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In this study, the WRF-Chem v4.4 model was utilized to evaluate the sensitivity of O3 simulations with three bottom-up emission inventories (EDGAR-HTAP v2 and v3 and KORUS v5) using surface and aircraft data in East Asia during the Korea-United States Air Quality (KORUS-AQ) campaign period in 2016. All emission inventories were found to reproduce the diurnal variations of O3 and its main precursor NO2 as compared to the surface monitor data. However, the spatial distributions of the daily maximum 8 h average (MDA8) O3 in the model do not completely align with the observations. The model MDA8 O3 had a negative (positive) bias north (south) of 30° N over China. All simulations underestimated the observed CO by 50 %–60 % over China and South Korea. In the Seoul Metropolitan Area (SMA), EDGAR-HTAP v2 and v3 and KORUS v5 simulated the vertical shapes and diurnal patterns of O3 and other precursors effectively, but the model underestimated the observed O3, CO, and HCHO concentrations. Notably, the model aromatic volatile organic compounds (VOCs) were significantly underestimated with the three bottom-up emission inventories, although the KORUS v5 shows improvements. The model isoprene estimations had a positive bias relative to the observations, suggesting that the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04 overestimated isoprene emissions. Additional model simulations were conducted by doubling CO and VOC emissions over China and South Korea to investigate the causes of the model O3 biases and the effects of the long-range transport on the O3 over South Korea. The doubled CO and VOC emission simulations improved the model O3 simulations for the local-emission-dominant case but led to the model O3 overestimations for the transport-dominant case, which emphasizes the need for accurate representations of the local VOC emissions over South Korea.

Abstract Background We performed an integrated biomarker evaluation in pembrolizumab‐treated patients with R/M HNSCC enrolled in KEYNOTE‐012 or KEYNOTE‐055. The relationship between biomarkers and HPV status was explored. Methods We evaluated PD‐L1 (combined positive score [CPS]), TMB, T‐cell‐inflamed gene expression profile (TcellinfGEP), and HPV status. Associations between biomarkers were evaluated by logistic regression (ORR) and Cox regression (PFS, OS). Results Two hundred and fifty‐seven patients (KEYNOTE‐012, n = 106; KEYNOTE‐055, n = 151) had TMB data available; of these, 254 had PD‐L1 and 236 had TcellinfGEP. TMB, PD‐L1, and TcellinfGEP were each significantly associated with ORR (p

Purpose: Local treatment for bone metastases is becoming increasingly complex. National guidelines traditionally focus only on radiation therapy (RT), leaving a gap in clinical decision support resources available to clinicians. The objective of this study was to reach expert consensus regarding multidisciplinary management of non-spine bone metastases, which would facilitate standardizing treatment within an academic-community partnership. Methods and Materials: A multidisciplinary panel of physicians treating metastatic disease across the Memorial Sloan Kettering (MSK) Cancer Alliance, including community-based partner sites, was convened. Clinical questions rated of high importance in the management of non-spine bone metastases were identified via survey. A literature review was conducted, and panel physicians drafted initial recommendation statements. Consensus was gathered on recommendation statements through a modified Delphi process from a full panel of 17 physicians from radiation oncology, orthopaedic surgery, medical oncology, interventional radiology, and anesthesia pain. Consensus was defined a priori as 75% of respondents indicating “agree” or “strongly agree” with the consensus statement. Strength of Recommendation Taxonomy was employed to assign evidence strength for each statement. Results: Seventeen clinical questions were identified, of which 11 (65%) were selected for the consensus process. Consensus was reached for 16 of 17 answer statements (94%), of which 12 were approved after Round 1 and additional 4 approved after Round 2 of the modified Delphi voting process. Topics included indications for referral to surgery or interventional radiology, radiation fractionation and appropriate use of stereotactic approaches, and the handling of systemic therapies during radiation. Evidence strength was most commonly C (n = 7), followed by B (n = 5) and A (n = 3). Conclusions: Consensus among a multidisciplinary panel of community and academic physicians treating non-spine bone metastases was feasible. Recommendations will assist clinicians and potentially provide measures to reduce variation across diverse practice settings. Findings highlight areas for further research such as pathologic fracture risk estimation, pre-operative radiation, and percutaneous ablation.

