Improved atmospheric constraints on Southern Ocean CO2 exchange.

We present improved estimates of air-sea CO2 exchange over three latitude bands of the Southern Ocean using atmospheric CO2 measurements from global airborne campaigns and an atmospheric 4-box inverse model based on a mass-indexed isentropic coordinate (Mθe). These flux estimates show two features not clearly resolved in previous estimates based on inverting surface CO2 measurements: a weak winter-time outgassing in the polar region and a sharp phase transition of the seasonal flux cycles between polar/subpolar and subtropical regions. The estimates suggest much stronger summer-time uptake in the polar/subpolar regions than estimates derived through neural-network interpolation of pCO2 data obtained with profiling floats but somewhat weaker uptake than a recent study by Long et al. [Science 374, 1275-1280 (2021)], who used the same airborne data and multiple atmospheric transport models (ATMs) to constrain surface fluxes. Our study also uses moist static energy (MSE) budgets from reanalyses to show that most ATMs tend to have excessive diabatic mixing (transport across moist isentrope, θe, or Mθe surfaces) at high southern latitudes in the austral summer, which leads to biases in estimates of air-sea CO2 exchange. Furthermore, we show that the MSE-based constraint is consistent with an independent constraint on atmospheric mixing based on combining airborne and surface CO2 observations.

Unexpected anthropogenic emission decreases explain recent atmospheric mercury concentration declines.

Anthropogenic activities emit ~2,000 Mg y-1 of the toxic pollutant mercury (Hg) into the atmosphere, leading to long-range transport and deposition to remote ecosystems. Global anthropogenic emission inventories report increases in Northern Hemispheric (NH) Hg emissions during the last three decades, in contradiction with the observed decline in atmospheric Hg concentrations at NH measurement stations. Many factors can obscure the link between anthropogenic emissions and atmospheric Hg concentrations, including trends in the reemissions of previously released anthropogenic ("legacy") Hg, atmospheric sink variability, and spatial heterogeneity of monitoring data. Here, we assess the observed trends in gaseous elemental mercury (Hg0) in the NH and apply biogeochemical box modeling and chemical transport modeling to understand the trend drivers. Using linear mixed effects modeling of observational data from 51 stations, we find negative Hg0 trends in most NH regions, with an overall trend for 2005 to 2020 of -0.011 ± 0.006 ng m-3 y-1 (±2 SD). In contrast to existing emission inventories, our modeling analysis suggests that annual NH anthropogenic emissions must have declined by at least 140 Mg between the years 2005 and 2020 to be consistent with observed trends. Faster declines in 95th percentile Hg0 values than median values in Europe, North America, and East Asian measurement stations corroborate that the likely cause is a decline in nearby anthropogenic emissions rather than background legacy reemissions. Our results are relevant for evaluating the effectiveness of the Minamata Convention on Mercury, demonstrating that existing emission inventories are incompatible with the observed Hg0 declines.

Estimated Impacts of Prescribed Fires on Air Quality and Premature Deaths in Georgia and Surrounding Areas in the US, 2015-2020.

Smoke from wildfires poses a substantial threat to health in communities near and far. To mitigate the extent and potential damage of wildfires, prescribed burning techniques are commonly employed as land management tools; however, they introduce their own smoke-related risks. This study investigates the impact of prescribed fires on daily average PM2.5 and maximum daily 8-h averaged O3 (MDA8-O3) concentrations and estimates premature deaths associated with short-term exposure to prescribed fire PM2.5 and MDA8-O3 in Georgia and surrounding areas of the Southeastern US from 2015 to 2020. Our findings indicate that over the study domain, prescribed fire contributes to average daily PM2.5 by 0.94 ± 1.45 µg/m3 (mean ± standard deviation), accounting for 14.0% of year-round ambient PM2.5. Higher average daily contributions were predicted during the extensive burning season (January-April): 1.43 ± 1.97 µg/m3 (20.0% of ambient PM2.5). Additionally, prescribed burning is also responsible for an annual average increase of 0.36 ± 0.61 ppb in MDA8-O3 (approximately 0.8% of ambient MDA8-O3) and 1.3% (0.62 ± 0.88 ppb) during the extensive burning season. We estimate that short-term exposure to prescribed fire PM2.5 and MDA8-O3 could have caused 2665 (95% confidence interval (CI): 2249-3080) and 233 (95% CI: 148-317) excess deaths, respectively. These results suggest that smoke from prescribed burns increases the mortality. However, refraining from such burns may escalate the risk of wildfires; therefore, the trade-offs between the health impacts of wildfires and prescribed fires, including morbidity, need to be taken into consideration in future studies.

