Anthropogenic activities impact on atmospheric environmental quality in a gas-flaring community: application of fuzzy logic modelling concept

Maternal and fetal exposure to metal (loid)s, maternal nutrition status, and impact on prenatal growth in an energy rich zone and an urban area along the Persian Gulf

The main purposes of the study were to determine whether maternal and cord blood (MB and CB) levels of metal (loid)s (MLs) are different between industrial and urban areas. Subjects were derived from the petrochemical and gas area (PGA) of Asaluyeh County and the urban area (UA) of Bushehr County in Iran. Adjusted models revealed significant differences between the PGA and UA for maternal Fe, Cu, Mg, Cd, Zn, Ni, V and Pb levels, likewise cord Cu, V, Pb and Ni levels, which were higher in the PGA. Unadjusted models revealed there were decreases in neonatal weight and head circumference by 16.550 g and 0.059 cm, respectively for each Ln unit increase in maternal Cr levels. The mean blood levels of MLs significantly associated with food consumption frequency. The MLs were detected at higher levels in the body's fetuses and their mothers who resided in the PGA than those from the UA. So, the fetuses of mothers living in an industrial areas may be at higher risk from anthropometric measures. The impact of prenatal exposure to MLs on prenatal growth should be considered for better understanding in the further studies.

Childhood exposure to metal(loid)s in industrial and urban areas along the Persian Gulf using toenail tissue as a biomarker

Metal(loid)s (MLs) with natural or anthropogenic sources may cause adverse health effects in children. This study aimed to compare the childhood exposure to ΣMLs (essential, non-essential and toxic) in an industrial and an urban area in Southwest Iran using toenail tissue as a biomarker. The present study was carried out with school children in the age range of 7-12 years, who were living in an industrial area in the petrochemical and gas area (PGA) of the Central District of Asaluyeh County and in an urban area (UA) located in the Kaki District. A total of 270 boys and girls were recruited in January to April 2019. The ICP-MS was used for determination of the studied MLs. A multi-linear regression model was constructed to assess the effect of residence area on toenail ML levels. A significantly higher level of ΣMLs in toenail from the PGA was observed compared to the level in the UA (8.839 vs. 7.081 μg/g, β = -0.169 and p < 0.05). However, all of the 15 MLs studied were detected in the toenail samples from both study sites. Significant differences for the mean Cr (β = -0.563), Fe (β = -0.968), Mn (β = -0.501), Ni (β = -0.306), and Pb (β = -0.377) levels were found between toenail samples from the study areas (p < 0.05), with higher levels in the PGA. The results of this study suggest that children in industrial area are prone to a greater risk for ML exposures compared with those living in a non-industrial urban area.

Exploring social determinants of municipal solid waste management: survey processing with fuzzy logic and self-organized maps

In the present study, the establishment of decision-making criteria and a novel and robust interdisciplinary approach for systematically characterizing effects of uncertainties in social determinants of municipal solid waste management using an important fuzzy logic methodology is demonstrated. The primary goal is to highlight the social benefits of this waste management option such as job creation, hygiene and health protection, and working safety as well as to indicate certain side effects occurring during waste processing (odor and leachate production, social trust). The current research is based on a social survey in an agro-industrial region, Thessaly, Greece, and indicates a set of diversified key factors that are related to public acceptance of municipal waste management schemes. These features are input to Kohonen Self-Organized Maps (a special type of Artificial Neural Networks) for clustering residents according to their social perception and attitudes in terms of solid waste collecting and recycling. Both analyses highlight the environmental concern, social perception, hygiene and health, economic status, and lifestyle as the primary social determinants in affecting the public attitudes towards recycling. In both cases, these soft computing techniques seem to outperform the classical statistical and logical regression methodologies and become very promising in accurately predicting waste management practice and possibly other environmental behaviors.