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Changes in material use, energy use and emissions profiles of industry are the result of complex interrelationships among a multitude of technological and economic drivers. To better understand and guide such changes requires that attention is paid to the time-varying consequences that technology and economic influences have on an industry's choice of inputs and its associated (desired and undesired) outputs. This paper lays out an approach to improving our understanding of the dynamics of large industrial systems. The approach combines engineering and econometric analysis with a detailed representation of an industry's capital stock structure. A transparent dynamic computer modeling approach is chosen to integrate information from these analyses in ways that foster participation of stakeholders from industry and government agencies in all stages of the modeling process-from problem definition and determination of system boundaries to generation of scenarios and interpretation of results. Three case studies of industrial energy use in the USA are presented-one each for the iron and steel, pulp and paper, and ethylene industry. Dynamic models of these industries are described and then used to investigate alternative carbon emissions and investment-led policies. A comparison of results clearly points towards two key issues: the need for industry specific policy approaches in order to effectively influence industrial energy use, fuel mix and carbon emissions, and the need for longer time horizons than have typically been chosen for the analysis of industrial responses to climate change policies.

There is concern that the carbon prices generated through climate policies are too low to create the incentives necessary to stimulate technological development. This paper empirically analyzes how the Swedish carbon dioxide (CO2) tax and the European Union emission trading system (EU ETS) have affected productivity development in the Swedish pulp and paper industry 1998-2008. A Luenberger total factor productivity (TFP) indicator is computed using data envelopment analysis. The results show that climate policy had a modest impact on technological development in the pulp and paper industry, and if significant it was negative. The price of fossil fuels, on the contrary, seems to have created important incentives for technological development. Hence, the results suggest that the carbon prices faced by the industry through EU ETS and the CO2 tax have been too low. Even though the data for this study is specific for Sweden, the models and results are applicable internationally. When designing policy to mitigate CO2 emissions, it is vital that the policy creates a carbon price that is high enough - otherwise the pressure on technological development will not be sufficiently strong., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Abstract: This paper discusses the relationship between environmental and economic performance and the influence of corporate strategies with regard to sustainability and the environment. After formulating a theoretical model, results are reported from an empirical analysis of the European paper manufacturing industry. New data are used to test hypotheses derived from the theoretical model, using environmental performance indices representing different corporate environmental strategy orientations. In particular, an emissions-based index largely reflecting end-of-pipe strategies and an inputs-based index reflecting integrated pollution prevention are distinguished. For the emissions-based index, a predominantly negative relationship between environmental and economic performance is found, whereas for the inputs-based index no significant link is found. This is consistent with the theoretical model, which predicts the possibility of different relationships. The results also show that for firms with pollution prevention-oriented corporate environmental strategies, the relationship between environmental and economic performance is more positive, thus making improvements in corporate sustainability more likely. Based on this last insight, managerial implications of this are discussed with regard to strategy choices, investment decisions and operations management. [Copyright &y& Elsevier]

Ozone pollution is the focus of current environmental governance in China and high-quality prediction of ozone concentration is the prerequisite to effective policymaking. The studied ozone pollution time series exhibits distinct seasonality and secular trends and is associated with various factors. This study developed an interpretable hybrid model by combining STL decomposition and the Transformer (STL-Transformer) with the prior information of ozone time series and global multi-source information as prediction basis. The STL decomposition decomposes ozone time series into trend, seasonal, and remainder components. Then, the three components, along with other air quality and meteorological data, are integrated into the input sequence of the Transformer. The experiment results show that the STL-Transformer outperforms the other five state-of-the-art models, including the standard Transformer. Specially, the univariate forecasting for ozone relies on mimicking the patterns and trends that have occurred in the past. In contrast, multivariate forecasting can effectively capture complex relationships and dependencies involving multiple variables. The method successfully grasps the prior and global multi-source information and simultaneously improves the interpretability of ozone prediction with high precision. This study provides new insights for air pollution forecasting and has reliable theoretical value and practical significance for environmental governance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Green assets are sensitive to environmental changes and are pivotal in achieving sustainable development. Some empirical studies have shown that air pollution, as part of environmental change, affects stock market investment; however, the mechanisms by which air pollution affects investment in green assets have not been explored in depth. This study investigates the impact of air pollution on green stocks through investor sentiment and green preference. Our empirical analysis reveals that declining air quality prompts negative investor sentiment, exerting an adverse impact on the stock market. Conversely, green preference exerts a positive influence on green stocks, with the magnitude of this effect being contingent on the degree of greenness (i.e., depth and breadth) of the stocks. Companies with a high degree of greenness experience a significant positive correlation between air pollution and stock prices, whereas this relationship is not significant for companies with lower greenness. Furthermore, the COVID-19 pandemic has served to alleviate the impact of air pollution on the stock market due to the diversion of public attention by COVID-19. These findings show that governments can take measures to mitigate negative impacts and facilitate green preference to promote green economic transformation and upgrading., Competing Interests: Declaration of competing interest All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study introduces a novel integrated quantitative modeling framework to assess the multiple environmental, health, and economic benefits from implementing carbon capture technology in the power sector of Basra province, Iraq. This province is struggling with significant environmental challenges and air pollution caused by extensive oil extraction operations. First, the developed modeling framework quantifies the captured CO 2 emissions and the equivalent avoided PM 2.5 emissions resulting from the use of carbon capture units in existing power plants. This is achieved through a detailed simulation of the monoethanolamine (MEA) capture process using Aspen. Second, the impact of avoided PM 2.5 exposure on public health is evaluated by developing and applying a dynamic dispersion model across the districts where the power plants are located. Third, it quantifies the expected health benefits, using the health impact assessment method. This method is based on a comprehensive meta-analysis of concentration-response functions, and it utilizes a Recurrent Neural Network prediction framework based on the Long-Short Term Memory (LSTM) method to predict the relative risk value of six health outcomes. Finally, the economic value of avoided health burdens is estimated by employing the Value of Statistical Life (VSL) and the Cost of Illness (COI) approaches. According to the findings, implementing new carbon capture units in the selected power plants in the area will lead to a reduction of 7.697 million tons of carbon dioxide per year in the total emission of pollutants from the current power generation units in the region. The integrated assessment results demonstrate a significant reduction in PM 2.5 emissions, amounting to 2299 tons per year, leading to the avoidance of 1328 premature deaths and 217 hospital admissions, resulting in annual savings of $1846 million from the avoided mortalities and morbidities cases and creation of 29,607 green jobs in Basra Province., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In recent years, China has adopted numerous policies and regulations to control NO x emissions to further alleviate the adverse impacts of NO 3 - -N deposition. However, the variation in wet NO 3 -N deposition and its sources changes after such air pollution control through field observation and isotope tracing. Results showed that the annual mean concentrations of NO - -N deposition under such policies is not clear. In this study, the southeastern area, with highly developed industries and traditional agriculture, was selected to explore the variation in NO 3 - -N deposition and its sources changes after such air pollution control through field observation and isotope tracing. Results showed that the annual mean concentrations of NO 3 - -N in precipitation were 0.67 mg L -1 and 0.54 mg L -1 in 2014-2015 and 2021-2022, respectively. The average wet NO 3 - -N depositions in 2014-2015 and 2021-2022 was 7.76 kg N ha -1 yr -1 and 5.03 kg N ha -1 yr -1 , respectively, indicating a 35% decrease. The δ 15 -N deposition, followed by vehicle exhaust (22.9%), other sources (17.1%), and soil emissions (6.4%) during 2014-2015. However, the contribution of vehicle exhaust (33.3%) overpassed the coal combustion (32.3%) and followed by other sources (25.4%) and soil emissions (9.0%) in 2021-2022. Apart from the control of air pollution, meteorological factors such as temperature, precipitation, and solar radiation are closely related to the changes in atmospheric N transformation and deposition. The results suggest phased achievements in air pollution control and that more attention should be paid to the control of motor vehicle exhaust pollution in the future, at the same time maintaining current actions and supervision of coal-powered plants.3 - and δ 18 O-NO 3 - values were lower in warm seasons and higher in cold seasons, and both showed a lower trend in 2021-2022 compared with 2014-2015. The Bayesian model results showed that the NO x emitted from coal-powered plants contributed 53.6% to wet NO 3 - -N deposition, followed by vehicle exhaust (22.9%), other sources (17.1%), and soil emissions (6.4%) during 2014-2015. However, the contribution of vehicle exhaust (33.3%) overpassed the coal combustion (32.3%) and followed by other sources (25.4%) and soil emissions (9.0%) in 2021-2022. Apart from the control of air pollution, meteorological factors such as temperature, precipitation, and solar radiation are closely related to the changes in atmospheric N transformation and deposition. The results suggest phased achievements in air pollution control and that more attention should be paid to the control of motor vehicle exhaust pollution in the future, at the same time maintaining current actions and supervision of coal-powered plants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Despite concerted efforts in emission control, air pollution control remains challenging. Urban planning has emerged as a crucial strategy for mitigating PM 2.5 pollution. What remains unclear is the impact of urban form and their interactions with seasonal changes. In this study, base on the air quality monitoring stations in the Yangtze River Delta urban agglomeration, the relationship between urban spatial indicators (building morphology and land use) and PM 2.5 concentrations was investigated using full subset regression and variance partitioning analysis, and seasonal differences were further analysed. Our findings reveal that PM 2.5 pollution exhibits different sensitivities to spatial scales, with higher sensitivity to the local microclimate formed by the three-dimensional structure of buildings at the local scale, while land use exerts greater influence at larger scales. Specifically, land use indicators contributed sustantially more to the PM 2.5 prediction model as buffer zone expand (from an average of 2.41% at 100 m range to 47.30% at 5000 m range), whereas building morphology indicators display an inverse trend (from an average of 13.84% at 100 m range to 1.88% at 5000 m range). These results enderscore the importance of considering building morphology in local-scale urban planning, where the increasing building height can significantly enhance the disperion of PM 2.5 pollution. Conversely, large-scale urban planning should prioritize the mixed use of green spaces and construction lands to mitigate PM 2.5 pollution. Moreover, the significant seasonal differences in the ralationship between urban spatical indicatiors and PM 2.5 pollution were observed. Particularly moteworthy is the heightened association between forest, water indicators and PM 2.5 concentrations in summer, indicating the urban forests may facilitate the formation of volatile compunds, exacerbating the PM 2.5 pollution. Our study provides a theoretical basis for addressing scale-related challenges in urban spatial planning, thereby forstering the sustainable development of cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

In urban areas, high levels of air pollution pose significant risks to human health, emphasising the need for detailed air quality (AQ) monitoring. However, traditional AQ monitoring relies on the data from Reference Monitoring Stations, which are sparsely distributed and provide only hourly or daily data, failing to capture the spatial and temporal variability of air pollutant concentrations. Addressing this challenge, we introduce in this article the ExpoLIS system, an all-weather mobile AQ monitoring system that integrates various AQ low-cost sensors (LCSs), providing high spatio-temporal resolution data. This study demonstrates that the inclusion of an extended sampling device may mitigate the effect of the meteorological parameters and other disturbances on readings. At the same time, it did not reduce the quality of the data, both in static conditions and in motion, as we were able to maintain a certain level of agreement between the LCSs. In conclusion, the ExpoLIS system proves its versatility by enabling the collection of large quantities of accurate data, allowing a deeper understanding of the AQ dynamics in urban environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results. 20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates. The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Air pollution has been one of the biggest environmental challenges for cities in their pursuit of sustainability. The removal of air pollutants usually entails a cost that can negatively impact economic activities. Assessing urban environmental performance can offer valuable insights for cities to balance their economic production and environmental protection. This paper develops a novel approach to measuring environmental performance by using the Euclidean distance function. An appealing feature of this approach is its ability in endogenously allocating the optimization pathway to each emitter, thereby avoiding arbitrary estimation results and distorted managerial implications. We apply this approach to study the environmental performance of Chinese key environmental protection cities. We find the heterogeneity in performance estimates and endogenous optimization pathways. Prioritizing the reduction of a specific type of emissions while simultaneously increasing industrial output value seems to be the most appropriate objective for the majority of cities. Our study can serve as a basis for urban governments to pinpoint the underlying factors contributing to low urban environmental performance and establish diverse objectives for enhancing environmental performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Nitrogen dioxide (NO 2 ) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO 2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO 2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO 2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R 2 values of 0.92 and 0.93, respectively. The study revealed that mean NO 2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO 2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO 2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In response to the dual challenges of air pollution control and carbon mitigation, China has strategically shifted its focus towards the synergistic reduction of air pollutants and CO 2 emissions. This study identifies the potential areas and specific air pollutant species (including CO, NO x , and SO 2 ) for co-reduction with carbon mitigation. We also reveal the driving forces behind the emissions of each air pollutant at both the national and regional scales. Our findings are as follows: (1) The potential for synergistic reduction of CO and SO 2 with CO 2 emissions has diminished in economically developed areas. There is a significant opportunity for co-reduction of SO 2 and CO 2 in the western and northern regions of China, particularly within Heilongjiang Province. (2) NOx is the key species for synergistic reduction with CO 2 emissions across China, especially in the Chengyu Plain. (3) Cleaner production and the synergistic reduction effect are the primary contributors to national air pollutant reduction in China from 2008 to 2017. Conversely, efforts in economic development and energy efficiency have led to emission increases. Energy and industrial structures have only made limited contributions to emission reductions, and carbon mitigation shows an inhibition effect on emission reductions. These results offer valuable insights for developing targeted regional strategies for deeper air pollution control, considering the specific characteristics and needs of each region. Additionally, our findings highlight the importance of addressing policy misalignments and strengthening mutual-influence mechanisms between air pollution control and carbon mitigation, ensuring that policies for carbon reduction also effectively contribute to air quality improvements., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Wildfire intensity and severity have been increasing in the Iberian Peninsula in recent years, particularly in the Galicia region, due to rising temperatures and accumulating drier combustible vegetation in unmanaged lands. This leads to substantial emissions of air pollutants, notably fine particles (PM 2.5 ), posing a risk to public health. This study aims to assess the impact of local and regional wildfires on PM 2.5 levels in Galicia's main cities and their implications for air quality and public health. Over a decade (2013-2022), PM 2.5 data during wildfire seasons were analyzed using statistical methods and Lagrangian tracking to monitor smoke plume evolution. The results reveal a notable increase in PM 2.5 concentration during the wildfire season (June-November) in Galicia, surpassing health guidelines during extreme events and posing a significant health risk to the population. Regional wildfire analyses indicate that smoke plumes from Northern Portugal contribute to pollution in Galician cities, influencing the seasonality of heightened PM 2.5 levels. During extensive wildfires, elevated PM 2.5 concentration values persisted for several days, potentially exacerbating health concerns in Galicia. These findings underscore the urgency of implementing air pollution prevention and management measures in the region, including developing effective alerts for large-scale events and improved wildfire management strategies to mitigate their impact on air quality in Galician cities., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Cesar Quishpe-Vasquez reports financial support was provided by University of Alcala. Patricia Oliva reports financial support was provided by Community of Madrid. Alejandro Casallas reports financial