[HTML][HTML] Tutorial: Guidelines for implementing low-cost sensor networks for aerosol monitoring

N Zimmerman - Journal of Aerosol Science, 2022 - Elsevier
Over the past decade, there has been exponential growth in low-cost air pollution sensing
technology. While low-cost sensors can provide a path towards more accessible air quality …

A Comparative and Systematic Study of Machine Learning (ML) Approaches for Particulate Matter (PM) Prediction

A Pandya, R Nanavaty, K Pipariya, M Shah - Archives of Computational …, 2024 - Springer
Air quality in metropolitan areas has deteriorated due to growing urbanisation and
industrialisation, leading to severe health and significant economic consequences. This …

Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter

ML Aix, S Schmitz, DJ Bicout - Science of The Total Environment, 2023 - Elsevier
Low concentrations of pollutants may already be associated with significant health effects.
An accurate assessment of individual exposure to pollutants therefore requires measuring …

Significance of sources and size distribution on calibration of low-cost particle sensors: Evidence from a field sampling campaign

V Malyan, V Kumar, M Sahu - Journal of Aerosol Science, 2023 - Elsevier
Low-cost sensors (LCS) are gathering the interest of researchers and monitoring agencies
worldwide due to their compact size and economic feasibility. However, the data recorded …

Thermal conductivity prediction of nano enhanced phase change materials: a comparative machine learning approach

F Jaliliantabar - Journal of Energy Storage, 2022 - Elsevier
Thermal conductivity is one of the crucial properties of nano enhanced phase change
materials (NEPCM). Then, in this study three different machine learning methods namely …

Spatiotemporal analysis of fine particulate matter for India (1980–2021) from MERRA-2 using ensemble machine learning

V Kumar, V Malyan, M Sahu, B Biswal, M Pawar… - Atmospheric Pollution …, 2023 - Elsevier
Particle exposure affects more humans globally than any other air pollutant. However, due to
expensive instruments and infrastructural deficiency, a high spatiotemporal network of …

Improving groundwater nitrate concentration prediction using local ensemble of machine learning models

H Mahboobi, A Shakiba, B Mirbagheri - Journal of Environmental …, 2023 - Elsevier
Groundwater is one of the most important water resources around the world, which is
increasingly exposed to contamination. As nitrate is a common pollutant of groundwater and …

Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain

G Kosmopoulos, V Salamalikis, S Wilbert, LF Zarzalejo… - Sensors, 2023 - mdpi.com
Low-cost sensors (LCSs) for particulate matter (PM) concentrations have attracted the
interest of researchers, supplementing their efforts to quantify PM in higher spatiotemporal …

Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS

T Zhu, Y Chen, C Tao - Science of The Total Environment, 2023 - Elsevier
As an essential environmental property, the aqueous solubility quantifies the hydrophobicity
of a compound. It could be further utilized to evaluate the ecological risk and toxicity of …

Mathematical optimization and prediction of Febuxostat xanthine oxidase inhibitor solubility through supercritical CO2 system using machine-learning approach

U Hani, ZAB Sinnah, AJ Obaidullah, J Alanazi… - Journal of Molecular …, 2023 - Elsevier
Scientific research towards the synthesis and discovery of novel therapeutic agents with
optimal safety, great biological efficiency and acceptable toxicity profile is known as the …