نشریه علوم محیطی
سال بیست و دوم شماره 2 (پیاپی 84، تابستان 1403)

Quantitative assessment of fish abundance is the basis of scientific research andmanagement of their population. Demographic studies of stream-dwelling salmonids haveshown that variation in their abundance on a spatio-temporal scale is common, and troutpopulations are no exception. Understanding this variation is crucial for several reasons,including designing and interpreting environmental impact assessment studies and monitoringfishery management strategies. The present study aimed to estimate the spatial and temporalvariations in the density of Caspian Sea trout in some of the Lar National Park streams andinvestigate the relationship between the density of fish in these streams and the density ofbenthic invertebrates as their primary food sources.

Fish densities were calculated using the Le Cren depletion method toachieve reliable population estimates. After two removal steps, the total number of fish caughtwas standardized and expressed as density per 100 m2 of the river channel. To investigate thetemporal changes of fish abundance, the study results in 2009 were qualitatively compared withthe results of another study in 2017.

According to the 2009 results, the highest Caspian Sea trout densitywas recorded in the Delichay (44 per 100 m2), Lar (43.2 per 100 m2), and Siah Palas sites (33.4per 100 m2), respectively, and the lowest densities were recorded in the Lar (Kharsang, 10.5per 100 m2) and Lar (Sorkhak, 11.5 per 100 m2). In 2017, the highest density of trout wasdocumented in Siah Palas (175 per 100 m2), Delichay (Vararo, 118 per 100 m2), and Elam (112per 100 m2) streams, and the lowest density was recorded in the Delichay (downstream, 48 per100 m2) and Absefid (49 per 100 m2) streams.The distribution and abundance of stream-dwelling salmons are primarily regulated by food andspace. The abundance of Caspian Sea trout on a local scale indicates variety in quality and habitataccess for Caspian Sea trout in the Lar National Park. We speculated that habitat diversity hasinfluenced the density of Caspian Sea trout in various streams in the region by affecting theabundance of aquatic invertebrates, which are the main prey source. However, the results ofregression (R2= 0.02, p value = 0.72) and correlation (Spearman, r = 0.24) analyses showed nosignificant relationship between the density of fish and benthic invertebrates in the Lar National Parkstreams. Despite the enough abundance of prey in Lar streams, the fish were low in density. Thecause of this incongruence is probably related to the salmon fishing in Lar National Park in the past.Comparing fish densities in 2009 and 2017 showed that the trout density in 2017 was higherthan the trout density in 2009.

Considering that in 2014, the Department of Environment stopped issuing fishinglicenses in this area, one of the reasons for the increase in fish density is probably a positiveeffect of the fishing prohibition on this species. Since Caspian Sea trout abundances in the LarNational Park streams are spatio-temporally variable, it is necessary to provide reasonablemanagement strategies and continuous monitoring to prevent them from global warming andconserve them in the streams of the Lar National Park. Furthermore, due to climate change andbeing at risk of other populations of Caspian Sea trout in different habitats in Iran, thispopulation should be protected as a support population.

Iran stands out as one of the world's biodiversity hotspots. Simultaneously, river fish species within the country face imminent threats from various human activities. Climate change, among other challenges, poses a significant risk to the biodiversity of Iran. Consequently, there is a critical need to anticipate potential habitat changes for freshwater fish species in response to climate change. This study focuses on evaluating the impact of climate change on the habitats of the Song fish, a species with economic value and conservation significance. The Song fish is currently classified as Vulnerable (VU) by the International Union for Conservation of Nature (IUCN), making the examination of climate change effects crucial for the future population of this species.

This study employs the MaxEnt model to predict the spatial distribution of the Song fish at two different time scales (2050 and 2080 AD) under both optimistic (RCP2.6) and pessimistic (RCP8.5) climate scenarios. The R software environment was used for the analysis. Environmental variables, including slope, annual temperature range, flow accumulation, annual precipitation, annual mean temperature, and upstream drainage area, were considered. The data related to the observations of this species have been collected from sampling and various library sources. Modeling performance in predicting species distribution has also been evaluated by the AUC (Area Under the Curve) criterion.

