Chemical Transport Model Simulations Research Articles

Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models

Approximately half of satellite aerosol retrievals are missing that limits the application of satellite data in PM2.5 pollution monitoring. To obtain spatiotemporally continuous PM2.5 distributions, various gap-filling methods have been developed, but have rarely been evaluated. Here, we reviewed and summarized four types of gap-filling strategies, and applied them to a random forest PM2.5 prediction model that incorporated ground observations, chemical transport model (CTM) simulations, and satellite AOD for predicting daily PM2.5 concentrations at a 1-km resolution in 2013 in the Beijing-Tianjin-Hebei region and the Yangtze River Delta. The model out-of-bag predictions were compared with national station measurements and external measurements to assess the performance of different gap-filling methods. We also conducted a by-city cross-validation and characterized the spatial distributions of PM2.5 prediction when the AOD coverage was low. We found that the methods filling in missing data by regression, i.e. multiple imputation and decision tree, performed robustly to characterizing PM2.5 variation at a high spatial resolution and the method filling in missing PM2.5 predictions with decision tree overcame the problem of time-consuming computations. The method using spatiotemporal trends to fill in missing data, i.e. ordinary kriging and generalized additive mixed model, may be overrated in statistical evaluation tests, and predicted artificially oversmoothed PM2.5 spatial distributions. We also revealed that CTM simulations benefited the prediction of PM2.5 spatial distribution in all the models with various gap-filling strategies with higher prediction accuracy in the by-city cross-validation. We noticed that the PM2.5 prediction was not sensitive to the resolution of CTM simulations and even the 12-km resolution CTM simulations benefited the high-resolution PM2.5 prediction.

Historical total ozone radiative forcing derived from CMIP6 simulations

Radiative forcing (RF) time series for total ozone from 1850 up to the present day are calculated based on historical simulations of ozone from 10 climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, RF is calculated for ozone fields prepared as an input for CMIP6 models without chemistry schemes and from a chemical transport model simulation. A radiative kernel for ozone is constructed and used to derive the RF. The ozone RF in 2010 (2005–2014) relative to 1850 is 0.35 W m−2 [0.08–0.61] (5–95% uncertainty range) based on models with both tropospheric and stratospheric chemistry. One of these models has a negative present-day total ozone RF. Excluding this model, the present-day ozone RF increases to 0.39 W m−2 [0.27–0.51] (5–95% uncertainty range). The rest of the models have RF close to or stronger than the RF time series assessed by the Intergovernmental Panel on Climate Change in the fifth assessment report with the primary driver likely being the new precursor emissions used in CMIP6. The rapid adjustments beyond stratospheric temperature are estimated to be weak and thus the RF is a good measure of effective radiative forcing.

APIFLAME v2.0 biomass burning emissions model: impact of refined input parameters on atmospheric concentration in Portugal in summer 2016

