Aerosol Optical Depth Observations Research Articles

Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations

Abstract. Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM2.5) air quality, but this requires better understanding of the AOD–PM2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM2.5 composition, PM10, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions.

A Global Bottom‐Up Approach to Estimate Fuel Consumed by Fires Using Above Ground Biomass Observations

AbstractReal‐time estimates of the fuel consumed during a fire (dry‐matter) relies on indirect estimate from remotely sensed released energy combined with biome dependent conversion coefficients. The uncertainties in the conversions lead to the use of inflation factors to avoid large underestimations in the prediction of aerosol load during fires. In this study, adopting two different fire inventories for burned areas, we apply above ground biomass (AGB) observations [from Soil Moisture and Ocean Salinity (SMOS) L‐band vegetation optical depth] as proxy for fuel load in deriving estimates of dry‐matter. These new estimates are then converted into biomass burning aerosols and validated against independent aerosol optical depth observations from the AERONET in situ global network. Results showed that use of AGB as a proxy observation of fuel load improves fire emission estimates and substituting the need for indirect dry‐matter estimates from remotely sensed fire activity or for the use of inflation factors.

Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas

Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor.

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.

Quantitative Retrieval of Volcanic Sulphate Aerosols from IASI Observations

We developed a new retrieval algorithm based on the Infrared Atmospheric Sounding Interferometer (IASI) observations, called AEROIASI-H2SO4, to measure the extinction and mass concentration of sulphate aerosols (binary solution droplets of sulphuric acid and water), with moderate random uncertainties (typically ∼35% total uncertainty for column mass concentration estimations). The algorithm is based on a self-adapting Tikhonov–Phillips regularization method. It is here tested over a moderate-intensity eruption of Mount Etna volcano (18 March 2012), Italy, and is used to characterise this event in terms of the spatial distribution of the retrieved plume. Comparisons with simultaneous and independent aerosol optical depth observations from MODIS (Moderate Resolution Imaging Spectroradiometer), SO2 plume observations from IASI and simulations with the CHIMERE chemistry/transport model show that AEROIASI-H2SO4 correctly identifies the volcanic plume horizontal morphology, thus providing crucial new information towards the study of volcanic emissions, volcanic sulphur cycle in the atmosphere, plume evolution processes, and their impacts. Insights are given on the possible spectroscopic evidence of the presence in the plume of larger-sized particles than previously reported for secondary sulphate aerosols from volcanic eruptions.

SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe

This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.

Validation and comparison of high-resolution MAIAC aerosol products over Central Asia

Aerosols are an important contributor to global atmospheric environmental changes and have critical effects on the global climate system and human health. Central Asia is one of the most important sources of dust aerosols in the world and produces a significant portion of global aerosols. Central Asia is also a scarce aerosol-data area, so it is of great significance to obtain and verify new aerosol data from this region. In this study, based on the aerosol optical depth (AOD) data from remote sensing (MYD04_L2) and ground-based observations (AERONET and Microtops II), the applicability of multiangle implementation of atmospheric correction (MAIAC) AOD in Central Asia was comprehensively analyzed, and the variation in AOD in Central Asia over the last 20 years was analyzed by the information entropy method. The results indicate that MAIAC AOD has good application prospects in Central Asia and can effectively compensate for the lack of observational data from Central Asia. MAIAC AOD exhibits excellent spatiotemporal consistency with MYD04 deep blue (DB) AOD and has a better ability than MYD04 DB AOD to describe local fine-scale features. Furthermore, MAIAC AOD demonstrates high consistency with ground-based AOD observations, showing high R (0.737) and low RMSE (0.067) values and having 65.2% of samples falling within the expected error (EE) envelope. When employing the ground-based AOD observations as a bridge, MAIAC exhibits superiority to MYD04 DB in both the richness number of valid high-quality retrievals and the retrieval accuracy of various evaluation indicators. The annual variation in AOD in Central Asia exhibits a unimodal distribution, with AOD being largest in April, followed by March and May, and comparable rangeability. Based on information entropy, interannual variation in AOD exists in most areas of Central Asia, with AOD in the Taklimakan Desert area being significantly increased and that in northern Central Asia (Kazakhstan) showing a downward trend.