The model demonstrated excellent performance in predicting species distribution, as indicated by the high AUC (Area Under the Curve) criterion value of 0.989. Among the environmental variables, annual average temperature and slope emerge were the most influential factors in determining the distribution of the Song fish. Furthermore, the study revealed a projected decrease in the distribution range of this species under both optimistic and pessimistic scenarios for 2050 and 2080.

To safeguard the Song fish, it is imperative for managers to identify and implement appropriate measures. These measures should aim to mitigate the effects of climate change and alleviate threats associated with these changes.

Volatile Organic Compounds (VOCs), despite their small share in the atmospheric pollutants, are the main cause of environmental problems and human health risks. In the biological processes, all the pollution is converted into carbon dioxide and biomass. A large number of industries discharge a mixture of pollutants such as n-hexane and methanol into the atmosphere. The different chemical and physical properties of these materials affect the rate of biological degradation and their removal efficiency by a specific microbial population. Among these properties, the solubility of each component may affect the interaction between the compounds during the biodegradation of the n-hexane/methanol mixture. To overcome the limitation of mass transfer and the low solubility of hydrophobic VOCs such as n-hexane in the water phase, it has been reported to be useful to add a non-aqueous phase (NAP) to the culture medium to absorb the hydrophobic compound. According to the previous researches, it is clear that the presence of silicone oil as an NAP has a positive effect on the simultaneous removal efficiency of methanol as a hydrophilic and n-hexane as a hydrophobic pollutant. However, its effect on the kinetic of biodegradation of VOCs is not well known. Therefore, this study aims to determine the effect of different volume fractions of silicone oil on the kinetics of simultaneous biodegradation of n-hexane and methanol.

n-hexane (5 g.m-3) was selected as a hydrophobic pollutant , methanol (1 g.m-3) as a hydrophilic pollutant, and silicone oil as the NAP. Microbial consortium used in this research is a mixed culture that was isolated from the adapted activated sludge from the return sludge flow of Unit No. 6 of the South Tehran Wastewater Treatment Plant. A gas chromatography (GC) device equipped with a capillary column and flame ionization detector (FID) was used to measure the concentrations of n-hexane and methanol. To measure the concentration of carbon dioxide, a thermal conductivity detector (TCD) was used. To measure the biomass concentration a spectrophotometer was used to determine the optical density (OD) of the samples.

The specific degradation rate (SDR) of methanol decreased from 1.42 mgmethanol.(gbiomass.day)-1) to 1.08 mgmethanol.(gbiomass.day)-1) by increasing the volume fraction of silicone oil from 1% to 20% v/v. In contrast, when volume fraction of silicone oil reached to 10% v/v, the SDR of n-hexane increased from 17.5 (mghexane.(gbiomass.day)-1) to 21 (mghexane.(gbiomass.day)-1). Further Increasing in the volume fraction of silicone oil up to 20% v/v did not have much effect on the SDR of n-hexane. The kinetics of n-hexane biodegradation followed the Michaelis-Menten model. The addition of 1 g.m-3 of methanol to the culture medium had a negative effect on the SDR of n-hexane, and the presence of methanol increased the Ks value to 21 g.m-3 and decreased the maximum SDR of n-hexane from 141.4 to 123.1 (mghexane.(gbiomass.day)-1).

The presence of silicone oil in the culture medium reduced the negative effect of methanol on the biological degradation of n-hexane. Increasing in the amount of silicone oil up to 10% v/v was effective on the biodegradation rate of n-hexane, and its further increase did not have effect on the SDR of n-hexane.

Investigating changes in land cover is one of the most important aspects of natural resource management and environmental changes. With the increase of urban and rural areas and the necessities of human life, changes are made on the surface of the earth. These changes are caused by the conflict between the needs of human societies and environmental constraints on Earth. In this research, using remote sensing techniques, the monitoring of land surface changes in Khoda Afarin and Kalibar cities was investigated using remote sensing technology during a period of 20 years (2000-2020).

In order to perform statistical and visual analysis of MODIS sensor images installed on Terra and Aqua satellites, for the period of 2000 to 2020, the method of classification and extraction of the earth's surface cover was studied. The IGBP classification method was used in 17 classes to classify land use. Then, based on the model and classification method, they were processed in the Arc GIS software environment. The area of each land use was calculated in each year. Natural factors affecting land use changes were used from the daily rainfall data for the studied period. Based on these values, the trend of changes in precipitation and drought was drawn according to the SPI standard precipitation index in the software environment.