Purpose: Treatment patterns for head and neck squamous cell carcinoma (HNSCC) vary among older adults because of concerns about their health status. Geriatric assessment may guide treatment for older adults with HNSCC by assessing their health status. Methods and Materials: We conducted a retrospective review of adjuvant treatment received by older patients with HNSCC who completed a novel geriatric assessment, the electronic Rapid Fitness Assessment, before treatment. The electronic Rapid Fitness Assessment yields an accumulated geriatric deficits (AGD) score. Higher AGD score indicates greater frailty. Comparators were age and performance status. The Wilcoxon rank sum test compared differences between those who did and did not receive adjuvant radiation therapy and chemotherapy. Results: The cohort included 73 patients, of whom 56 (77%) had oral cavity cancer. The most common geriatric deficits were major distress, social activity limitation, depression, and impaired activities of daily living. AGD score, age, and performance status were not associated with receipt of adjuvant radiation. Patients who received adjuvant chemotherapy had a significantly lower median AGD score than those who did not (3 vs 6; P = .044), but there was no association with age and performance status. Of the 17 patients with newly diagnosed disease and either positive margins or extranodal extension, only 9 received adjuvant radiation and only 3 received systemic therapy. Most often, systemic therapy was omitted because of patient preference or comorbidities and poor performance status. There was a nonstatistically significant lower AGD score between patients who did and did not receive standard fractionated radiation therapy (median, 4 vs 6.5; P = .13). Conclusions: Receipt of adjuvant chemotherapy was associated with frailty. Rates of chemotherapy utilization were very low, indicating the need for novel strategies to mitigate the toxicity burden in this patient population. Receipt of adjuvant radiation therapy was not associated with frailty; however, there was a trend toward lower frailty among those who did receive radiation therapy.

Sub-grid variability (SGV) in atmospheric trace gases within satellite pixels is a key issue in satellite design and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m×250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by the mean of the sub-grid GeoTASO values within the same satellite pixel) for different hypothetical satellite pixel sizes over urban regions. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS-AQ field campaign and over the Los Angeles Basin, USA, during the 2017 Student Airborne Research Program (SARP) field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ∼10 % for 0.5 km×0.5 km pixel size to ∼35 % for 25 km×25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability in the retrieved NO2 VC within the same hypothetical satellite pixels (represented by the difference of retrieved values at two or more different times in a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over the SMA. For a given small (e.g., ≤4 h) sampling time difference within the same satellite pixels, the temporal variability in the retrieved NO2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles Basin. The results of this study have implications for future satellite design and retrieval interpretation and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime.

In this work, the impact of Los Angeles Basin pollution transport and stratospheric intrusions on the surface ozone levels observed in the San Gabriel Mountains is investigated based on a combination of surface and lidar measurements as well as WRF-Chem (Weather Research and Forecasting with Chemistry) and WACCM (Whole Atmosphere Community Climate Model) runs. The number of days with observed surface ozone levels exceeding the National Ambient Air Quality Standards exhibit a clear seasonal pattern, with a maximum during summer, when models suggest a minimum influence of stratospheric intrusions and the largest impact from Los Angeles Basin pollution transport. Additionally, measured and modeled surface ozone and PM10 were analyzed as a function of season, time of the day, and wind direction. Measurements and models are in good qualitative agreement, with maximum surface ozone observed for southwest and west winds. For the prevailing summer wind direction, slightly south of the ozone maximum and corresponding to south-southwest winds, lower ozone levels were observed. Back trajectories suggest that this is associated with transport from the central Los Angeles Basin, where titration limits the amount of surface ozone. A quantitative comparison of the lidar profiles with WRF-Chem and WACCM models revealed good agreement near the surface, with models showing an increasing positive bias as function of altitude, reaching 75 % at 15 km above sea level. Finally, three selected case studies covering the different mechanisms affecting the near-surface ozone concentration over the San Gabriel Mountains, namely stratospheric intrusions and pollution transport, are analyzed based on surface and ozone lidar measurements, as well as co-located ceilometer measurements and models.

KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM2.5, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm−3 in the model vs. observed average of 1.5 g cm−3) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011–2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea–United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated datasets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a factor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori profiles) but mostly to OMI's large footprint (>312 km2).

Fine particulate air pollution (PM _2.5 ) is decreasing in most areas of the United States, except for areas most affected by wildfires, where increasing trends in PM _2.5 can be attributed to wildfire smoke. The frequency and duration of large wildfires and the length of the wildfire season have all increased in recent decades, partially due to climate change, and wildfire risk is projected to increase further in many regions including the western United States. Increasingly, empirical evidence suggests differential health effects from air pollution by class and race; however, few studies have investigated such differential health impacts from air pollution during a wildfire event. We investigated differential risk of respiratory health impacts during the 2008 northern California wildfires by a comprehensive list of socio-economic status (SES), race/ethnicity, and smoking prevalence variables. Regardless of SES level across nine measures of SES, we found significant associations between PM _2.5 and asthma hospitalizations and emergency department (ED) visits during these wildfires. Differential respiratory health risk was found by SES for ED visits for chronic obstructive pulmonary disease where the highest risks were in ZIP codes with the lowest SES levels. Findings for differential effects by race/ethnicity were less consistent across health outcomes. We found that ZIP codes with higher prevalence of smokers had greater risk of ED visits for asthma and pneumonia. Our study suggests that public health efforts to decrease exposures to high levels of air pollution during wildfires should focus on lower SES communities.

Wildfires have been increasing in frequency in the western United States (US) with the 2017 and 2018 fire seasons experiencing some of the worst wildfires in terms of suppression costs and air pollution that the western US has seen. Although growing evidence suggests respiratory exacerbations from elevated fine particulate matter (PM2.5) during wildfires, significantly less is known about the impacts on human health of ozone (O3) that may also be increased due to wildfires. Using machine learning, we created daily surface concentration maps for PM2.5 and O3 during an intense wildfire in California in 2008. We then linked these daily exposures to counts of respiratory hospitalizations and emergency department visits at the ZIP code level. We calculated relative risks of respiratory health outcomes using Poisson generalized estimating equations models for each exposure in separate and mutually-adjusted models, additionally adjusted for pertinent covariates. During the active fire periods, PM2.5 was significantly associated with exacerbations of asthma and chronic obstructive pulmonary disease (COPD) and these effects remained after controlling for O3. Effect estimates of O3 during the fire period were non-significant for respiratory hospitalizations but were significant for ED visits for asthma (RR = 1.05 and 95% CI = (1.022, 1.078) for a 10 ppb increase in O3). In mutually-adjusted models, the significant findings for PM2.5 remained whereas the associations with O3 were confounded. Adjusted for O3, the RR for asthma ED visits associated with a 10 μg/m3 increase in PM2.5 was 1.112 and 95% CI = (1.087, 1.138). The significant findings for PM2.5 but not for O3 in mutually-adjusted models is likely due to the fact that PM2.5 levels during these fires exceeded the 24-hour National Ambient Air Quality Standard (NAAQS) of 35 μg/m3 for 4976 ZIP-code days and reached levels up to 6.073 times the NAAQS, whereas our estimated O3 levels during the fire period only occasionally exceeded the NAAQS of 70 ppb with low exceedance levels. Future studies should continue to investigate the combined role of O3 and PM2.5 during wildfires to get a more comprehensive assessment of the cumulative burden on health from wildfire smoke. Keywords: Wildfires, Ozone, Particulate matter, Respiratory disease

