The rising temperature due to climate change leads to glaciers melting, causing increased sea levels and sea surface temperatures (SST). The rising SST and glacier melting elevate sea levels around the globe. The rising sea level is hazardous for the coastal regions worldwide, and therefore, calls for continuous monitoring to manage this threat. Pakistan, with a coastline of 990 km long, is among those countries most vulnerable to sea-level rise (SLR). Around 10% of Pakistan's population lives near the coastal zones, which are most marginalized and dependent on fishing and small agricultural farms. An SLR of a few millimeters per year is a serious threat to Pakistan's coastal areas that can, directly and indirectly, cause land loss of low-lying areas and destruction of infrastructures, human settlements, agriculture lands, and coastal ecosystems and other resources. The coastal communities of developing countries such as Pakistan, lacking essential resources, are the most disadvantageous and adversely affected people of the world due to these disasters. To overcome resources and capacity constraints, remote sensing (RS) and GIS techniques can employ to monitor coastal zones at larger extents. In this study, SLR and SST around the coast of Karachi have been investigated using multi-mission satellite radar altimetry (SRA) and MODIS Terra L3 SST product. The SRA is used to monitor sea levels using data from 1993 to 2018. Similarly, monthly SST data from 2000 to 2019 have been acquired to monitor SST trends. The rises in sea levels and temperatures were estimated as 3.9 mm/year and 0.0138 °C/year respectively. Different extrapolation techniques can further be utilized to predict the potential of SLR and SST in the study area for developing future coastal zone management plans.

The riparian zone plays an important role in the ecological stability of rivers. In particular, the quality of the riparian vegetation is a significant component of the hydromorphological status. In Europe, the QBR index (Qualitat del Bosc de Ribera) and the River Habitat Survey (RHS) are commonly used for the qualitative assessment of the riparian vegetation taking into account the riparian vegetation cover, the cover structure and the channel alterations. In this study, we estimated the QBR index and the Riparian Quality index, which is derived from the RHS method, for 124 river reaches of the National Monitoring Network of Greece. These reaches were surveyed during the summer periods of 2018, 2019 and 2020, through the implementation of the National Monitoring Program in compliance with the Water Framework Directive (WFD). The aim of this study is to assess the riparian vegetation status by comparing these two indices and to identify linkages with the dominant land uses within the catchment. The Riparian Quality Index and the Habitat Modification Score, also estimated from the RHS method, were positively correlated showing that the overall hydromorphological alteration is associated with the degradation of the riparian vegetation. In addition, the QBR index and the Riparian Quality Index correlated negatively with the agricultural land uses in the catchment area and positively with the natural ones (e.g. forests). Both indices seem to present similar quality assessments. These results constitute a first assessment of the status of the riparian zones in Greek rivers in accordance with the WFD and set the basis for further research for the development of new and effective tools for a rapid quality assessment of the riparian zones.

A three-dimensional intermediate test system with the ability to control boundary conditions and soil moisture variations was developed. The setup had the advantage of being able to accurately characterize the heterogeneity through packing with test soils with well-defined properties and to control the boundary and initial conditions that are not possible in field settings. A distributed soil moisture sensor system was tested under controlled conditions in the test facility before field deployment. The developed 3-D tank has dimensions of L=4.87 m, W= 2.44 m packed to a depth of 0.40 m. The tank was packed with a heterogeneous configuration using five uniform silica sand with the effective sieve numbers #70, #16, #8, #12/30, and #20/30 (Accusands Unimin Corp Ottawa, MN), respectively. Soil moisture variations were monitored using 30 soil moisture sensors (ECH2O EC-5 and 5TE, TEROS12). The testing focused on observing and recording soil moisture patterns and the performance of the sensors under various imbibition and drainage scenarios expected in the field. The sensors were able to successfully capture the complex spatial and temporal variations of the soil moisture in the tank. Each sensor was individually calibrated for each type of the test sands used to provide unique fitting parameters relating to the sensor’s measured voltage to known water content. During the experiments, the head at one of the boundaries was kept constant, resulting in full saturation at this boundary that was captured by the sensors. Based on the time-series data, the variations in the specific properties of the sand in the packing led to different saturations. The varying hydraulic properties of the packed sand affected the water flow and soil moisture dynamics that were captured by the sensors. Even under such highly controlled test conditions in laboratory settings, heterogeneities resulting from packing imperfections and compaction introduced some uncertainties in the measurements. These observations suggest the importance of incorporating any available information on the natural heterogeneity when designing sensor deployment strategies in the field.