Twenty MODIS sensor images were analyzed for the period under study and annual maps and change trend charts of each land use were drawn. The land use coverage in the years 2001, 2002 and 2003 showed that the largest area was related to agriculture in 2001, followed by pasture. This trend continued from 2002 to 2003.. In these years, the process of forest destruction and change of use from forest to pasture and then agriculture has started. From 2004 to 2006, Bayer's unfavorable coverage is visible. In the years 2007 to 2009, regarding dam construction the water coverage and the area under cultivation increased and in the years 2010 to 2012, the city and man-made buildings increased significantly, and from 2012 to 2020, the process of destroying forests and turning them into pastures and then agricultural lands in the year. The end of the investigated period is also quite evident, and also by examining the natural factors, the amount of rainfall and drought during the 20-year period has had a downward trend.

The results show that the area of forest and shrub cover has decreased, and instead, there has been a significant increase in barren, water, pasture and agricultural cover, although the increase in water cover is due to the construction of dams in the study area. Also, the results of examining the amount of precipitation and drought show that the trend of precipitation is decreasing and relatively severe droughts have occurred in the region.

Improving living standards in developing countries and rapid population growthhave significant effects on the economy and environment. Population growth leads to anincrease in demand for agricultural products, which increases environmental pollution, reducesthe productivity of natural resources, and has a negative effect on economic growth. Therefore,the aim of this research is to investigate the short-term and long-term relationship betweenagricultural production, economic growth, and environmental pollution in Iran.

In this research, time series data for the period of 1991-2020 werecollected from the database of the World Bank and Food and Agriculture Organization (FAO),and the Autoregressive Distributed Lag (ARDL) models were used. First, the augmentedDickey-Fuller (ADF) and Phillips-Perron tests were performed to test the stationarity. Then,according to the values of Akaike, Schwarz, and Bayesian information criterion, the optimalnumber of lags was selected. ARDL bounds test was used to test the presence of the long-runrelationship between the variables, and then short and long-run relationships and errorcorrection models (ECM) were estimated. Finally, the causality between pairwise variables wasinvestigated by using the Granger causality test.

The results of short-term relationships show that a one percentincrease in economic growth, rural population, gross capital formation, and agriculturalproduction increases CO2 emissions by 0.307%, decreases by 2.937%, and increases by0.087%, and 0.065%, respectively. The effect of foreign direct investment on CO2 emissions inthe short term was not significant. However, a one percent increase in the lag of foreign directinvestment will increase CO2 emissions by 0.01%. The long-term results show that a onepercent increase in economic growth, rural population, agricultural products, foreign directinvestment, and gross capital formation will increase CO2 emissions by 0.662%, decrease by3.807%, and increase by 0.141%, by 0.024% and 0.188%, respectively. The results of theGranger causality test show the bidirectional causality relationship between economic growth,agricultural production, foreign direct investment, and CO2 emissions, as well as foreign directinvestment and agricultural production. Also, there is causality in only one direction betweengross capital formation and CO2 emissions, agricultural production and economic growth,foreign direct investment and economic growth, agricultural production and rural population,rural population and foreign direct investment, and rural population and CO2 emissions. Inaddition, there is a long-term positive and significant relationship between CO2 emissions andeconomic growth, gross capital formation, agricultural production, and foreign directinvestment. The long-run result demonstrated by the FMOLS and DOLS methods was the sameas the finding of the ARDL approach.

In Iran, enhancing agricultural mechanization, promoting renewable energy,enforcing environmental regulations, adopting green technologies, investing in R&D, andattracting foreign investments are crucial to reduce CO2 emissions and pollution. Fossil fueldependentindustries, capital formation, and rural populations also impact environmentalpollution, emphasizing the need for employment opportunities in rural areas to mitigate theseeffects.

The Caspian Kutum (Rutilus frisii) is one of the most important bony fish speciesof the Caspian Sea and has high conservation and commercial value. There were decreasingtrends in its catch levels in the last years. Identifying temporal trends of its catch could helpadopt proper plans to maintain the stocks of this important species and achieve sustainableexploitation goals. In the present study, we conducted a time-series analysis for catch data ofthe species over a decadal period.