We apply a high-resolution chemical transport model (GEOS-Chem CTM) with updated treatment of volatile organic compounds (VOCs) and a comprehensive suite of airborne datasets over North America to (i) characterize the VOC budget and (ii) test the ability of current models to capture the distribution and reactivity of atmospheric VOCs over this region. Biogenic emissions dominate the North American VOC budget in the model, accounting for 70 % and 95 % of annually emitted VOC carbon and reactivity, respectively. Based on current inventories anthropogenic emissions have declined to the point where biogenic emissions are the dominant summertime source of VOC reactivity even in most major North American cities. Methane oxidation is a 2× larger source of nonmethane VOCs (via production of formaldehyde and methyl hydroperoxide) over North America in the model than are anthropogenic emissions. However, anthropogenic VOCs account for over half of the ambient VOC loading over the majority of the region owing to their longer aggregate lifetime. Fires can be a significant VOC source episodically but are small on average. In the planetary boundary layer (PBL), the model exhibits skill in capturing observed variability in total VOC abundance (R2=0.36) and reactivity (R2=0.54). The same is not true in the free troposphere (FT), where skill is low and there is a persistent low model bias (∼ 60 %), with most (27 of 34) model VOCs underestimated by more than a factor of 2. A comparison of PBL : FT concentration ratios over the southeastern US points to a misrepresentation of PBL ventilation as a contributor to these model FT biases. We also find that a relatively small number of VOCs (acetone, methanol, ethane, acetaldehyde, formaldehyde, isoprene + oxidation products, methyl hydroperoxide) drive a large fraction of total ambient VOC reactivity and associated model biases; research to improve understanding of their budgets is thus warranted. A source tracer analysis suggests a current overestimate of biogenic sources for hydroxyacetone, methyl ethyl ketone and glyoxal, an underestimate of biogenic formic acid sources, and an underestimate of peroxyacetic acid production across biogenic and anthropogenic precursors. Future work to improve model representations of vertical transport and to address the VOC biases discussed are needed to advance predictions of ozone and SOA formation.

Recent increases in natural gas (NG) production through hydraulic fracturing have called the climate benefit of switching from coal-fired to natural gas-fired power plants into question. Higher than expected levels of methane, non-methane hydrocarbons (NMHC), and NOx have been observed in areas close to oil and NG operation facilities. Large uncertainties in the oil and NG operation emission inventories reduce the confidence level in the impact assessment of such activities on regional air quality and climate, as well as in the development of effective mitigation policies. In this work, we used ethane as the indicator of oil and NG emissions and explored the sensitivity of ethane to different physical parameterizations and simulation setups in the Weather Research and Forecasting with Chemistry (WRF-Chem) model using the US EPA National Emission Inventory (NEI-2011). We evaluated the impact of the following configurations and parameterizations on predicted ethane concentrations: planetary boundary layer (PBL) parameterizations, daily re-initialization of meteorological variables, meteorological initial and boundary conditions, and horizontal resolution. We assessed the uncertainties around oil and NG emissions using measurements from the FRAPPÉ and DISCOVER-AQ campaigns over the northern Front Range metropolitan area (NFRMA) in summer 2014. The sensitivity analysis shows up to 57.3 % variability in the normalized mean bias of the near-surface modeled ethane across the simulations, which highlights the important role of model configurations on the model performance and ultimately the assessment of emissions. Comparison between airborne measurements and the sensitivity simulations indicates that the model–measurement bias of ethane ranged from −14.9 to −8.2 ppb (NMB ranged from −80.5 % to −44 %) in regions close to oil and NG activities. Underprediction of ethane concentration in all sensitivity runs suggests an actual underestimation of the oil and NG emissions in the NEI-2011. An increase of oil and NG emissions in the simulations partially improved the model performance in capturing ethane and lumped alkanes (HC3) concentrations but did not impact the model performance in capturing benzene, toluene, and xylene; this is due to very low emission rates of the latter species from the oil and NG sector in NEI-2011.

Exposure to wildland fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for assessing these smoke events (ground-based measurements, satellite observations, and chemical transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using daily social media posts from Facebook regarding smoke, haze, and air quality to assess population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook dataset to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), daily (24 h) average surface particulate matter measurements, and model-simulated (WRF-Chem) surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well correlated (R2 generally above 0.5) with the other methods in smoke-impacted regions. The Facebook dataset is better correlated with surface measurements of PM2. 5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation. We also present an example case for Washington state in 2015, for which we combine this Facebook dataset with MODIS observations and WRF-Chem-simulated PM2. 5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct ground-based particulate matter measurements.

