Ground-level Observations Research Articles

Evaluating the reliability of UAV-based carbon dioxide measurements in the lower troposphere

Vertical profiles of carbon dioxide (CO2) were captured in rural Ontario, Canada, using an Aeroqual Series 500 monitor mounted onboard an Uncrewed Aerial Vehicle (UAV). Measurements at various altitudes within the Transport Canada regulations height limit (5m – 95m) were compared to simultaneous measurements taken by an identical sensor at ground-level. Results demonstrate a high degree of agreement between onboard and ground-level observations, with a mean different of -6.02 ppm, and the majority of differences falling within the sensor’s factory calibration accuracy (±10 ppm + 5%). The study also demonstrates the ability to capture the homogeneity of CO2 in the lower troposphere, with Intraclass Correlation Coefficients (ICC) exceeding 0.75 at all altitudes, without the addition of turbulence by UAV flight. The vertical profiles captured in this study validate the use of UAV-based measurements for understanding the distribution and transport of greenhouse gases near the surface of the Earth. This research has implications for routine air quality monitoring to improve atmospheric models, environmental impact assessments and the development of targeted emission reduction strategies.

Global population datasets overestimate flood exposure in Sweden

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies.

Validation of GPM DPR Rainfall and Drop Size Distributions Using Disdrometer Observations in the Western Mediterranean

Dual-frequency precipitation radar (DPR) on the Core GPM satellite provides spaceborne three-dimensional observations of precipitation fields and surface rainfall rate with quasi-global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and drop size distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR-derived products, version 07, from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground references. Four matching techniques between satellite estimates and ground level observations were tested, and the best results were found for the so-called optimal comparison approach. Overall, GPM DPR products captured the variability of the observed DSD well at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw were observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, the lowest errors were found for radar reflectivity factor and Dm, and the highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested, and a substantial overestimation of stratiform cases compared to disdrometer observations were found.

An intercomparison of satellite, airborne, and ground-level observations with WRF–CAMx simulations of NO2 columns over Houston, Texas, during the September 2021 TRACER-AQ campaign

Abstract. Nitrogen dioxide (NO2) is a precursor of ozone (O3) and fine particulate matter (PM2.5) – two pollutants that are above regulatory guidelines in many cities. Bringing urban areas into compliance of these regulatory standards motivates an understanding of the distribution and sources of NO2 through observations and simulations. The TRACER-AQ campaign, conducted in Houston, Texas, in September 2021, provided a unique opportunity to compare observed NO2 columns from ground-, airborne-, and satellite-based spectrometers. In this study, we investigate how these observational datasets compare and simulate column NO2 using WRF–CAMx with fine resolution (444 × 444 m2) comparable to the airborne column measurements. We compare WRF-simulated meteorology to ground-level monitors and find good agreement. We find that observations from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument were strongly correlated (r2 = 0.79) to observations from Pandora spectrometers with a slight high bias (normalized mean bias (NMB) = 3.4 %). Remote sensing observations from the TROPOspheric Monitoring Instrument (TROPOMI) were generally well correlated with Pandora observations (r2 = 0.73) with a negative bias (NMB = −22.8 %). We intercompare different versions of TROPOMI data and find similar correlations across three versions but slightly different biases (from −22.8 % in v2.4.0 to −18.2 % in the NASA MINDS product). Compared with Pandora observations, the WRF–CAMx simulation had reduced correlation (r2 = 0.34) and a low bias (−21.2 %) over the entire study region. We find particularly poor agreement between simulated NO2 columns and GCAS-observed NO2 columns in downtown Houston, an area of high population and roadway densities. These findings point to a potential underestimate of NOx emissions (NOx = NO + NO2) from sources associated with the urban core of Houston, such as mobile sources, in the WRF–CAMx simulation driven by the Texas state inventory, and further investigation is recommended.