The commercial catch data of Caspian Kutum, over the seine netfishing points of the northern coastal regions of Iran during catch seasons 2002/03 to 2011/12,were used as catch-per-unit-of-effort (CPUE). A 5-point moving average of CPUE was used todistinguish the fishing points as optimum (with normalized CPUEs ≥ 0.6) and non-optimum(with normalized CPUEs < 0.6) fishing locations. Time series modeling was conducted usingthe seasonal autoregressive integrated moving average (SARIMA) model based on seasonal 3-month intervals. The performance and predictive ability of the models were assessed using aset of indices, including Akaike’s information criteria (AIC), Bayesian information criterion(BIC), root mean squared error (RMSE), normalized root mean squared error (nRMSE), meanabsolute error (MAE), normalized mean absolute error (nMAE) and the Pearson correlationcoefficient (r). CPUE trends over the five years of 2013 to 2017 were predicted using the bestfittedSARIMA models.

Five optimum (HR) and six non-optimum ranges (CR) were identifiedover the whole fishing points range (WR). The fitted SARIMA models based on the whole dataof all fishing locations as well as classified optimum and non-optimum ranges of fishinglocations did not have significant non-seasonal autoregressive and moving averagecomponents, indicating no increasing nor decreasing trends for CPUE over the study period,while for some of the ranges of fishing points, there were significant autoregressive and movingaverage components with clear seasonal increasing trends. The overall trend of CPUEs showedmainly an increase from 2002 to 2006, and then after relatively constant levels, there weredecreases from 2009 to 2013. The obtained predictions from the models for data sets havingsudden temporal fluctuations were less accurate. In contrast, higher accuracy levels ofpredictions and trends were observed for fish catch time series with no sudden alterations inCPUE levels over the studied period. Most of the obtained predictions for 2013-2017 similarlypresented stationary fluctuation trends with apparent seasonal increases in CPUEs.

Time-series modeling for R. frisii using the SARIMA method mainly indicatedclear increasing seasonal trends without any general trend of change over the whole fishingpoints. The simplicity of the obtained models considering the obtained seasonal and nonseasonalcomponents could be explained by the short time frame and the low number of datapoints. However, spatial classification of fishing points resulted in more detailed models andhigher recognition potential of them. The findings of this research could lead to a betterunderstanding of the temporal trends in catch levels of Caspian Kutum and use them by fisheriesmanagers to adopt efficient management plans regarding the available stocks of this species inthe future.

Dust is a common phenomenon in arid and semi-arid regions. One of the factorsthat play a role in the occurrence of this phenomenon is the change in weather conditions, whichleads to drought, and the drying of lakes and rivers has caused crises such as sandstorms. Thisphenomenon leads to negative effects on agriculture, water and soil pollution, and respiratorydiseases, as well as environmental and social challenges such as reduced visibility and roadaccidents. The purpose of this research is to investigate the relationship between the DSI dustindex and SPI standardized precipitation drought index in Semnan Province.

The studied area of Semnan Province is located in the southern regionof the Alborz Mountain range. To investigate the effect of drought on the amount of dust, thedaily data of dust, monthly rainfall, temperature, relative humidity and evapotranspiration offive synoptic stations of Semnan Province within a period of 15 years (2003-2017) were usedon an annual scale. During a 15-year period, the drought storm index (DSI) was determined forthe synoptic stations using the dust storm index, and then the cumulative DSI index wascalculated. Afterwards, statistical and experimental evaluations was done on annualfluctuations to identify potential trends. Also, experiment and linear regression analysis wereused to evaluate the correlation of standard precipitation index and dust storms, and Arc GISsoftware was used for zoning.

The results showed that the average annual temperature in the regionhas increased from 2003 to 2017. This rapid warming has caused drought and as a result,temperature, evaporation and transpiration have also increased and led to a decrease in rainfall.A month-by-month survey of wind speed in different stations showed that there is a possibilityof dust in the province in the months of June and July when the maximum wind speed prevails.On the contrary, the months of December and January experienced the least occurrence of dust.The annual changes of DSI (2003 to 2017) showed an irregular shape and do not have a trend.Thus, in 2011, the intensity of dust was high and decreased in the following year, and the samewas observed in 2016. According to the SPI index, Semnan Province was in the normal droughtcategory with a score of 0.59. The in-depth analysis of the correlation chart between thestandardized rainfall indices and DSI showed that the DSI has increased along with the severityof drought during the study period and there is a good correlation between the DSI index andthe meteorological drought in the region, so that when there was a drought period, the value ofDSI decreased and when it was a drought period, the value of the dust index increased.However, there is no significant relationship between these two indicators (p =0.07 andR2=0.22) during the 15-year research period.

