Ground-level Particulate Matter Concentrations Research Articles

Fast and operational gap filling in satellite-derived aerosol optical depths using statistical techniques

Satellite observations, used worldwide in the atmospheric sciences, are extremely useful for providing aerosol information within a wide spatial range. However, the coverage of aerosol data by satellite observations is sometimes of inferior quality because of the effects of surface reflectivity and clouds. To fill the gaps in aerosol optical depths (AODs) retrieved from geostationary ocean color imager observations, this study applies operational statistical techniques, including radial basis functions (RBFs) with four different weightings (i.e., linear, multiquadric, thin-plate, and inverse), Poisson, and ordinary Kriging. Based on computation time and accuracy of the individual gap-filling techniques, Poisson and the liner RBF are selected as the two best methods and then averaged with weights using one-dimensional weighted average (1D-WAVG) and two-dimensional weighted average (2D-WAVG) root mean square errors. All methods produce reliable results, yielding a correlation coefficient between 0.74 and 0.87 over the entire research domain. Out of the individual techniques, the Poisson, with an initial estimation from a zonal mean of AODs, is the most accurate with the lowest computational costs, even for a large number of missing pixels and most regions, excluding East China (EC). The Poisson’s high bias over EC is compensated in 1D- and 2D-WAVGs by taking more accurate estimations of the linear RBF than those of the Poisson over the region. If we consider 1D- and 2D-WAVGs in our analysis, the highest correlation is obtained from the 2D-WAVG over all regions. Because of its reliability and fast computation time, applying the 2D-WAVG can be a good solution to provide spatial–temporal continuous aerosol information. In addition to air pollution studies, such as real-time air quality predictions, estimation of ground-level particulate matter concentrations, and other applications, the fast and operational gap-filling technique can also be expanded to remote sensing data obtained from satellite observations to provide helpful and useful information for the public.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM10) and <2.5 μm (PM2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM10 and PM2.5 were R2 = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM10 and PM2.5 were R2 = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Estimation of ground-level particulate matter concentrations based on synergistic use of MODIS, MERRA-2 and AERONET AODs over a coastal site in the Eastern Mediterranean

Satellite-derived aerosol optical depth (AOD) products are widely used to estimate the spatial and temporal characteristics of ground-level particulate matters (PM). Satellite-based approaches in the operational use of PM estimation models have limitations such as missing values in satellite-based variables due to the impact of cloud cover, relative humidity, and aerosol vertical distribution. Therefore, the spatial-temporal resolution of the estimation models is forced to be improved by using AOD products having different temporal resolutions from different platforms. In this study, ground-based PM10 concentrations were aimed to be estimated using different gap-filled AOD datasets between 2008 and 2016 over a coastal site in the Eastern Mediterranean. For the estimation of PM10 concentrations, daily AOD data were mainly used from MODIS. Then, two different gap-filled MODIS AOD datasets with both AERONET and MERRA-2 AODs were also examined separately on both multi-annual and multi-seasonal basis since the number of MODIS AOD data was temporally limited in the region. A pattern recognition neural network (PRNN) model was used for the estimation of PM10 concentrations. AOD with several meteorological parameters from an on-site meteorological station and aerosol diagnostic products from MERRA-2 were used as inputs to the estimation model. The most significant variables for the model were identified in 23 independent variables using the multiple linear regression (MLR). The results indicated that the best estimation (R = 0.74) was obtained with the gap-filled AODMODIS+MERRA dataset for the period covering all years whereas the AODMODIS dataset alone showed the poorest performance (R = 0.62). However, the performance of the gap-filled datasets varied with the seasons. For example, the AODMODIS dataset alone showed the best estimation performance (R = 0.67) in the summer whereas the gap-filled AODMODIS+AERONET dataset had the best performance (R = 0.59) in the winter. Overall results suggest that for estimating ground-level PM concentrations, an approach of gap-filled AOD dataset usage for estimation models is useful especially in rainy seasons such as the winter and autumn where MODIS AOD retrievals are limited.

Temporal and spatial variations of aerosol optical properties over the Korean peninsula during KORUS-AQ

