Journal of Environmental Resources Research
Volume:11 Issue: 1, Winter-Spring 2023

Sustainable land-use planning refers to the effort to establish a balance between economic growth, ecological structures, environmental protection, and social progress. Therefore, land-use suitability assessment and extract comprehensive objectives are essential. In recent years, the use of artificial intelligence (AI) tools significantly increased for land-use planning. In this study, the Multi-Objective Land Allocation (MOLA) algorithm, Gravitational Search Algorithm (GSA), and Image Processing (IP) technique have been applied to urban land use location of the Birjand watershed based on a comprehensive set of sustainable development goals. The objectives used include maximizing fitness functions (e.g., environmental and ecological suitability, compression functions, and landscape stability), minimizing land-use conversion, imposing limitations of flood protected areas with an above 70% slope, the demands of urban areas, placement one land use per pixel. Visual assessment, statistical and landscape metrics analysis were employed to compare algorithms' outcomes. results showed that the MOLA (with an average of 215.136) had better allocation concerning land use suitability assessment for urban development. Also, MOLA and IP algorithms (with standard deviations of 41.037 and 41.729, respectively) were placed in better positions than GSA. Additionally, landscape metrics analysis indicated that there were relative efficiency and superiority between different metrics of the studied algorithms.

This research was conducted in order to evaluate the effect of conservation agriculture on energy indicators in common agricultural ecosystems using a split plot design in the form of randomized complete blocks with three replications in the climatic conditions of Birjand. In this research, the studied treatments include tillage methods in three levels of conventional tillage methods (plough+disc+leveling+farrower+planting with seeds), reduced plowing (chisel packer or light disc + farrower+planting with seeds) and no plowing. (Direct planting with seed drill) in the main plots and plant residues at three levels without residues, 30% retention and 60% retention of wheat residues in secondary plots. This study was investigated in the rotation system of wheat, barley and cotton. The results showed that in the period under study, the largest contribution of electricity was 68.7%, nitrogen was 11.9% and fuel was 8.9%. The share of direct energy from the total energy input for all three tillage methods was more than 75%. The effect of tillage practices was only significant on the efficiency of energy consumption; So that the change of tillage methods from conventional tillage to no tillage and reduced tillage was associated with a decrease in energy consumption by 11.6 and 9.9, respectively. The results of the energy index analysis indicated that the use of conservation tillage methods is recommended in terms of the superiority of the energy consumption efficiency index for wheat, barley and cotton cropping systems in the climatic conditions of Birjand.

Heavy metals have contaminated various ecosystems. Omnivorous animals such as rodents with high fertility, abundance and low motility can be good indicators for heavy metal contamination of their habitat. Rural- agricultural areas around seven cities in Golestan and Mazandaran Provinces were selected to trap five brown rats (Rattus norvegicus) from each region. After the preparation of serum samples, Pb concentration was measured by atomic absorption spectrometry. Also, GIS software was used to survey the effect of environmental factors on the Pb distribution pattern. The average Pb concentration was similar in males and females. The average Pb concentrations in rural areas of Golestan and Mazandaran Provinces were 6.7 and 6.3 μg/dL, respectively (p≥ 0.05). The highest average Pb concentration (7.55 μg/dL) belonged to rats sampled in areas around Ghargomishan road (around Neka City), and the lowest average Pb concentration (5.20 μg/dL) belonged to rats sampled from Kaleh agricultural areas (between Bahnamir and Babolsar Cities). Using the sensitivity method of the linear multiple regression model, it was found that the temperature and humidity have the greatest effect and altitude and precipitation have the lowest impacts. The results of this study indicated Pb contamination of rural-agricultural areas of Golestan and Mazandaran Provinces and the transmission risk of this hazardous metal to humans and other animals living in these areas. Therefore, the communication of information to the authorities and the use of adequate measures to prevent Pb entrance to sampled areas seem necessary.

Energy use efficiency is a measure of how efficiently energy is used in an agricultural production system. It considers the amount of energy inputs required to produce a given output, such as a unit of cumin or fennel. The province of Qazvin, Iran was selected for the study of the cultivation of medicinal plants in 2022. A life cycle assessment (LCA) is a comprehensive analysis of the environmental impact of a product or process throughout its entire life cycle, from raw material extraction to disposal. It considers the environmental impact of various stages, including production, transportation, use, and disposal, and assesses the impact on categories such as climate change, water use, and land use. The present study investigates energy use and environmental impacts of cumin and fennel production. The results showed that fennel had higher productive energy and that its energy output was 18206.04 MJ ha-1. The highest consumption of inputs, which was over 40%, was related to nitrogen fertilizers. The negative addition of net energy indicates that more care should be taken in medicinal plant farms as to how energy inputs, especially chemical fertilizers and diesel fuel, are consumed. LCA is a suitable instrument to investigate and quantify the environmental effects of agricultural products and food industries. The effects of environmental emissions of medicinal plant production were calculated as an important part of human health. The weighting results showed that the human health category has more environmental emissions for both crops