Parsimonious estimation of hourly surface ozone concentration across China during 2015–2020

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015–2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands

Abstract. Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

Identification of atmospheric ozone generated from seismic activity during the occurrence of multiple earthquakes across Japan in 2022

Ground-level atmospheric ozone (tropospheric ozone) is a well-known indicator of photochemical air pollution. Atmospheric ozone may also be an indicator of earthquake generation. Ground-level observations of atmospheric ozone at multiple sites were conducted in this study at the time of multiple earthquake occurrences in Japan in 2022. As the result of this study, ozone peaks of unknown origin were detected during the occurrence of multiple earthquakes across Japan in 2022. The level of these ozone peaks began to increase from a few days to a few hours before each earthquake, reaching a maximum concentration coincident with the time of earthquake generation. ‘Unknown ozone’ were detected at near earthquake epicentres, except in the case of deep earthquakes. The ‘unknown ozone’ were also associated with earthquake seismic intensities, which were monitored at seismograph stations in various Japanese cities, towns, and villages. A laboratory experiment by Baragiola et al. (2011) indicated that ppm levels of ozone can be generated in air by the crushing and grinding of terrestrial crustal rocks, generated by exo-electrons emitted by high electric fields, resulting from charge separation during rock fracture. Baragiola et al. (2011) proposed a hypothesis whereby atmospheric ozone could be generated via rock fracturing during the occurrence of earthquakes. This hypothesis could explain the ozone peaks correlating with multiple Japanese earthquakes observed in this study. The significance of atmospheric ozone generated by seismic activity is discussed in terms of it being a possible indicator of earthquake generation and prediction.

Land subsidence susceptibility mapping in urban settlements using time-series PS-InSAR and random forest model

Land subsidence in urban settlements is globally becoming prevalent and severe due to sea level rise and accelerated construction. However, few studies have analyzed the susceptibility of land subsidence in urban settlements and the subsidence rate thresholds that have a great impact on the reliability of the land subsidence susceptibility map (LSSM). This work aims to provide a novel LSSM framework for decision makers to conduct risk control in regional urban settlements. Herein, the COSMO-SkyMed Synthetic Aperture Radar (SAR) data from July 2016 to June 2021 was acquired for remote sensing interpretation in Wuhan, China. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology, combined with geostatistical analysis, was employed to map regional land subsidence rates. A total of 12 impact factors, identified through Pearson correlation coefficient (PCC) and multicollinearity tests, were selected as input features for the machine learning model. The performance of the model was assessed using Receiver Operating Characteristic (ROC) curves, and the segmentation of PS points for land subsidence rate was discussed. Furthermore, the correspondence between the measured ground-level observations and the predicted LSSM was compared. The results demonstrate that the random forest (RF) model outperforms other models in the test set, achieving an Area Under the Curve (AUC) of 0.940. The optimal threshold of −10 mm/year is proposed for segmenting PS points, which exhibits the strongest reliability in the distribution characteristics of the susceptibility index (SI). Combined with a high-performance hybrid model, this work is expected to provide a promising reference for land subsidence susceptibility prediction in similar urban settlements worldwide.

A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020

Human-perceived temperature (HPT) describes the joint effects of multiple climatic factors such as temperature and humidity. Extreme HPT events may reduce labor capacity and cause thermal discomfort and even mortality. These events are becoming more frequent and more intense under global warming, posing severe threats to human and natural systems worldwide, particularly in populated areas with intensive human activities, e.g., the North China Plain (NCP). Therefore, a fine-scale HPT dataset in both spatial and temporal dimensions is urgently needed. Here we construct a daily high-resolution (~1 km) human thermal index collection over NCP from 2003 to 2020 (HiTIC-NCP). This dataset contains 12 HPT indices and has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. Moreover, it exhibits high spatiotemporal consistency with ground-level observations. The dataset provides a reference for human thermal environment and could facilitate studies such as natural hazards, regional climate change, and urban planning.

