Recent increases in the frequency and scale of wildfires worldwide have raised concerns about the influence of climate change and associated socio-economic costs. In the western U.S., the hazard of wildfire has been increasing for decades. Here, we use a combination of physical, epidemiological, and economic models to estimate the economic impacts of California wildfires in 2018, including the value of destroyed and damaged capital, the health costs related to air pollution exposure, and indirect losses due to broader economic disruption cascading along with regional and national supply chains. Our estimation shows that wildfire damages in 2018 totaled $148.5 (126.1-192.9) billion (roughly 1.5% of California's annual GDP), with $27.7 billion (19%) in capital losses, $32.2 billion (22%) in health costs, and $88.6 billion (59%) in indirect losses. Our results reveal that the majority of economic impacts related to California wildfires may be indirect and often affect industry sectors and locations distant from the fires (e.g., 52% of the indirect losses-31% of total losses-in 2018 were outside of California). Our findings and methods provide new information for decision-makers tasked with protecting lives and key production sectors and reducing the economic damages of future wildfires.
A size-composition resolved aerosol model (SCRAM) is coupled to the Polyphemus air quality platform and evaluated over Greater Paris. SCRAM simulates the particle mixing state and solves the aerosol dynamic evolution taking into account the processes of coagulation, condensation/evaporation, and nucleation. Both the size and mass fractions of chemical components of particles are discretized. The performance of SCRAM to model air quality over Greater Paris is evaluated by comparison to PM 2.5 , PM 10 , and Aerosol Optical Depth (AOD) measurements. Because air quality models usually assume that particles are internally mixed, the impact of the mixing state on aerosols formation, composition, optical properties, and their ability to be activated as cloud condensation nuclei (CCN) is investigated. The simulation results show that more than half (up to 80% during rush hours) of black carbon particles are barely mixed at the urban site of Paris, while they are more mixed with organic species at a rural site. The comparisons between the internal-mixing simulation and the mixing state-resolved simulation show that the internal-mixing assumption leads to lower nitrate and higher ammonium concentrations in the particulate phase. Moreover, the internal-mixing assumption leads to lower single scattering albedo, and the difference of aerosol optical depth caused by the mixing state assumption can be as high as 72.5%. Furthermore, the internal-mixing assumption leads to lower CCN activation percentage at low supersaturation, but higher CCN activation percentage at high supersaturation.
Air quality models are used to simulate and forecast pollutant concentrations, from continental scales to regional and urban scales. These models usually assume that particles are internally mixed, i.e. particles of the same size have the same chemical composition, which may vary in space and time. Although this assumption may be realistic for continental-scale simulations, where particles originating from different sources have undergone sufficient mixing to achieve a common chemical composition for a given model grid cell and time, it may not be valid for urban-scale simulations, where particles from different sources interact on shorter time scales. To investigate the role of the mixing state assumption on the formation of particles, a size-composition resolved aerosol model (SCRAM) was developed and coupled to the Polyphemus air quality platform. Two simulations, one with the internal mixing hypothesis and another with the external mixing hypothesis, have been carried out for the period 15 January to 11 February 2010, when the MEGAPOLI winter field measurement campaign took place in Paris. The simulated bulk concentrations of chemical species and the concentrations of individual particle classes are compared with the observations of Healy et al. (Atmos. Chem. Phys., 2013, 13, 9479-9496) for the same period. The single particle diversity and the mixing-state index are computed based on the approach developed by Riemer et al. (Atmos. Chem. Phys., 2013, 13, 11423-11439), and they are compared to the measurement-based analyses of Healy et al. (Atmos. Chem. Phys., 2014, 14, 6289-6299). The average value of the single particle diversity, which represents the average number of species within each particle, is consistent between simulation and measurement (2.91 and 2.79 respectively). Furthermore, the average value of the mixing-state index is also well represented in the simulation (69% against 59% from the measurements). The spatial distribution of the mixing-state index shows that the particles are not mixed in urban areas, while they are well mixed in rural areas. This indicates that the assumption of internal mixing traditionally used in transport chemistry models is well suited to rural areas, but this assumption is less realistic for urban areas close to emission sources.
Abstract. The Size-Composition Resolved Aerosol Model (SCRAM) for simulating the dynamics of externally mixed atmospheric particles is presented. This new model classifies aerosols by both composition and size, based on a comprehensive combination of all chemical species and their mass-fraction sections. All three main processes involved in aerosol dynamics (coagulation, condensation/evaporation and nucleation) are included. The model is first validated by comparison with a reference solution and with results of simulations using internally mixed particles. The degree of mixing of particles is investigated in a box model simulation using data representative of air pollution in Greater Paris. The relative influence on the mixing state of the different aerosol processes (condensation/evaporation, coagulation) and of the algorithm used to model condensation/evaporation (bulk equilibrium, dynamic) is studied.
Ammonium salts such as ammonium nitrate and ammonium sulfate constitute an important fraction of the total fine particulate matter (PM 2.5 ) mass. While the conversion of inorganic gases into particulate-phase sulfate, nitrate, and ammonium is now well understood, there is considerable uncertainty over interactions between gas-phase ammonia and secondary organic aerosols (SOAs). Observations have confirmed that ammonia can react with carbonyl compounds in SOA, forming nitrogen-containing organic compounds (NOCs). This chemistry consumes gas-phase NH 3 and may therefore affect the amount of ammonium nitrate and ammonium sulfate in particulate matter (PM) as well as particle acidity. In order to investigate the importance of such reactions, a first-order loss rate for ammonia onto SOA was implemented into the Community Multiscale Air Quality (CMAQ) model based on the ammonia uptake coefficients reported in the literature. Simulations over the continental US were performed for the winter and summer of 2011 with a range of uptake coefficients (10 −3 -10 −5 ). Simulation results indicate that a significant reduction in gas-phase ammonia may be possible due to its uptake onto SOA; domainaveraged ammonia concentrations decrease by 31.3 % in the winter and 67.0 % in the summer with the highest uptake coefficient (10 −3 ). As a result, the concentration of particulate matter is also significantly affected, with a distinct spatial pattern over different seasons. PM concentrations decreased during the winter, largely due to the reduction in ammonium nitrate concentrations. On the other hand, PM concentrations increased during the summer due to increased biogenic SOA (BIOSOA) production resulting from enhanced acid-catalyzed uptake of isoprene-derived epoxides. Since ammonia emissions are expected to increase in the future, it is important to include NH 3 + SOA chemistry in air quality models.
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