Global Gridded Emission Inventory of Organophosphate Flame Retardants from 2010 to 2020.

As a substitute for brominated flame retardants, organophosphate flame retardants (OPFRs) have become a global concern due to their high toxicity and bioaccumulation. To paint an overall picture of OPFRs in the global environment, the present study develops a gridded global emission inventory of OPFRs on a spatial resolution of 1 × 1° from 2010 to 2020. Revealing a 3.31% average annual increase in emissions, totaling 21,324.42 tons. The production process is the primary source, accounting for 55.43% of emissions, with consumption processes making up the rest. Major sources are in Asia, North America, and Europe. The inventory is verified by implementing emission data into a global atmospheric transport model to predict OPFR concentrations in the global environment and comparing modeled concentrations with field sampled data. The results indicate that the inventory is reliable except for the pristine polar region, where the emission inventory and modeled concentrations underestimate OPFR levels in the atmosphere, likely resulting from ignorance of chemical reactions and the secondary derivative of parent OPFRs during their global long-distance atmospheric transport in the model. This comprehensive data set aids in formulating OPFR emission control policies and assessing health risks.

Societal Co-benefits of Zero-Emission Vehicles in the Freight Industry.

This study was set in the Greater Toronto and Hamilton Area (GTHA), where commercial vehicle movements were assigned across the road network. Implications for greenhouse gas (GHG) emissions, air quality, and health were examined through an environmental justice lens. Electrification of light-, medium-, and heavy-duty trucks was assessed to identify scenarios associated with the highest benefits for the most disadvantaged communities. Using spatially and temporally resolved commercial vehicle movements and a chemical transport model, changes in air pollutant concentrations under electric truck scenarios were estimated at 1-km2 resolution. Heavy-duty truck electrification reduces ambient black carbon and nitrogen dioxide on average by 10 and 14%, respectively, and GHG emissions by 10.5%. It achieves the highest reduction in premature mortality attributable to fine particulate matter chronic exposure (around 200 cases per year) compared with light- and medium-duty electrification (less than 150 cases each). The burden of all traffic in the GTHA was estimated to be around 600 cases per year. The benefits of electrification accrue primarily in neighborhoods with a high social disadvantage, measured by the Ontario Marginalization Indices, narrowing the disparity of exposure to traffic-related air pollution. Benefits related to heavy-duty truck electrification reflect the adverse impacts of diesel-fueled freight and highlight the co-benefits achieved by electrifying this sector.

Model Simulations and Predictions of Hydroxymethanesulfonate (HMS) in the Beijing-Tianjin-Hebei Region, China: Roles of Aqueous Aerosols and Atmospheric Acidity.

Hydroxymethanesulfonate (HMS) has been found to be an abundant organosulfur aerosol compound in the Beijing-Tianjin-Hebei (BTH) region with a measured maximum daily mean concentration of up to 10 µg per cubic meter in winter. However, the production medium of HMS in aerosols is controversial, and it is unknown whether chemical transport models are able to capture the variations of HMS during individual haze events. In this work, we modify the parametrization of HMS chemistry in the nested-grid GEOS-Chem chemical transport model, whose simulations provide a good account of the field measurements during winter haze episodes. We find the contribution of the aqueous aerosol pathway to total HMS is about 36% in winter in Beijing, due primarily to the enhancement effect of the ionic strength on the rate constants of the reaction between dissolved formaldehyde and sulfite. Our simulations suggest that the HMS-to-inorganic sulfate ratio will increase from the baseline of 7% to 13% in the near future, given the ambitious clean air and climate mitigation policies for the BTH region. The more rapid reductions in emissions of SO2 and NOx compared to NH3 alter the atmospheric acidity, which is a critical factor leading to the rising importance of HMS in particulate sulfur species.

