Hasil untuk "Environmental effects of industries and plants"

Ho Nguyen, Ta Hoang Trung, Tran The Dinh

The Vietnamese Mekong Delta (VMD), Vietnam's “rice bowl”, is a vital agricultural hub due to its extensive network of rivers and fertile floodplains. This study evaluates land use/land cover (LULC) changes in the Upper Delta - Dong Thap Province from 1990 to 2023, using Landsat imagery and random forest algorithms. LULC maps were analyzed across five classes: cropland, wetlands, permanent crops, built-up areas, and open water. We monitored these classes at three points: 1990, 2005, and 2023, noting improvements in classification accuracy from 89% in 1990 to 94% in 2023. Significant transformations were observed; from 1990 to 2005, the most extensive change was the conversion of 477.32 km² of wetlands to cropland. Conversely, the minimal change involved only 0.55 km²  of open water area converted to built-up areas. Between 2005 and 2023, transitions from cropland to permanent crops dominated, peaking at 242.03 km². Over the three decades, the province experienced substantial shifts in LULC, primarily from wetlands to croplands, totaling 513.24 km². These changes reflect both natural dynamics and human impacts, underscoring the influence of past policies on land development. This longitudinal study provides crucial insights for policymakers, emphasizing the need for informed, sustainable land management strategies in Dong Thap Province.

Lehua Gao, Wenwen Sun, Wu-lan-tuo-ya Bao et al.

Against the backdrop of the intertwining challenges of global climate change and agricultural sustainable development, agriculture serves not only as a fundamental industry but also as a significant source of greenhouse gas emissions. As a major agricultural nation, China still lacks a clear understanding of the carbon flow processes within its agricultural industrial chain, which hampers the formulation of targeted emission reduction strategies. To systematically reveal the transfer structure and evolutionary patterns of agriculture-related carbon emissions and support the low-carbon transition of the industry in alignment with the “dual carbon” goals, this study develops an integrated “input-output and complex network” coupling framework and conducts an analysis based on six years of input-output tables. Key findings reveal a three-phase evolution of agricultural carbon emissions (growth, peak, and plateau), with 71.55 % of emissions concentrated in four sectors: agriculture, food processing, food manufacturing, and fertilizer production. The construction (S31) and basic chemical raw materials manufacturing (S16) are identified as the primary sectors for embodied carbon inflow, while electricity and heat production and supply (S21) and petroleum and nuclear fuel processing (S15) serve as the core sectors for embodied carbon outflow. Food processing (S5) and specialty chemical products manufacturing (S17) play critical intermediary roles. The “fertilizer manufacturing → agriculture” pathway shows the highest carbon transfer volume, while “agriculture → food processing” remains a stable high-carbon-flow route. The “pesticide manufacturing → agriculture” pathway has intensified significantly since 2012. Notably, the livestock sector achieved reduced embodied carbon transfer despite production scale expansion. At the upstream stage of the industrial chain, agriculture (S1) and the food processing industry (S5) exhibit a clear transition in the carbon emission structure of the high-carbon sectors they drive during production—shifting from reliance on petroleum-based fuels toward electricity as the dominant energy source. At the downstream stage, driven by consumption demand from food-related industries, the resulting carbon emissions are highly concentrated in the paper and paper products industry (S29), accounting for more than 80 % of the total. Community detection identifies stable modular structures, revealing the systematic dependencies of embodied carbon flows. The study concludes by proposing enhanced technology adoption and optimized intermediate input management as key policy recommendations for agricultural carbon mitigation.

