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This study explores the diurnal variations and influencing factors of PM 2.5 , NO 2 , and ozone concentrations in educational buildings. Utilizing an integrated system of indoor and outdoor sensors, building automation control networks, and walk-through inspections, air quality data along with relevant characteristics were collected from ten educational buildings in Central Florida. Advanced Neural Network models (RNNs and CNNs), including the Long Short-Term Memory (LSTM) and the Attention Temporal Convolutional Network (ATCN) algorithms based on the Least Absolute Shrinkage and Selection Operator (LASSO), were developed to accurately identify diurnal patterns in indoor air quality (IAQ) and the differences in influencing factors. The findings indicate greater variability in diurnal differences and factors influencing indoor NO 2 and ozone concentrations compared to PM 2.5. Although the factors influencing day and night PM 2.5 levels were similar, there were significant differences in the contribution weights of these factors. Optimized RNNs and CNNs significantly outperformed standard Artificial Neural Network (ANN) models in dynamically simulating and predicting target pollutants. Comparative analysis of the root-mean-square error (RMSE) demonstrated that LASSO-LSTM models comprehensively outperformed LASSO-ATCN models by averaging 13.4% (p < 0.05). These results can be referenced in studies concerning Indoor Air Quality (IAQ) control conducted in similar environmental settings. • Indoor and outdoor air pollution measurements were conducted in ten BACnet-equipped buildings. • Indoor PM 2.5 , NO 2 , and O 3 exhibited diurnal fluctuations in their influencing factors. • The D/N ratio of indoor NO 2 and its contributions from influencing factors are unstable. • LASSO-based ANNs significantly outperformed standard ANNs in simulating targeted pollutants. • LASSO-LSTM outperformed LASSO-ATCN by averaging 13.4% (p < 0.05). [ABSTRACT FROM AUTHOR]

Rhodomyrtus tomentosa (Ait.) Hassk. is widely distributed in tropical Asia and is an invasive species in Florida, USA. The purpose of the present study was to investigate the reproductive ecology of the species in order to provide information that may be useful in elucidating factors contributing to its invasiveness. The research was carried out at Heshan Hilly Land Integrated Experimental Station, the Chinese Academy of Sciences, Guangdong Province, China in 2003 and 2004. Rhodomyrtus tomentosa flowers in March or April and the flowering period lasts for about 3 months. Flowers open early in the morning and last 2-3 days. The pollen remain viable for about 12 h and the stigma is only receptive on the blooming day. The breeding system is a mixed mating system (outcrossing and geitonogamy). The species is bee-pollinated with pollen as the floral reward. Almost all pollinators appeared before 12 am. Amegilla florea (Smith) Brooks and Xylocopa nasalis Westwood are the primary pollinators for the species. The results indicate that the reproductive features of R. tomentosa - with the mixed mating system and common generalist pollinators - are not limiting factors when colonization occurs in a new environment. [ABSTRACT FROM AUTHOR]

Exposures to air pollutants have been associated with various acute respiratory diseases and detrimental human health. Analysis and further interpretation of air pollutant patterns are correspondingly important as monitoring them. In the present study, the 24-h and four-month indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were measured simultaneously for a laboratory in Gainesville city, Florida. The indoor PM2.5, PM10, and NO2 concentrations were predicted using multiple linear regression (MLR), time series regression (TSR), and artificial neural networks (ANN) models. The modeling conducted in this study aims to perform a cross comparison study between these models in a symmetric environment. The value of root-mean-square error was improved by 18.33% in comparison with the MLR model. In addition, the value of the coefficient of determination was improved by 24.68%. The ANN model had the best performance and could predict the target air pollutants at 10-min intervals of the studied building with 90% accuracy levels. The TSR model showed slightly better performance compared to the MLR model. These results can be accordingly referred for studies analyzing indoor air quality in similar building types and climate zones. [ABSTRACT FROM AUTHOR]

The 24 h and 14-day relationship between indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were assessed for an elementary school (site 1), a laboratory (site 2), and a residential unit (site 3) in Gainesville city, Florida. The primary aim of this study was to introduce a biplot-based PCA approach to visualize and validate the correlation among indoor and outdoor air quality data. The Spearman coefficients showed a stronger correlation among these target environmental measurements on site 1 and site 2, while it showed a weaker correlation on site 3. The biplot-based PCA regression performed higher dependency for site 1 and site 2 (p < 0.001) when compared to the correlation values and showed a lower dependency for site 3. The results displayed a mismatch between the biplot-based PCA and correlation analysis for site 3. The method utilized in this paper can be implemented in studies and analyzes high volumes of multiple building environmental measurements along with optimized visualization. [ABSTRACT FROM AUTHOR]