Reduction Of Pollutant Emissions Research Articles

Study of condensable particulate matter from stationary combustion sources: Source profiles, emissions, and impact on ambient fine particulate matter

Although significant progress has been made in controlling emissions from stationary combustion sources in China over the past decade, understanding of condensable particulate matter (CPM) emissions from these sources and their impact on ambient PM2.5 remains limited. In this study, we established the source profiles and emission inventories of CPM from coal-fired industrial boilers (CFIBs), coal-fired power plants (CFPPs), and iron and steel industry (ISIs) for the Yangtze River Delta (YRD) region of China; furthermore, the air quality model (Community Multiscale Air Quality, CMAQ) was used to evaluate the impact of CPM emissions from these three types of stationary combustion sources on ambient PM2.5 during Feb. 2018, a month characterized by elevated PM2.5 concentrations. The results indicated that CPM emissions from these three sources in the YRD region before and after the implementation of the ultra-low emissions (ULE) policy amounted to 109,839 and 43,338 tons, respectively, with particularly high emission intensity along the Yangtze River. The implementation of CFPPs ULE policy was shown to reduce the impact of CPM emissions from these three stationary sources on monthly PM2.5 concentrations from 0.92 μg/m3 to 0.41 μg/m3 (with a maximum of 5.35 μg/m3). This reduction exceeded the 0.31 μg/m3 decrease in PM2.5 concentrations resulting from the emission reductions of conventional pollutants (FPM, SO2 and NOx). CPM emissions from these three stationary sources were found to increase the PM2.5 by 0.68 μg/m3 during pollution periods. The largest components of PM2.5 contributed by CPM emissions from stationary combustion sources were sulfate, organic carbon, and nitrate, accounting for 21.4 %, 21.1 %, and 18.2 %, respectively. Particularly, contributions from CPM emissions to PM2.5 varied by altitude, with a relatively large impact at altitudes between 220 and 460 m. Attention should be given to CPM emission control, with particular priority placed on implementing ULE measures for ISIs and CFIBs.

Synergies quantification and evolution on air pollutant and CO2 emissions driven by different socio-economic effects

Realizing a synergistic reduction of air pollutant and CO2 emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results indicated that the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO2 emissions was about 48% lower when compared with reductions of SO2 (95%), NOx (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NOx and CO2 emissions were stronger driven by final demand structure and type effects, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. The structural decomposition analysis based on input-output model combined with the cross-elasticity analysis method to quantitively synergies between APCE from the consumption (demand) perspective, considering the connections between industrial sectors with socio-economic developing, which would contribute to the industrial synergistic reduction and green transformation as the consumption driven gradually increasing.

Graph theory-enhanced integrated distribution network reconfiguration and distributed generation planning: A comparative techno-economic and environmental impacts analysis

In today's world, eco-friendly solutions are crucial for efficient power delivery and assessing their corresponding economic and environmental benefits is essential. As innovators, it is also imperative to continually improve on existing techniques to solve a problem. Evaluating the existing literature in this area of study, gaps of improving the optimization techniques by reducing the amount of infeasible configurations the reconfiguration procedure encounters was established, additionally, the need to utilize distributed generations that significantly reduce carbon footprint in the environment was also ascertained. Hence, this paper presents an effective integration method for the simultaneous reconfiguration of Radial Distribution Networks (RDNs) and Photovoltaic (PV) DGs allocation, considering the tripodal issues of cost, operational efficiency, and environmental sustainability. A modification of the adaptive mountain gazelle optimizer (AMGO) enhanced with graph theory is deployed for the optimization procedures. The crucial feature of the proposed approach is the reduction of unfeasible configurations throughout the optimization procedure toward satisfying the network's radiality constraints, achieving consistent convergence and reduced computation time. The technical benefits are active power loss minimization, voltage stability, and voltage profile improvement. The economic benefits are analyzed by estimating the purchased power, the associated cost of power losses, and the cost of DGs and switches over a planning period of 20 years. The consequent environmental benefits are analyzed in detail, highlighting the significant reduction in pollutant emissions. The proposed model was tested on the IEEE 33- and 69-bus RDN, considering several scenarios, including synchronous network reconfiguration and DG installations. From the results procured, the simultaneous network reconfiguration and DG allocation provided better outcomes, yielding minimum active power loss of 35.36 kW, minimum voltage of 0.9541 p.u., voltage stability index of 1.9936 p.u., total planning cost of $3.456 million, and emission of 1.744 million lb/hr, respectively, for the 33-bus systems. The corresponding value for the 69-bus system is 32.57 kW, 0.9832 p.u., 2.3847 p.u., $ 2.524 million, and 2.53 million lb/hr, respectively. The proposed model was compared with other reported techniques for performance validation, and its efficacy and superior performance was established.

