Assessment of temporal and spatial water quality in international Gomishan Lagoon, Iran, using multivariate analysis

Water quality stress to Amirkalayeh Wetland, Northern Iran

Amirkalayeh Wetland, listed as wetlands of International Importance in the Ramsar Convention, exposes to severe water quality stress resulted from increase in dissolved ions and nutrients concentrations. In addition to in situ measurements of physicochemical parameters (electrical conductivity, pH, and temperature), a total number of 28 water samples were collected from Amirkalayeh Wetland and surrounding surface and groundwater resources to investigate the most important factors increasing trophic state and decreasing the water quality of the wetland. Water samples were analyzed for major ions and dissolved plant nutrients (nitrate, nitrite, ammonium, and phosphate). Up to three-time increase in salinity and dramatic rising in nutrients level was observed in Amirkalayeh Wetland from 2017 to 2021. The excessive nutrients intake resulted in hyper-eutrophication trophic status of Amirkalayeh Wetland. Our hydrological, hydrochemical, and statistical studies investigating the role of a variety of possible water quality degrading factors show that surface and subsurface drainage of agricultural return water into the Amirkalayeh Wetland are the major cause of increase in trophic level and decreasing water quality. Results of this work indicate that Amirkalayeh Wetland is exposed to severe water quality stress that threatens the aquatic life and ecosystem of this wetland. Therefore, preventing the inflow of surface and subsurface agricultural drains to the wetland and providing a sustainable water management plan are vital to improve water quality of the wetland.

Measurement and ecological risk assessment of heavy metals accumulated in sediment and water collected from Gomishan international wetland, Iran

This study aimed to measure and ecologically assess heavy metals, including As, Cr, Pb, Cd, and Ni in water and sediment samples taken from Gomishan, an international wetland located in Golestan, Iran. Four sampling stations were selected to cover all parts of the wetland. The analyses of the heavy metals were performed by ICP-MS. Based on the content of the heavy metals in the sediments, the values of risks for individual heavy metals, as Er, and for total heavy metals, as IR, were estimated. Igeo and EF also presented the soil quality in terms of accumulated contamination. The average content of the heavy metals in water was 23.12, 4.14, 10.04, 6.71, and 94.48 μg/L for As, Cd, Cr, Ni, and Pb, respectively. The heavy metal concentrations in sediments were decreased in the following order: Pb (2130 ppb) > As (655 ppb) > Cr (295 ppb) > Ni (148.8 ppb) > Cd (148.8 ppb). The potential risk values for individual heavy metals were in the low range, Er < 40, except for Cd, which mostly posed a moderate ecological risk. The values of EF and Igeo showed that the sediments sampled from the Gomishan wetland were minimally enriched and contaminated. As the Gomishan wetland has a moderate risk of heavy metal contamination, conservative and monitoring activities should be performed.

Rapid and non-destructive approach for the detection of fried mustard oil adulteration in pure mustard oil via ATR-FTIR spectroscopy-chemometrics

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometrics was effectively applied for the rapid detection and accurate quantification of fried mustard oil (FMO) adulteration in pure mustard oil (PMO). PMO was adulterated with FMO in the range of 0.5-50% v/v. Principal component analysis (PCA) elucidated the studied adulteration using two components with an explained variance of 97%. The linear discriminant analysis (LDA) was adopted to classify the adulterated PMO samples with FMO. LDA model showed 100% accuracy initially, as well as when cross-validated. To enhance the overall quality of models, characteristic spectral regions were optimized, and principal component regression (PCR) and partial least square regression (PLS-R) models were constructed with high accuracy and precision. PLS-R model for the 2nd derivative of the optimized spectral region 1260-1080 cm^-1 showed best results for prediction sample sets in terms of high R² and residual predictive deviation (RPD) value of 0.999 and 31.91 with low root mean square error (RMSE) and relative prediction error (RE %) of 0.53% v/v and 3.37% respectively. Thus, the suggested method can detect up to 0.5% v/v of adulterated FMO in PMO in a short time interval.

A simulation-based method to develop strategies for nitrogen pollution control in a creek watershed with sparse data

Well-defined targets for nitrogen (N) release into the local environment are essential for water management in creeks, but difficulties often arise from working with data that are too sparse to achieve reliable evaluations. Here, a simulation-optimization approach based on the QUAL2K model was developed to put forward strategies for nitrogen pollution control in a creek with sparse data in Shixi Creek, southeast China. The model showed good agreement with field observations from 22 sampling sites sampled over the period from March 2017 to February 2019, with normalized objective function (NOF) less than 0.360. Based on this model, the water pollutant sources in the creek were distinguished and analyzed. Rural sewage discharge in Shixi Creek was the major factor threatening water quality in the stream. Seasonal variations may influence the transformation of riverine N. To make more than 80% of the area in Shixi Creek meet the water quality standard of grade III, an optimized approach is to reduce more than 55% of the N pollution from point source pollution and 10% from nonpoint source pollution. This study proposed an approach that can effectively evaluate strategies for water management in a creek watershed with sparse data.

