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Changing the level of pollution in the urban environment is one of the consequences of Covid-19. Litter are one of the most important urban pollutants affected by the Covid-19 pandemic. In this research, the pollution level of urban areas during the Covid-19 pandemic was investigated by studying the urban environment. To this end, the protocol of observation and counting was used and litter were studied in two groups including common litter and Covid-19 related litter in Yasuj, Iran. The results were interpreted based on the clean environment index (CEI). The time of observation was selected based on the peak of the disease and the decline in the incidence rate. The results showed that on average, at the peak of the disease, the density of the litter was reduced by 19% compared to the low lockdown related to Covid-19. The CEI on average was 4.76 at the peak of the disease that was interpreted in the clean status, while the CEI on average was 5.94 at the low lockdown related to Covid-19 so interpreted in the moderate status. Among urban land uses, recreational areas with a difference of more than 60% showed the greatest impact caused by Covid-19, while in commercial areas this difference was less than 3%. The effect of Covid-19 related litter on the calculated index was 73% in the worst case and 0.8% in the lowest case. Although Covid-19 decreased the number of litter in urban areas, the emergence of Covid-19 lockdown related litter was a cause for concern and led to increasing the CEI. [ABSTRACT FROM AUTHOR]

This study aimed to assess the groundwater quality and human health risks of nitrate, nitrite, and fluoride contamination in rural areas of Khaf County, Iran. For this aim, 28 groundwater samples were collected from open dug wells during August 2018. Twelve parameters (pH, EC, TDS TH, Cl-, SO42-, HCO3−, NO3−, F−, Ca2+, Mg2+, and Na+) have been used to evaluate the water quality index. Also, point estimation using HQ formula and sensitivity analysis using the Monte Carlo Simulation (MCS) method, by 10,000 repetitions in Oracle Crystal Ball®, were applied to determine the additional risk of non-carcinogenicity in Nitrate, Nitrite, and Fluoride in four groups: infant, children, teenager, and adult. WQI showed that 7%, 60%, 10% and 21% of groundwater samples fall within the class of excellent, good, poor, and very poor quality, respectively. The HQ values of nitrate for infant, children, teenagers, and adults in 32%, 14%, 10% and 7% of the samples from rural areas were above the safe limit of 1. The HQ values of nitrite were mostly acceptable for infants, children, teenagers, and adults in all the rural areas because the mean HQ values of fluoride were lower than 1. Also, the calculated HQ values of fluoride for infants, children, and teenagers in 60.7%, 21.4% and 10% of the rural areas were above the safe limit. The results of HQ point estimation showed that nitrate and fluoride had non-carcinogenic risk in most exposed population groups, but exposure to nitrite did not have non-carcinogenic risk. The results of sensitivity analysis, to determine the most effective parameter in increasing non-carcinogenic risk, showed that the concentration parameter of Nitrate, Nitrite, and Fluoride had the most effect on increasing sensitivity in the four studied exposed groups. [ABSTRACT FROM AUTHOR]

High concentrations of fluoride and nitrate in drinking water cause significant impacts on human health. The aim of this research was to estimate the risk to human health induced by fluoride and nitrate due to the intake of the groundwater through drinking water in Sabzevar city, Iran. Eighteen groundwater samples were collected from nine wells in the region during 2018 to determine the fluoride and nitrate concentrations. The non-cancer risk, reflected by the hazard quotient (HQ) and the hazard index (HI), was assessed for adults, children and infants. The water samples had overall mean fluoride and nitrate levels in the range of 0.197–1.32 mg/L and 8.8–47.2 mg/L, respectively. All the wells had fluoride levels below the WHO guideline of 1.5 mg/L. For nitrate, all the wells were within the standard. Among the nine groundwater samples, the total HI values at 22%, 55% and 55% of wells exceeded safe levels of 1 for adults, children and infants, which indicates that the local groundwater is detrimental to these age groups in the mentioned locations. The results of this research could help to develop proper strategies for comprehensive water resource management, particularly by providing scientific documents for decision and management of groundwater sources applied for drinking water supply. [ABSTRACT FROM AUTHOR]