Towards Green and Low-Carbon Transformation via Optimized Polygeneration System: A Case Study of the Iron and Steel Industry

Polygeneration systems have significant potential for energy conservation and emission reduction and can effectively promote green and low-carbon development in energy-intensive industries, such as the iron and steel industry. However, its application faces the difficulty in technology selection under multiple objectives simultaneously, which is to determine the technology portfolio to achieve the synergy of energy conservation goals and air pollutant emission reduction goals, as well as ensure the economic benefits of the enterprises. This study investigated a case polygeneration system where the iron and steel plant are the core with four polygeneration paths and twenty polygeneration technologies. A multi-objective optimization model is developed to select the optimal technology combination of each polygeneration path under energy conservation, emission reduction, and cost control objectives, which is solved by the non-dominated sorting genetic algorithm-II (NSGA-II). The optimal results can reach significant energy conservation and emission reduction effects while obtaining economic benefits. However, synergistic and conflicting relationships among the objectives exist in both scales of iron and steel plants. The final decision scheme can achieve the mitigations equivalent to 15.9–27.1% and 16.3–42.6% of the energy consumption and air pollutant emissions of the steel enterprises with annual production of 3 Mt/a and 9 Mt/a, respectively. There are thirteen and twelve technologies that are selected as the final decision scheme in the polygeneration system in these two case enterprises. These findings demonstrate the significant roles the polygeneration system plays and provide critical insights and methodology in the technical selection of the polygeneration system.

Spatiotemporal Characteristics and Influencing Factors of the Synergistic Effect of Pollution Reduction and Carbon Reduction in China

Based on the use of the coupling coordination model to calculate the coupling coordination degree of carbon emission and pollutant control, the national, regional, and provincial spatiotemporal characteristics of the synergistic effect of pollution control and carbon emissions reduction in China were further analyzed, facilitating the crucial to identification of key areas. The fixed effects regression models and provincial panel data from 2006 to 2020 were used to explore factors contributing to better synergizing the reduction of pollution and carbon emissions in China. On this basis, the adjustment variable of R&D investment intensity was introduced, and the regulation effect model was constructed to further explore the influence mechanism of the synergistic effect of pollution reduction and carbon reduction. The results showed that： synergy exists between carbon emission reduction and the air pollution control system, the evolution of the synergistic effect of pollution reduction and carbon reduction in China presented an inverted "U"-shaped trend from 2006 to 2020, and there was spatial aggregation and a spatial spillover effect in pollution reduction and carbon reduction. The synergistic governance of carbon emission and pollutant control was still at a relatively low level. The carbon emission and air pollutant emission systems were still in an unstable and uncoordinated state. The results showed that： The degree of coordination of eastern China, central China, and western China decreased in turn. At the national level, energy consumption structure, per capita GDP, and the proportion of green investment were the main factors affecting the synergistic effect of pollution reduction and carbon. The heterogeneity of the influencing factors existed in the central, eastern, and western regions on industrial structure, energy consumption structure, energy utilization efficiency, per capita GDP, urbanization rate, the proportion of green investment, and transportation structure. The intensity of R&D played a significant moderating effect in the whole country, eastern, and central regions. However, no significant moderating effect was identified in the western region. In the eastern region, the urbanization rate, the proportion of green investment, and the transportation structure could not have a significant effect on the synergistic effect of pollution reduction and carbon reduction alone, and it must be coordinated with the intensity of R&D.

