Short-term Exposure to Wildfire-Specific PM2.5 and Diabetes Hospitalization: A Study in Multiple Countries and Territories.

OBJECTIVE: To evaluate associations of wildfire fine particulate matter ≤2.5 mm in diameter (PM2.5) with diabetes across multiple countries and territories. RESEARCH DESIGN AND METHODS: We collected data on 3,612,135 diabetes hospitalizations from 1,008 locations in Australia, Brazil, Canada, Chile, New Zealand, Thailand, and Taiwan during 2000-2019. Daily wildfire-specific PM2.5 levels were estimated through chemical transport models and machine-learning calibration. Quasi-Poisson regression with distributed lag nonlinear models and random-effects meta-analysis were applied to estimate associations between wildfire-specific PM2.5 and diabetes hospitalization. Subgroup analyses were by age, sex, location income level, and country or territory. Diabetes hospitalizations attributable to wildfire-specific PM2.5 and nonwildfire PM2.5 were compared. RESULTS: Each 10 µg/m3 increase in wildfire-specific PM2.5 levels over the current day and previous 3 days was associated with relative risks (95% CI) of 1.017 (1.011-1.022), 1.023 (1.011-1.035), 1.023 (1.015-1.032), 0.962 (0.823-1.032), 1.033 (1.001-1.066), and 1.013 (1.004-1.022) for all-cause, type 1, type 2, malnutrition-related, other specified, and unspecified diabetes hospitalization, respectively. Stronger associations were observed for all-cause, type 1, and type 2 diabetes in Thailand, Australia, and Brazil; unspecified diabetes in New Zealand; and type 2 diabetes in high-income locations. An estimate of 0.67% (0.16-1.18%) and 1.02% (0.20-1.81%) for all-cause and type 2 diabetes hospitalizations were attributable to wildfire-specific PM2.5. Compared with nonwildfire PM2.5, wildfire-specific PM2.5 posed greater risks of all-cause, type 1, and type 2 diabetes and were responsible for 38.7% of PM2.5-related diabetes hospitalizations. CONCLUSIONS: We show the relatively underappreciated links between diabetes and wildfire air pollution, which can lead to a nonnegligible proportion of PM2.5-related diabetes hospitalizations. Precision prevention and mitigation should be developed for those in advantaged communities and in Thailand, Australia, and Brazil.

In-kitchen aerosol exposure in twelve cities across the globe.

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 (PM2.5 and PM10) 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 âˆ¼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM2.5 (PM10) 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 PM2.5 and PM10 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 PM2.5 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 m3; 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.

Daily submicron particle doses received by populations living in different low- and middle-income countries.

In the present study, the daily dose in terms of particle surface area received by citizens living in different low- and middle-income countries, characterized by different lifestyles, habits, and climates, was evaluated. The level of exposure to submicron particles and the dose received by the populations of Accra (Ghana), Cairo (Egypt), Florianopolis (Brazil), and Nur-Sultan (Kazakhstan) were analyzed. A direct exposure assessment approach was adopted to measure the submicron particle concentration levels of volunteers at a personal scale during their daily activities. Non-smoking adult volunteers performing non-industrial jobs were considered. Exposure data were combined with time-activity pattern data (characteristic of each population) and the inhalation rates to estimate the daily dose in terms of particle surface area. The received dose of the populations under investigation varied from 450 mm2 (Florianopolis, Brazil) to 1300 mm2 (Cairo, Egypt). This work highlights the different contributions of the microenvironments to the daily dose with respect to high-income western populations. It was evident that the contribution of the Cooking & Eating microenvironment to the total exposure (which was previously proven to be one of the main exposure routes for western populations) was only 8%-14% for low- and middle-income populations. In contrast, significant contributions were estimated for Outdoor day and Transport microenvironments (up to 20% for Cairo, Egypt) and the Sleeping & Resting microenvironment (up to 28% for Accra, Ghana), highlighting the effects of different site-specific lifestyles (e.g. time-activity patterns), habits, socioeconomic conditions, climates, and outdoor air quality.