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This study introduces two integrated methods to model the short- to long-term droughts in terms of the Standardized Precipitation Index (SPI). In this regard, the monthly precipitation data from three stations located in different climates of Iran was used. Firstly, the capability of Random Forest (RF) and M5 Pruning tree (M5P) models were assessed for short- to long-term drought forecasting. Then, the impact of data multi-pre-processing on improving the models' efficiency was investigated. Therefore, inputs were serially decomposed via Discrete Wavelet Transform (DWT) and Variational Mode Decomposition (VMD). The most dominate subseries were selected based on their energy values and used as inputs of the RF and M5P. Furthermore, since the drought variable consists of two deterministic and stochastic sections and in the modeling process the stochastic part is not often considered, in the third step, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model was applied for determining the random parts of the SPIs. Results showed that the integrated models led to better outcomes compared to the standalone models. The second and third steps increased the modeling accuracy up to 45% and 55%, respectively. Results showed that the distribution range of the Root Mean Square Errors (RMSE) criteria for the SPI-3 modeling decreased from 0.438–0.551 (in using raw data) to 0.184–0.26 (in using the integrated-GARCH model). Results showed that using the integrated-GARCH models, SPIs could be successfully simulated with considering only one input variable. [ABSTRACT FROM AUTHOR]

In addition to the waste of resources and economic losses, environmental damage by gas flaring is widespread and significant. Since flaring the associated gas gives no added value in exchange for its pollution and greenhouse gas (GHG) emissions, it could be identified as a top priority for mitigation. Iran is the third gas flaring country after Russia and Iraq among those facing this issue, and is responsible for 12.1% of the world's gas flaring. While the necessity of developing a method for the precise estimation of flaring GHG emissions is clear, especially for evaluating the result of countries' efforts to meet their nationally determined contribution target, there are huge uncertainties and discrepancies in the values of emission factors among various data sources due to the lack of actual measurements of the volume and diversity of the composition of flare gas. This study aimed to fill the gap in providing authentic data on Iran's gas flaring GHG and air pollutant emissions by developing a model based on satellite data on flare volumes, gas compositions, and combustion equations. Our results revealed that based on 2021 data on flaring volume, Iranian gas flares are emitting approximately 50 million metric tons of CO2 equivalent to the atmosphere annually, which could be reduced to 43 by only enhancing the flares' efficiency. It accounted for 5.5%–6% of the total GHG emissions of the country. Integr Environ Assess Manag 2023;19:735–748. © 2022 SETAC. Key Points: Iranian gas flares are emitting some 50 million metric tons of CO2 equivalent to the atmosphere annually, accounting for 5.5%–6% of the country's total GHG emissions.Slight increases in the flare's destruction efficiency can cause a huge decrease in its value of global warming potential.The gas flare's emission factors suggested by USEPA would be reliable only if a combustion efficiency of 98% and a destruction efficiency of 99% were considered, while emission factors suggested by Ecoinvent seem to be too optimistic to be true.By taking advantage of fair access to financial opportunities and international cooperation envisaged through the Convention (COP21), the Paris Agreement could be a historic opportunity for the complete elimination of gas flaring from Iran's upstream and downstream oil industries. [ABSTRACT FROM AUTHOR]

Drought as a natural disaster is one of the human's ecological, hydrological, agricultural, and economic concerns. In this study, multiscale intelligence methods were proposed for drought severity detection and mapping in the northwest part of Iran for the years of 2007 to 2020. In the modeling process two scenarios were considered and in-situ and remote sensing datasets were adopted with two machine learning models namely M5 Pruning tree (M5P) and Random Forest (RF). In the first scenario, the in-situ datasets including the precipitation, relative humidity, evaporation, and temperature were used as inputs of the intelligence models to assess drought severity in terms of the Standardized Precipitation Index. In the second scenario, the SM2RAIN-ASCAT precipitation product and Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) products of the MODIS were considered as inputs. During the drought severity modeling process, the input time series were first broken down into several subseries via the Variational Mode Decomposition; then, the most effective subseries were selected and imposed into the M5P and RF as inputs. Also, the potential of the relatively new TemperatureVegetation Water Stress Index (T-VWSI), which has developed based on the NDVI and LST, was assessed in drought severity monitoring. The results proved the appropriate efficiency of the proposed multiscale methods in effectively detecting drought severity. Also, it was observed that the T-VWSI could be successfully used for detecting drought occurrences in areas without meteorological datasets. [ABSTRACT FROM AUTHOR]

