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Sustainable urban soil management is becoming increasingly crucial due to its vital role in climate and water regulation and its significant potential for storing soil organic carbon (SOC). This significance is emphasized considering the ongoing urbanization and climate change issues. Although SOC is influenced by many factors, such as soil type and climate fluctuations (temperature, precipitation patterns), on a regional scale, land use and management practices (e.g., fertilization, irrigation) can have a more significant impact on SOC storage and the balance of soil-atmosphere carbon fluxes. However, there is still a limited understanding of the amount of humus content in urban soils and the effects of urban development and management practices on soil health and carbon storage. We investigated how management practices in urban green spaces influence soil carbon storage as the primary indicator of soil health.
The present study was carried out in the Bonn-Rhein-Sieg area, as the region is vital in terms of sustainable urban and regional development with a high population density (Rhein-Sieg district: 338.4, Bonn: 520.9 inhabitants/km2) in Germany. A survey was conducted with owners and managers of urban private (e.g., allotment and backyard garden) and public green spaces on the practices for the most common vegetation types (e.g., lawn, vegetable, ornamental). In the autumn and winter of 2022, 248 soil samples (0–20 cm depth) were collected from 95 private and public green spaces in the study area and analyzed for physiochemical and biological properties. Multivariate Analysis of Variance (MANOVA) was performed to assess the effects of different management practices on soil properties.
Our results indicate that the average SOC stock in public green areas (94.67 Mg ha-1) is substantially higher than in private ones (house garden 67.72 Mg ha-1, allotment garden 73.15 Mg ha-1). Moreover, urban green spaces with vegetables (91.66 Mg ha-1) and ornamentals (85.05 mg ha-1) show greater SOC stock levels when comparing vegetation types (lawn 62.48 Mg ha-1). Significant differences in SOC are also found for various management practices. Specifically, the monthly fertilization schedule resulted in higher SOC levels (127.37 Mg ha⁻¹) compared to the yearly fertilization schedule (76.88 Mg ha⁻¹). Additionally, the use of organic fertilizers contributed to increased SOC levels (84.40 Mg ha⁻¹) in contrast to mineral fertilizer applications (65.31 Mg ha⁻¹). The average SOC stock in all the studied urban green spaces (85 mg ha-1) was higher than the average SOC stock in arable soils in Germany (47.30 Mg ha-1). The higher SOC in the region could be due to vegetation types and fertilization frequencies, which show statistically significant effects (p-value <0.001). Other management practices (e.g., irrigation type and frequency) did not show a significant effect. Our findings highlight the significance of soil management practices, particularly in selecting vegetation types and determining fertilization frequency, as essential factors influencing urban SOC.
Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model’s capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region.
Green infrastructure has been widely recognized for the benefits to human health and biodiversity conservation. However, knowledge of the qualities and requirements of such spaces and structures for the effective delivery of the range of ecosystem services expected is still limited, as well as the identification of trade-offs between services. In this study, we apply the One Health approach in the context of green spaces to investigate how urban park characteristics affect human mental health and wildlife support outcomes and identify synergies and trade-offs between these dimensions. Here we show that perceived restorativeness of park users varies significantly across sites and is mainly affected by safety and naturalness perceptions. In turn, these perceptions are driven by objective indicators of quality, such as maintenance of facilities and vegetation structure, and subjective estimations of biodiversity levels. The presence of water bodies benefited both mental health and wildlife. However, high tree canopy coverage provided greater restoration potential whereas a certain level of habitat heterogeneity was important to support a wider range of bird species requirements. To reconcile human and wildlife needs in green spaces, cities should strategically implement a heterogeneous green infrastructure network that considers trade-offs and maximizes synergies between these dimensions.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.