U.S. Air Quality and Health Benefits from Avoided Climate Change under Greenhouse Gas Mitigation

Garcia–Menendez, Fernando; Saari, Rebecca K.; Monier, Erwan; Selin, Noelle E.

doi:10.1021/acs.est.5b01324

Cited by 93 publications

(83 citation statements)

References 67 publications

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“…Ensemble-mean projections estimate a climate-induced increase in U.S. annual population-weighted PM 2.5 of 0.5 ± 0.1 μg/m 3 by midcentury and 1.5 ± 0.1 μg/m 3 by the end of the century under the REF scenario (statistical significance evaluated with a Student's t test for 95% confidence). The interactions within these ensemble simulations and impacts on different PM 2.5 components are further discussed in Garcia-Menendez et al (2015). The simulated changes reflect multiple pathways through which meteorology influences PM 2.5 concentrations, including increased SO 2 oxidation and nitrate partitioning to the gas phase under warmer temperatures, higher 10.1029/2019EF001195 stagnation, and variations in precipitation, consistent with previous studies analyzing the linkages between climate and air quality (Fiore et al, 2012).…”

Section: Resultssupporting

confidence: 81%

“…In CAM-Chem, secondary organic aerosol (SOA) production is represented by a two-product scheme that links SOA formation to oxidation of atmospheric nonmethane hydrocarbons (Lamarque et al, 2012). Additional details and discussion about the CAM-Chem simulations and ensemble-mean PM 2.5 projections used here are included in Garcia-Menendez et al (2015). CAM-Chem has been previously used to model air quality and its ability to replicate surface concentrations of different aerosol species is evaluated against surface observations in Lamarque et al (2012).…”

Section: Methodsmentioning

confidence: 99%

“…To account for natural variability, most modeling studies have relied on simulations covering multiple years. However, the influence of natural variability on PM 2.5 , the pollutant potentially responsible for the majority of health costs associated with the "climate penalty" on air quality (Garcia-Menendez et al, 2015;Silva et al, 2017), has not been specifically assessed. A review of 45 prior studies, listed in Table S1, assessing the impact of climate change on PM 2.5 , or particulate matter components, reveals that over half used five years or less of simulated air quality to represent present or future climates and 85% relied on 10 years or less.…”

Section: Citationmentioning

confidence: 99%

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Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution

et al. 2019

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Variations in meteorology associated with climate change can impact fine particulate matter (PM 2.5 ) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model-based projections of PM 2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM 2.5 pollution in the United States. We find that natural variability in simulated PM 2.5 can be comparable or larger than reported estimates of anthropogenic-induced climate impacts. Relative to mean concentrations, the variability in projected PM 2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM 2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic-forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate-induced impacts on PM 2.5 . Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits.Plain Language Summary Climate change can worsen air pollution caused by small particles in the atmosphere as it alters temperature, precipitation, and other weather variables. Models have been used to project the effects climate change can have on airborne particle concentrations. However, natural year-to-year and longer-term variations in weather can make it difficult to estimate the impacts specifically caused by human forces. In this study, we use a large set of climate and air quality simulations to assess the effect of natural variability in model-based projections of climate change impacts on air pollution over the United States. Our results show that natural variations in predictions of climate change effects on fine particle levels can be significant. We recommend that projections use longer simulation lengths than typically applied, 10 years or more, in order to filter out this natural variability and better reflect the effect of human-caused climate change on particle pollution. Natural variability can also make it challenging to confidently project human-caused climate impacts in some regions or if efforts are taken to reduce greenhouse gas emissions. By using larger modeling lengths or simulation sets, projections of climate change impacts on air pollution and climate policy benefit analyses can be impr...

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Section: Resultssupporting

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Section: Citationmentioning

confidence: 99%

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Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution

et al. 2019

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“…As described in the previous sections, predictions indicate that climate change will worsen O 3 and PM 2.5 pollution across the United States and increase associated health effects (28). Increased temperatures lead to drier conditions, more dust generation, higher risk for wildfires, and increased demand for air conditioning that is largely powered by fossil fuel combustion, all of which contribute to higher PM concentrations (29 (32).…”

Section: Health Implications Of Physical Changes In Earth Climate Sysmentioning

confidence: 95%

Environment, Global Climate Change, and Cardiopulmonary Health

Bayram

Bauer

Abdalati

et al. 2017

Am J Respir Crit Care Med

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“…Many studies have shown that ambient air pollution is related to public health, ecological plant growth, and regional and global climates [1][2][3][4][5][6][7]. A critical component of air pollution is atmospheric PM, which includes fine particles with small diameters that remain suspended in air and do not settle.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Particulate Pollution (PM2.5 and PM10) and Their Spacescale-Dependent Relationships with Meteorological Elements in China

Chen

Zeng

et al. 2017

Sustainability

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Particulate matter (PM) pollution in China has an obvious characteristic of spatial distribution. It is well known that intensive anthropogenic activities, such as fossil fuel combustion and biomass burning, have great influence on the spatial distribution of PM pollution. However, the spacescale-dependent relationships between PM concentrations and weather conditions remain unclear. Here, we investigated the characteristics of two types of particulate pollution, including PM 2.5 and PM 10 , and their spatial relationships with meteorological elements in 173 cities throughout China from March 2014 to February 2015. Results: (1) High PM 2.5 concentrations were distinctly located southeast of the Hu Line, and high PM 10 concentrations were distinctly situated north of the Yangtze River; (2) Spacescale-dependent relationships were found between PM pollution and meteorological elements. The influence of temperature had similar inverted V-shaped characteristics, namely, there was serious PM pollution when temperature was about 15 • C, and there was slight PM pollution when temperature was less or more than 15 • C. Annual precipitation, wind speed, and relative humidity were negatively correlated with PM, while annual atmospheric pressure was positively correlated with PM; (3) The ideal meteorological regions were identified according to the quantified spatial relationships between PM and meteorological elements, which could be defined by a combination of the following conditions: (a) temperature <10 • C or >21 • C; (b) precipitation >1500 mm; (c) atmospheric pressure <900 hPa; (d) wind speed >3 m/s; and (e) relative humidity >65%, where air pollutants can easily be scavenged. The success of this research provides a meteorological explanation to the spatial distribution characteristics of PM pollution in China.

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U.S. Air Quality and Health Benefits from Avoided Climate Change under Greenhouse Gas Mitigation

Cited by 93 publications

References 67 publications

Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution

Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution

Environment, Global Climate Change, and Cardiopulmonary Health

Characteristics of Particulate Pollution (PM2.5 and PM10) and Their Spacescale-Dependent Relationships with Meteorological Elements in China

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