We investigated the temporal and spatial variations of aerosol optical properties over the Korean peninsula during the KORUS-AQ (KORea–United States Air Quality) experiment with ground-based aerosol optical properties measured by remote and in-situ techniques. On the ground, AErosol RObotic NETwork (AERONET) and ground-level particulate matter (PM) concentration from air quality monitoring stations were used. From the NASA DC-8 research aircraft, the airborne Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research instrument (4STAR), an aerosol mass spectrometer (AMS), and a single particle soot photometer (SP2) provided aerosol information. Average hourly fine- and coarse-mode aerosol optical depth (AOD) and fine mode fraction (FMF), were divided into four clusters (background, Seoul Metropolitan Area (SMA), southwest, and east) representing different temporal/spatial variations; the results of those clusters were similar to the clustering results using PM from air quality monitoring stations. The downwind region of SMA was dominant by light-scattering fine-mode aerosols likely due to secondary aerosol formation with high fine-mode AOD and single scattering albedo compared to other regions, even Seoul. The fine-mode aerosols were more spatially homogeneous than coarse-mode aerosols, especially in the west-to-east direction, because fine-mode aerosols are usually transported on a regional scale by westerlies rather than emitted from local emission sources. However, the aerosol size distribution was spatially more homogeneous because of a consistent contribution of fine- and coarse-mode AOD to total AOD regardless of direction between the AERONET sites. During high-aerosol loading episodes, the temporal and spatial variations of aerosol optical properties were similar to those derived from 4STAR and ground-level PM concentrations, providing detailed information on aerosol behavior and characteristics. Using missed-approach flight segments (touchdown and take-off without a full-stop), investigation of diurnal variations over the SMA revealed a significant increase in AOD and Angstrom exponent (AE) in the afternoon compared to morning and noon, especially in the downwind region, because of more active secondary formation resulting from advected and/or emitted local pollutants and precursors. The diurnal variation of PM1 in the downwind region was similar to that of AOD; it was mainly increased during the day by secondary organic aerosols and ammonium nitrate due to large amounts of isoprene and meteorological conditions that supported secondary aerosol formation. Ammonium sulfate also partially led to increasing the PM1, but its behavior was unclear, because RH decreased during the day, which would imply the reduced aqueous-phase oxidation (the major pathway for sulfate production). Compared to the downwind region, ammonium nitrate and refractory black carbon decreased in Seoul because of a slightly decreased traffic volume, dissociation of ammonium nitrate, and increased boundary layer height according to meteorological conditions in the afternoon. The results of this study provide detailed information on aerosol behavior given the synergy of the various measurement platforms used. Along with the evidence of active photochemical reactions taking place in the downwind region, such data will be useful in formulating policies that improve air quality in Korea.

Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing

Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM2.5) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM2.5 concentrations have limited the assessment of the health impact of these exposures, both in the native populations and previously deployed military personnel. As part of a Department of Veterans Affairs Cooperative Studies Program health study of land-based U.S. military personnel who were previously deployed to these countries, we developed a novel approach to estimate spatially and temporarily resolved daily PM2.5 exposures 2001–2018. Since visibility is proportional to ground-level particulate matter concentrations, we were able to take advantage of extensive airport visibility data that became available as a result of regional military operations over this time period. First, we combined a random forest machine learning and a generalized additive mixed model to estimate daily high resolution (1 km × 1 km) visibility over the region using satellite-based aerosol optical depth (AOD) and airport visibility data. The spatially and temporarily resolved visibility data were then used to estimate PM2.5 concentrations from 2001 to 2018 by converting visibility to PM2.5 using empirical relationships derived from available regional PM2.5 monitoring stations. We adjusted for spatially resolved meteorological parameters, land use variables, including the Normalized Difference Vegetation Index, and satellite-derived estimates of surface dust as a measure of sandstorm activity. Cross validation indicated good model predictive ability (R2 = 0.71), and there were considerable spatial and temporal differences in PM2.5 across the region. Annual average PM2.5 predictions for Iraq and Kuwait were 37 and 41 μg/m3, respectively, which are greater than current U.S. and WHO standards. PM2.5 concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM2.5 concentrations above 45 μg/m3 with weekly averages as high as 150 μg/m3 depending on calendar year. The highest annual PM2.5 concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM2.5 values were observed in 2014. On average, July had the highest concentrations, and November had the lowest values, consistent with seasonal patterns of air pollution in this region. This is the first study that estimates long-term PM2.5 exposures in Iraq and Kuwait at a high resolution based on measurements data that will allow the study of health effects and contribute to the development of regional environmental policies. The novel approach demonstrated may be used in other parts of the world with limited monitoring networks.

Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo

The spatiotemporal pattern of particulate matter (PM) concentrations is an important factor in predicting health issues in inhabitants of urban areas. The integration of satellite-derived aerosol optical depth (AOD) data with ground-level PM concentration data, obtained from monitoring networks, has contributed to better characterization of the spatiotemporal variability of aerosols worldwide. However, before using satellite AOD data as a proxy for PM in epidemiological and air quality studies in specific regions, the applicability of that strategy must be evaluated. In this study, we evaluate the use of the high-resolution AOD, derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, as a predictor of surface PM concentrations in the Metropolitan Area of São Paulo (MASP). We found relatively weak or negative correlations between PM concentrations and MAIAC AOD, even after vertical correction by planetary boundary layer height and the hygroscopic growth factor. The weak correlations reported in this study are mainly due to the mismatch between the current MAIAC aerosol model and the properties of local aerosols in the MASP. Our results suggest that sources of aerosol particles in the MASP are quite diverse and that there is therefore no single optical model suitable for use with satellite-derived AOD.