Health equity and synergistic abatement strategies of carbon dioxide and air pollutant emissions reduction in China’s eastern coastal area

Abstract Quantifying regional health disparities linked to air pollution is essential for enhancing air quality and attaining carbon neutrality objectives. Nonetheless, the efficacy of proactive policies in ensuring equitable health protection in China’s Eastern Coastal Area (ECA) remains uncertain. Here, we employed an integrated assessment model to assess the combined reduction of carbon dioxide (CO2) and atmospheric pollutants and their health repercussions in the ECA of China. Our findings reveal that 273 000 premature deaths are attributable to air pollution in 2060 in the ECA in the absence of mitigation policies. Conversely, carbon reduction policies are poised to curtail 80% of CO2 emissions, alongside reductions of 76% for NOx, 79% for SO2, 80% for PM2.5, 72% for VOCs, and 66% for NH3 emissions. Air pollution control policies could mitigate premature deaths by 19 600, while carbon reduction policies could potentially lower them by 50 800. The health inequality coefficient among provinces stands at 0.19, primarily attributable to significantly higher mortality rates in Hebei and Shandong. These findings yield valuable insights for crafting synergistic abatement strategies in similarly imbalanced developmental regions grappling with comparable environmental challenges.

A hybrid optimization for distributed generation and D-STATCOM placement in radial distribution network: a multi-faceted evaluation

The deregulation of the power system, upward growth in electrical energy demand and network expansion have resulted in an increasing integration of distributed generation (DG) and distribution static synchronous compensator (D-STATCOM) into radial distribution systems (RDS). Nonetheless, the optimal allocation of these devices is highly important to derive immense benefits. This investigation narrows down on optimizing DG and D-STATCOM placement in IEEE 33-bus RDS with a view to increase bus voltages, decrease power losses as well as maximize economic gains. The study undertakes a comprehensive analysis comparing the technical, economic and environmental performance of DG and D-STATCOM; thereby enabling power engineers to make informed choices concerning which device will be most advantageous when it comes to delivering power in RDS. A fuzzy enhanced firefly optimization (FEFO) approach is proposed for the optimization and a multifaceted evaluation in terms of technical, financial and environmental is presented for effective decision-making on distributed energy resource deployment. D-STATCOM and wind DG integrations led to notable reductions in power loss and pollutant emissions, highlighting their effectiveness in improving power quality and reducing reliance on fossil fuels. While wind DG incurred a higher installation cost ($3,100,749.2) compared to D-STATCOM ($90,566.6), it achieved greater yearly power loss cost savings ($69,198 versus $47,619). FEFO’s efficiency in optimization stands out, aiding engineers in making informed decisions for optimizing D-STATCOM and wind-DG integration in the IEEE-33 RDS, ultimately enhancing system performance and cost-effectiveness through proactive planning. The integration of D-STATCOM and wind DG led to a significant improvement in distribution system efficiency, with D-STATCOM reducing real power loss by 28.7% and reactive power loss by 27.8%, while wind DG achieved greater reductions of 41.8% in real power loss and 37.5% in reactive power loss, alongside reductions in pollutant emissions of 1.5% and 2.2%, respectively.