We describe the University of Colorado mobile Solar Occultation Flux instrument (CU mobile SOF). The instrument consists of a digital mobile solar tracker that is coupled to a Fourier transform spectrometer (FTS) of 0.5 cm−1 resolution and a UV–visible spectrometer (UV–vis) of 0.55 nm resolution. The instrument is used to simultaneously measure the absorption of ammonia (NH3), ethane (C2H6) and nitrogen dioxide (NO2) along the direct solar beam from a moving laboratory. These direct-sun observations provide high photon flux and enable measurements of vertical column densities (VCDs) with geometric air mass factors, high temporal resolution of 2 s and spatial resolution of 5–19 m. It is shown that the instrument line shape (ILS) of the FTS is independent of the azimuth and elevation angle pointing of the solar tracker. Further, collocated measurements next to a high-resolution FTS at the National Center for Atmospheric Research (HR-NCAR-FTS) show that the CU mobile SOF measurements of NH3 and C2H6 are precise and accurate; the VCD error at high signal to noise ratio is 2–7 %. During the Front Range Air Pollution and Photochemistry Experiment (FRAPPE) from 21 July to 3 September 2014 in Colorado, the CU mobile SOF instrument measured median (minimum, maximum) VCDs of 4.3 (0.5, 45) × 1016 molecules cm−2 NH3, 0.30 (0.06, 2.23) × 1016 molecules cm−2 NO2 and 3.5 (1.5, 7.7) × 1016 molecules cm−2 C2H6. All gases were detected in larger 95 % of the spectra recorded in urban, semi-polluted rural and remote rural areas of the Colorado Front Range. We calculate structure functions based on VCDs, which describe the variability of a gas column over distance, and find the largest variability for NH3. The structure functions suggest that currently available satellites resolve about 10 % of the observed NH3 and NO2 VCD variability in the study area. We further quantify the trace gas emission fluxes of NH3 and C2H6 and production rates of NO2 from concentrated animal feeding operations (CAFO) using the mass balance method, i.e., the closed-loop vector integral of the VCD times wind speed along the drive track. Excellent reproducibility is found for NH3 fluxes and also, to a lesser extent, NO2 production rates on 2 consecutive days; for C2H6 the fluxes are affected by variable upwind conditions. Average emission factors were 12.0 and 11.4 gNH3 h−1 head−1 at 30 °C for feedlots with a combined capacity for ∼ 54 000 cattle and a dairy farm of ∼ 7400 cattle; the pooled rate of 11.8 ± 2.0 gNH3 h−1 head−1 is compatible with the upper range of literature values. At this emission rate the NH3 source from cattle in Weld County, CO (535 766 cattle), could be underestimated by a factor of 2–10. CAFO soils are found to be a significant source of NOx. The NOx source accounts for ∼ 1.2 % of the N flux in NH3 and has the potential to add ∼ 10 % to the overall NOx emissions in Weld County and double the NOx source in remote areas. This potential of CAFO to influence ambient NOx concentrations on the regional scale is relevant because O3 formation is NOx sensitive in the Colorado Front Range. Emissions of NH3 and NOx are relevant for the photochemical O3 and secondary aerosol formation.

We present airborne measurements made during the 2014 Front Range Air Pollution and Photochemistry Experiment (FRAPPÉ) project to investigate the impacts of the Denver Cyclone on regional air quality in the greater Denver area. Data on trace gases, non-refractory submicron aerosol chemical constituents, and aerosol optical extinction (βext) at λ = 632 nm were evaluated in the presence and absence of the surface mesoscale circulation in three distinct study regions of the Front Range: In-Flow, Northern Front Range, and the Denver metropolitan area. Pronounced increases in mass concentrations of organics, nitrate, and sulfate in the Northern Front Range and the Denver metropolitan area were observed during the cyclone episodes (27–28 July) compared to the non-cyclonic days (26 July, 2–3 August). Organic aerosols dominated the mass concentrations on all evaluated days, with a 45 % increase in organics on cyclone days across all three regions, while the increase during the cyclone episode was up to ∼ 80 % over the Denver metropolitan area. In the most aged air masses (NOx / NOy βext displayed strong correlations (r = 0.71) with organic and nitrate in the Northern Front Range and only with organics (r = 0.70) in the Denver metropolitan area, while correlation of βext during the cyclone was strongest (r = 0.86) with nitrate over Denver. Mass extinction efficiency (MEE) values in the Denver metropolitan area were similar on cyclone and non-cyclone days despite the dominant influence of different aerosol species on βext. Our analysis showed that the meteorological patterns associated with the Denver Cyclone increased aerosol mass loadings in the Denver metropolitan area mainly by transporting aerosols and/or aerosol precursors from the northern regions, leading to impaired visibility and air quality deterioration.

