The huge amounts of sewage sludge produced by municipal wastewater treatment plants induce major environmental and economical issues, calling for advanced disposal methods. Traditional methods for sewage sludge disposal increase greenhouse gas emissions and pollution. Moreover, biochar created from sewage sludge often cannot be used directly in soil applications due to elevated levels of heavy metals and other toxic compounds, which alter soil biota and earthworms. This has limited the application of sewage sludge-derived biochar as a fertilizer. Here, we review biomass and sewage sludge co-pyrolysis with a focus on the stabilization of heavy metals and toxicity reduction of the sludge-derived biochar. We observed that co-pyrolyzing sewage sludge with biomass materials reduced heavy metal concentrations and decreased the environmental risk of sludge-derived biochar by up to 93%. Biochar produced from sewage sludge and biomass co-pyrolysis could enhance the reproduction stimulation of soil biota by 20‒98%. Heavy metals immobilization and transformation are controlled by the co-feed material mixing ratio, pyrolysis temperature, and pyrolysis atmosphere.

Harnessing solar energy is one of the most important practical insights highlighted to mitigate the severe climate change (CC) phenomenon. Therefore, this study aims to focus on the use of hybrid solar dryers (HSDs) within an environmentally friendly framework, which is one of the promising applications of solar thermal technology to replace traditional thermal technology that contributes to increasing the severity of the CC phenomenon. The HSD, based on a traditional electrical energy source (HS_TEE) and electrical energy from photovoltaic panels (HS_PVSE), was evaluated compared to a traditional electrical (TE) dryer for drying some medicinal and aromatic plants (MAPs). This is done by evaluating some of the drying outputs, energy consumed, carbon footprint, and financial return at 30, 40, and 50°C. The best quality of dried MAP samples in terms of essential oil (EO, %) and microbial load was achieved at 40°C. The HS_TEE dryer has reduced energy consumption compared to the TE dryer by a percentage ranging from 37% to 54%. The highest CO₂ mitigated ratio using the HS_TEE dryer was recorded in lavender, thyme, basil, lemongrass, and sage samples with values ranging from 45% to 54% at 30, and 50°C. The highest financial return obtained from energy consumption reduction and carbon credit footprint was achieved at 50°C, with values ranging from 5,313.69 to 6,763.03 EGP/year (EGP ≈ 0.0352 USD) when coal was used as a fuel source for the generation of electricity. Moreover, the HS_PVSE dryer achieved a 100% reduction in traditional energy consumption and then reduced CO₂ emissions by 100%, which led to a 100% financial return from both energy reduction and carbon credit. The highest financial returns were observed at 50°C, with values ranging from 13,872.56 to 15,007.02, 12,927.28 to 13,984.43, and 11,981.99 to 12,961.85 EGP/year (EGP ≈ 0.0352 USD) for coal, oil, and natural gas, respectively. The HS dryers show potential for environmental conservation contribution; furthermore, earning money from energy savings and carbon credit could help improve the living standards and maximize benefits for stakeholders.

Background: Recently, there has been a growing demand for the mechanization of flower harvesting to enhance harvesting efficiency. Therefore, the purpose of the current research was to design a jasmine flower automated picker system (JFAPS). The picking system incorporates a gripper that moves along the third vertical axis using an Intel depth camera and the You Only Look Once (YOLO-V5) deep learning system to locate and detect the flowers. Results: For different design cross-sections, it was observed that the least safe factor of design safety was considered high enough to marginalize any mechanical failure potential. Furthermore, according to the prediction box, the flowers’ center point on the pixel plane was detected, while the real vertical position of the flowers was computed using a deep learning system. Consequently, the gripper moves down to pick the flowers and convey them to the storage system. In these conditions, the detection method’s average precision and recall of flowers were 100% and 90%, respectively. Conclusions: The JFAPS was balanced and efficient in detecting flowers. Therefore, future efforts will be directed at evaluating this system and confirming its efficacy in collecting flowers on an experimental farm.