Application of rapid environmental impacts assessment matrix and Iranian matrix in environmental impact assessment of solid waste landfill of Shahrekord

زمینه و هدف: یکی از مهمترین معضلات محیط زیستی تولید بیش از اندازه مواد زائد جامد شهری و مدیریت این مواد است. ارزیابی اثرات محیط زیستی (Environmental Impact Assessment= EIA) به عنوان راهکاری جهت به حداقل رساندن اثرات منفی دارای اهمیت است. هدف از این مطالعه استفاده از فرآیند EIA برای به حداقل رساندن اثرات محیط زیستی محل دفن زباله های شهرکرد و ارائه گزینه مناسب برای مدیران و برنامه ریزان این شهر بود. روش بررسی: در این مطالعه توصیفی-تحلیلی ارزیابی اثرات محیط زیستی محل دفن پسماند جامد شهرکرد در سال 1391 با دو روش ماتریس ارزیابی سریع اثرات محیط زیستی (Rapid Impact Assessment Matrix= RIAM) و ماتریس ایرانی (ماتریس اصلاح شده لئوپولد) مبتنی بر بازدیدهای میدانی و جمع آوری اطلاعات از منابع متعدد انجام شد. مقایسه چهار گزینه موجود شامل ادامه دفن به شیوه کنونی، ارتقاء کیفیت دفن، احداث محل دفن بهداشتی جدید و احداث کارخانه کمپوست و بازیافت انجام شد. یافته ها: ادامه روند کنونی دفن دارای امتیاز در RIAM، 1443- و در ماتریس ایرانی 9/3- بود. امتیاز گزینه کمپوست بازیافت در RIAM، 816- و در ماتریس ایرانی 1 برآورد شد. همچنین در نتایج دو روش برای اولویت گذاری ارتقاء کیفیت دفن و احداث محل دفن بهداشتی اختلاف مشاهده شد. نتیجه گیری: بر اساس هر دو روش، ادامه دفن به شیوه کنونی از لحاظ بهداشتی غیر قابل قبول و ادامه روند کنونی همراه با آسیب های محیط زیستی شدید است. کارخانه کمپوست-بازیافت با توجه به پتانسیل پسماندهای تولیدی در این شهر در اولویت گزینه های موجود قرار دارد

Bathymetric modelin from satellite imagery via Single Band Algorithm (SBA) and Principal Components Analysis (PCA) in southern Caspian Sea

Remotely sensed imagery is proving to be a useful tool to estimate water depths in coastal zones. Bathymetric algorithms attempt to isolate water attenuation and hence depth from other factors by using different combinations of spectral bands. In this research, images of absolute bathymetry using two different but related methods in a region in the southern Caspian Sea coasts has been produced. The first method used a Single Band Algorithm (SBA) and assumed a constant water attenuation coefficient throughout the blue band. The second method used Principal Components Analysis (PCA) to adjust for varying water attenuation coefficients without additional ground truth data. PCA method (r=-0.672394) appears to match our control points slightly better than single band algorithm (r=-0.645404). It is clear that both methods can be used as rough estimates of bathymetry for many coastal zone studies in the southern Caspian Sea such as near shore fisheries, coastal erosion, water quality, recreation siting and so forth. The presented methodology can be considered as the first step toward mapping bathymetry in the southern Caspian Sea. Further research must investigate the determination of the nonlinear optimization techniques as well as the assessment of these models’ performance in the study area

Influence of vertical distribution of phytoplankton on remote sensing signal of Case II waters : southern Caspian Sea case study

Reliable monitoring of coastal waters is not possible without using remote sensing data. On the other hand, it is quite difficult to develop remote sensing algorithms that allow one to retrieve water characteristics (like chlorophyll-a concentration) in optically complex coastal and inland waters (called also Case II waters) as the concentrations of optically active substances (phytoplankton, suspended matter, and colored dissolved organic matter) vary independently from each other and the range of variability is often high. Another problem related to developing remote sensing algorithms for retrieving concentrations of optically active substances in such complex waters is vertical distribution of these substances. For example, phytoplankton distribution in the water column is often characterized with maxima just below the surface mixed layer, and some phytoplankton species even have the capability to migrate in the water column and tend to form layers at depths optimal for their growth. Twenty-three field campaigns were performed during the spring-summer period in the coastal waters of the southern Caspian Sea where vertical distribution of phytoplankton was measured by means of chlorophyll-a fluorometer. There results showed that there is usually a chlorophyll-a maximum between 10 and 20 m where the concentration is about one order of magnitude higher than in the top mixed layer. The Hydrolight 5.0 radiative transfer model used to estimate if the vertical distribution of biomass have detectable impact on remote sensing signal in these waters. For that purpose, several stations with distinctly different chlorophyll-a profiles were selected and two simulations for each of those measuring stations was carried out. First the Hydrolight was run with the actual chlorophyll-a vertical distribution profile and second a constant chlorophyll-a value (taken as an average of measured chlorophyll-a in the surface layer) was used in the model simulation. The modelling results show that the “deep” chlorophyll maximum has negligible effect on the remote sensing reflectance spectra. Consequently, there is no need to take into account the vertical distribution of phytoplankton while developing remote sensing algorithms for the Caspian Sea coastal water