Multi-Objective Energy Management in Microgrids: Improved Honey Badger Algorithm with Fuzzy Decision-Making and Battery Aging Considerations

A multi-objective energy management and scheduling strategy for a microgrid comprising wind turbines, solar cells, fuel cells, microturbines, batteries, and loads is proposed in this work. The plan uses a fuzzy decision-making technique to reduce pollution emissions, battery storage aging costs, and operating expenses. To be more precise, we applied an improved honey badger algorithm (IHBA) to find the best choice variables, such as the size of energy resources and storage, by combining fuzzy decision-making with the Pareto solution set and a chaotic sequence. We used the IHBA to perform single- and multi-objective optimization simulations for the microgrid’s energy management, and we compared the results with those of the conventional HBA and particle swarm optimization (PSO). The results showed that the multi-objective method improved both goals by resulting in a compromise between them. On the other hand, the single-objective strategy makes one goal stronger and the other weaker. Apart from that, the IHBA performed better than the conventional HBA and PSO, which also lowers the cost. The suggested approach beat the alternative tactics in terms of savings and effectively reached the ideal solution based on the Pareto set by utilizing fuzzy decision-making and the IHBA. Furthermore, compared with the scenario without this cost, the results indicated that integrating battery aging costs resulted in an increase of 7.44% in operational expenses and 3.57% in pollution emissions costs.

Review of gliding arc plasma assisted ignition and combustion for gas turbine application

Abstract The potential of gliding arc plasma-assisted ignition and combustion technology to enhance ignition and combustion performance is attracting increasing attention from the scientific community. A multitude of experimental studies have been conducted by scientists and engineers on its application in gas turbine combustors. This paper presents a review of the research conducted on gliding arc plasma-assisted ignition and combustion over the past five years. Gliding arc plasma exerts a multitude of effects on combustion processes. These effects can be broadly categorized as follows: (1) reduction in ignition delay time; (2) expansion of ignition and lean blowout boundaries; (3) enhancement of ultra-lean burning combustion and low-temperature flame stability; (4) improvement in combustion efficiency; (5) reduction in pollutant emissions; (6) augmentation of stability of unstable fuels such as ammonia. Finally, a prospection on the application of gliding arc plasma assisted ignition and combustion technology in gas turbine combustor is presented.

The mechanism and path of pollution reduction and carbon reduction affecting high quality economic development - taking the Yangtze River Delta urban agglomeration as an example

In the wake of the COVID19 pandemic, what is crucial for the continued promotion of ecological civilization construction and sustainable green development in China is to achieve the dynamic and balanced development between pollution reduction, carbon emission reduction, and economic growth. Therefore, the indexes of CO2 emissions, pollution reduction intensity, and high-quality economic development are quantified in this study by using the panel data related to 26 cities in the Yangtze River Delta urban agglomeration(YRDUA) from 2010 to 2020. Furthermore, kernel density analysis, GIS spatial analysis, and other methods are employed to demonstrate the spatial and temporal patterns of pollution reduction intensity, CO2 emission levels, and the characteristics of high-quality economic development in the YRDUA. In order to reveal the dynamic relationship between pollution reduction, carbon emission reduction, and high-quality economic development, a panel vector autoregressive model (PAVR) was established and the system generalized method of moments was used to estimate the model coefficients. Additionally, Granger causality tests, impulse response analysis, and variance decomposition were conducted. There is a fluctuating upward trend shown by the overall level of CO2 emissions in the YRDUA, with significant spatial nonuniformity exhibited in carbon emissions. Despite the self-promotion mechanism playing a more significant role in the high-quality economic development in the YRDUA and its three regions, the impact of pollutant and carbon emissions on high-quality economic development is quite limited. A significant promoting effect is exhibited in those high and moderately high-quality economic development regions, while nonlinear effects are observed in those high and moderately high-quality economic development regions. As for the interactive relationships, the three factors are correlated with each other to a more significant extent in those high and moderately high-quality economic development regions, which contributes to mutual influence and causal relationship. However, there is no effective interactive mechanism observed among the three factors in the whole area and low-quality economic development regions, with high-quality economic development playing a major role in environmental pollution and carbon emission reduction.