Bacterial Emission Factors: A Foundation for the Terrestrial-Atmospheric Modeling of Bacteria Aerosolized by Wildland Fires.

Wildland fire is a major global driver in the exchange of aerosols between terrestrial environments and the atmosphere. This exchange is commonly quantified using emission factors or the mass of a pollutant emitted per mass of fuel burned. However, emission factors for microbes aerosolized by fire have yet to be determined. Using bacterial cell concentrations collected on unmanned aircraft systems over forest fires in Utah, USA, we determine bacterial emission factors (BEFs) for the first time. We estimate that 1.39 × 1010 and 7.68 × 1011 microbes are emitted for each Mg of biomass consumed in fires burning thinning residues and intact forests, respectively. These emissions exceed estimates of background bacterial emissions in other studies by 3-4 orders of magnitude. For the ∼2631 ha of similar forests in the Fishlake National Forest that burn each year on average, an estimated 1.35 × 1017 cells or 8.1 kg of bacterial biomass were emitted. BEFs were then used to parametrize a computationally scalable particle transport model that predicted over 99% of the emitted cells were transported beyond the 17.25 x 17.25 km model domain. BEFs can be used to expand understanding of global wildfire microbial emissions and their potential consequences to ecosystems, the atmosphere, and humans.

Influence of Oil and Gas End-Use on Summertime Particulate Matter and Ozone Pollution in the Eastern US.

The influence of oil and gas end-use activities on ambient air quality is complex and understudied, particularly in regions where intensive end-use activities and large biogenic emissions of isoprene coincide. In these regions, vehicular emissions of nitrogen oxides (NOx≡NO + NO2) modulate the oxidative fate of isoprene, a biogenic precursor of the harmful air pollutants ozone, formaldehyde, and particulate matter (PM2.5). Here, we investigate the direct and indirect influence of the end-use emissions on ambient air quality. To do so, we use the GEOS-Chem model with focus on the eastern United States (US) in summer. Regional mean end-use NOx of 1.4 ppb suppresses isoprene secondary organic aerosol (OA) formation by just 0.02 µg m-3 and enhances abundance of the carcinogen formaldehyde by 0.3 ppb. Formation of other reactive oxygenated volatile organic compounds is also enhanced, contributing to end-use maximum daily mean 8-h ozone (MDA8 O3) of 8 ppb. End-use PM2.5 is mostly (67%) anthropogenic OA, followed by 20% secondary inorganic sulfate, nitrate and ammonium and 11% black carbon. These adverse effects on eastern US summertime air quality suggest potential for severe air quality degradation in regions like the tropics with year-round biogenic emissions, growing oil and gas end-use and limited environmental regulation.

In vitro screening model for compound interactions with human and dairy animal BCRP orthologs.

Orthologs of breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transmembrane transporter, are present in several species. The list of compounds known to interact with BCRP is growing, and many questions remain concerning species-specific variations in substrate specificity and affinity and the potency of inhibitors. As the most abundant efflux transporter known to be present in the blood-milk barrier, BCRP can increase the elimination of certain xenobiotics to milk, posing a risk for suckling offspring and dairy product consumers. Here we developed a model that can be employed to investigate species-specific differences between BCRP substrates and inhibitors. Membrane vesicles were isolated from transiently transduced human embryonic kidney (HEK) 293 cells, overexpressing BCRP, with human, bovine, caprine, and ovine cDNA sequences. To confirm BCRP transport activity in the transduced cells, D-luciferin efflux was measured and to confirm transport activity in the membrane vesicles, [3H] estrone-3-sulfate ([3H]E1S) influx was measured. We also determined the Michaelis-Menten constant (Km) and Vmax of [3H]E1S for each species. We have developed an in vitro transport model to study differences in compound interactions with BCRP orthologs from milk-producing animal species and humans. BCRP transport activity was demonstrated in the species-specific transduced cells by a reduced accumulation of D-luciferin compared with the control cells, indicating BCRP-mediated efflux of D-luciferin. Functionality of the membrane vesicle model was demonstrated by confirming ATP-dependent transport and by quantifying the kinetic parameters, Km and Vmax for the model substrate [3H]E1S. The values were not significantly different between species for the model substrates tested. This model can be insightful for appropriate inter-species extrapolations and risk assessments of xenobiotics in lactating woman and dairy animals.

