首页|University of Birmingham Reports Findings in Machine Learning (Local spatiotempo ral dynamics of particulate matter and oak pollen measured by machine learning a ided optical particle counters)
University of Birmingham Reports Findings in Machine Learning (Local spatiotempo ral dynamics of particulate matter and oak pollen measured by machine learning a ided optical particle counters)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Birmingham, United Kingdom, by NewsRx correspondents, research stated, “Conventional techniq ues for monitoring pollen currently have significant limitations in terms of lab our, cost and the spatiotemporal resolution that can be achieved. Pollen monitor ing networks across the world are generally sparse and are not able to fully rep resent the detailed characteristics of airborne pollen.” Our news editors obtained a quote from the research from the University of Birmi ngham, “There are few studies that observe concentrations on a local scale, and even fewer that do so in ecologically rich rural areas and close to emitting sou rces. Better understanding of these would be relevant to occupational risk asses sments for public health, as well as ecology, biodiversity, and climate. We pres ent a study using low-cost optical particle counters (OPCs) and the application of machine learning models to monitor particulate matter and pollen within a mat ure oak forest in the UK. We characterise the observed oak pollen concentrations , first during an OPC colocation period (6 days) for calibration purposes, then for a period (36 days) when the OPCs were distributed on an observational tower at different heights through the canopy. We assess the efficacy and usefulness o f this method and discuss directions for future development, including the requi rements for training data. The results show promise, with the derived pollen con centrations following the expected diurnal trends and interactions with meteorol ogical variables. Quercus pollen concentrations appeared greatest when measured at the canopy height of the forest (20-30 m). Quercus pollen concentrations were lowest at the greatest measurement height that is above the canopy (40 m), whic h is congruent with previous studies of background pollen in urban environments. ”
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