Dry deposition of ozone is an important sink of ozone in near‐surface air. When dry deposition occurs through plant stomata, ozone can injure the plant, altering water and carbon cycling and reducing crop yields. Quantifying both stomatal and nonstomatal uptake accurately is relevant for understanding ozone's impact on human health as an air pollutant and on climate as a potent short‐lived greenhouse gas and primary control on the removal of several reactive greenhouse gases and air pollutants. Robust ozone dry deposition estimates require knowledge of the relative importance of individual deposition pathways, but spatiotemporal variability in nonstomatal deposition is poorly understood. Here we integrate understanding of ozone deposition processes by synthesizing research from fields such as atmospheric chemistry, ecology, and meteorology. We critically review methods for measurements and modeling, highlighting the empiricism that underpins modeling and thus the interpretation of observations. Our unprecedented synthesis of knowledge on deposition pathways, particularly soil and leaf cuticles, reveals process understanding not yet included in widely used models. If coordinated with short‐term field intensives, laboratory studies, and mechanistic modeling, measurements from a few long‐term sites would bridge the molecular to ecosystem scales necessary to establish the relative importance of individual deposition pathways and the extent to which they vary in space and time. Our recommended approaches seek to close knowledge gaps that currently limit quantifying the impact of ozone dry deposition on air quality, ecosystems, and climate.

Atmospheric ozone loss through dry deposition to vegetation is a critically important process for both air quality and ecosystem health. The majority of atmospheric chemistry models calculate dry deposition using a resistance‐in‐series parameterization by Wesely (1989), which is dependent on many environmental variables and lookup table values. The uncertainties contained within this parameterization have not been fully explored, ultimately challenging our ability to understand global scale biosphere‐atmosphere interactions. In this work, we evaluate the GEOS‐Chem model simulation of ozone dry deposition using a globally distributed suite of observations. We find that simulated daytime deposition velocities generally reproduce the magnitude of observations to within a factor of 1.4. When correctly accounting for differences in land class between the observations and model, these biases improve, most substantially over the grasses and shrubs land class. These biases do not impact the global ozone burden substantially; however, they do lead to local absolute changes of up to 4 ppbv and relative changes of 15% in summer surface concentrations. We use MERRA meteorology from 1979 to 2008 to assess that the interannual variability in simulated annual mean ozone dry deposition due to model input meteorology is small (generally less than 5% over vegetated surfaces). Sensitivity experiments indicate that the simulation is most sensitive to the stomatal and ground surface resistances, as well as leaf area index. To improve ozone dry deposition models, more measurements are necessary over rainforests and various crop types, alongside constraints on individual depositional pathways and other in‐canopy ozone loss processes.

[1] We explore the value of multispectral CO retrievals from NASA/Terra Measurement of Pollution In The Troposphere (MOPITT v5), along with Atmospheric CO 2 Observations from Space (ACOSv2.9) CO 2 retrievals from the Japan Aerospace Exploration Agency Greenhouse Gases Observing Satellite (GOSAT), for characterizing emissions from anthropogenic combustion. We use these satellite retrievals to analyze observed CO 2 /CO enhancement ratios (ΔCO 2 /ΔCO) over megacities. Since CO is coemitted with CO 2 in anthropogenic combustion, the observed ΔCO 2 /ΔCO characterizes the general trend in combustion activity. Our analyses show patterns in ΔCO 2 /ΔCO that correspond well with the developed/developing status of megacities, and ΔCO 2 /ΔCO that agree well with available literature and emission inventories to approximately 20%. Comparisons with ΔCO 2 /ΔCO derived from Total Carbon Column Observing Network measurements show similar agreement, where some of the differences in observed ΔCO 2 /ΔCO are due to representativeness and limited GOSAT data. Our results imply potential constraints in anthropogenic combustion from GOSAT/MOPITT, particularly in augmenting our carbon monitoring systems. Citation: Silva, S. J., A. F. Arellano, and H. Worden (2013), Toward anthropogenic combustion emission constraints from space-based analysis of urban CO 2 /CO sensitivity, Geophys. Res. Lett., 40,[4971][4972][4973][4974][4975][4976]

Abstract:We present joint analyses of satellite-observed combustion products to examine bulk characteristics of combustion in megacities and fire regions. We use retrievals of CO, NO 2 and CO 2 from NASA/Terra Measurement of Pollution In The Troposphere, NASA/Aura Ozone Monitoring Instrument, and JAXA Greenhouse Gases Observing Satellite to estimate atmospheric enhancements of these co-emitted species based on their spatiotemporal variability (spread, σ) within 14 regions dominated by combustion emissions. We find that patterns in σ XCO /σ XCO 2 and σ XCO /σ XNO 2 are able to distinguish between combustion types across the globe. These patterns show distinct groupings for biomass burning and the developing/developed status of a region that are not well represented in global emissions inventories. We show here that such multi-species analyses can provide constraints on emission inventories, and be useful in monitoring trends and understanding regional-scale combustion.

