Assessment of Urban Environmental Quality by Socioeconomic and Environmental Variables Using Open‐Source Datasets

Transactions in GIS, Journal Year: 2024, Volume and Issue: 28(7), P. 2526 - 2544

Published: Sept. 22, 2024

ABSTRACT In this era of rapid development, environmental quality is an essential aspect sustainable development. A healthy urban environment supports, regulates, and provides livable conditions. areas, considerably affected by socioeconomic factors such as population expansion economic For decision‐making, it also significant for stakeholders policymakers to understand the impact on quality. While previous studies have examined quality, they often focused single cities or limited parameters. This research addresses these limitations conducting a comparative analysis two major Asian with similar demographic features, utilizing comprehensive set variables. Our innovative approach combines open‐source datasets advanced remote sensing techniques provide more holistic assessment over decades. We analyzed last decades selected parameters: surface greenness, moisture, land temperature. Lahore (Pakistan) Wuhan (China) were having approximately same features. Correlation matrix has been used assess relationship between variables social‐economic variables: carbon emission. coefficient indicated that correlates negatively greenness moisture both (−0.67 −0.71) District (−0.5 −0.75), respectively, while had positive relation temperature: 0.65 0.57 District, respectively. These effects are prominent within 10 km distance from city center, where substantial observed during time window.

Language: Английский

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Assessment of the Groundwater Quality of the Jessore Municipality by Using Weighted Arithmetic Water Quality Index

Next research., Journal Year: 2025, Volume and Issue: 2(1), P. 100187 - 100187

Published: Feb. 3, 2025

Language: Английский

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Decadal Dynamics of Rangeland Cover Using Remote Sensing and Machine Learning Approach

Rangeland Ecology & Management, Journal Year: 2025, Volume and Issue: 100, P. 1 - 13

Published: Feb. 14, 2025

Language: Английский

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Groundwater quality assessment in selected polluted hotspots of Tamil Nadu, India using geospatial and statistical approaches

Desalination and Water Treatment, Journal Year: 2025, Volume and Issue: unknown, P. 101047 - 101047

Published: Feb. 1, 2025

Language: Английский

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Spatio-temporal analysis of urban expansion and land use dynamics using google earth engine and predictive models

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

Published: Feb. 27, 2025

Urban expansion and changes in land use/land cover (LULC) have intensified recent decades due to human activity, influencing ecological developmental landscapes. This study investigated historical projected LULC urban growth patterns the districts of Multan Sargodha, Pakistan, using Landsat satellite imagery, cloud computing, predictive modelling from 1990 2030. The analysis images was grouped into four time periods (1990–2000, 2000–2010, 2010–2020, 2020–2030). Google Earth Engine cloud-based platform facilitated classification 5 ETM (1990, 2000, 2010) 8 OLI (2020) Random Forest model. A simulation model integrating Cellular Automata an Artificial Neural Network Multilayer Perceptron MOLUSCE plugin QGIS employed forecast resulting maps showed consistently high accuracy levels exceeding 92% for both across all periods. revealed that Multan's built-up area increased 240.56 km2 (6.58%) 440.30 (12.04%) 2020, while Sargodha experienced more dramatic 730.91 (12.69%) 1,029.07 (17.83%). Vegetation remained dominant but significant variations, particularly peri-urban areas. By 2030, is stabilize at 433.22 km2, primarily expanding southeastern direction. expected reach 1,404.97 showing balanced multi-directional toward northeast north. presents effective analytical method processing, GIS, change modeling evaluate spatiotemporal changes. approach successfully identified main transformations trends areas highlighting potential urbanization zones where opportunities exist developing planned managed settlements.

Language: Английский

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Modelling sustainable land management programme intervention effect on soil loss rate in the watershed region

Soil Use and Management, Journal Year: 2025, Volume and Issue: 41(1)

Published: Jan. 1, 2025

Abstract This paper modelled the sustainable land management programme intervention effect on soil loss rate in Hoha and Temba watersheds, Western Ethiopia. In area, (SLMP) has been doing many soil–water conservation measurements since 2011. However, an assessment of before after implementation project not yet conducted area because operational issues high costs gathering on‐ground data. Because this, we have developed a Revised Universal Soil Loss Equation (RUSLE) framework fully integrated with geographic information system (GIS) for spatial resolution (30 m) erosion 2010 (before SLMP was implemented area) 2015 2021 (after area). The results showed that mean annual study 13.04, 1.88 2.06 t ha −1 year 9.58, 1.53 1.68 watershed years, 2010, 2021, respectively. line our also indicated increment forest cover reduction bare both watersheds throughout time. terms reduction, significant role through improvement watersheds.

