Search

Little is known of how low-income residents of urban heat islands engage their knowledge, attitudes, behaviors, and resources to mitigate the health impacts of heat waves. In this qualitative study, we conducted semi-structured interviews with 40 adults in two such neighborhoods in Los Angeles California to explore their adaptation resources and behaviors, the impacts of heat waves on physical and mental health, and threat assessments of future heat waves. Eighty percent of participants received advanced warning of heat waves from television news and social media. The most common resource was air conditioning (AC) units or fans. However, one-third of participants lacked AC, and many of those with AC engaged in limited use due primarily to the high cost of electricity. Adaptation behaviors include staying hydrated, remaining indoors or going to cooler locations, reducing energy usage, and consuming certain foods and drinks. Most of the participants reported some physical or mental health problem or symptom during heat waves, suggesting vulnerability to heat waves. Almost all participants asserted that heat waves were likely to increase in frequency and intensity with adverse health effects for vulnerable populations. Despite limited resources, low-income residents of urban heat islands utilize a wide range of behaviors to minimize the severity of health impacts, suggesting they are both vulnerable and resilient to heat waves.

Little is known about how low-income residents of urban communities engage their knowledge, attitudes, behaviors, and resources to mitigate the health impacts of wildfire smoke and other forms of air pollution. We interviewed 40 adults in Los Angeles, California, to explore their threat assessments of days of poor air quality, adaptation resources and behaviors, and the impacts of air pollution and wildfire smoke on physical and mental health. Participants resided in census tracts that were disproportionately burdened by air pollution and socioeconomic vulnerability. All participants reported experiencing days of poor air quality due primarily to wildfire smoke. Sixty percent received advanced warnings of days of poor air quality or routinely monitored air quality via cell phone apps or news broadcasts. Adaptation behaviors included remaining indoors, circulating indoor air, and wearing face masks when outdoors. Most (82.5%) of the participants reported some physical or mental health problem or symptom during days of poor air quality, but several indicated that symptom severity was mitigated by their adaptive behaviors. Although low-income residents perceive themselves to be at risk for the physical and mental health impacts of air pollution, they have also adapted to that risk with limited resources.

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 (Hyytiala, 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 similar to 0.1). The same DNN model, when driven by assimilated meteorology at 2 degrees x 2.5 degrees 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. Plain Language Summary Ozone in the lower atmosphere is a toxic pollutant and greenhouse gas. In this work, we use a machine learning technique known as deep learning, to simulate the loss of ozone to Earth's surface. We show that our deep learning simulation of this loss process outperforms existing traditional models and demonstrate the opportunity for using machine learning to improve our understanding of the chemical composition of the atmosphere.

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use–land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

Graph theoretical methods have revolutionized the exploration of complex systems across scientific disciplines. Here, we demonstrate their applicability to the investigation and comparison of three widely-used atmospheric chemical mechanisms of varying complexity: the Master Chemical Mechanism v3.3, GEOS-Chem v12.6, and the Super-Fast chemical mechanism. We investigate these mechanisms using a class of graphical models known as species-reaction graphs and find similarities between these chemical reaction systems and other systems arising in nature. Several graph theoretical properties are consistent across mechanisms, including strong dynamical system disequilibrium and clustering of chemically-related species. This formalism also reveals key differences between the mechanisms, some of which have characteristics inconsistent with domain knowledge; for example, isoprene and peroxy radical chemistry exhibit substantially different graph properties in each mechanism. Graph theoretical methods provide a promising set of tools for investigating atmospheric chemical mechanisms, complementing existing computational approaches, and potentially opening new avenues for scientific discovery.

The activation of aerosol into cloud droplets is an important step in the formation of clouds, and strongly influences the radiative budget of the Earth. Explicitly simulating aerosol activation in Earth system models is challenging due to the computational complexity required to resolve the necessary chemical and physical processes and their interactions. As such, various parameterizations have been developed to approximate these details at reduced computational cost and accuracy. Here, we explore how machine learning emulators can be used to bridge this gap in computational cost and parameterization accuracy. We evaluate a set of emulators of a detailed cloud parcel model using physically regularized machine learning regression techniques. We find that the emulators can reproduce the parcel model at higher accuracy than many existing parameterizations. Furthermore, physical regularization tends to improve emulator accuracy, most significantly when emulating very low activation fractions. This work demonstrates the value of physical constraints in machine learning model development and enables the implementation of improved hybrid physical-machine learning models of aerosol activation into next generation Earth system models.

