The Huatanay River in Cusco-Peru, is affected by wastewater discharges along its course. In order to evaluate this impact, we evaluate antibiotic residues and their impact on the increase of bacterial resistance in the city of Cusco treatment plant. For this purpose, water samples from the influent and effluent of the treatment plant were analyzed by chromatographic methods; additionally, sensitivity tests were performed with three bacterial strains (Escherichia coli, Salmonella sp., and Klebsiella sp.), which were isolated from the same place. Six antibiotic residues were identified (ceftriaxone, amoxicillin, trimethoprim, sulfamethoxazole, dicloxacillin, and lincomycin). Those found in the highest concentration were: amoxicillin (91495 and 0 µg/L) and lincomycin (33970 and 10800 µg/L) in the influent and effluent, respectively. There is more resistance in the effluent than the influent in the case of E. coli shows resistance in the effluent to cephalexin (30 µg) and azithromycin (15 µg). Salmonella sp. is resistant to amoxicillin (15 µg), dicloxacillin (1 µg), lincomycin (2 µg), ceftriaxone (30 µg), cephalexin (30 µg), and ciprofloxacin (5 µg). Finally, Klebsiella sp. is sensitive to ceftriaxone (30 µg), amoxicillin (15 µg), and cephalexin (30 µg). This confirms that the antibiotic residues contained in the wastewater of Cusco generate resistance in the isolated bacteria.

Elevated level of fluoride (> 1.0 mg/L) in drinking water leads to both dental and skeletal fluorides. Present research is dedicated to check the efficacy of duck shell dust towards removal of fluoride. Various analytical tools (XRF, XRD, SEM-EDAX and zero point charge) were used to characterize the present adsorbent. The entire batch mode study results were further optimized by Response Surface Methodology (RSM). The results revealed that Langmuire isotherm is best fitted (R2 = 0.819) with adsorption capacity 4.894 mg/g. However, kinetic study suggest that the fluoride adsorption followed pseudo-second-order kinetic equation (R2 = 0.956). Similarly, thermodynamic study revealed that the fluoride adsorption by duck shell dust is endothermic and entropy driven process. Finally, optimization study demonstrated the optimized condition such as initial concentration, adsorbent dose, contact time and pH are 89.29 mg/L, 1.112 g/100 mL, 42.5 min and 9.91, respectively. Therefore, it may be concluded that duck shell dust could be a promising adsorbent for decontamination of fluoride from contaminated body.

Clogging of the drainage layer is the main reason for the inefficiency and failure of the leachate collection system in municipal solid waste (MSW) landfills. One of the most important challenges in the design and operation of landfills is to identify the factors affecting the drainage layer clogging and the extent of their influence especially in the real scale. In this study, five experimental columns were designed to investigate the effective factors on the clogging of the drainage system in the MSW landfills, making it possible to measure the effect of different parameters on the drainage layer clogging through simulating the real conditions. The designed columns are capable to apply the boundary conditions of the MSW landfill including temperature, pressure, and leachate recirculation as well as measuring the permeability of drainage layer. High strength real leachate recirculated in the experimental columns to monitor the degree of drainage layer clogging through the regular measurement of permeability in the different columns. The results showed hydraulic conductivity of the drainage layer decreased between 20 to 50 percent in different samples over time. Although the particle size of drainage materials directly influences the reduction of hydraulic conductivity, the common concentration of calcium carbonate in the materials of the drainage layer does not considerably affect this issue. Formation of biofilm in the drainage layer was observed through scanning electron microscope (SEM) and visual inspection in all columns indicating the proper performance of clogging process simulator which is designed and developed in this research.

Tractor 10E is an Alpha-cypermethrin based insecticide. It is one of the commonly used insecticides. The toxic effect of this product was assessed using the Lens culinaris (edible lentil) and Lepidium sativum (watercress) test. The seeds of Lens culinaris and Lepidium sativum were subjected to 6 increasing concentrations of Tractor 10E (25, 50, 100, 200, 400 and 800mg/l) and the control (distilled water). After 7 days, weight, root and stem development were measured.The results of the statistical study revealed the notable effect of this toxic product on growth, especially at high concentrations for the two species which are the subject of plant toxicity tests. Stem length growth is the most sensitive parameter. IC50 is equal to 136.99mg/l for lentil and 136.42mg/l for watercress. The results of this study reveal that this alpha-cypermethrin insecticide has the ability to alter the growth of plants as non-target organisms, which imposes the effective use and management of these toxicants and even replacing them with biopesticides to preserve human health and the environment.

As a RAMSAR site (no.1204), Ashtamudi Lake has very productive and significant ecosystem services. Currently, the lake is being threatened by severe pollution stress, especially with heavy metals. Heavy metal pollution is a great concern of matter as it enters in the bioaccumulation and  bio-magnification processes of the aquatic food chain. The present study seasonally investigated the bioaccumulation   of seven heavy metals (Pb, Zn, Fe, Cu, Cr, Cd and Co) in the body tissues of an edible bivalve, Marcia recens, from three sampling stations (viz. Neendakara, Kureepuzha and Asramam) of the lake during the study period in 2019. The results showed that, as per the standard permissible limits (FAO/USEPA, 1989), some of the metal accumulations  were extremely high in the bivalve tissues. Highest Metal Pollution Index was observed in station Ⅱ. There were significant spatial and temporal variations in the accumulation of heavy metals in the the examined bivalve. Two way ANOVA analysis also reveals a statistically significant differences (p<0.5), in the heavy metal accumulation in the bivalve,  among the stations as well as seasons. The continual exposure to even a relatively low levels of these metals by regular consumption of contaminated bivalves, may entail adverse health issues. Implementation of appropriate scientific and sustainable conservation strategies will ensure the health of the estuaries and the sustainability of bio-resources.

