Utilization and Characterization of Microbes for Heavy Metal Remediation DOI Open Access
Imran Imran, Asad Ali Khan,

Abid Kamal

et al.

Polish Journal of Environmental Studies, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 5, 2024

Heavy metals in forest soils have substantial ecological implications, affecting the soil and surrounding ecosystem.These naturally occurring elements, with high atomic weights, become toxic at elevated concentrations.Notable heavy include lead, cadmium, mercury, arsenic.The profound toxicity of metal pollution poses a significant risk to modern agriculture, potential for accumulation crops soils, threatening food security.A crucial aspect addressing this challenge involves promptly restoring disrupted agricultural land.This study contributes restoration by employing combination microorganisms, which proven effective alleviating pollution.When paired Sunflower (Helianthus annus), ensures enhances security.The investigated microorganisms from contaminated soil, revealing Gram-negative bacilli cocci arrangements.The colony characteristics, including hues diameters, were assessed, notable findings samples 1, 2, 5.The microbial ability remove (Pb, As, Hg, Ni, Cd) was quantified, highlighting diverse capacities among isolates.Selected isolates (1, 3, 6, 7, 10) exhibited 25% higher biomass than control, extracting least 40 mg/L each metal.Bacterial identification using Vitek 2 Compact analyzer revealed Pantoea sp., Achromobacter denitrificans, Klebsiella oxytoca, Rhizobium radiobacter, Pseudomonas fluorescens.Biocompatibility testing led formation consortia remediation, Coalition D (Achromobacter radiobacter 1:1:2 ratio) confirming removal Ni Pb.Various showed differing performances removing composite contaminants, being promising, indicating phytoremediation.Optimal cultivation conditions identified, excelling accumulation.The temperature, pH, efficiency, combining them phytoremediation techniques promise.Laboratory experiments sunflower seedlings confirmed efficacy enhancing phytoremediation, improving plant survival, mixed metals.

Language: Английский

Addressing the impact of land use land cover changes on land surface temperature using machine learning algorithms DOI Creative Commons
Sajid Ullah,

Xiuchen Qiao,

Mohsin Abbas

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 13, 2024

Over the past two and a half decades, rapid urbanization has led to significant land use cover (LULC) changes in Kabul province, Afghanistan. To assess impact of LULC on surface temperature (LST), province was divided into four classes applying Support Vector Machine (SVM) algorithm using Landsat satellite images from 1998 2022. The LST assessed data thermal band. Cellular Automata-Logistic Regression (CA-LR) model applied predict future patterns for 2034 2046. Results showed classes, as built-up areas increased about 9.37%, while bare soil vegetation decreased 7.20% 2.35%, respectively, analysis annual revealed that highest mean LST, followed by vegetation. simulation results indicate an expected increase 17.08% 23.10% 2046, compared 11.23% Similarly, indicated area experiencing class (≥ 32 °C) is 27.01% 43.05% 11.21% increases considerably decreases, revealing direct link between rising temperatures.

Language: Английский

Citations

27
Comprehensive Survey of Artificial Intelligence Techniques and Strategies for Climate Change Mitigation DOI
Zahra Mohtasham‐Amiri, Arash Heidari, Nima Jafari Navimipour

et al.

Energy, Journal Year: 2024, Volume and Issue: 308, P. 132827 - 132827

Published: Aug. 29, 2024

Language: Английский

Citations

26
Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation DOI Creative Commons

Chaitanya B. Pande,

Aman Srivastava, Kanak N. Moharir

et al.

Environmental Sciences Europe, Journal Year: 2024, Volume and Issue: 36(1)

Published: April 24, 2024

Abstract Land use and land cover (LULC) analysis is crucial for understanding societal development assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing under cloud limited ground truth data. To enhance accuracy comprehensiveness of descriptions changes, this investigation employed a combination advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, addition to computing capabilities Google Earth Engine (GEE) platform. Additionally, study incorporated random forest (RF) algorithm. This aimed generate continuous maps 2014 2020 Shrirampur area Maharashtra, India. A novel multiple composite RF approach based on classification utilized final utilizing RF-50 RF-100 tree models. Both models seven input bands (B1 B7) as dataset classification. By incorporating these bands, were able influence spectral information captured by each band classify categories accurately. The inclusion enhanced discrimination classifiers, increasing assessment classes. indicated that exhibited higher training validation/testing (0.99 0.79/0.80, respectively). further revealed agricultural land, built-up water bodies have changed adequately undergone substantial variation among classes area. Overall, research provides insights into application machine learning (ML) emphasizes importance selecting optimal enhancing reliability GEE different present enabled interpretation pixel-level interactions while improving image suggested best through identification

Language: Английский

Citations

20
Spatio-Temporal Dynamics of Rangeland Transformation using machine learning algorithms and Remote Sensing data DOI

Ningde Wang,

Iram Naz, Rana Waqar Aslam

et al.

Rangeland Ecology & Management, Journal Year: 2024, Volume and Issue: 94, P. 106 - 118

Published: March 23, 2024

Language: Английский

Citations

19
Impact of urbanization induced land use and land cover change on ecological space quality- mapping and assessment in Delhi (India) DOI Open Access
Manob Das, Arijit Das, Paulo Pereira

et al.