Dynamic Spatial Modeling of Urban Growth through Cellular Automata in a GIS Environment

Urban settlements and their connectivity will be the dominant driver of global change during the twenty-first century. In an attempt to assess the effects of urban growth on available land for other uses and its associated impacts on environmental parameters, we modeled the change in the extent of Gorgan City, the capital of the Golestan Province of Iran. We used Landsat TM and ETM+ imagery of the area and evaluated possible scenarios of future urban sprawl using the SLEUTH method. The SLEUTH is a cellular automaton dynamic urban-growth model that uses geospatial data themes to simulate and forecast change in the extent of urban areas. We successfully modeled and forecasted the likely change in extent of the Gorgan City using slope, land use, exclusion zone, transportation network, and hillshade predictor variables. The results illustrated the utility of modeling in explaining the spatial pattern of urban growth. We also showed the method to be useful in providing timely information to decision makers for adopting preventive measures against unwanted change in extent and location of the built-up areas within in the city limits

A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: a case of the Nekarood watershed, Iran

Land use (LU) or land cover (LC) is a critical factor dictating the amount of available water in runoff and groundwater. LU/LC Land use (LU) or land cover (LC) is a critical factor which can determine amount of available water in runoff and groundwater. In this study future effect of LU/LC change on stream flow in Nekarood watershed is investigated by Soil Water Assessment Tool (SWAT). Land use maps (1986–2016) based on Landsat TM and ETM+ satellite imagery are used. Furthermore, land use projection is performed by CA-Markov for the future period of 2016–2030. According to the results, agriculture and residential land use are increased by 40%, 28%, 38% and 31% respectively, and forest area is reduced by 12% and 6% during 1986–2001 and 2001–2016, respectively. Moreover, land use projection results showed that from 2016 to 2030 forest area will decrease by 6%, residential areas and agricultural will increase by 34% and 19%, respectively

Land cover change modelling in Hyrcanian forests, Northern Iran: a landscape pattern and transformation analysis perspective

El objetivo principal de este trabajo es analizar los patrones espaciotemporales de cambios del uso del suelo (1984-2010) y generar escenarios para el horizonte temporal 2030 en la cuenca del río Neka, en el norte de Irán. Dicha cuenca alberga bosques hircanios, de gran riqueza ecológica y que sólo se encuentran en algunos sectores del entorno del mar Caspio. Para ello se han utilizado mapas muy detallados de usos del suelo para los periodos 1984 y 2010. Los resultados evidencian procesos moderados de deforestación, fundamentalmente debidos a la expansión de zonas agrícolas y urbanas. Además los resultados indican una evolución hacia bosques más fragmentados y con una pérdida de conectividad. La metodología para simular cambios de usos del suelo fue capaz de reproducir adecuadamente (con un acierto del 96.4%) los usos del suelo simulados para el año 2010. Los escenarios para 2030 muestran una continuidad en los cambios observados durante el periodo 1984-2010, apareciendo nuevos cultivos y zonas urbanas dentro de las zonas actualmente ocupadas por bosques. Si bien la deforestación del bosque hircanio es moderada y ocupa un pequeño porcentaje de la superficie total, representa una afección evidente a los ecosistemas de la región y puede tener impactos asociados en la producción de escorrentía, recarga de acuíferos, explotaciones forestales y procesos erosivos. Así, la información generada puede resultar una herramienta de utilidad para los gestores del territorio y la gestión de los bosques hircanios en la cuenca del río Neka.The main objective of this study is to analyze the spatio-temporal changes in land cover and land use, (1984–2010), as well to simulate future land cover for 2030 in the Neka River Basin, including the Hyrcanian forest, in northern Iran. For this purpose, we used detailed land cover maps for the years 1984, 2001 and 2010. The results showed that the highest deforestation occurred in the boundaries between forest and agriculture areas between 1984 and 2010. Comparing the observed and predicted land cover in 2010 yielded agreement of 96.41%. From 1984 to 2010, landscape metrics showed that the forest area evolved to more fragmentation, with less shape complexity and less connectivity. Projections for the future are consistent with observed changes for the Neka landscape, with a tendency to continue disaggregating and increasing diversity in a number of different patch types. Between 2010 and 2030, we observed the arrival of new crops, rangelands, and urban areas within the remaining areas of homogeneous forest. Changes in the Hyrcanian forest will cause alteration in ecosystem services, such as erosion control, water yield, timber harvest, and ground water reservation. Results of this work may represent a useful tool to provide strategies and territorial planning for sustainable management of the fragile Hyrcanian forest ecosystems in the Neka Basin.

