Local spatiotemporal dynamics of particulate matter and oak pollen measured by machine learning aided optical particle counters DOI Creative Commons
Sophie A. Mills, A. R. MacKenzie, Francis D. Pope

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 941, P. 173450 - 173450

Published: May 25, 2024

Conventional techniques for monitoring pollen currently have significant limitations in terms of labour, cost and the spatiotemporal resolution that can be achieved. Pollen networks across world are generally sparse not able to fully represent detailed characteristics airborne pollen. There few studies observe concentrations on a local scale, even fewer do so ecologically rich rural areas close emitting sources. Better understanding these would relevant occupational risk assessments public health, as well ecology, biodiversity, climate. We present study using low-cost optical particle counters (OPCs) application machine learning models monitor particulate matter within mature oak forest UK. characterise observed concentrations, first during an OPC colocation period (6 days) calibration purposes, then (36 when OPCs were distributed observational tower at different heights through canopy. assess efficacy usefulness this method discuss directions future development, including requirements training data. The results show promise, with derived following expected diurnal trends interactions meteorological variables. Quercus appeared greatest measured canopy height (20-30 m). lowest measurement is above (40 m), which congruent previous background urban environments. attenuation sources depleted also season heights, some evidence persist later level beneath (10 m) where catkins latest compared higher catkins.

Language: Английский

Isolating pollen signals from laser diode aerosol Optical Particle Counter (OPC) data through positive matrix factorization (PMF) and Unmix receptor models DOI

Rajat Prakash Singhal,

Sumit Khandelwal, Akhilendra Bhushan Gupta

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 931, P. 172793 - 172793

Published: April 28, 2024

Language: Английский

Citations

1
Local spatiotemporal dynamics of particulate matter and oak pollen measured by machine learning aided optical particle counters DOI Creative Commons
Sophie A. Mills, A. R. MacKenzie, Francis D. Pope

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 941, P. 173450 - 173450

Published: May 25, 2024

Conventional techniques for monitoring pollen currently have significant limitations in terms of labour, cost and the spatiotemporal resolution that can be achieved. Pollen networks across world are generally sparse not able to fully represent detailed characteristics airborne pollen. There few studies observe concentrations on a local scale, even fewer do so ecologically rich rural areas close emitting sources. Better understanding these would relevant occupational risk assessments public health, as well ecology, biodiversity, climate. We present study using low-cost optical particle counters (OPCs) application machine learning models monitor particulate matter within mature oak forest UK. characterise observed concentrations, first during an OPC colocation period (6 days) calibration purposes, then (36 when OPCs were distributed observational tower at different heights through canopy. assess efficacy usefulness this method discuss directions future development, including requirements training data. The results show promise, with derived following expected diurnal trends interactions meteorological variables. Quercus appeared greatest measured canopy height (20-30 m). lowest measurement is above (40 m), which congruent previous background urban environments. attenuation sources depleted also season heights, some evidence persist later level beneath (10 m) where catkins latest compared higher catkins.

Language: Английский

Citations

0