Low-cost sensor networks, data science and forecasting
Many cheap sensors, carefully corrected, plus models that predict tomorrow’s air
How the work is done
Co-location calibration against reference monitors. Humidity and temperature correction. Machine learning and deep learning time-series models, evaluated honestly – out-of- period test sets, multiple random seeds, and reported uncertainty rather than a single best number. Interpretation of model drivers, so a prediction can be explained and not just issued.
This description is provisional. The wording is the site maintainer's and the science is the Principal Investigator's; it has not yet been approved by him.
Which records carry this theme is derived from each record’s title, not chosen by its authors. The rules are in data/research-themes.yaml and every tag stores the term that produced it, shown in the chip’s tooltip. Theses use the laboratory’s own “Area of Research” where it is specific enough. Three publications and one thesis match no theme and are deliberately left untagged.
Publications 9
-
Interpretable machine learning tools to analyze PM2.5 sensor network data so as to quantify local source impacts and long-range transport
Atmospheric Research, 311, 107656
Low-cost sensor networks, data science and forecasting Source apportionment and emission sources Urban and indoor air quality in Dhaka
-
Characterizing Indoor-Outdoor PM2.5 Concentrations Using Low-Cost Sensor Measurements in Residential Homes in Dhaka, Bangladesh
American Chemical Society (ACS)
Low-cost sensor networks, data science and forecasting Urban and indoor air quality in Dhaka
10.26434/chemrxiv-2024-7wrlw
Open access (green) -
Particulate Matter Emissions at Different Microenvironments Using Low-Cost Sensors in Megacity Dhaka, Bangladesh
Atmosphere, 15(8), 897
Low-cost sensor networks, data science and forecasting Source apportionment and emission sources Urban and indoor air quality in Dhaka
10.3390/atmos15080897
Open access (gold) -
Research Priorities of Applying Low-Cost PM2.5 Sensors in Southeast Asian Countries
International Journal of Environmental Research and Public Health, 19(3), 1522
Low-cost sensor networks, data science and forecasting Urban and indoor air quality in Dhaka
10.3390/ijerph19031522
Open access (gold) -
Distinguishing Air Pollution Due to Stagnation, Local Emissions, and Long-Range Transport Using a Generalized Additive Model to Analyze Hourly Monitoring Data
ACS Earth and Space Chemistry, 5(9), 2329-2340
Low-cost sensor networks, data science and forecasting Source apportionment and emission sources Urban and indoor air quality in Dhaka
-
Global Sources of Fine Particulate Matter: Interpretation of PM<sub>2.5</sub> Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model
Environmental Science & Technology
Aerosol optical properties and remote sensing Low-cost sensor networks, data science and forecasting Urban and indoor air quality in Dhaka
Theses 1
-
Spatial and Temporal Variations of PM2.5 Concentrations across Dhaka Using TSI BlueSkyTM Sensors
Supervisor: Dr. Abdus Salam
Co-supervisor: Shatabdi RoyLow-cost sensor networks, data science and forecasting Urban and indoor air quality in Dhaka