Having data about atmospheric concentrations in an entire urban area is difficult, hence interpolation methods are helpful. Their choice will depend on minimising the error. In this work, two deterministic (Inverse Distance Weight and Local Polynomial Interpolation) and two stochastic methods (Simple and Ordinary Kriging) were applied to predict seasonal and annual atmospheric methane (CH4) concentration means. Two sampling networks were designed in an intermediate city, covering a wide variety of urban densities, with different sampling site numbers. The main objective was to find the interpolation model that best predicts CH4 concentration and to analyse if the network's expansion improves the metric errors - the mean error (ME) and the root-mean-square error (RMSE). The ME values were close to zero in all cases, and the stochastic methods had the smallest RMSE for both networks. Besides, adding more sampling sites improved up to 50% of the RMSE values. Finally, an integrated map was obtained incorporating all the best interpolation models, which gave a difference of less than 4% between the measured and the estimated CH4 concentration. This type of study is helpful to evaluate the design of a sampling network, the territorial planning and future installations of CH4 sources.

During grazing, some of the nutrients ingested by cattle are returned to grassland as urine and dung patches and can be lost as greenhouse gases. Sites where cattle congregate are more likely to have overlapping excreta patches favouring enhanced nitrous oxide (N2O) emissions. However, there is no consensus about the magnitude of these or simultaneous methane (CH4) emissions or potential mitigation options. This study investigated the effect of combined cattle dung and urine depositions on N2O and CH4 emissions, compared with emissions from separate depositions, under different weather conditions. Local emission factors (EFs) were then calculated for both gases. A quantitative assessment of published studies was also performed to search for N2O emissions drivers. Two field experiments were performed during two 98-day trials under dry and wet conditions in Tandil, Argentina. Treatments included fresh excreta patches of urine (0.75 L), dung (2.50 kg), dung + urine (2.50 kg + 0.75 L) from Holstein dairy cows, and a control (without excreta). Soil and excreta properties were analysed, and N2O and CH4 fluxes from the patches were measured using the static chamber technique. Patches containing dung were shown to be localised CH4 hotspots. Urine applied to soil, and the addition of urine to dung patches had a negligible effect on CH4 fluxes. Urine, dung and combined patches were found to be localised N2O sources. Adding urine to dung patches under wet weather had a significant synergetic effect (threefold increase) on cumulative N2O emissions compared with the theoretical sum of separate excreta patches. Adding urine to dung patches under dry conditions gave an additive effect on N2O. These findings suggest that preventing overlapping excreta patches under wet conditions can help mitigate N2O emissions from temperate managed grazed pastures. The effect of combining excreta patches was also evident in the EF values obtained. That for CH4 was consistent with the default IPCC value (0.75 g CH4 kg⁻¹ VS), while N2O (EF = 0.03–0.39%) was lower than the updated IPCC 2019 value of 0.6%.

Methane (CH4) is the second more important greenhouse gas (GHG), respecting its potential global warming. Although cities represent only 2% of the global surface, they are responsible for 70% of the GHGs emissions. Thus, it is necessary to study their atmospheric concentration variations to identify the main sources and mitigate their emissions. The main objective of this study is to estimate the CH4 urban concentration using satellite products. To do this, first the atmospheric CH4 concentration was analyzed in 16 sites in the city of Tandil (Argentina) for one year; thus, the observed data could be registered. It was found that in winter and autumn, the concentrations were higher than in summer and spring. Then, the data from Landsat 8 satellite were used to obtain the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Linear regression was applied, taking into account the seasonal CH4 concentration as the dependent variable, and the NDVI and LST as the independent variables. The adjusted R2 was 0.53, and the principal variable that affected the CH4 concentration was NDVI, which is related to urbanization. Finally, the mathematical expression from the regression was applied to obtain CH4 urban concentration, which allows us to analyze the temporal and spatial variations.

Background Atmospheric methane (CH 4) is responsible for approximately 20% of global warming since the preindustrial era. Forests are land ecosystems whose role is crucial for mitigating the greenhouse effect due to their capacity to capture and store C and preserve other processes such as CH 4 oxidation in the soil. On the other hand, in the particular case of afforestation, there are contradictory results about the magnitude of CH 4 uptake variation due to changes in methanotrophic bacteria activity and its relationship with micro-environmental conditions. Results The average potential CH 4 oxidation rate in the laboratory (MOL) of afforested soil was 186% greater than that of the grassland, which could be marginally attributed to differences in soil physicochemical parameters like bulk density, pH and organic matter. A seasonal pattern in MOL was observed in both land uses, with the highest values at the warm and rainy season. MOL magnitude increased with soil depth up to 10-15 cm, which corresponds with the mineral layer. Conclusion Pine afforestation would improve the biological soil attributes linked to methane oxidising bacteria compared to the grassland systems. Keywords: Land use change; Methanotrophic bacteria; methane uptake; GHG mitigation; ecological services

The reuse of effluents from intensive dairy farms combined with localized irrigation techniques (fertigation) has become a promising alternative to increase crop productivity while reducing the environmental impact of waste accumulation and industrial fertilizers production. Currently, the reuse of dairy effluents through fertigation by subsurface drip irrigation (SDI) systems is of vital importance for arid regions but it has been poorly studied. The present study aimed to assess the greenhouse gas (GHG) emissions, soil properties, and crop yield of a maize crop fertigated with either treated dairy effluent or dissolved granulated urea applied through an SDI system at a normalized N application rate of 200 kg N ha−1. Fertilizer application was divided into six fertigation events. GHG fluxes were measured during fertigation (62-day) using static chambers. Soil properties were measured previous to fertilizer applications and at the harvest coinciding with crop yield estimation. A slight increase in soil organic matter was observed in both treatments for the 20–60 cm soil depth. Both treatments also showed similar maize yields, but the dairy effluent increased net GHG emissions more than urea during the fertigation period. Nevertheless, the net GHG emissions from the dairy effluent were lower than the theoretical CO2eq emission that would have been emitted during urea manufacturing or the longer storage of the effluent if it had not been used, showing the need for life-cycle assessments. Local-specific emission factors for N2O were determined (0.07%), which were substantially lower than the default value (0.5%) of IPCC 2019. Thus, the subsurface drip irrigation systems can lead to low GHG emissions, although further studies are needed.