Soil organic carbon sequestration as affected by afforestation: the Darab Kola forest (north of Iran) case study

Following the ratification of the Kyoto Protocol, afforestation of formerly arable lands and/or degraded areas has been acknowledged as a land-use change contributing to the mitigation of increasing atmospheric CO(2) concentration in the atmosphere. In the present work, we study the soil organic carbon sequestration (SOCS) in 21 year old stands of maple (Acer velutinum Bioss.), oak (Quercus castaneifolia C.A. Mey.), and red pine (Pinus brutia Ten.) in the Darab Kola region, north of Iran. Soil samples were collected at four different depths (0-10, 10-20, 20-30, and 30-40 cm), and characterized with respect to bulk density, water content, electrical conductivity, pH, texture, lime content, total organic C, total N, and earthworm density and biomass. Data showed that afforested stands significantly affected soil characteristics, also raising SOCS phenomena, with values of 163.3, 120.6, and 102.1 Mg C ha(-1) for red pine, oak and maple stands, respectively, vs. 83.0 Mg C ha(-1) for the control region. Even if the dynamics of organic matter (OM) in soil is very complex and affected by several pedo-climatic factors, a stepwise regression method indicates that SOCS values in the studied area could be predicted using the following parameters, i.e., sand, clay, lime, and total N contents, and C/N ratio. In particular, although the chemical and physical stabilization capacity of organic C by soil is believed to be mainly governed by clay content, regression analysis showed a positive correlation between SOCS and sand (R = 0.86(**)), whereas a negative correlation with clay (R = -0.77(**)) was observed, thus suggesting that most of this organic C occurs as particulate OM instead of mineral-associated OM. Although the proposed models do not take into account possible changes due to natural and anthropogenic processes, they represent a simple way that could be used to evaluate and/or monitor the potential of each forest plantation in immobilizing organic C in soil (thus reducing atmospheric C concentration), as well as to select more appropriate species during forestation plan management at least in the north of Iran.     

Classification and mapping forest sites using geographic information system (GIS): a case study in Artvin Province

The productivity of forest sites has been indirectly determined with solo wood production objective in forest management. Forest site productivity should, however, be determined directly in order to implement ecosystem based multipurpose forest management philosophy. This article tackles the problem in distinguishing and mapping forest sites using both direct method and indirect method in Genya Mountain located in central of Artvin State Forest Enterprise. About 112 sample plots were designed and distributed over the area. In each sample plot, soil samples were collected and the classical timber inventory measurements were taken. According to direct method, Soil Moisture Regime (SMR) method is preferred due to a water deficiency in the study area. Water holding capacity was used as an essential criterion for the classification of the forest site. Forest site classifications were assigned regarding the physiographic factors such as landform, aspect, and slope. Five different forest sites classes; dry, moderate fresh, fresh, humid and hygric were determined. According to direct method, the guiding curve was used to generate anamorphic site index (SI) equations and three site index classes; good (SI=I–II), medium (SI=III) and low (SI=IV–V) were determined. Some important differences between the methods were realized. The forest sites determined with site index estimation method indicate that site index I and II is 505.99 ha, III 1095.79 ha and IV and V 992.95 ha, whereas forest sites determined with direct method related to dry site of 937.58 ha, moderate fresh site of 931.90 ha, fresh site of 1,797.71 ha, humid site of 80.48 ha and hygric site of 356.55 ha. The forest site maps of both methods were created using GIS functions. The forest sites of open and degraded areas should be determined according to direct method.     

Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain,Iran)

Understanding the spatiotemporal relationships between land use/cover changes (LUCC) and groundwater resources is necessary for effective and efficient land use management. In this paper, geographically weighted regression (GWR) and ordinary least squares (OLS) models have been expanded to analyze varying spatial relationships between groundwater quantity changes and LUCC for three periods: 1987–2000, 2000–2010, and 1987–2010 in the Khanmirza Plain of southwestern Iran. For this purpose, TM images were used to generate LUCC (rainfed, irrigated, meadow, and bare lands). Groundwater quantity variables, including groundwater level changes (GLC) and groundwater withdrawal differences (GWD), were gathered from piezometric and agricultural wells data. The analysis of spatial autocorrelation (Moran’s I and local indicators of spatial association ) demonstrated that GWR has a better ability to model spatially varying data with very minimal clustering of residuals. The results R ² and corrected Akaike’s Information Criterion parameters revealed that the GWR has the lowest similarity in space and time in neighboring situations and it has the high ability to explain more variance in the LUCC as a function of the groundwater quantity changes. All results of the distribution of local R ² values from GWR confirm our assertion that there is a spatiotemporal relationship between types of land use and each of groundwater quantity variables within the region. According to the t test results from GWR, there are significant differences between the GLC and GWD and the land use types in different places of region in each of the three time series. The GWR results can help decision-makers to make appropriate decisions for future planning.     