Passive samplers have been used widely as an alternative to the traditional grab water sampling method as they i) allow pre-concentration of micropollutants, ii) integrate average contaminant concentrations over extended sampling periods and ii) minimize the sampling logistic¹. The use of passive samplers is particularly beneficial when environmental concentrations vary strongly over time. In this case, the grab sampling approach can miss important input from short events such e.g. storm-events and bias interpretation of micropollutant transfer. Compound-specific isotope analysis (CSIA) allows teasing apart the contribution of non-degradative (dilution, sorption) and degradative processes both resulting in overall pollutant dissipation². CSIA is widely applied for industrial contaminated sites, with a relatively high concentration of contaminants. However, is less often extended to micropollutants, as herbicides and pharmaceuticals, due to smaller environmental concentrations. Here we developed the application of commercially available polar organic chemical integrative sampling (POCIS) for simultaneous evaluation of concentration and carbon CSIA of herbicides (atrazine, metalaxyl, terbutryn, S-metolachlor, tebuconazole, dimethomorph) in artificial wetlands receiving pesticide runoff from a vineyard catchment (42.7 ha, Alsace, France).

Prior to field deployment, POCIS performance was tested for sorption and subsequent isotope analysis of selected herbicides for different concentrations, matrices (distilled vs. pond water), flow conditions, and hydrogeological events patterns. In parallel, self-made POCIS samplers were prepared and compared to the commercially available passive samplers. Our results show that i) pesticide sorption on POCIS is linear for concentrations below 10 µg/L, ii) extraction from POCIS causes no significant isotopic effect for studied herbicides, iii) commercial and self-made POCIS did not differ in terms of concentration and δ¹³C measured in extracts and iv) POCIS records accurately changes of isotope signatures of pollutants in water. As POCIS techniques are not selective to the molecules of interest, the only limitation arises sporadically from matrix effects for some compounds in complex matrices, a common problem in CSIA from environmental samples. This issue can be solved in the future with a further combination of POCIS with further matrix cleanup techniques.

Finally, POCIS was deployed into the storm-water pond located at the outlet of the vineyard catchment for intervals of 28 days over 3 months. All the compounds detected in the grab water samples (10L) taken in parallel were found in POCIS. In particular, the widely used fungicide dimetomorph was pre-concentrated in POCIS extracts up to 2 mg/L compared to the surrounding water average concentration of 100 ng/L. This high preconcentration factor (x 20’000) makes CSIA of dimetomorph feasible, supporting assessment of degradation in the field. Altogether, we anticipate this study to be a starting point for the application of multi-element CSIA to assess the fate of a variety of micropollutants in surface waters.

Literature:

(1) Alvarez, D. A.; Petty, J. D.; Huckins, J. N.; Jones-Lepp, T. L.; Getting, D. T.; Goddard, J. P.; Manahan, S. E. Development of a Passive, in Situ, Integrative Sampler for Hydrophilic Organic Contaminants in Aquatic Environments. Environ. Toxicol. Chem. 2004. https://doi.org/10.1897/03-603.

(2) Hunkeler, D.; Meckenstock, R. U.; Sherwood Lollar, B.; Schmidt, T. C.; Wilson, J. T. A Guide for Assessing Biodegradation and Source Identification of Organic Ground Water Contaminants Using Compound Specific Isotope Analysis (CSIA). USEPA Publ. 2008, 600/R-08/1 (December), 1–82.

