Environmental Pollution Emissions Research Articles

Exploring the Mechanisms of Regional Environmental Collaborative Legislation in China: Policy Effectiveness, Practical Challenges, and Policy Suggestions

The legislation for regional environmental collaboration is of significant importance in enhancing the effectiveness of environmental protection. The existing literature focuses on the power struggle between central and local governments regarding environmental regulation within the realm of regional environmental governance, lacking an examination of the collaborative governance mechanisms and their effectiveness. Therefore, this study takes China’s regional environmental collaborative legislation as its research object. It employs a difference-in-differences model to test the effectiveness of regional environmental collaborative legislation concerning environmental governance and conducts a qualitative analysis to examine the current practical challenges facing regional environmental collaborative legislation. The quantitative analysis results indicate that regional environmental collaborative legislation can effectively suppress the emission of environmental pollutants. Specifically, the implementation of regional environmental collaborative legislation results in a 19.7% reduction in prefecture-level wastewater emissions and a 26.6% reduction in sulfur dioxide emissions. Qualitative analysis results show that regional environmental collaborative legislation currently faces challenges such as localism, difficulties in legislative authority allocation, and a lack of cooperation mechanisms. Therefore, this paper proposes to establish a collaboration platform, balance the interests of stakeholders, and improve the allocation mechanism of environmental regulatory powers in order to better optimize the regional environmental collaborative legislation and enhance the government’s environmental governance capacity.

Environmental Management Technologies, Environmental Policy Stringency, Energy Productivity and Pollution Emission: Fresh Evidence from Indonesia under STIRPAT Framework

The rapid pace of climate change and global warming has become an ever growing challenge all over the world. Global warming and carbon concentrations can be mitigated by adopting strict environmental policies and effective management of the environment. Therefore, the study aims to empirically investigate the effect of environmental policy stringency (EPS), energy productivity and environmental management technologies (EMT) on pollution emission in Indonesia- one of the most polluting countries of the world. To this end, the study collects data over the 1990 to 2020 period. The study uses Autoregressive Distributed Lag Model (ARDL) approach to carry out empirical estimation. The findings reveal that environmental management technologies, energy productivity and environmental policy stringency had negative impact on CO2 emission. However, economic growth (GDP) and urbanization had positive role in increasing CO2 emission in the long run and the short run. On the basis of the outcomes, the study recommends that Indonesia should adopt strict environmental policies and legislations to mitigate environmental deterioration. Indonesian government is recommended to promote the adoption of environmental management technologies by providing substantial incentives and investment opportunities for businesses.

How does digital transformation affect the emissions of environmental pollutants? From the perspective of nonlinear nexuses

How does digital transformation affect the emissions of environmental pollutants? From the perspective of nonlinear nexuses

Sustainable management of land use patterns and water allocation for coordinated multidimensional development

The sustainable and integrated management of resources is a crucial approach to achieving sustainable regional development in the face of limited land and increasingly scarce water resources. This paper proposes a multidimensional optimization model to jointly optimize land use patterns and water resource allocation under the conditions of maximizing economic benefits and minimizing social differences, ecological loads, environmental pollution, and carbon emissions. The optimization model is solved using the affiliation function method and then combined with random forest (RF) and cellular automata (CA) models to further obtain the optimal spatial layout of land use. This paper takes the Mid-Yangtze River City Cluster as an example and optimizes the land use pattern and water allocation in the region in 2020 and 2030. The results of the study show that the optimized water and land resource allocation scheme performs better than the status quo in all dimensions. The optimized economic benefits, social effects, ecological effects, environmental effects, and overall sustainability in 2020 are increased by 45.97%, 22.89%, 10.07%, 24.65%, and 42.73%, respectively. By 2030, overall sustainability reaches 0.69. The developed modeling framework helps to elucidate the internal linkages between land use and water allocation, and it provides decision-makers with diversified approaches to land and water resource management and adjustment for different cities in different periods, which promotes the sustainable development of urban agglomerations.

