The main GHGs (CO2, NOx, and SO2) have been quantified based on national energy and population statistics. The results show that the contribution of households' energy consumption in the West Bank to global CO2 emission is about 0.016%, while contribution of total energy consumption by all sectors is about 0.041%. The results show that wood is the most polluting energy source in terms of CO2 and NOx emission, while electricity is the most polluting source in terms of SO2. Other sources like diesel, kerosene, and LPG that contribute to the GHGs emission are also quantified. The total amounts of CO2, NOx, and SO2 by households in the West Bank are 4.7 million tonne per year, 3.02 thousand tonne per year, and 2.23 thousand tonne per year respectively. This study presents a set of measures that might help in reducing the level of GHGs emission and protect the environment.

Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO2. In agricultural soils, N2O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N2O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N2O emission, and to compare the performances of RF with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N2O emissions from local information.

DayCent is a biogeochemical model of intermediate complexity widely used to simulate greenhouse gases (GHG), soil organic carbon and nutrients in crop, grassland, forest and savannah ecosystems. Although this model has been applied to a wide range of ecosystems, it is still typically parameterized through a traditional "trial and error" approach and has not been calibrated using statistical inverse modelling (i.e. algorithmic parameter estimation). The aim of this study is to establish and demonstrate a procedure for calibration of DayCent to improve estimation of GHG emissions. We coupled DayCent with the parameter estimation (PEST) software for inverse modelling. The PEST software can be used for calibration through regularized inversion as well as model sensitivity and uncertainty analysis. The DayCent model was analysed and calibrated using N2O flux data collected over 2 years at the Iowa State University Agronomy and Agricultural Engineering Research Farms, Boone, IA. Crop year 2003 data were used for model calibration and 2004 data were used for validation. The optimization of DayCent model parameters using PEST significantly reduced model residuals relative to the default DayCent parameter values. Parameter estimation improved the model performance by reducing the sum of weighted squared residual difference between measured and modelled outputs by up to 67 %. For the calibration period, simulation with the default model parameter values underestimated mean daily N2O flux by 98 %. After parameter estimation, the model underestimated the mean daily fluxes by 35 %. During the validation period, the calibrated model reduced sum of weighted squared residuals by 20 % relative to the default simulation. Sensitivity analysis performed provides important insights into the model structure providing guidance for model improvement. © 2013 Springer Science+Business Media Dordrecht.

Rewetting of agriculturally used peatlands has been proposed as a measure to stop soil subsidence, conserve peat and rehabilitate ecosystem functioning. Unintended consequences might involve nutrient release and changes in the greenhouse gas (GHG) balance towards CH₄-dominated emission. To investigate the risks and benefits of rewetting, we subjected soil columns from drained peat- and clay-covered peatlands to different water level treatments: permanently low, permanently inundated and fluctuating (first inundated, then drained). Surface water and soil pore water chemistry, soil-extractable nutrients and greenhouse gas fluxes were measured throughout the experiment. Permanent inundation released large amounts of nutrients into pore water, especially phosphorus (up to 11.7 mg P-PO₄ l⁻¹) and ammonium (4.8 mg N-NH₄ l⁻¹). Phosphorus release was larger in peat than in clay soil, presumably due to the larger pool of iron-bound phosphorus in peat. Furthermore, substantial amounts of phosphorus and potassium were exported from the soil matrix to the surface water, risking the pollution of local species-rich (semi-)aquatic ecosystems. Rewetting of both clay and peat soil reduced CO₂ emissions. CH₄ emissions increased, but, in contrast to the expectations, the fluxes were relatively low. Calculations showed that rewetting reduced net cumulative GHG emissions expressed as CO₂ equivalents.

Coal mine methane is a significant greenhouse gas source as well as a potential lost energy resource if not effectively used. In recent years, mine ventilation air (MVA) capture and use has become a key element of research and development due to comparatively larger methane emissions by MVA than other coal mine sources. Technologies have been evaluated to treat the low methane concentrations in MVA such as thermal-based technologies or processing by biofiltration. This review initially considers the techniques available for treating the low methane concentrations encountered in MVA, after which it focuses on developments in biofiltration systems. Biofiltration represents a simple, energy-efficient, and cheap alternative to oxidize methane from MVA. Major factors influencing biofilter performance along with knowledge gaps in relation to its application to MVA are identified and discussed.