Distribution, Population Density, and Behavior of Dwarf Galagos (Paragalago sp.) in Taita Hills, Kenya

AbstractA good understanding of taxonomy, distribution, and population density is needed for conservation. Scientific knowledge of dwarf galagos (Paragalago sp.) remains limited. Two populations of Taita dwarf galagos survive in the largest remnants of moist montane forest in Taita Hills, Kenya. We obtained acoustic data on the two remaining populations using AudioMoths—small, passive recorders—more than 231 h in January and February 2021. We measured forest structure by using airborne LiDAR and ground-level observations and tested the influence of forest size and height, canopy coverage, and density on calls per hour as an indicator of population density. Forest size significantly affected population size. Taita dwarf galagos prefer dense canopy coverage, and they prefer the lower 20–30-m tall forest. Mbololo forest (185 ha) has steady population, whereas dwarf galagos are nearly extinct in Ngangao forest (120 ha). The calls of Taita dwarf galagos resemble those of Kenya coast dwarf galagos (Paragalago cocos). DNA studies are urgently needed to resolve the taxonomic status of both surviving populations of dwarf galagos in the Taita Hills. This simple study design should be conducted across African forests to obtain crucial knowledge for galago conservation.

Chemical processes of wintertime aerosols over the East China Sea based on aircraft and ground measurements: Comparison with the Sea of Japan

Aircraft and ground-level observations of wintertime aerosols over the East China Sea (ECS) around Japan's southwestern islands were conducted from the boundary layer to the middle troposphere (0–4.5 km) and compared to those over the Sea of Japan (SOJ) during the same period, for the purpose of comprehensive elucidation on modification process of chemical composition in aerosols at the Asian outflow receptor regions. The deficient values of Cl− in both cases for ECS and SOJ were higher at ground level than at high altitudes because of the acidic gas (HNO3, H2SO4) concentrations. Most Na+ and Cl− at high altitudes and at ground level originated from sea salt, because the (Na+ + Cl−) concentrations of which in the boundary layer observed were linearly related to the production flux of sea spray aerosol. The contribution of sea salt aerosols to cloud condensation nuclei appears to be low over the ECS, contrary to the SOJ. The non-sea salt (nss)-K+ concentrations and the path of backward trajectories suggest a significant impact of biomass burning on the SOJ associated with woodburning for residential heating in the Korean Peninsula. A similar difference was observed in NH4+/nss-SO42– equivalent ratios (∼0.5 for the ECS; ∼1 for the SOJ), which likely indicates ammonium sulfate aerosols produced in urban and industrial regions of Korea. The high aerosol Ca2+ concentration in the boundary layer over the ECS was accompanied by those of Na+, Cl−, NO3−, and nss-SO42– during cold front passages, as observed in SOJ. This indicates that Ca(NO3)2, CaSO4, and CaCl2 were formed via the reactions of acidic gases and Ca-rich dust particles from desert areas in the Asian continent, followed by the production of NaNO3 by uptake of acidic gases into sea salt in the ECS. This notion is supported by high Se(VI)/Se (IV) ratios (>2) that indicate an oxidative process during long-range transport from coal combustion sources.

Estimating nitrogen and sulfur deposition across China during 2005 to 2020 based on multiple statistical models

Abstract. Due to the rapid development of industrialization and a substantial economy, China has become one of the global hotspots of nitrogen (N) and sulfur (S) deposition following Europe and the USA. Here, we developed a dataset with full coverage of N and S deposition from 2005 to 2020, with multiple statistical models that combine ground-level observations, chemistry transport simulations, satellite-derived vertical columns, and meteorological and geographic variables. Based on the newly developed random forest method, the multi-year averages of dry deposition of oxidized nitrogen (OXN), reduced nitrogen (RDN), and S in China were estimated at 10.4, 14.4, and 16.7 kg N/S ha−1 yr−1, and the analogous numbers for total deposition were respectively 15.2, 20.2, and 25.9 kg N/S ha−1 yr−1 when wet deposition estimated previously with a generalized additive model (GAM) was included. The dry to wet deposition ratio (Rdry/wet) of N stabilized in earlier years and then gradually increased especially for RDN, while that of S declined for over 10 years and then slightly increased. The RDN to OXN deposition ratio (RRDN/OXN) was estimated to be larger than 1 for the whole research period and clearly larger than that of the USA and Europe, with a continuous decline from 2005 to 2011 and a more prominent rebound afterwards. Compared with the USA and Europe, a more prominent lagging response of OXN and S deposition to precursor emission abatement was found in China. The OXN dry deposition presented a descending gradient from east to west, while the S dry deposition a descending gradient from north to south. After 2012, the OXN and S deposition in eastern China declined faster than the west, attributable to stricter emission controls. Positive correlation was found between regional deposition and emissions, while smaller deposition to emission ratios (D/E) existed in developed eastern China, attributed to more intensive human activities and thereby anthropogenic emissions.