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.

This paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of compact phase space retrievals of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (R2). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of compact phase space retrievals (CPSRs). CPSRs are obtained by preprocessing retrieval data sets with an algorithm that (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by ∼ 35 %, while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.

This study examines differences in the surface black carbon (BC) aerosol loading between the Bay of Bengal (BoB) and the Arabian Sea (AS) and identifies dominant sources of BC in South Asia and surrounding regions during March–May 2006 (Integrated Campaign for Aerosols, Gases and Radiation Budget, ICARB) period. A total of 13 BC tracers are introduced in the Weather Research and Forecasting Model coupled with Chemistry to address these objectives. The model reproduced the temporal and spatial variability of BC distribution observed over the AS and the BoB during the ICARB ship cruise and captured spatial variability at the inland sites. In general, the model underestimates the observed BC mass concentrations. However, the model–observation discrepancy in this study is smaller compared to previous studies. Model results show that ICARB measurements were fairly well representative of the AS and the BoB during the pre-monsoon season. Elevated BC mass concentrations in the BoB are due to 5 times stronger influence of anthropogenic emissions on the BoB compared to the AS. Biomass burning in Burma also affects the BoB much more strongly than the AS. Results show that anthropogenic and biomass burning emissions, respectively, accounted for 60 and 37% of the average ± standard deviation (representing spatial and temporal variability) BC mass concentration (1341 ± 2353 ng m−3) in South Asia. BC emissions from residential (61%) and industrial (23%) sectors are the major anthropogenic sources, except in the Himalayas where vehicular emissions dominate. We find that regional-scale transport of anthropogenic emissions contributes up to 25% of BC mass concentrations in western and eastern India, suggesting that surface BC mass concentrations cannot be linked directly to the local emissions in different regions of South Asia.

Triolein-containing semipermeable membrane devices (SPMDs) and butyl rubber (BR) based sorbents were employed as passive samplers in 14 coastal stations of Turkey including shipyards and marinas to characterize time-integrated levels of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) and their relationship to potential pollution sources. Passive samplers of SPMDs and BR sorbents were deployed for 30 days in the spring of 2012. The maximum concentrations of total PAH and PCB compounds sequestered by SPMDs were 3338 ng g−1 SPMD and 4247 pg g−1 SPMD. (END)-I and DDT-related compounds were dominant OCP compounds for most of the sites in passive samplers. Total PAH concentrations in SPMDs were found 1.2 to 8 times higher than the concentrations in BRs. However, BR sorbents were able to sample some PAHs which could not be sampled by SPMDs. The concentrations of PCBs and OCPs in BRs were similar or higher than SPMDs. SPMD-data were used to estimate the average ambient water concentrations of the contaminants. Two existing theoretical approaches have been used to derive the concentrations of hydrophobic pollutants in the ambient waters. The results were found very similar and range from 7318 to 183864 pg L−1 for PAHs, from 2 to 186 pg L−1 for PCBs, and from 98 to 848 pg L−1 for OCPs. Furthermore, a simple numerical model was designed to estimate the boat-related water concentrations in marinas by using the seawater data supplied by SPMDs. The model was mainly built on the water concentration and the capacities of a particular marina and then applied to two sites in the second marina. A good correlation was found between the model outputs and SPMD-water data. Keywords: Passive water sampling, PAH, POP, Shipyard, Marina, Pollution