Chemometrics for environmental monitoring: a review

Environmental monitoring is necessary to ensure the overall health and conservation of an ecosystem. However, ecosystems (e.g. air, water, soil), are complex, involving numerous processes (both native and external), inputs, contaminants, and living organisms. As such, monitoring an environmental system is not a trivial task. The data obtained from natural systems is often multifaceted and convoluted, as a multitude of inputs can be intertwined within the matrix of the information obtained as part of a study. This means that trends and important results can be easily overlooked by conventional and single dimensional data analysis protocols. Recently, chemometric methods have emerged as a powerful method for maximizing the details contained within a chemical data set. Specifically, chemometrics refers to the use of mathematical and statistical analysis methods to evaluate chemical data, beyond univariant analysis. This type of analysis can provide a quantitative description of environmental measurements, while also having the capacity to reveal previously overlooked trends in data sets. Applying chemometrics to environmental data allows us to identify and describe the inter-relationship of environmental drivers, sources of contamination, and their potential impact upon the environment. This review aims to provide a detailed understanding of chemometric techniques, how they are currently used in environmental monitoring, and how these techniques can be used to improve current practices. An enhanced ability to monitor environmental conditions and to predict trends would be greatly beneficial to government and research agencies in their ability to develop environmental policies and analytical procedures.

Identifying the Key Information and Land Management Plans for Water Conservation under Dry Weather Conditions in the Border Areas of the Syr Darya River in Kazakhstan

Due to an increase in poorly planned anthropogenic activities, the water quality of several Asian big rivers is highly being affected. Although the assessment of heavy metal contents is vital to develop and design sustainable water management plans, several areas in Central Asia such as Kazakhstan do not have recent studies available that evaluate this situation. One representative example of this lack of information is the Syr Darya River. Thus, this study carried out the first approach to a water quality assessment in Kazakhstan’s Syr Darya River, where a massive expansion of irrigation canals, pastures in middle- and lower-reaches and an increase in industrialization and population have lowered its potential water capacity. To achieve this goal, various physicochemical parameters were analyzed for forty-three water samples along the river under dry weather conditions at 25 cm water depth. The obtained results were analyzed using standard methods (e.g., Multi N/C 2100 S analyzer or an atomic absorption spectrometer) and evaluated by multivariate techniques (cluster analysis (CA), principal component analysis (PCA), and non-metric multidimensional scaling (NDMS)) and a heavy metal pollution index (HPI). In the CA, five cluster groups were obtained. It is important to remark that the first cluster consists of the highest number of water sampling points (8). The last cluster is made up of only one point, which shows the highest difference against the other sites in our model. The NDMS also confirmed that some specific points along the river are different. Five components were extracted from the PCA: (1) COD (chemical oxygen demand), Zn, Cu, Pb, Ni and Mn; (2) Cu, Cd, Ni and Co; (3) T (water temperature), pH and DO (dissolved oxygen); (4) T and Fe; and (5) COD and OC (organic carbon). The HPI showed very high values (279.9), which were locally confirmed in some hotspots close to the Aral Sea, industrialized areas and agricultural fields. Therefore, our results demonstrate that, under dry weather conditions, surface water resources could be mismanaged in the Syr Darya River in Kazakhstan in specific areas. For the future, considering the important role that agriculture and pasture play in the Kazakh economy, we insist upon the importance of applying water quality control measures applying nature-based solutions and efficient management plans. Moreover, we confirmed the necessity to conduct further research related to sampling under other weather situations such as wet and cold conditions, different river water depths and other locations considering specific land uses, for example, grazing, mining, railways or industries.

Issues of Meander Development: Land Degradation or Ecological Value? The Example of the Sajó River, Hungary

The extensive destruction of arable lands by the process of lateral bank erosion is a major issue for the alluvial meandering type of rivers all around the world. Nowadays, land managers, stakeholders, and scientists are discussing how this process affects the surrounding landscapes. Usually, due to a land mismanagement of agroforestry activities or urbanization plans, river regulations are designed to reduce anthropogenic impacts such as bank erosion, but many of these regulations resulted in a degradation of habitat diversity. Regardless, there is a lack of information about the possible positive effects of meandering from the ecological point of view. Therefore, the main aim of this study was to investigate a 2.12 km long meandering sub-reach of Sajó River, Hungary, in order to evaluate whether the process of meander development can be evaluated as a land degradation processes or whether it can enhance ecological conservation and sustainability. To achieve this goal, an archive of aerial imagery and UAV (Unmanned Aerial Vehicle)-surveys was used to provide a consistent database for a landscape metrics-based analysis to reveal changes in landscape ecological dynamics. Moreover, an ornithological survey was also carried out to assess the composition and diversity of the avifauna. The forest cover was developed in a remarkable pattern, finding a linear relationship between its rate and channel sinuosity. An increase in forest areas did not enhance the rate of landscape diversity since only its distribution became more compact. Eroding riverbanks provided important nesting sites for colonies of protected and regionally declining migratory bird species such as the sand martin. We revealed that almost 70 years were enough to gain a new habitat system along the river as the linear channel formed to a meandering and more natural state.