support was provided by European Union. Ellie Anne Lopez-Barrera reports financial support was provided by Sergio Arboleda University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Volatile organic compounds (VOCs) emitted into the atmosphere negatively affect the environment and human health. Biotrickling filtration, an effective technology for treating VOC-laden waste gases, faces challenges in removing hydrophobic VOCs due to their low water solubility and therefore limited bioavailability to microorganisms. Consequently, the addition of (bio)surfactants has proven to be a promising strategy to enhance the removal of hydrophobic VOCs in biotrickling filters (BTFs). Yet, up to now, no single study has ever performed a mass transfer characterization of a BTF under (bio)surfactants addition. In this study, the effect of (bio)surfactant addition on the gas-liquid mass transfer characteristics of two BTFs was measured by using oxygen (O 2 ) as a model gas. Through an empirical correlation, the mass transfer coefficients (k L a) of two hydrophobic VOCs, toluene and hexane, which are of industrial and environmental significance, were estimated. One BTF was filled with expanded perlite, while the other with a mixture of compost and wood chips (C + WC). Both BTFs were operated under different liquid velocities (U L : 0.95 and 1.53 m h -1 ). Saponin, a biological surfactant, and Tween 80, a synthetic surfactant, were added to the recirculating liquid at different critical micelle concentrations (CMCs: 0-3 CMC). The higher interfacial and surface area of the perlite BTF compared to the C + WC BTF led to higher k L aO 2 values regardless of the operational condition: 308 ± 18-612 ± 19 h -1 versus 42 ± 4-177 ± 24 h -1 , respectively. Saponin addition at 0.5 and 1 CMC had positive effects on the perlite BTF, with k L aO 2 values two times higher compared to those at 0 CMC. Tween 80 exhibited a neutral or slightly positive effect on the mass transfer of both BTFs under all conditions. Overall, the CMC, along with the physical characteristics of the packing materials and the operational conditions evaluated explained the results obtained. This study provides fundamental data essential to improve the performance and design of BTFs for hydrophobic VOCs abatement., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Wildfires are increasing in duration and intensity across the United States' Pacific West region, resulting in heightened particulate matter from smoke in the atmosphere. Levels of peak particulate matter are concurrent to peak visitor attendance at National Parks, given seasonal alignment with summer vacation travel and heightened forest fire conditions. Particulate matter threatens visitor health and safety and contributes to poor visibility and a deteriorated visitor experience. To assess visitation response to diminished air quality, we utilized wildfire-generated particulate matter (PM2.5) data in conjunction with monthly attendance records for three ecoregions containing eight national parks in Washington, Oregon, and California from 2009 to 2019. We analyzed daily PM2.5 levels from data gridded at the 10 km scale for National Park Service units by Level III forest ecoregions within the National Park Service's Pacific West Unit. Data were then compared to normalized monthly visitation trends for each of the ecoregions using two statistical methods Kendall's Tau and Analysis of Variance (ANOVA) with post-hoc Tukey tests. Results demonstrate that attendance at these national parks does not decrease in response to increased PM2.5 levels. Instead, we see several statistically significant increases in attendance across these ecoregions during periods of reduced air quality. Of 115 shifts between air quality categories during the busy season of July to September, there are no significant decreases in attendance as air quality worsens. These findings suggest that visitors are willing to tolerate reduced air quality compared to other factors such as temperature or precipitation. Given that park units within each ecoregion feature diverse historical contexts, varied built environments, and unique ecological systems, our discussion specifically addresses managerial concerns associated with maintained high levels of visitation during suboptimal, and potentially dangerous, conditions. There is substantial need for specific, scalable approaches to mitigate adverse health and experiential impacts as visitors are exposed to increased risks during a range of exertional activities associated with diverse settings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Recent years have witnessed growing public concern over air pollution in China, posing a challenge to the government's environmental management efforts. Empirical evidence indicates that the digital economy contributes to mitigating environmental pollution. Given that national audits are a crucial part of the national oversight system and considering the significant role of digital technology in audit governance, it is relevant to explore how the digital economy can support national audits in enhancing China's environmental quality. This study investigates the environmental impact of national audit governance, utilizing a dataset from 1540 counties in China spanning from 2005 to 2018. The findings reveal that effective national audits contribute to reducing haze pollution (HP) levels, with the digital economy playing a moderating role. The results also demonstrate heterogeneity; national audits are particularly effective in regions characterized by high urbanization rates, severe HP, and stringent environmental regulations. The mechanism analysis suggests that industrial transformation and enhanced government governance are the key mechanisms through which national audits reduce regional HP. Additionally, reforming the audit management system can amplify the effects of national audits on reducing HP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Livestock is one of major sources of greenhouse gas (GHG) emissions in China. Clarifying spatiotemporal characteristics of GHG emissions from livestock and exploring influencing factors can provide reference for grasping regional changes of GHG emission and formulate strategies of carbon reduction for livestock industry. However, existing literatures considered both spatial and temporal impacts and dynamic evolution trend of these factors seldomly. This paper used the life cycle assessment (LCA) method to estimate GHG emissions of livestock in 114 cities of the YRB from 2000 to 2021. On this basis, spatiotemporal heterogeneity of influencing factors was analyzed by using geographically and temporally weighted regression (GTWR) model. Finally, future evolution trend of GHG emissions from livestock was predicted by combining traditional and spatial Markov chain. Four main results were listed as follows. Firstly, GHG emission in the life cycle of livestock industry increased from 57.202 million tons (Mt) carbon dioxide equivalent (CO 2 e) in 2000 to 77.568 Mt CO 2 e in 2021. Secondly, structure of livestock industry, labor flow and mechanization were vital factors that led to increase of GHG emissions from livestock. Positive effects of labor flow and mechanization were increasing year by year, while negative effect of urbanization and positive effect of economic development were decreasing year by year. Markov chain analysis shown that probability of keeping high level of GHG emissions of livestock in the YRB unchanged were 96% (T = 1) and 90% (T = 5), and there also existed a Matthew effect. In addition, probability of level transfer of GHG emission in urban livestock was spatially dependent. Government should formulate strategies for livestock development and optimize low-carbon transformation of energy structure for livestock and poultry husbandry based on local conditions and key driving factors in the future. Meanwhile, boundaries of administrative divisions should be broken to promote reduction of GHG emissions in livestock comprehensively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In electric vehicles, the Heating, Ventilation and Air-Conditioning (HVAC) function is often performed by a heat pump. Heating and cooling the cabin air drains energy directly from the vehicle's battery. In addition, these vehicles may operate in environments with high level of air pollution. In the cabin, passengers are confined to a small space where particles and harmful gases can accumulate. In addition, the ventilation system must also handle the air which does not enter the cabin through blower operation. This "infiltration" is a function of the vehicle speed and allows pollution to enter the cabin without being filtered or thermally treated. The objective of the study is to optimize the competing goals of the HVAC system: achieving the best air quality while maintaining good thermal comfort, at minimum energy costs. A system simulation tool is calibrated to represent the heating and cooling of an electric car. With this model, the influence of key factors is evaluated. Depending on ambient conditions and other parameters (number of occupants, vehicle speed, etc.), the blower flow rate and recirculation ratio can be adjusted to reach the objectives. The management of the proportion of fresh and recirculated air allows to regulate the humidity and carbon dioxide levels. Optimum controls are proposed as good trade-offs to reduce the power consumption, while maintaining a safe and comfortable environment for occupants. Compared to the full fresh air mode, the driving range gains are estimated in cold (-15 °C) and hot (30 °C) scenarios at 9 and 26 km respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Education expenditure is essential in mitigating air pollution, but the relationship between education expenditure and air pollution lacks in-depth discussion. Utilizing data at the county level in China during 2007-2021, this study estimates the effect of education expenditure from local governments on air pollution. Our findings demonstrate that education expenditure significantly and negatively affects air pollution, which remains robust after addressing endogeneity. The mechanism analysis presents that education expenditure reduces air pollution through the composition, technique, and income effects. The heterogeneity analysis indicates that the impact of education expenditure exhibits marked regional heterogeneity. Specifically, the