Abstract. Biomass burning emissions are a major source of trace gases and aerosols. Wildfires being highly variable in time and space, calculating emissions requires a numerical tool able to estimate fluxes at the kilometer scale and with an hourly time step. Here, the APIFLAME model version 2.0 is presented. It is structured to be modular in terms of input databases and processing methods. The main evolution compared to version 1.0 is the possibility of merging burned area and fire radiative power (FRP) satellite observations to modulate the temporal variations of fire emissions and to integrate small fires that may not be detected in the burned area product. Accounting for possible missed detection due to small fire results in an increase in burned area ranging from ∼5 % in Africa and Australia to ∼30 % in North America on average over the 2013–2017 time period based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) Collection 6 fire products. An illustration for the case of southwestern Europe during the summer of 2016, marked by large wildfires in Portugal, is presented. Emissions calculated using different possible configurations of APIFLAME show a dispersion of 80 % on average over the domain during the largest wildfires (8–14 August 2016), which can be considered as an estimate of uncertainty of emissions. The main sources of uncertainty studied, by order of importance, are the emission factors, the calculation of the burned area, and the vegetation attribution. The aerosol (PM10) and carbon monoxide (CO) concentrations simulated with the CHIMERE regional chemistry transport model (CTM) are consistent with observations (good timing for the beginning and end of the events, ±1 d for the timing of the peak values) but tend to be overestimated compared to observations at surface stations. On the contrary, vertically integrated concentrations tend to be underestimated compared to satellite observations of total column CO by the Infrared Atmospheric Sounding Interferometer (IASI) instrument and aerosol optical depth (AOD) by MODIS. This underestimate is lower close to the fire region (5 %–40 % for AOD depending on the configuration and 8 %–18 % for total CO) but rapidly increases downwind. For all comparisons, better agreement is achieved when emissions are injected higher into the free troposphere using a vertical profile as estimated from observations of aerosol plume height by the Multi-angle Imaging SpectroRadiometer (MISR) satellite instrument (injection up to 4 km). Comparisons of aerosol layer heights to observations by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) show that some parts of the plume may still be transported at too low an altitude. The comparisons of the different CTM simulations to observations point to uncertainties not only on emissions (total mass and daily variability) but also on the simulation of their transport with the CTM and mixing with other sources. Considering the uncertainty of the emission injection profile and of the modeling of the transport of these dense plumes, it is difficult to fully validate emissions through comparisons between model simulations and atmospheric observations.

Spatial Correlation of Ultrafine Particle Number and Fine Particle Mass at Urban Scales: Implications for Health Assessment.

The epidemiological evidence for ultrafine particles (UFP; particles with diameter <100 nm) causing chronic health effects independent of fine particulate matter (PM2.5) mass is inconclusive. A prevailing view is that urban UFP and PM2.5 mass have different spatial patterns, which should allow epidemiological studies to distinguish their independent, chronic health effects. We investigate intraurban spatial correlation of PM2.5 and UFP exposures in Pittsburgh, Pennsylvania. Measurements and predictions of a land-use regression model indicate moderate spatial correlation between particle number concentrations (PNC; a proxy for UFP) and PM2.5 (R2 of 0.38 and 0.41, respectively). High-resolution (1-km) chemical transport model simulations predict stronger spatial correlation (R2 ≈ 0.8). The finding of moderate to strong spatial correlation was initially surprising because secondary aerosol contributes the vast majority of PM2.5 mass. However, intraurban spatial patterns of both PNC and PM2.5 are driven by local emissions and both pollutants largely behave as passive tracers at time scales of 1 day or less required for transport across most urban environments. Although previous research has shown little temporal correlation between PNC and PM2.5, our finding of moderate to strong spatial correlation may complicate epidemiological analyses to separate the chronic health effects of PNC from PM2.5 mass.

Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM). By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods that achieve lower accuracy despite requiring 20+ CTM simulations (the benchmark statistical model). Our results demonstrate the utility of deep learning approaches for capturing the nonlinearity of atmospheric chemistry and physics and the prospects of the new method to support effective policymaking in other environment systems.

Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO2 measurements from 1999 to 2017.

Understanding changes in terrestrial carbon balance is important to improve our knowledge of the regional carbon cycle and climate change. However, evaluating regional changes in the terrestrial carbon balance is challenging due to the lack of surface flux measurements. This study reveals that the terrestrial carbon uptake over the Republic of Korea has been enhanced from 1999 to 2017 by analyzing long-term atmospheric CO2 concentration measurements at the Anmyeondo Station (36.53°N, 126.32°E) located in the western coast. The influence of terrestrial carbon flux on atmospheric CO2 concentrations (ΔCO2 ) is estimated from the difference of CO2 concentrations that were influenced by the land sector (through easterly winds) and the Yellow Sea sector (through westerly winds). We find a significant trend in ΔCO2 of -4.75ppm per decade (p<.05) during the vegetation growing season (May through October), suggesting that the regional terrestrial carbon uptake has increased relative to the surrounding ocean areas. Combined analysis with satellite measured normalized difference vegetation index and gross primary production shows that the enhanced carbon uptake is associated with significant nationwide increases in vegetation and its production. Process-based terrestrial model and inverse model simulations estimate that regional terrestrial carbon uptake increases by up to 18.9 and 8.0TgC for the study period, accounting for 13.4% and 5.7% of the average annual domestic carbon emissions, respectively. Atmospheric chemical transport model simulations indicate that the enhanced terrestrial carbon sink is the primary reason for the observed ΔCO2 trend rather than anthropogenic emissions and atmospheric circulation changes. Our results highlight the fact that atmospheric CO2 measurements could open up the possibility of detecting regional changes in the terrestrial carbon cycle even where anthropogenic emissions are not negligible.

