoxide levels between weekdays and weekends, the T-Test did not highlight a significant weekend ozone effect in a mid-sized city as Craiova. Using Python, the daily values were calculated and compared with the limit values recommended by the World Health Organization (WHO) and European Environment Agency (EEA). The WHO O3 recommended levels were exceeded for 13 times in one year. This study offers a comprehensive understanding of ozone pollution in a mid-sized city as Craiova, serving as a valuable reference for local decision-makers. It provides critical insights into the seasonal dynamics of ozone levels, emphasizing the significant role of temperature in ozone formation and the complex interactions between various pollutants and meteorological factors.

Factors affecting compliance with vehicular environmental laws and pro-environmental behavior in Lahore, Pakistan

Lahore, Pakistan, is considered the most polluted city in the world and is suffering from environmental injustice due to a lack of compliance with vehicular environmental laws and deficient observance of pro-environmental behavior. This study analyzes factors affecting public compliance with vehicular environmental laws among drivers in Lahore with an extended pro-environmental behavior approach. The study utilized several factors such as personal benefits prioritization (PBP), economic incentives perception (EIP), government system (IGS), perceived eco-policy effectiveness and enforcement (PEPEE), environmental knowledge and awareness (EKA), theory of planned behavior (TPB), and value belief norm theory (VBNT). Using purposive sampling in the data collection, two hundred fifty-one participants voluntarily answered the survey through a self-administered online questionnaire utilizing the partial least square structural equation modeling (PLS-SEM). Results showed that personal benefits prioritization (PBP) significantly affected economic incentive perception (EIP). EIP and perceived eco-policy effectiveness and enforcement (PEPEE) showed a significant direct relationship with environmental knowledge and awareness (EKA). Interestingly, the government system (IGS) has the highest direct significance with PEPEE. EKA significantly affected the theory of planned behavior (TPB) and the value belief norm theory (VBNT). Thus, this study can be a foundation for related sectors to enhance the air quality of Lahore, Pakistan, by enhancing vehicular environmental laws and ensuring compliance. Increasing awareness through improving education and enforcement strategies is expected to contribute to successful compliance among Lahore residents, eventually aligning with national ecological sustainability policies. Moreover, the paper provides a comprehensive roadmap for stakeholders to address the environmental challenges identified and contribute to a more sustainable and environmentally just future globally.