C. F. Guerra, L. Reyes-Bozo, E. Vyhmeister et al.

Abstract Green ammonia can be produced using fossil fuels or any renewable energy source combined with heat or electricity. Chile has one of the highest rates of solar irradiation and also the environmental conditions that support the development of solar industry. Other renewable sources of energy are the wind and the hydraulic dams. These energies could be used to generate ammonia by a standard method such as the Haber-Bosch process, which transforms hydrogen and nitrogen using high temperatures and a catalyst. The ammonia has several desirable characteristics that suggest its use as a way to store hydrogen. Firstly, it can be liquified under mild conditions. Secondly, the ammonia has a high weight fraction of hydrogen. Thus, it is deemed necessary to study technical and economic variables that may support the use of green ammonia as an energy carrier for hydrogen. The aim of this work is to develop a technical-economic analysis about the production of ammonia using hydrogen by means of electrolysis (carried out with solar, wind, and hydraulic renewable energies). The aforementioned green ammonia would be produced in Chile and it would be necessary to transport it to Japan. Sensitivity analysis of main parameters (plant operating hours, size of the ammonia synthesis plant - related to the capacity of the electrolysis plant size in MW-, electricity price, electrolyzer cost, Haber-Bosch cycle cost, and ammonia sales price) was performed to report what factors are primordial at the moment of carried out techno-economic analyses. An optimization process that minimize the NPV was run in order to settle the most convenient size of the electrolyser stack. A net present value (NPV) of base case is €77,414,525 and 7.62 years of pay-back period were calculated for this green ammonia production plant, which considered hydrogen production via electrolysis, Haber-Bosch processes, and trade values of different operational units. The sensitivity analysis has determined that the main variables affecting the NPV are the size of the ammonia synthesis plant and the electricity price. Furthermore, by considering triangular probability distribution of specific variables, it was observed that with a 95% of confidence, the NPV would be positive with a 76.1% of occurrence. The optimization process defined that a stack of 164.21 MW would be the most convenient electrolyser stack dimension, which was estimated by considering all different effects over the operational expenditure (OPEX), capital expenditure (CAPEX), and investments risk. Therefore, the hydrogen production in Chile and its transportation to Japan using ammonia as an energy carrier would be a technically viable and profitable solution, which also has environmental benefits.

Utik Tri Wulan Cahya, Wani Hadi Utomo, Waego Hadi Nugroho et al.

Management of water resources in watersheds requires an in-depth understanding of the factors that influence the runoff coefficient. This study aimed to analyze the influence of land cover, slope, and soil physical properties on the runoff coefficient in the Upper Brantas Sub-watershed and develop a prediction model using multiple linear regression. The research was conducted in Pesanggrahan Village, Batu City, using nine observation plots consisting of three types of land cover (dense canopy, moderate canopy, and sparse canopy) with three slope classes (15%, 25%, and 35%). Surface runoff measurements were conducted using 150 m² plots during the rainy season. Pearson correlation analysis showed that the runoff coefficient was significantly negatively correlated with land cover percentage (r = -0.551; p<0.001) and Dry Microaggregate Ratio (DMR) index (r = -0.439; p<0.001), and significantly positively correlated with slope (r = 0.265; p<0.001) and sand content (r = 0.410; p<0.001). The selected regression model (C = -0.031 - 0.074X1 + 0.015X2 - 0.001X4 + 0.110X6) showed land cover/X1 had the strongest influence (? = -0.074, p<0.0001), followed by slope class/X2 (? = 0.015, p<0.0001), bulk density/X4 (? = 0.110, p<0.001), and silt content/X6 (? = -0.001, p<0.036). The model performed well with a validation R² of 46.3% and a Root Mean Square Error (RMSE) of 0.0331. This research presents a practical model for estimating runoff coefficients, supporting soil and water conservation planning in mountainous areas.

Donghui Liu, Pengfei Li, Chang Liu et al.

Updating and quantifying the carbon footprint (CF) of rice production systems can provide valuable insights for developing effective agricultural emission strategies and policies. This study broadens the scope of CF research and calculated the temporal and spatial variations in the CF of a typical emerging rice production system in China from 1992 to 2022. The results show that from 1992 to 2022, the net CF per hectare (CFArea) increased from 5.2 to 6.3 t CO2 equivalents (CO2 eq) hm−2, while the net CF per ton (CFYield) decreased from 1.11 to 0.84 t CO2 eq t−1. The carbon emission (CE) related to methane emission (CECH4) became the largest contributor to CE, rising from 16.8 % to 49.5 %. The CE related to straw utilization (CEStraw) decreased from 59.6 % to 29.5 %. The CE related to agricultural materials input (CEMaterials) and N2O and NH3 emissions (CENitro) ranging from 8.8 % to 10.5 % and 11.1 % to15.4 %, respectively. The carbon sequestration related to soil (CSSoil) could offset 14.3 % to20.9 % CE. Spatial analysis shows that regions like Jiamusi and Harbin are facing high CFs and significant environmental pressures. Increasing straw return to fields helped reduce both CEStraw and CEMaterials, while enhancing CSSoil, although it had minimal effect on reducing CECH4. Scenario analysis suggests that straw management is more effective in reducing emissions compared to managing agricultural materials. In conclusion, implementing sustainable straw return practices and enhancing soil carbon storage will be key strategies in reducing emissions from rice production systems.