Language: Английский

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Influence of Geographical Locations on Drinking Water Quality in Rural Pavlodar Region, Kazakhstan

Water, Journal Year: 2025, Volume and Issue: 17(7), P. 945 - 945

Published: March 24, 2025

Drinking water quality in rural areas is impacted by industrial and agricultural runoff, treatment infrastructure, household economic conditions. This study explores the relationship between drinking quality, sources, land cover types northeastern Kazakhstan. The Water Quality Index (WQI) was calculated for each village using Horton Equation. Land mapped Sentinel-2 Level-2A imagery. Statistical differences among villages were analyzed through one-way ANOVA t-tests. A Structural Equation Model (SEM) built Maximum Likelihood estimators, with significance set at p < 0.05. Significant variations manganese, hydrocarbonates, chlorides observed based on distance from Irtysh River sources. Grasslands had greatest influence parameters (−14.89), followed croplands (5.96), urban lands (2.15), other (2), forests having least effect. Biological indicators, such as Actinomycetes sp., significantly correlated (2.32) types. reduce mineral content groundwater, while contribute to enrichment, particularly nitrates croplands. Urban increase chemical loads manganese levels decrease River. Chlorides hydrocarbonates are highest near river. Rural infrastructure should be improved, stricter pollution controls enforced, sustainable use practices promoted runoff. Additionally, incentives filtration, regular monitoring, a coordinated watershed management approach can enhance long-term security.

Language: Английский

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Research on national pattern innovation fusion and packaging design system based on efficient net image recognition and transfer learning algorithm

GeoJournal, Journal Year: 2025, Volume and Issue: 90(2)

Published: April 1, 2025

Language: Английский

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Integrative Assessment of Surface Water Contamination Using GIS, WQI, and Machine Learning in Urban–Industrial Confluence Zones Surrounding the National Capital Territory of the Republic of India

Water, Journal Year: 2025, Volume and Issue: 17(7), P. 1076 - 1076

Published: April 4, 2025

This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence impact of organic inorganic pollutants across urban–industrial confluence zones (UICZ) surrounding National Capital Territory (NCT) India. Surface samples (n = 118) were systematically collected from Gautam Buddha Nagar, Ghaziabad, Faridabad, Sonipat, Gurugram, Jhajjar, Baghpat districts assess physical, chemical, microbiological parameters. The application spatial interpolation techniques, such as kriging inverse distance weighting (IDW), enhances WQI estimation in unmonitored areas, improving regional assessments remediation planning. GIS mapping highlighted stark disparities, with industrial hubs, like Faridabad exhibiting values exceeding 600 due untreated discharges wastewater, while rural regions, Jhajjar Baghpat, recorded below 200, reflecting minimal anthropogenic pressures. employed four ML models—linear regression (LR), random forest (RF), Gaussian process (GPR_PUK), support vector machines (SVM_Poly)—to predict high precision. SVM_Poly emerged most effective model, achieving testing CC, RMSE, MAE 0.9997, 11.4158, 5.6085, respectively, outperforming RF (0.9925, 29.8107, 21.7398) GPR_PUK (0.9811, 68.4466, 54.0376). By leveraging models, this prediction beyond conventional computation, enabling extrapolation early contamination detection data-scarce regions. Sensitivity analysis identified total suspended solids critical predictor influencing WQI, underscoring its relevance monitoring programs. research uniquely integrates algorithms analytics, providing a novel methodological contribution assessment. findings emphasize urgency mitigating fate transport protect Delhi’s hydrological ecosystems, presenting robust decision-support system for policymakers environmental managers.

Language: Английский

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Spatial distribution, potential health risks, and sources of groundwater contamination in the semi-arid region

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2025, Volume and Issue: unknown, P. 103952 - 103952

Published: April 1, 2025

Language: Английский

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