Pollution Prevention Strategies Research Articles

Sustainable End-of-Life Tyre Management: A Comprehensive Analysis of Environmental Impacts and Crumb Rubber Integration in Composite Concretes

The research explores pollution prevention strategies associated with end-of-life tyres (ELTs) through sustainable practices, focusing on repurposing ELTs into crumb rubber (CR) for use in composite concretes for civil engineering applications. Three disposal approaches are considered: recycling into crumb rubber for use in cement composites to enhance acoustic properties, incineration for cement production, and landfill disposal. The study aims to assess the environmental impact of each method, particularly in terms of carbon dioxide (CO2) emissions during the recycling phase, utilizing the OpenLCA program for a comprehensive life cycle analysis. The primary objective is to provide insights into the sustainability of incorporating recycled rubber in concrete mixes as fine aggregate to improve concrete acoustic and shock absorbance properties. The research also gathers data on CO2 emissions from ELT incineration for cement production and the environmental implications of ELT’s landfill disposal. By comparing these three strategies, the study offers a holistic perspective on the environmental ramifications of ELT management. Notably, a recent study highlights the energy recovery and CO2 emissions from ELT incineration, demonstrating the potential benefits of recycling. The research identifies a gap in existing studies, emphasizing the need to consider the entire life cycle, including the transportation and use stages. Notably, the use stage significantly contributes to environmental impacts, with carbon emissions ranging from 550 to 840 kg CO2 eq., per car tyre. Additionally, a proposed inventory analysis method evaluates greenhouse gas emissions from Portland cement concrete pavement construction, revealing that raw material production accounts for most emissions. The findings of this research are providing valuable insights for policymakers, industry professionals, and environmentalists aiming for sustainable practices in the construction sector. By allocating a fraction of tyre production CO2 emissions to obtain raw material for concrete composites, the study suggests a potential avenue for reducing environmental impact. Further calculations using the OpenLCA program may refine these recommendations.

Material Flow Analysis and Occupational Exposure Assessment in Additive Manufacturing End-of-Life Material Management.

Additive manufacturing (AM) offers a variety of material manufacturing techniques for a wide range of applications across many industries. Most efforts at process optimization and exposure assessment for AM are centered around the manufacturing process. However, identifying the material allocation and potentially harmful exposures in end-of-life (EoL) management is equally crucial to mitigating environmental releases and occupational health impacts within the AM supply chain. This research tracks the allocation and potential releases of AM EoL materials within the US through a material flow analysis. Of the generated AM EoL materials, 58% are incinerated, 33% are landfilled, and 9% are recycled by weight. The generated data set was then used to examine the theoretical occupational hazards during AM EoL material management practices through generic exposure scenario assessment, highlighting the importance of ventilation and personal protective equipment at all stages of AM material management. This research identifies pollution sources, offering policymakers and stakeholders insights to shape pollution prevention and worker safety strategies within the US AM EoL management pathways.

Characterizing carbonaceous aerosols in residential coal combustion: Insights from thermal/spectral carbon analyzer coupled with photoionization mass spectrometry analysis

This study aims to identify unique signatures from residential coal combustion in China across various combustion conditions and coal types. Using a Thermal/Spectral Carbon Analyzer with a Photoionization Time-of-Flight Mass Spectrometer (TSCA-PI-TOF-MS), we focus on the optical properties and organic mass spectra of the emissions. Bituminous coal emerged as the primary emitter of total carbon, releasing 729 μg C/mg PM2.5 under smoldering and 894 μg C/mg PM2.5 under flaming. Carbon fractions mainly comprised OC1 and OC2, except for anthracite's dominance of EC1 under smoldering. Pyrolysis carbon absorption shifted from 405, 445 and 532 nm during smoldering to near-infrared bands (635–980 nm) during flaming for both bituminous and anthracite coal. Conversely, clean coal exhibited an inverse trend, attributed to additives enhancing oxygen-containing organic compounds and long-chain hydrocarbons released in charring process. Sample of bituminous coal began charring at OC3 step, while anthracite began earlier at OC2 step, particularly pronounced under flaming. Clean coal displayed unconventional charring at OC1 step under smoldering condition, producing signature compounds like butenal, methylfuran, furanylalcohol, and naphthol. The mass spectra of bituminous coal featured characteristic peaks, including m/z 192 (methylphenanthrene), 206, 220 (alkylated phenanthrenes), and 234 (retene). Anthracite coal showed a potential tracer at m/z 223, shifting from OC1 in smoldering to OC2 in flaming. Clean coal under flaming condition exhibited elevated levels of aromatic compounds, indicating potential toxicity, with peaks at m/z 178 (phenanthrene), 228 (chrysene/benz[a]anthracene), 234 (retene), 242 (methylchrysene), and 252 (benzo[a]pyrene, benzo[k]fluoranthene). Results also showed that the broader mass spectra range in the OC3 and OC4 steps across all coal types suggests that high-temperature pyrolysis promotes diversity. These findings contribute to refined source apportionment of carbon emissions from residential coal combustion and provide the scientific basis for the formulation of air pollution prevention strategies, crucial for coal-dependent regions.