The burning of gases in torches in oil and refinery areas is a significant source of air pollution, releasing harmful pollutants into the atmosphere. This study focused on the 9th refinery of the South Pars Gas Complex in the Assaluyeh region as a case study to understand the distribution and impact of pollutants caused by combustion processes and refinery industry flares. The researchers collected air pollutant and flare emission data, weather information, and physical stack and flare information to model the distribution of pollutants using the AERMOD software. The study found that carbon monoxide emissions were higher than the standard range in autumn and winter of 2020 and summer of 2021, while NO2 emissions were lower than the standard range, and SO2 emissions were higher than the standard range across all seasons. The highest concentration of pollutants was observed in the refinery range and to the south and west of the refinery, leading to the sea, which could affect the ecosystem. The study highlights the importance of having sufficient data and database models to provide effective analysis and management strategies to control and reduce the effects of air pollutants. Overall, this study provides valuable insights into the distribution and impact of pollutants caused by combustion processes and refinery industry flares, which can inform future management strategies to mitigate the harmful effects of air pollution.

Salinity is the second most important abiotic stress in the world, especially in Iran. The research dedicated to reveal the responses of the some physiological and biochemical parameters (proline content, carbohydrates, total protein and peroxides enzymes) of Chrysopogon zizanioides to soil salinity in vitro. Prior to salinity application, the plant seedlings placed in greenhouse for adaptation purpose. To apply soil salinity stress on Chrysopogon zizanioides, sodium chloride solution was applied in 6 different concentrations in the period of 4 days. All the measurements, including leaf proline content, soluble and non-soluble sugar, protein content, and peroxidase enzyme, were measured after 2 month of salinity treatment application. The results indicated that salinity stress could have a significant increasing effects on proline content, soluble sugar and non-soluble one of leaves (p≤0.05). Leaf total protein content and peroxidase enzyme are also significantly affected by salinity stress, showing an increasing trend up to 32 ds/m following by a decreasing trend at 44 ds/m. According to physiologic and biochemical parameters, the results confirms high tolerance of Vetiver grass against 32 ds/m salinity in short term.

Runoff is a crucial hydrological variable that provides vital information for water resource management and planning. In this study, we used the Soil and Water Assessment Tool (SWAT) to simulate monthly runoff in the Neyshabur watershed, Khorasan Razavi province, for a ten-year period from 2000 to 2009. We considered all available rain gauges, synoptic, and evapotranspiration stations within and around the watershed. We calibrated the model parameters and coefficients using the SUFI2 algorithm in the SWAT-CUP software package. We found that the parameters related to the infiltration process, such as CH_K2, CN2, SOL_AWC, and REVAPMN, had the most significant impact on the runoff. We evaluated the model's performance during the calibration and validation periods using parameters such as P-factor, R-factor, Kling-Gupta efficiency (KGE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2). The simulation results showed good agreement with the observed monthly runoff for both the calibration and validation periods. The NSE and R2 values were 0.84 and 0.87, respectively, at the Zarande Andarab station during the calibration period, and 0.74 and 0.78, respectively, during the validation period. The Hosseinabad Jangal station showed even better performance, with NSE and R2 values of 0.93 and 0.93, respectively, during the calibration period, and 0.90 and 0.90, respectively, during the validation period. Comparing our results with previous studies in the same watershed, we found that utilizing a more comprehensive monitoring network and increasing the statistical period of the study can significantly enhance the model's performance and reduce uncertainties in the calibration and validation stages.

In this research, land-use changes in Azadshahr County were investigated from 1998 to 2009, using the imageries from Landsat 5 satellite and an integration of the Markov chain and Cellular Automata methods. Using the object-based support-vector-machine image classification method, land-use maps were classified into three major categories, namely agriculture fields, forest lands and built-up areas for the years of 1987, 1998 and 2009; their overall accuracies have been obtained 91.0%(1987), 91.0% (1998) and 88.8% (2009), with the respective Kappa values of 86.5%(1987), 86.5% (1998) and 83.2%(2009). The built-up areas had the greatest changes by increasing 2.02% and 2.17% for the periods of 1987-1998 (as first period) and 1998-2009 (as second period), respectively. During the first period, forest area has shrunk by approximately -1.80%. However, as a result of the afforestation project during 1998-2009, forest area has increased 1.59%, while over the 22-year period the total area of forest has merely reduced by -0.21%. Agricultural areas on the one hand has shrunk in favor of the built-up areas, and on the other hand, increased by the conversion of the forest lands, making a total reduction of -0.22 and -3.75% for the first and second periods, respectively. The land-use pattern of 2020 was simulated using the MULOSCE extension of the QGIS software based on the integrated cellular automata and Markov chain technique. It is expected for this period to encounter a 0.62% increase in built-up areas, with 0.48% and 0.15% reduction in agriculture fields and forest lands, respectively.