Shiyu Pei, Zonghan Li, Yi Liu et al.

Urban water systems (UWSs) continuously evolve in response to changes in urban populations, technological advancements, and lifestyle shifts, resulting in significant changes in greenhouse gas (GHG) emissions. Understanding how GHG emissions vary across the different developmental stages of a UWS is crucial for charting pathways toward carbon neutrality under varying levels of urbanization and infrastructure maturity. To explore the long-term patterns of GHG emissions from the UWS, we developed a systematic accounting framework encompassing four energy-related subsystems: water extraction, water supply, residential water use, and wastewater treatment. We applied this framework to China’s UWS across its transitional trajectory—from early development to system-wide maturity (1980–2020) at the provincial level. Results show that over the 40 years, GHG emissions from China’s UWS increased approximately 14-fold, surpassing the overall rate of population growth by 143.9%. From the early 1990s till now, residential water use emerged as the dominant source of UWS-related emissions, accounting for approximately 77.6% of total emissions. Our scenario analysis estimates a potential 34.0% reduction in China’s carbon emissions (128.3 Mt CO2-eq) by 2030 through water-saving strategies. This study offers critical insights into promoting low-carbon operations and sustainable management of UWS, and serves as an important reference for global efforts net-zero water infrastructure.

Saeed Aghel, Mehdi Gholamalifard, Nader Bahramifar et al.

The construction of the Tehran-Shomal Freeway is a major infrastructural project in Iran aimed at enhancing connectivity between Tehran and northern regions. This study evaluates the environmental impacts associated with the construction of the third section of this freeway. Using Life Cycle Assessment (LCA), this study evaluates the construction of four distinct spatial pathways of varying lengths, focusing on key environmental impact categories at both the midpoint and endpoint levels. The research emphasizes the construction phase and contributes a method that can be used to develop and analyze construction phase life-cycle inventories. The normalized results indicate that Human Carcinogenic Toxicity is the most significant contributor to environmental impacts across all scenarios, with the Tunnel subproject identified as the dominant source, followed by bridge construction. Machine operation, Portland cement, and electricity emerge as the primary contributors to environmental impact within the tunnel construction process. Among the evaluated scenarios, the S3 scenario stands out as the most sustainable option, exhibiting the lowest overall environmental impact. It achieves a global warming potential of 1,062,439 tons of CO2 equivalent and a final score of 35.5 MPt. These results emphasize the critical importance of adopting optimized construction strategies to reduce the environmental footprint of transportation infrastructure projects. However, selecting the most appropriate scenario should extend beyond LCA outcomes to include economic, social, and ecological considerations. This integrated approach is essential to achieving a balanced and holistic decision-making process that supports sustainable development objectives.

Ruli Wandri, Kurniatun Hairiah, Didik Suprayogo et al.

Oil palm plantations face sustainability challenges with variable yields and significant greenhouse gas emissions. To optimize nutrient cycling while maintaining soil carbon stocks, this study investigated the effects of fertilization intensity and spatial management on organic matter decomposition. A factorial experiment in South Sumatra (Indonesia) used a completely randomized block design with three fertilization levels (low, intermediate, high). Decomposition was monitored in three spatial zones (weeded circle, frond stack, interrow) using litter bags over 52 weeks, with sequential harvesting at 13 time points. Soil properties, litter quality, and environmental factors were analyzed using ANOVA and regression models. Results showed decomposition constants ranging from 0.0180 to 0.0258 week?¹ and half-life times of 16 to 32 weeks, with high fertilization treatments accelerating decomposition by 28% but reducing soil carbon (2.05% to 2.77%) below the litter bags compared to low fertilization (4.37%). Frond stack zones exhibited 35% faster decomposition while maintaining higher carbon levels. The regression model combining soil and frond C/N ratios explained 73% of the variance in decomposition. These findings reveal trade-offs between rapid nutrient cycling and carbon storage, demonstrating that sustainable oil palm production requires precision spatial management rather than uniform high fertilization. This study recommends implementing reduced-intensity inorganic fertilization, avoiding nutrient application in frond stacking zones, and expanding organic matter placement in inter-row areas. Future research should prioritize quantifying belowground carbon dynamics and fine root turnover to develop management frameworks balancing immediate productivity with long-term sustainability.