Water is exceptionally vital for all living beings and socio-economic development. This study aimed to investigate the groundwater suitability for drinking in rural areas of Kamyaran city, Kurdistan province, Iran, by using the water quality index (WQI) and evaluating the non-carcinogenic health risk caused by nitrate from the drinking route. Forty-five groundwater samples were collected (2019) from operated dug-wells, and twelve parameters (TDS, pH, TH, EC, HCO3−, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, and NO3−) were measured to the calculation of WQI. Hazard Quotient (HQ) and sensitivity analysis (SA) using the Monte-Carlo Simulation technique with 10,000 iterations were employed to determine the non-carcinogenic effects of Nitrate in different exposed groups (Infant, children, teenagers, and adults). The results of WQI showed that 74% of groundwater samples fall within the excellent water quality class, and 26% of rural areas fall in the category of good water type. The nitrate concentration in drinking water ranged from 22.42 ± 11.44 mg/L. The HQ mean for infants, children, teenagers, and adults were 0.5606, 0.7288, 0.5606, and 0.438, respectively. Probability estimation showed the HQ values for the 5th, and 95th percentile in infants, children, teenagers, and adult groups were (0.25–1.81), (0.13–1.08), (0.13–0.97), and (0.07–0.51), respectively. The SA showed that the most significant parameter of non-carcinogenic risk in all exposed populations was nitrate concentration. Generally, nitrate concentration in the study area was relatively high, and remarkably in agriculture and fertilizer management required more attention. [ABSTRACT FROM AUTHOR]

Climate change, as an emerging phenomenon, has led to changes in the distribution, movement, and even risk of extinction of various wildlife species and this has raised concerns among conservation biologists. Different species have two options in the face of climate change, either to adopt or follow their climatic niche to new places through the connectivity of habitats. The modeling of interpatch landscape communications can serve as an effective decision support tool for wildlife managers. This study was conducted to assess the effects of climate change on the distribution and habitat connectivity of the endangered subspecies of Asian black bear (Ursus thibetanus gedrosianus) in the southern and southeastern Iran. The presence points of the species were collected in Provinces of Kerman, Hormozgan, and Sistan-Baluchestan. Habitat modeling was done by the Generalized Linear Model, and 3 machine learning models including Maximum Entropy, Back Propagation based artificial Neural Network, and Support Vector Machine. In order to achieve the ensemble model, the results of the mentioned models were merged based on the method of "accuracy rate as weight" derived from their validation. To construct pseudo-absence points for the use in the mentioned models, the Ensemble model of presence-only models was used. The modeling was performed using 15 habitat variables related to climatic, vegetation, topographic, and anthropogenic parameters. The three general circulation models of BCC-CSM1, CCSM4, and MRI-CGCM3 were selected under the two scenarios of RCP2.6 and RCP8.5 by 2070. To investigate the effect of climate change on the habitat connections, the protected areas of 3 provinces were considered as focal nodes and the connections between them were established based on electrical circuit theory and Pairwise method. The true skill statistic was employed to convert the continuous suitability layers to binary suitable/unsuitable range maps to assess the effectiveness of the protected areas in the coverage of suitable habitats for the species. Due to the high power of the stochastic forest model in determining the importance of variables, this method was used. The results showed that presence/absence models were successful in the implementation and well distinguished the points of presence and pseudo-absence from each other. Based on the random forests model, the variables of Precipitation of Driest Quarter, Precipitation of Coldest Quarter, and Temperature Annual Range have the greatest impact on the habitat suitability. Comparing the modeling findings to the realities of the species distribution range indicated that the suitable habitats are located in areas with high humidity and rainfall, which are mostly in the northern areas of Bandar Abbas, south of Kerman, and west and south of Sistan-Baluchestan. The area of suitable habitats, in the MRI-CGCM3 (189731 Km2) and CCSM4 (179007 Km2) models under the RCP2.6 scenario, is larger than the current distribution (174001 Km2). However, in terms of the performance of protected areas, the optimal coverage of the species by the boundary of the protected areas, under each of the RCP2.6 and RCP8.5 scenarios, is less than the present time. According to the electric circuit theory, connecting the populations in the protected areas of Sistan-Baluchestan province to those in the northern Hormozgan and the southern Kerman would be based on the crossing through the heights of Sistan-Baluchestan and Hormozgan provinces and the plains between these heights would be the movement pinch points under the current and future scenarios. Populations in the protected areas of Kerman have higher quality patch connections than that of the other two provinces. The areas such as Sang-e_Mes, Kouh_Shir, Zaryab, and Bahr_Aseman in Kerman Province and Kouhbaz and Geno in Hormozgan Province can provide suitable habitats for the species in the distribution models. The findings revealed that the conservation of the heights along with the caves inside them could be a protective priority to counteract the effects of climate change on the species. [ABSTRACT FROM AUTHOR]