Increase in greenhouse production in Yazd province is along with increased fertilizer and pesticide use, so it is necessary to reduce the use of chemicals in greenhouse production. The aim of this study was to investigate the factors influencing the process of transition to organic agriculture in greenhouse cucumber producers in Yazd province. This study was a descriptive-survey research and data collection tools were questionnaires and face to face interviews with 144 producers of greenhouse cucumber. Data analysis was done using descriptive analysis, t-test and logit econometric model. Of the 144 greenhouse cucumber producers, 59 people have a positive trend to become organic farmers and 85 people showed no interest. The results showed that the low information of producers in the field of organic farming, lack of sufficient support of organic farming from government, high-risk of organic farming, the lack of specific market and the lack of knowledge and skills are the major obstacles to the production of organic products. Logit model results showed that there is a significant positive relationship between the level of production history, age, education, sanitation, positive attitude towards the environment and desire to have organic farming. Also, there is a significant negative correlation between the use of fertilizers and chemical pesticides and desire to have organic farming. Due to lack of information in the field of technical, management and sales of organic products, government support and control of the production in this area is essential.

Soil and water conservation (SWC) is important for reducing the damaging effects of different soil erosion problems and improving the sustainability and rehabilitation of the natural environment. This study evaluated the effect of SWC in conserved areas on soil properties of two different climatic regions, including semi-arid (Rastab region) and humid cold temperate (Kohrang region) climates in Chaharmahal and Bakhtiari province, Iran. A total of 24 soil samples were taken as soil cores from two layers, including 0–30 cm and 30-60 cm from each region. The soil physicochemical properties were analyzed based on standard laboratory procedures.Based on the results, soil properties in a five-year conserved area experiment showed improvement. However, no significant trend was observed in soil bulk density. The storage of soil carbon (SOC) and total nitrogen (TN) significantly increased after five years in the conserved area, while lime (CaCO3) decreased significantly. Moreover, significant improvement was found based on the infiltration rate in the conserved area management. Therefore, conserved areas for five years significantly improved the soil quality and potential carbon sequestration and infiltration rate in both semi-arid and humid cold temperate climates conserved areas. Overall, conserved areas in the area with a colder climatic regime improved the soil quality more than the semi-arid regime. Thus, SWC should be adopted and scaled up in areas exposed to severe land degradation due to its positive effects.

Spatio-temporal accessibility to water, especially, for household use is expedient to a healthy living. This work attempted the development of predictive models using multiple regression to forecast household water use and also assess the significance of water demand forecasting to water use efficiency and its accessibility in homes. Data used for the study were generated through questionnaire administration and these were analyzed using descriptive and inferential statistics. Domestic water uses consist of 10 defined components (Drinking, Cooking, Bathing, Washing, Cleaning, Car wash, Lawn watering, Coolant/Chiller, Incidental and Livestock) proportionately distributed within Coolant/Chiller and Washing regime and ranged from 0.12% to 38.53% respectively among the regime components. Results of the regression analyses revealed that two home water use components namely Washing and Car wash predict home water use at 95% level of confidence with R2 of 0.872 and the Standard Error of 28.91. With this result, Model 3 showed better accuracy than Models 1 and 2 comparatively. The Incidental use component was not significant and may be ignored in the computation. Two predictive components namely Washing and Car wash generally explain excessive use of water in homes and must be considered to enhance the efficient use and unrestricted accessibility of this vital resource.

The aim of this research was to predict soil organic matter (SOM) using kriging and cokriging methods using soil auxiliary data. Soil samples were gathered from an area of 63 km2 in Bonab plain in Iran and overall of 78 samples from depth 0-20 cm were collected. SOM and ten other soil physicochemical properties were measured. Later correlation between SOM and soil properties was determined and those properties with high correlation in 1% probability level with SOM were used to develop cross-semivariograms. Later SOM prediction was done on a grid of 100 m with kriging and cokriging methods using BMElib package developed for MATLAB software. Results showed that among studied soil properties, CCE, silt, sand and wet aggregate stability (WAS) had the highest correlations with SOM and therefore they were chosen as auxiliary data in cokriging of SOM. Spatial prediction of SOM with kriging method resulted in MSE and RMSE of 0.055 % and 0.234 % respectively. However, SOM prediction with developed cross-semivarigrams by using auxiliary data revealed that CCE and silt could improve SOM prediction with MSE and RMSE of 0.047%, 0.032% and 0.216%, 0.178 % respectively. Selecting appropriate soil parameters with high correlation with SOM and high spatial dependency can improve spatial prediction of SOM and thus, a step forward in sustainable management of SOM as a key soil quality index, especially in areas with salinization and desertification danger.