Heru Hendrayana, Indra Agus Riyanto, Azmin Nuha

With its abundant groundwater potential, East Java faces a growing risk of contamination due to rapid industrial growth. This study assessed groundwater quality in four regional groundwater basins (GWB) using the Water Quality Index (WQI), water quality standard comparison, Piper diagram, and hydrogeochemical ion analysis. The WQI analysis revealed that 59% of the samples were classified as excellent and good for consumption, predominantly found in volcanic, river alluvial, and limestone hill areas. In comparison, 11% were unsuitable for consumption due to contamination, particularly near coastal, industrial, and agricultural zones. The Piper diagram showed that most groundwater samples were unpolluted, reflecting the natural interaction between groundwater and surrounding lithology. However, ion standard comparison identified samples exceeding acceptable ion levels, and ion correlation analysis confirmed contamination from industrial, agricultural, anthropogenic, and municipal wastewater activities. These findings highlight the need for targeted groundwater management, particularly in areas vulnerable to contamination.

Bing Wang, Ximing Li, Yiming Wang et al.

The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effects. Accordingly, automatically distinguishing misinformation, dubbed as Misinformation Detection (MD), has become an increasingly active research topic. The mainstream methods formulate MD as a static learning paradigm, which learns the mapping between the content, links, and propagation of news articles and the corresponding manual veracity labels. However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). The basic idea of MISDER lies in learning a social environmental representation for each period and employing a temporal model to predict the representation for future periods. In this work, we specify the temporal model as the LSTM model, continuous dynamics equation, and pre-trained dynamics system, suggesting three variants of MISDER, namely MISDER-LSTM, MISDER-ODE, and MISDER-PT, respectively. To evaluate the performance of MISDER, we compare it to various MD baselines across 2 prevalent datasets, and the experimental results can indicate the effectiveness of our proposed model.

Herve Goeau, Pierre Bonnet, Alexis Joly

The world is estimated to be home to over 300,000 species of vascular plants. In the face of the ongoing biodiversity crisis, expanding our understanding of these species is crucial for the advancement of human civilization, encompassing areas such as agriculture, construction, and pharmacopoeia. However, the labor-intensive process of plant identification undertaken by human experts poses a significant obstacle to the accumulation of new data and knowledge. Fortunately, recent advancements in automatic identification, particularly through the application of deep learning techniques, have shown promising progress. Despite challenges posed by data-related issues such as a vast number of classes, imbalanced class distribution, erroneous identifications, duplications, variable visual quality, and diverse visual contents (such as photos or herbarium sheets), deep learning approaches have reached a level of maturity which gives us hope that in the near future we will have an identification system capable of accurately identifying all plant species worldwide. The PlantCLEF2023 challenge aims to contribute to this pursuit by addressing a multi-image (and metadata) classification problem involving an extensive set of classes (80,000 plant species). This paper provides an overview of the challenge's resources and evaluations, summarizes the methods and systems employed by participating research groups, and presents an analysis of key findings.

Nikolaos Kalyviotis, Christopher D. F. Rogers, Geoffrey J. D. Hewings

Five life cycle assessment (LCA) methods to calculate a project s environmental value are described: (a) process-based, (b) hybrid, (c) pseudo, (d) simplified, and (e) parametric. This paper discusses in detail and compares the two methods with the least inherent uncertainty: process-based LCA (a bottom-up methodology involving mapping and characterising all processes associated with all life cycle phases of a project) and a hybrid LCA (the EXIOBASE analysis, which incorporates top-down economic input output analysis and is a wider sector-by-sector approach). The bottom-up nature of process-based LCA, which quantifies the environmental impacts for each process in all life cycle phases of a project, is particularly challenging when applied to the evaluation of infrastructure as a whole. Conversely, combining the environmental impact information provided in EXIOBASE tables with the corresponding input output tables allows decision makers to more straightforwardly choose to invest in infrastructure that supports positive environmental outcomes. Employing LCA and a bespoke model using Pearson s correlation coefficient to capture environmental interdependencies between the transport sector and the other four economic infrastructures showed the transport and energy sectors to be most closely linked. Both integrated planning and innovative technologies are needed to radically reduce adverse environmental impacts and enhance sustainability across transport, waste, water, and communication sectors.