Vertical Evolution of Ozone Formation Sensitivity Based on Synchronous Vertical Observations of Ozone and Proxies for Its Precursors: Implications for Ozone Pollution Prevention Strategies.

Photochemical ozone (O3) formation in the atmospheric boundary layer occurs at both the surface and elevated altitudes. Therefore, the O3 formation sensitivity is needed to be evaluated at different altitudes before formulating an effective O3 pollution prevention and control strategy. Herein, we explore the vertical evolution of O3 formation sensitivity via synchronous observations of the vertical profiles of O3 and proxies for its precursors, formaldehyde (HCHO) and nitrogen dioxide (NO2), using multi-axis differential optical absorption spectroscopy (MAX-DOAS) in urban areas of the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions in China. The sensitivity thresholds indicated by the HCHO/NO2 ratio (FNR) varied with altitude. The VOC-limited regime dominated at the ground level, whereas the contribution of the NOx-limited regime increased with altitude, particularly on heavily polluted days. The NOx-limited and transition regimes played more important roles throughout the entire boundary layer than at the surface. The feasibility of extreme NOx reduction to mitigate the extent of the O3 pollution was evaluated using the FNR-O3 curve. Based on the surface sensitivity, the critical NOx reduction percentage for the transition from a VOC-limited to a NOx-limited regime is 45-72%, which will decrease to 27-61% when vertical evolution is considered. With the combined effects of clean air action and carbon neutrality, O3 pollution in the YRD and PRD regions will transition to the NOx-limited regime before 2030 and be mitigated with further NOx reduction.

Using an extended model of the reasoned action approach to explore individual behavioral intentions regarding litter and plastic pollution prevention in a developing country.

Implementing litter and plastic pollution prevention strategies is essential for cities of developing countries, mainly due to the prevailing high incidence of littering and the urgent need to realize the adverse per capita environmental impact target of the sustainable development goals. In this article, we report the use of the prominent reasoned action approach-in its original state and an extended model with moral norms-for exploring the critical socio-cognitive determinants of individuals' litter prevention intentions in Ghana. By analyzing the valid answers of 447 participants to a structured questionnaire on litter prevention, we found attitudes (β = 0.35, SE = 0.014, p < 0.001) and moral norms (β = 0.57, SE = 0.099, p < 0.001) as the most influencing determinants to individual intentions in the original and the extended models, respectively. The analysis suggests that individuals will stop littering their environments if environmentally friendly interventions are implemented to elicit self-responsibility and moral obligation. Campaigns that demonstrate the effects of littering on drain blockage, flooding, and disease outbreaks may improve individual litter prevention attitudes. Installing waste receptacles in public spaces and communicating persuasive messages may facilitate personal antilittering intentions. Apart from contributing to the implementation of a litter management strategy to reduce the flood risk and enhance the resilience of the Greater Accra region of Ghana, this research helps to close the literature gaps in litter prevention behavior in developing countries, as well as support the implementation of the sustainable development goals and the global plastic action partnership.