Mechanistic modeling reveals the importance of turgor-driven apoplastic water transport in wheat stem parenchyma during carbohydrate mobilization.

During stem elongation, wheat (Triticum aestivum) increases its stem carbohydrate content before anthesis as a reserve for grain filling. Hydraulic functioning during this mobilization process is not well understood, and contradictory results exist on the direct effect of drought on carbohydrate mobilization. In a dedicated experiment, wheat plants were subjected to drought stress during carbohydrate mobilization. Measurements, important to better understand stem physiology, showed some unexpected patterns that could not be explained by our current knowledge on water transport. Traditional water flow and storage models failed to properly describe the drought response in wheat stems during carbohydrate mobilization. To explain the measured patterns, hypotheses were formulated and integrated in a dedicated model for wheat. The new mechanistic model simulates two hypothetical water storage compartments: one where water is quickly exchanged with the xylem and one that contains the carbohydrate storage. Water exchange between these compartments is turgor-driven. The model was able to simulate the measured increase in stored carbohydrate concentrations with a decrease in water content and stem diameter. Calibration of the model showed the importance of turgor-driven apoplastic water flow during carbohydrate mobilization. This resulted in an increase in stem hydraulic capacitance, which became more important under drought stress.

Use of Gastrointestinal Simulator, Mass Transport Analysis, and Absorption Simulation to Investigate the Impact of pH Modifiers in Mitigating Weakly Basic Drugs' Performance Issues Related to Gastric pH: Palbociclib Case Study.

It is well known that reduced gastric acidity, for example with concomitant administration of acid reducing agents, can result in variable pharmacokinetics and decreased absorption of weakly basic drugs. It is important to identify the risk of reduced and variable absorption early in development, so that product design options to address the risk can be considered. This article describes the utilization of in vitro and in silico tools to predict the effect of gastric pH, as well as the impact of adding pH modifiers, in mitigating the effect of acid reducing agents on weak base drugs' dissolution and absorption. Palbociclib, a weakly basic drug, was evaluated in low and high gastric pH conditions in a multicompartmental dissolution apparatus referred to as a gastrointestinal simulator (GIS). The GIS permits the testing of pharmaceutical products in a way that better assesses dissolution under physiologically relevant conditions of pH, buffer concentration, formulation additives, and physiological variations including GI pH, buffer concentrations, secretions, stomach emptying rate, residence time in the GI, and aqueous luminal volume. To predict drug dissolution in the GIS, a hierarchical mass transport model was used and validated using in vitro experimental data. Dissolution results were then compared to observed human clinical plasma data with and without proton pump inhibitors using a GastroPlus absorption model to predict palbociclib plasma profiles and pharmacokinetic parameters. The results showed that the in silico model successfully predicted palbociclib dissolution in the GIS under low and high gastric pH conditions with and without pH modifiers. Furthermore, the GIS data coupled with the in silico tools anticipated (1) the reduced palbociclib exposure due to proton pump inhibitor coadministration and (2) the mitigating effect of a pH-modifying agent. This study provides tools to help in the development of orally administered formulations to overcome the effect of elevated gastric pH, especially when formulating with pH modifiers.

Modeling long-distance seed dispersal of the invasive tree Spathodea campanulata in the Society Islands.

Long-distance dispersal is a key factor explaining the success of invasive alien species, particularly across oceanic islands. However, it is often not feasible to reliably measure long-distance seed dispersal (LDD) over many kilometers in the field. Here, we used a three-dimensional kinematic trajectory model (Computing Atmospheric Trajectory tool [CAT]) initiated on the basis of regional wind field data to assess the potential for LDD of a wind-dispersed invasive tree, Spathodea campanulata (African tulip tree), across the Society Islands (French Polynesia, South Pacific Ocean) following its initial planting and spread on the island of Tahiti. The main objective of our study was to determine whether S. campanulata could be expected to spread naturally among islands. Atmospheric dynamics, seed terminal velocity, precipitation, and temperature of air masses were considered to assess the potential for LDD between oceanic islands, with the island of Tahiti serving as the island source for multiple, geographically distant invasions. Aerial trajectories of modeled S. campanulata seeds indicated that wind-dispersed seeds originating from trees on the island of Tahiti could reach most of the Society Islands and disperse as far as 1364 km. This result suggests that Spathodea can be expected to spread naturally among the Society Islands. When rainfall events were modeled as causal agents of seed settlement, fewer seeds reached distant islands, but more seeds settled on the closest island (20 km away). Including effects of island topography ("barrier effects") also resulted in more seeds settling on the closest island and fewer seeds reaching the most distant islands. Overall, our findings suggest that recent atmospheric models can provide valuable insights into LDD and invasion patterns of wind-dispersed invasive species.

