Sostenipra Research Group (SGR 01412), Institute of Environmental Sciences and Technology (CEX2019-000940-M), Z Building, Universitat Autònoma de Barcelona (UAB), Campus UAB, Bellaterra, 08193 Barcelona, Spain.
Sostenipra Research Group (SGR 01412), Institute of Environmental Sciences and Technology (CEX2019-000940-M), Z Building, Universitat Autònoma de Barcelona (UAB), Campus UAB, Bellaterra, 08193 Barcelona, Spain; Department of Chemical, Biological and Environmental Engineering, Universitat Autònoma de Barcelona (UAB), Campus UAB, 08193 Bellaterra, Barcelona, Spain.
Sci Total Environ. 2022 May 20;822:153514. doi: 10.1016/j.scitotenv.2022.153514. Epub 2022 Jan 29.
Geographically explicit datasets reflecting local management of crops are needed to help improve direct nitrous oxide (NO) emission inventories. Yet, the lack of geographically explicit datasets of relevant factors influencing the emissions make it difficult to estimate them in such way. Particularly, for local peri-urban agriculture, spatially explicit datasets of crop type, fertilizer use, irrigation, and emission factors (EFs) are hard to find, yet necessary for evaluating and promoting urban self-sufficiency, resilience, and circularity. We spatially distribute these factors for the peri-urban agriculture in the Metropolitan Area of Barcelona (AMB) and create NO emissions maps using crop-specific EFs as well as Tier 1 IPCC EFs for comparison. Further, the role of the soil types is qualitatively assessed. When compared to Tier 1 IPCC EFs, we find 15% more emissions (i.e. 7718 kg NO-N year) than those estimated with the crop-specific EFs (i.e. 6533 kg NO-N year) for the entire AMB. Emissions for most rainfed crop areas like cereals (e.g. oat and barley) and non-citric fruits (e.g. cherries and peaches), which cover 24% and 13% of AMB's peri-urban agricultural area respectively, are higher with Tier 1 EF. Conversely, crop-specific EFs estimate higher emissions for irrigated horticultural crops (e.g. tomato, artichoke) which cover 33% of AMB's peri-urban agricultural area and make up 70% of the total NO emissions (4588 kg NO-N year using crop-specific EFs). Mapping the emissions helps evaluate spatial variability of key factors such as fertilizer use and irrigation of crops but carry uncertainties due to downscaling regional data to represent urban level data gaps. It also highlighted core emitting areas. Further the usefulness of the outputs on mitigation, sustainability and circularity studies are briefly discussed.
为了帮助改进直接一氧化二氮（NO）排放清单，需要具有反映作物当地管理情况的具有明确地理位置的数据集。然而，由于缺乏影响排放的相关因素的具有明确地理位置的数据集，因此难以进行此类估算。特别是对于当地城郊农业，很难找到作物类型、肥料使用、灌溉和排放因子（EF）等具有明确地理位置的数据集，而这些数据对于评估和促进城市自给自足、弹性和循环性是必要的。我们为巴塞罗那大都市区（AMB）的城郊农业进行了这些因素的空间分布，并使用特定于作物的 EF 以及用于比较的 Tier 1 IPCC EF 来创建 NO 排放图。此外，还定性评估了土壤类型的作用。与 Tier 1 IPCC EF 相比，我们发现整个 AMB 的排放量增加了 15%（即 7718 千克 NO-N 年），而使用特定于作物的 EF 估算的排放量为 6533 千克 NO-N 年）。对于覆盖 AMB 城郊农业区 24%和 13%的雨养作物区（如燕麦和大麦）和非柑橘类水果（如樱桃和桃子），Tier 1 EF 的排放量更高。相反，具有明确地理位置的 EF 估计灌溉园艺作物（如番茄、朝鲜蓟）的排放量更高，这些作物覆盖了 AMB 城郊农业区的 33%，占总 NO 排放量的 70%（使用特定于作物的 EF 为 4588 千克 NO-N 年）。排放图有助于评估关键因素（如作物施肥和灌溉）的空间变异性，但由于将区域数据缩小到代表城市层面的数据差距，因此存在不确定性。它还突出了核心排放区。此外，简要讨论了输出在缓解、可持续性和循环性研究方面的有用性。
