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Biomass plays an important role in the world primary energy supplies, currently providing ∼14% of the world's primary energy needs and being the fourth largest contributor following coal, oil and natural gas. Over the past decade, domestic biomass heating has received more governmental and public supports than ever before in many developed countries, such as the UK. Although biomass combustion releases some combustion pollutants, biomass is renewable and produces little net CO 2 emissions to the atmosphere. Owing to the low sulphur and low nitrogen contents of many biomass materials, substituting biomass for fossil fuels, particularly coal, can reduce SO x and NO x emissions. This study investigated flue gas emissions, particularly carbon monoxide and nitrogen oxides, of a domestic biomass boiler under various operating conditions. The biomass boiler used in this study satisfies the current EU regulation (EN 303-05) on emissions of domestic biomass boilers. Emissions of the boiler had been measured not only under normal combustion conditions, but also under 'idle' combustion conditions when the boiler was not in but was ready for full operation. The experimental results are analysed and presented in this paper. Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

Biomass plays an important role in the world primary energy supplies, currently providing ∼14% of the world's primary energy needs and being the fourth largest contributor following coal, oil and natural gas. Over the past decade, domestic biomass heating has received more governmental and public supports than ever before in many developed countries, such as the UK. Although biomass combustion releases some combustion pollutants, biomass is renewable and produces little net CO 2 emissions to the atmosphere. Owing to the low sulphur and low nitrogen contents of many biomass materials, substituting biomass for fossil fuels, particularly coal, can reduce SO x and NO x emissions. This study investigated flue gas emissions, particularly carbon monoxide and nitrogen oxides, of a domestic biomass boiler under various operating conditions. The biomass boiler used in this study satisfies the current EU regulation (EN 303-05) on emissions of domestic biomass boilers. Emissions of the boiler had been measured not only under normal combustion conditions, but also under 'idle' combustion conditions when the boiler was not in but was ready for full operation. The experimental results are analysed and presented in this paper. Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

Considering recent environmental regulations, the need to adapt domestic biomass combustion systems to models that generate less emissions has gained relative importance. The present research proposes an analysis of the bed cooling effects on emission patterns, specifically focusing on the concentration, typology and morphological aspects of the released particles. The study was carried out by comparing the behaviour of a small-scale pilot plant with air stratification, with and without bed cooling. The results revealed an optimal behaviour of the facility with distributions of 30% primary-70% secondary air, accompanied by a significant decrease in emissions due to the reduction in the operating temperatures. More than 75% of the particles were retained in the bed on the cooled surfaces due to the effect of the prominent temperature gradient that was produced. Among the types of emitted particles (mostly with sizes below 0.1 μm), the presence of partial biomass degradation remnants was observed, representing three-quarters of the total collected matter. To a lesser extent, the presence of carbonaceous agglomerates was detected and usually in very compact clusters; however, in cases of high primary air supply, large amounts of immature soot were observed. Agencia Estatal de Investigación | Ref. RTI2018-100765-B-I00

Considering recent environmental regulations, the need to adapt domestic biomass combustion systems to models that generate less emissions has gained relative importance. The present research proposes an analysis of the bed cooling effects on emission patterns, specifically focusing on the concentration, typology and morphological aspects of the released particles. The study was carried out by comparing the behaviour of a small-scale pilot plant with air stratification, with and without bed cooling. The results revealed an optimal behaviour of the facility with distributions of 30% primary-70% secondary air, accompanied by a significant decrease in emissions due to the reduction in the operating temperatures. More than 75% of the particles were retained in the bed on the cooled surfaces due to the effect of the prominent temperature gradient that was produced. Among the types of emitted particles (mostly with sizes below 0.1 μm), the presence of partial biomass degradation remnants was observed, representing three-quarters of the total collected matter. To a lesser extent, the presence of carbonaceous agglomerates was detected and usually in very compact clusters; however, in cases of high primary air supply, large amounts of immature soot were observed. Agencia Estatal de Investigación | Ref. RTI2018-100765-B-I00

Scarcity of water resources in the entire country is more serious in the northern provinces like Golestan. Therefore, farmers have to use saline water or waste waters. One way to adapt to this condition is to use unconventional water such as Caspian Sea water, with lower salinity as compared with water from free seas, for common crops like soybean. Thus feasibility of using Caspian Sea water that has integrated with narrow common water and the calibration of Aquacrop model under the effect of salinity and water stress for soybean crop in Golestan province are more important. The Aquacrop model assumes a linear relationship between the biomass yield (BY) and crop transpiration one one hand and water productivity (WP) value on the other (Steduto and Albrizio, 2005). This model, as compared to other crop models, requires minimal input data and its new version 4.0 (June, 2012) has a salinity module which was used in this research to simulate the grain yield (GY) and WP of soybean under deficit and saline water irrigation. Furthermore, the AquaCrop model has not often been tested to simulate the yield of soybean under saline conditions in the semi-arid climate of Golestan province. This study aims at evaluating the efficiency of AquaCrop model in soybean yield simulation. The results are to be used for optimizing water consumption under water and saline stress. To this end, an experiment was carried out at the research farm of Gorgan University of Agricultural Sciences and Natural Resources.

Scarcity of water resources in the entire country is more serious in the northern provinces like Golestan. Therefore, farmers have to use saline water or waste waters. One way to adapt to this condition is to use unconventional water such as Caspian Sea water, with lower salinity as compared with water from free seas, for common crops like soybean. Thus feasibility of using Caspian Sea water that has integrated with narrow common water and the calibration of Aquacrop model under the effect of salinity and water stress for soybean crop in Golestan province are more important. The Aquacrop model assumes a linear relationship between the biomass yield (BY) and crop transpiration one one hand and water productivity (WP) value on the other (Steduto and Albrizio, 2005). This model, as compared to other crop models, requires minimal input data and its new version 4.0 (June, 2012) has a salinity module which was used in this research to simulate the grain yield (GY) and WP of soybean under deficit and saline water irrigation. Furthermore, the AquaCrop model has not often been tested to simulate the yield of soybean under saline conditions in the semi-arid climate of Golestan province. This study aims at evaluating the efficiency of AquaCrop model in soybean yield simulation. The results are to be used for optimizing water consumption under water and saline stress. To this end, an experiment was carried out at the research farm of Gorgan University of Agricultural Sciences and Natural Resources.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.