Analysis of Community Social Capital Inclusiveness Towards Waste Pollution Prevention by Shrimp Farming Company: A Case Study of Waste Pollution in Pasir Kuning Beach Area

Sewage pollution originating from the activities of shrimp farming companies in the Pasir Kuning Beach area raises serious concerns about its impact that damages the environment and disrupts the balance of the ecosystem. This research aims to understand and analyze the inclusiveness of social capital in local communities as an effort to prevent sewage pollution. Using a qualitative approach, this research conducted in-depth interviews with community members in the Pasir Kuning Beach Area. Respondents were selected based on criteria that ensured a balanced representation of various community groups, and secondary data was used to provide a broader context. The research findings highlight the central role of social capital in shaping relationships, trust, and cooperation among communities. The implications include developing more effective sewage pollution prevention strategies and providing a foundation for understanding community contributions to coastal ecosystem conservation. The focus on social capital inclusivity as a catalyst for positive change demonstrates the determination of local communities to overcome the negative impacts of shrimp farming activities. In the context of Bangka Belitung, which is affected by the mining sector, this research is significant in efforts to reduce environmental damage. It is hoped that these findings provide valuable insights for stakeholders in formulating sustainable solutions that support the ecology and well-being of local communities in the future.

A decadal atmospheric ammonia reanalysis product in China

Atmospheric ammonia has great environmental implications due to its important role in ecosystem and nitrogen cycle, as well as contribution to formation of secondary particles. China is recognized as a hotspot of NH3 pollution owing to agricultural and livestock intensification. In the quest to achieve a comprehensive understanding of atmospheric ammonia load and to quantify its environmental impacts in China, relying solely either on existing measurements or on model simulations falls short. Their limitations, either in spatial coverage and integrity or in data quality, fails to meet the needs. Available reanalysis products exhibit a marked deficiency in ammonia data. We therefore aim to propose an integrated ammonia reanalysis product in China, adeptly melding satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI) NH3 retrievals with chemical transport model simulation, capitalizing on the robust Ensemble Kalman Filter (EnKF) data assimilation methodology. The product is validated in high quality via the comparison against independent measurements from ground monitoring stations. Spanning a decade from 2013 to 2022, our reanalysis uncovers not just the spatial intricacies of NH3 concentrations but also their temporal dynamics. Our findings pinpointed the spatial disparities in atmospheric ammonia intensities, highlighting regional hotspots in the NCP, SCB, and Northeast China, and identified annual and seasonal patterns. Our research provides crucial insights for shaping future NH3 pollution prevention and control strategies in China.

Integrated pollution analysis, pollution area identification and source apportionment of heavy metal contamination in agricultural soil

Given the global prevalence of soil heavy metal contamination, knowledge concerning of soil environmental quality assessment, pollution area identification and source apportionment is critical for implementation of soil pollution prevention and safe utilization strategies. In this study, soil static environmental capacity (QI) for heavy metals was selected to evaluate pollution risks in agricultural soils of Wenzhou, southeast China. Combined with geostatistical methods, the pollution area was identified along with uncertainty analysis. Potential sources were quantitatively apportioned using a positive matrix factorization model (PMF). Results showed that agricultural soils in this study were mainly contaminated by Cd and Pb based on both Nemerow and QI indices. The environmental capacity assessment found more than 90% areas were identified as polluted soils for Qi-Zn, Qi-Cd and Qi-Pb, with minor uncertain areas. Cu was identified as having a high proportion of uncertain pollution area status, which was similar to the results of the integrated environmental capacity for all metals. PMF results indicated that industrial discharge, agrochemicals and parent material accounted for 32.1%, 32.2% and 35.7% of heavy metal accumulation in soils, respectively. Implementation of strict policies to reduce anthropogenic source emissions and remediate soil pollution are crucial to minimize metal pollution inputs, improve agricultural soil quality and enhance food safety.

Implementation of the resource efficiency and cleaner production tool for green enterprises in Moldova

In order to solve environmental problems in the Republic of Moldova, it is necessary to implement effective tools based on the increased application of environmental pollution prevention strategies to processes, products and services with the aim of increasing efficiency and reducing risks for the human factor and the environment. An effective tool is Resource Efficiency and Cleaner Production (RECP), which refers to the methods and practices of Cleaner Production as tools that generate multiple benefits relevant to solving global environmental problems. The RECP tool approach involves integrating environmental policies to create appropriate incentives for green business actors. In the framework of the research, the author aims to conceptually study the RECP tool for companies in the Republic of Moldova, through the lens of existing programs and projects to produce sustainable effects in the future. This study was carried out within the State Program 20.80009.0807.22 Development of the circular economy training mechanism in the Republic of Moldova.