Leaf-litter decomposition in forested streams is a key process that fuels food-webs. However, drivers of this process in intermittent streams are poorly understood. Our goal was to compare the microbial-mediated decomposition of three leaf species [all native in temperate riparian forests from Portugal (alder, chestnut and oak)] in the channel and its riparian zone of an intermittent stream. The leaves were incubated for 88 days, the stream channel presenting a total of 8 days of water flow, 49 days of isolated pools, and 35 days running dry, while the riparian zone remained emerged the whole time. Alder decomposed faster than the other species in both areas which is related to its higher quality. Independently of the species, decomposition rates were higher in the stream channel (1.4- to 1.8-fold) than in the riparian area. Fungal biomass also differed among species, oak having the lowest value. The different quality between alder and chestnut was not translated into differences in fungal biomass, which may be related to an earlier fungal peak and faster decomposition of the former. Between zones, leaves incubated in-channel presented higher fungal biomass than those in the riparian zone, being higher for chestnut (3.8-fold) in comparison with the other species (1.8- fold for alder and 2.0-fold for oak). These results strengthen the key role of water (sensu lato) as a driver of litter decomposition. The synchronous or asynchronous presence of lotic and lentic environments in the streambed, associated with soil humidity in drier periods, seem to enhance fungal growth, accelerating litter degradation in-stream. A delayed terrestrial degradation and the distinct leaf species-specific susceptibility to changes in environmental conditions may constitute an insurance, for the stream communities, of a higher quality of leaf material supplied to the stream by the riparian area when flow resumes.

Bangladesh is considered one of the most suitable regions for fisheries in the world, with the world's largest flooded wetland and the third largest aquatic biodiversity in Asia after China and India. Fish firming has brought a significant changes in the economy of the country. But it is a matter of great sorrow that, most of owners of the fisheries in our country are not well educated enough or they don’t have enough knowledge about the firm water. So every year a large number of fisheries counts losses in business due to death of fishes for non-suitable water in the firm. In 2018 total 1200 tones of fishes died in 4 districts reported by fisheries and livestock secretary.

The quick growth of economy brings new problem for our environment. One of the main problem we are facing is the water pollution. The most water quality factors are dissolved oxygen, total ammonia nitrogen, nitrite, PH, Alkalinity, Co2, salinity, iron, chlorine, hydrogen. These parameters have certain standard values for the fishes to live in the water. If the dissolved oxygen is below of the standard value the fishes cannot survive in water. Another parameter is ammonia in the water and it has to have certain standard level for the fishes to lives else fishes will die in one night. The temperature of the water is also important parameter for the fishes to live. Another important parameter is the PH level which has to have standard value for the fishes to live as well. The main reason of fish death is considered to be due to lack of standard values of the parameters in water as mentioned earlier. In our country the water of most of the rivers and ponds close to Dhaka city are seriously polluted. The main reason for this is the waste materials of the industries. This is usually noticed near the Dhaka city and other city areas of Bangladesh. In our country, there are many leather processing companies near the Dhaka and other districts which are close to Dhaka. These companies don't process their waste materials properly. Due to not proper processing technique and management the waste materials and contaminated water of these industries fall into the rivers and nearby ponds which pollute the rivers and pond's water seriously. In addition, our country there are nearly 7000 garments factories. They also don't properly manage their waste materials although there is pressure from proper authority but it is not possible to control this issue by the authority. The waste polluted water from the garments industries are not purified properly. Therefore waste materials and contaminated water from different industries fall into the rivers and nearby ponds which pollutes the water. This ingredients of the waste contaminated water form garments industries, leather processing industries and other industries pollute the water which have very bad effects to the aquatic creatures and also human health. It is very urgent to take necessary action to monitor and prevent the water pollution. Due to the above mentioned reasons the quality of the elements of water such as dissolved oxygen, Nitrogen, pH ,water temperature, nitrite, ammonium, Co2 do not have standard value. This causes serious problem for the fishes to live in the water. It also has bad effect to the environment and human health.