Distinct responses of PM2.5 and O3 extremes to persistence of weather conditions in eastern China

Climate change may aggravate air pollution through altering emissions, ventilation, chemical production, and deposition. Under a warming climate, persistent weather patterns are likely to increase in both number and intensity, yet their implications on future air quality have been less explored. Here we use ground-level observations of air pollutants and meteorological reanalysis dataset to explore how persistence of weather conditions would affect extremes of wintertime PM2.5 and summertime O3 in China. Associated changes in the shapes of their distributions are discussed also with contrasting weather conditions. As air stagnation days persist, median summertime O3 significantly increases, with 0.07, 0.12 and 0.19 standard deviation per day (about 1.97, 3.12 and 4.63 ppb per day) for the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions, respectively. As high temperature days continue, significant increases in median (about 0.31 ppb per day) and 90th percentile (about 0.78 ppb per day) of summertime O3 is found only in the YRD region, although the highest probability of O3 extreme (90th percentile) is identified on the last day of four-day high temperature events for both the BTH and YRD regions. In contrast, persistent stagnation causes a significant increase in the 90th percentile of PM2.5 of about 17.87 and 10.88 μg/m3 per day for BTH and YRD regions, rather than the median. Stagnation events lasting for five, four and five days are the best indicators for PM2.5 extreme (90th percentile) in the BTH, YRD and PRD regions, respectively. Analysis on contrasting monthly weather conditions suggests that summertime O3 extreme is amplified by higher mean daily maximum air temperature (T) in the BTH region and co-occurrences of high T and stagnation in the YRD region, while wintertime PM2.5 extreme is significantly amplified by stronger stagnation. These results suggest that megacities with relatively high emissions are more vulnerable to a hotter and more stable future.

Snowfall in Northern Finland derives mostly from ice clouds

Abstract. Clouds and precipitation play a critical role in the Earth's water cycle and energy budget. We present ground-level observations of snowfall coinciding with radiosonde launches in Sodankylä, Finland (67.367∘ N, 26.629∘ E) through a period of 8 cold months (October–April) in 2019 and 2020. They comprise 7401 depositing snow particles detected by a snowflake camera and 468 radiosonde profiles. Our results show that precipitating clouds were extending from ground to at least 2.7 km in altitude. Approximately one-quarter of them were mixed phase and the rest were likely fully glaciated. Estimations of the cloud top temperatures indicate that in roughly half of the snowfall events, ice might have been initiated through heterogeneous freezing. For such cases, the predicted ice-nucleating particle concentrations active at cloud top temperatures could explain the analysed ice crystal particle concentrations observed near ground in approximately one- to two-thirds of the cases. For the rest, ice multiplication was likely active. In a warmer climate, the relative proportion of solid to liquid cloud particles will probably decrease, with implications on the radiation balance.