role of education expenditure is significant in strong regulation, key, eastern, and central regions. By considering interaction terms, we identify the moderating effects of human capital, economic development, infrastructure construction, and public service for education expenditure. The cost-benefit analysis emphasizes that education expenditure improves social welfare. Our findings can inspire local governments to place more emphasis on air quality and public education expenditure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Continuous Emission Monitoring Systems (CEMS) are devices used to measure and report real-time emission of air pollutants. Although CEMS have been extensively deployed in developed countries to ensure compliance with emission standards and enhance their environmental performance, their adoption in India is still in its early stages. The present study aims to evaluate the effectiveness of CEMS in India, identify obstacles in terms of policy, regulation, technology and finance that impede their adoption and suggest mechanisms and incentives to facilitate their expansion. The findings indicate that CEMS offer benefits for air pollution control in India by improving monitoring accuracy, transparency, accountability and enforcement. The study also highlights institutional challenges faced by CEMS, including the absence of a certification system, lack of quality assurance measures, issues with data validation and challenges in its calibration as well as integration concerns with existing regulatory framework. To address these challenges effectively it is recommended that India must develop a policy framework for CEMS along with regulations. Essential steps such as establishing a certification and accreditation system should be taken while enhancing stakeholders' capacity and awareness., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ashish Tiwari reports a relationship with Uttar Pradesh Climate Change Authority Directorate of Environment Vineet Khand-6, Gomti Nagar, Lucknow, Uttar Pradesh 226,010 that includes: employment. NA If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Recorded particulate matter (PM2.5) hourly trends are compared for fifteen urban recording sites distributed across central England for the period 2018 to 2022. They include 10 urban-background and five urban-traffic (roadside) sites with some located within the same urban area. The sites all show consistent background and peak distributions with mean annual values and standard deviations higher for 2018 and 2019 than for 2020 to 2022. The objective of this study is to demonstrate that trend attributes extracted from hourly recorded univariate PM2.5 trends at these sites can be used to provide reliable short-term hourly predictions and provide valuable insight into the regional variations in the recorded trends. Fifteen trend attributes extracted from the prior 12 h (t-1 to t-12) of recorded PM2.5 data were compiled and used as input to four supervised machine learning models (SML) to forecast PM2.5 concentrations up to 13 h ahead (t0 to t+12). All recording sites delivered forecasts with similar ranges of error levels for specific hours ahead which are consistent with their PM2.5 recorded ranges. Forecasting results for four representative sites are presented in detail using models trained and cross-validated with 2020 and 2021 hourly data to forecast 2021 and 2022 hourly data, respectively. A novel optimized feature selection procedure using a suite of five optimizers is used to improve the efficiency of the forecasting models. The LASSO and support vector regression models generate the best and most generalizable hourly PM2.5 forecasts from trained and validated SML models with mean average error (MAE) of between ∼1 and ∼3 μg/m 3 for t0 to t+3 h ahead. A novel overfitting indicator, exploiting the cross-validation mean values, demonstrates that these two models are not affected by overfitting. Forecasts for t+6 to t+12 h forward generate higher MAE values between ∼3 and ∼4 μg/m 3 due to their tendency to underestimate some of the extreme PM2.5 peaks. These findings indicate that further model refinements are required to generate more reliable short-term predictions for the t+6 to t+24 h ahead., Competing Interests: Declaration of competing interest The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The photocatalytic cementitious materials (PCM) are expected to alleviate air pollution due to the direct utilization of natural solar energy. However, the color properties of cementitious materials have significant effect on the photocatalytic performance of PCM. In the present study, the colorful PCM is prepared using various colorants. The effect of color properties of cementitious materials on the NOx removal capacity of PCM is researched, and the related mechanism is analyzed by optical analysis. Furthermore, the effect of colorants on the compressive strength of PCM is studied. Results showed that the NOx removal capacity of PCM is decreased by the presence of colorants. As the 5% of black, yellow, red, and blue colorants are used, the NOx removal capacity of PCM is reduced by 73%, 48%, 21%, and 19%, respectively. Both the nano-TiO 2 and cement in the PCM can absorb UV light. Colorants could enhance the UV light absorption capacity of cement, leading to a decrease in the UV light absorption of nano-TiO 2 , which is harmful to the generation of electron-hole pairs. Moreover, the Fe-phase in colorants could improve the surface charge separation resistance of nano-TiO 2 , limiting the efficiently separated electron-hole pairs. Therefore, the photocatalytic performance of PCM is weakened by the presence of colorants. The compressive strength of PCM is decreased by using colorants, but the reduction ratio at 28 d is no more than 10%, with the content of colorants within 5%. This research can guide the color design of PCM in practical applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This paper examines the impact of air pollution control policies targeting key polluting enterprises, highlighting a strategic shift towards precision pollution control that concentrates on high-emission, high-risk businesses. The paper explores the efficacy of these policies and their potential spatial spillover effects, utilizing panel data from 259 Chinese cities from 2013 to 2021. Employing the difference-in-differences (DID) model and spatial Durbin model, the study analyzes both the direct local effects and the broader spatial consequences of these regulatory measures on air quality. The findings indicate a significant reduction in air pollutant concentrations in urban areas, attributing this improvement to factors such as industrial restructuring, increased investment in science and technology, and economic growth. Spatial econometric analysis further reveals a substantial positive correlation in air quality among Chinese cities. However, estimates of the spillover effect indicate that while such policies successfully reduce pollution locally, they could unintentionally degrade air quality in adjacent areas. The study highlights the need for nuanced policy strategies to mitigate unintended spatial spillovers and enhance overall effectiveness. It recommends tailored policies that integrate environmental and socioeconomic objectives, national and regional coordination for consistent enforcement, technology-driven compliance strategies, and incentives for sustainable enterprise practices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The crushing of beneficiation plants will produce a large amount of dust containing hot air flow, seriously polluting the atmospheric environment if discharged directly without treatment. The key to control is to dust and cool the exhaust. In order to improve the efficiency of the device, the airflow disturbance between the chord grid should be enhanced to promote the collision probability between the dust and the droplet and the surface of the chord grid. Based on the above analysis, the lattice Boltzmann method (LBM) is used to simplify the chord grid wire into an infinitely long cylinder structure, and a mesoscopic model is established to explore the flow characteristics of the airflow through the wet chord grid wires. The results show that there is a critical flow direction spacing ratio of L/D = 2.5; when the critical spacing is exceeded, vortex shedding occurs on the upstream cylinder, the boundary layer is separated, and the time-average drag coefficient C d -M on the cylinder surface changes sharply, when the spacing ratio is less than this critical ratio, the downstream cylinder is immersed in the near wake region of the upstream cylinder. The gap flows smoothly from the downstream cylinder gap. The sequential double-chord grid wires show the flow characteristics around a single blunt body, and the time-average drag coefficient of the cylinder surface changes smoothly. According to the research results, the wet chord grid wires purification and heat dissipation device is applied to the beneficiation plants. The parameter design is carried out to make the flow direction spacing ratio (FDSR) L/D ≥ 3.5 to ensure that the development and migration of vortices in the wake of the upstream cylinder are not inhibited by the downstream cylinder. The longitudinal spacing ratio (LSR) is 1.35≤W/D ≤ 2.5 to ensure that the velocity ratio behind the upstream cylinder is u/u 0  ≥ 0.5 to promote the mixing of the fluid. The test results show that when the concentration of exhaust dust in the beneficiation plants is 38.27 mg/m 3 , the dust concentration of outlet air will be reduced to 0.39 mg/m 3 after the wet chord grid wires purification and heat treatment, the total dust removal efficiency is 98.98%, the inlet air temperature is 32 °C, and the outlet air temperature is about 27 °C. The maximum temperature drop is 5 °C, and the air quality meets environmental emission standards., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this study, efficiencies of eight indigenous plants of Baishnabghata Patuli Township (BPT), southeast Kolkata, India, were explored as green barrier species and potentials of plant leaves were exploited for biomonitoring of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs). The present work focused on studying PM capturing abilities (539.32-2766.27 μg cm -2 ) of plants (T. divaricata, N. oleander and B. acuminata being the most efficient species in retaining PM) along with the