Heavy metal concentrations in water, fish, and sediments from Bangladesh's haor region were investigated. Fish and sediment samples were taken once a season and evaluated using standard methods, while water samples were collected monthly. Results showed that metals in water (mg/L) were found in order of Mn (0.1694) > Cu (0.0189) > Zn (0.0045) > Pb (0.0040) > Cd (0.0028) within the maximum permissible level of Environment Conservation Rules (ECR). Mean concentrations of metal in fish (mg/kg–dry wt.) found in order of Zn (56.16) > Cu (25.47) > Mn (4.36) > Pb (2.19) > Cd (1.27) that were higher than maximum allowable level of Food and Agricultural Organization (FAO) except Cu. Metal in sediments (mg/kg) found in order of Mn (127.61) > Zn (32.51) > Pb (10.09) > Cu (5.40) > Cd (0.43), and except Cu all metal concentrations were lower than the Environmental Protection Agency's (EPA) probable effect concentrations. In water and sediments, pollution indices revealed a critical pollution threshold for water, and a range of unpolluted to highly polluted for sediments. Sampling sites had low potential ecological risk, despite the fact that metals were showing signs of a negative impact on people' health. Furthermore, bio-concentration factor for fish and water was low to extremely high, but for fish and sediment was low. The level of heavy metal contamination in haor shows the situation is alarming for biota and residents of the region. The relevant authority should control and monitor the aquatic ecology in order to protect it.

The present study aims at investigating land use changes (as one of the effective human factors on water systems) as well as its relation with water quality at spatial scales of the entire basin, sub-basin and defined buffers (10 and 15 km) in Zayandeh-Rud Basin, Isfahan, Iran. By means of supervised classification method along with maximum likelihood algorithm, it classifies the land use map into five categories, including agriculture, bare lands, urban areas, vegetation, and water. The research collects data for 11 water quality parameters in seven sampling stations of Zayandeh-Rud Basin in 2002, 2009, and 2015 from Isfahan Water and Sewerage Organization. Correlation analysis is then conducted to investigate the effect of land use changes on water quality at different spatial scales. Land use analysis in the entire basin shows that despite an increase in urban and agricultural lands from 2002 to 2015, bare lands, vegetation, and water covers have had a decreasing trend. Moreover, various land uses at different scales show some correlation with water quality parameters. The strongest correlations in this study belong to sub-basin scale. Therefore, it is recommended to use this spatial scale to investigate the relation between land use and water quality parameters

This study was performed to determine the chemical compositions and heavy metals in the muscle of Scomberomorus commerson from the Oman Sea, during the two seasons, pre-monsoon and post-monsoon in 2018. The protein, fat, moisture, and ash contents were determined by AOAC (Association of Official Analytical Chemists) methods. Heavy metal (Zn, Cu, and Pb) analyses were performed by atomic absorption spectrophotometer after acid digestion. There were significant differences between protein, fat, moisture, and ash values in muscle tissue in two seasons (P< 0.05). The highest content of protein (22.53±2.09%) and fat (4.15±1.25%) was recorded in pre-monsoon. The mean concentrations of heavy metals (μg g-1dw) in muscle tissue were 0.08-0.05 for Zn, 0.04-0.02 for Cu, and 0.02-0.01 for Pb in the pre and post-monsoon, respectively. The accumulation of heavy metals in muscle followed the Zn>Cu>Pb. The amounts of Zn, Cu, and Pb were below maximum permissible limits (MPL) recommended by international standards (FAO, FAO/WHO, and MAFF). Results revealed that estimated daily and weekly intakes of Zn, Cu, and Pb were far below the permissible tolerable daily intake (PTWI) recommended by FAO/WHO. Therefore, consumption of S. commerson in the pre and post-monsoon has no risks for human health in the Oman Sea.

