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[HTML][HTML] A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective …
Land use regression (LUR) models are widely used in epidemiological and environmental
studies to estimate humans' exposure to air pollution within urban areas. However, the early …
studies to estimate humans' exposure to air pollution within urban areas. However, the early …
Low‐cost air quality monitoring networks for long‐term field campaigns: A review
The application of low‐cost air quality monitoring networks has substantially grown over the
last few years, following the technological advances in the production of cheap and portable …
last few years, following the technological advances in the production of cheap and portable …
[HTML][HTML] Calibrating networks of low-cost air quality sensors
Ambient fine particulate matter (PM 2.5) pollution is a major health risk. Networks of low-cost
sensors (LCS) are increasingly being used to understand local-scale air pollution variation …
sensors (LCS) are increasingly being used to understand local-scale air pollution variation …
Flexible, non-contact and multifunctional humidity sensors based on two-dimensional phytic acid doped co-metal organic frameworks nanosheets
The development of high-performance humidity sensors is of great significance to explore
their practical applications in the fields of environment, energy saving and safety monitoring …
their practical applications in the fields of environment, energy saving and safety monitoring …
[HTML][HTML] Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction
Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is
of paramount importance, especially in densely populated urban areas. However, precise …
of paramount importance, especially in densely populated urban areas. However, precise …
High-spatiotemporal-resolution PM2. 5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data
High-resolution real-time air quality forecasting can alert decision-makers and residents
about forthcoming air pollution events and refine air quality management. The …
about forthcoming air pollution events and refine air quality management. The …
Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment
Atmospheric particulate matter (PM) poses a significant threat to human health, infiltrating
the lungs and brain and leading to severe issues such as heart and lung diseases, cancer …
the lungs and brain and leading to severe issues such as heart and lung diseases, cancer …
Assessment of the applicability of a low-cost sensor–based methane monitoring system for continuous multi-channel sampling
Abstract Systems that are made of several low-cost gas sensors with automatic gas
sampling may have the potential to serve as reliable fast methane analyzers. However, there …
sampling may have the potential to serve as reliable fast methane analyzers. However, there …
Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi
Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily
transportable solution to address global gaps in air quality (AQ) observations. However …
transportable solution to address global gaps in air quality (AQ) observations. However …
Prediction of influencing atmospheric conditions for explosion Avoidance in fireworks manufacturing Industry-A network approach
This research study uses Artificial Neural Networks (ANNs) to predict occupational accidents
in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the …
in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the …