estimation of foliar contents of PM adhered to leaf surfaces (total sPM (large + coarse): 526.59-2731.76 μg cm -2 ) and embedded within waxes (total wPM (large + coarse): 8.73-34.51 μg cm -2 ). SEM imaging used to analyse leaf surfaces affirmed the presence of innate corrugated microstructures as main drivers for particle capture. Accumulation capacities of PAHs of vehicular origin (total index, TI > 4) were compared among the species based on measured concentrations (159.92-393.01 μg g -1 ) which indicated T. divaricata, P. alba and N. cadamba as highest PAHs accumulators. Specific leaf area (SLA) of plants (71.01-376.79 cm 2  g -1 ), a measure of canopy-atmosphere interface, had great relevance in PAHs diffusion. Relative contribution (>90%) of 4-6 ring PAHs to total carcinogenic equivalent and potential as well as 5-6 ring PAHs to total mutagenic equivalent and potential had also been viewed with respect to benzo[a]pyrene. In-depth analysis of foliar traits and adoption of plant-based ranking strategies (air pollution tolerance index (APTI) and anticipated performance index (API)) provided a rationale for green belting. Each of the naturally selected plant species showed evidences of adaptations during abiotic stress to maximize survival and filtering effects for reductive elimination of ambient PM and PAHs, allowing holistic management of green spaces., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Black carbon (BC) significantly affects climate, environmental quality, and human health. This study utilised Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), which can compensate for the shortcomings of ground BC monitoring in spatial-temporal distribution to study the pollution characteristics of BC and potential pollution sources in a typical industrial city (Xinxiang) with serious air pollution in northern China. The results showed that average daily ground observation and MERRA-2 concentration of BC of 7.33 μg m -3 and 9.52 μg m -3 . The mean BC concentration derived from MERRA-2 reanalysis data was higher than ground measurement due to resolution limitations and pollution from the northern regions. The reliability of the MERRA-2 data was confirmed through correlation analysis. Consideration of the spatial distribution of BC from MERRA-2 and incorporating the potential source contribution function (PSCF), concentration-weighted trajectory (CWT), and emission inventory, other possible source areas and primary sources of BC in Xinxiang were investigated. The results indicated that implementing transportation and residential emission control measures in Henan Province and its surrounding provinces, such as Hebei Province, will effectively decrease the BC level in Xinxiang City. A passively smoked cigarettes model was used to evaluate the risk of BC exposure. The percentage of lung function decrement (PLFD) was the highest in school-age children, while the impact on lung cancer (LC) health risk was comparatively lower. Notably, the BC health risk in Xinxiang was lower than in most cities across Asia., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Spatial misallocation of resource elements impedes the coordination in regional co-benefit in carbon emission and pollution reduction. Guided by scope economy principles, this study aims to quantifies co-benefits and explores spatial variation patterns to facilitate mutual synergy. The findings offer valuable insights for identifying optimal focus points and shaping targeted collaborative policies. Based on the multi-source provincial panel data spanning 2000 to 2021, this paper quantitatively assesses the co-benefit through the lens of marginal abatement cost. Then, the Dagum Gini coefficient was employed to investigate the disparities at the spatial level. Additionally, geographical detector is introduced to analyze the source of disparities at the factor level. Results indicate: (1) Under joint reduction, the marginal abatement cost reduces by 57.86% and 79.97% respectively, with an overall 68.77% increase in co-benefit fluctuation. (2) Provinces with low co-benefit significantly decreased after 2007, while provinces with high co-benefit concentrated in the northwest, southwest, and east. (3) Overall disparities decreased, east-central regions hold the lowest spatial disparities in co-benefit and east-west hold the most; intragroup differences and supervariable density primarily contributes to the disparity. (4) FDI significantly influence the co-benefit over the past two decades, with spatial disparities influenced by both endogenous and exogenous factors across development stages., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Artificial intelligence (AI) technology represents a disruptive innovation that has garnered significant interest among researchers for its potential applications in ecological and environmental management. While many studies have investigated the impact of AI on carbon emissions, relatively few have delved into its relationship with air pollution. This study sets out to explore the causal mechanisms and constraints linking AI technologies and air pollution, using provincial panel data collected from 2007 to 2020 in China. Furthermore, this study examines the distinct pathways through which AI technology can ameliorate air pollution and reduce carbon emissions. The findings reveal the following key insights: (1) AI technologies have the capacity to significantly reduce air pollution, particularly in terms of PM 2.5 and SO 2 levels. (2) AI technologies contribute to enhanced air quality by facilitating adjustments in energy structures, improving energy efficiency, and strengthening digital infrastructure. Nonetheless, it is important to note that adjusting the energy structure remains the most practical approach for reducing carbon emissions. (3) The efficacy of AI in controlling air pollution is influenced by geographical location, economic development level, level of information technology development, resource dependence, and public attention. In conclusion, this study proposes novel policy recommendations to offer fresh perspectives to countries interested in leveraging AI for the advancement of ecological and environmental governance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Food waste has received wide attention due to its hazardous environmental effects, such as soil, water, and air pollution. Evaluating food waste treatment techniques is imperative to realize environmental sustainability. This study proposes an integrated framework, the complex q-rung orthopair fuzzy-generalized TODIM (an acronym in Portuguese for interactive and multi-criteria decision-making) method with weighted power geometric operator, to assess the appropriate technique for food waste. The assessment of food waste treatment techniques can be divided into three phases: information processing, information fusion, and ranking alternatives. Firstly, the complex q-rung orthopair fuzzy set flexibly describes the information with periodic characteristics in the processing process with various parameters q. Then, the weighted power geometric operator is employed to calculate the weight of the expert and form the group evaluation matrix, in which the weight of each input rating depends upon the other input ratings. It can simulate the mutual support, multiplicative preferences, and interrelationship of experts. Next, the generalized TODIM method is employed to rank the food waste treatment techniques, considering experts' psychological characteristics and bounded behavior. Subsequently, a real-world application case examines the practicability of the proposed framework. Furthermore, the sensitivity analysis verifies the validity and stability of the presented framework. The comparative study highlights the effectiveness of this framework using the existing frameworks. According to the result, Anaerobic digestion (0.0043) has the highest priority among the considered alternatives, while Incineration (-0.0009) has the lowest., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

To investigate the particle sources before, during, and after the 2022 Beijing Winter Olympic and Paralympic (WOP) in Beijing, ambient particles were passively collected from January to March 2022. The physicochemical properties including morphology, size, shape parameters, and elemental compositions were analyzed by the IntelliSEM EPAS (an advanced computer-controlled scanning electron microscopy [CCSEM] system). Using the user-defined classification rules, 37,174 individual particles were automatically classified into 27 major groups and further attributed to seven major sources based on the source-associated characteristics, including mineral dust, secondary aerosol, combustion/industry, carbonaceous particles, salt-related particles, biological particles, and fiber particles. Our results showed that mineral dust (66.5%), combustion/industry (12.6%), and secondary aerosol (6.3%) were the three major sources in a wide size range of 0.2-42.8 μm. During the Winter Olympic Games period, low emission of anthropogenic particles and favorable meteorological conditions contributed to significantly improved air quality. During the Winter Paralympic Games period, more particles sourced from the dust storm, secondary formed particles, and the adverse meteorological conditions resulted in relatively worse air quality. The secondary aerosol all decreased during the competition period, while increased during the non-competition period. Sulfate-related particles had explosive growth and further aggravate the pollution degree during the non-competition period, especially under adverse meteorological conditions. These results provide microscopic evidence revealing variations of physicochemical properties and sources in response to the control measures and meteorological conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

