Tree planters in British Columbia have reported symptoms that are congruent with musculoskeletal stress and organophosphate or carbamate pesticide intoxication. The purpose of this research was to determine the existence of any physiological or biochemical correlate supporting the existence of these potential hazards in tree planting. Worker's health complaints were assessed from regularly distributed questionnaires. Blood samples were obtained from 14 male and three female Canadian subjects before and after tree planting work on 10 occasions throughout a tree planting season. The strenuous physical challenge of tree planting was confirmed by a significant elevation of serum enzyme activity (ESEA) at the beginning of the season, which did not return to a normal level during the remainder of the season. Significant (p < or = 0.05) inhibition of erythrocyte acetylcholinesterase activity (AChE) postwork was observed in 15.9% of individuals, and a significant group mean prework-postwork difference of AChE or plasma pseudocholinesterase (PChE) was observed on two days of testing, indicating a potential toxicological hazard from pesticide absorption. No correlation was found between the degree of ESEA or cholinesterase inhibition and the number of health complaints.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/00140139308967959 | DOI Listing |
Sci Rep
January 2025
Department of Electronic Engineering, Universidad Tecnica Federico Santa Maria, Valparaiso, Chile.
Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets.
View Article and Find Full Text PDFJ Environ Manage
January 2025
INRAE, Aix-Marseille Univ., UMR RECOVER, Aix-en-Provence, France.
Drought stress during the plant's growing season is a serious constraint to plant establishment in arid and semiarid Mediterranean ecosystems. Plant growth promoting rhizobacteria (PGPR) as environmentally friendly and innovative management approach can be used to produce seedlings better adapted to these environments. We tested native PGPR strains isolated from drought-tolerant tree and shrub species originating from two climatically contrasting regions: hot-dry (Dehloran) and milder Mediterranean climate (Ilam).
View Article and Find Full Text PDFJ Environ Manage
January 2025
University Center of International Programmes of Studies, International Hellenic University, Thessaloniki, 57001, Greece. Electronic address:
The use of treated wastewater (TWW) for agricultural irrigation is becoming more popular as a sustainable alternative to freshwater due to increasing water scarcity. While considerable research exists on the effects of TWW on soil microorganisms, its impact on soil nematodes, key indicators of soil health remains unexplored. This study assessed the effects of two years of TWW irrigation on soil nematode communities in abandoned fields cultivated with Lavender, Anise, Olive and Pomegranate trees.
View Article and Find Full Text PDFEcotoxicol Environ Saf
January 2025
Universidad Nacional de San Juan, Facultad de Ingeniería (FI-UNSJ), Av. Lib. San Martín (Oeste) 1109, San Juan, San Juan 5400, Argentina; Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria San Juan, Calle 11 y Vidart, Pocito, San Juan 5427, Argentina. Electronic address:
Seeds of four native species of trees and shrubs (Larrea cuneifolia, Bulnesia retama, Plectrocarpa tetracantha and Prosopis flexuosa) were exposed to soil contaminated with As, Cu, Cd, and Zn from an abandoned gold mine to identify adaptation strategies. Several physiological, morpho-anatomical, and biochemical parameters were determined. The seed germination of L.
View Article and Find Full Text PDFPLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!