Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges
L Liang - Environmental Research, 2021 - Elsevier
Low-cost sensors (LCSs) are widely acknowledged for bringing a paradigm shift in
supplemental traditional air monitoring by air regulatory agencies. However, there is …
supplemental traditional air monitoring by air regulatory agencies. However, there is …
Low-cost air quality sensing towards smart homes
The evolution of low-cost sensors (LCSs) has made the spatio-temporal map** of indoor
air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their …
air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their …
Long-term time-series pollution forecast using statistical and deep learning methods
Tackling air pollution has become of utmost importance since the last few decades. Different
statistical as well as deep learning methods have been proposed till now, but seldom those …
statistical as well as deep learning methods have been proposed till now, but seldom those …
Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis
S Mahajan - Scientific Reports, 2022 - nature.com
Cities around the world are struggling with environmental pollution. The conventional
monitoring approaches are not effective for undertaking large-scale environmental …
monitoring approaches are not effective for undertaking large-scale environmental …
Evaluation of low-cost sensors for quantitative personal exposure monitoring
Observation of air pollution at high spatio-temporal resolution has become easy with the
emergence of low-cost sensors (LCS). LCS provide new opportunities to enhance existing …
emergence of low-cost sensors (LCS). LCS provide new opportunities to enhance existing …
From Do-It-Yourself (DIY) to Do-It-Together (DIT): Reflections on designing a citizen-driven air quality monitoring framework in Taiwan
Air pollution is a serious problem and has caused public health concerns all over the world.
Despite the evidence, the preparedness and response of citizens has been limited. This …
Despite the evidence, the preparedness and response of citizens has been limited. This …
Trend decomposition aids forecasts of air particulate matter (PM2. 5) assisted by machine and deep learning without recourse to exogenous data
DA Wood - Atmospheric Pollution Research, 2022 - Elsevier
A near-past, trend-attribute extraction technique is proposed for short-term hourly particulate
matter (PM2. 5) forecasting. Multiple attributes are extracted from the univariate PM2. 5 time …
matter (PM2. 5) forecasting. Multiple attributes are extracted from the univariate PM2. 5 time …
PM2. 5 forecasting model using a combination of deep learning and statistical feature selection
This paper proposed a PM 2.5 forecasting model using Long Short-Term Model (LSTM)
sequence to sequence combined with the statistical method. Correlation Analysis, XGBoost …
sequence to sequence combined with the statistical method. Correlation Analysis, XGBoost …
AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality
Increasing urbanisation and a better understanding of the negative health effects of air
pollution have accelerated the use of Internet of Things (IoT)-based air quality sensors. Low …
pollution have accelerated the use of Internet of Things (IoT)-based air quality sensors. Low …
MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas
H Wang, L Zhang, R Wu - Atmosphere, 2023 - mdpi.com
The accurate prediction of PM2. 5 concentration, a matter of paramount importance in
environmental science and public health, has remained a substantial challenge …
environmental science and public health, has remained a substantial challenge …