Greenhouse gas (GHG) emission in wastewater treatment plants of the pulp-and-paper industry was estimated by using a dynamic mathematical model. Significant variations were shown in the magnitude of GHG generation in response to variations in operating parameters, demonstrating the limited capacity of steady-state models in predicting the time-dependent emissions of these harmful gases. The examined treatment systems used aerobic, anaerobic, and hybrid—anaerobic/aerobic—biological processes along with chemical coagulation/flocculation, anaerobic digester, nitrification and denitrification processes, and biogas recovery. The pertinent operating parameters included the influent substrate concentration, influent flow rate, and temperature. Although the average predictions by the dynamic model were only 10 % different from those of steady-state model during 140 days of operation of the examined systems, the daily variations of GHG emissions were different up to ±30, ±19, and ±17 % in the aerobic, anaerobic, and hybrid systems, respectively. The variations of process variables caused fluctuations in energy generation from biogas recovery by ±16, ±17, and ±14 % in the three examined systems, respectively. The lowest variations were observed in the hybrid system, showing the stability of this particular process design.

Wastewater treatment is an important source of nitrous oxide (N₂O), which is a strong greenhouse gas and dominate ozone-depleting substance. The purpose of this study was to evaluate the effect of carbon source on N₂O emission from anoxic/oxic biological nitrogen removal process. The mechanisms of N₂O emission were also studied. Long-term experiments were operated to evaluate the effect of three different carbon sources (i.e., glucose, sodium acetate, and soluble starch) on N₂O emission characteristics. And batch experiments, in the presence or absence of specific inhibitors, were carried out to identify the sources of N₂O emission. The ammonia-oxidizing bacteria (AOB) and denitrifiers community compositions under different circumstances were also analyzed based on which the underlying mechanisms of N₂O emission were elucidated. The conversion ratios of N₂O in reactors with glucose, sodium acetate, and soluble starch were 5.3 %, 8.8 %, and 2.8 %, respectively. The primary process responsible for N₂O emission was nitrifier denitrification by Nitrosomonas-like AOB, while denitrification by heterotrophic denitrifiers acted as the sink. Reactor with sodium acetate showed the highest N₂O emission, together with the highest nitrogen and phosphate removal ratios. Carbon source has a significant impact on N₂O emission quantity and relatively minor effect on its production mechanism.

In this study, an environmental assessment on a soil washing process for the remediation of a Pb-contaminated shooting range site was conducted, using a green and sustainable remediation tool, i.e., SiteWise ver. 2, based on data relating specifically to the actual remediation project. The entire soil washing process was classified into four major stages, consisting of soil excavation (stage I), physical separation (stage II), acid-based (0.2 N HCl) chemical extraction (stage III), and wastewater treatment (stage IV). Environmental footprints, including greenhouse gas (GHG) emissions, energy consumption, water consumption, and critical air pollutant productions such as PM10, NO x , and SO x , were calculated, and the relative contribution of each stage was analyzed in the environmental assessment. In stage I, the relative contribution of the PM10 emissions was 55.3 % because the soil excavation emitted the fine particles. In stage II, the relative contribution of NO x and SO x emissions was 42.5 and 52.5 %, respectively, which resulted from electricity consumption for the operation of the separator. Stage III was the main contributing factor to 63.1 % of the GHG emissions, 67.5 % of total energy used, and 37.4 % of water consumptions. The relatively high contribution of stage III comes from use of consumable chemicals such as HCl and water-based extraction processes. In stage IV, the relative contributions of GHG emissions, total energy used, and NO x and SO x emissions were 23.2, 19.4, 19.5, and 25.3 %, respectively, which were caused by chemical and electricity demands for system operation. In conclusion, consumable chemicals such as HCl and NaOH, electric energy consumption for system operation, and equipment use for soil excavation were determined to be the major sources of environmental pollution to occur during the soil washing process. Especially, the acid-based chemical extraction process should be avoided in order to improve the sustainability of soil washing processes.