Health and Safety Assessment in Lakhra Coal Mines and Its Mitigation Measures

oai:ojs.localhost:article/200The coal mine excavation, transportation and coal cutting process are involved in hazards and risks that can result in fatalities, injuries and diseases, if these are not properly managed. This study has been undertaken for assessment of the safety and health issues amongst the mines workers. Convenience sampling technique was exercised upon 97 mine workers and interviewed with the help of set questionnaire. Personnel protection to workplace environment was monitored by using physical observation and scientific analysis. All parameters were measured against national and international protocols pertaining to labor law at coal mines. It has been determined that very high risk was persisting while mine excavation, coal cutting and transportation processes. Previous record of last five years was suggesting that 04 deaths happened due to roof fall, 03 fatalities occurred through suffocation by inhaling toxic gases, one causality happened via rope haulage pulley, and also one death due to stone fall down from mine shaft. 121 workers injured in different kinds of accidents within five years. It has been learnt from in-depth analysis that maximum of health risk and subsequent health damages are triggering due to lack of awareness, non-compliance of labor as well as mines laws. Thus, it is recommended that government should not allow coal mining contractors and companies, those which are failing in compliance with the suggested standards

Nitrite Accumulation at Low Ammonia Concentrations in Wastewater Treatment Plants

At higher ammonium concentrations, nitritation can be easily attained by picking out the inhibitor. In low-concentrated reactors, nitrite accumulation without using any chemical inhibitor is a challenging process. In this study, two continuous stirred-tank reactors (CSTR) with biofilm and without biofilm were operated with total ammonium nitrogen feed concentrations of ~50 mg/L and ~30 mg/L and effluent concentrations of ~1 mg/L. A CSTR without biofilm was operated in three phases. In phase 1, a substrate-shock concentration of 1 to 2000 mg total ammonium nitrogen (TAN)/L was tested. It was found that the shock concentration was not successful in long-term operations because nitrite-oxidizing bacteria (NOB) recovered rapidly. In phases 2 and 3, the sludge-treatment method was applied, and a high nitrite accumulation efficiency was achieved (~98%). In a CSTR with biofilm, the free ammonia shock concentration was ~91.7 mg/L, and a nitrite accumulation efficiency of ~90% was achieved

Experimental Investigation of Methane Generation in the Presence of Surface and Un-Surface Nanoparticles of Iron Oxide

The exploitation and harnessing of renewable energies are becoming increasingly important throughout the world. This study presents a method of methane (CH4) generation using biological disintegration of food waste (FW) by anaerobic digestion (AD). The CH4 production was enhanced by the addition of three different types of iron oxide (Fe3O4) nanoparticles (NPs) (Cetyletrimethlebromide (CTAB), urea-capped Fe3O4 NPs and Fe3O4 NPs without capping). The bio generation of CH4 and biodegradation of volatile solids (VS) were carried out in an AD treatment at mesophilic conditions (35–37 °C) for more than 50 days in batch mode. The concentration of all three types of NPs was kept constant at 75 mg/L. It was noticed that urea-capped NPs produced the maximum CH4 (5.386 L), followed by Fe3O4 NPs (5.212 L). Methane production in the control bioreactor was 2.143 L. The experimental results of CH4 generation (a dependent variable) were analyzed against the concentrations of NPs used (as independent variables) in multiple regression analysis (MRA). The overall model for the experiments resulted in R2 and R-adjusted values of 0.995 and 0.993, respectively

Arsenic removal through bio sand filter using different bio-adsorbents

Arsenic is one of the most harmful pollutants in groundwater. In this paper, the Nepali bio sand filter (BSF) was modified with different bio-adsorbents, and proved to be an efficient method for arsenic removal from groundwater. Three different bio-adsorbents were used to modify the Nepali BSF. Iron nails and biochar BSF, ~96% and ~93% arsenic removal was achieved, within the range of WHO guidelines. In iron nails, BSF and biochar BSF ~15 dm3∙h–1 arsenic content water was treated. In the other two BSFs, rice-husk and banana peel were used, the arsenic removal efficiency was ~83% of both BSFs. Furthermore, the efficiency of rice-husk and banana peel BSFs can be increased by increasing the surface area of the adsorbent or by reducing the flow rate

Detailed evaluation of physicochemical properties and microbial activities of Hanna Lake and Spin Karez

