An integrated overview of metals contamination, source-specific risks investigation in coal mining vicinity soils.

Heavy metals in soil are harmful to natural biodiversity and human health, and it is difficult to estimate the effects accurately. To reduce pollution and manage risk in coal-mining regions, it is essential to evaluate risks for heavy metals in soil. The present study reviews the levels of 21 metals (Nb, Zr, Ag, Ni, Na, K, Mg, Rb, Zn, Ca, Sr, As, Cr, Fe, Pb, Cd, Co, Hg, Cu, Mn and Ti) in soils around Barapukuria coal-mining vicinity, Bangladesh which were reported in literature. An integrated approach for risk assessments with the positive matrix factorization (PMF) model, source-oriented ecological and health hazards were applied for the study. The contents of Rb, Ca, Zn, Pb, As, Ti, Mn, Co, Ag, Zr, and Nb were 1.63, 1.10, 1.97, 14.12, 1.20, 3.13, 1.22, 3.05, 3.85, 5.48, and 7.21 times greater than shale value. About 37%, 67%, 12%, and 85% of sampling sites posed higher risks according to the modified contamination factor, Nemerow pollution index, Nemerow integrated risk index, and mean effect range median quotient, respectively. Five probable metal sources were computed, including industrial activities to coal mining (17%), agricultural activities (33%), atmospheric deposition (19%), traffic emission (16%), and natural sources (15%). Modified Nemerow integrated risk index reported that agricultural activities, industrial coal mining activities, and atmospheric deposition showed moderate risk. Health hazards revealed that cancer risk values computed by the PMF-HHR model with identified sources were higher than the standard value (1.0E-04) for children, adult male, and female. Agricultural activities showed higher cancer risks to adult male (39%) and children (32%) whereas traffic emission contributed to female (25%). These findings highlight the ecological and health issues connected to potential sources of metal contamination and provide useful information to policymakers on how to reduce such risks.

Heavy metals contamination, receptor model-based sources identification, sources-specific ecological and health risks in road dust of a highly developed city.

The present study quantified Ni, Cu, Cr, Pb, Cd, As, Zn, and Fe levels in road dust collected from a variety of sites in Tangail, Bangladesh. The goal of this study was to use a matrix factorization model to identify the specific origin of these components and to evaluate the ecological and health hazards associated with each potential origin. The inductively coupled plasma mass spectrometry was used to determine the concentrations of Cu, Ni, Cr, Pb, As, Zn, Cd, and Fe. The average concentrations of these elements were found to be 30.77 ± 8.80, 25.17 ± 6.78, 39.49 ± 12.53, 28.74 ± 7.84, 1.90 ± 0.79, 158.30 ± 28.25, 2.42 ± 0.69, and 18,185.53 ± 4215.61 mg/kg, respectively. Compared to the top continental crust, the mean values of Cu, Pb, Zn, and Cd were 1.09, 1.69, 2.36, and 26.88 times higher, respectively. According to the Nemerow integrated pollution index (NIPI), pollution load index (PLI), Nemerow integrated risk index (NIRI), and potential ecological risk (PER), 84%, 42%, 30%, and 16% of sampling areas, respectively, which possessed severe contamination. PMF model revealed that Cu (43%), Fe (69.3%), and Cd (69.2%) were mainly released from mixed sources, natural sources, and traffic emission, respectively. Traffic emission posed high and moderate risks for modified NIRI and potential ecological risks. The calculated PMF model-based health hazards indicated that the cancer risk value for traffic emission, natural, and mixed sources had been greater than (1.0E-04), indicating probable cancer risks and that traffic emission posed 38% risk to adult males where 37% for both adult females and children.

A review on toxic metal pollution and source-oriented risk apportionment in road dust of a highly polluted megacity in Bangladesh.

Heavy metal enrichment in road dust has resulted from intensive anthropogenic activity, particularly urbanization, industrial activities and traffic emission, posing a hazard to urban ecosystems and human health. To promote optimal road dust management in urban environments, it is necessary to assess the possible ecological and health impact of toxic elements in road dust. In a heavily populated megacity like Dhaka, Bangladesh, large-scale risk assessments of contamination in road dust with heavy metals are limited. The present study aims at presenting a concentration of twenty-five metals in road dust (Na, K, Cs, Rb, Mg, Ca, Sr, Ba, Al, Zn, Cd, Pb, As, Sb, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zr and W) in Dhaka megacity. We used a critical source-based positive matrix factorization model, source-oriented potential ecological risks and health risks. Out of the studied metals, Na, Ca, Zn, Cd, Cu, Zr and W exceeded the shale value. About 73%, 48%, 29% and 32% of sampling sites showed a higher level of pollution based on PLI, NIPI, PER and NIRI, respectively. PMF model identified that Cd (85.3%), Cr (62.4%), Ni (58.2%), Zn (81.8%) and Mn (65.9%) in road dust were primarily attributed to traffic emission, fuel combustion, metal processing, transport sources and natural sources, respectively. Fuel combustion and metal processing posed considerable and high risks based on modified potential ecological risk and NIRI. Based on health hazards, traffic emission posed a high cancer risk in adult males (29%), whereas transport sources contributed to females (21%) and children (23%).