Seasonal variation in ambient PM mass and number concentrations (case study: Tehran, Iran)

Tehran, the capital city of Iran, is an important industrial and commercial center. This city is one of the worst cities in the world in terms of air pollution, which is mostly due to mobile sources rather than stationary sources. Particulate matter (PM), which is a complex mixture of extremely small particles and liquid droplets, is considered as an important source of air pollution in Tehran. In this study, our objective was to study PM₁₀, PM_2.5, and PM_1.0 mass and number concentrations and find the correlations of these two parameters in the west-central parts of Tehran during two consecutive warm and cold seasons. The particles collected from five stations were analyzed for their mass and number simultaneously by a laser-based Grimm dust monitor. In general, it was found that the accumulation of the PM in this region is more in the cold season. PM₁₀ mass concentration increases almost twofold and PM_2.5 and PM_1.0 almost three times in this season. The mean number concentration of the particles (0.3–20 μm) was found to be almost 4.8 times in the cold season. It was also noticed that the average dimensions of the particles decrease in that season.     

Use of forest inventories and geographic information systems to estimate biomass density of tropical forests: Application to tropical Africa

One of the most important databases needed for estimating emissions of carbon dioxide resulting from changes in the cover, use, and management of tropical forests is the total quantity of biomass per unit area, referred to as biomass density. Forest inventories have been shown to be valuable sources of data for estimating biomass density, but inventories for the tropics are few in number and their quality is poor. This lack of reliable data has been overcome by use of a promising approach that produces geographically referenced estimates by modeling in a geographic information system (GIS). This approach has been used to produce geographically referenced, spatial distributions of potential and actual (circa 1980) aboveground biomass density of all forests types in tropical Africa. Potential and actual biomass density estimates ranged from 33 to 412 Mg ha^–1 (10⁶g ha^–1) and 20 to 299 Mg ha^–1, respectively, for very dry lowland to moist lowland forests and from 78 to 197 Mg ha^–1 and 37 to 105 Mg ha^–1, respectively, for montane-seasonal to montane-moist forests. Of the 37 countries included in this study, more than half (51%) contained forests that had less than 60% of their potential biomass. Actual biomass density for forest vegetation was lowest in Botswana, Niger, Somalia, and Zimbabwe (about 10 to 15 Mg ha^–1). Highest estimates for actual biomass density were found in Congo, Equatorial Guinea, Gabon, and Liberia (305 to 344 Mg ha^–1). Results from this research effort can contribute to reducing uncertainty in the inventory of country-level emission by providing consistent estimates of biomass density at subnational scales that can be used with other similarly scaled databases on change in land cover and use.     

GIS-based pollution hazard mapping and assessment framework of shallow lakes: southeastern Pampean lakes (Argentina) as a case study

The assessment of water vulnerability and pollution hazard traditionally places particular emphasis on the study on groundwaters more than on surface waters. Consequently, a GIS-based Lake Pollution Hazard Index (LPHI) was proposed for assessing and mapping the potential pollution hazard for shallow lakes due to the interaction between the Potential Pollutant Load and the Lake Vulnerability. It includes easily measurable and commonly used parameters: land cover, terrain slope and direction, and soil media. Three shallow lake ecosystems of the southeastern Pampa Plain (Argentina) were chosen to test the usefulness and applicability of this suggested index. Moreover, anthropogenic and natural medium influence on biophysical parameters in these three ecosystems was examined. The evaluation of the LPHI map shows for La Brava and Los Padres lakes the highest pollution hazard (≈30 % with high to very high category) while Nahuel Rucá Lake seems to be the less hazardous water body (just 9.33 % with high LPHI). The increase in LPHI value is attributed to a different loading of pollutants governed by land cover category and/or the exposure to high slopes and influence of slope direction. Dissolved oxygen and biochemical oxygen demand values indicate a moderately polluted and eutrophized condition of shallow lake waters, mainly related to moderate agricultural activities and/or cattle production. Obtained information by means of LPHI calculation result useful to perform a local diagnosis of the potential pollution hazard to a freshwater ecosystem in order to implement basic guidelines to improve lake sustainability.     