Our findings indicated an increase in dust from the west to the east of the provincedue to the increase in average dust storm days. Correlation results between SPI and DSI indexshowed that although DSI index increased during the analyzed period along with the severityof drought, the correlation between the two was not significant. However, the DSI index trendpattern was consistent with the drought pattern trend. Finally, the correlation between droughtand DSI has always fluctuated according to droughts.

Climate change and global warming, caused by the increase in the concentrationof greenhouse gases, have garnered significant attention across various national and internationalsectors. The emission of greenhouse gases has long been recognized as one of the most pressingenvironmental issues, sparking widespread concern. Since the Industrial Revolution, the demandfor energy and the consumption of fossil fuels have escalated, leading to increased greenhouse gasemissions. While the input and output of atmospheric carbon dioxide have traditionally remainedin balance with nature, human activities and carbon dioxide emissions have disrupted thisequilibrium in recent decades, giving rise to climate change and global warming.

There are various methods for measuring atmospheric carbon dioxide.Remote sensing technology, in particular, has emerged as a solution, overcoming the limitationsof ground-based measurement methods by offering continuous monitoring and global coverageof greenhouse gases. Despite the absence of ground stations for monitoring greenhouse gases inthe Middle East region, spanning 7,207,570 square kilometers, this study investigates the monthly,seasonal, and annual atmospheric concentrations of carbon dioxide using data from satellites suchas SCIAMACHY, GOSAT, and OCO. The study period spans from 2003 to 2020.

The findings indicate a significant increase in atmospheric carbondioxide concentration over the 18 years examined by all three satellites. In addition toexamining annual changes, this study also investigated seasonal and monthly variations inatmospheric CO2 concentration. The lowest concentrations of this greenhouse gas occurredduring the summer months, particularly in August and September, while the highestconcentrations were observed during the spring months, specifically in April and May.Furthermore, the analysis of differences in atmospheric CO2 between seasons revealed the mostsignificant changes from spring to summer, with an average decrease of 6 ppm. Conversely,the highest increases in atmospheric CO2 between seasons were observed from summer toautumn, with a recorded average increase of approximately 4 ppm.

This research indicates a notable increase in atmospheric CO2 concentration in theMiddle East region from 2003 to 2020, accompanied by seasonal and monthly fluctuationsconsistent with global trends of this greenhouse gas. This long-term rise in greenhouse gaslevels can lead to various detrimental effects in the region, including temperature escalation,alterations in rainfall patterns, heightened drought severity, and damage to natural ecosystems.Consequently, the socio-economic stability of the region could be jeopardized, impactingagriculture, water resources, human health, and biodiversity. To effectively manage andmitigate greenhouse gas emissions, immediate action is imperative at both national andinternational levels. Such measures may involve promoting renewable energy sources,enhancing energy efficiency, curbing industrial pollution, advancing emission reductiontechnologies, and fostering regional and international collaboration in greenhouse gas reductionefforts. Moreover, public awareness campaigns and policy interventions are essential tomobilize stakeholders and facilitate the transition to a low-carbon economy in the Middle East,ensuring sustainable development and climate resilience for future generations.

The health of the river basin is determined by the health of its ecosystem to provide important and valuable resources and services for human use and the basin itself. However, the changes in the land use pattern, which is an effective reflection of anthropic activities, have greatly disturbed the health of the river and are the main driver of water quality reduction. Examining the relationship between land use patterns and river water quality provides an important basis for water quality safety and effective land use management. Therefore, the aim of this study is to investigate the influence of land use patterns on surface water quality based on the water quality monitoring data and land use data from 38 sampling points in the sub-basin of the Maroon River.