Drought is one of the severe natural disasters that has devastating effects on various parts of the environment. Therefore, spatiotemporal monitoring of drought using reliable methods is very important. In this study, using a new Discrete Wavelet Transform (DWT)-Variational Mode Decomposition (VMD)-Energy multi-scale approach, the efficiency of the new Soil Moisture to RainAdvanced SCATterometer (SM2RAIN-ASCAT) precipitation product was investigated in monitoring the spatiotemporal patterns of short- to long-term droughts for the Northwest part of Iran. In this regard, after validating the accuracy of the SM2RAIN-ASCAT datasets, a multi-scale method was developed based on the serial decomposition of the drought signals. The mean energy amounts of the obtained subseries were imposed into the K-means (K signifies the number of clusters) to identify the drought-prone regions. Results showed that the highest Correlation Coefficient (R) between SM2RAIN-ASCAT and in-situ observations are found at monthly time scales, in which 73% of the stations had R higher than 0.7. Investigating the reliability of this product for drought monitoring showed that for short- and mid-term timescales in 75% of the stations, the values of R were higher than 0.7. The applied methodology successfully showed the drought-prone regions and a direct relationship was found between energy and drought intensity. The capability of the applied methodology was verified via the CPC Merged Analysis of Precipitation (CMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) products and the obtained results proved the desirable performance of the SM2RAIN-ASCAT datasets. It was found that SOI, Nino3, and Nino3.4 had major effects on the drought index in the selected area, while AO had the least effect. The relationship between the LST and SPIs was negative, while there was a positive relationship between NDVI and SPIs. [ABSTRACT FROM AUTHOR]

In this study, the spatiotemporal variations of drought and salinity were assessed for the Urmia Lake basin, which is a very critical and challenging ecosystem problem. The lowering of water level in the Urmia Lake and the associated risk of becoming a completely saline land has become an important issue in Iran. In the first step of the study, using the ground- and satellite-based datasets, a new multiscale technique based on Empirical Wavelet Transform (EWT) and Differential Symbolic Entropy (DSE) was proposed to monitor the variations of drought in the selected region. In the next step, variations in the water level and salinity of Urmia Lake and its impacts on the environment were investigated using satellite datasets. Results showed that the Southern parts of the basin were more prone to severe droughts, and there was a direct relationship between entropy and drought intensity. Based on the results, it was concluded that climate change had an impact on the shrinkage of the lake; however, these changes were not solely responsible of causing the dramatic water loss of the Lake. Variations of the Salinity Index (SI) showed that saline lands were mostly seen in the Eastern and Southern parts of the lake, causing negative impacts on air quality and agricultural activities in these parts. It was found that the proper management of water resources would play an effective role in the restoration of the lake. [Display omitted] • Applying a new multiscale technique based on the EWT-DSE for drought monitoring. • Investigating the soil salinity variations via remotely sensing-based datasets. • Detecting saline land via the salinity index obtained from the satellite datasets. • Accuracy of the used methodology was proved in drought and soil salinity monitoring. [ABSTRACT FROM AUTHOR]

Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area with different climates. Therefore, 31 stations located in Iran were selected and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were computed during the 1951-2016 period. A hybrid methodology including Maximal Overlap Discrete Wavelet Transform (MODWT) and K-means methods was used for obtaining the SPIs time-frequency properties and multiscale zoning of the area. The energy amounts of the decomposed subseries via the MODWT were applied as inputs for K-means. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the northern parts where the lowest values were obtained for drought duration. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and the mean SPIs, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that increasing the degree of SPI led to more similarity between the stations of each cluster. Also, the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6. [ABSTRACT FROM AUTHOR]

Beside in situ observations, satellite-based products can provide an ideal data source for spatiotemporal monitoring of drought. In this study, the spatiotemporal pattern of drought was investigated for the northwest part of Iran using ground- and satellite-based datasets. First, the Standardized Precipitation Index series were calculated via precipitation data of 29 sites located in the selected area and the CPC Merged Analysis of Precipitation satellite. The Maximal Overlap Discrete Wavelet Transform (MODWT) was used for obtaining the temporal features of time series, and further decomposition was performed using Ensemble Empirical Mode Decomposition (EEMD) to have more stationary time series. Then, multiscale zoning was done based on subseries energy values via two clustering methods, namely the self-organizing map and K-means. The results showed that the MODWT-EEMD-K-means method successfully identified homogenous drought areas. On the other hand, correlation between the satellite sensor data (i.e. the Normalized Difference Vegetation Index, the Vegetation Condition Index, the Vegetation Healthy Index, and the Temperature Condition Index) was evaluated. The possible links between central stations of clusters and satellite-based indices were assessed via the wavelet coherence method. The results revealed that all applied satellite-based indices had significant statistical correlations with the ground-based drought index within a certain period. [ABSTRACT FROM AUTHOR]

Accurate prediction of drought indices is a useful method to reduce its undesirable consequences. In this study, the workability of newly integrated hybrid forecasting approach based on Meta model and data processing methods was assessed for forecasting the Standardized Precipitation Evapotranspiration Index (SPEI) in districts with different climates. The short-, mid-, and long-term SPEIs series (i.e. timescale of 3, 9, and 24 month) were computed during the period of 1951–2019 for five sites located in Iran. In this regard, first temporal features of the SPEIs were broken down using Wavelet Transform (WT). Then, for obtaining features with higher stationary properties, Ensemble Empirical Mode Decomposition (EEMD) was applied to further decompose the obtained subseries. Finally, the most efficient subseries were selected and inserted to Meta model approaches [i.e. Feed Forward Neural Network (FFNN), Kernel Extreme Learning Machine (KELM), and Gaussian Process Regression (GPR)] as inputs. Results showed that the proposed methods enhanced the models' capability between 35 to 45%. The capability of the proposed model was verified via Overlap Discrete Wavelet Transform (MODWT) method. Results showed that the distribution range of the Root Mean Square Errors (RMSE) criteria for integrated methods decreased from 0.036–0.172 (in raw data) to 0.025–0.109 (in decomposed data). The Monte Carlo uncertainty analysis was used to assess the applied models dependability. Results showed that the integrated model with having values of 72.8% to 89.2% for the 95PPU indicator had an allowable degree of uncertainty in short- to long-tern drought modeling. [ABSTRACT FROM AUTHOR]

Drought as a severe natural disaster has devastating effects on the environment; therefore, reliable drought prediction is an important issue. In the current study, based on lower upper bound estimation, hybrid models including data preprocessing, permutation entropy, and artificial intelligence (AI) methods were used for point and interval predictions of short- to long-term series of Standardized Precipitation Index in the Northwest of Iran. Ground-based and remote sensing precipitation data were used covering the period of 1983-2017. In the modeling process, first, the data processing capability via variational mode decomposition (VMD), ensemble empirical mode decomposition, and permutation entropy (PE) was investigated in drought point prediction. Then, interval prediction was applied for tolerating increased uncertainty and providing more details for practical operation decisions. The simulation results demonstrated that the proposed integrated models could achieve significantly better performance compared to single models. Hybrid PE models increased the modeling accuracy up to 40 and 55%. Finally, the efficiency of developed models was verified for Normalized Difference Vegetation Index prediction. Results demonstrated that the proposed methodology based on remote sensing data and VMD-PE-AI approaches could be successfully used for drought modeling, especially in limited or non-gauged areas. [ABSTRACT FROM AUTHOR]

