Uncontrolled combustion of domestic waste has been observed in many countries, creating concerns for air quality; however, the health implications have not yet been quantified. We incorporate the ...Wiedinmyer et al (2014 Environ. Sci. Technol. 48 9523-30) emissions inventory into the global chemical-transport model, GEOS-Chem, and provide a first estimate of premature adult mortalities from chronic exposure to ambient PM2.5 from uncontrolled combustion of domestic waste. Using the concentration-response functions (CRFs) of Burnett et al (2014 Environ. Health Perspect. 122 397-403), we estimate that waste-combustion emissions result in 270 000 (5th-95th: 213 000-328 000) premature adult mortalities per year. The confidence interval results only from uncertainty in the CRFs and assumes equal toxicity of waste-combustion PM2.5 to all other PM2.5 sources. We acknowledge that this result is likely sensitive to choice of chemical-transport model, CRFs, and emission inventories. Our central estimate equates to 9% of adult mortalities from exposure to ambient PM2.5 reported in the Global Burden of Disease Study 2010. Exposure to PM2.5 from waste combustion increases the risk of premature mortality by more than 0.5% for greater than 50% of the population. We consider sensitivity simulations to uncertainty in waste-combustion emission mass, the removal of waste-combustion emissions, and model resolution. A factor-of-2 uncertainty in waste-combustion PM2.5 leads to central estimates ranging from 138 000 to 518 000 mortalities per year for factors-of-2 reductions and increases, respectively. Complete removal of waste combustion would only avoid 191 000 (5th-95th: 151 000-224 000) mortalities per year (smaller than the total contributed premature mortalities due to nonlinear CRFs). Decreasing model resolution from 2° × 2.5° to 4° × 5° results in 16% fewer mortalities attributed to waste-combustion PM2.5, and over Asia, decreasing resolution from 0.5° × 0.666° to 2° × 2.5° results in 21% fewer mortalities attributed to waste-combustion PM2.5. Owing to coarse model resolution, our global estimates of premature mortality from waste-combustion PM2.5 are likely a lower bound.
Residential biomass burning for heating purposes is an important source of air pollutants during winter. Here we test the hypothesis that significant secondary organic aerosol production can take ...place even during winter nights through oxidation of the emitted organic vapors by the nitrate (NO3) radical produced during the reaction of ozone and nitrogen oxides. We use a mobile dual smog chamber system which allows the study of chemical aging of ambient air against a control reference. Ambient urban air sampled during a wintertime campaign during nighttime periods with high concentrations of biomass burning emissions was used as the starting point for the aging experiments. Biomass burning organic aerosol (OA) was, on average, 70 % of the total OA at the beginning of our experiments. Ozone was added in the perturbed chamber to simulate mixing with background air (and subsequent NO3 radical production and aging), while the second chamber was used as a reference. Following the injection of ozone, rapid OA formation was observed in all experiments, leading to increases in the OA concentration by 20 %–70 %. The oxygen-to-carbon ratio of the OA increased on average by 50 %, and the mass spectra of the produced OA was quite similar to the oxidized OA mass spectra reported during winter in urban areas. Furthermore, good correlation was found for the OA mass spectra between the ambient-derived emissions in this study and the nocturnal aged laboratory-derived biomass burning emissions from previous work. Concentrations of NO3 radicals as high as 25 ppt (parts per trillion) were measured in the perturbed chamber, with an accompanying production of 0.1–3.2 µg m−3 of organic nitrate in the aerosol phase. Organic nitrate represented approximately 10 % of the mass of the secondary OA formed. These results strongly indicate that the OA in biomass burning plumes can chemically evolve rapidly even during wintertime periods with low photochemical activity.
A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the ...near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN(0.2)) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer.