R. Krishna Kumari

Amidst the shifting tides of the educational landscape, this research article embarks on a transformative journey delving into the fusion of theoretical principles and pragmatic implementations within the realm of Graph Machine Learning (GML), particularly accentuated within the sphere of nature, environment, and pollution technology. GML emerges as a potent and indispensable tool, adeptly leveraging the intrinsic interconnectedness embedded within environmental datasets. Its application extends far beyond mere analysis towards the profound ability to forecast ecological patterns, prescribe sustainable interventions, and tailor pollution mitigation strategies with precision and efficacy. This article does not merely scratch the surface of GML’s applications but dives deep into its tangible implementations, unraveling its potential to revolutionize environmental science and pollution technology. It endeavors to bridge the gap between theory and practice, weaving together relevant ecological theories and empirical evidence that underpin the theoretical foundations supporting GML’s practical utility in environmental domains. By synthesizing theoretical insights with real-world applications, this research elucidates the profound transformative potential of GML, paving the way for proactive and data-driven approaches toward addressing pressing environmental challenges. In essence, this harmonization of theory and application catalyzes advancing the adoption of GML in environmental science and pollution technology. It not only illuminates the path towards sustainable practices but also lays the groundwork for fostering a holistic understanding of our ecosystem. Through this integration, GML emerges as a beacon guiding us toward a future where environmental stewardship is informed by data-driven insights, leading to more effective and sustainable solutions for the benefit of our planet and future generations.

Muhammad Arbab Arshad, Talukder Jubery, James Afful et al.

We evaluate different Neural Radiance Fields (NeRFs) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods usually fail to capture the complex geometric details of plants, which is crucial for phenotyping and breeding studies. We evaluate the reconstruction fidelity of NeRFs in three scenarios with increasing complexity and compare the results with the point cloud obtained using LiDAR as ground truth. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score after 30 minutes of training on the GPU, highlighting the efficacy of NeRFs for 3D reconstruction in challenging environments. Additionally, we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4% in the average F1 score. This optimization process significantly enhances the speed and efficiency of 3D reconstruction using NeRFs. Our findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.

Hambur Wang

Air pollution has emerged as a serious challenge for China, posing a threat to public health and hindering the progress of sustainable economic development. In response to air pollution and other environmental issues, the Chinese government introduced a new Environmental Protection Law in 2015. This paper investigates the impact of the new Environmental Protection Law's implementation and corporate Environmental, Social, and Governance (ESG) investments on air pollution and public subjective well-being. Using panel data at the macro level, we employ a difference-in-differences (DID) model, with Chinese provinces and municipalities as units of analysis, to examine the combined effects of the new Environmental Protection Law and changes in corporate ESG investment intensity. The study evaluates their impacts on air quality and public subjective well-being. Findings indicate that these policies and investment behaviors significantly improve public subjective well-being by reducing air pollution. Notably, an increase in ESG investment significantly reduces air pollution levels and is positively associated with enhanced well-being. These results underscore the critical role of environmental legislation and corporate social responsibility in improving public quality of life and provide empirical support for promoting sustainable development in China and beyond.

Hamed Kazemi Shariat Panahi, M. Dehhaghi, M. Aghbashlo et al.