Access to clean drinking water is a major issue in many regions of the world, particularly in areas where groundwater is scarce. This study aims at determining the water quality of two lakes. Some parameters in this investigation were found to be above the WHO limit, while most were within the limit. For instance, the average value of electrical conductivity in Hanna Lake was 537.4 μS/cm, while it was 758.9 μS/cm in Spin Karez, which was above the WHO limits (>500 μS/cm). Turbidity in Hanna Lake was 4.17 nephelometric turbidity units (NTU), within the WHO limits (<5 NTU), while in Spin Karez, it was 9.5 NTU above the WHO limits, and dissolved oxygen concentration average values were 9.06 mg/l in Hanna Lake and 8.86 mg/l in Spin Karez, above the permitted limits provided by the WHO (6.5–8 mg/l). The study also found that both lakes had high concentrations of microbial colonies, with 65 CFU/100 ml in Hanna Lake and 56 CFU/100 ml in Spin Karez. Based on these findings, an efficient water treatment technique should be adopted to remove these highly concentrated parameters in both lakes for purified water and future water demands. HIGHLIGHTS Detailed evaluation of all parameters.; Clean drinking water.; Microbial activities.; Climate change.; Lakes.

Cascade Reservoirs: An Exploration of Spatial Runoff Storage Sites for Water Harvesting and Mitigation of Climate Change Impacts, Using an Integrated Approach of GIS and Hydrological Modeling

Torrents play an essential role in water resources through rainfall in arid to semi-arid mountainous regions, serving large populations worldwide, and are also crucial in maintaining the downstream environment. The natural flows (floods, ephemeral flows) in arid hill regions result in potential hydrological fluctuations caused by climate change. However, the feasibility of eventual storage in remote hilly catchments would force a more sudden change. The current study was conducted in the lower part of the Khirthar National Range in the Sindh province of Pakistan, with the aim to explore spatial runoff storage sites for sustainable development to mitigate the impacts of climate change in arid areas. In total, 83 years of precipitation data were used to estimate water availability, along with satellite imagery for LULC pre- and post-monsoon conditions, delineation of watersheds, and identification of potential runoff storage locations and return periods, using Remote Sensing (RS)/Geographical Information System (GIS) 10.5.1, HEC-HMS 3.1, and Origin Pro 9.0 for statistical approaches. The model delineated two potential watersheds: Goth Sumar, covering an area of 61.0456 km2, wherein ten cascading reservoirs were identified, and Goth Baro, covering an area of 14,236 km2, wherein two cascading reservoirs were identified. Different storage capacities were determined for the cascade-type reservoirs. The maximum live volumetric potential storage of the reservoirs varies from 0.25 to 1.32 million cubic meters (MCM) in the villages of Baro and Sumar. The return periods have been estimated at 5, 10, 20, 25, 50, and 75 years, corresponding to 12.35, 16.47, 21.43, 21.72, 25.21, and 40.53 MCM for Goth Sumar, while Goth Baro’s storage capacity has been estimated at 2.88, 3.84, 5.00, 5.06, 5.88, and 9.45 MCM, respectively. All results obtained were authenticated using accuracy assessment, validation, and sensitivity analysis. The proposed potential storage sites were recommended for a planning period of five years. The live storage capacity of the identified cascade reservoirs can be improved by raising the marginal banks and developing the spillways to control inlet and outlet flow in order to maintain internal pressure on the reservoir banks. The stored water can be used for climate-friendly agricultural activities to increase crop production and productivity. The proposed study area has extensive experience with flood irrigation systems and rainwater harvesting to sustain agriculture due to rainfall being the only water resource (WR) in the region. However, the study area has enormous potential for surface runoff WRs, especially during the rainy season (monsoon); the current 2022 monsoon is showing flooding. The modeling approaches of Remote Sensing, GIS, and HEC-HMS play an important role in delineating watershed areas, developing hydrographs, and simulating water availability for different return periods by minimizing cost and time

Machine learning, Water Quality Index, and GIS-based analysis of groundwater quality

Water is essential for life, as it supports bodily functions, nourishes crops, and maintains ecosystems. Drinking water is crucial for maintaining good health and can also contribute to economic development by reducing healthcare costs and improving productivity. In this study, we employed five different machine learning algorithms – logistic regression (LR), decision tree classifier (DTC), extreme gradient boosting (XGB), random forest (RF), and K-nearest neighbors (KNN) – to analyze the dataset, and their prediction performance were evaluated using four metrics: accuracy, precision, recall, and F1 score. Physiochemical parameters of 30 groundwater samples were analyzed to determine the Water Quality Index (WQI) of Pano Aqil city, Pakistan. The samples were categorized into the following four classes based on their WQI values: excellent water, good water, poor water, and unfit for drinking. The WQI scores showed that only 43.33% of the samples were deemed acceptable for drinking, indicating that the majority (56.67%) were unsuitable. The findings suggest that the DTC and XGB algorithms outperform all other algorithms, achieving overall accuracies of 100% each. In contrast, RF, KNN, and LR exhibit overall accuracies of 88, 75, and 50%, respectively. Researchers seeking to enhance water quality using machine learning can benefit from the models described in this study for water quality prediction. HIGHLIGHTS Groundwater quality is evaluated using the Water Quality Index method.; Machine learning algorithms are used for forecasting groundwater quality.; The predictive capabilities of decision tree classifier, extreme gradient boosting, logistic regression, random forest, and K-nearest neighbors models have been evaluated and compared.