Stand-alone hybrid energy systems based on solar and energy storage are an effective option for rural areas to meet the load demand. The objective of the current work is to the optimal configuration of a stand-alone hybrid photovoltaic/battery energy storage system with the help of an efficient metaheuristic algorithm, improved harmony search, to supply electrical of a residential load in Iran. The objective function is a minimization of total life cycle cost (TLCC) subject to the reliability index (loss of load probability). The optimal configurations of hybrid systems are compared in respect of different losses of load probability (0 to 20%). Sensitivity analysis and effects of economic parameters based on photovoltaic and battery prices are carried out to study the possibility of the suggested scheme. The results show that, by increasing the reliability index from 0 to 20%, the optimal number of panels and batteries decreases by 52 and 1202. Also, it is found that the TLCC of the system and cost of system components are increased by decreasing of the reliability index value. [ABSTRACT FROM AUTHOR]

High consumption of fossil fuel resources has always been associated with large amounts of pollutants' production. Due to this, Tehran weather condition reaches emergency state every year and causes a health threat to residents. Residential sector consumes over 40% of the total energy in Iran and causes a high amount of emission. In this article, the amount of loss reduction is calculated when new insulations are used (instead of stone-wool) in a building located in district 3 of Tehran. Innovative insulations involve the use of expanded polystyrene (EPS) and hollow silica nanospheres (HSN). To validate the previous part and observe the wall condition in detail, walls with current and new insulations are modelled by ANSYS FLUENT. Results indicate that EPS and HSN reduce total energy demand in the studied apartment by 1.2% and 5.1%, respectively. Finally, Tehran's under-construction buildings during 2015 are assumed to utilize mentioned insulations to observe their effects. The annual amount of reduced six air pollutants (CO2, SPM, CO, SO2, CH4 and NOx) by using EPS and HSN are 16,681 and 60,712 tonnes, respectively. It is found out that by using EPS and HSN, CO2 external costs can be decreased by 4,376,846$ and 15,929,919$ per year. [ABSTRACT FROM AUTHOR]

Growing energy demand, rising greenhouse gas emissions and the depletion of fossil fuels necessitates the development of renewable energy sources. In order to improve renewable energy utilization, it is necessary to determine the most important factors influencing energy strategy. The energy system of Iran is highly dependent on fossil fuels; however, Iran has a high potential for solar energy development and several policies are being pursued by the government to develop power generation by renewable energy resources. This study presents and discusses several important parameters of energy policy and security which make the installation of photovoltaic (PV) systems crucial for the future. Installation of solar PV will improve energy security, foster job creation and reduce environmental problems. It also introduces and investigates those institutions and organizations involved in developing renewable energies, especially solar energy. These include the Ministry of Energy and universities, both of which play key roles in the development of solar energy. [ABSTRACT FROM AUTHOR]