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest.

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

An assessment of the potential environmental effects of bridge construction in Boga, Patuakhali, Bangladesh.

Bangladesh Road Transport Authority announced a plan to build a bridge over the Lohalia River in Boga, Patuakhali, which will significantly modify the entire communication system and lead to significant economic empowerment in the southeastern part of Bangladesh. This study was designed to help decision-makers through the identification and evaluation of all potential social and environmental consequences of this proposed project using an integrated methodology combining GIS mapping, environmental impact value assessment, and critical evaluation of the environmental impact through the Leopold matrix. The necessary information required for this study has been collected through questionnaire surveys, participatory community risk assessments (CRA), focused group discussions, key informant interviews, and reviews of previously published documents. According to this study, the proposed Boga Bridge construction will have some adverse environmental consequences including agricultural land and productivity loss, the decline of ecosystem health, extinction of endangered species, deterioration of water, air and soil quality, sedimentation and changes in river flow. Despite these adverse impacts this project will improve the life and livelihood of the coastal community and foster economic growth and industrialization over the long run through easily assessable road transportation. Additionally, the estimated overall environmental impact value (-2) and Leopold matrix's impact value (-1.51) revealed that this project has low adverse effects on the surrounding environment. Moreover, the majority of the environmental consequences were found to be transient because they were only limited to the construction phase which makes it simple to control with the proper implementation of appropriate mitigation strategies. Therefore, this study furnished some effective mitigation strategies incorporating mitigation hierarchy principals to avoid and minimize adverse impacts as well as enhance the positive impacts of this project. Finally, this study recommends constructing the proposed Boga Bridge after ensuring rigorous implementation and monitoring of all impact mitigation strategies proposed in this study.

Comprehensive evaluation of chemical properties and toxic metals in the surface water of Louhajang River, Bangladesh.

Louhajang River, Bangladesh, which crosses Tangail as a densely industrialized and urbanized city, supplies water for different purposes. This study reports the levels of pH, electrical conductivity (EC), and some toxic heavy metals in 40 water samples collected during the summer and winter seasons from Louhajang River. The winter season reported higher levels of the examined parameters than the summer season with significant variation (p < 0.05) for all parameters, with the exception of Cd. The metal contents were assessed against local and international standards for drinking, irrigation, and aquatic life purposes where different trends were observed. The heavy metal evaluation index and the ecological risk index reported low to moderate risks. The spatial distribution of metal contents assigned hot spots in some sites along the riverbed. The health risk assessment for three population categories, i.e., adult male, adult female, and children, was examined. Cr and Cd recorded hazard index > 1 in all cases, indicating possible non-cancer risk. The total carcinogenic risk values during both seasons were > 1.0 × 10-6, indicating possible cancer risk. The adopted collection of different approaches (comparison against standard levels of toxicants, statistical analysis, spatial distribution, and health risk assessment) successfully demonstrates a whole picture of the environmental status of Louhajang River, Bangladesh.

The effect of parental history of myopia on eye size of pre-school children: a pilot study.

PURPOSE: To evaluate parental history of myopia as a predictor of refractive error and eye size in Chinese pre-school children. METHODS: A total of 514 pre-school children (aged 2.3--6.4 years) were examined. Parental history of myopia, amount of near work performed, refractive status and ocular biometry were recorded. RESULTS: There was no significant difference in spherical equivalent refraction (SER) among children with no myopic parents (mean+0.94+/-0.05 D), one myopic parent (mean+0.77+- 0.07 D) and two myopic parents (mean+0.79+/- 0.12 D) (p=0.102) after controlling for age and amount of near work. Further, children with more myopic parents did not have longer eyeballs (p=0.335). CONCLUSIONS: In this study in Chinese pre-school children, parental history of myopia was not found to be associated with a myopic refractive error or increased eyeball length. Further studies with larger sample sizes would help to confirm these results.