Trends of Precipitation Acidification and Determining Factors in China During 2006–2015

AbstractPrecipitation acidification, often termed as acid rain (pH &lt; 5.6), has been recognized as one of the major environmental issues in China since the 1980s and exerted detrimental effects on terrestrial ecosystems. In this study, we present the first investigation on the evolution of precipitation acidification over China during 2006–2015 based on a long‐term nationwide precipitation observation network and chemical transport model (Weather Research and Forest model coupled with Chemistry) simulations. The observations demonstrate a clearly decreasing trend in precipitation acidification across China from 2006 to 2015. The percentages of the number of the stations experiencing heavy acidic precipitation (annual mean pH &lt; 5.0) decrease from ~50% in 2006 to 20% in 2015, and those with pH &lt; 4.5 decrease from 25% to less than 5%. Our model simulations reproduced this trend and show the notable decrease in the terrestrial area (from 30% to 16%) experiencing the mean precipitation pH &lt; 5.6. The simulation experiments indicate that the mitigation of the acidification issue in China stemmed from the large reduction in SO2 emissions (by about 60% from 2006 to 2015), which would limit the formation of sulfate and also increase the wet scavenging of gaseous ammonia. The contributions of sulfuric acid on precipitation acidification were estimated to decrease from 0.70–0.82 to 0.44–0.60 from 2006 to 2015 over northern, southern, and southwestern China. This study suggests that nitric acid plays a more important role in acidifying precipitation in northern China at present, while sulfuric acid is still the dominant factor in southern areas.

Bayesian geostatistical modelling of high-resolution [formula omitted] exposure in Europe combining data from monitors, satellites and chemical transport models

Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO2) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO2 concentrations at 1 km2 spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO2 derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration’s (NASA’s) Aura satellite were converted to near ground NO2 concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625°spatial resolution and surface-to-column NO2 ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° × 0.1°were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar NO2 values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface NO2 estimates, showing that, in 2016, 16.17 (95% C.I. 6.34–29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for NO2. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO2 in 2016.

Random forest models for PM2.5 speciation concentrations using MISR fractional AODs

It is increasingly recognized that various chemical components of PM2.5 might have differential toxicities to human health, although such studies are hindered by the sparse or non-existent coverage of ground PM2.5 speciation monitors. The Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite has an innovative design to provide information about aerosol shape, size and extinction that are more related to PM2.5 speciation concentrations. In this study, we developed random forest models that incorporated ground measurements of PM2.5 species, MISR fractional AODs, simulated PM2.5 speciation concentrations from a chemical transport model (CTM), land use variables and meteorological fields, to predict ground-level daily PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) concentrations in California between 2005 and 2014. Our models had out-of-bag R2 of 0.72, 0.70, 0.68 and 0.70 for sulfate, nitrate, OC and EC, respectively. We also conducted sensitivity tests to explore the influence of variable selection on model performance. Results show that if there are sufficient ground measurements and predictor data to support the most sophisticated model structure, fractional AODs and total AOD have similar predicting power in estimating PM2.5 species. Otherwise, models using fractional AODs outperform those with total AOD. PM2.5 speciation concentrations are more sensitive to land use variables than other supporting data (e.g., CTM simulations and meteorological information).