Investigation of Irrigation Water Requirements for Major Crops Using CROPWAT Model Based on Climate Data

Water is one of the most important natural resources and is widely used around the globe for various purposes. In fact, the agricultural sector consumes 70% of the world&rsquo;s accessible water, of which about 60% is wasted. Thus, it needs to be managed scientifically and efficiently to maximize food production to meet the requirements of an ever-increasing population. There is a lack of information on water requirements of crops and irrigation scheduling concerning the Shaheed Benazirabad district, Pakistan. Thus, the present study was conducted to determine the irrigation water requirements (IWR) and irrigation scheduling for the major crops in the Shaheed Benazirabad district, Sindh, Pakistan, using agro-climatic data and the CROPWAT model. Agro-climatic data such as rainfall, maximum and minimum temperature, sunshine hours, humidity, and wind speed were obtained from the NASA website, CLIMWAT 2.0, and world weather However, data about studied crops and soils were obtained from FAO (Food and Agriculture Organization). Analysis revealed that the IWRs per irrigation round for the four major crops&mdash;sugarcane, banana, cotton, and wheat&mdash;were as 3108.0 mm, 1768.5 mm, 1655.7 mm, and 402.5 mm, respectively. It was observed the IWRs are more sensitive in the hot season because of high temperatures and low relative humidity, and vice versa in the cold season. The use of scientific tools such as CROPWAT is recommended to assess IWRs with a high degree of accuracy and to compute irrigation scheduling. Accordingly, the study results will be helpful for improving food production and supervision of water resources

Estimation of irrigation water requirement and irrigation scheduling for major crops using the CROPWAT model and climatic data

The world is facing an acute water shortage. The present irrigation techniques used in the Hyderabad district, Pakistan, are not demand-driven. The present study was carried out to determine the crop water requirement (CWR), irrigation water requirement (IWR), and irrigation scheduling for major crops grown in the Hyderabad district using the CROPWAT model based on climatic, soil, and crop data. The analysis revealed that the total CWR for the entire growing season for sugarcane, banana, cotton, and wheat were 3,127.0; 2,012.3; 1,073.5; and 418.9 mm, respectively. However, the IWR for sugarcane, banana, cotton, and wheat for the entire growing season was found to be 2,964.0; 1,966.7; 1,052.7; and 407.6 mm, respectively. However, the contribution of rainfall was 163.0, 45.6, 20.8, and 11.3 mm during sugarcane, banana, cotton, and wheat, respectively. The CWR and IWR were higher during the dry season due to high temperatures and low relative humidity. However, the IWR of each crop was low in the initial stage which increased with the growing stage until the peak at the full growth stage. The study recommends the use of CROPWAT to investigate the irrigation water requirements with accuracy. HIGHLIGHTS Investigation for crop water requirement (CWR) for wheat, cotton, banana, and sugarcane.; Investigation for irrigation water requirement (IWR).; Investigation for irrigation scheduling.; Use of climatic, soil, and crop data.; Use of scientific tools, i.e., CROPWAT and CLIMAT models.

Study of GIS-based groundwater potential zones for agricultural sustainability in the arid region

The cluster-wise area of shallow and deep aquifer zones is used to estimate the potential of groundwater. The potential of the shallow aquifer zone is estimated at 4.61 MCM (million cubic meters) and for the deep aquifer zone at 17,509.03 MCM, while the total groundwater potential for both aquifer zones is estimated at 17,513.64 MCM. The Geographical Information System (GIS) was employed efficiently to estimate the subsurface volume of the lithological rock layers using cost-effective and time-saving techniques, while the Rockwork software integrated with GIS was successfully used to visualize the subsurface lithology and stratigraphy of the aquifer zones. The estimated potential of groundwater can be uncovered by using the alternative solar pumping system to improve the agricultural system in the study area, thereby reducing the migration rate, reducing poverty, and improving the socio-economic conditions of livelihood. In the future, too, it will be essential to design water quality studies to ensure the proper use of groundwater. HIGHLIGHTS The current study discovered two potential groundwater zones of the (shallow and deep) aquifer.; The potential of the groundwater has been estimated using GIS for future planning and development.; The overall groundwater potential for both aquifer zones is estimated at 17,513.64 MCM.