High-performance gas sensor utilizing g-C3N4/In2O3 composite for low concentration prediction to NO2

The demand for gas sensors is experiencing rapid growth, driven by the increasing need for air quality detection in the face of environmental pollution and industrial emissions. However, the widely used metal oxide semiconductor (MOS) gas sensors suffer from drawbacks such as low ability to predict gas concentration and poor selectivity. In this work, a sensor array was fabricated consisting of 10 various gas sensors and combined with an advanced deep learning algorithm to achieve precise predictions of nitrogen dioxide (NO2) at low concentrations. First, graphitic carbon nitride (g-C3N4) and In2O3 nanoparticles were composited with different mass ratios, resulting in the formation of distinct g-C3N4/In2O3 composites as sensing materials. Afterward, SEM, TEM, and XRD were employed to characterize the morphology, structure, and elemental composition of the samples. Furthermore, sensing properties including response, selectivity, and repeatability, are studied for 10 gas sensors based on g-C3N4/In2O3 composites. Finally, the convolutional neural network-efficient channel attention-gate recursive unit (CNN-AGRU) model was proposed to analyze the patterns of the signals collected from the sensor array when exposed to various NO2 concentrations from 1 to 9 ppm. The results demonstrated the integration of high-selectivity sensing materials to NO2 and an advanced deep learning algorithm CNN-AGRU enables to realize a high concentration prediction accuracy of about 97.04% and a low prediction error of 0.20527 ppm for NO2 gas.

ZnO based ultrasensitive CO chemiresistive gas sensor

The detection of hazardous gases in the atmosphere is a topic of interest for public security, environmental pollution and industrial emission. In recent years, semiconductor-based gas sensor attracted wide attention in all over the world. Among various hazardous gases, detection of CO at low temperature with fast response and recovery time is still a challenge. In this work, we have adopted single step synthesis of zinc oxide (ZnO) nanoroads for the fabrication of CO gas sensor. This sensor is found to have ∼8% sensitivity with fast response time of the order of 10 s at 50 °C. Also, selectivity of sensor has been investigated for different gases and observed that ZnO is more active for CO gas at 50 °C. The sensitivity of ZnO based sensor is explained in terms of interaction of CO molecules with adsorbed oxygen vacancies in ZnO which promote better pathway to the charge carrier. The sensing material is characterized by x-ray diffraction (XRD), UV–vis spectroscopy and scanning electron microscopy (SEM). To the best of our knowledge, the detection of 15 ppm CO at 50 ºC with good sensitivity is reported first time.

High potential in synergizing the reduction of dissolved organic carbon concentration and carbon dioxide emissions for submerged-vegetation-covered river networks

Various technologies and projects have been explored and developed for the synergetic control of environmental pollution and carbon emissions in aquatic ecosystems. Planting submerged vegetation in shallow waters was also expected to achieve this purpose. However, the magnitude and mechanism of carbon dioxide (CO2) emission affected by submerged vegetation is not clear enough in complex aquatic ecosystems. This study investigated the influences of submerged plants on CO2 emission, ecosystem metabolism features, and microbial community traits based on observations in river networks on the Changjiang River Delta. The results showed that CO2 emission from planted waters accounted for 73% of unplanted waters. Meanwhile, planted waters had higher dissolved organic carbon removal capacity in overlying water and higher potential of carbon sequestration in sediment at the same time. These distinctions between the two habitats were attributed to (1) improved CO2 and bicarbonate consumption in water columns via enhancing photosynthesis and (2) inhibited CO2 production by reconstructing the benthic microbial community. Additional eco-advantages were found in planted sediments, such as a high potential of methane oxidation and xenobiotics biodegradation and a low risk of becoming black and odorous. In brief, submerged vegetation is beneficial in promoting pollution removal and carbon retention synchronously. This study advances our understanding of the feedback between aquatic metabolism and CO2 emission.

Assessment of Syngas Storage Tank Hazards Taking Account of the Domino Effect

In most countries energy needs are satisfied using fossil fuels. Fossil fuel combustion involves environmental pollution and greenhouse gas emissions. The effect of the depletion of natural resources and the growing awareness of the need to protect the environment are the reasons that clean energy and alternative energy sources have been significant research issues. One of the most important technologies enabling efficient generation of low-emission energy is the gasification process of synthesis gas production. Syngas is primarily composed of hydrogen and carbon monoxide, but depending on the feedstock, it can also contain smaller concentrations, e.g., of carbon dioxide, methane and nitrogen. Because synthesis gas contains flammable and toxic substances, it may pose hazards to humans and the environment at every stage of gas production, storage, transport or final utilization if released uncontrollably. This paper presents the results of analyses related to hazards created by an uncontrollable release of synthesis gas during storage. A failure of a syngas system may cause damage to other, subsequent technological systems and facilities located in the neighborhood and containing dangerous substances. The problem gains special significance if syngas is stored in many tanks, where a failure of one may result in damage to subsequent tanks due to the so-called domino effect. The conditions in which the domino effect may occur are analyzed and the effect occurrence probability is determined depending on the mutual location of the tanks.