Retrieving PM2.5 with high spatio-temporal coverage by TOA reflectance of Himawari-8

Particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) has drawn considerable concern due to its significant air quality and public health effects. Aerosol optical depth (AOD) from polar-orbiting satellites has been widely used for estimating ground-level PM2.5 concentration. However, the PM2.5 estimation from the polar-orbiting satellites AOD are limited in spatio-temporal coverage due to its low revisit rate and the applicability issue of the AOD retrieval method. This study proposed a method to estimate PM2.5 distributions using the top-of-the-atmosphere reflectance (TOAR) of the Himawari-8 geostationary satellite over China. The results show that PM2.5 estimations from AOD and TOAR present a similar accuracy, with an R2 (RMSE) of 0.86 (17.2 μg/m3). For the marginal regions where valid AOD observations unavailable, the R2 (RMSE) of PM2.5 estimated from TOAR also reaches 0.83 (23.7 μg/m3). The spatio-temporal coverage of PM2.5 estimated by TOAR is about four times based on AOD data, which is important to capture the true distribution of fine particles and explore their diurnal evolution.

Spatio-Temporal Characteristics of PM2.5, PM10, and AOD over the Central Line Project of China’s South-North Water Diversion in Henan Province (China)

The spatio-temporal characteristics of particulate matter with a particle size less than or equal to 2.5 μm (PM2.5), particulate matter with a particle size less than or equal to 10 μm (PM10), meteorological parameters from September 2018 to September 2019, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol optical depth (AOD) from 2007 to 2019 were investigated over the Central Line Project of China’s South-North Water Diversion (CSNWD) in Henan Province. To better understand the characteristics of the atmospheric environment over the CSNWD, air quality monitoring stations were installed in Nanyang (in the upper reaches), Zhengzhou (in the middle reaches), and Anyang (in the lower reaches). In this study, daily, monthly, and seasonal statistical analyses of PM2.5 and PM10 concentrations were performed and their relationship with meteorological parameters was investigated. The results show extremely poor air quality conditions over the Zhengzhou Station compared with the Nanyang and Anyang Stations. The annual average PM2.5 concentration did not meet China’s ambient air secondary standard (35 μg/m3 annual mean) over all the stations, while the annual average PM10 concentration satisfied China’s ambient air secondary standard (100 μg/m3 annual mean) over the Anyang and Nanyang Stations, except for the Zhengzhou Station. The highest PM2.5 and PM10 concentrations were observed during winter compared with the other seasons. The results show that PM2.5 and PM10 concentrations were negatively correlated with wind speed and temperature at the Nanyang and Zhengzhou Stations, but positively correlated with relative humidity. However, no significant negative or positive correlation was observed at Anyang Station. There is a strong linear positive correlation between PM2.5 and PM10 (R = 0.99), which indicates that the particulate matter at the three stations was mainly caused by local emissions. Additionally, the AOD values at the three stations were the highest in summer, which may be related to the residues of crops burned in Henan Province in summer.

New in situ aerosol hyperspectral optical measurements over 300–700 nm – Part 1: Spectral Aerosol Extinction (SpEx) instrument field validation during the KORUS-OC cruise

