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Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM 2.5 and PM 10 ) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with ∼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM 2.5 (PM 10 ) concentrations were in Dhaka 185 ± 48 (220 ± 58) μg m -3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) μg m -3 . Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM 2.5 and PM 10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM 2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15 m 3 ; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Car microenvironments significantly contribute to the daily pollution exposure of commuters, yet health and socioeconomic studies focused on in-car exposure are rare. This study aims to assess the relationship between air pollution levels and socioeconomic indicators (fuel prices, city-specific GDP, road density, the value of statistical life (VSL), health burden and economic losses resulting from exposure to fine particulate matter ≤2.5 µm; PM 2.5 ) during car journeys in ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Data collected by portable laser particle counters were used to develop a proxy of car-user exposure profiles. Hotspots on all city routes displayed higher PM 2.5 concentrations and disproportionately high inhaled doses. For instance, the time spent at the hotspots in Guangzhou and Addis Ababa was 26% and 28% of total trip time, but corresponded to 54% and 56%, respectively, of the total PM 2.5 inhaled dose. With the exception of Guangzhou, all the cities showed a decrease in per cent length of hotspots with an increase in GDP and VSL. Exposure levels were independent of fuel prices in most cities. The largest health burden related to in-car PM 2.5 exposure was estimated for Dar-es-Salam (81.6 ± 39.3 μg m -3 ), Blantyre (82.9 ± 44.0) and Dhaka (62.3 ± 32.0) with deaths per 100,000 of the car commuting population per year of 2.46 (2.28-2.63), 1.11 (0.97-1.26) and 1.10 (1.05-1.15), respectively. However, the modest health burden of 0.07 (0.06-0.08), 0.10 (0.09-0.12) and 0.02 (0.02-0.03) deaths per 100,000 of the car commuting population per year were estimated for Medellin (23 ± 13.7 μg m -3 ), São Paulo (25.6 ± 11.7) and Sulaymaniyah (22.4 ± 15.0), respectively. Lower GDP was found to be associated with higher economic losses due to health burdens caused by air pollution in most cities, indicating a socioeconomic discrepancy. This assessment of health and socioeconomic parameters associated with in-car PM 2.5 exposure highlights the importance of implementing plausible solutions to make a positive impact on peoples' lives in these cities., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)