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Tahir I, Alkheraije KA. A review of important heavy metals toxicity with special emphasis on nephrotoxicity and its management in cattle. Front Vet Sci 2023; 10:1149720. [PMID: 37065256 PMCID: PMC10090567 DOI: 10.3389/fvets.2023.1149720] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 02/21/2023] [Indexed: 03/30/2023] Open
Abstract
Toxicity with heavy metals has proven to be a significant hazard with several health problems linked to it. Heavy metals bioaccumulate in living organisms, pollute the food chain, and possibly threaten the health of animals. Many industries, fertilizers, traffic, automobile, paint, groundwater, and animal feed are sources of contamination of heavy metals. Few metals, such as aluminum (Al), may be eliminated by the elimination processes, but other metals like lead (Pb), arsenic (As), and cadmium (Ca) accumulate in the body and food chain, leading to chronic toxicity in animals. Even if these metals have no biological purpose, their toxic effects are still present in some form that is damaging to the animal body and its appropriate functioning. Cadmium (Cd) and Pb have negative impacts on a number of physiological and biochemical processes when exposed to sub-lethal doses. The nephrotoxic effects of Pb, As, and Cd are well known, and high amounts of naturally occurring environmental metals as well as occupational populations with high exposures have an adverse relationship between kidney damage and toxic metal exposure. Metal toxicity is determined by the absorbed dosage, the route of exposure, and the duration of exposure, whether acute or chronic. This can lead to numerous disorders and can also result in excessive damage due to oxidative stress generated by free radical production. Heavy metals concentration can be decreased through various procedures including bioremediation, pyrolysis, phytoremediation, rhizofiltration, biochar, and thermal process. This review discusses few heavy metals, their toxicity mechanisms, and their health impacts on cattle with special emphasis on the kidneys.
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Affiliation(s)
- Ifrah Tahir
- Department of Parasitology, University of Agriculture, Faisalabad, Pakistan
| | - Khalid Ali Alkheraije
- Department of Veterinary Medicine, College of Agriculture and Veterinary Medicine, Qassim University, Buraidah, Saudi Arabia
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Quiroga Gutierrez AC, Lindegger DJ, Taji Heravi A, Stojanov T, Sykora M, Elayan S, Mooney SJ, Naslund JA, Fadda M, Gruebner O. Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1473. [PMID: 36674225 PMCID: PMC9861515 DOI: 10.3390/ijerph20021473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/31/2022] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.
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Affiliation(s)
| | | | - Ala Taji Heravi
- CLEAR Methods Center, Department of Clinical Research, Division of Clinical Epidemiology, University Hospital Basel and University of Basel, 4031 Basel, Switzerland
| | - Thomas Stojanov
- Department of Orthopaedic Surgery and Traumatology, University Hospital of Basel, 4031 Basel, Switzerland
| | - Martin Sykora
- School of Business and Economics, Centre for Information Management, Loughborough University, Loughborough LE11 3TU, UK
| | - Suzanne Elayan
- School of Business and Economics, Centre for Information Management, Loughborough University, Loughborough LE11 3TU, UK
| | - Stephen J. Mooney
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - John A. Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Marta Fadda
- Institute of Public Health, Università Della Svizzera Italiana, 6900 Lugano, Switzerland
| | - Oliver Gruebner
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, 8001 Zurich, Switzerland
- Department of Geography, University of Zurich, 8057 Zurich, Switzerland
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Dutta D, Pal SK. Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 195:223. [PMID: 36544059 PMCID: PMC9771789 DOI: 10.1007/s10661-022-10761-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 11/12/2022] [Indexed: 06/17/2023]
Abstract
The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM10 and PM2.5). However, the effect of the lockdown is most prominent on PM2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM2.5 and PM10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM2.5 and PM10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.
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Affiliation(s)
- Debashree Dutta
- Center for Soft Computing Research, Indian Statistical Institute, Kolkata, 700108 India
| | - Sankar K. Pal
- Center for Soft Computing Research, Indian Statistical Institute, Kolkata, 700108 India
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Al-Masnay YA, Al-Areeq NM, Ullah K, Al-Aizari AR, Rahman M, Wang C, Zhang J, Liu X. Estimate earth fissure hazard based on machine learning in the Qa' Jahran Basin, Yemen. Sci Rep 2022; 12:21936. [PMID: 36536056 PMCID: PMC9763334 DOI: 10.1038/s41598-022-26526-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa' Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures.