Diesel Engine Turbocharger Monitoring by Processing Accelerometric Signals through Empirical Mode Decomposition and Independent Component Analysis

In this study, a method for the monitoring of internal combustion engine operation by vibration signals is proposed. The work falls within the context of the increasingly stringent standards relating to the environmental impact of engines and the development of monitoring and control techniques to ensure increased engine performance as well as fuel saving and reduction of pollutant emissions. Experimentation was performed on a turbocharged light-duty compression ignition direct-injection engine. Two monoaxial accelerometers were installed on the engine compressor case, the speed of which has been demonstrated to be closely related to the engine operation. Vibration measurements of the engine compressor case have been processed by combining the Empirical Mode Decomposition technique with Independent Component Analysis and Short Time Fourier Transform to indirectly estimate the turbocharger speed. The obtained traces have been compared to the direct turbocharger velocity measures during the stationary running of the engine (speed and load conditions varied in the complete engine’s range of operation). The results point out the potentiality of the methodology in algorithms devoted to identifying modifications of the combustion development regarding regular operation via indirect turbocharger speed monitoring.

An inter-provincial coordinate model under Renewable Portfolio Standards policy based on tradable green certificate options trading

The current territorial management model under Renewable Portfolio Standards in China cannot fully develop renewable energy due to the mismatch between high energy-consumption regions and renewable energy-rich regions. Therefore, we proposed an inter-provincial coordinate model based on tradable green certificate options trading (CMTOT), which includes four components: (1) A dual-objective optimization model is established to minimize the RPS target realization cost and maximize the pollutant emission reduction based on Blacke-Scholes option pricing model. (2) The identification of buyer and seller provinces of tradable green certificate (TGC) options based on the above model. (3) An optimization model in buyers' or sellers' cooperation alliance is established. (4) The cost allocation models by Shapley value and Nucleolus are established to ensure the fairness in different scenarios. A case study of Tianjin, Shandong, Shanxi and Inner Mongolia shows that the CMTOT can effectively reduce the RPS target realization cost by 16.79 % and increase the pollutant emission reduction by 4.32 %. A sensitivity analysis confirms the robustness of the CMTOT. The results show that the CMTOT can both fully develop renewable energy and effectively reduce pollutant emission. Therefore, we proposed some policy recommendations to support the implementation of the CMTOT.

Environmental and economic impact analysis of levying VOCs environmental protection tax in China

China is one of the largest volatile organic compounds (VOCs) emitters worldwide. The emission levels of and harm caused by VOCs have attracted much attention. China has implemented multiple policies for VOCs prevention and control but lacks economic control measures for VOCs In this study, the input-output (IO) price model was used to simulate and analyze the emission reduction and economic effects resulting from the imposition of a VOCs environmental protection tax (EPT) in 31 provinces in China. The results show that, first, the collection of a VOCs-EPT can achieve not only VOCs emission reductions but also the synergistic emission reductions of other major pollutants. Second, the collection of a VOCs-EPT could have a negative impact on the macroeconomy, i.e., the greater the tax scope and the higher the tax rate are, the greater the negative economic impact. Third, differences in the level of economic development, the structure of pollution emissions and the stringency of tax policies among regions would cause the emission reduction effect and related negative economic impact to vary across regions. Finally, the collection of a VOCs-EPT could have heterogeneous impacts on various industries, as high-emission industries would suffer greater negative impacts. Therefore, each region should set tax rates that match its provincial economic and environmental development levels. Furthermore, a VOCs-EPT can be levied on key industries, and reasonable preferential tax policies can be formulated to reduce negative macroeconomic benefits.