Aerosol from Biomass Combustion in Northern Europe: Influence of Meteorological Conditions and Air Mass History

Alkali-containing submicron particles were measured continuously during three months, including late winter and spring seasons in Gothenburg, Sweden. The overall aims were to characterize the ambient concentrations of combustion-related aerosol particles and to address the importance of local emissions and long-range transport for atmospheric concentrations in the urban background environment. K and Na concentrations in the particulate matter PM1 size range were measured by an Alkali aerosol mass spectrometer (Alkali-AMS) and a cluster analysis was conducted. Local meteorological conditions and trace gas and PM concentrations were also obtained for a nearby location. In addition, back trajectory analyses and chemical transport model (CTM) simulations were included for the evaluation. The Alkali-AMS cluster analysis indicated three major clusters: (1) biomass burning origin, (2) mixture of other combustion sources, and (3) marine origin. Low temperatures and low wind speed conditions correlated with high concentrations of K-containing particles, mainly owing to local and regional emissions from residential biomass combustion; transport of air masses from continental Europe also contribute to Cluster 1. The CTM results indicate that open biomass burning in the eastern parts of Europe may have contributed substantially to high PM2.5 concentrations (and to Cluster 1) during an episode in late March. According to the CTM results, the mixed cluster (2) is likely to include particles emitted from different source types and no single geographical source region seems to dominate for this cluster. The back trajectory analysis and meteorological conditions indicated that the marine origin cluster was correlated with westerly winds and high wind speed; this cluster had high concentrations of Na-containing particles, as expected for sea salt particles.

Time-series analysis of ambient PM2.5 and cardiorespiratory emergency room visits in Lima, Peru during 2010-2016.

IntroductionThere have been no time-series studies of air pollution in Peru. Here we evaluate the effect of ambient PM2.5 on emergency room (ER) visits in Lima.MethodsWe estimated daily PM2.5 levels at a 1 km2 resolution during 2010–2016 using ground measurements, satellite data, and chemical transport model simulations. Population-weighted average daily PM2.5 levels were calculated for each district in Lima (n=40), and assigned to patients based on residence. ER visits for respiratory and circulatory diseases were gathered from 9 large public hospitals. Poisson regression was used to estimate the rate ratio for daily ER visits with change in daily PM2.5, controlling for meteorology, time trends, and district.ResultsFor each interquartile range (IQR) increase in PM2.5, respiratory disease ER visits increased 4% (95% CI: 0–5%), stroke visits 10% (3–18%), and ischemic heart disease visits (adults, 18–64 years) 11% (−1%, 24%). Districts with higher poverty showed significantly stronger associations of PM2.5 and respiratory disease ER visits than districts with lower poverty. Effects were diminished 24–42% using Lima-wide instead of district-specific PM2.5 levels.ConclusionShort-term exposure to ambient PM2.5 is associated with increases in ER visits in Lima for respiratory diseases and stroke, and among middle-aged adults, ischemic heart disease.

Constraining Emissions of Volatile Organic Compounds Over the Indian Subcontinent Using Space-Based Formaldehyde Measurements.