In order to sample from the study area, first, the boundary of the sub-basin of the maroon river was determined, and then the sub-basin was divided into different smaller sub-basins using the hydrological analysis tool in ArcGIS software, and the map of waterways in the region was also prepared. Then, sampling stations were selected from the exit point of each of these sub-basins in the main sub-basin area. In order to measure the water quality parameters of the studied area, sampling was done from 38 monitoring stations in the sub-basins in triplicates in the spring of 2023. The land use map was prepared using Landsat satellite images to evaluate the changes and the impact of land use patterns on the water quality status of the Maroon River sub-basin. Then, using geographic information system and multivariate statistical techniques, the effects of land use on river water quality in the Maroon sub-basin were estimated. In addition, in order to evaluate the quality of river water in different sub-basins, water quality data was analyzed using the water quality index (WQI).

The results of the evaluation of the distribution of land use patterns showed that the use of pasture is the dominant use in the sub-basin of the Maroon River and the water quality parameters exhibited significant changes in the stations covered by agricultural and forest use. The results of correlation analysis and linear regression of water quality parameters and land use patterns in the Maroon River sub-basin showed that agricultural land has a negative relationship with pH and DO parameters and a positive relationship with parameters ,  and Ca2+, forest land has a positive correlation with DO and a negative correlation with , and urban land also showed a negative correlation with DO. WQI values in different monitoring stations were estimated between 73.80 and 288.73 which showed that the health level of the river upstream of the sub-basin was better than downstream. In general, based on the WQI classification, 62.5% of the water quality of the Maroon River sub-basin was in the "poor" class, 25% in the "very poor" class, and 12.5% in the "good" class.

The findings of the research showed that agricultural land was the key factor affecting the water quality parameters and as a result the decrease in water quality in the sub-basin of the Maroon River, so limiting the discharge of runoff from agricultural activities is critical for improving water quality in the study area. This study highlights the importance of LULC changes in water quality for making informed decisions on proper watershed planning and management.

Understanding access to natural ecosystems and identifying potential priorities for harnessing the benefits of nature will be crucial factors in reducing inequality of access. Additionally, this understanding will inform the future planning and management of protected areas. The degree to which an individual can access ecosystem services depends on a complex range of mechanisms including social relations, institutions, capabilities, property rights, and various capitals. Various frameworks have been developed to conceptualize these linkages and describe the relationships between ecosystem services and human well-being, which include social and natural sciences and objective and subjective criteria, One of the significant and important frameworks in this field is the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES). In this study, the factors outlined by Ribot and Peluso (2003) in the access theory were integrated with components of the IPBES framework, providing a theoretical foundation for investigating the determinants of access to ecosystem services in the Zaribar Wetland.

The current research is applied, qualitative, and has an inductive approach. In order to analyze the data obtained from the semi-structured interviews, the content analysis method was used. The study area of the research is the villages around Zaribar Wetland in Marivan City, there are 10 villages with a population of 2853 households, which are in close interaction and depend on the services of the Wetland. Non-probability purposeful snowball sampling was used to select the investigated individuals or samples. The selection of samples continued until the adequacy and the theoretical saturation of the data were reached, and finally the researcher reached the theoretical saturation by conducting 22 interviews. In this study, Guba and Lincoln's four criteria—validity, transferability, reliability, and verifiability (Guba & Lincoln, 1989) were employed to assess the validity of the research. Data analysis was structured around the 'IPBES framework' and the 'access theory'.

The process of qualitative content analysis was used to analyze the factors affecting residents' access to Zaribar wetland ecosystem services in Marivan City. First, 106 primary codes were extracted. By multiple revisions and integration of codes based on similarity during several stages, five main categories and 17 subcategories were extracted. The five main categories and themes identified in this analysis were institutional-management, structural-communication, infrastructural-technological, economic-capital and environmental-contextual.

Access to ecosystem services in wetland communities, including those in Zaribar, is complex and dynamic, influenced by interwoven relationships and affected by economic, environmental, and other factors. The results of this study show that different regulations and organizational arrangements regarding people's access, instead of strengthening interaction and proper relationships, have caused different types of exchange relationships and unequal rights in the field of this ecosystem. Most ecosystem services are produced through both ecosystem processes and social actions, and thus their assessment cannot be separated from the social context in which they are embedded. As a result, it is recommended that more ecosystem service research be done with process-oriented, context-specific and integrated approaches, based on the recognition of the complexity of social-ecological realities.