The main objective of this paper is to propose a smart IoT based water sensor system which will efficiently measure quality parameters of the water. There will be various sensors to measure different parameters of the water. This system will help us to monitor the parameters of water quality and pollution level of water which can help to prevent the water pollution. However there will be Arduino UNO microcontroller to receive the data from different sensors and process them. Wireless module will be integrated with the system as well. These measures parameters bys the various sensors will be stored in the cloud and from there they will be displayed on the Mobile application. Data also can be displaced in the website. An android mobile application will be developed for this system. The application of this proposed system is not limited to only fish farming in Bangladesh but it has other applications in many areas. This system can be used to the places of the industries where the waste materials and water passes to monitor the quality of the water in real time and proper authority can take legal action to prevent it. However, in this paper authors proposed to use this IoT based smart water monitoring service for the application of fish farming in Bangladesh. This IoT based whole system will help fish farmers to increase the quality and production of fishes. For the solution of the mentioned problem a IOT based device is proposed that have ability to sense the quality of the elements of the water such as dissolved oxygen, Nitrogen, pH ,water temperature, nitrite, ammonium, Co2, and it will be sent the measured data by sensors to a mobile via a mobile application.

Proposed device will be set into the water of any ponds, lakes or any source of water in the fisheries so that it can monitor the condition of the water in real time and the parameters will be sent to mobile app of the users. Users will be able to control the device through that mobile app and take treatment measure by observing the situation. There will be other advanced features integrated with the app. The IoT based sensor device will be movable so it can move every places in the water of pond, river or lakes to monitor the oxygen level and other parameters in water because the level of oxygen is not equal to all places in the water. Different fishes stay in the water at different level of water. By using the device user will get real time analysed information about the water and can easily take proper steps to make the water quality better so that fish can live there without facing any problems. Mobile app will have many features added for advance monitoring and action plan. This system will be very helpful for the fish farmers of our country and eventually country will be benefited by exporting fishes in abroad and earn foreign currency which will help to develop the economy of the country.

The paper provides an overview of the agricultural economy in terms of agricultural development, especially in the field of water protection against nitrate pollution from agricultural sources. Environmental protection and sustainable management of natural resources, prioritization of action behavior in terms of good practice methods in soil treatments, especially vulnerabilities on the types of fertilizers used, are part of the soil-plant-air-water equation. The change of paradigms in agriculture with climate change involves the adaptation of agricultural systems the risks of using fertilizers in soil treatment, the interdependence of plant-soil-water in agricultural practice is also highlighted in the paper. One of the main objectives in the field of agriculture is to maintain a low level of greenhouse gas emissions from the agricultural sector without diminishing the importance of biosphere protection. The role of research and studies has shown an important factor in reducing the carbon footprint per tonne of food produced from organic farming compared to conventional farming, mainly due to the abandonment of the use of chemical fertilizers and pesticides. The purpose of the following research is to collect data and information on the most effective management models that will create the premises for agricultural practices applied to the soil by preventing pollution of groundwater and surface water with nitrates. from agricultural sources and by promoting the use of good practices farm. The Common Agricultural Policy (CAP) supports the Nitrates Directive by granting direct assistance and through rural development measures. There is also a growing trend for agro-ecological initiatives for which farmers can receive payments. nutrient management measures, such as the creation of buffer zones, as an element to stimulate environmental protection. During the research we tried to highlight aspects that, in our opinion, are important for the development of the agricultural sector through innovative rural development measures as part of the economy.

The adverse effects of improper disposal of collected and treated wastewater have become inevitable. In order to achieve the desired environmental standards, in addition to the construction of a wastewater treatment plant, there is also a need to evaluate the continuous performance of treatment systems. In Iran, treated wastewater is mostly used in agriculture. Therefore, the use of wastewater with poor quality characteristics can endanger health. In this study, the efficiency of the neural network model in order to predict the performance of the Parkandabad waste water treatment plant in Mashhad, with a semi-mechanical treatment system, was investigated. The first step in predicting the performance of the treatment plant was identification of factors affecting the Total Biochemical Oxygen Demand (TBOD) parameter which is one of the quality indicators of the effluent. In the next step, the neural network model optimized with a genetic algorithm, and effective features as network inputs were used for the predictions of the performance of the treatment plant. Based on the results obtained from the model, the parameters that affect the prediction of TBOD concentration the most were singled out. They are flow rate, organic matter load, dissolved oxygen concentration, temperature, and some active aerators. Paper will consider replacing the semi-mechanical treatment system with the activated sludge process.