India is an air pollution mortality hot spot, but regional emissions are poorly understood. We present a high-resolution nested chemical transport model (GEOS-Chem) simulation for the Indian subcontinent and use it to interpret formaldehyde (HCHO) observations from two satellite sensors (OMI and GOME-2A) in terms of constraints on regional volatile organic compound (VOC) emissions. We find modeled biogenic VOC emissions to be overestimated by ~30-60% for most locations and seasons, and derive a best estimate biogenic flux of 16 Tg C/year subcontinent-wide for year 2009. Terrestrial vegetation provides approximately half the total VOC flux in our base-case inversions (full uncertainty range: 44-65%). This differs from prior understanding, in which biogenic emissions represent >70% of the total. Our derived anthropogenic VOC emissions increase slightly (13-16% in the base case, for a subcontinent total of 15 Tg C/year in 2009) over RETRO year 2000 values, with some larger regional discrepancies. The optimized anthropogenic emissions agree well with the more recent CEDS inventory, both subcontinent-wide (within 2%) and regionally. An exception is the Indo-Gangetic Plain, where we find an underestimate for both RETRO and CEDS. Anthropogenic emissions thus constitute 37-50% of the annual regional VOC source in our base-case inversions and exceed biogenic emissions over the Indo-Gangetic Plain, West India, and South India, and over the entire subcontinent during winter and post-monsoon. Fires are a minor fraction (<7%) of the total regional VOC source in the prior and optimized model. However, evidence suggests that VOC emissions in the fire inventory used here (GFEDv4) are too low over the Indian subcontinent.

Application of the SHERPA source-receptor relationships, based on the EMEP MSC-W model, for the assessment of air quality policy scenarios

Abstract Source-receptor relationships (SRRs) are simplified air quality models. They are usually used to replace fully-fledged Chemical Transport models (CTMs) when simulating a huge number of emission reduction scenarios, in policy-related contexts. Even if SRRs do not contain the same richness of information as CTMs (i.e. in terms of spatial/temporal resolution) and are merely a statistical approximation of the original models, their application is usually deemed to be sufficient in the policy arena, when a condensed (i.e. yearly average concentrations instead of hourly detailed ones) representation of the real scenarios is required; and when “simulation time” is a constraint (as i.e. optimization processes). In this paper we identify and validate SRRs, based on the EMEP MSC-W Chemical Transport Model. The proposed statistical SRR approach is implemented using a limited number of CTM simulations. Also, it allows for a flexible selection of the emission reduction scenarios to be simulated, both from a geographical and a sectoral point of view. The validation results, performed on various domains and sectors, demonstrate that the proposed methodology can be used in a policy context, even if certain limitations on its use needs to be recognized.

Rapid transition in winter aerosol composition in Beijing from 2014 to 2017: response to clean air actions

Abstract. The clean air actions implemented by the Chinese government in 2013 have led to significantly improved air quality in Beijing. In this work, we combined the in situ measurements of the chemical components of submicron particles (PM1) in Beijing during the winters of 2014 and 2017 and a regional chemical transport model to investigate the impact of clean air actions on aerosol chemistry and quantify the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the changes in aerosol chemical composition from 2014 to 2017. We found that the average PM1 concentration in winter in Beijing decreased by 49.5 % from 2014 to 2017 (from 66.2 to 33.4 µg m−3). Sulfate exhibited a much larger decline than nitrate and ammonium, which led to a rapid transition from sulfate-driven to nitrate-driven aerosol pollution during the wintertime. Organic aerosol (OA), especially coal combustion OA, and black carbon also showed large decreasing rates, indicating the effective emission control of coal combustion and biomass burning. The decreased sulfate contribution and increased nitrate fraction were highly consistent with the much faster emission reductions in sulfur dioxide (SO2) due to phasing out coal in Beijing compared to reduction in nitrogen oxides emissions estimated by bottom-up inventory. The chemical transport model simulations with these emission estimates reproduced the relative changes in aerosol composition and suggested that the reduced emissions in Beijing and its surrounding regions played a dominant role. The variations in meteorological conditions and regional transport contributed much less to the changes in aerosol concentration and its chemical composition during 2014–2017 compared to the decreasing emissions. Finally, we speculated that changes in precursor emissions possibly altered the aerosol formation mechanisms based on ambient observations. The observed explosive growth of sulfate at a relative humidity (RH) greater than 50 % in 2014 was delayed to a higher RH of 70 % in 2017, which was likely caused by the suppressed sulfate formation through heterogeneous reactions due to the decrease in SO2 emissions. Thermodynamic simulations showed that the decreased sulfate and nitrate concentrations have lowered the aerosol water content, particle acidity, and ammonium particle fraction. The results in this study demonstrate the response of aerosol chemistry to the stringent clean air actions and identify that the anthropogenic emission reductions are a major driver, which could help to further guide air pollution control strategies in China.

Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014.

Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, NO3−, NH4+, EC, OC, SO42−) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R2 values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R2 values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2.

Why Do Antarctic Ozone Recovery Trends Vary?

We use satellite ozone records and Global Modeling Initiative chemistry transport model simulations integrated with Modern Era Retrospective for Research and Analysis 2 meteorology to identify a metric that accurately captures the trend in Antarctic ozone attributable to the decline in ozone depleting substances (ODSs). The GMI CTM Baseline simulation with realistically varying ODS levels closely matches observed interannual to decadal scale variations in Antarctic September ozone over the past four decades. The expected increase or recovery trend is obtained from the differences between the Baseline simulation and one with identical meteorology and fixed 1995 ODS levels. The differences show that vortex-averaged column O3 has the greatest sensitivity to ODS change from 1 to 20 September. The observed vortex-averaged column O3 during this period produces a trend consistent with the expected recovery attributable to ODS decline. Trends from dates after 20 September have smaller sensitivity to ODS decline and are more uncertain due to transport variability. Simulations show that the greatest decrease in O3 loss (i.e., recovery) occurs inside the vortex near the edge. The polar cap metrics have vortex size-dependent bias and do not consistently sample this region. Because the 60-90°S 220 Dobson unit O3 mass deficit metric does not sample the edge region, its trend is lower than the expected trend; this is improved by area weighting. The 250-Dobson unit O3 mass deficit metric samples more of the edge region, which increases its trend. Approximately 25% of the September Antarctic O3 increase is due to higher O3 levels in June prior to winter depletion.

A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation

Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) has been linked to various adverse health outcomes. PM2.5 arises from both natural and anthropogenic sources, and PM2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM2.5 measurements, potentially limiting the accuracy of PM2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R2. The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.

Estimating power plant CO2 emission using OCO-2 XCO2 and high resolution WRF-Chem simulations

Anthropogenic CO2 emission from fossil fuel combustion has major impacts on the global climate. The Orbiting Carbon Observatory 2 (OCO-2) observations have previously been used to estimate individual power plant emissions with a Gaussian plume model assuming constant wind fields. The present work assesses the feasibility of estimating power plant CO2 emission using high resolution chemistry transport model simulations with OCO-2 observation data. In the new framework, 1.33 km Weather Research and Forecasting-Chem (WRF)-Chem simulation results are used to calculate the Jacobian matrix, which is then used with the OCO-2 XCO2 data to obtain power plant daily mean emission rates, through a maximum likelihood estimation. We applied the framework to the seven OCO-2 observations of near mid-to-large coal burning power plants identified in Nassar et al (2017 Geophys. Res. Lett. 44, 10045–53). Our estimation results closely match the reported emission rates at the Westar power plant (Kansas, USA), with a reported value of 26.67 ktCO2/day, and our estimated value at 25.82–26.47 ktCO2/day using OCO-2 v8 data, and 22.09–22.80 ktCO2/day using v9 data. At Ghent, KY, USA, our estimations using three versions (v7, v8, and v9) range from 9.84–20.40 ktCO2/day, which are substantially lower than the reported value (29.17 ktCO2/day). We attribute this difference to diminished WRF-Chem wind field simulation accuracy. The results from the seven cases indicate that accurate estimation requires accurate meteorological simulations and high quality XCO2 data. In addition, the strength and orientation (relative to the OCO-2 ground track) of the XCO2 enhancement are important for accurate and reliable estimation. Compared with the Gaussian plume model based approach, the high resolution WRF-Chem simulation based approach provides a framework for addressing varying wind fields, and possible expansion to city level emission estimation.