Abstract. In situ observations of spectrally resolved aerosol extinction coefficients (300–700 nm at ∼ 0.8 nm resolution) from the May–June 2016 Korea–United States Ocean Color (KORUS-OC) oceanographic field campaign are reported. Measurements were made with the custom-built Spectral Aerosol Extinction (SpEx) instrument that previously has been characterized only using laboratory-generated aerosols of known size and composition. Here, the performance of SpEx under realistic operating conditions in the field was assessed by comparison to extinction coefficients derived from commercial instruments that measured scattering and filter-based absorption coefficients at three discrete visible wavelengths. Good agreement was found between these two sets of extinction coefficients with slopes near unity for all three wavelengths within the SpEx measurement error (± 5 Mm−1). The meteorological conditions encountered during the cruise fostered diverse ambient aerosol populations with varying sizes and composition at concentrations spanning 2 orders of magnitude. The sampling inlet had a 50 % size cut of 1.3 µm diameter particles such that the in situ aerosol sampling suite deployed aboard ship measured fine-mode aerosols only. The extensive hyperspectral extinction data set acquired revealed that nearly all measured spectra exhibited curvature in logarithmic space, such that Ångström exponent (α) power law fits could lead to large errors compared to measured values. This problem was particularly acute for α values calculated over only visible wavelengths and then extrapolated to the UV, highlighting the need for measurements in this wavelength range. Second-order polynomial fits to the logarithmically transformed data provided a much better fit to the measured spectra than the linear fits of power laws. Building on previous studies that used total column aerosol optical depth observations to examine the information content of spectral curvature, the relationship between α and the second-order polynomial fit coefficients (a1 and a2) was found to depend on the wavelength range of the spectral measurement such that any given α maps into a line in (a1, a2) coefficient space with a slope of −2LN(λch), where λch is defined as the single wavelength that characterizes the wavelength range of the measured spectrum (i.e., the “characteristic wavelength”). Since the curvature coefficient values depend on λch, it must be taken into account when comparing values from spectra obtained from measurement techniques with different λch. Previously published work has shown that different bimodal size distributions of aerosols can exhibit the same α yet have differing spectral curvature with different (a1, a2). This implies that (a1, a2) contain more information about size distributions than α alone. Aerosol size distributions were not measured during KORUS-OC, and the data reported here were limited to the fine fraction, but the (a1, a2) maps obtained from the SpEx data set are consistent with the expectation that (a1, a2) may contain more information than α – a result that will be explored further with future SpEx and size distribution data sets.

A model framework to reduce bias in ground-level PM2.5 concentrations inferred from satellite-retrieved AOD

We present a new method to infer ground-level fine particulate matter (PM2.5) from satellite remote sensing observations of aerosol optical depth (AOD). The conventional method generally uses a range of modelling approaches to determine PM2.5:AOD relationships that are subsequently used to infer ground-level PM2.5 concentrations from satellite-retrieved AOD. Here, we use a high-resolution atmospheric chemistry simulation to explore how changes in the vertical distribution of aerosol extinction coefficients affects the PM2.5:AOD relationship and how we can use that information to improve the robustness of inferred estimates of ground-level PM2.5 over eastern China. We define a metric, ΓPBLAOD, that describes the fraction of AOD that resides in the planetary boundary layer compared with the total columnar AOD. We determine physically-meaningful PM2.5:AOD relationships using data for which ΓPBLAOD≥50%, a criterion based on sensitivity analyses on data clusters that we identify using a hierarchical clustering method. We use statistical and machine learning methods to develop independent models that describe these PM2.5:AOD relationships, and use a Monte Carlo approach to quantify the improvement after our selection of more physically relevant data records. Benefiting from the improved representativeness of AOD for ground-level PM2.5, our method effectively reduces bias in inferred estimates of ground-level PM2.5 by 10–15% (9–12%) for space-borne sensors passing over in the morning (afternoon). It also captures more variations in ground-level PM2.5 by up to 8% (5%) for space-borne sensors passing over in the morning (afternoon), particularly over areas dominated by natural aerosols such as dust. Accordingly, our method improves the seasonal ground-level PM2.5 maps, e.g. the bias of the autumn (winter) mean of ground-level PM2.5 estimates over Qinghai and Gansu (Shaaxi, Shanxi, and Henan) provinces reduces from −8% to −5% (11%–6%).

Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance

ABSTRACT Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 µg m–3 for the former and R2 = 0.65 and RMSE = 15.69 µg m–3 for the later. Additionally, we find a large discrepancy in PM2.5 estimates between reflectance-based and AOD-based approaches in terms of annual mean and their spatial distribution, which is mainly due to the sampling difference, especially over northern YRD in winter. Overall, reflectance-based approach can provide robust PM2.5 estimates for both annual mean values and probability density function of hourly PM2.5. Our results further show that almost all population lives in non-attainment areas in YRD using annual mean PM2.5 from reflectance-based approach. This study suggests that reflectance-based approach is a valuable way for providing robust PM2.5 estimates and further for constraining health impact assessments.

