ID Number		2456
Study Type		Plant Studies
Model		Studies on plants exposed to low-level RF exposure.
Details		AUTHORS' ABSTRACT: Halgamuge et al. 2015 (IEEE #5903): The aim of this work was to study possible effects of environmental radiation pollution on plants. The association between cellular telephone (short duration, higher amplitude) and base station (long duration, very low amplitude) radiation exposure and the growth rate of soybean (Glycine max) seedlings was investigated. Soybean seedlings, pre-grown for 4 days, were exposed in a gigahertz transverse electromagnetic cell for 2h to global system for mobile communication (GSM) mobile phone pulsed radiation or continuous wave (CW) radiation at 900 MHz with amplitudes of 5.7 and 41 Vm(-1) , and outgrowth was studied one week after exposure. The exposure to higher amplitude (41 Vm(-1)) GSM radiation resulted in diminished outgrowth of the epicotyl. The exposure to lower amplitude (5.7 Vm(-1)) GSM radiation did not influence outgrowth of epicotyl, hypocotyls, or roots. The exposure to higher amplitude CW radiation resulted in reduced outgrowth of the roots whereas lower CW exposure resulted in a reduced outgrowth of the hypocotyl. Soybean seedlings were also exposed for 5 days to an extremely low level of radiation (GSM 900 MHz, 0.56 Vm(-1)) and outgrowth was studied 2 days later. Growth of epicotyl and hypocotyl was found to be reduced, whereas the outgrowth of roots was stimulated. Our findings indicate that the observed effects were significantly dependent on field strength as well as amplitude modulation of the applied field. AUTHOR'S ABSTRACT: Halgamuge 2016 (IEEE #6510): AIM: The aim of this article was to explore the hypothesis that non-thermal, weak, radiofrequency electromagnetic fields (RF-EMF) have an effect on living plants. SUBJECT AND METHODS: In this study, we performed an analysis of the data extracted from the 45 peer-reviewed scientific publications (1996-2016) describing 169 experimental observations to detect the physiological and morphological changes in plants due to the non-thermal RF-EMF effects from mobile phone radiation. Twenty-nine different species of plants were considered in this work. RESULTS: Our analysis demonstrates that the data from a substantial amount of the studies on RF-EMFs from mobile phones show physiological and/or morphological effects (89.9%, p < 0.001). Additionally, our analysis of the results from these reported studies demonstrates that the maize, roselle, pea, fenugreek, duckweeds, tomato, onions and mungbean plants seem to be very sensitive to RF-EMFs. Our findings also suggest that plants seem to be more responsive to certain frequencies, especially the frequencies between (i) 800 and 1500 MHz (p < 0.0001), (ii) 1500 and 2400 MHz (p < 0.0001) and (iii) 3500 and 8000 MHz (p = 0.0161). CONCLUSION: The available literature on the effect of RF-EMFs on plants to date observed the significant trend of radiofrequency radiation influence on plants. Hence, this study provides new evidence supporting our hypothesis. Nonetheless, this endorses the need for more experiments to observe the effects of RF-EMFs, especially for the longer exposure durations, using the whole organisms. The above observation agrees with our earlier study, in that it supported that it is not a well-grounded method to characterize biological effects without considering the exposure duration. Nevertheless, none of these findings can be directly associated with human; however, on the other hand, this cannot be excluded, as it can impact the human welfare and health, either directly or indirectly, due to their complexity and varied effects (calcium metabolism, stress proteins, etc.). This study should be useful as a reference for researchers conducting epidemiological studies and the long-term experiments, using whole organisms, to observe the effects of RF-EMFs. AUTHORS' ABSTRACT; Halgamuge and Davis 2019 (IEEE #7339): This paper applies Machine Learning (ML) algorithms to peer-reviewed publications in order to discern whether there are consistent biological impacts of exposure to non-thermal low power radio-frequency electromagnetic fields (RF-EMF). Expanding on previous analysis that identified sensitive plant species, we extracted data from 45 articles published between 1996 and 2016 that included 169 experimental case studies of plant response to RF-EMF. Raw-data from these case studies included six different attributes: frequency, specific absorption rate (SAR), power flux density, electric field strength, exposure time and plant type (species). This dataset has been tested with two different classification algorithms: k-Nearest Neighbor (kNN) and Random Forest (RF). The outputs are estimated using k-fold cross-validation method to identify and compare classifier mean accuracy and computation time. We also developed an optimization technique to distinguish the trade-off between prediction accuracy and computation time based on the classification algorithm. Our analysis illustrates kNN (91.17%) and RF (89.41%) perform similarly in terms of mean accuracy, nonetheless, kNN takes less computation time (3.38/s) to train a model compared to RF (248.12/s). Very strong correlations were observed between SAR and frequency, and SAR with power flux density and electric field strength. Despite the low sample size (169 reported experimental case studies), that limits statistical power, nevertheless, this analysis indicates that ML algorithms applied to bioelectromagnetics literature predict impacts of key plant health parameters from specific RF-EMF exposures. This paper addresses both questions of the methodological importance and relative value of different methods of ML and the specific finding of impacts of RF-EMF on specific measures of plant growth and health. Recognizing the importance of standardizing nomenclature for EMF-RF, we conclude that Machine Learning provides innovative and efficient RF-EMF exposure prediction tools, and we propose future applications in occupational and environmental epidemiology and public health.
Findings		Effects
Status		Completed With Publication
Principal Investigator		Dept Electrical and Electronics Eng, U Melbourne,
Funding Agency		?????
Country		AUSTRALIA
References		Halgamuge , MN et al. Bioelectromagnetics., (2015) 36:87-95 Halgamuge, MN Electromagn Biol Med., (2017) 36:213-235 Halgamuge, MN et al. Environmental Research., (2019) 178:(108634)-
Comments