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Highlights • Monitoring is done at 12 indoors and 5 outdoors locations, spread across university. • GP based data mining technique is implemented to identify multi-nonlinear models. • Outdoor PM 2.5 is much higher than the permissible limits by USEPA and EEA. • GP based models are perfectly able to mimic the behavioral trends of outdoor PM. • The model predictions are very close to the measured values. Abstract Airborne particulate matter (PM) is considered to be an essential indicator of outdoor and indoor air quality. In this study, indoor and outdoor PM 1 , PM 2.5 , PM 10 concentrations were monitored at different locations within the Tehran University campus. It is found that 10% of PM 1 , PM 2.5 and PM 10 concentrations were higher than 36.11, 52.48 and 92.13 μg/m3 for indoors respectively. Genetic programming (GP) based methodology is implemented to identify the influence of outdoor PM on the indoor PM and established significant empirical models. The best GP model is identified based on fitness measure and root mean square error. It was observed that the GP based models are perfectly able to mimic the behavioural trends of outdoor particulate matter for PM 1 , PM 2.5 and PM 10 concentrations. The model predictions are very similar to the measured values and their variation was less than ± 8%. This analysis confirms the performance of GP based data driven modeling approach to predict the relationship between the outdoor particulate matter and its influence on the indoor particulate matter concentration. [ABSTRACT FROM AUTHOR]

[Display omitted] Oxidative stress and tissue damage (OSD) play a pivotal role as an early-stage process in chronic disease pathogenesis. However, there has been little research to better understand the temporal (χρόνος[chronos]) dimensions of OSD process associated with environmental (non-genetic, including behaviors/lifestyle) and/or occupational stressors, like night shift work. OSD processes have recently attracted attention in relation to time-resolved external stressor trajectories in personalized medicine (prevention) initiatives, as they seem to interact with circadian clock systems towards the improved delineation of the early stages of (chronic) disease process. This work critically reviewed human studies targeting the temporal dynamics of OSD and circadian clock system's activity in response to environmental/occupational stressors; the case of night shift work was examined. Being a key stressor influencing OSD processes and circadian rhythm, night shift work was evaluated as part of a scoping review of research in OSD, including inflammatory and metabolic processes to determine the extent of OSD research undertaken in human populations, methodologies, tools and biomarkers used and the extent that the temporal dimensions of exposure and biological effect(s) were accounted for. Online databases were searched for papers published from 2000 onwards, resulting in the selection of 53 original publications. The majority of studies (n = 41) took place in occupational settings, while the rest were conducted in the general population or patient groups. Most occupational studies targeted outcomes of oxidative stress/damage (n = 19), followed by the combination of OSD with inflammatory response (n = 10), and studies focused on metabolic outcomes (n = 12). Only a minor fraction of the studies measured biomarkers related to circadian rhythm, such as, melatonin, its metabolite, or cortisol. Night shift work was associated with select biomarkers of OSD and inflammation, albeit with mixed results. Although much progress in delineating the biological mechanisms of OSD process has been made, an equally thorough investigation on the temporal trajectory of OSD processes as triggered by environmental/occupational stressors in human studies has yet to fully evolve. [ABSTRACT FROM AUTHOR]

Bisphenol-A (BPA) is an important industrial chemical generally introduced into the environment through the wastes of factories producing, for example, paper, plastics and plastic products-making industries. Consequently, factory effluents and sewage containing BPA are sources of contamination in the aquatic environments. The objective of this research is to study the efficiency of BPA removal from aqueous solutions using two chitosans, (1) synthesized chitosan; a waste derived adsorbent from waste seafood shell and which was synthesized in our laboratory and (2) commercial chitosan a chitosan sample obtained commercially. The effects of process parameters such as solution pH, adsorbent dose, contact time, and initial concentration of BPA have been examined and compared. The results obtained from this study indicated that the efficiency of removal declines with increased contaminant concentration and decreased adsorbent dose. The optimal pH for maximum adsorption for both chitosan adsorbents was 5. The adsorption data were fitted into six isotherm models namely Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, Redlich–Peterson and Sips and the isotherm parameters were determined. Furthermore, the kinetics of the process were correlated by three kinetic models, namely, the pseudo-first-order, pseudo-second-order and intra-particle diffusion models. The kinetic experiments revealed that both chitosan adsorbents follow the pseudo-second-order kinetic model. The isotherm studies demonstrated that both adsorbents follow the Langmuir model. Based on the results, chitosan, synthesized (SC) and commercial (CC), is an efficient, novel and inexpensive alternative for the removal of BPA from aqueous solution. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the applicability of the adsorption process for the removal of organophosphorus pesticide malathion 57% from water by using multi-walled carbon nanotubes (MWCNTs). The impact of various experimental conditions such as pH, quantity of adsorbent, concentration of pesticides, contact time and temperature was studied and optimized for the maximum removal of malathion. Unlike conventional optimization, a limited number of experiments (26 steps) were performed in a cost-effective manner for different independent variables such as MWCNTs concentration (0.1–0.5 g/L), the malathion (57%) concentration (6 mg/L and 10 mg/L), contact time (2–30 min) and pH (neutral range). Based on the experimental data obtained in a lab-scale batch study, a three-factor response surface modeling (RSM) approach was implemented in order to optimize the conditions for maximum removal of malathion, and compare experimental results with standardized malathion samples. The optimized conditions to achieve the maximum removal of malathion (100%) were determined to be a malathion concentration of 6 mg/L, an initial MWCNTs concentration of 0.5 g/L, and a contact time of 30 min. Findings of this study clearly indicated that 100% of the malathion could be cost-effectively removed by MWCNTs in conditions predicted by the proposed optimization methodology. [ABSTRACT FROM AUTHOR]