Turbulent Flux Measurements of the Near‐Surface and Residual‐Layer Small Particle Events

AbstractAccording to recent field studies, almost half of the New Particle Formation (NPF) events occur aloft, in a residual layer, near the top of the boundary layer. Therefore, measurements of the meteorological parameters, precursor gas concentrations, and aerosol loadings conducted at the ground level are often not representative of the conditions where the NPFs take place. This paper presents new measurements obtained during the Turbulent Flux Measurements of the Residual Layer Nucleation Particles, conducted at the Southern Great Plains research site. Vertical turbulent fluxes of 3–10 nm‐sized particles were measured using a sonic anemometer and two condensation particle counters with nominal cutoff diameters of 3 nm and 10 nm mounted at the top of the 10‐m telescoping tower. Aerosol number size distribution (5–300 nm) was determined through the ground‐based Scanning Mobility Particle Sizers. The size selected (15–50 nm) particle hygroscopicity was derived with the Humidified Tandem Differential Mobility Analyzer. The ground level observations were supplemented by vertically‐resolved measurements of horizontal and vertical wind speeds and aerosol backscatter. The data analysis suggests that (a) turbulent flux measurements of 3–10 nm particles can distinguish between near‐surface and residual‐layer small particle events; (b) sub‐50 nm particles had a hygroscopicity value of 0.2, suggesting that organic compounds dominate atmospheric nanoparticle chemical composition at the site; and (c) current methodologies are inadequate for estimating the dry deposition velocity of sub‐10 nm particles because it is not feasible to measure particle concentration very near the surface, in the diffusion sublayer.

Optimal Planning of Air Quality-Monitoring Sites for Better Depiction of PM2.5 Pollution across China.

A myriad of studies have attempted to use ground-level observations to obtain gap-free spatiotemporal variations of PM2.5, in support of air quality management and impact studies. Statistical methods (machine learning, etc.) or numerical methods by combining chemical transport modeling and observations with data assimilation techniques have been typically applied, yet the significance of site placement has not been well recognized. In this study, we apply five proper orthogonal decomposition (POD)-based sensor placement algorithms to identify optimal site locations and systematically evaluate their reconstruction ability. We demonstrate that the QR pivot is relatively more reliable in deciding optimal monitoring site locations. When the number of planned sites (sensors) is limited, using a lower number of modes would yield lower estimation errors. However, the dimension of POD modes has little impact on reconstruction quality when sufficient sensors are available. The locations of sites guided by the QR pivot algorithm are mainly located in regions where PM2.5 pollution is severe. We compare reconstructed PM2.5 pollution based on QR pivot-guided sites and existing China National Environmental Monitoring Center (CNEMC) sites and find that the QR pivot-guided sites are superior to existing sites with respect to reconstruction accuracy. The current planning of monitoring stations is likely to miss sources of pollution in less-populated regions, while our QR pivot-guided sites are planned based on the severity of PM2.5 pollution. This planning methodology has additional potentials in chemical data assimilation studies as duplicate information from current CNEMC-concentrated stations is not likely to boost performance.

Aerosol–light interactions reduce the carbon budget imbalance

Current estimates of the global land carbon sink contain substantial uncertainties on interannual timescales which contribute to a non-closure in the global carbon budget (GCB) in any given year. This budget imbalance (BIM) partly arises due to the use of imperfect models which are missing or misrepresenting processes. One such omission is the separate treatment of downward direct and diffuse solar radiation on photosynthesis. Here we evaluate and use an improved high-resolution (6-hourly), gridded dataset of surface solar diffuse and direct fluxes, over 1901–2017, constrained by satellite and ground-level observations, to drive two global land models. Results show that tropospheric aerosol–light interactions have the potential for substantial land carbon impacts (up to 0.4 PgCyr-1 enhanced sink) at decadal timescales, however large uncertainties remain, with models disagreeing on the direction of change in carbon uptake. On interannual timescales, results also show an enhancement of the land carbon sink (up to 0.9 PgCyr-1) and subsequent reduction in BIM by 55% in years following volcanic eruptions. We therefore suggest GCB assessments include this dataset in order to improve land carbon sink estimates.