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm’s efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

The effects of green finance on pollution and carbon reduction: Evidence from China’s industrial firms

Reducing pollution and carbon emissions is important but requires a large amount of capital. Green finance is becoming an important market-based instrument. Few literatures have focused on the direct impact of green finance on corporate environmental performance. This study investigates the effects of green finance on air pollution and carbon reduction in China’s industrial firms from 2008 to 2015. Results show that green finance promotes CO2 and air pollution reduction. Moreover, green finance has a synergistic effect on SO2 and carbon reduction, but not on reduction of dust and carbon emissions. Our findings suggest that green finance reduces emissions by improving corporate energy utilization efficiency. Furthermore, we find that the effects of green finance on emission reduction are more significant among non-state and non-listed firms. These findings have important implications for the development of green finance and synergistic reduction of air pollutants and carbon.

Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China

Artificial intelligence (AI) has become a key driver in the latest wave of scientific and technological advancement, and its rapid development, proliferation, and environmental impacts cannot be ignored. China and numerous emerging economies are confronted with the dual challenges of environmental degradation and climate change. Hence, it is imperative to assess whether the advancement of AI can contribute to a synergistic reduction in pollutant and CO2 emissions. This paper utilizes the system-generalized method of moments (SYS-GMM) to study the synergistic effect of artificial intelligence on mitigating pollutant and carbon emissions. The following three main conclusions are drawn: (1) AI plays a major role in synergistically decreasing pollutant and CO2 emissions; (2) AI indirectly helps lower pollutant and CO2 emissions by fostering technological advancements and enhancing industrial structures. Although it contributes to an increase in emissions by expanding production scale, its suppression effect dominates overall; (3) The impact of AI applications is particularly vital in cities with strict environmental controls, especially in the central and eastern regions. Finally, we suggest some policy measures to augment the influence of AI in reducing emissions and attaining sustainable development.

Spatiotemporal Distribution Characteristics of Ground-level-ozone and Its Relationship with Meteorological Conditions in a Representative City in the Bohai Rim from 2017 to 2022

In recent years, ground-level-ozone（O3） pollution in urban areas in the Bohai Rim has attracted wide attention. Based on the analysis of the spatiotemporal distribution characteristics of O3 concentration in Dongying, a representative city in the Bohai Rim from 2017 to 2022, the effects of meteorological factors and sea-land breeze circulation on O3 concentration were evaluated. The results showed that： ① From 2017 to 2022, the annual assessment value of O3 concentration in Dongying showed a fluctuating upward trend, and the pollution days with O3 as the primary pollutant increased. O3 pollution mainly occurred in spring, summer, and autumn, with the most severe O3 pollution episodes typically occurring in May and June, and the duration of O3 pollution season tended to be longer. The monthly variation in the daily maximum 8-h average ozone （MDA8 O3） presented a bimodal distribution, with significant increases in the 5th and 25th percentiles, and the spatial distribution was "high in the north and south, low in the middle." In addition, the nocturnal O3 concentration in recent years in Dongying also showed a significant increase trend. ② Meteorological factors greatly influenced O3 concentration in Dongying. When the temperature was greater than 30℃, the relative humidity was less than 50%, and the wind direction was south-southwest or east-northeast, a high O3 value was more likely to occur. Meteorological factors contributed 30% of the MDA8 O3 variation in Dongying during the study period. In the case of moderate and severe O3 pollution, the contribution of meteorological factors to the change in MDA8 O3 could be as high as 40%. ③ To some extent, sea-land breeze contributed to the occurrence of MDA8 O3 exceeding the secondary standard limit value of the National Ambient Air Quality Standard. In the afternoon, the hourly concentration of O3 during the sea-land breeze days was approximately 20 μg·m-3 higher than that during the non-sea-land breeze days. On the days of moderate and severe O3 pollution, the O3 concentration during the sea-land breeze days from 10：00 to 16：00 was higher than that during non-sea-land breeze days, and the O3 concentration was also at a high level from 20：00 to 23：00 on sea-land breeze days. In the O3 pollution season, the sea-land breeze could significantly affect the O3 level in coastal cities, which could bring significant challenges for O3 pollution prevention and control in this region. In the future, cities in the Bohai Rim need to further strengthen regional joint prevention and control of O3 pollution and increase emission reduction efforts of nitrogen oxides and volatile organic compounds. This strategy could effectively lower pollutant concentrations within the land breeze air mass, consequently reducing the impact of the sea breeze air mass on air quality in cities in the Bohai Rim.