Urban Climate, Journal Year: 2024, Volume and Issue: 53, P. 101818 - 101818

Published: Jan. 1, 2024

Language: Английский

Citations

18
Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review DOI Creative Commons
Arjun Upadhyay, Yu Zhang, Cengiz Koparan

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 225, P. 109363 - 109363

Published: Aug. 22, 2024

Language: Английский

Citations

17
The impact of urbanization on land use land cover change using geographic information system and remote sensing: a case of Mizan Aman City Southwest Ethiopia DOI Creative Commons
Addis Bikis, Mastawesha Misganaw Engdaw, Digvijay Pandey

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 8, 2025

Land use land cover change due to urbanization is the prime driving forces environmental problem and surface temperature. The gap of study lack awareness stakeholders regarding protection native forests, fruit trees, BEBEKA coffee plantations. Deforestation for urban functions, including timber production, construction materials, firewood, adversely affects environment. aim this was analyze effect on Use Cover Change (LULCC) at Mizan Aman city, southwest Ethiopia from 1992 2022 using geographic information systemand remote sensing technique. employed systematic sampling household surveys high-resolution techniques identify impact temperature change. Sample survey focused family size, education level, parcel, year house, type employment monthly income. LULC classification were based eight class (settlement, dense forest, moderate sparse closed grassland, open shrub land, annual crop land). Preprocessing, images accuracy assessment tested separately kappa coefficient. analysis incorporates factor graph optimization ambiguity resolution. results indicated that cumulative 81.52%, 82.96%, 85.41% 84.46% coefficient 82.41%, 84.86%, 89.45% 88.76%% 1992, 2002, 2012 respectively. This research showed forest significantly decreased by 68.96%, 24.60%, 31.36% 8.28% respectively in last 30 years. Urban settlement increased alarming rate demand housing, infrastructure manufacturing. Therefore, planners must prioritize sustainable management, integrated zoning, active community involvement order protect against unsustainable changes cover. For future research, incorporating methodologies such as multi-source imaging will help differentiate more effectively. City experiences a nine-month rainy season with hot climate, cloud can affect image quality, making it challenging map covers clearly. Utilizing SENTINEL data enhance resolution improve spatio-temporal monitoring frameworks. Furthermore, integrating CO2 estimation could offer deeper insights into associated urbanization.

Language: Английский

Citations

8
Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project DOI Creative Commons
Kaleem Mehmood, Shoaib Ahmad Anees, Sultan Muhammad

et al.

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(2)

Published: Feb. 1, 2025

ABSTRACT This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel‐2 imagery, we observed an increase tree cover from 25.02% 2015 29.99% 2023 a decrease barren land 20.64% 16.81%, with accuracy above 85%. Hotspot spatial clustering analyses revealed significant vegetation recovery, high‐confidence hotspots rising 36.76% 42.56%. A predictive model for Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture precipitation as primary drivers of growth, ANN achieving R 2 0.8556 RMSE 0.0607 on testing dataset. These results demonstrate effectiveness integrating learning framework support data‐driven afforestation efforts inform sustainable environmental management practices.

Language: Английский

Citations

3
Mitigating cadmium contamination in soil using Biochar, sulfur-modified Biochar, and other organic amendments DOI

Tianzhi Huang,

Imran Ahmad

International Journal of Phytoremediation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: Jan. 26, 2025

Biochar is a novel approach to remediating heavy metal-contaminated soil. Using various organic amendments like phyllosilicate-minerals (PSM), compost, biochar (BC) and sulfur-modified (SMB), demonstrates superior adsorption capacity stability compared unmodified (BC). The mechanisms of SMB are identified for its potential increase soil-pH reduce available cadmium (Cd). study reveals the BC in immobilizing Cd contaminated demonstrated highest Cd, followed by BC, PSM, with capacities ranging from 7.47 17.67 mg g-1. Both exhibit high (12.82 g-1, respectively) low desorption percentages (4.46-6.23%) at ion strengths 0.01 0.1 mol-L-1 pH levels 5 7. showed higher (17.67 g-1) lower percentage BC. mechanism involves surface-precipitation, exchange, formation Cd(OH)2 CdCO3 precipitates, as well interactions between sulfur, leading more stable-Cd CdHS+ compounds. Adding 1% increased soil significantly reduced demonstrating pollutant remediation. underscores promise providing sustainable solution Cd-contaminated

Language: Английский

Citations

2
Analysis and early warning management of resource and environmental carrying capacity in agricultural provinces: A case study of Henan Province DOI Creative Commons
Weidong Chen,

Meng Lian

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0318848 - e0318848

Published: Feb. 12, 2025

Resources and Environmental Carrying Capacity (RECC) is a comprehensive concept that encompasses the interactions between resources, environment, human activities, serving as foundation for social development strategies. To adequately reflect this complex relationship, multi-level, multi-dimensional evaluation indicator system must be developed. This paper constructs regional soil environmental incorporating PM2.5 indicators, which in line with relevant protection policies planning orientations our country from 2014 to 2023. It analyzes level trend of RECC Henan Province proposes measures effective management. The results indicate following: (1) demonstrates downward trajectory, marked by temporary fluctuations over time. hit its nadir 2019, subsequently undergoing gradual resurgence; (2) Analysis individual dimension indicators reveals natural carrying capacity has declined medium relatively weaker level. Meanwhile, shown slight but generally remained stable. In contrast, socio-economic demonstrated an upward trend, rising strong terms early warning measures, it essential establish red zone, implement credit record accountability system, develop monitoring database along information technology platform. evaluating across different dimensions holds significant reference value assessing similar regions.

Language: Английский

Citations

2