Scott Archer-Nicholls

Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia.

Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153–17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.

The Regional Impacts of Cooking and Heating Emissions on Ambient Air Quality and Disease Burden in China

Exposure to air pollution is a major risk factor globally and particularly in Asia. A large portion of air pollutants result from residential combustion of solid biomass and coal fuel for cooking and heating. This study presents a regional modeling sensitivity analysis to estimate the impact of residential emissions from cooking and heating activities on the burden of disease at a provincial level in China. Model surface PM2.5 fields are shown to compare well when evaluated against surface air quality measurements. Scenarios run without residential sector and residential heating emissions are used in conjunction with the Global Burden of Disease 2013 framework to calculate the proportion of deaths and disability adjusted life years attributable to PM2.5 exposure from residential emissions. Overall, we estimate that 341 000 (306 000-370 000; 95% confidence interval) premature deaths in China are attributable to residential combustion emissions, approximately a third of the deaths attributable to all ambient PM2.5 pollution, with 159 000 (142 000-172 000) and 182 000 (163 000-197 000) premature deaths from heating and cooking emissions, respectively. Our findings emphasize the need to mitigate emissions from both residential heating and cooking sources to reduce the health impacts of ambient air pollution in China.

Seasonal and Diurnal Air Pollution from Residential Cooking and Space Heating in the Eastern Tibetan Plateau.

Residential combustion of solid fuel is a major source of air pollution. In regions where space heating and cooking occur at the same time and using the same stoves and fuels, evaluating air-pollution patterns for household-energy-use scenarios with and without heating is essential to energy intervention design and estimation of its population health impacts as well as the development of residential emission inventories and air-quality models. We measured continuous and 48 h integrated indoor PM2.5 concentrations over 221 and 203 household-days and outdoor PM2.5 concentrations on a subset of those days (in summer and winter, respectively) in 204 households in the eastern Tibetan Plateau that burned biomass in traditional stoves and open fires. Using continuous indoor PM2.5 concentrations, we estimated mean daily hours of combustion activity, which increased from 5.4 h per day (95% CI: 5.0, 5.8) in summer to 8.9 h per day (95% CI: 8.1, 9.7) in winter, and effective air-exchange rates, which decreased from 18 ± 9 h(-1) in summer to 15 ± 7 h(-1) in winter. Indoor geometric-mean 48 h PM2.5 concentrations were over two times higher in winter (252 μg/m(3); 95% CI: 215, 295) than in summer (101 μg/m(3); 95%: 91, 112), whereas outdoor PM2.5 levels had little seasonal variability.

Quasi-Newton Methods for Atmospheric Chemistry Simulations: Implementation in UKCA UM vn10.8

A key and expensive part of coupled atmospheric chemistry–climate model simulations is the integration of gas-phase chemistry, which involves dozens of species and hundreds of reactions. These species and reactions form a highly coupled network of differential equations (DEs). There exist orders of magnitude variability in the lifetimes of the different species present in the atmosphere, and so solving these DEs to obtain robust numerical solutions poses a “stiff problem”. With newer models having more species and increased complexity, it is now becoming increasingly important to have chemistry solving schemes that reduce time but maintain accuracy. While a sound way to handle stiff systems is by using implicit DE solvers, the computational costs for such solvers are high due to internal iterative algorithms (e.g. Newton–Raphson methods). Here, we propose an approach for implicit DE solvers that improves their convergence speed and robustness with relatively small modification in the code. We achieve this by blending the existing Newton–Raphson (NR) method with quasi-Newton (QN) methods, whereby the QN routine is called only on selected iterations of the solver. We test our approach with numerical experiments on the UK Chemistry and Aerosol (UKCA) model, part of the UK Met Office Unified Model suite, run in both an idealised box-model environment and under realistic 3-D atmospheric conditions. The box-model tests reveal that the proposed method reduces the time spent in the solver routines significantly, with each QN call costing 27 % of a call to the full NR routine. A series of experiments over a range of chemical environments was conducted with the box model to find the optimal iteration steps to call the QN routine which result in the greatest reduction in the total number of NR iterations whilst minimising the chance of causing instabilities and maintaining solver accuracy. The 3-D simulations show that our moderate modification, by means of using a blended method for the chemistry solver, speeds up the chemistry routines by around 13 %, resulting in a net improvement in overall runtime of the full model by approximately 3 % with negligible loss in the accuracy. The blended QN method also improves the robustness of the solver, reducing the number of grid cells which fail to converge after 50 iterations by 40 %. The relative differences in chemical concentrations between the control run and that using the blended QN method are of order ∼ 10−7 for longer-lived species, such as ozone, and below the threshold for solver convergence (10−4) almost everywhere for shorter-lived species such as the hydroxyl radical.