Graduate Program in Environmental Engineering, Federal University of Technology, Av. Pioneiros 3131, 86036-370 Londrina, PR, Brazil.
Department of Environmental Engineering, Federal University of Technology, Av. Pioneiros 3131, 86036-370 Londrina, PR, Brazil.
Sci Total Environ. 2022 Sep 15;839:156332. doi: 10.1016/j.scitotenv.2022.156332. Epub 2022 May 28.
Black carbon (BC) inventories for cities are scarce, especially in developing countries, despite their importance to tackle climate change and local air pollution. Here, we draw on results from a case study in a Brazilian city to discuss the challenges of compiling a BC inventory for different activity sectors. We included traditionally inventoried sectors, such as industries and on-road transportation, other less reported sectors (food establishments and aviation), and open burning of household solid waste (HSW), typically found in developing countries. We present a machine-learning technique (Random Forest) as a novel approach to obtain HSW burning activity using a set of spatial predictors. The BC inventory was based on PM emissions weighted by the fraction of PM emitted as BC and developed for the year 2018. We also reported the disaggregated spatial PM emissions for the same combustion sources, and documented the databases used for activity data and emission factors (EF). The total estimated BC and PM emissions amounted to 57.88 and 234.75 tons, respectively, with on-road vehicle exhaust emissions and industrial combustion as the main BC sources (63 and 22%, respectively). For PM emissions, on-road transportation (exhaust and non-exhaust) contributed 48%, followed by industrial combustion (21%) and food establishments (20%). Population density, number of vacant lots, and property tax values were identified as the most important features to predict the HSW fire activity. A comparison with other inventories revealed that the BC emission profile of Londrina is similar to the profile reported for Greater Mexico City, another Latin American city. Thus, the methodology used in this study could be extended to other cities with similar local BC sources. Finally, we highlight that the lack of local activity data, representative EF, and even methodology may undermine the development of reliable BC inventories, and intensive research should be conducted to characterize the emission sources.
黑碳(BC)的城市清单很少,尤其是在发展中国家,尽管它们对应对气候变化和当地空气污染很重要。在这里,我们利用巴西一个城市的案例研究结果来讨论为不同活动部门编制 BC 清单所面临的挑战。我们包括了传统上被列入清单的部门,如工业和道路交通,以及其他报道较少的部门(饮食场所和航空),以及发展中国家常见的家庭固体废物(HSW)露天焚烧。我们提出了一种机器学习技术(随机森林),作为使用一组空间预测因子获取 HSW 燃烧活动的新方法。BC 清单是基于 PM 排放加权的,权重为 PM 排放中作为 BC 排放的部分,并为 2018 年编制。我们还报告了相同燃烧源的细分空间 PM 排放,并记录了用于活动数据和排放因子(EF)的数据库。估计的总 BC 和 PM 排放量分别为 57.88 和 234.75 吨,其中道路交通车辆尾气排放和工业燃烧是 BC 的主要来源(分别为 63%和 22%)。对于 PM 排放,道路交通(尾气和非尾气)贡献了 48%,其次是工业燃烧(21%)和饮食场所(20%)。人口密度、空地数量和财产税价值被确定为预测 HSW 火灾活动的最重要特征。与其他清单的比较表明,隆德里纳的 BC 排放特征与另一个拉丁美洲城市大墨西哥城的报告特征相似。因此,本研究中使用的方法可以扩展到具有类似本地 BC 源的其他城市。最后,我们强调缺乏本地活动数据、有代表性的 EF,甚至缺乏方法可能会破坏可靠的 BC 清单的编制,应进行密集研究以表征排放源。