Environmental Sustainability: A Challenge for Leather Industry

Adopting pollution prevention strategies (PPS) can ensure environmental sustainability (ES) in the leather industry and can help the industry achieve several sustainable development goals (SDGs). In order to ensure the ES of the leather industry, there is a need for research to identify a comprehensive list of PPS. This research aims at identifying the most effective PPS for the leather industry, which are categorized into 4R (reduce, reuse, recycle, and recover) dimensions. This is a case study where four leather processing companies from Bangladesh were purposively selected. Through an extensive literature review and experts’ opinions, 21 PPS are identified for the leather industry’s ES. Also, this study shows several benefits of PPS through which various SDGs can be achieved in the leather industry. This study will certainly guide the leather industry managers to ensure the ES of the leather industry and, consequently, assist in achieving several SDGs.

A quantitative exploration of the interactions and synergistic driving mechanisms between factors affecting regional air quality based on deep learning

Air pollution represents a significant environmental challenge on a global scale. The exploration of factors and mechanisms influencing air quality has garnered considerable attention and produced a substantial body of research. Nonetheless, the absence of effective research tools has resulted in a limited understanding of the nonlinear interactive effects of multiple factors on air quality, as well as their patterns and spatiotemporal variability characteristics. Furthermore, the efficacy of multifactor joint control strategies in practical air pollution management remains inadequate. A novel regional air environmental quality prediction model was developed utilizing a neural network with long short-term memory (LSTM) in deep learning based on an analysis of the spatiotemporal heterogeneity of regional air environmental quality in the Yangtze River Economic Belt (YEB). The model results were attributed and interpreted by integrating the SHapley Additive explanation (SHAP) method, focusing on the possible interactive effects and synergistic driving mechanisms of PM2.5 and O3 as the main pollutants and other factors on the air quality index (AQI) in a quantitative way, respectively. The SHAP interaction values of five factor pairs, namely PM2.5 and temperature, PM2.5 and precipitation, O3 and temperature, O3 and NO2, and O3 and PM2.5, are depicted in dependence scatter plots that exhibit three distinct trajectory shapes, namely linear, U-shaped, and irregular. The linear pattern of the dependence scatter plots for PM2.5 and precipitation in the YEB, midstream, and downstream regions, as well as for O3 and temperature in the upstream region, is evident. The irregular patterns of PM2.5 and precipitation, O3 and NO2, O3 and PM2.5 in the upstream, and PM2.5 and temperature in the downstream are observed to be correspondingly related. Additionally, a U-shaped pattern is exhibited by O3 and temperature in the YEB, midstream and downstream, PM2.5 and temperature in the upstream and midstream, O3 and NO2 in the YEB, midstream and downstream, and O3 and PM2.5 in the YEB. Of the three models, the U-shaped model's identification of inflection points and corresponding quantitative determination of factor thresholds hold greater practical implications for decision-making in air environment management. This pattern suggests that the synergistic effect of factor pairs initially decreases with changes in each factor but subsequently begins to increase after reaching inflection points. It is anticipated that this research will contribute to the advancement of theoretical inquiry into the nonlinear multifactor interaction effect and synergistic driving model of regional air quality. Additionally, it is expected to furnish a valuable foundation for decision-making in the development of joint air pollution prevention and control strategies aimed at addressing compound air pollution issues.

Complexities of Water Pollution: A Review of Surface Water Contamination in Sri Lanka