Unveiling Microbial Nitrogen Metabolism in Rivers using a Machine Learning Approach.

Microbial nitrogen metabolism is a complicated and key process in mediating environmental pollution and greenhouse gas emissions in rivers. However, the interactive drivers of microbial nitrogen metabolism in rivers have not been identified. Here, we analyze the microbial nitrogen metabolism patterns in 105 rivers in China driven by 26 environmental and socioeconomic factors using an interpretable causal machine learning (ICML) framework. ICML better recognizes the complex relationships between factors and microbial nitrogen metabolism than traditional linear regression models. Furthermore, tipping points and concentration windows were proposed to precisely regulate microbial nitrogen metabolism. For example, concentrations of dissolved organic carbon (DOC) below tipping points of 6.2 and 4.2 mg/L easily reduce bacterial denitrification and nitrification, respectively. The concentration windows for NO3--N (15.9-18.0 mg/L) and DOC (9.1-10.8 mg/L) enabled the highest abundance of denitrifying bacteria on a national scale. The integration of ICML models and field data clarifies the important drivers of microbial nitrogen metabolism, supporting the precise regulation of nitrogen pollution and river ecological management.

Hollow-structured cobalt sulfide electrocatalyst for alkaline oxygen evolution reaction: Rational tuning of electronic structure using iron and fluorine dual-doping strategy

Utilizing renewable electricity for water electrolysis offers a promising way for generating high-purity hydrogen gases while mitigating the emission of environmental pollutants. To realize the water electrolysis, it is necessary to develop highly active and precious metal-free electrocatalyst for oxygen evolution reaction (OER) which incurs significant overpotential due to its complicated four-electron transfer mechanism. Hence, we propose a facile preparation method for hollow-structured Fe and F dual-doped CoS2 nanosphere (Fe-CoS2-F) as an efficient OER electrocatalyst. The uniform hollow and porous structure of Fe-CoS2-F enlarge the specific surface area and increase the number of exposed active sites. Furthermore, the Fe and F dual-dopants synergistically contributed to the adjustment of electronic structure, thereby promoting the adsorption/desorption of oxygen-containing reaction intermediates on active sites during the alkaline OER procedure. As a result, the prepared Fe-CoS2-F exhibits outstanding OER activity, characterized by a low overpotential of 298 mV to achieve a current density of 10 mA cm−2 and a Tafel slope as small as 46.0 mV dec−1. Based on computational theoretical calculations, the introduction of the dual-dopants into CoS2 structure reduce the excessively strong adsorption energy of reaction intermediate in the rate determining step, leading to effectively promoted electrocatalytic cycle for OER in alkaline environment. This study presents an effective strategy for preparing noble metal-free OER electrocatalysts with promising potential for large-scale industrial water electrolysis.

Investigating global warming's influence on food security in Benin: in-depth analysis of potential implications of climate variability on maize production

Climate change has emerged as a pressing concern affecting nations worldwide, particularly within the African continent, including Benin. Given that maize stands as a staple cereal in Benin, this research to assess the impact and predict the effects of climate change on maize production by the year 2050. To attain this objective, an assortment of data encompassing climatic conditions, demographic factors, fertilizer application levels, and emissions of environmental pollutants has been collected and analyzed. The data analysis based on ARDL and ARIMA models has unveiled those variables such as emissions of CO2 and CH4 through food waste, peak temperatures, precipitation patterns, and rural population density exert considerable immediate influence over maize production volumes. The predictive models portend an upswing in national maize production volume, albeit accompanied by a probable decline in per capita availability. Policies aimed at controlling activities that generate high levels of air pollutants should be formulated to increase production capacities.

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

Long-Term Prevalence Trends in Children in Relation to Environmental Issues in the Astrakhan Region