Trends in water quality, either increasing or decreasing over the long term, are becoming an essential guide to understanding water quality. This study aims to analyse the trends in water quality in the upstream part of the Bernam River Basin, Malaysia from 1998 to 2018. This study involved the collection of data on water quality from the Department of Environment, Malaysia. Six main parameters of the water quality index (WQI) were chosen, including the dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH3-N), total suspended solids (TSS) and pH. The analysis methods applied are the Mann-Kendall test and Sen’s estimator of slope. The results of the WQI value trend analysis revealed that most stations have decreasing trends. A trend analysis of the parameters found that most stations had increasing trends for the DO, BOD, NH3-N and pH parameters, while decreasing trends for the COD and TSS parameters were observed. An increasing trend indicated that the water quality parameters were getting better, and a decreasing trend indicated the opposite. This study will benefit the parties responsible for planning and monitoring developments to reduce water pollution around the upstream Bernam River Basin. In the upstream of Bernam River Basin, land use changes have occurred rapidly, especially forest areas have been explored for human settlements, agricultural and industrial activities. Thus, the sustainability of the river basin can be maintained and valued by the various parties in the future. The river basin is also important as a domestic water supply for the residents of Selangor and Perak.

Drilling operations release considerable amounts of drilling fluids that contain pollutants such as heavy metals and hydrocarbons. By means of the ICP-OES device, the concentration of metals in the drilling mud deposited in the accumulation pools (around the drill rigs), the drilling mud used in the well (initial, input, and output mud), the soil around the drilling rig, and the control sample can be determined. Comparison of metal accumulation volume with current standards (upper crust and the region’s soil) indicates that the initial drilling mud contains As, Cd, Mo, Cu, and Ag metals. Also, the outflow and associated cuttings are contaminated with Sb, showing a geological formation origin for this metal. In case of Pb, Ba, Cr, and Mn, the level of contamination is in a medium range wherein the source of the contamination is recycled mud and drilling rocks in the previous phases. Contamination of other metals is evaluated in the low range. Geo-accumulation index (Igeo), Enrichment Coefficient (EF), and Contamination Factor (CF) are used to determine the level of pollution. Results show that As, Pb, Ba, Mn, Mo, Cd, Co, Cu, and Zn have caused a lot of pollution in the area and their amounts should be controlled.

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily CO concentration as a function of 12 input variables. Then, forward selection (FS) technique was applied to reduce the number of input variables. After converting 12 input variables to 7 using the FS, they were fed to SVM models (FS-(-SVM) and FS-(-SVM)). Finally, a comparison among SVM models operation and previously developed techniques, i.e. classical regression model and artificial intelligent methods such as ANN and adaptive neuro-fuzzy inference system (ANFIS) was carried out. Determination of coefficient (R2) and mean absolute error (MAE) for -SVM (-SVM) were 0.87 (0.40) and 0.87 (0.41), respectively, while they were 0.90 (0.39) and 0.91 (0.35) for ANN and ANFIS, respectively. Results of developed SVM models indicated that both FS-(-SVM) and FS-(-SVM) regression techniques were superior. Furthermore, it was founded that the performance of FS-(-SVM) and FS-(-SVM) models were generally a bit better than the best FS-ANFIS and FS-ANN solutions for short term forecasting of CO concentrations.

Emerging contaminants are distributed in to the environment from various anthropogenic activities. These Emerging contaminants (ECs) are mainly composed of products, such as pharmaceuticals, personal care products (PCPs), surfactants, plasticizers, pesticides etc. The present conventional system of waste treatment are not designed to treat these contaminants. Complex structure of these pollutants and their existence at low concentration makes them untraceable and hence found to be difficult in removal of these by present waste treatment. These chemicals are considered as threat to human health and environment. Therefore, disposal and treatment of these chemicals of emerging concern have been a key concern in the field of water treatment and its reuse. Biosensors can be used for biomonitoring of these contaminants with of biological system. Bioremediation plays an important role in the treatment of these pollutants of emerging concern. This review discusses about the sources, effects, and challenges in biomonitoring and bioremediation related to these emerging contaminants.