Water is indispensable for sustaining life, food production, economic growth, and well-being. However, the growing population and industrialization have intensified the demand for freshwater, posing significant challenges to water resources in Sri Lanka. This review paper focuses on understanding the types and causes of water pollution, with a particular emphasis on surface water pollution, as well as exploring preventive measures in the context of Sri Lanka. Given its severe consequences and the global issue of water scarcity, water pollution has gained attention from researchers, scientists, and organizations. Surface water bodies, such as lakes and rivers, face pollution primarily due to inadequate management of sewage and industrial effluents. Insufficient sanitation facilities in low-income settlements further exacerbate the problem, affecting the country. Despite existing regulations, the lack of monitoring allows improper waste disposal practices to persist. Rural areas experience groundwater contamination from agrochemicals, while urban areas suffer from pollution caused by domestic sewage. Considering the limited resources, prioritizing pollution prevention proves to be a cost-effective approach. Effective control measures are required to address marine pollution, adversely impacting fisheries and tourism. Recognizing the interconnected nature of all types of water pollution is crucial, as they contribute to ecological degradation. To safeguard water resources, several measures must be implemented. These include improving sewage treatment systems, implementing better management practices for industrial effluents, prioritizing pollution prevention strategies, and strengthening monitoring mechanisms. Prioritizing water resource preservation will safeguard ecosystems, support sustainable development, and ensure well-being.

Impact of pollution prevention strategies on environment sustainability: role of environmental management accounting and environmental proactivity.

Environmental sustainability involves meeting current needs without compromising the abilities of future generations. Practical steps must be taken to prevent pollution, decrease waste, and utilize resources. This study investigates the predictors of environmental sustainability in SMEs, focusing on the role of environmental management accounting and environmental proactivity in linking pollution prevention strategies to environmental sustainability using the natural resource-based view theory. This study surveyed 308 Pakistani SMEs employees and tested the data using SPSS v28 and AMOS v26. The results show that pollution control initiatives increased the usage of environmental management accounting, which in turn had a positive impact on environmental sustainability. The study also finds that environmental proactivity is significantly moderate and that environmental management accounting mediates these associations. To the best of our knowledge, this study is unique in that it is the first to provide sustainable implications based on the current framework. This study highlights the importance of implementing pollution prevention strategies, integrating environmental management accounting practices, and fostering environmental proactivity to enhance environmental sustainability performance in SMEs in Pakistan, ultimately leading to improved business competitiveness, positive environmental impacts, and a sustainable future. SMEs can focus on implementing proactive pollution prevention strategies such as waste reduction, energy conservation, and resource optimization. By adopting a proactive approach to pollution prevention, SMEs can reduce their environmental impact and enhance their sustainability performance.

Source apportionment of heavy metal(loid)s in sediments of a typical karst mountain drinking-water reservoir and the associated risk assessment based on chemical speciations.

As important place for water storage and supply, drinking-water reservoirs in karst mountain areas play a key role in ensuring human well-being, and its water quality safety has attracted much attention. Source apportionment and ecological risks of heavy metal(loid)s in sediments of drinking-water reservoir are important for water security, public health, and regional water resources management, especially in karst mountain areas where water resources are scarce. To expound the accumulation, potential ecological risks, and sources of heavy metal(loid)s in a drinking-water reservoir in Northwest Guizhou, China, the surface sediments were collected and analyzed based on the combined use of the geo-accumulation index (Igeo), sequential extraction (BCR), ratios of secondary phase and primary phase (RSP), risk assessment code (RAC), modified potential ecological risk index (MRI), as well as the positive matrix factorization methods. The results indicated that the accumulation of Cd in sediments was obvious, with approximately 61.9% of the samples showing moderate to high accumulation levels, followed by Pb, Cu, Ni, and Zn, whereas the As and Cr were at low levels. A large proportion of BCR-extracted acid extractable and reducible fraction were found in Cd (72.5%) and Pb (40.3%), suggesting high bioavailability. The combined results of RSP, RAC, and MRI showed that Cd was the major pollutant in sediments with high potential ecological risk, while the risk of other elements was low. Source apportionment results of heavy metal(loid)s indicated that Cd (75.76%) and Zn (23.1%) mainly originated from agricultural activities; As (69.82%), Cr (50.05%), Cu (33.47%), and Ni (31.87%) were associated with domestic sources related to residents' lives; Cu (52.36%), Ni (44.57%), Cr (34.33%), As (26.51%), Pb (24.77%), and Zn (23.80%) primarily came from natural geological sources; and Pb (47.56%), Zn (22.46%) and Cr (13.92%) might be introduced by mixed sources of traffic and domestic. The contribution ratios of the four sources were 18.41%, 36.67%, 29.48%, and 15.44%, respectively. Overall, priority control factors for pollution in relation to agricultural sources included Cd, while domestic sources are primarily associated with As. It is crucial to place special emphasis on the impacts of human activities when formulating pollution prevention and control measures. The results of this study can provide valuable reference and insights for water resources management and pollution prevention and control strategies in karst mountainous areas.