Digital transformation and synergistic reduction in pollution and carbon Emissions——An analysis from a dynamic capability perspective

Pollution control and carbon emission reduction pose significant challenges for developing countries, and achieving synergistic reductions in pollution and carbon emissions (SRPCs) has emerged as the optimal choice. Digitization, a prevailing trend in the current era, presents new opportunities for realizing the SRPC. We utilize data from Chinese A-share listed companies from 2007 to 2022 to investigate the impact of corporate digital transformation on the SRPC. The research reveals the following. (1) Enterprise digital transformation significantly promotes the SRPC, a conclusion validated through a series of robustness tests. However, the SRPC resulting from digital transformation is characterized by weak synergy, which is primarily observed in the coordinated reduction in air pollution and carbon emissions. (2) This effect is notably stronger for companies near environmental regulatory authorities, those receiving government environmental subsidies, and state-owned enterprises (SOEs). (3) Enhancing managerial collaborative management capabilities, promoting corporate technological innovation capabilities, and alleviating financing pressure are the primary mechanisms at play. This research provides important policy and practical insights for promoting the SRPC in the context of digitization, fostering sustainable development.

Photovoltaics Energy Potential in the Largest Greek Cities: Atmospheric and Urban Fabric Effects, Climatic Trends Influences and Socio-Economic Benefits

This comprehensive study explores the influence of aerosols and clouds on solar radiation in the urban environments of nine of Greece’s largest cities over the decade from 2014 to 2023. Utilizing a combination of Earth Observation data, radiative transfer models, and geographic information systems, the research undertook digital surface modeling and photovoltaic simulations. The study meticulously calculated the optimal rooftop areas for photovoltaic installation in these cities, contributing significantly to their energy adequacy and achieving a balance between daily electricity production and demand. Moreover, the research provides an in-depth analysis of energy and economic losses, while also highlighting the environmental benefits. These include a reduction in pollutant emissions and a decrease in the carbon footprint, aligning with the global shift towards local energy security and the transformation of urban areas into green, smart cities. The innovative methodology of this study, which leverages open access data, sets a strong foundation for future research in this field. It opens up possibilities for similar studies and has the potential to contribute to the creation of an updated, comprehensive solar potential map for continental Greece. This could be instrumental in climate change mitigation and adaptation strategies, thereby promoting sustainable urban development and environmental preservation.

Role of primary drivers leading to emission reduction of major air pollutants and CO2 from global power plants

Electricity production is a significant source of air pollution. Various factors, including electricity demand, generation efficiency, energy mix, and end-of-pipe control measures, are responsible for the emission changes during electricity generation. Although electricity production more than doubled from 1990 to 2017, air pollutant emissions showed a moderate increase or decrease, which was attributed to mitigating drivers such as increased clean energy use, improved power generation efficiency, and widespread installation of end-of-pipe control facilities. The absence of these mitigating drivers would have increased CO2, fine particulate matter (PM2.5), black carbon, SO2, and NOx emissions in 2017 by 165 %, 403 %, 1070 %, 614 %, and 274 % than their actual levels, respectively. The improved electricity generation efficiency reduced potential CO2, PM2.5, SO2, and NOx emissions by 30 %, 295 %, 119 %, and 52 % compared to actual emissions, respectively. Meanwhile, the installation of end-of-pipe facilities reduced potential SO2 and PM2.5 emissions by 34.7 and 4.0 Tg, respectively. Considerable differences in emissions among countries were found to be attributable to their differences in electricity demand and the implementation of local mitigating polices.