Black carbon (BC) aerosol plays an important role in the Earth’s climate system because it absorbs solar radiation and therefore potentially warms the climate; however, BC can also act as a seed for ...cloud particles, which may offset much of its warming potential. If BC acts as an ice nucleating particle (INP), BC could affect the lifetime, albedo, and radiative properties of clouds containing both supercooled liquid water droplets and ice particles (mixed-phase clouds). Over 40% of global BC emissions are from biomass burning; however, the ability of biomass burning BC to act as an INP in mixed-phase cloud conditions is almost entirely unconstrained. To provide these observational constraints, we measured the contribution of BC to INP concentrations (INP) in real-world prescribed burns and wildfires. We found that BC contributes, at most, 10% to INP during these burns. From this, we developed a parameterization for biomass burning BC and combined it with a BC parameterization previously used for fossil fuel emissions. Applying these parameterizations to global model output, we find that the contribution of BC to potential INP relevant to mixed-phase clouds is ∼5% on a global average.
The annual premature mortality in India attributed to exposure to ambient particulate matter (PM2.5) exceeds 1 million (Cohen et al., 2017, https://doi.org/10.1016/S0140‐6736(17)30505‐6). Studies ...have estimated sector‐specific premature mortality from ambient PM2.5 exposure in India and shown residential energy use is the dominant contributing sector. In this study, we estimate the contribution of PM2.5 and premature mortality from six regions of India in 2012 using the global chemical‐transport model. We calculate how premature mortality in India is determined by the transport of pollution from different regions. Of the estimated 1.1 million annual premature deaths from PM2.5 in India, about ~60% was from anthropogenic pollutants emitted from within the region in which premature mortality occurred, ~19% was from transport of anthropogenic pollutants between different regions within India, ~16% was due to anthropogenic pollutants emitted outside of India, and ~4% was associated with natural PM2.5 sources. The emissions from Indo Gangetic Plain contributed to ~46% of total premature mortality over India, followed by Southern India (13%). Indo Gangetic Plain also contributed (~8%) to the most premature mortalities in other regions of India through transport. More than 50% of the premature mortality in Northern, Eastern, Western, and Central India was due to transport of PM2.5 from regions outside of these individual regions. Our results indicate that reduction in anthropogenic emissions over India, as well as its neighboring regions, will be required to reduce the health impact of ambient PM2.5 in India.
Key Points
In India, transport of PM2.5 between regions within India and from outside of India influences estimated premature mortality
Pollutant transport to other Indian regions is highest from the Indo Gangetic Plain
For better air quality, anthropogenic emissions throughout India and its neighboring regions need to be reduced
Familiarity with the use of face coverings to reduce the risk of respiratory disease has increased during the coronavirus pandemic; however, recommendations for their use outside of the pandemic ...remains limited. Here, we develop a modeling framework to quantify the potential health benefits of wearing a face covering or respirator to mitigate exposure to particulate air pollution. This framework accounts for the wide range of available face coverings and respirators, fit factors and efficacy, air pollution characteristics, and exposure‐response data. Our modeling shows that N95 respirators offer robust protection against different sources of particulate matter, reducing exposure by more than a factor of 14 when worn with a leak rate of 5%. Synthetic‐fiber masks offer less protection with a strong dependence on aerosol size distribution (protection factors ranging from 4.4 to 2.2), while natural‐fiber and surgical masks offer reductions in the exposure of 1.9 and 1.7, respectively. To assess the ability of face coverings to provide population‐level health benefits to wildfire smoke, we perform a case study for the 2012 Washington state fire season. Our models suggest that although natural‐fiber masks offer minor reductions in respiratory hospitalizations attributable to smoke (2%–11%) due to limited filtration efficiency, N95 respirators and to a lesser extent surgical and synthetic‐fiber masks may lead to notable reductions in smoke‐attributable hospitalizations (22%–39%, 9%–24%, and 7%–18%, respectively). The filtration efficiency, bypass rate, and compliance rate (fraction of time and population wearing the device) are the key factors governing exposure reduction potential and health benefits during severe wildfire smoke events.
Plain Language Summary
The use of face coverings (e.g., cloth masks, surgical masks, and N95 respirators) has increased dramatically during the coronavirus pandemic; however, recommendations for their use outside of the pandemic, for example, during wildfire episodes, remains limited. In this study, we investigate the potential health benefits of wearing a face covering to reduce the amount of inhaled particulate air pollution. Our model accounts for different types of face coverings, how well they fit, the characteristics of air pollution, and the risk of air pollution causing respiratory disease. We find that N95 respirators, a special type of face covering that meets regulatory standards, offer a promising means to reduce the inhalation of particulate air pollution and thereby reduce the risk of negative health effects; however, the public health benefits are strongly dependent on how often the respirator is worn and by how many people. In a case study in Washington state in 2012, we estimate that the use of N95 respirators could reduce respiratory hospitalizations caused by wildfire smoke by 22%–39%. Conversely, cloth masks offer only limited protection against air pollution due to poor filtration efficiency and poor fit.