Atmospheric aerosol over the North Atlantic Ocean impacts regional clouds and climate. In this work, we use a set of sun photometer observations of aerosol optical depth (AOD) located on the Graciosa and Cape Verde islands, along with the GEOS‐Chem chemical transport model to investigate the sources of these aerosol and their transport over the North Atlantic Ocean. At both locations, the largest simulated contributor to aerosol extinction is the local source of sea‐salt aerosol. In addition to this large source, we find that signatures consistent with long‐range transport of anthropogenic, biomass burning, and dust emissions are apparent throughout the year at both locations. Model simulations suggest that this signal of long‐range transport in AOD is more apparent at higher elevation locations; the influence of anthropogenic and biomass burning aerosol extinction is particularly pronounced at the height of Pico Mountain, near the Graciosa Island site. Using a machine learning approach, we further show that simulated observations at these three sites (near Graciosa, Pico Mountain, and Cape Verde) can be used to predict the simulated background aerosol imported into cities on the European mainland, particularly during the local winter months, highlighting the utility of background AOD monitoring for understanding downwind air quality., Key Points Sun photometer AOD observations at island sites characterize long‐range transport of aerosol species across the North Atlantic OceanModel simulations suggest that higher elevation observations provide a pronounced signature of anthropogenic and biomass burning aerosolThese island sites contain information useful for predicting imported aerosol to the European continent

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.

[1] We explore the value of multispectral CO retrievals from NASA/Terra Measurement of Pollution In The Troposphere (MOPITT v5), along with Atmospheric CO2 Observations from Space (ACOSv2.9) CO2 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 CO2/CO enhancement ratios (ΔCO2/ΔCO) over megacities. Since CO is coemitted with CO2 in anthropogenic combustion, the observed ΔCO2/ΔCO characterizes the general trend in combustion activity. Our analyses show patterns in ΔCO2/ΔCO that correspond well with the developed/developing status of megacities, and ΔCO2/ΔCO that agree well with available literature and emission inventories to approximately 20%. Comparisons with ΔCO2/ΔCO derived from Total Carbon Column Observing Network measurements show similar agreement, where some of the differences in observed ΔCO2/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.

Over recent decades oil palm plantations have rapidly expanded across Southeast Asia (SEA). According to the United Nations, oil palm production in SEA increased by a factor of 3 from 1995 to 2010. We investigate the impacts of current (2010) and near-term future (2020) projected oil palm expansion in SEA on surface–atmosphere exchange and the resulting air quality in the region. For this purpose, we use satellite data, high-resolution land maps, and the chemical transport model GEOS-Chem. Relative to a no oil palm plantation scenario (∼ 1990), overall simulated isoprene emissions in the region increased by 13 % due to oil palm plantations in 2010 and a further 11 % in the near-term future. In addition, the expansion of palm plantations leads to local increases in ozone deposition velocities of up to 20 %. The net result of these changes is that oil palm expansion in SEA increases surface O₃ by up to 3.5 ppbv over dense urban regions, and in the near-term future could rise more than 4.5 ppbv above baseline levels. Biogenic secondary organic aerosol loadings also increase by up to 1 µg m⁻³ due to oil palm expansion, and could increase by a further 2.5 µg m⁻³ in the near-term future. Our analysis indicates that while the impact of recent oil palm expansion on air quality in the region has been significant, the retrieval error and sensitivity of the current constellation of satellite measurements limit our ability to observe these impacts from space. Oil palm expansion is likely to continue to degrade air quality in the region in the coming decade and hinder efforts to achieve air quality regulations in major urban areas such as Kuala Lumpur and Singapore., National Science Foundation (U.S.) (AGS- 1238109)

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[subscript 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[subscript 3] of more than 1 ppb in some areas. The O[subscript 3] response to the projected change in emissions is affected by the ratio of baseline NO[subscript x] : VOC concentrations, suggesting that in addition to the degree of land cover change, tree mortality impacts depend on whether a region is NO[subscript x]-limited or NO[subscript x]-saturated. Consequently, air quality (as diagnosed by the number of days that 8 h average O[subscript 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[superscript −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., Natural Sciences and Engineering Research Council of Canada, National Science Foundation (U.S.) (AGC-1238109)