Vertical Characteristics of Secondary Aerosols Observed in the Seoul and Busan Metropolitan Areas of Korea during KORUS-AQ and Associations with Meteorological Conditions

In this study, the chemical components of aerosols observed at ground level and in upper layers during the Korea–United States Air Quality (KORUS-AQ) campaign were analyzed in two representative metropolitan areas of Korea: the Seoul metropolitan area (SMA) and the Busan-containing southeastern metropolitan area (BMA). First, we characterized emissions using the Clean Air Policy Support System (CAPSS) emission statistics, and compared them with both ground- and aircraft-based measurements obtained during the KORUS-AQ campaign. The emission statistics showed that the SMA had higher NOx levels, whereas BMA had significantly higher SO2 levels. Ground-level observations averaged for the summer season also showed SMA–nitrate and BMA–sulfate relationships, reflecting the CAPSS emission characteristics of both areas. However, organic carbon (OC) was higher in BMA than SMA by a factor of 1.7, despite comparable volatile organic compound (VOC) emissions in the two areas. DC-8 aircraft-based measurements showed that, in most cases, nitrogen-rich localities were found in the SMA, reflecting the emission characteristics of precursors in the two sampling areas, whereas sulfur-rich localities in the BMA were not apparent from either ground-based or aircraft observations. KORUS-AQ measurements were classified according to two synoptic conditions, stagnant (STG) and long-range transport (LRT), and the nitrate-to-sulfate (N/S) ratio in both ground and upper layers was higher in the SMA for both cases. Meanwhile, organic aerosols reflected local emissions characteristics in only the STG case, indicating that this stagnant synoptic condition reflect local aerosol characteristics. The LRT case showed elevated peaks of all species at altitudes of 1.0–3.5 km, indicating the importance of LRT processes for predicting and diagnosing aerosol vertical distributions over Northeast Asia. Other chemical characteristics of aerosols in the two metropolitan areas were also compared.

WRF-Chem Modeling of Summertime Air Pollution in the Northern Great Plains: Chemistry and Aerosol Mechanism Intercomparison

Oil and gas production in the Bakken region increased dramatically during the past decade. A WRF-Chem modeling study of the Northern Great Plains was conducted for a July 2010 baseline scenario prior to the largest of these production increases. Simulations using the RACM-MADE/SORGAM, CBMZ-MOSAIC, and MOZART-MOSAIC chemistry-aerosol mechanisms were compared to each other and against ground level observations. All three gas-aerosol modules produced similar prediction results for O3, and NO2, with moderate correlation to hourly measurements and monthly average values overpredicted by 20% for O3 and underpredicted by 5% for NO2. Monthly average PM2.5 concentrations were relatively accurate, but correlation to hourly measurements was very low and PM2.5 subspecies exhibited high variability with a mix of over and underpredictions depending on the mechanism. Pollutant concentrations were relatively low across the mostly rural study domain, especially in the Bakken region. Results from this work can be used as a basis of comparison for studies of more recent time periods that include increased oil and gas-related emissions.

Predicting Spatial Variations in Multiple Measures of Oxidative Burden for Outdoor Fine Particulate Air Pollution across Canada.

Fine particulate air pollution (PM2.5) is a leading contributor to the overall global burden of disease. Traditionally, outdoor PM2.5 has been characterized using mass concentrations which treat all particles as equally harmful. Oxidative potential (OP) (per μg) and oxidative burden (OB) (per m3) are complementary metrics that estimate the ability of PM2.5 to cause oxidative stress, which is an important mechanism in air pollution health effects. Here, we provide the first national estimates of spatial variations in multiple measures (glutathione, ascorbate, and dithiothreitol depletion) of annual median outdoor PM2.5 OB across Canada. To do this, we combined a large database of ground-level OB measurements collected monthly prospectively across Canada for 2 years (2016-2018) with PM2.5 components estimated using a chemical transport model (GEOS-Chem) and satellite aerosol observations. Our predicted ground-level OB values of all three methods were consistent with ground-level observations (cross-validation R2 = 0.63-0.74). We found that forested regions and urban areas had the highest OB, predicted primarily by black carbon and organic carbon from wildfires and transportation sources. Importantly, the dominant components associated with OB were different than those contributing to PM2.5 mass concentrations (secondary inorganic aerosol); thus, OB metrics may better indicate harmful components and sources on health than the bulk PM2.5 mass, reinforcing that OB estimates can complement the existing PM2.5 data in future national-level epidemiological studies.