CO 2 utilization is one of several tools available to us to mitigate climate change. This paper aims to study and optimize the urea production in the largest CO 2 utilization plant in Iran. The location, magnitude, and source of thermodynamic inefficiencies of the plant are evaluated using exergy, exergoeconomic and exergoenvironmental analyses. The optimization process uses as decision variables the temperature of the lean monoethanolamine solution and loading, along with the height of the absorber and the stripper of the plant. The decision variables and objective functions are trained with a hybrid combination of an artificial neural network with a genetic algorithm. The multi-objective genetic algorithm results in a pareto front of solutions. The exergy efficiency of the overall system is found to be 30.89% and 3.57, 2.86, 2.21% of the exergy of the fuel provided to the plant is destroyed in the soda ash wash direct contact, the stripper, and the absorber columns (424.07, 339.71 and 258.61 kW), respectively. The exergoeconomic analysis shows that the heat exchangers E-7 and E-8 result in relatively low exergoeconomic factor; therefore, an enhancement in the thermodynamic performance of the heat exchangers should be considered. The absorber column is found to have the largest environmental impact, equal to approximately 0.1205 mPt/s. The highest environmental impact of exergy destruction, equal to 33,256.3 mPts/hr is found in the stripper. • An industrial CO 2 capture and utilization integrated to a urea plant is investigated. • The system is optimized with an artificial neural network coupled to a genetic algorithm. • The plant is evaluated with exergy, exergoeconomic and exergoenvironmental analyses. [ABSTRACT FROM AUTHOR]

Carbon capture and utilization (CCU) may offer a response to climate change mitigation from major industrial emitters. CCU can turn waste CO2 emissions into valuable products such as chemicals and fuels. Consequently, attention has been paid to petrochemical industries as one of the best options for CCU. The largest industrial CO2 removal monoethanol amine-based plant in Iran has been simulated with the aid of a chemical process simulator, i.e., Aspen HYSYS® v.10. The thermodynamic properties are calculated with the acid gas property package models, which are available in Aspen HYSYS®. The results of simulation are validated by the actual data provided by Kermanshah Petrochemical Industries Co. Results show that there is a good agreement between simulated results and real performance of the plant under different operational conditions. The main parameters such as capture efficiency in percent, the heat consumption in MJ/kg CO2 removed, and the working capacity of the plant are calculated as a function of inlet pressure and temperature of absorber column. The best case occurred at the approximate temperature of 40 to 42 °C and atmospheric pressure with CO2 removal of 80.8 to 81.2%; working capacity of 0.232 to 0.233; and heat consumption of 4.78 MJ/kg CO2. [ABSTRACT FROM AUTHOR]

This investigation presents a powerful and useful platform for the feasibility study of hybrid renewable systems for rural electrification and potable water supply, which can be used in each country with different values of economic inputs and local renewable potentials. The main objective of present investigation is to introduce feasible solutions in remote villages with different climate zones. Since Iran is a country with different climates, five remote villages are selected as case studies. The selected villages have different values of prime and deferrable loads. In continue, the decision-making between the utilization of hybrid renewable energy systems and grid extension is conducted. To assess the feasibility of grid extension scenario, the distance from the central grid for selected villages is determined using the Geographic Information System (GIS). To supply the required potable water, the Reverse Osmosis (RO) desalination system is employed. By employing HOMER Pro software, several energy configurations for each village are simulated. The results illustrate that for all selected villages, the stand-alone (off-grid) system is the most cost-effective option. The pure grid extension scenario due to the high values of Net Present Cost (NPC) is not recommended for small and large villages. The CO 2 emissions of pure extension are more than 105 kg/year for all selected villages. By using pure extension option, about 25–43% of the share of overall NPC is related to the grid extension cost. With the use of stand-alone systems instead of pure extension, about 10–19% of energy losses and 100000–200000 kg/year of CO 2 emissions reduce. Finally, to determine the breakeven distance for each village, the sensitivity analysis on load demand and extension distance is implemented. For instance, for selected villages with the climates of Semi-Arid and Cold, the breakeven distance is computed to be 13.5 km and 10.3 km, respectively. Image 1 • Decision-making between the hybrid renewable energy systems and grid extension is conducted. • When the load demand of a remote village is high, the integration of grid extension and renewable system is cost-effective. • The pure grid extension scenario due to the high values of NPC is not recommended for small and large villages. • The CO 2 emissions of pure extension are more than 105 kg/year for all selected villages. • For selected village with the climate of Semi-Arid, the breakeven distance is computed to be 13.5 km. [ABSTRACT FROM AUTHOR]