Investigating the Land Cover Changes in Mazandaran Province Using Landscape Ecology’s Metrics Between 1984 - 2010

Land cover has rapidly changed due to the relatively high population density, high rate of seasonal and permanent migrants, favorable conditions of natural and cultural, frequency of industrial units, coastal border and harbour and great rate of construction in the mazandaran province in recent years. Land cover changes are led to fragmentation, perforation, dissection, shirinkage, attrition and coalescence in the landscape, which each of them has important concept in the landscape ecology science. In this study, after preparing land cover maps, landscape metrics were extracted then Principal component analysis (PCA) were used in order to selection appropriate metrics for Mazandaran landscape changes analysis during 1984 and 2010. Finally five metric including Class Area (CA), Number of Patches (NP), Largest Patch Index (LPI), Perimeter-Area Fractal Dimension (PAFRAC) and Shannon Diversity Index (SHDI) were selected as the appropriate metrics. The results show an increase in the extent of residential, agricultural, pasture, roads covers (7387, 54655, 88986, 4768 ha, respectively) and sharp decline in forests (162,867 ha). Such that, LPI of forest cover decreased in the Neka, Savadkooh, Sari and Tonekabon cities 17.5, 13.8, 8.6 and 4.9 respectively, during the study period and matrix change were observed from forest to pasture and agriculture in Ramsar and Behshahr cites. More changes have been happened due to digestion forest of patches to anthropogenic covers especially integration of agricultural land

Non-point Source Pollution Modeling Using Geographic Information System (GIS) for Representing Best Management Practices (BMP) in the Gorganrood Watershed

The most important pollutants that cause water pollution are nitrogen and phosphorus from agricultural runoff called Non-Point Source Pollution (NPS). To solve this problem, management practices known as BMPs or Best Management Practices are applied. One of the common methods for Non-Point Source Pollution prediction is modeling. By modeling, efficiency of many practices can be tested before application. In this study, land use changes were studied from the years 1984 till 2010 that showed an increase in agricultural lands from 516908.52 to 630737.19 ha and expansion of cities from 5237.87 to 15487.59 ha and roads from 9666.07 to 11430.24 ha. Using L-THIA model (from nonpoint source pollution models) for both land use categories, the amount of pollutant and the volume of runoff were calculated that showed high growth. Then, the seventh sub-basin was recognized as a critical zone in terms of pollution among the sub-basins. In the end, land use change was considered as a BMP using Multi-Criteria Evaluation (MCE) based on which a more suitable land use map was produced. After producing the new land use map, L-THIA model was run again and the result of the model was compared to the actual land use to show the effect of this BMP. Runoff volume decreased from 367.5 to 308.6 M3/ha and nitrogen in runoff was reduced from 3.26 to 1.58 mg/L and water BOD from 3.61 to 2.13 mg/L. Other pollutants also showed high reduction. In the end, land use change is confirmed as an effective BMP for Non-Point Source Pollution reduction

Application of Multivariate Statistical Techniques to Assess Seasonal Variation in Water Quality Parameters in Gorganrood Watershed, Iran

Nonpoint source (NPS) pollution is a major surface water contaminant commonly caused by agricultural runoff. The purpose of this study was to assess seasonal variation in water quality parameters in Gorganrood watershed (Golestan Province, Iran). It also tried to clarify the effects of agricultural practices and NPS pollution on them. Water quality parameters including potassium, sodium, pH, water flow rate, total dissolved solids (TDS), electrical conductivity (EC), hardness, sulfate, bicarbonate, chlorine, magnesium, and calcium ions during 1966-2010 were evaluated using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was implemented to determine the significance of differences between mean seasonal values. Discriminant analysis (DA) was also carried out to identify correlations between seasons and the water quality parameters. Parameters of water quality index were measured through principal component analysis (PCA) and factor analysis (FA). Based on the results of statistical tests, climate (freezing, weathering and rainfall) and human activities such as agriculture had crucial effects on water quality. The most important parameters in differentiation between seasons in descending order were potassium, pH, carbonic acid, calcium, and magnesium. According to load factor analysis, chlorine, calcium, and potassium were the most important parameters in spring and summer, indicating the application of fertilizers (especially potassium chloride fertilizer) and existence of NPS pollution during these seasons. In the next stage, the months during which crops had excessive water requirements were detected using CROPWAT software. Almost all water requirements of the area’s major crops, i.e. cotton, rice, soya, wheat, and oat, happen in the late spring until mid/late summer. According to our findings, agricultural practices had a great impact on water pollution. Results of analysis with CROPWAT software also confirmed this conclusion