Increasing volatile organic compounds emission from massive industrial coating consumption require more comprehensive prevention

Coating is an important raw material in industrial production. Global consumption of industrial coatings has approximated 28.84 Tg in 2019, with China accounting for more than half which generated plenty of Volatile Organic Compounds (VOCs). To learn the pollution characteristic and prevention strategy, we estimated the speciated VOCs emission of coating-consuming industries in China through field investigation, sample collection and analytical analysis. The environmental and health influences in 2019, 2025 and 2030 were then assessed based on scenario prediction with taking low-VOCs containing coating (LCC) application into consideration which was an important action in the ongoing prevention measures. Ozone formation potential (OFP), secondary organic aerosol potential (SOAP) and the newly established index of total health risks (THR) were employed to indicate the environmental influence and health threaten. Conclusions showed that (1) VOCs emissions would increase from 6.78 Tg to 10.46 Tg during 2019–2030 which result in a much higher OFP, SOAP and THR. (2) Replacing traditional organic coating (TOC) with LCC could effectively reduce VOCs emission. For every 1% increase in LCC proportion, VOCs emission, OFPs, SOAPs, and THRs decreased by 1.24%, 1.44%, 1.49%, and 1.70% on average, respectively. (3) Potential O3 and SOA yields approximated 25.61 Tg and 19.12 Tg (5.87% and 4.39% higher than 2019) even in the most stringent scenario for 2030 which meant continuous increases in industrial coating consumption could offset the positive effects produced by LCC substitution. (4) Esters, ethers, alcohols, and ketones are proposed as main composition of coatings to effectively reduce VOCs emission and their influence on account of better technical accessibility. These findings reveal that more innovative pollution prevention methods and production technologies are urgently needed to compensate for increased pollutant emissions derived from the growing demand for coating-associated commodities.

Combined effect of simulated microgravity and low-dose ionizing radiation on structure and antibiotic resistance of a synthetic community model of bacteria isolated from spacecraft assembly room

Understanding the structural and antibiotic resistance changes of microbial communities in space environments is critical for identifying potential pathogens that may pose health risks to astronauts and for preventing and controlling microbial contamination. The research to date on microbes under simulated space factors has primarily been carried out on single bacterial species under the individual effects of microgravity or low-dose radiation. However, microgravity (MG) and low-dose ionizing radiation (LDIR) coexist in the actual spacecraft environment, and microorganisms coexist as communities in the spacecraft environment. Thus, the microbial response to the real changes present during space habitation has not been adequately explored. To address this knowledge gap, we compared the dynamics of community composition and antibiotic resistance of synthetic bacterial communities under simulated microgravit, low-dose ionizing radiation, and the conditions combined, as it occurs in spacecraft. To ensure representative bacteria were selected, we co-cultured of 12 bacterial strains isolated from spacecraft cleanrooms. We found that the weakened competition between communities increased the possibility of species coexistence, community diversity, and homogeneity. The number of Bacilli increased significantly, while different species under the combined conditions showed various changes in abundance compared to those under the individual conditions. The resistance of the synthetic community to penicillins increased significantly under low doses of ionizing radiation but did not change significantly under simulated microgravity or the combined conditions. The results of functional predictions revealed that antibiotic biosynthesis and resistance increased dramatically in the community under space environmental stress, which confirmed the results of the drug sensitivity assays. Our results show that combined space environmental factors exert different effects on the microbial community structure and antibiotic resistance, which provides new insights into our understanding of the mechanisms of evolution of microorganisms in spacecraft, and is relevant to effective microbial pollution prevention and control strategies.

Analysis of the economic impact of preventing air pollution based on a computable general equilibrium model

ABSTRACT This paper reports an analysis using the computable general equilibrium (CGE) model to calculate the economic development and emissions under different air pollution prevention strategies, with a focus on the Beijing-Tianjin-Hebei rim surrounding the cities in the ‘Capital Economic Circle’. It appeared that raising the emission tax effectively suppressed air pollution but also suppressed economic development. Raising the emission subsidy promoted economic development, but did not suppress air pollution. A stepwise tax collection mode can be used to formulate the emission tax. When using emission subsidies to encourage manufacturers to improve their exhaust treatment technology, the policy should be tilted towards small enterprises to reduce economic pressure on them.