Introduction: One of the tasks of preventive medicine and hygiene is to study the relationship between environmental factors and human health. The purpose of the study was to compare pediatric prevalence rates in the Astrakhan Region and in the city of Astrakhan in 2007–2020 with those for the Russian Federation in relation to per capita emissions of environmental pollutants. Materials and methods: We used official statistics on the size of population, prevalence rates in children, and emissions of air pollutants in the Astrakhan Region for the years under study. Methods of mathematical and statistical analysis, construction of trend lines and calculation of linear regression equations using built-in Microsoft Excel software were applied. An online Spearman’s Rho Calculator was used to compute correlation coefficients. Results: The prevalence in children showed a statistical downward trend both in Astrakhan and in the Astrakhan Region as a whole with its rates being, on average, 7.8 % and 15.6 % lower than the national ones, respectively. The Spearman’s Rho (rs) showed the presence of a high and direct statistically significant correlation between per capita emissions of air pollutants and disease prevalence in the child population in the Astrakhan Region (rs = 0.73; p = 0.003) with a moderate direct, yet insignificant, correlation for the Russian Federation as a whole (rs = 0.32; p = 0.41) and null correlation for the data computed for the of Astrakhan (rs = 0.26; p = 0.32). Conclusion: Along with the predicted decreasing trend in prevalence in the pediatric population, we established a statistical relationship between local environmental problems and children’s health, which should be taken into account in the development of disease prevention programs.

Carbon emission trading, technological progress, synergetic control of environmental pollution and carbon emissions in China

This study introduces three production technology shocks (Energy manufactures, Brown enterprises, and Green enterprises) by constructing different models, namely, the environment sector (baseline) model, the carbon emissions trading scheme (ETS) model, and the carbon emissions rights trading mechanism. The fluctuation trend of China's macroeconomic and environmental quality before and after the establishment of carbon market is compared and analyzed. Additionally, the study examines the welfare of the implementation of carbon market policies. The carbon trading market policy can promote the synergistic efficiency of China's pollution reduction and carbon reduction using energy and green production technologies. From the perspective of social welfare, the optimal range of the initial carbon quota ratio issued by the government to enterprises is [0.7,0.8]. The findings of this study provide theoretical support and contribute toward understanding the impact mechanisms of carbon market and technological progress on synergizing the reduction of pollution and carbon emissions in China.

Assessment of environmental concern for enterprise pollution reduction

Environmental regulation produces different effects in different regions. Public concern for the environment in different regions may affect the effectiveness of environmental regulation. Using the pollution emission data of the Chinese Industrial Enterprise Database and the Baidu Index, this paper uses econometric models to evaluate the effect of public environmental concern on enterprise pollution emissions. Results show that public environmental concern reduces enterprise emissions with a coefficient of -3.026. Further research shows that the negative relationship between public environmental concern and pollution emissions is significant for state-owned enterprises, longer-established enterprises, and enterprises in cities with high GDP, in populated areas, and in cities with a higher concentration of secondary industries. Through mechanism analysis, we found that the public can urge enterprises to reduce emissions through centralized treatment of wastewater, harmless treatment of garbage, and increasing green areas, and enterprises should take corresponding measures for different emissions. Finally, we present suggestions for pluralistic co-governance from the perspectives of the government, enterprises, and the media.

DPSIR framework to evaluate and analyze Iran's energy security

Energy security has become an increasing concern for many countries, policymakers, and decision-makers. Beyond the environmental challenges of fossil energy sources, energy security remains a prominent concern for economic development. Therefore, evaluating energy security can be a valuable tool for policy formulation. This article models the energy security of Iran using the DPSIR framework (driving forces, pressures, states, impacts, and responses). The period under study is from 2012 to 2021. The dimensions of this framework include five aspects: driving forces, pressures, status, impacts, and responses. The TOPSIS model with entropy weighting is used to calculate the weights of indices and ultimately determine the final energy security. The research findings indicate that throughout the examined period, there have been fluctuations in the trend. However, Iran's energy security has been bullish and has reached 0.61 in 2021 from 0.38 in 2012. The indicators of driving forces and impacts have enhanced energy security threats. International sanctions, population growth, urbanization, reliance on fossil fuel consumption, and the emission of environmental pollutants were the most influential factors contributing to decreased energy security. Iran requires the implementation of suitable strategies for long-term control of these factors.

R&D and Application of Smart Park Energy Control Platform Combined with MLM Technology

Abstract As the core of park intelligence, the smart park energy control platform realizes the efficient management and optimal use of park energy by integrating and applying IoT technology. This study focuses on the research development and application of a smart park energy management and control platform using IoT technology, firstly constructing the IoT smart park platform, designing the login module, the park information module, and the energy consumption analysis module, and describing the steps of the smart park energy collection algorithm, and then designing the model of the energy prediction and planning algorithms, i.e.In the construction of the LSTM heating temperature prediction model and the GRU network modeling. Testing and research can be done by using a smart park as an example. The empirical study shows that the construction of the IoT smart park platform can improve the accuracy of monitoring, and the difference between the actual collection results and the system monitoring results is no more than 0.03 kW. After optimizing the configuration, the park’s energy supply system can meet the electric heating and cooling loads with optimal efficiency. Through the comprehensive evaluation of the operation of the extensive energy information monitoring platform, the environmental pollutant emissions are reduced by about 20%, and the platform has a remarkable effect on energy saving and emission reduction.