This study investigates the connection between nonrenewable energy productivity, renewable energy productivity, and air quality degradation in Taiwan from 2002 to 2019. We specifically emphasize the novel contribution of analyzing the productivity of renewable energy consumption. Robust estimation models, namely Nonlinear Autoregressive Distributed Lag (NARDL) and Robust Standard Estimation, are employed for comprehensive analyses. Our findings reveal a strong correlation between nonrenewable energy productivity and increased air pollutants, highlighting the significant impact of fossil fuels on air quality deterioration. Although renewable energy productivity demonstrates a negative association with air degradation, its effect is not statistically significant. This can be attributed to Taiwan's continued reliance on non-renewable energy sources within the overall energy mix. Hence, reducing dependence on fossil fuels is crucial for improving air quality. Importantly, the identified relationships have long-term implications, underscoring the necessity of persistent policy measures that promote renewable energy transition and emissions reduction over time. Our research emphasizes the urgency of addressing fossil fuel dependency to mitigate air pollution and highlights the potential benefits of enhancing renewable energy efficiency to achieve cleaner and healthier environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The potential cause-effect relationship between synoptic meteorological conditions and levels of criteria air pollutants, including CO, NO 2 , O 3 , PM 10 , PM 2.5 and SO 2 , in Bahia Blanca, Argentina, was assessed for the period of 2018-2019. Daily back-trajectories and global meteorological data fields were employed to characterize the primary transport paths of air masses reaching the study site, and to identify the synoptic meteorological patterns responsible for these atmospheric circulations. Time series of surface-level meteorological parameters and midday mixing layer height were collected to examine the impact of the synoptic meteorological patterns on local meteorology. Furthermore, the NAAPS global aerosol model was utilized to identify days when contributions from long-range transport processes, such as dust and/or biomass burning smoke, impacted air quality. By applying this methodology, it was determined that the air masses coming from the N, NW and W regions significantly contributed to increased mean concentrations of coarse particles in this area through long-range transport events involving dust and smoke. Indeed, the high average levels of PM 10 recorded in 2018-2019 (annual mean values of 47 and 52 μg/m 3 , respectively) represent the main air quality concern in Bahía Blanca. Moreover, PM 10 , PM 2.5 and NO 2 emissions should be reduced in order to meet recommended air quality guidelines. On the other hand, the results from this study suggest that the sources and meteorological processes leading to the increase in the concentrations of CO and SO 2 have a local-regional origin, although these air pollutants did not reach high values probably as a consequence of the strong wind speed registered in this region during any synoptic meteorological pattern., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

As surface ozone (O 3 ) gains increasing attention, there is an urgent need for high temporal resolution and accurate O 3 monitoring. By taking advantage of the progress in artificial intelligence, deep learning models have been applied to satellite based O 3 retrieval. However, the underlying physical mechanisms that influence surface O 3 into model construction have rarely been considered. To overcome this issue, we considered the physical mechanisms influencing surface O 3 and used them to select relevant variable features for developing a novel deep learning model. We used a wide and deep model architecture to account for linear and non-linear relationships between the variables and surface O 3 . Using the developed model, we performed hourly inversions of surface O 3 retrieval over China from 2017 to 2019 (9:00-17:00, local time). The validation results based on sample-based (site-based) methods yielded an R 2 of 0.94 (0.86) and an RMSE of 12.79 (19.13) μg/m 3 , indicating the accuracy of the models. The average surface O 3 concentrations in China in 2017, 2018, and 2019 were 82, 78, and 87 μg/m 3 , respectively. There was a diurnal pattern in surface O 3 in China, with levels rising significantly from 55 μg/m 3 at 9:00 a.m. to 96 μg/m 3 at 15:00. Between 15:00 and 16:00, the O 3 concentration remained stable at 95 μg/m 3 and decreased slightly thereafter (16:00-17:00). The results of this study contribute to a deeper understanding of the physical mechanisms of ozone and facilitate further studies on ozone monitoring, thereby enhancing our understanding of the spatiotemporal characteristics of ozone., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In megacities, vehicle emissions face urgent challenges related to air pollution and CO 2 control. To achieve the refinement of vehicle control policies for the co-control of air pollutants and CO 2 , this study established a vehicle emission inventory with high spatial and temporal resolution based on the hourly traffic flow in Shanghai and analyzed the spatial and temporal distribution characteristics of the real-time vehicle emissions. Meanwhile, a policy evaluation framework was constructed by combining pollutant emission predictions with quantitative co-control effect assessments. The results indicated that spatio-temporal variations in different air pollutants and CO 2 could mainly be attributed to primary contributing vehicle types. The pollutants (CO 2 , CO and VOCs) primarily contributed by private cars exhibited a bimodal pattern in 24-h time series and their spatial distribution was concentrated in the urban city center. The spatial distribution of NOx and PM primarily contributed by heavy trucks was still obvious on non-urban center areas. Furthermore, the results of synergistic effect analysis revealed that the alternative energy replacement scenario demonstrated the most significant potential for the co-control. Based on temporal-spatial and co-benefit analysis, the precise control policy of vehicle emissions can be established through time-, region-, and model-control. This study provides references and research methods for the formulation of the vehicle refinement control policies in worldwide megacities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Cities occupy a central position in addressing climate change and promoting sustainable regional development. Synergistic control of urban gas emissions at the city level is one of the main issues typically explored. The confounding effect and the interactions between the urban indicators of population and area have been ignored in previous studies. In this study, we examined the spatial distribution characteristics and synergy between greenhouse gases (CO 2 ) and air pollutants (SO 2 and NO X ) using spatial population and gas emission data. By upgrading the city clustering algorithm (CCA), we established a method for defining active areas of gas emissions (spatial element-coupled clustering, SECC) and identified active areas of gas emissions in China. In this study, we created a research framework that can simultaneously consider the effects of population and area, as well as the possible interactions between these indicators in active areas. The superlinear scaling relationship between the above three gases was revealed at the active zone level, and the existence of synergy between the emission patterns of the three gases was confirmed. Via further model application, we measured the synergistic efficiency of the three gases. It was found that for every 1% increase in SO 2 and NO X in an active zone, CO 2 increases by 0.86%. In this study, we explored a new perspective and approach to explain the synergy between greenhouse gases and air pollutants. This is essential to promote national competition among cities to achieve synergistic control of CO 2 and local air pollutants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Elevated levels of ground-level ozone (O 3 ) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O 3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O 3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R 2 of 0.91. Internal and external validation procedures and verification of O 3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O 3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O 3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Several cities in the developing world, of which the capital city of India, New Delhi, is an example, often experience air quality in which pollutant levels go way above the levels considered hazardous for human health. To bring down the air quality to within permissible limits quickly, the measures typically taken involve shutting down certain high-polluting activities for some time to enable the air quality to recover temporarily. This paper presents a first-ever model based on artificial neural networks to forecast the extent of reduction in air quality parameters that can be achieved and the time period within which a change can be experienced when the source of the emissions is cut off temporarily. The model is based on the extensive data on the extent of reduction in air quality parameters that occurred during the lockdown that was imposed during the COVID-19 pandemic. The non-linear autoregressive exogenous network-based model chosen for the purpose employs the hour since stopping of emissions, relative humidity, wind speed, wind direction, and ambient temperature as input parameters to predict the rate of change of PM 2.5 with respect to the concentration at the start of the stopping of the emissions. Air quality data from a key monitoring station in New Delhi was used to develop the model. The model predicted the rate of drop in PM 2.5 with an R and MSE of 0.0044 and 0.9736, respectively, while training and 0.0095 and 0.9583 while testing. The model was then tested with data from 19 other stations in New Delhi, and accuracy of the model was found to be exceptionally accurate, with the correlation between the measured and the predicted PM 2.5 levels ranging from 0.74 to 0.94 and the MSE ranging from 0.0110 to 1.0746. Thus, the model can be employed to determine the number of hours of temporary stoppage of emissions required for the PM 2.5 concentration to reach safe levels. The methodology of development of the model can be extrapolated to construct models tailored for use in other parts of the world as well., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Aerosol Atmospheric Rivers (AARs) are elongated and narrow regions that carry high concentrations of aerosols (tiny particles suspended in the atmosphere) across large distances, exerting effects on both air quality and human health (Chakraborty et al., 2021, 2022). Monitoring and modeling these aerosols present