Contamination of ground and surface water resources with Nitrate (NO3), Fluoride (F), Trihalomethanes (THMs), radon, and heavy metals is the most important global concern due to its possible health risks to people. This study reviews the drinking water contaminants and their health outcomes examined in Iran. A review search was conducted using Scopus, Web of Knowledge, PubMed, and Embase databases for associated released articles from 2014 to 2023, resulting in 86 articles relevant to the objective of this study. According to the results of this review, different emerging contaminants were found in potable water, including THMs, NO3, F, radon and heavy elements (i.e., As,  Pb, Ni, Cd, Zn, Cu, and Cr). Health outcomes of exposure to radon, F, NO3, THMs, and heavy metals in potable water have been expressed in various epidemiological research studies. More than 65% of the studies reported hazard index (HI) or hazard quotient (HQ) of heavy elements to be greater than one in potable water in Iran for infants, children, teenagers, and adults. Children and infants are at higher health risk than adults in these areas. The level of Arsenic, Cadmium, Lead, Nickel, Zinc, and Chromium, in 26, 26, 17.39, 13.04, 8.69, and 4.3% of the papers was more than the allowable limits, respectively. Various groups of emerging pollutants have been found in potable water in Iran, while epidemiological research studies on their health outcomes are still insufficient.

Environmental pollution with heavy metals has a negative impact on lawn grasses. Heavy metals are one of the priority pollutants of anthropogenic ecosystems. Earlier, plants Agrostis stolonifera, resistant to cadmium, were obtained using biotechnological method. Plants that are resistant to one heavy metal may be cross-resistant to another. The assessment of the resistance of plants obtained by biotechnological methods to other heavy metals is of practical value. The object of our study was to lawn grass - Agrostis stolonifera L. The aim of this work was to assess the tolerance of the next generation descendants  of the regenerant  Agrostis stolonifera, resistant to cadmium, to one of the most phytotoxic heavy metals - copper. Cadmium -tolerant plants were more resistant to copper. The tolerance of cadmium – resistant plants to copper is associated with  nonspecific mechanisms.  However, the increase in plant resistance was not very significant. Therefore, it is more expedient to obtain plants that are resistant to copper.

Diazinon is an organophosphorus insecticide that was widely used in agriculture to control pests on crops. It acts as an acetylcholinesterase inhibitor, which means that it interferes with the normal functioning of the nervous system of insects, leading to their death. Diazinon can also have an impact on human health and the environment, as it can contaminate water and soil and pose a risk to non-target species, including humans and animals. This review paper shows the progress made in the last years in analytical methods applied for the purpose of extraction, detection and degradation of Diazinon as an important environmental pollutant. A variety of sampling and analytical methods have been developed to measure diazinon and its metabolites in different media. The most popular methods for the identification and analysis of Diazinon are liquid and gas chromatography, liquid-liquid extraction, and solid-phase extraction (SPE). The focus of this review is on the identification, measurement, and elimination of diazinon as a major soil pollutant. It begins with a discussion of analytical techniques, followed by an examination of methods for removing diazinon from soil.

The present investigation examined the impact of highways on the global dispersion patterns of metallic elements present in dust and snow. A total of 18 mixed snow samples were collected from both sides of the Moscow-Tambo-Astrakhan Caspian Highway by the end of the winter season. The analysis of the samples indicated the presence of 35 distinct chemical elements, where Al, Ba, Ca, Fe, K, Mg, Na, and Zn were identified as the primary contaminants. The primary area of pollution on the windward side originating from the road spans a distance of 20-40 meters, while on the leeward side, it extends to 10 meters. The data presented suggests that the metals found in highways exhibited variability in terms of their solubility in water and concentration. Our findings demonstrate that the predominant wind directions affect the dispersion of pollutants. Furthermore, it was observed that the region with a higher concentration of metal on the side of the road facing the wind had a thickness that was 2-3 times less than that of the opposite side. It is advisable to conduct a subsequent inquiry within the ensuing five years to obtain dependable data regarding the extent of metal pollution.

In this study, a multitude of statistical tools were used to examine PM10 concentration trends and their seasonal behavior from 2015 to 2021 in Tehran. The results of the integrated analysis have led to a better understanding of current PM10 trends which may be useful for future management policies. The Kruskal – Wallis test indicated the significant impact of atmospheric phenomena on the seasonal fluctuations of PM10. The seasonal decomposition of PM10 time series was conducted for better analysis of trends and seasonal oscillations. The seasonal Mann-Kendall test illustrated the significant possibility of a monotonic seasonal trend of PM10 (p = 0.026) while showing its negative slope simultaneously (Sen = -1.496). The forecasting procedure of PM10 until 2024 comprised 15 time series models which were validated by means of 8 statistical criteria. The model validation results indicated that ARIMA (0,1,2) was the most satisfactory case for predicting the future trend of PM10. This model estimated the concentration of PM10 to reach approximately 79.04 (µg/m3) by the end of 2023 with a 95% confidence interval of 51.38 – 107.42 (µg/m3). Overall, it was concluded that the use of the aforementioned analytical tools may help decision-makers gain a better insight into future forecasts of ambient airborne particulate matter.