The human health impacts caused due to exposure to criteria outdoor air pollutants PM2.5, PM10 and NO2 were assessed in present study. The human health effects associated with exposure to atmospheric air pollution in NCT Delhi were estimated utilizing the AirQ+ v1.3 software tool integrated with Ri-MAP during the study period 2013-2018 considering 80% of the whole population subjected to air pollution exposure. Taking into account the World Health Organization (WHO) (2016) guidelines, the inter-annual average concentrations of PM2.5, PM10, and NO2, concentration response relationships and population attributable fraction (AF) or impact fraction (IF) concepts were adopted. The excess number of cases (ENCs) of Mortality (all) natural cases 30+ years, acute lower respiratory infection (ALRI), lung cancer (LC), ischaemic heart disease (IHD), stroke, incidence of chronic bronchitis in children, postneonatal infant mortality, chronic obstructive pulmonary disease (COPD), prevalence of bronchitis in children, incidence of asthma symptoms in asthmatic children in the year 2013 were 48332, 2729, 5645, 26853, 22737, 120754, 34510, 5125, 9813, 3054, 17203 and 682, respectively. Within half of a decade i.e. in year 2018, the ENCs of Mortality (all) natural cases 30+ years, ALRI, COPD, LC, IHD, stroke, incidence of chronic bronchitis in children, postneonatal infant mortality, prevalence of bronchitis in children, incidence of asthma symptoms in asthmatic children increased significantly and were 72254, 3471, 6547, 7568, 32358, 28233, 150110, 50810, 9019, 862, 29570 and 1189, respectively.

With an increase in the global pollution, there is requirement for an alternative to the fossil fuels. Non-edible vegetable oils are highly promising for producing liquid fuels like diesel. Jatropha is a potential feedstock for biodiesel, currently utilized in India and many parts of the world. The optimization of reaction conditions such as temperature, time, catalyst and molar ratio for biodiesel production is important in reactor design. However, oils have characteristics reaction properties for optimum yield. Therefore, there is the need to identify such parameters in Jatropha oil ethyl esters production. Preparation of biodiesel from Jatropha oil ethyl esters using conventional homogeneous process. Optimization of Jatropha ethyl esters using Response surface methodology is done and data so obtained are fed to the design experiment software for analysis. The Jatropha ethyl esters yield was 92.62%. Maximum production of Jatropha oil ethyl ester was achieved with the process parameters viz molar ratio 8.5, reaction time 89.67min, reaction temperature 70.1°C and catalyst.0.62wt%.

The aim of this study was to compare the selected aquatic plants ability to remove nitrate from wastewater. Excess of these nutrients in water can directly affect human health (methemoglobinaemia) or indirectly through the products of secondary pollution include eutrophication. Negative impact of nutrients excess in surface water often causes the destruction of water ecosystems, and therefore, common substances of these elements must be monitored and managed. Spectrophotometric technique was commonly used for quick and simple analyses of nutrients in waste water. There are calibration curves for each nutrient and for the determination of their concentration. Phytotechnology is one of the biological wastewater treatment methods or processes to eliminate nitrate contaminant from aquatic system. So as to avoid the eutrophic formation of fresh water and to determine the efficiency of nitrate utilization by specific aquatic plants which include Utricularia aurea and Salvinia molesta were collected from a eutrophic lake at Theerthamkara, Kerala. The samples were allowed to grow in nitrate solution for about one month at different concentrations. The optical density (OD) of nitrate solution at 410 nm was measured on alternative days of the experiment by using UV spectrophotometer. After 33 days of treatment periods, the maximum amount of nitrate removed in terms of percentage was found to be 95% by Utricularia aurea and 92% by Salvinia molesta at 100 ppm nitrate concentration. The results revealed that the aquatic plant (carnivorous) based system of phytotechnology was productively removed the nitrate load from the synthetic wastewater containing nitrate.