Spatial variability and hotspot prediction of heavy metal pollution in soil-rice system: based on a new geostatistical method

ABSTRACT The goal of this research was to better understand the spatial characteristics and hot areas of heavy metal concentrations in the soil-rice system, which was critical for implementing heavy metal pollution prevention and control strategies. The amounts of heavy metals in soil-rice were determined using high-precision testing methods such as XRF, ICP-MS, and AFS based on the systematic collection of 285 pairs of soil-rice samples from Wanjiang Economic Zone, China. The Getis-Ord index, Kriging method, and geostatistical method were used to reveal the spatial distribution of heavy metal contamination hot spots in the soil-rice system. The results revealed that the average concentration of heavy metals in soil was Zn>Cr>Cu>Pb>Ni>As>Cd>Hg, whereas it was Zn>Cu>Ni>Cr>Cd>As>Pb>Hg in rice. The average concentrations of heavy metals in the soil were all well below the maximum allowable limit, except for Cd, which was slightly above the allowable limit. Only Cd and Ni concentrations in rice exceeded the maximum permitted limits. In soil, Cd, Hg, Pb, and Zn pollution hotspots were mostly found in the southeast, while Cr and Ni were mostly found in the northeast. An intriguing discovery was that there was a weak relationship between heavy metal concentrations in soil and rice and the spatial pattern of their distribution. The study’s findings offer a scientific foundation for establishing a long-term agricultural development strategy for the soil-rice system in the Wanjiang Economic Zone.

Transitioning to a zero-emission energy system towards environmental sustainability

Emissions reductions can improve business competitiveness and represent an extension of its inevitable drive towards increased efficiency. The productivity of labor and capital came first, followed by the productivity of raw materials - producing more with less. Therefore, zero emissions can be viewed as a new standard of efficiency. The issue is organizing industrial clusters based on the zero emissions principle. This context has led to the development of case studies and demonstration projects to demonstrate how the zero emissions approach differs from other pollution prevention strategies. We investigate the impact of net-zero emissions by 2050 on the environmental sustainability of the top three CO2-emitting countries, using natural resource rents, economic growth, and financial development as moderator variables from 1971 to 2019. To achieve this objective, we developed an advanced applied econometric methodology. Firstly, Westerlund’s panel co-integration suggests long-term relationships within the variables. Secondly, net-zero emissions by 2050 are negatively associated with environmental sustainability. Total natural resources rents, economic growth, and financial development are positively associated with the ecological footprint. Findings show that net-zero emissions by 2050 will improve environmental sustainability in the top three CO2-emitting countries. Moreover, natural resource depletion, economic growth, and financial development deteriorate the ecological sustainability of these economies. To maintain the environment’s equilibrium and conserve ecosystems, governments should put research, development, deployment, and demonstration at the center of climate and energy policy.

On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method

In recent years, air pollution has attracted considerable attention worldwide. As an effective air protection method, the accurate prediction of air pollutants can help provide an early warning against harmful air pollutants and plan air pollution prevention and control strategies. Due to its high prediction capability, a novel method using a Takagi-Sugeno fuzzy model is presented to predict air pollutants in this study. This work investigates fuzzy model identification based on a larger dataset, and a hierarchical clustering-based identification method with model structure selection is proposed. The novelty of this work is as follows: First, a novel hierarchical clustering method has been employed in air quality forecasting. This method improves the widely used BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing a refinement phase for handling clusters with arbitrary shapes. Second, a cluster validity measure automatically determines the number of clusters. Then, the estimation of the model order selection and the model parameters is formulated as a sparse optimization problem, which can be solved using global optimization. Finally, the proposed Takagi-Sugeno model can learn the local trend pattern and spatial-temporal dependencies of multivariate air quality-related time series data. The presented method is demonstrated with air quality measurements in Shanghai, and the results show that the proposed method can achieve acceptable prediction performance.