The effects of socioeconomic parameters on household solid-waste generation and composition in developing countries (a case study: Ahvaz,Iran)

Environment problems associated with the generation of waste are part of societal changes where households play an important role. These societal changes influence the size, structure and characteristic of given households. For the effective planning of solid-waste handling infrastructure, it is essential to know the quantity of waste generation and its composition. This paper presents the findings of a study carried out in an urban municipal area in Iran to determine the household solid-waste generation rate and waste composition based on field surveys and to determine the related socioeconomic parameters. The dependent variables were waste generation and composition, and independent variables were family size, family employment, age, number of room and education. Over 400 sample households were selected for the study using a stratified random sampling methodology and from five different socioeconomic groups. Waste collected from all groups of households were segregated and weighted. Waste generation rate was 5.4 kg/household/day. Household solid waste comprised of ten categories of wastes and with the largest component (76.9%). The generation and composition of household solid waste were correlated with family size, education level and households income. This paper adequately suggests new insights concerning the role of socioeconomic parameters in affecting the generation of household waste.     

Habitat evaluation using suitability index and habitat type diversity: a case study involving a shallow forest stream in central Taiwan

In recent years, the Taiwanese government has strongly promoted the concept of ecological engineering in the hope that doing so will encourage the maintenance of the ecosystem and its integrity. As a result, the riprap spur dike is one of the most commonly used measures for protecting stream banks. Traditionally, a spur dike is used at concave banks to prevent their scouring and/or to increase their stabilization. An additional benefit of deflector structures, like spur dikes, may be to increase the weighted usable area (WUA) for aquatic life survival during periods of increased flow (examples include typhoon, flood, etc.). A two-dimensional river habitat simulation program (River2D) coupled with a developed shallow water habitat type diversity module was used for the case study at a headwater stream in central Taiwan. The habitat suitability index for this study was established using substrate, depth, and velocity from field surveys for the fish family Cyprinidae by prepositioned area electrofisher. The ungauged flood conditions were calculated using digital elevation models within a watershed delineation and hydrological modeling system in accordance with local regulations. Simulated results indicate that the spur dikes currently in use on the stream in this study need be improved from a WUA point of view more effectively handle a flood event.     

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station,USA

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott’s index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).     

Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: case study of Ardabil province,Iran

Wind energy is a renewable energy resource that has increased in usage in most countries. Site selection for the establishment of large wind turbines, called wind farms, like any other engineering project, requires basic information and careful planning. This study assessed the possibility of establishing wind farms in Ardabil province in northwestern Iran by using a combination of analytic network process (ANP) and decision making trial and evaluation laboratory (DEMATEL) methods in a geographical information system (GIS) environment. DEMATEL was used to determine the criteria relationships. The weights of the criteria were determined using ANP and the overlaying process was done on GIS. Using 13 information layers in three main criteria including environmental, technical and economical, the land suitability map was produced and reclassified into 5 equally scored divisions from least suitable to most suitable areas. The results showed that about 6.68 % of the area of Ardabil province is most suitable for establishment of wind turbines. Sensitivity analysis shows that significant portions of these most suitable zones coincide with suitable divisions of the input layers. The efficiency and accuracy of the hybrid model (ANP-DEMATEL) was evaluated and the results were compared to the ANP model. The sensitivity analysis, map classification, and factor weights for the two methods showed satisfactory results for the ANP-DEMATEL model in wind power plant site selection.     

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.     

Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China

Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant policies. Maotiao River Basin is located in the province of Guizhou, China, which has a developed agricultural industry in the karst mountain areas. This paper selected biophysical and social–economic factors as independent variables, and constructed a multiple logistic regression of farmland spatial distribution probability by random sampling. Then, by using GIS technology and integrating the 2000 data, this study predicted the farmland spatial pattern. When the predicted map was compared with the actual farmland map for 2000, we noted that 71% of the simulation is in accordance with the 2000 farmland pattern. The result satisfactorily proves the reasonability and applicability of our model.     