distinct challenges due to their dynamic nature and complex interactions within the atmosphere. In this context, the present study detects and predicts the AARs using MERRA-2 reanalysis datasets with their seasonal climatology of key aerosol species, including Black Carbon (BC), Dust (DU), Organic Carbon (OC), Sea Salt (SS), and Sulphates (SU). The study employs an innovative Integrated Aerosol Transport (IAT) based AAR algorithm from 2015 to 2022. A total count of 44,020 BC AARs, 13,280 DU AARs, 21,599 OC AARs, 17,925 SS AARs, and 31,437 SU AARs were detected globally. The seasonal climatology of BC and OC AARs intensifies in areas such as the Amazon rainforest and Congo during AMJJAS (April-September) due to forest fires. Similarly, DU AARs are more frequent in regions near the Saharan desert, primarily around the equator during AMJJAS. SS AARs tend to predominate over the oceans, while SU AARs are predominantly found in the northern hemisphere, primarily due to higher anthropogenic emissions. Furthermore, convolutional autoencoder-based models were developed for key aerosol species, strengthening predictive accuracy by effectively capturing complex data relationships and delivering precise predictions for the last 5-time frames. During validation, the model evaluation parameters for image prediction such as the Structural Similarity Index ranged from 0.86 to 0.94, Peak Signal-to-Noise Ratio fluctuated between 1.14 and 42.25 dB, Root Mean Square Error varied from 2.39 to 296.4 mg/(m-sec), and Mean Square Error fell within the range of 1.55-17.22 mg/(m-sec). These collectively reflect image closeness, quality, dissimilarity, and accuracy in AAR prediction. This study demonstrates the effectiveness of advanced machine and deep learning models in predicting AARs, offering the potential for advanced forecasting and enhancing resilience in high-aerosol concentration regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Evidence from the past has previously established that the air information disclosure program has an impact on people's air pollution perception. However, the influence of the concrete information communicated has always been overlooked. In this study, we examine the impact of the announced air pollution level information on public pollution perception. We collect air-related Weibo posts from June 1, 2019, to May 31, 2021, in China, and apply a regression discontinuity (RD) design to quantify the impact. According to our research, when a higher pollution level is announced, people's pollution perception expressed online rises by 3.5%-3.7%, which is approximately equivalent to the response to a more than 100 Air Quality Index (AQI) increase. The episode-based analyses reveal that the impact of pollution level information would fade away along with the persistence or the frequency of the announced pollution episode, which shows a phenomenon of information fatigue. The heterogeneity analyses reveal that the impact of pollution level is significant only when the original pollution level is "Good" or "Lightly Polluted", leading to an increase of pollution perception by 2.8% and 4.8%, respectively. We also find that men, highly-educated, and urban residents are more responsive to the pollution level information. Our study illustrates a detailed picture of how people's perception responds to the announced pollution level information under the AQI standards, and can provide guidance for policymakers when developing the AQI standards to maximize the welfare of the air information., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Although considerable health effects are gained from air quality improvement action plans implemented in China recently, they may have been amplified or offset due to the complexity and uncertainty of the changing demographic factors. In this study, we developed a framework for analyzing the effects of demographic factors on environmental health effects, focusing on three aspects: population scale, age structure, and spatial distribution. We quantified the above three effects by investigating how the health endpoint changed by the three demographic factors, based on a strategy of counterfactual and step-by-step relaxing hypothesis. We found that the increasing population scale and population aging caused 44,279 to 292,442 premature deaths, which offset the health effect of air quality improvement efforts for China. The change in population spatial distribution, in general, has little impact on the health effects of air quality improvement. Furthermore, the three effects are distributed unevenly across regions, especially the spatial distribution effect. Considering the widespread effect of demographic factors, PM 2.5 concentration should be further reduced, and the aged population and mega-cities should be targeted for managing air quality in a cost-effective manner., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Indoor air quality (IAQ) of urban public transportation stations (UPTS) has adverse health impacts on the station employees and commuters. However, there is a lack of comprehensively evaluations of IAQ in waiting rooms of UPTS. Therefore, it is crucial to select appropriate air quality indexes (including fuzzy synthetic index (B), comprehensive index (P), I 1 index, and indoor air quality index (IAQI)) to assess air quality and potential health risks. Our study is a subsample of the CPPEHS 2019 study, which included 224 UPTS in 126 cities of China. We found that P index showed an excellent air quality rate of 95.96% in the railway stations and 83.19% in the inter-city bus stations. The P index was correlated with UPTS usage years, useable area, and per passenger useable area. Furthermore, waiting rooms in UPTS with good (OR = 1.9187, 95% CI: 1.1204, 3.2859) and bad (OR = 2.0854, 95% CI: 1.2182, 3.5698) air quality evaluated by P index had a higher risk of rhinitis compared to those with excellent air quality. Similarly, UPTS with good (OR = 2.2202, 95% CI: 1.3427, 3.6711), bad (OR = 1.7897, 95% CI: 1.0807, 2.9637), and serious (OR = 1.7478, 95% CI: 1.0098, 3.0250) air quality evaluated by P index were associated with a higher risk of pharyngitis. These findings suggested that P index is the optimal index for assessing air quality in UPTS, while IAQI may exaggerate indoor air pollution and the B index may underestimate it. Overall, this study aims to identify and evaluate the more suitable air quality index (P) in nationwide UPTS, providing valuable insights for control of IAQ and guiding the air quality management and standards., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Biotrickling filters (BTF) have been applied to treat waste air. Controlling the water content (WC) of BTFs is fundamental to ensure an adequate interfacial mass transfer and the support of the biofilm activity. A sensor system based on electrical conductance measurement was developed for the online assessment of the WC in a bench scale polyurethane foam (PUF) packed bed. ON/OFF and rule-based control algorithms were applied to keep the water content in a biofilm-free PUF-packed bed within the optimal range of 70-90% through the activation of a hydraulic pump. The applied control algorithms activated the pump at various times, which yielded different energy and water savings even under similar WC conditions suitable for biotrickling filtration of waste air. It was estimated that using the ON/OFF and rule-based system control schemes reduced the electrical power consumption by 98% and 99.8%, respectively, compared to systems under continuous water irrigation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Transboundary air pollution poses a significant challenge in China, and collaborative governance presents a promising approach to tackle this problem. However, such approach is rarely implemented due to the influence of inter-regional trade dynamics. To address this issue, a comprehensive, responsibility-based fair compensation mechanism was established in the present study. The aim of the mechanism is to bridge the existing gap through the integrated utilization of air pollutant emission inventories, multi-regional input-output analysis, emission responsibility allocation, and an ecological compensation model. In 2017, approximately 50% of China's air pollutant emissions were embedded in inter-provincial trade, primarily originating from wealthier provinces and directed towards less-developed regions with higher pollution intensity. There exists a significant imbalance between the inter-provincial transfers of air pollution and economic benefits, owing to which Inner Mongolia and Liaoning have suffered from serious environmental inequity brought about by trade. The allocation of responsibility for trade-related emissions between producers and consumers is determined by the economic surplus they gain from trade. Such approach results in an intermediate outcome that falls between production-based and consumption-based emissions for the majority of provinces. The scheme highlights the import-related share of responsibility for economically developed provinces and the export-related share for less-developed, pollution-intensive provinces, which can primarily be attributed due to their differences in value chain specialization. The inter-provincial compensation volumes for trade-related emissions are determined based on the economically equitable emission responsibility sharing scheme. More developed provinces, characterized by industrial structures focused on low-pollution, high-value-added products, primarily function as compensation providers. In contrast, less-developed provinces with higher pollution intensity seek compensation from these more affluent regions. The present findings can offer a quantitative basis for the ecological compensation and collaborative control mechanism of trade-driven transboundary air pollution in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Steady increase in electricity generation and heavy reliance on coal in Mainland Southeast Asia (M-SEA) create huge pressure on the environment. This study used information collected from individual thermal power plants (TPPs) in M-SEA to calculate emissions of air pollutants and greenhouse gases (GHG) for 2010, 2015 and 2019. The emissions were projected to 2030 following the latest national Power Development Plans. The emission results were analyzed in relation to the power development by country and fuel type, and environmental