Multi-atomic loaded C2N1 catalysts for CO2 reduction to CO or formic acid.

In recent years, the development of highly active and selective electrocatalysts for the electrochemical reduction of CO2 to produce CO and formic acid has aroused great interest, and can reduce environmental pollution and greenhouse gas emissions. Due to the high utilization of atoms, atom-dispersed catalysts are widely used in CO2 reduction reactions (CO2RRs). Compared with single-atom catalysts (SACs), multi-atom catalysts have more flexible active sites, unique electronic structures and synergistic interatomic interactions, which have great potential in improving the catalytic performance. In this study, we established a single-layer nitrogen-graphene-supported transition metal catalyst (TM-C2N1) based on density functional theory, facilitating the reduction of CO2 to CO or HCOOH with single-atom and multi-atomic catalysts. For the first time, the TM-C2N1 monolayer was systematically screened for its catalytic activity with ab initio molecular dynamics, density of states, and charge density, confirming the stability of the TM-C2N1 catalyst structure. Furthermore, the Gibbs free energy and electronic structure analysis of 3TM-C2N1 revealed excellent catalytic performance for CO and HCOOH in the CO2RR with a lower limiting potential. Importantly, this work highlights the moderate adsorption energy of the intermediate on 3TM-C2N1. It is particularly noteworthy that 3Mo-C2N1 exhibited the best catalytic performance for CO, with a limiting potential (UL) of -0.62 V, while 3Ti-C2N1 showed the best performance for HCOOH, with a corresponding UL of -0.18 V. Additionally, 3TM-C2N1 significantly inhibited competitive hydrogen evolution reactions. We emphasize the crucial role of the d-band center in determining products, as well as the activity and selectivity of triple-atom catalysts in the CO2RR. This theoretical research not only advances our understanding of multi-atomic catalysts, but also offers new avenues for promoting sustainable CO2 conversion.

A study on the relationships among green finance, environmental pollution and economic development

With the rapid development of China's economy, China's energy consumption is increasing. There has always been an objective contradiction between economic development and environmental pollution control. Green finance is an important tool to solve the problem. After testing that there is endogeneity among green finance, environmental pollution and economic development, this paper uses the data in China to explore the spatial effect and interaction relationship of the three by establishing a spatial simultaneous equation. The empirical results show that: Economic development and pollutant emissions constitute an inverted U-shaped EKC; Green finance has a significant positive effect on economic development, and economic development will also bring more green finance investment; The increase of environmental pollutant emissions requires more investment in green finance, and green finance has a certain governance effect on pollutant emissions.

Utilization of carbon-based nanomaterials for wastewater treatment and biogas enhancement: A state-of-the-art review

The management of environmental pollution and carbon dioxide (CO2) emissions is a challenge that has spurred increased research interest in determining sustainable alternatives to decrease biowaste. This state-of-the-art review aimed to describe the preparation and utilization of carbon-based nanomaterials (CNM) for biogas enhancement and wastewater contaminant (dyes, color, and dust particles) removal. The novelty of this review is that we elucidated that the performance of CNMs in the anaerobic digestion (AD) varies from one system to another. In addition, this review revealed that increasing the pyrolysis temperature can facilitate the transition from one CNM type to another and outlined the methods that can be used to develop CNMs, including arc discharge, chemical exfoliation, and laser ablation. In addition, this study showed that methane (CH4) yield can be slightly increased (e.g. from 33.6% to 60.89%) depending on certain CNM factors, including its type, concentration, and feedstock. Temperature is a fundamental factor involved in the method and carbon sources used for CNM synthesis. This review determined that graphene oxide is not a good additive for biogas and CH4 yield improvement compared with other types of CNM, such as graphene and carbon nanotubes. The efficacy of CNMs in wastewater treatment depends on the temperature and pH of the solution. Therefore, CNMs are good adsorbents for wastewater contaminant removal and are a promising alternative for CO2 emissions reduction. Further research is necessary to determine the relationship between CNM synthesis and preparation costs while accounting for other factors such as gas flow, feedstock, consumption time, and energy consumption.