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Affiliation(s)
- Yousef A. Al-Masnay
- grid.27446.330000 0004 1789 9163Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024 People’s Republic of China ,grid.216417.70000 0001 0379 7164Department of Surveying and Remote Sensing, School of Geosciences and Info-Physics, Central South University, Changsha, 410083 China
| | - Nabil M. Al-Areeq
- grid.444928.70000 0000 9908 6529Department of Geology and Environment, Thamar University, Thamar, Yemen
| | - Kashif Ullah
- grid.503241.10000 0004 1760 9015Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, People’s Republic of China
| | - Ali R. Al-Aizari
- grid.33763.320000 0004 1761 2484Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, 300072 China
| | - Mahfuzur Rahman
- grid.443015.70000 0001 2222 8047Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka, 1230 Bangladesh
| | - Changcheng Wang
- grid.216417.70000 0001 0379 7164Department of Surveying and Remote Sensing, School of Geosciences and Info-Physics, Central South University, Changsha, 410083 China
| | - Jiquan Zhang
- grid.27446.330000 0004 1789 9163Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024 People’s Republic of China ,grid.27446.330000 0004 1789 9163Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun, 130024 People’s Republic of China ,grid.27446.330000 0004 1789 9163State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, 130024 People’s Republic of China
| | - Xingpeng Liu
- grid.27446.330000 0004 1789 9163Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024 People’s Republic of China ,grid.27446.330000 0004 1789 9163Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun, 130024 People’s Republic of China ,grid.27446.330000 0004 1789 9163State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, 130024 People’s Republic of China
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Highway Proneness Appraisal to Landslides along Taiping to Ipoh Segment Malaysia, Using MCDM and GIS Techniques. SUSTAINABILITY 2022. [DOI: 10.3390/su14159096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Landslides are geological hazards that claim lives and affect socio-economic growth. Despite increased slope failure, some constructions, such as road constructions, are still being performed without proper investigation of the susceptibility of slope mass movement. This study researches the susceptibility of landslides in a study area encompassing a major highway that extends from Taiping to Ipoh, Malaysia. After a comprehensive literature review, 10 landslide conditioning factors were considered for this study. As novel research in this study area, multi-criteria decision-making (MCDM) models such as AHP and fuzzy AHP were used to rank the conditioning factors before generating the final landslide susceptibility mapping using Geographical Information System (GIS) software. The landslide susceptibility map has five classes ranging from very low (9.20%) and (32.97%), low (18.09%) and (25.60%), moderate (24.46%) and (21.36%), high (27.57%) and (13.26%), to very high (20.68%) and (6.81%) susceptibility for the FAHP and AHP models, respectively. It was recorded that the area is mainly covered with moderate to very high landslide risk, which requires proper intervention, especially for subsequent construction or renovation processes. The highway was overlayed on the susceptibility map, which concludes that the highway was constructed on a terrain susceptible to slope instability. Therefore, decision-makers should consider further investigation and landslide susceptibility mapping before construction.
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Air quality prediction models based on meteorological factors and real-time data of industrial waste gas. Sci Rep 2022; 12:9253. [PMID: 35661145 PMCID: PMC9166716 DOI: 10.1038/s41598-022-13579-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/16/2022] [Indexed: 11/08/2022] Open
Abstract
With the rapid economic growth, air quality continues to decline. High-intensity pollution emissions and unfavorable weather conditions are the key factors for the formation and development of air heavy pollution processes. Given that research into air quality prediction generally ignore pollutant emission information, in this paper, the random forest supervised learning algorithm is used to construct an air quality prediction model for Zhangdian District with industrial waste gas daily emissions and meteorological factors as variables. The training data include the air quality index (AQI) values, meteorological factors and industrial waste gas daily emission of Zhangdian District from 1st January 2017 to 30th November 2019. The data from 1st to 31th December 2019 is used as the test set to assess the model. The performance of the model is analysed and compared with the backpropagation (BP) neural network, decision tree, and least squares support vector machine (LSSVM) function, which has better overall prediction performance with an RMSE of 22.91 and an MAE of 15.80. Based on meteorological forecasts and expected air quality, a daily emission limit for industrial waste gas can be obtained using model inversion. From 1st to 31th December 2019, if the industrial waste gas daily emission in this area were decreased from 6048.5 million cubic meters of waste gas to 5687.5 million cubic meters, and the daily air quality would be maintained at a good level. This paper deeply explores the dynamic relationship between waste gas daily emissions of industrial enterprises, meteorological factors, and air quality. The meteorological conditions are fully utilized to dynamically adjust the exhaust gas emissions of key polluting enterprises. It not only ensures that the regional air quality is in good condition, but also promotes the in-depth optimization of the procedures of regional industrial enterprises, and reduces the conflict between environmental protection and economic development.
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Vehicular Traffic in Urban Areas: Health Burden and Influence of Sustainable Urban Planning and Mobility. ATMOSPHERE 2022. [DOI: 10.3390/atmos13040598] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Vehicular traffic is one of the major sources of air pollution in European cities. This work aims to understand which characteristics of the urban environment could influence mobility-related air pollution, quantify the health impacts of exposure to traffic-derived PM2.5 and NO2 concentrations, and assess the potential health benefits expected from traffic interventions. The health benefits modeled are intended to provide a set of comparable data to support decision-makers and encourage informed decision-making to design healthier cities. Targeting a large geographical coverage, 12 European cities from 9 countries were comparatively assessed in terms of mean daily traffic volume/area, the number of public transport stops/area, and the percentage of green and outdoor leisure areas, among other urban indicators. This was implemented using an open-source data mining tool, which was seen as a useful engine to identify potential strategies to improve air quality. The comparison of urban indicators in the selected cities evidenced two trends: (a) cities with the most heterogeneous distribution of public transport stops, as an indicator of poor accessibility, are also those with the lowest proportion of km dedicated to cycleways and footways, highlighting the need in these cities for more sustainable mobility management; and (b) the percentage of green and outdoor leisure areas may influence the share of journeys by bicycle, pointing out that promoting the perception of green routes is relevant to enhance the potential of active transport modes. Socioeconomic factors can be key determinants of the urban indicators and would need further consideration. For the health impact assessment (HIA), two baseline scenarios were evaluated and compared. One is based on mean annual traffic contributions to PM2.5 concentrations in each target city (ranging between 1.9 and 13 µg/m3), obtained from the literature, and the second is grounded on mean annual NO2 concentrations at all available traffic and urban background stations within each city (17.2–83.5 µg/m3), obtained from the European Environment Agency database. The intervention scenarios modeled were designed based on traffic mitigation strategies in the literature, and set to ranges of 6–50% in traffic-derived PM2.5 concentrations and of 4–12.5% in NO2 concentrations. These scenarios could result in only a 1.7% (0.6–4%) reduction in premature mortality due to exposure to traffic-derived PM2.5, and 1.0% (0.4–2%) due to exposure to NO2, as the mean for all the cities. This suggests that more ambitious pollution abatement strategies should be targeted.
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