Mapping Microscale PM2.5 Distribution on Walkable Roads in a High-Density City

Monitoring pollution of PM2.5 on walkable roads is important for resident health in high-density cities. Due to the spatiotemporal resolution limitations of aerosol optical depth observation, fixed-point monitoring, or traditional mobile measurement instruments, the microscale PM2.5 distribution in the walking environment cannot be fully estimated at the fine scale. In this article, by the integration of mobile measurement data, OpenStreetMap (OSM) data, Landsat images, and other multisource data in land-use regression (LUR) models, a novel framework is proposed to estimate and map PM2.5 distribution in a typical microscale walkable environment of the high-density city Hong Kong. First, the PM2.5 data on the typical walking paths were collected by the handheld mobile measuring instruments, to be selected as the dependent variables. Second, geographic prediction factors calculated by Google Street View, OSM data, Landsat images, and other multisource data were further selected as independent variables. Then, these dependent and independent variables were put into the LUR models to estimate the PM2.5 concentration on sidewalks, footbridges, and footpaths in the microscale walkable environment. The proposed models showed high performance relative to those in similar studies (adj <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 0.593 to 0.615 [sidewalks]; 0.641 to 0.682 [footpaths]; 0.783 to 0.797 [footbridges]). This article is beneficial for mapping PM2.5 concentration in the microscale walking environment and the identification of hot spots of air pollution, thereby helping people avoid the PM2.5 hotspots and indicating a healthier walking path.

Surface and satellite observations of air pollution in India during COVID-19 lockdown: Implication to air quality

The strict nationwide lockdown imposed in India starting from 25th March 2020 to prevent the spread of COVID-19 disease reduced the mobility and interrupted several important anthropogenic emission sources thereby creating a temporary air quality improvement. This study conducts a multi-scale (national-regional-city), multi-species, and multi-platform analysis of air pollutants and meteorological data by synergizing surface and satellite observations. Our analysis suggests a significant reduction in surface measurements of nitrogen dioxide (NO2) (46–61 %) and fine particulate matter (PM2.5) (42–60 %) during the lockdown period that are also corroborated by the reduction in satellite observed aerosol optical depth (AOD) (3–56 %) and tropospheric NO2 column density (25–50 %) data over multiple cities. Other species, namely coarse particulate matter (PM10) (24–62 %), ozone (22–56 %) also showed a substantial reduction whereas carbon monoxide (16–46 %), exhibited a moderate decline. In contrast, sulfur dioxide (SO2) levels did not show any defined reduction trend but rather increased in Mumbai, Bengaluru, and Kolkata. The temporary air quality improvement achieved by the painful natural experiment of this pandemic has helped demonstrate the importance of reducing emissions from other sectors along with transportation and industry to achieve the national air quality targets in the future.

A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain.

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5.

Exploring the Constraints on Simulated Aerosol Sources and Transport Across the North Atlantic With Island‐Based Sun Photometers

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

Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke

IntroductionEstimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use. MethodsUsing ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008–2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model. ResultsEnsemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers. ConclusionOur method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies.

Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54

Abstract. It was hypothesized that using mineral dust emission climatologies in global chemistry climate models (GCCMs), i.e. prescribed monthly-mean dust emissions representative of a specific year, may lead to misrepresentations of strong dust burst events. This could result in a negative bias of model dust concentrations compared to observations for these episodes. Here, we apply the aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, third generation) as part of the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model. We employ two different representations of mineral dust emissions for our model simulations: (i) a prescribed monthly-mean climatology of dust emissions representative of the year 2000 and (ii) an online dust parametrization which calculates wind-driven mineral dust emissions at every model time step. We evaluate model results for these two dust representations by comparison with observations of aerosol optical depth from ground-based station data. The model results show a better agreement with the observations for strong dust burst events when using the online dust representation compared to the prescribed dust emissions setup. Furthermore, we analyse the effect of increasing the vertical and horizontal model resolution on the mineral dust properties in our model. We compare results from simulations with T42L31 and T63L31 model resolution (2.8∘×2.8∘ and 1.9∘×1.9∘ in latitude and longitude, respectively; 31 vertical levels) with the reference setup (T42L19). The different model versions are evaluated against airborne in situ measurements performed during the SALTRACE mineral dust campaign (Saharan Aerosol Long-range Transport and Aerosol-Cloud Interaction Experiment, June–July 2013), i.e. observations of dust transported from the Sahara to the Caribbean. Results show that an increased horizontal and vertical model resolution is able to better represent the spatial distribution of airborne mineral dust, especially in the upper troposphere (above 400 hPa). Additionally, we analyse the effect of varying assumptions for the size distribution of emitted dust but find only a weak sensitivity concerning these changes. The results of this study will help to identify the model setup best suited for future studies and to further improve the representation of mineral dust particles in EMAC-MADE3.

Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: lidar observations

Abstract. This paper presents the first results about the assimilation of CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) extinction coefficient measurements onboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite in the MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) chemistry transport model of Météo-France. This assimilation module is an extension of the aerosol optical depth (AOD) assimilation system already presented by Sič et al. (2016). We focus on the period of the TRAQA (TRAnsport à longue distance et Qualité de l’Air dans le bassin méditerranéen) field campaign that took place during summer 2012. This period offers the opportunity to have access to a large set of aerosol observations from instrumented aircraft, balloons, satellite and ground-based stations. We evaluate the added value of CALIOP assimilation with respect to the model free run by comparing both fields to independent observations issued from the TRAQA field campaign. In this study we focus on the desert dust outbreak which happened during late June 2012 over the Mediterranean Basin (MB) during the TRAQA campaign. The comparison with the AERONET (Aerosol Robotic Network) AOD measurements shows that the assimilation of CALIOP lidar observations improves the statistics compared to the model free run. The correlation between AERONET and the model (assimilation) is 0.682 (0.753); the bias and the root mean square error (RMSE), due to CALIOP assimilation, are reduced from −0.063 to 0.048 and from 0.183 to 0.148, respectively. Compared to MODIS (Moderate-resolution Imaging Spectroradiometer) AOD observations, the model free run shows an underestimation of the AOD values, whereas the CALIOP assimilation corrects this underestimation and shows a quantitative good improvement in terms of AOD maps over the MB. The correlation between MODIS and the model (assimilation) during the dust outbreak is 0.47 (0.52), whereas the bias is −0.18 (−0.02) and the RMSE is 0.36 (0.30). The comparison of in situ aircraft and balloon measurements to both modelled and assimilated outputs shows that the CALIOP lidar assimilation highly improves the model aerosol field. The evaluation with the LOAC (Light Optical Particle Counter) measurements indicates that the aerosol vertical profiles are well simulated by the direct model but with a general underestimation of the aerosol number concentration, especially in the altitude range 2–5 km. The CALIOP assimilation improves these results by a factor of 2.5 to 5. Analysis of the vertical distribution of the desert aerosol concentration shows that the aerosol dust transport event is well captured by the model but with an underestimated intensity. The assimilation of CALIOP observations allows the improvement of the geographical representation of the event within the model as well as its intensity by a factor of 2 in the altitude range 1–5 km.

Enhancing Accuracy of Air Quality and Temperature Forecasts During Paddy Crop Residue Burning Season in Delhi Via Chemical Data Assimilation

AbstractThis paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) generated 72 hr fine particulate matter (PM2.5) forecasts in Delhi during the crop residue burning season of October‐November 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol‐radiation interactions. The assimilation significantly pushes the model AOD and PM2.5 toward the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF‐Chem forecast with MODIS AOD assimilation, aerosol‐radiation feedback turned on, and real‐time fire emissions reduce the mean bias by 88–195 μg/m3 (70–86%) with the largest improvement during the peak air pollution episode of 6–13 November 2017. Aerosol‐radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third days of PM2.5 forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM2.5 forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol‐radiation feedback extends the benefits of assimilating satellite AOD beyond PM2.5 forecasts to surface temperature forecast with a reduction in the mean bias of 0.9–1.5°C (17–30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.