Driving co-abatement of Greenhouse Gas (GHGs) and Air Pollutants (APs) in the city level is crucial for fostering societal green and low-carbon transitions, yet comprehensive and refined researches at this level remain limited. To facilitate urban fine management of GHGs control and APs reduction, this study targeted nine categories of anthropogenic emission sources in Shanghai, a typical megacity of China, analyzing the co-benefits of three types of GHGs (CO, CH, NO) and seven types of APs (SO, NOx, CO, VOCs, NH, PM, PM) via emissions flow, spatial distribution, hotspot regions identification, and scenario prediction. Results highlighted the source heterogeneity of different types and significant contributions of energy consumption. CO emissions showed a strong spatial correlation with SO, NOx, and CO, followed by VOCs and PM. Hotspot regions for CO-VOCs, CO-NOx and CO-SO co-abatement included power plants, petrochemical enterprises and chemical industrial parks in the southern coastal areas, iron and steel enterprises and power plants in the northern coastal areas, and airport areas in the central and eastern coastal areas, presenting great potential maximum reduction benefits. Achieving positive co-benefits in industrial sector would depend on the steady decline of CO emissions in power generation and steel industries. Introducing carbon capture devices and improving energy efficiency would be more beneficial to CO emission reduction, while increasing the share of clean energy and phasing out outdated vehicles, machinery, or production capacities are more effective in reducing APs. These mitigation measures could achieve 68.8 % and 47.6 % reduction for CO and APs by 2050, respectively, and the co-effect of CO and APs emission reduction would gradually increase with the continuous implementation of these measures.
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http://dx.doi.org/10.1016/j.scitotenv.2024.175569 | DOI Listing |
Sci Rep
January 2025
U.S. Geological Survey, Wetland and Aquatic Research Center, 700 Cajundome Boulevard, Lafayette, LA, 70506, USA.
Blue carbon refers to organic carbon sequestered by oceanic and coastal ecosystems. This stock has gained global attention as a high organic carbon repository relative to other ecosystems. Within blue carbon ecosystems, tidally influenced wetlands alone store a disproportionately higher amount of organic carbon than other blue carbon systems.
View Article and Find Full Text PDFMany sharks, rays and skates are highly threatened and vulnerable to overexploitation, as such reliable monitoring of elasmobranchs is key to effective management and conservation. The mobile and elusive nature of these species makes monitoring challenging, particularly in temperate waters with low visibility. Environmental DNA (eDNA) methods present an opportunity to study these species in the absence of visual identification or invasive techniques.
View Article and Find Full Text PDFJ Plant Res
January 2025
College of Marine and Biological Engineering, Yancheng Institute of Technology, Yancheng, 224002, Jiangsu, China.
Barley (Hordeum vulgare L.) is an important cereal crop used in animal feed, beer brewing, and food production. Waterlogging stress is one of the prominent abiotic stresses that has a significant impact on the yield and quality of barley.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Virginia Institute of Marine Science, William & Mary, P.O. Box 1346, Gloucester Point, VA, 23062, USA.
Coastal ecosystems are degraded worldwide and oyster reefs are among the most threatened coastal habitats. Oysters are a critical ecosystem engineer and valuable fishery species, thus effective management strategies must balance tradeoffs between protecting reef ecosystems and continued human use. Management practices for oysters commonly incorporate shell replenishment (provisioning hard substrates to increase reef relief) and spatial management (rotational harvest areas or sanctuaries); however, the impact of these practices on reef dynamics and fisheries outcomes are poorly understood, particularly on harvested reefs.
View Article and Find Full Text PDFMar Pollut Bull
January 2025
JK Laxmipat University, Jaipur, Rajasthan, India.
Marine pollution due to oil spills presents major risks to coastal areas and aquatic life, leading to serious environmental health concerns. Oil Spill detection using SAR data has transitioned from traditional segmentation to a variety of machine learning & deep learning models like UNET proving its efficiency for the task. This research paper proposes a GSCAT-UNET model for efficient oil spill detection and discrimination from lookalikes.
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