Key Points
We developed a framework to quantify potential health benefits of wearing a face mask or respirator during episodes of severe air pollution
N95 respirators offer protection against wildfire PM2.5, reducing exposure by more than a factor of 14 and hospitalizations by 22%–39%
Natural‐fiber (e.g., cotton) masks offer only minor protection against wildfire PM2.5, reducing hospitalizations by only 2%–11%
Atmospheric aerosols are evolving mixtures of chemical species. In global climate models (GCMs), this “aerosol mixing state” is represented in a highly simplified manner. This can introduce errors in ...the estimates of climate-relevant aerosol properties, such as the concentration of cloud condensation nuclei. The goal for this study is to determine a global spatial distribution of aerosol mixing state with respect to hygroscopicity, as quantified by the mixing state metric χ . In this way, areas can be identified where the external or internal mixture assumption is more appropriate. We used the output of a large ensemble of particle-resolved box model simulations in conjunction with machine learning techniques to train a model of the mixing state metric χ . This lower-order model for χ uses as inputs only variables known to GCMs, enabling us to create a global map of χ based on GCM data. We found that χ varied between 20% and nearly 100%, and we quantified how this depended on particle diameter, location, and time of the year. This framework demonstrates how machine learning can be applied to bridge the gap between detailed process modeling and a large-scale climate model.
Open, uncontrolled combustion of domestic waste is a potentially significant source of aerosol; however, this aerosol source is not generally included in many global emissions inventories. To provide ...a first estimate of the aerosol radiative impacts from domestic-waste combustion, we incorporate the Wiedinmyer et al. (2014) emissions inventory into GEOS-Chem-TOMAS, a global chemical-transport model with online aerosol microphysics. We find domestic-waste combustion increases global-mean black carbon and organic aerosol concentrations by 8 and 6 %, respectively, and by greater than 40 % in some regions. Due to uncertainties regarding aerosol optical properties, we estimate the globally averaged aerosol direct radiative effect to range from −5 to −20 mW m−2; however, this range increases from −40 to +4 mW m−2 when we consider uncertainties in emission mass and size distribution. In some regions with significant waste combustion, such as India and China, the aerosol direct radiative effect may exceed −0.4 W m−2. Similarly, we estimate a cloud-albedo aerosol indirect effect of −13 mW m−2, with a range of −4 to −49 mW m−2 due to emission uncertainties. In the regions with significant waste combustion, the cloud-albedo aerosol indirect effect may exceed −0.4 W m−2.