Composition of Clean Marine Air and Biogenic Influences on VOCs during the MUMBA Campaign

Volatile organic compounds (VOCs) are important precursors to the formation of ozone and fine particulate matter, the two pollutants of most concern in Sydney, Australia. Despite this importance, there are very few published measurements of ambient VOC concentrations in Australia. In this paper, we present mole fractions of several important VOCs measured during the campaign known as MUMBA (Measurements of Urban, Marine and Biogenic Air) in the Australian city of Wollongong (34°S). We particularly focus on measurements made during periods when clean marine air impacted the measurement site and on VOCs of biogenic origin. Typical unpolluted marine air mole fractions during austral summer 2012-2013 at latitude 34°S were established for CO2 (391.0 ± 0.6 ppm), CH4 (1760.1 ± 0.4 ppb), N2O (325.04 ± 0.08 ppb), CO (52.4 ± 1.7 ppb), O3 (20.5 ± 1.1 ppb), acetaldehyde (190 ± 40 ppt), acetone (260 ± 30 ppt), dimethyl sulphide (50 ± 10 ppt), benzene (20 ± 10 ppt), toluene (30 ± 20 ppt), C8H10 aromatics (23 ± 6 ppt) and C9H12 aromatics (36 ± 7 ppt). The MUMBA site was frequently influenced by VOCs of biogenic origin from a nearby strip of forested parkland to the east due to the dominant north-easterly afternoon sea breeze. VOCs from the more distant densely forested escarpment to the west also impacted the site, especially during two days of extreme heat and strong westerly winds. The relative amounts of different biogenic VOCs observed for these two biomes differed, with much larger increases of isoprene than of monoterpenes or methanol during the hot westerly winds from the escarpment than with cooler winds from the east. However, whether this was due to different vegetation types or was solely the result of the extreme temperatures is not entirely clear. We conclude that the clean marine air and biogenic signatures measured during the MUMBA campaign provide useful information about the typical abundance of several key VOCs and can be used to constrain chemical transport model simulations of the atmosphere in this poorly sampled region of the world.

Attribution of the Hemispheric Asymmetries in Trends of Stratospheric Trace Gases Inferred From Microwave Limb Sounder (MLS) Measurements

AbstractUsing Microwave Limb Sounder (MLS) satellite observations, ERA‐Interim reanalysis data, and a chemistry transport model simulation, we analyze and investigate the causes of the asymmetric hemispheric trends of N2O, CH4, and HCl in the stratosphere during the period 2004–2012. We find significant hemispheric asymmetries in the trends of these trace gases in the midlatitude middle and lower stratosphere. With regard to N2O and CH4, the enhanced downwelling branch of the residual circulation in the Northern Hemisphere (NH) middle and upper stratosphere transports more N2O/CH4‐poor air from the upper stratosphere to the lower stratosphere. The enhanced poleward meridional branch of the residual circulation in the Southern Hemisphere (SH) stratosphere brings more N2O/CH4‐rich air from lower to middle latitudes. These processes therefore contribute to the negative trends of N2O and CH4 in the NH lower stratosphere and the positive trends in the SH middle stratosphere. A corresponding positive trend is found for HCl in the NH, where the deep branch of the residual circulation located in the middle and upper stratosphere strengthens, bringing more HCl‐rich air downward to the lower stratosphere, while the shallow branch of the residual circulation in the lower stratosphere weakens and leads to enhanced conversion of chlorine‐containing source gases of different lifetimes to HCl. A reversed picture emerges in the SH, where the deep branch of the residual circulation in the middle and upper stratosphere weakens, while the shallow branch in the lower stratosphere strengthens, resulting in less HCl there. In addition, the southward shift of the upwelling branch of the residual circulation in recent decades can partly explain trace gas trends above 20 hPa, while the eddy mixing has a small effect on the trends. Understanding these contributions from different processes to the hemispheric asymmetries in trends of these trace gases can help us to evaluate more accurately future changes in stratospheric composition.