Compilation of a city-scale black carbon emission inventory: Challenges in developing countries based on a case study in Brazil.

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 PM2.5 emissions weighted by the fraction of PM2.5 emitted as BC and developed for the year 2018. We also reported the disaggregated spatial PM2.5 emissions for the same combustion sources, and documented the databases used for activity data and emission factors (EF). The total estimated BC and PM2.5 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 PM2.5 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.

Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches.

Characterizing the spatiotemporal variability of the Urban Heat Island (UHI) and its drivers is a key step in leveraging thermal comfort to create not only healthier cities, but also to enhance urban resilience to climate change. In this study, we developed specific daytime and nighttime multiple linear regression (MLR) and random forest (RF) models to analyze and predict the spatiotemporal evolution of the Urban Heat Island intensity (UHII), using the air temperature (Tair) as the response variable. We profited from the wealth of in situ Tair data and a comprehensive pool of predictors variables - including land cover, population, traffic, urban geometry, weather data and atmospheric vertical indices. Cluster analysis divided the study period into three main groups, each dominated by a combination of weather systems that, in turn, influenced the onset and strength of the UHII. Anticyclonic circulations favored the emergence of the largest UHII (hourly mean of 5.06 °C), while cyclonic circulations dampened its development. The MLR models were only able to explain a modest percentage of variance (64 and 34% for daytime and nighttime, respectively), which we interpret as part of their inability to capture key factors controlling Tair. The RF models, on the other hand, performed considerably better, with explanatory power over 96% of the variance for daytime and nighttime conditions, capturing and mapping the fine-scale Tair spatiotemporal variability in both periods and under each cluster condition. The feature importance analysis showed that the meteorological variables and the land cover were the main predictors of the Tair. Urban planners could benefit from these results, using the high-performing RF models as a robust framework for forecasting and mitigating the effects of the UHI.

Open waste burning causes fast and sharp changes in particulate concentrations in peripheral neighborhoods.

The open burning of municipal solid waste (MSW) -frequently observed in developing countries- emits harmful pollutants, including fine particulate matter (PM2.5) and black carbon (BC), and deteriorates the air quality in urban areas. This work reports on PM2.5 and BC measurements (fixed and mobile) conducted in a residential neighborhood on the outskirts of a Brazilian city (Londrina), complemented by a public opinion survey to understand the open burning in the context of waste management. Mean (± standard deviation) BC concentration (1.48 ± 1.40 µg m-3) at the fixed sites of the neighborhood was lower than downtown, while PM2.5 (9.68 ± 8.40 µg m-3) concentration was higher. The mobile monitoring showed higher mean PM2.5 concentrations but lower BC/PM2.5 ratios than downtown, with sharp and fast spikes (up to 317.87 and 565.21 µg m-3 for BC and PM2.5, respectively). The large spatial heterogeneity of particulate concentrations was associated with the occurrence of MSW burning events. Our observations were verified by the survey respondents who identified poor waste management practices: garbage in streets, waste burning, and illegal dump sites. Even though the area has a municipal waste collection service, the majority of the respondents (87%) had seen waste burning close to their homes on a weekly basis, and think that people burn waste out of habit (54%) and because they are not patient to wait for the collection services (67%). To combat this illegal practice, we suggest raising the public awareness through campaigns at local level, adopting education initiatives and economic incentives for correct waste segregation, and enforcing regular inspection of burning events by the authorities. Our research method proved to be a time- and cost-effective approach for mapping particulate concentrations and for identifying undesirable waste practices, and could be effectively applied to other global cities.

Bus commuter exposure and the impact of switching from diesel to biodiesel for routes of complex urban geometry.

We report on commuters' exposure to black carbon (BC), PM2.5 and particle number (PN, with aerodynamic diameter, da, in the range 0.01