impacts. The region collective annual TPP emissions in 2019, in Gg/yr, were 27 PM 2.5 , 77 PM 10 , 0.7 BC, 4.9 OC, 255 SO 2 , 451 NO x , 91 CO, 12 NMVOC, 0.4 NH 3 , 260 CO 2 , 13 CH 4 , and 26 N 2 O. Coal-fired TPPs dominated the emissions of most species while NG-fired contributed the largest amounts of NH 3 and CH 4 . Bi-decadal increase in energy production from TPPs of nearly 3 times is accompanied by 2.7 times increase in emissions. The 2010-2019 period saw average emissions increase by 1.9 times (TPPs' energy production increased 1.6 times), slightly higher than the rate of 1.4 times projected for 2019-2030 (double TPPs' energy production). The current intrusion rate of renewable energy accompanied by phasing-out of old TPPs are still by far insufficient to reverse the emission trend. Aggressive power development in Vietnam with its heavy coal reliance made it the largest emitter in 2019 and the projected for 2030, followed by Thailand. Spatially, higher emissions are seen over locations of large coal-fired TPPs in Vietnam and Thailand. Available rainwater composition monitoring data showed higher deposition amounts of sulfate and nitrate in areas located near or downwind of large TPPs. Significant GHG emissions projected for TPPs in 2030 indicated that TPPs should be the priority for emission reduction to achieve Nationally Determined Contribution targets. Emission database produced by this study can be used in dispersion modeling studies to assess impacts of TPPs on air quality, health, and acid deposition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Since especially the 12th Five-Year Plan (2011-2015), China has made great efforts to reverse the increasing trend of NO x emissions through end-of-pipe measures. With the Ozone Monitoring Instrument (OMI) level 2 swath product of tropospheric NO 2, this study explores the temporal-spatial patterns of NO x concentrations over China's coal-fired power plants from 2005 to 2020 and investigates the evolution of its control strategy. The nationwide deployment of flue-gas denitration facilities was a critical measure to mitigate NO x emissions from coal-fired power plants, while this study externally assesses the implementation gap of their operation. Our results illustrate that, besides the impacts of economic cycles, China's control strategy experienced a dramatic transformation from an ad hoc campaign style for meeting short-term temporary targets to more sustainable, technology- and governance-centered institutional arrangements for ensuring long-term fundamental solutions. Furthermore, the satellite-based assessment may provide not only ex post evaluation, but also in-time and independent data for more effective and efficient environmental compliance monitoring., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In recent decades, the low birth weight (LBW) rate in New Mexico has consistently exceeded the Unites States average. Maternal exposure to air pollution during pregnancy may be a significant contributor to LBW in offspring. This study investigated the links between maternal residential exposure to air pollution from industrial sources and the risk of LBW in offspring. The analysis included 22,375 LBW cases and 233,340 controls. It focused on 14 common chemicals listed in the Toxic Release Inventory (TRI) and monitoring datasets, which have abundant monitoring samples. The Emission Weighted Proximity Model (EWPM) was used to calculate maternal air pollution exposure intensity. Adjusted odds ratios (adjORs) were calculated using binary logistic regressions to examine the association between maternal residential air pollution exposure and LBW, while controlling for potential confounders, such as the maternal age, race/ethnicity, gestational age, prenatal care, education level, consumption of alcohol during pregnancy, public health regions, child's sex, and the year of birth. Multiple comparison correction was applied using the False Discovery Rate approach. The results showed that maternal residential exposure to 1,2,4-trimethylbenzene, benzene, chlorine, ethylbenzene, and styrene had significant positive associations with LBW in offspring, with adjusted odds ratios ranging from 1.10 to 1.13. These five chemicals remained as significant risk factors after dividing the estimated exposure intensities into four categories. In addition, significant linear trends were found between LBW and maternal exposure to each of the five identified chemicals. Furthermore, 1,2,4-trimethylbenzene was identified as a risk factor to LBW for the first time. The findings of this study should be confirmed through additional epidemiological, biological, and toxicological studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

In recent years, China has achieved numerous economic miracles but it has also been plagued by severe air pollution. The frequent hazy weather has severely restricted China's sustainable development. To investigate the nonlinear threshold effect of socio-economic factors on urban haze in China, this study constructs a spatial econometric Smooth Transition Autoregressive Regression (STAR) model based on the STIRPAT theory by using the remote sensing inversion PM 2.5 data of 223 prefecture-level and above cities in China mainland during 2004-2016. In this study, the ARAR-STAR model is estimated by quasi-maximum likelihood estimation, and the accuracy of parameter estimation is verified by Monte Carlo simulation, which proves that the ARAR-STAR model constructed in this study is robust. It is concluded that: there is a complex spatial nonlinear relationship between socio-economic factors such as economic development level, population density, advanced industrial structure, energy consumption, opening-up, and haze pollution. The effect of socio-economic factors on haze emission reduction under the spatial influence has complex heterogeneity with the smooth transition between high and low regimes with economic development. The ARAR-STAR model constructed in this paper, which has both individual fixed effects and time fixed effects, expands the form of existing spatial panel nonlinear models and enriches and implements the application of spatial panel smooth transfer threshold models in the environmental field. Not only can it provide policy recommendations for China to achieve "coordinated efficiency in pollution reduction and carbon reduction" as soon as possible, but it also contributes to China's plan to address global climate change and promote global sustainable development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

The relationship between fiscal regimes and urban industrial pollution emissions is unclear. This paper aims to explore the effects and mechanisms of fiscal centralization on urban industrial pollution emissions and environmental quality. Using the vertical reform of environmental administrations (VREA) in China as a quasi-natural experiment of fiscal centralization, this study applies a staggered difference-in-differences (DID) model to explore the differences in industrial pollution emissions between centralization cities and decentralization cities. The main findings are: (1) VREA significantly inhibits regional industrial pollution emissions, and the reform effect increases over time. This conclusion still holds after considering a series of robustness issues. (2) Industrial sulfur dioxide (SO 2 ) and solid particulate emissions in the fiscal centralization cities have decreased significantly by 0.3281% and 0.2240%, respectively. However, there is no significant change in industrial wastewater discharges. (3) Environmental regulations, environmental expenditures, and pollution control investments of local governments are the main channels through which VREA reduces industrial pollution emissions. (4) The effects of VREA are more significant in central and western cities and small cities. (5) Relative to decentralization cities, centralization cities have improved air and water quality by 0.0825% and 0.1628%, respectively. These findings help to accurately assess the effects of fiscal centralization on regional environmental governance and provide a decision-making reference for further deepening environmental centralization reform in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Quantity-based and concentration-based limits are two common environmental permitting approaches utilized by government worldwide in environmental management. While existing literature is still unambiguous about roles played by quantity-based versus concentration-based limits in environmental management, it becomes evident that relying exclusively on concentration-based or quantity-based limits to control plant emissions may not necessarily result in improved environmental quality. This paper leverages a unique opportunity arising from a recent reform in China's Pollutant Emission Permit System (PEPS) initiated in 2016 to analyze how the introduction of quantity-based limits in addition to concentration-based limits through the PEPS reform impact emissions at the plant level. Utilizing a unique plant-level continuous emission monitoring system data collected from Shaanxi Province (located in western China), the paper finds a significant reduction in air pollutant emissions as a result of the PEPS reform (nitrogen oxides (NOx) by 39%, sulfur dioxide (SO 2 ) by 15% and particulate matter (PM) by 13%). The heterogeneity analyses show emission reductions in plants differ across those with varying quantity limits specified in their permits, distinct emission ratios and diverse ownership structures. Furthermore, plants that fall under the classified management system with more stringent regulations imposed, especially those operating in high-pollution sectors, situated within industrial parks, or classified as large-sized plants, attain higher pollutant quantity limits. Findings of the paper carry important implications for effective environmental management, particularly within developing countries, and shed some light on carbon emission reduction policies in China., Competing Interests: Declaration of competing interest This work is supported by the National Natural Science Foundation of China (Grant no. 71773135), Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (Grant no. 21XNH057) and the Fund for Building World-class Universities (disciplines) of Renmin University of China (Grant no. KYGJA2022004). Authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)