Selenge River is a transboundary river, which rises in the Khanghai Mountains in Mongolia and flows into Lake Baikal in the Republic of Buryatia (Russia). We studied the Selenga River basin (446 000 km²) located in western Siberia (Transbaikalia, Russia), and northern and central Mongolia between the Tuva-Mongol and Dzabkhan microcontinents in the west, the Siberia craton in the north and the Amurian superterrane in the south. Water resources are very limited in this basin because annual precipitation is only 250-400 mm. Moreover, 90% of annual precipitation occurs in summer, which causes frequent flooding in summer and severe shortage of water during the rest of the year. The transition from a planned economy to a market economy has resulted in inefficiently operated wastewater treatment systems which pollute the river, and reckless deforestation have increased non-point pollution sources on the lower Selenge River in Russia. In recent years, of industrial water consumption more than 50% of withdrawn water was used by the mining industry in Mongolia. The mining enterprises are located in watersheds of small tributaries, the second order of the Selenge River. In this work we present the current state of the Selenge River chemistry and estimate heavy metal pollution. From 2004, the requirement of federal law of Russia «On the protection of Lake Baikal» the new variant of regional norms MAC (Maximum Allowable Concentration) were elaborated. The concentration of the main polluting substances were compared with norms MAC.

The dissolved concentrations of major, trace elements, REE (rare earth elements), and dissolved organic carbon (DOC) have been measured in Selenge River draining watersheds with various surface areas. Measured pH for all sampling sites were within the standard range for river water, which is between 6 and 9, meantime the highest measured pH 9.2 may be due to anthropogenic influences in the area. The total dissolved solids load (TDS) is 108-297 mg l^-1. Along the river Ca/Na ratio varies 2.1-9.8 that can be explained by the heterogeneity of the crystalline rock and soil.

Mongolian and Russian parts of Selenge River having different drainage areas and hydrological parameters were sampled over a 6-year period. Collected data show that the river present the same monthly seasonal variations, with higher concentrations in winter and lower concentrations during other seasons. In waters, DOC and insoluble element concentrations (e.g., Al, Fe, and Th) were strongly correlated, which show the key role of organic colloidal matter in the transport of some insoluble elements.

In riverine flood-prone areas, the delineation of the spatial pattern of flood extents and durations allow flood planners to anticipate likely threats from floods and to formulate actions to mitigate these events. Rapid flood mapping is crucial for flood disaster estimation and evaluation in the early stage. Accurate and timely updates of flood inundation have been made possible by remote sensing techniques. The present study applies the Water indexes and Classification method to analyzes and estimates the riverine Spatio-temporal flood-2014 extent changes using Landsat-8 imagery in Lower Chenab Plain, Pakistan. The lower Chenab plain is particularly prone to frequent riverine flooding but is understudied. It has experienced history worst flooding in September 2014. Optical Landsat-8 data can be used for flood inundation mapping when the flooded areas are clouds free. Cloud free Landsat-8 data was acquired for pre-flood, during-flood, and post-flood periods for detailed analysis. We used different water indexes including Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) for the delineation of inundated areas based on increase water index value from pre-flood and post-flood Landsat-8 images. Satellite-derived Water Indexes which are mostly utilized for flood extent estimation that separates the flooded water area from non-flooded areas based on a threshold value. Further, we also used supervised classification to detect flooded areas and compare them with water indexes. The resulted analysis allowed us to compute flood extent area, duration, and flood recession. The inundated area values of used water indexes are higher in the post-flood instance as compared to the pre-flood instance. The proposed RS technique provides an empirical basis for the rapid identification of inundated areas, which would enable emergency response and relief efforts on newly flooded areas in future events. Thus, our study provides another perspective and substantial contributions to flood monitoring using free satellite data in Pakistan.