Rationale Extremes of heat and particulate air pollution threaten human health and are becoming more frequent because of climate change. Understanding the health impacts of coexposure to extreme heat and air pollution is urgent. Objectives To estimate the association of acute coexposure to extreme heat and ambient fine particulate matter (PM 2.5 ) with all-cause, cardiovascular, and respiratory mortality in California from 2014 to 2019. Methods We used a case-crossover study design with time-stratified matching using conditional logistic regression to estimate mortality associations with acute coexposures to extreme heat and PM 2.5 . For each case day (date of death) and its control days, daily average PM 2.5 and maximum and minimum temperatures were assigned (0- to 3-day lag) on the basis of the decedent’s residence census tract. Measurements and Main Results All-cause mortality risk increased 6.1% (95% confidence interval [CI], 4.1–8.1) on extreme maximum temperature-only days and 5.0% (95% CI, 3.0–8.0) on extreme PM 2.5 -only days, compared with nonextreme days. Risk increased by 21.0% (95% CI, 6.6–37.3) on days with exposure to both extreme maximum temperature and PM 2.5 . Increased risk of cardiovascular and respiratory mortality on extreme coexposure days was 29.9% (95% CI, 3.3–63.3) and 38.0% (95% CI, −12.5 to 117.7), respectively, and were more than the sum of individual effects of extreme temperature and PM 2.5 only. A similar pattern was observed for coexposure to extreme PM 2.5 and minimum temperature. Effect estimates were larger over age 75 years. Conclusions Short-term exposure to extreme heat and air pollution alone were individually associated with increased risk of mortality, but their coexposure had larger effects beyond the sum of their individual effects.

Dry deposition is a major sink of tropospheric ozone. Increasing evidence has shown that ozone dry deposition actively links meteorology and hydrology with ozone air quality. However, there is little systematic investigation on the performance of different ozone dry deposition parameterizations at the global scale and how parameterization choice can impact surface ozone simulations. Here, we present the results of the first global, multidecadal modelling and evaluation of ozone dry deposition velocity (v d ) using multiple ozone dry deposition parameterizations. We model ozone dry deposition velocities over 1982-2011 using four ozone dry deposition parameterizations that are representative of current approaches in global ozone dry deposition modelling. We use consistent assimilated meteorology, land cover, and satellite-derived leaf area index (LAI) across all four, such that the differences in simulated v d are entirely due to differences in deposition model structures or assumptions about how land types are treated in each. In addition, we use the surface ozone sensitivity to v d predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated v d values from four different parameterizations are evaluated against field observations, and while performance varies considerably by land cover types, our results suggest that none of the parameterizations are universally better than the others. Discrepancy in simulated mean v d among the parameterizations is estimated to cause 2 to 5 ppbv of discrepancy in surface ozone in the Northern Hemisphere (NH) and up to 8 ppbv in tropical rainforests in July, and up to 8 ppbv in tropical rainforests and seasonally dry tropical forests in Indochina in December. Parameterization-specific biases based on individual land cover type and hydroclimate are found to be the two main drivers of such discrepancies. We find statistically significant trends in the multiannual time series of simulated July daytime v d in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah, and Mongolia) or greening (high latitudes). The trend in July daytime v d is estimated to be 1 % yr −1 and leads to up to 3 ppbv of surface ozone changes over 1982-2011. The interannual coefficient of variation (CV) of July daytime mean v d in NH is found to be 5 %-15 %, with spatial distribution that varies with the dry deposition parameterization. Our sensitivity simulations suggest this can contribute between 0.5 to 2 ppbv to interannual variability (IAV) in surface ozone, but all models tend to underestimate interannual CV when compared to long-term ozone flux observations. We also find that IAV in some dry deposition parameterizations is more sensitive to LAI, while in others it is more sensitive to climate. Comparisons with other published estimates of the IAV of background ozone confirm that ozone dry deposition can be an important part of natural surface ozone variability. Our results...

Abstract. Land use and land cover changes impact climate and air quality by altering the exchange of trace gases between the Earth's surface and atmosphere. Large-scale tree mortality that is projected to occur across the United States as a result of insect and disease may therefore have unexplored consequences for tropospheric chemistry. We develop a land use module for the GEOS-Chem global chemical transport model to facilitate simulations involving changes to the land surface, and to improve consistency across land-atmosphere exchange processes. The model is used to test the impact of projected national-scale tree mortality risk through 2027 estimated by the 2012 USDA Forest Service National Insect and Disease Risk Assessment. Changes in biogenic emissions alone decrease monthly mean O 3 by up to 0.4 ppb, but reductions in deposition velocity compensate or exceed the effects of emissions yielding a net increase in O 3 of more than 1 ppb in some areas. The O 3 response to the projected change in emissions is affected by the ratio of baseline NO x : VOC concentrations, suggesting that in addition to the degree of land cover change, tree mortality impacts depend on whether a region is NO x -limited or NO x -saturated. Consequently, air quality (as diagnosed by the number of days that 8 h average O 3 exceeds 70 ppb) improves in polluted environments where changes in emissions are more important than changes to dry deposition, but worsens in clean environments where changes to dry deposition are the more important term. The influence of changes in dry deposition demonstrated here underscores the need to evaluate treatments of this physical process in models. Biogenic secondary organic aerosol loadings are significantly affected across the US, decreasing by 5-10 % across many regions, and by more than 25 % locally. Tree mortality could therefore impact background aerosol loadings by between 0.5 and 2 µg m −3 . Changes to reactive nitrogen oxide abundance and partitioning are also locally important. The regional effects simulated here are similar in magnitude to other scenarios that consider future biofuel cropping or natural succession, further demonstrating that biosphere-atmosphere exchange should be considered when predicting future air quality and climate. We point to important uncertainties and further development that should be addressed for a more robust understanding of land cover change feedbacks.

The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiälä, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus ~0.1). The same DNN model, when driven by assimilated meteorology at 2° × 2.5° spatial resolution, outperforms the Wesely scheme as implemented in the GEOS‐Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models.