Over the Mediterranean region, elevated tropospheric ozone (O3) values are recorded, especially in summer. We use the thermal Infrared Atmospheric Sounding Interferometer (IASI) and the Weather Research and Forecasting Model with Chemistry (WRF-Chem) to understand and interpret the factors and emission sources responsible for the high O3 concentrations observed in the Mediterranean troposphere. Six years (2008–2013) of IASI data have been analyzed and results show consistent maxima during summer, with an increase of up to 22% in the [0–8] km O3 column in the eastern part of the basin compared to the middle of the basin. We focus on summer 2010 to investigate the processes that contribute to these summer maxima. Using two modeled O3 tracers (inflow to the model domain and local anthropogenic emissions), we show that, between the surface and 2 km, O3 is mostly formed from anthropogenic emissions, while above 4 km it is mostly transported from outside the domain or from stratospheric origins. Evidence of stratosphere-to-troposphere exchange (STE) events in the eastern part of the basin is shown, and corresponds to a low water vapor mixing ratio and high potential vorticity.

This study examines the effect of a typical pre-monsoon season dust storm on tropospheric chemistry through a case study in northern India. Dust can alter photolysis rates by scattering and absorbing solar radiation and provide surface area for heterogeneous reactions. We use the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate the dust storm that occurred during 17–22 April 2010 and investigate the contribution of different processes on mixing ratios of several key trace gases including ozone, nitrogen oxides, hydrogen oxides, methanol, acetic acid and formaldehyde. We revised the Fast Troposphere Ultraviolet Visible (F-TUV) photolysis scheme to include effects of dust aerosols on photolysis rates in a manner consistent with the calculations of aerosol optical properties for feedback to the meteorology radiation schemes. In addition, we added 12 heterogeneous reactions on the dust surface, for which 6 reactions have relative-humidity-dependent reactive uptake coefficients (γ). The inclusion of these processes in WRF-Chem is found to reduce the difference between observed and modeled O3 from 16 ± 9 to 2 ± 8 ppbv and that in NOy from 2129 ± 1425 to 372 ± 1225 pptv compared to measurements at the high-altitude site Nainital in the central Himalayas, and reduce biases by up to 30% in tropospheric column NO2 compared to OMI retrievals. The simulated dust storm acted as a sink for all the trace gases examined here and significantly perturbed their spatial and vertical distributions. The reductions in these gases are estimated as 5–100%, and more than 80% of this reduction was due to heterogeneous chemistry. The RH dependence of γ is also found to have substantial impact on the distribution of trace gases, with changes of up to 20–25% in O3 and HO2, 50% in H2O2 and 100% in HNO3. A set of sensitivity analyses revealed that dust aging could change H2O2 and CH3COOH levels by up to 50% but has a relatively small impact on other gases.

The impact of a typical pre-monsoon season (April–June) dust storm event on the regional aerosol optical properties and radiation budget in northern India is analyzed. The dust storm event lasted from 17 to 22 April 2010 and the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) estimated total dust emissions of 7.5 Tg over the model domain. Both in situ (AERONET – Aerosol Robotic Network) and satellite observations show significant increase (> 50%) in local to regional scale aerosol optical depth (AOD) and decrease (> 70%) in the Ångström exponent (α) during this period. Amongst the AERONET sites in this region, Kanpur was influenced the most, where the AOD reached up to 2.1 and the α decreased to −0.09 during the dust storm period. The WRF-Chem model reproduced the spatial and temporal distributions of dust plumes and aerosol optical properties but generally underestimated the AOD. The average MODIS and WRF-Chem AOD (550 nm) values in a subregion (70–80° E, 25–30° N) affected the most by the dust storm are estimated as 0.80 ± 0.30 and 0.68 ± 0.28, respectively. Model results show that dust particles cool the surface and the top of the atmosphere, but warm the atmosphere itself. The radiative perturbation due to dust aerosols averaged over the subregion is estimated as −2.9 ± 3.1 W m−2 at the top of the atmosphere, 5.1 ± 3.3 W m−2 in the atmosphere and −8.0 ± 3.3 W m−2 at the surface. The simulated instantaneous cooling under the dust plume was much higher and reached −227 and −70 W m−2 at the surface and the top of the atmosphere, respectively. The impact of these radiative perturbations on the surface energy budget is estimated to be small on a regional scale but significant locally.