Diurnal variation of air pollutants and their relationship with land surface temperature in Bengaluru and Hyderabad cities of India

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 35, P. 101204 - 101204

Published: April 25, 2024

Language: Английский

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Prediction of land surface temperature using spectral indices, air pollutants, and urbanization parameters for Hyderabad city of India using six machine learning approaches

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 35, P. 101265 - 101265

Published: June 2, 2024

Language: Английский

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Machine Learning Approach for Predicting Perfluorooctanesulfonate Rejection in Efficient Nanofiltration Treatment and Removal

ACS ES&T Water, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

Language: Английский

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Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

ACS ES&T Water, Journal Year: 2024, Volume and Issue: 4(6), P. 2619 - 2631

Published: May 1, 2024

Land area optimization for horizontal flow constructed wetlands (HFCWs) with a low organic loading rate (OLR) needs special considerations as the microflora changes dramatically OLR. The P-k-C* approach does not lead to an accurate calculation of k-values in these wetlands. In this research, nonlinear machine learning models [Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN)] are applied predict realistic k-values. Data from 37 low-OLR HFCWs (n = 544) were analyzed, calculated found vary markedly (0.059–0.249 average 0.113 ± 0.090 m/day). classification based on OLR, rate, media depth leads reduction standard deviations (SDs) 83.40 35.27%. least SDs needed optimal design CWs. SVR, RF, ANN tested, best prediction efficiency testing datasets was achieved through model R2(kTKN)= 0.768 (RMSE 0.067) total Kjeldahl nitrogen (TKN), R2(kTN)= 0.835 0.043) (TN), R2(kTP) 0.723 0.087) phosphorus (TP). outcome validated using primary data HFCWs, which also confirmed superiority ANN-based model, can be used customization HFCWs.

Language: Английский

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Understanding the multifaceted influence of urbanization, spectral indices, and air pollutants on land surface temperature variability in Hyderabad, India

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 470, P. 143284 - 143284

Published: Aug. 1, 2024

Language: Английский

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A Futuristic Approach to Subsurface-Constructed Wetland Design for the South-East Asian Region Using Machine Learning

ACS ES&T Water, Journal Year: 2024, Volume and Issue: 4(9), P. 4061 - 4074

Published: Aug. 29, 2024

This study investigates the optimized design of horizontal flow constructed wetlands (HFCWs) to enhance pollutant removal efficiency while minimizing surface area requirements, particularly in Southeast Asian region. By refining first-order rate coefficient (k) for organics and nutrients, research aims meet specific performance benchmarks across three scenarios, ensuring compliance with discharge or reuse standards. Utilizing a data set comprising 1680 entries, five machine learning models─multiple linear regression (MLR), eXtreme Gradient Boosting (XGBoost), random forest (RF), artificial neural network (ANN), support vector (SVR)─were employed predict k values. Pearson's correlation, heat maps, ANOVA analysis identified most influential parameters affecting k-value predictions. The values ranged from 0.01 0.52 per day using P–k–C* method, essential effective removal. SVR model demonstrated highest predictive accuracy, R2 0.91 kBOD, 0.90 kTN, 0.82 kTKN, 0.76 kTP. optimization reduced standard deviations significantly, 136.90% 2.28%. Consequently, required wetland was by up 68% biochemical oxygen demand (BOD), 60% TN (total nitrogen), 67% TP phosphorus) larger systems, supporting tailored HFCWs targeted

Language: Английский

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High‐Resolution Analysis of Severe Heat Wave Dynamics and Thermal Discomfort Across India

International Journal of Climatology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

ABSTRACT The study explores variability and dynamical characteristics of heatwaves during March–June for 1990–2020 over India. Normalised T max anomaly is used to identify different heatwave spells in vulnerable regions North‐central India (NCI) Southeast coast (SECI) using Meteorological Department (IMD, 1° × resolution) observations, Indian Monsoon Data Assimilation Analysis (IMDAA, 0.12° 0.12°), ECMWF Reanalysis v5 (ERA5, 0.25° 0.25°). Results highlight that IMDAA exhibited a total 202 days (181 days) duration NCI while ERA5 132 (89 days), respectively, compared with those IMD (195 163 days). primary periods (10 April 20 June) SECI region (1 May 10 are well captured by IMDAA, unlike ERA5. average length the 7.8, 7.5, 7.76 (8.15, 7.72, 6.1 IMD, ERA5, respectively. high heat stress more frequent than common May–June (May only), as seen (ERA5). middle upper‐level anticyclone stronger heatwaves. Heat advection 850‐hPa north‐westerlies (~10 ms −1 ) abates sea breeze coastal region, aiding longer region. Ascending motion induced surface heating confined lower levels due subsidence anomalous anticyclone, stagnating higher temperatures atmosphere, depicting dome. slightly (31°C–39°C) (30°C–37°C). However, double moist dome has witnessed conditions NCI. Higher relative humidity contributed maritime winds from Bay Bengal Arabian Sea, soil moisture, so forth. highlights value atmospheric moisture differentiating conditions.

Language: Английский

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Evolution of Heat Wave Monitoring and Forecasting in India

MAUSAM, Journal Year: 2025, Volume and Issue: 76(1), P. 303 - 316

Published: Jan. 16, 2025

The article describes the journey of India Meteorological Department as a nodal agency with respect to weather related aspects since its inception through lens Heat Wave forecasting and warning services. It deals evolution specific in terms heat waves definitions, scientific understanding, researches operational methodologies during past 150 years. also touches upon current situation monitoring strategies, tools techniques followed by IMD impact based early

Language: Английский

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Predicting temperature variability in major Indian cities using Random Forest Regression (RFR) Model

Journal of Earth System Science, Journal Year: 2025, Volume and Issue: 134(1)

Published: Jan. 28, 2025

Language: Английский

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Real-Time Estimation of Near-Surface Air Temperature over Greece Using Machine Learning Methods and LSA SAF Satellite Products

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1112 - 1112

Published: March 21, 2025

The air temperature near the Earth’s surface is one of most important meteorological and climatological parameters. Yet, accurate timely readings are not available in significant parts world. development first validation a methodology for estimation near-surface (NSAT) presented here. Machine learning satellite products at core developed model. Land Surface Analysis Satellite Application Facility (LSA SAF) related to radiation, humidity budgets, albedo land cover, along with static topography parameters weather station measurements, used analysis. A series experiments showed that Random Forest regression 20 selected predictors was optimum selection NSAT. mean absolute error (MAE) NSAT model 0.96 °C, while biased (MBE) −0.01 °C R2 0.976. Limited seasonality present efficiency model, an increase errors noted during morning afternoon hours. influence rather limited. Cloud-free conditions were associated only marginally smaller errors, supporting applicability under both cloud-free cloudy conditions.

Language: Английский

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