Characterizing the Transport and Surface Affinity of Extracellular Vesicles Isolated from Yeast and Bacteria in Well-Characterized Porous Media.

Extracellular vesicles (EVs) are membrane-bounded, nanosized particles, produced and secreted by all biological cell types. EVs are ubiquitous in the environment, operating in various roles including intercellular communication and plant immune modulation. Despite their ubiquity, the role of EV surface chemistry in determining transport has been minimally investigated. Using the zeta (ζ)-potential as a surrogate for surface charge, this work considers the deposition of EVs from the yeast, Saccharomyces cerevisiae, and two bacterial species, Staphylococcus aureus and Pseudomonas fluorescens, in well-characterized porous medium under various background conditions shown to influence the transport of other environmental colloidal particles: ionic strength and humic acid concentration. The affinity of S. cerevisiae EVs for the porous medium (glass beads) appeared to be sensitive to changes in ionic strength, as predicted by colloid stability (Derjaguin, Landau, Verwey, and Overbeek or DLVO) theory, and humic acid concentration, while P. fluorescens EVs deviated from DLVO predictions, suggesting that mechanisms other than charge stabilization may control the deposition of P. fluorescens. Calculations of attachment efficiency from these deposition studies were used to estimate EV transport using a clean-bed filtration model. Based on these calculations, EVs could be transported through such homogeneous porous media up to 15 m.

The contribution of black carbon to global ice nucleating particle concentrations relevant to mixed-phase clouds.

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.

Transport of pollutants in groundwater of domestic waste landfills in karst regions and its engineering control technologies.

Domestic waste produces leachate with a high concentration of pollutants in the landfill process due to biochemical degradation stages like compaction and fermentation. A large number of cases show that anti-seepage membranes widely used in refuse landfills tend to rupture under long-term tension and corrosion, causing leachate to enter the groundwater system and pollute the environment. To reveal the phenomenon of groundwater contamination in refuse landfills, typical domestic waste landfills in karst regions were examined, on the basis of a summary of hydrogeological conditions and hydrochemical characteristics, a three-dimensional groundwater flow model and solute transport model were constructed to analyze the pattern of pollutant diffusion, and its controlling factors, under the current conditions and massive rupture of anti-seepage membrane. The results show that with a minor rupture of the anti-seepage membrane, the area of the low pollution region increases first and then decreases while that of the slight pollution region continuously increases; When a massive rupture of the anti-seepage membrane appears, the ranges of heavy pollution region and total pollution regions continue to grow; Pollutant migrates along the same direction as the groundwater flow and diffuse from high concentration region to low concentration regions under the differential concentration effect. Based on the temporal-spatial distribution characteristics of groundwater pollutants, two engineering control schemes, namely, curtain grouting blocking and group well pumping, were established. A comparison of the two control schemes shows that group well pumping stably maintains water quality safety over the long term, pollutants overflow from both sides of the curtain after they have accumulated to a certain point of concentration, causing damage to the groundwater environment in the conservation area.

Sensitivity of WRF/Chem simulated PM2.5 to initial/boundary conditions and planetary boundary layer parameterization schemes over the Indo-Gangetic Plain.