It is obvious that Iran agricultural industry, unlike Brazil and USA, cannot afford to provide conventional biomass, i.e. sugary or starchy biomass for bioethanol production, mainly due to climatic and geographic conditions. With some exception of date (fruit), first-generation ethanol production triggers food vs. fuel debates in Iran and put nation to hunger. Agricultural products including apple, barley, carrot, corn, grape, orange, potato, rice, sugar beet, sugarcane, and wheat are consumed domestically, exported, or even lost because of poor harvesting and processing conditions such as transportation or packaging. These products may alone generate 21.56 million ton per annum green wastes upon processing in the food industry. Every year about 5.4 billion liters of bioethanol can be produced by establishing second-generation ethanol plants next to the food processing sectors. Seventy-seven-percent of this amount of bioethanol can easily support 5% ethanol (E5) policy to phase out the consumption of 4.2 billion liters methyl tert-butyl ether (MTBE) for raising the octane number of gasoline in the country. If more comprehensive policy is adopted, larger quantities of lignocellulosic feedstocks can be gathered from agro as well as forestry practices. Second-generation bioethanol technology can help Iran to tackle air pollution in its big cities and to address the adverse effects of MTBE on its populations and ecosystem. The other advantages are improvement of fuel security, mitigation of climate change, and development of economy. The motivation can be created through passing a framework policy to cut fossil fuel subsidies, to mandate bioethanol blends in gasoline, and to impose carbon taxes. Development of coherent socially and environmentally relevant strategies and facilitation of investment in bioethanol industry are also necessary.

Kai Wang, Xin Wang, Yangyao Chen

We investigate the environmental dependence of the gas-phase metallicity for galaxies at $z=0$ to $z\gtrsim 2$ and the underlying physical mechanisms driving this dependence using state-of-the-art cosmological hydrodynamical simulations. We find that, at fixed stellar mass, central galaxies in massive halos have lower gas-phase metallicity than those in low-mass halos. On the contrary, satellite galaxies residing in more massive halos are more metal-rich. The combined effect is that massive galaxies are more metal-poor in massive halos, and low-mass galaxies are more metal-rich in massive halos. By inspecting the environmental dependence of other galaxy properties, we identify that the accretion of low-metallicity gas is responsible for the environmental dependence of central galaxies at high $z$, whereas the AGN feedback processes play a crucial role at low $z$. For satellite galaxies, we find that both the suppression of gas accretion and the stripping of existing gas are responsible for their environmental dependence, with negligible effect from the AGN feedback. Finally, we show that the difference of gas-phase metallicity as a function of stellar mass between protocluster and field galaxies agrees with recent observational results, for example from the MAMMOTH-Grism survey.

Guy RY Coleman, Matthew Kutugata, Michael J Walsh et al.

Many advanced, image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but their ability to recognize growth stages, often with stark differences in appearance, is uncertain. Amaranthus palmeri (Palmer amaranth) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production, exhibiting highly variable plant morphology both across growth stages over a growing season, as well as between plants at a given growth stage due to high genetic diversity. In this paper, we investigate eight-class growth stage recognition of A. palmeri in cotton as a challenging model for You Only Look Once (YOLO) architectures. We compare 26 different architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 on an eight-class growth stage dataset of A. palmeri. The highest mAP@[0.5:0.95] for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar growth stages. With all growth stages grouped as a single class, performance increased, with a maximum mean average precision (mAP@[0.5:0.95]) of 67.05% achieved by v7-Original. Single class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures.

Weiying Li, Junlin Zhou, Zhi-yuan Zhang et al.

H. T. Qiao, B. W. Zhao and X. S. Yu

The study of competitive adsorption of heavy metals on soil is important for heavy metals contaminated soil remediation. However, there have been few studies on the impact of desorption reagents on heavy metal adsorption in soil. Batch adsorption studies were used to investigate the competitive adsorption mechanism of two heavy metals, Pb and Zn, on a loess soil in the presence of a new chelating surfactant, sodium N-lauroyl ethylenediamine triacetate (LED3A). Results showed that competitive adsorption equilibria of Pb and Zn were reached at 3 and 10 h, respectively. The maximum equilibrium adsorption capacities were 19.55 and 18.35 g.kg-1, respectively. LED3A affected the competitive adsorption kinetics of Pb and Zn by increasing the change in external mass transfer and reducing the change in internal mass transfer. LED3A reduced Pb and Zn adsorption capacities onto the soil through competitive chelation of the heavy metals. The heavy metal chelating ability of LED3A was higher for Zn than for Pb. When its concentration was larger than 5 g.L-1, LED3A showed a significant effect on the competitive adsorption of Pb and Zn. In the competitive system, the effect of Pb concentration on the Zn adsorption capacity was greater than the effect of Zn concentration on the Pb adsorption capacity. LED3A weakened the effect of Pb concentration and enhanced the effect of Zn concentration. LED3A showed a significant potential for efficiently leaching remediation of Pb and Zn co-contaminated soil.