Determining the most suitable areas for artificial groundwater recharge via an integrated PROMETHEE II-AHP method in GIS environment (case study: Garabaygan Basin, Iran)

Flood spreading is a suitable strategy for controlling and benefiting from floods. Selecting suitable areas for flood spreading and directing the floodwater into permeable formations are amongst the most effective strategies in flood spreading projects. Having combined geographic information systems (GIS) and multi-criteria decision analysis approaches, the present study sought to locate the most suitable areas for flood spreading operation in the Garabaygan Basin of Iran. To this end, the data layers relating to the eight effective factors were prepared in GIS environment. This stage was followed by elimination of the exclusionary areas for flood spreading while determining the potentially suitable ones. Having closely examined the potentially suitable areas using the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II and analytic hierarchy process (AHP) methods, the land suitability map for flood spreading was produced. The PROMETHEE II and AHP were used for ranking all the alternatives and weighting the criteria involved, respectively. The results of the study showed that most suitable areas for the artificial groundwater recharge are located in Quaternary Q_g and Q^gsc geologic units and in geomorphological units of pediment and Alluvial fans with slopes not exceeding 3 %. Furthermore, significant correspondence between the produced map and the control areas, where the flood spreading projects were successfully performed, provided further evidence for the acceptable efficiency of the integrated PROMETHEE II-AHP method in locating suitable flood spreading areas.     

Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region

Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E?~?1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period.     

Applications of behavioral science to biodiversity management in agricultural landscapes: conceptual mapping and a California case study

Regime shifts of major salinity constituents (Ca, Mg, Na, K, SO₄, Cl, HCO₃, and NO₃) in the lower Salinas River, an agricultural ecosystem, can have major impacts on ecosystem services central to continued agricultural production in the region. Regime shifts are large, persistent, and often abrupt changes in the structure and dynamics of social-ecological systems that occur when there is a reorganization of the dominant feedbacks in the system. Monitoring information on changes in the system state, controlling variables, and feedbacks is a crucial contributor to applying sustainability and ecosystem resilience at an operational level. To better understand the factors driving salinization of the lower Salinas River on the central coast of California, we examined a 27-year record of concentrations of major salinity constituents in the river. Although limited in providing an understanding of solute flux behavior during storm events, long-term “grab sampling” datasets with accompanying stream discharges can be used to estimate the actual history of concentrations and fluxes. We developed new concentration–discharge relationships to evaluate the dynamics of chemical weathering, hydrological processes, and agricultural practices in the watershed. Examinations of long-term records of surface water and groundwater salinity are required to provide both understanding and perspective towards managing salinity in arid and semi-arid regions while also enabling determination of the influence of external climatic variability and internal drivers in the system. We found that rock weathering is the main source of Ca, Mg, Na, HCO₃, and SO₄ in the river that further enables ion exchange between Ca, Mg, and Na. River concentrations of K, NO₃, and Cl were associated with human activities while agricultural practices were the major source of K and NO₃. A more direct anthropogenic positive trend in NO₃ that has persisted since the mid-1990s is associated with the lag or memory effects of field cropping and use of flood irrigation. Event to inter-year scale patterns in the lower Salinas River salinity are further controlled by antecedent hydrologic conditions. This study underscores the importance of obtaining long-term monitoring records towards understanding watershed changes-of-state and time constants on the range of driving processes.

     

Assessment of elemental concentrations in the urban air (case study: Tehran city)

Tehran is one of the megacities of the world with a population of over eight million. Its air is highly polluted mainly due to the suspended particulate matters, which encompasses a wide spectrum of chemical elements. These elements based on their type, size, and impact on the life cycle have various environmental and heath risks. In this research, the neutron activation method is used to determine the concentration levels of Al, Ba, Fe, Mg, and V in the urban air. Thus, two districts of Tehran with different characteristics are selected. District 21 includes much of the industries located in Tehran metropolitan and is considered as an industrial area. In contrast, district 22 lacks any significant industrial activity. It is a newly established and expanding district adjacent to district 21 with a great deal of constructional activities. For the measurement of the suspended particulate matters in the air, the various sections of the aforesaid districts with industrial, residential, heavily congested traffic, residential/commercial, residential/heavily congested traffic, and residential/industrial classifications were identified. Subsequently, 24 sampling stations were selected. The sampling of the suspended particulate matters was conducted with the aid of a high volume pump containing 125 mm cellulose filters in two different time intervals. After completion of the sampling process, the samples were prepared and sent to the research reactor of the Iran Nuclear Energy Organization for Neutron Activation. During the next steps, the radiations emitted from the samples were registered, the radiation curves were plotted, and the amounts of the trace elements were determined. As a result, the average concentration levels of Al, Ba, Fe, Mg, and V were identified to be 3.301140, 2.273658 × 10, 4.0681696 × 10^???1, 3.5525475 × 10^???1, and 3.04075 × 10^???2 μg/m³, respectively. Moreover, the emission sources of the aforesaid elements into the air were identified. The concentration levels of these elements in the industrial and heavily congested traffic sections were higher. Finally, it was concluded that the statistical analysis of these elements presents a meaningful correlation among them.