The ability of a chemical transport model to simulate accurate meteorological and chemical processes depends upon the physical parametrizations and quality of meteorological input data such as initial/boundary conditions. In this study, weather research and forecasting model coupled with chemistry (WRF-Chem) is used to test the sensitivity of PM2.5 predictions to planetary boundary layer (PBL) parameterization schemes (YSU, MYJ, MYNN, ACM2, and Boulac) and meteorological initial/boundary conditions (FNL, ERA-Interim, GDAS, and NCMRWF) over Indo-Gangetic Plain (Delhi, Punjab, Haryana, Uttar Pradesh, and Rajasthan) during the winter period (December 2017 to January 2018). The aim is to select the model configuration for simulating PM2.5 which shows the lowest errors and best agreement with the observed data. The best results were achieved with initial/boundary conditions from ERA and GDAS datasets and local PBL parameterization (MYJ and MYNN). It was also found that PM2.5 concentrations are relatively less sensitive to changes in initial/boundary conditions but in contrast show a stronger sensitivity to changes in the PBL scheme. Moreover, the sensitivity of the simulated PM2.5 to the choice of PBL scheme is more during the polluted hours of the day (evening to early morning), while that to the choice of the meteorological input data is more uniform and subdued over the day. This work indicates the optimal model setup in terms of choice of initial/boundary conditions datasets and PBL parameterization schemes for future air quality simulations. It also highlights the importance of the choice of PBL scheme over the choice of meteorological data set to the simulated PM2.5 by a chemical transport model.

Rice increases phosphorus uptake in strongly sorbing soils by intra-root facilitation.

Upland rice (Oryza sativa) is adapted to strongly phosphorus (P) sorbing soils. The mechanisms underlying P acquisition, however, are not well understood, and models typically underestimate uptake. This complicates root ideotype development and trait-based selection for further improvement. We present a novel model, which correctly simulates the P uptake by a P-efficient rice genotype measured over 48 days of growth. The model represents root morphology at the local rhizosphere scale, including root hairs and fine S-type laterals. It simulates fast- and slowly reacting soil P and the P-solubilizing effect of root-induced pH changes in the soil. Simulations predict that the zone of pH changes and P solubilization around a root spreads further into the soil than the zone of P depletion. A root needs to place laterals outside its depletion- but inside its solubilization zone to maximize P uptake. S-type laterals, which are short but hairy, appear to be the key root structures to achieve that. Thus, thicker roots facilitate the P uptake by fine lateral roots. Uptake can be enhanced through longer root hairs and greater root length density but was less sensitive to total root length and root class proportions.

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast.

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods rely on either chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product, NASA's Goddard Earth Observing System "Composition Forecasting" (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next 5 days at a 1 km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next 2 days had an overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following 3 forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with a validation normalized mean bias close to 0, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

Ozone modelling and mapping for risk assessment: An overview of different approaches for human and ecosystems health.

Tropospheric ozone (O3) is one of the most concernedair pollutants dueto its widespread impacts on land vegetated ecosystems and human health. Ozone is also the third greenhouse gas for radiative forcing. Consequently, it should be carefully and continuously monitored to estimate its potential adverse impacts especially inthose regions where concentrations are high. Continuous large-scale O3 concentrations measurement is crucial but may be unfeasible because of economic and practical limitations; therefore, quantifying the real impact of O3over large areas is currently an open challenge. Thus, one of the final objectives of O3 modelling is to reproduce maps of continuous concentrations (both spatially and temporally) and risk assessment for human and ecosystem health. We here reviewedthe most relevant approaches used for O3 modelling and mapping starting from the simplest geo-statistical approaches andincreasing in complexity up to simulations embedded into the global/regional circulation models and pro and cons of each mode are highlighted. The analysis showed that a simpler approach (mostly statistical models) is suitable for mappingO3concentrationsat the local scale, where enough O3concentration data are available. The associated error in mapping can be reduced by using more complex methodologies, based on co-variables. The models available at the regional or global level are used depending on the needed resolution and the domain where they are applied to. Increasing the resolution corresponds to an increase in the prediction but only up to a certain limit. However, with any approach, the ensemble models should be preferred.

Assessing the health estimation capacity of air pollution exposure prediction models.

BACKGROUND: The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations. METHODS: We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM2.5 predictions at 17 monitors in 8 US cities. RESULTS: In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R2 = 0.95) with health association bias compared to overall approaches (R2 = 0.57). For PM2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations. CONCLUSIONS: For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.