Summertime Arctic aerosol size distributions are strongly controlled by
natural regional emissions. Within this context, we use a chemical transport
model with size-resolved aerosol microphysics ...(GEOS-Chem-TOMAS) to interpret
measurements of aerosol size distributions from the Canadian Arctic
Archipelago during the summer of 2016, as part of the “NETwork on Climate
and Aerosols: Addressing key uncertainties in Remote Canadian Environments”
(NETCARE) project. Our simulations suggest that condensation of secondary organic
aerosol (SOA) from precursor vapors emitted in the Arctic and near Arctic
marine (ice-free seawater) regions plays a key role in particle growth events
that shape the aerosol size distributions observed at Alert (82.5∘ N,
62.3∘ W), Eureka (80.1∘ N, 86.4∘ W), and
along a NETCARE ship track within the Archipelago. We refer to this SOA as
Arctic marine SOA (AMSOA) to reflect the Arctic marine-based and likely
biogenic sources for the precursors of the condensing organic vapors. AMSOA from a simulated flux (500 µgm-2day-1, north of
50∘ N) of precursor vapors (with an assumed yield of unity) reduces the
summertime particle size distribution model–observation mean fractional
error 2- to 4-fold, relative to a simulation without this AMSOA. Particle
growth due to the condensable organic vapor flux contributes strongly
(30 %–50 %) to the simulated summertime-mean number of particles with
diameters larger than 20 nm in the study region. This growth couples with
ternary particle nucleation (sulfuric acid, ammonia, and water vapor) and
biogenic sulfate condensation to account for more than 90 % of this
simulated particle number, which represents a strong biogenic influence. The simulated fit to
summertime size-distribution observations is further improved at Eureka and
for the ship track by scaling up the nucleation rate by a factor of 100 to
account for other particle precursors such as gas-phase iodine and/or amines
and/or fragmenting primary particles that could be missing from our
simulations. Additionally, the fits to the observed size distributions and total
aerosol number concentrations for particles larger than 4 nm improve with
the assumption that the AMSOA contains semi-volatile species: the
model–observation mean fractional error is reduced 2- to 3-fold for the Alert and
ship track size distributions. AMSOA accounts for about half of the
simulated particle surface area and volume distributions in the summertime
Canadian Arctic Archipelago, with climate-relevant simulated summertime
pan-Arctic-mean top-of-the-atmosphere aerosol direct (−0.04 W m−2) and
cloud-albedo indirect (−0.4 W m−2) radiative effects, which due
to uncertainties are viewed as an order of magnitude estimate. Future work
should focus on further understanding summertime Arctic sources of AMSOA.
Despite the potential importance of black carbon (BC) for radiative forcing of the Arctic atmosphere, vertically resolved measurements of the particle light scattering coefficient (σsp) and light ...absorption coefficient (σap) in the springtime Arctic atmosphere are infrequent, especially measurements at latitudes at or above 80∘ N. Here, relationships among vertically distributed aerosol optical properties (σap, σsp and single scattering albedo or SSA), particle microphysics and particle chemistry are examined for a region of the Canadian archipelago between 79.9 and 83.4∘ N from near the surface to 500 hPa. Airborne data collected during April 2015 are combined with ground-based observations from the observatory at Alert, Nunavut and simulations from the Goddard Earth Observing System (GEOS) model, GEOS-Chem, coupled with the TwO-Moment Aerosol Sectional (TOMAS) model (collectively GEOS-Chem–TOMAS; Kodros et al., 2018) to further our knowledge of the effects of BC on light absorption in the Arctic troposphere. The results are constrained for σsp less than 15 Mm−1, which represent 98 % of the observed σsp, because the single scattering albedo (SSA) has a tendency to be lower at lower σsp, resulting in a larger relative contribution to Arctic warming. At 18.4 m2 g−1, the average BC mass absorption coefficient (MAC) from the combined airborne and Alert observations is substantially higher than the two averaged modelled MAC values (13.6 and 9.1 m2 g−1) for two different internal mixing assumptions, the latter of which is based on previous observations. The higher observed MAC value may be explained by an underestimation of BC, the presence of small amounts of dust and/or possible differences in BC microphysics and morphologies between the observations and model. In comparing the observations and simulations, we present σap and SSA, as measured, and σap∕2 and the corresponding SSA to encompass the lower modelled MAC that is more consistent with accepted MAC values. Median values of the measured σap, rBC and the organic component of particles all increase by a factor of 1.8±0.1, going from near-surface to 750 hPa, and values higher than the surface persist to 600 hPa. Modelled BC, organics and σap agree with the near-surface measurements but do not reproduce the higher values observed between 900 and 600 hPa. The differences between modelled and observed optical properties follow the same trend as the differences between the modelled and observed concentrations of the carbonaceous components (black and organic). Model-observation discrepancies may be mostly due to the modelled ejection of biomass burning particles only into the boundary layer at the sources. For the assumption of the observed MAC value, the SSA range between 0.88 and 0.94, which is significantly lower than other recent estimates for the Arctic, in part reflecting the constraint of σsp<15 Mm−1. The large uncertainties in measuring optical properties and BC, and the large differences between measured and modelled values here and in the literature, argue for improved measurements of BC and light absorption by BC and more vertical profiles of aerosol chemistry, microphysics and other optical properties in the Arctic.
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