Gahar Lake is located within Oshtorankooh Protected Area (east of Lorestan Province in Iran), which has extensive potentials for the development of the tourism industry. The aim of the present research was to determine the economic value of the Gohar Lake resort using the zonal travel cost method. Therefore, at first, 380 questionnaires were distributed among the tourists by the simple random sampling method based on appropriate spatiotemporal distribution during the visiting seasons. The questionnaire items were categorized as economic, social, and miscellaneous parts. The calculation results revealed a value of USD 84.538 per visitor and a value of USD 1,986,657.163 per year, indicating the high value and importance of the region. The analysis showed that socio-economic variables have a significant role in the use or non-use of the resort. The obtained R2 coefficient was 0.82, indicating that around 82% of the changes in the number of visitors can be justified by the variables introduced in the model. The results also revealed the need to pay more attention to this region and formulate a tourism development plan. [ABSTRACT FROM AUTHOR]

The coastal pollution has been evaluated using indexes like Clean Coast Index (CCI) in many countries. In this study, several coasts and urban areas in northern of Iran along the Caspian Sea, were assessed in terms of number and composition of litters. Furthermore, Clean Environment Index (CEI) was used for the first time to interpret the results. The results indicated that 60% of the coasts and 50% of the urban areas were in a dirty status and only 22% of the total surveyed areas were found to be in a clean status. The highest number of litters observed in the study areas was cigarette butt. Due to the impact of risk factor of different types of littered waste, it was obviously clear that CEI offers a more realistic and rigorous interpretation than CCI. Therefore, this new index can be considered to evaluate litters pollution in various areas. • Litters are most common pollution in beaches and urban environments. • Plastic group as most abundant litters consist 39.54% in total studied areas. • CCI showed that 40% of coasts are in clean, 40% in moderate, 20% in dirty status. • CEI showed that 60% of coasts and 50% of urban areas are in dirty status. • The number and composition of litters on the coasts varied with different land-uses. [ABSTRACT FROM AUTHOR]

Predicting environmental disturbances and evaluating their potential impacts on the habitats of various plant and animal species is a suitable strategy for guiding conservation efforts. Wildfires are a type of disturbance that can affect many aspects of an ecosystem and its species. Therefore, through the integration of spatial models and species distribution models (SDMs), we can make informed predictions of the occurrence of such phenomena and their potential impacts. This study focused on five focal species, namely, the brown bear (Ursus arctos), wild goat (Capra aegagrus), wild sheep (Ovis orientalis), wildcat (Felis silvestris), and striped hyena (Hyaena hyaena). This study used MODIS active fire data and ensemble machine learning methods to model the risk of wildfire occurrence in 2023 for spring, summer, and autumn separately. This study also investigated the suitability of habitats for focal species via SDMs. The predicted probability maps for wildfire risk and habitat suitability were converted to binary values via the true skill statistic (TSS) threshold. The overlap of the habitat suitability map and wildfire occurrence areas was analyzed via GAP analysis. The area prone to fire in spring, summer and winter is equal to 9077.32; 10,199.83 and 13,723.49 KM2 were calculated, which indicates an increase in wildfire risk. Proximity to roads is one of the most important factors affecting the possible effects of wildfires in all seasons. Most fire occurrences are concentrated on agricultural lands, which, when integrated with other land use types, have wildfire potential in all seasons. The use of fire to destroy agricultural residues is a critical factor in the occurrence of wildfires. The distribution range of each focal species is considered the most important component of fire susceptibility. Hence, the suitable habitat for Hyaena hyaena in spring, summer, and autumn, with areas of 5.257, 5.856, and 6.889 km2 respectively, is the most affected by the possibility of fire. In contrast, these areas have the lowest values for Ovis orientalis, with 162, 127, and 396 km2 respectively. Therefore, species that are dependent on human-based ecosystems have the highest vulnerability to wildfire. Conservation efforts should focus on familiarizing farmers with methods of destroying agricultural residues as well as the consequences of intentional fires. The findings of this study can be used to mitigate the negative impacts of wildfire and protect the habitat of focal species., Competing Interests: NO authors have competing interests Enter: The authors have declared that no competing interests exist., (Copyright: © 2024 Morovati, Karami. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)