Evaluation of potential toxic heavy metal contamination in soil, fly ash, vegetables and grain crops along with associated ecological and health risk assessment of nearby inhabitants of a thermal power station in Jharkhand (India).

The present study aimed to evaluate the potential toxic heavy metal (PTHM) concentrations and associated ecological and health risk of nearby inhabitants (child and adult populations) of the Bokaro thermal power station (BTPS), Jharkhand, India. The fly ash containing PTHMs is released from the thermal power plant and contaminates the soil of the nearby agricultural area, roadside area and residential area. Furthermore, PTHMs cause ecological risk as well as carcinogenic health effects in adults. Inhabitants are exposed to PTHMs through exposure pathways (for instance, direct ingestion, dermal contact and air inhalation), and PTHMs were examined in this study by calculating the estimated daily intake (EDI). The contamination factor (Cf) of Cd exceeded the threshold value throughout the study area, whereas the Cf of Hg was observed higher only in roadside soil (RSS). The geoaccumulation index (Igeo) of Cd was higher in the top soil of agricultural soil (AS) (7.39) and RSS (9.38). The level of PTHMs was monitored slightly higher in the topsoil of RSS. The ecological risk index (Eri) was detected to be the highest in RSS (1628.9). The permissible limit of Cd (0.05 mg kg-1) and Pb (0.1 mg kg-1) for all sampled vegetables and grain crops was surpassed. The transfer factor (TF) of the studied vegetables and grain crops was in the order of Cd > Cr > Pb > Hg > As. The results of the present study concluded that the hazard quotient (HQ) values for all PTHMs analysed in soil throughout the study area were < 1, which indicates that the child population was within the safe limit. However, the adult population was at a high risk of non-carcinogenic health effects due to increased permissible limits of Cd, Pb and Cr in vegetables and grain crops whereas the target carcinogenic risk (TCR) of Pb and Cr was higher and indicated that there may be a health risk of cancer in adults.

Current understanding on artificial intelligence and machine learning in orthopaedics - A scoping review.

Background: Artificial Intelligence (AI) has improved the way of looking at technological challenges. Today, we can afford to see many of the problems as just an input-output system rather than solving from the first principles. The field of Orthopaedics is not spared from this rapidly expanding technology. The recent surge in the use of AI can be attributed mainly to advancements in deep learning methodologies and computing resources. This review was conducted to draw an outline on the role of AI in orthopaedics. Methods: We developed a search strategy and looked for articles on PubMed, Scopus, and EMBASE. A total of 40 articles were selected for this study, from tools for medical aid like imaging solutions, implant management, and robotic surgery to understanding scientific questions. Results: A total of 40 studies have been included in this review. The role of AI in the various subspecialties such as arthroplasty, trauma, orthopaedic oncology, foot and ankle etc. have been discussed in detail. Conclusion: AI has touched most of the aspects of Orthopaedics. The increase in technological literacy, data management plans, and hardware systems, amalgamated with the access to hand-held devices like mobiles, and electronic pads, augur well for the exciting times ahead in this field. We have discussed various technological breakthroughs in AI that have been able to perform in Orthopaedics, and also the limitations and the problem with the black-box approach of modern AI algorithms. We advocate for better interpretable algorithms which can help both the patients and surgeons alike.

Groundwater Suitability Evaluation Using Entropy Weightage Quality Index (EWQI) Model and Human Health Cancer Risk Assessment of Heavy Metal in Eastern India.

This study evaluated the groundwater using the Entropy Weightage Quality Index model (EWQI). Eighteen samples were taken from the different wellbores during premonsoon seasons in 2021. The present study is aimed at developing a comprehensive approach for groundwater quality assessment and associated health risk along with the cancer risk due to the presence of heavy metals. The water quality of Ranchi city was found to be better except in the western zone. Principal component analysis (PCA) revealed that arsenic (As) was the most influencing element that deteriorated the potability of water which supports our study. The study looked at cancer and noncancer health hazards connected with heavy metal music. The value of hazardous quotient (HQ) was observed to be relatively higher in As (HQ > 1) and Ni, followed by Mn > Fe > Zn > Cu. Also, the children were at higher risk than adults. The cancer risk associated with arsenic was investigated and found that the northern part and southeast-west (lapung block) of the study are at higher risk. Prolonged ingestion of As causes diseases like arsenicosis that leads to enhanced chances of cancer risk. This research provides an immense research database to assess the potability of drinking water in a similar city like Ranchi.

Vehicular traffic noise modelling of urban area-a contouring and artificial neural network based approach.

Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causes health risks among the urban populations. In this study, we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and find the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles, and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, speed, traffic flow, road gradient, pavement, road side carriageway distance factors were taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59%, two wheelers and different vehicle specifications with varying speeds also affect driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and the highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicate high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 and .029 in training and 0.458 and 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ± 0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

In situ phytoremediation of heavy metal-contaminated soil and groundwater: a green inventive approach.

The heavy metal contamination of soil and groundwater is a serious threat to environment worldwide. The survival of human being primarily relies upon soil and groundwater sources. Therefore, the remediation of heavy metal-contaminated soil and groundwater is a matter of utmost concern. Heavy metals are non-degradable and persist in the environment and subsequently contaminate the food chain. Heavy metal pollution puts a serious impact on human health and it adversely affects our physical body. Although, numerous in situ conventional technologies have been utilized for the treatment purpose, but most of the techniques have some limitations such as high cost, deterioration of soil properties, disturbances to soil native flora and fauna and intensive labour. Despite that, in situ phytoremediation is a cost-effective, eco-friendly, solar-driven and novel approach with significant public acceptance. The past research reflects rare discussion addressing both (heavy metal in situ phytoremediation of soil and groundwater) in one platform. The present review article covers both the concepts of in situ phytoremediation of soil and groundwater with major emphasis on health risks of heavy metals, enhanced integrated approaches of in situ phytoremediation, mechanisms of in situ phytoremediation along with effective hyperaccumulator plants for heavy metals remediation, challenges and future prospects.

Assessment of Mine Water Quality Using Heavy Metal Pollution Index in a Coal Mining Area of Damodar River Basin, India.

A total no. of 16 mine water (underground and opencast coal mine pump discharges) samples were collected from East Bokaro coalfield during pre-monsoon, monsoon and post-monsoon seasons. The concentrations of Fe, Mn, Cu, Pb, Zn, Ni, As, Se, Al, Cd and Cr were determined using inductively coupled plasma mass spectrometry for the assessment of spatio-temporal variations, source apportionment and heavy metal pollution indexing. The results demonstrated that concentrations of the metals showed significant seasonality and most variables exhibited higher levels in the pre-monsoon season. The principle component analysis for ionic source identification was synthesized into three factors with eigen values cut off at greater than unity and explained about 64.8% of the total variance. The extracted factors seemed to be associated to the geogenic, extensive mining and allied transportation sources of the elements. The heavy metal pollution index (HPI) of the mine water calculated for the individual locations varied from 7.1 to 49.5. Most of the locations fall under low to medium classes of HPI except few locations which are under the influence of surface mining and associated transportation.

Estimation of Heavy Metal Contamination in Groundwater and Development of a Heavy Metal Pollution Index by Using GIS Technique.

Heavy metal (Al, As, Ba, Cr, Cu, Fe, Mn, Ni, Se and Zn) concentration in sixty-six groundwater samples of the West Bokaro coalfield were analyzed using inductively coupled plasma-mass spectroscopy for determination of seasonal fluctuation, source apportionment and heavy metal pollution index (HPI). Metal concentrations were found higher in the pre-monsoon season as compared to the post-monsoon season. Geographic information system (GIS) tool was attributed to study the metals risk in groundwater of the West Bokaro coalfield. The results show that 94 % of water samples were found as low class and 6 % of water samples were in medium class in the post-monsoon season. However, 79 % of water samples were found in low class, 18 % in medium class and 3 % in high class in the pre-monsoon season. The HPI values were below the critical pollution index value of 100. The concentrations of Al, Fe, Mn, and Ni are exceeding the desirable limits in many groundwater samples in both seasons.

Evaluation of Surface Water Quality by Using GIS and a Heavy Metal Pollution Index (HPI) Model in a Coal Mining Area, India.

Twenty eight surface water samples were collected from fourteen sites of the West Bokaro coalfield, India. The concentration of Mn, Cu, Zn, Ni, As, Se, Al, Cr, Ba, and Fe were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) for determination of seasonal fluctuations and a heavy metal pollution index (HPI). The HPI values were below the critical pollution index value of 100. Metal concentrations were higher in the pre-monsoon season as compared to the post-monsoon season. The Zn, Ni, Mn, As, Se, Al, Ba, Cu, and Cr concentrations did not exceed the desirable limits for drinking water in either season. However, at many sites, concentrations of Fe were above the desirable limit of the WHO (2006) and Indian drinking water standard (BIS 2003) in both seasons. The water that contained higher concentrations of Fe would require treatment before domestic use.