Effects of Low‐Carbon Energy Adoption on Airborne Particulate Matter Concentrations With Feedbacks to Future Climate Over California

AbstractCalifornia plans to reduce emissions of long‐lived greenhouse gases (GHGs) through adoption of new energy systems that will also lower concentrations of short‐lived absorbing soot contained in airborne particulate matter (PM). Here we examine the direct and indirect effects of reduced PM concentrations under a low‐carbon energy (GHG‐Step) scenario on radiative forcing in California. Simulations were carried out using the source‐oriented WRF/Chem (SOWC) model with 12 km spatial resolution for the year 2054. The avoided aerosol emissions due to technology advances in the GHG‐Step scenario reduce ground level PM concentrations by ~8.85% over land compared to the Business‐as‐Usual (BAU) scenario, but changes to meteorological parameters are more modest. Top‐of‐atmospheric forcing predicted by the SOWC model increased by 0.15 W m−2, surface temperature warmed by 0.001 K, and planetary boundary layer height (PBLH) increased by 2.20 cm in the GHG‐Step scenario compared to the BAU scenario. PM climate feedbacks are small because the significant changes in ground level PM concentrations associated with the GHG‐Step scenario are limited to the first few hundred meters of the atmosphere, with little change for the majority of the vertical column above that level. As an order‐of‐magnitude comparison, the long‐term effects of global reductions in GHG emissions (RCP8.5–RCP4.5) lowered average surface temperature over the California study domain by approximately 0.76 K. The effects of long‐lived climate pollutants such as CO2 are much stronger than the effects of short‐lived climate pollutants such as PM soot over California in the year 2054.

Estimating ground-level particulate matter concentrations using satellite-based data: a review

ABSTRACTParticulate matter (PM) is a widely used indicator of air quality. Satellite-derived aerosol products such as aerosol optical depth (AOD) have been a useful source of data for ground-level PM monitoring. However, satellite-based approaches for PM monitoring have limitations such as impacts of cloud cover. Recently, many studies have documented advances in modeling for monitoring PM over the globe. This review examines recent papers on ground-level PM monitoring for the past 10 years focusing on modeling techniques, sensor types, and areas. Satellite-based retrievals of AOD and commonly used approaches for estimating PM concentrations are also briefly reviewed. Research trends and challenges are discussed based on the review of 130 papers. The limitations and challenges include spatiotemporal scale issues, missing values in satellite-based variables, sparse distribution of ground stations for calibration and validation, unbalanced distribution of PM concentrations, and difficulty in the operational use of satellite-based PM estimation models. The literature review suggests there is room for further investigating: 1) the spatial extension of PM monitoring to global scale; 2) the synergistic use of satellite-derived products and numerical model output to improve PM estimation accuracy, gap-filling, and operational monitoring; 3) the use of more advanced modeling techniques including data assimilations; 4) the improvement of emission data quality; and 5) short-term (hours to days) PM forecasts through combining satellite data and numerical forecast model results.

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Abstract. Long-term exposure to particulate matter (PM) with aerodynamic diameters &lt; 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

An Improved Method for Monitoring Fine Particulate Matter Mass Concentrations via Satellite Remote Sensing

Ground level monitoring of Particulate Matter (PM) is limited by spatial coverage and resolution, in spite of possessing high temporal resolution and accuracy. Atmospheric Aerosol Optical Depth (AOD), a product of space-borne remote sensing, has shown significant potential for estimating ground level PM concentrations. Several approaches have been used to improve the correlation between AOD-PM by providing corrections for the aerosol vertical profile and ground level humidity. However, the effects of the vertical profile of humidity and aerosol size on the AOD-PM relationship requires further study. In this paper, we propose a method for developing an AOD-PM_(2.5) relationship by retrieving the vertical profile of relative humidity via ground observation data and aerosol size distribution in Beijing. Moreover, a series of Hanel growth coefficients (γ) are applied to determine the specific value, which maximizes the correlation. The results show that applying our proposed method can improve the correlation from R = 0.610 to R = 0.707 for Terra and R = 0.707 to 0.752 for Aqua. The best correlations were obtained for γ = 1.2 and 1.3 for Terra and Aqua, respectively. A good correlation (R = 0.8) between ground based and MODIS based PM_(2.5) measurements, together with employing MODIS to predict true air pollution levels (65% accuracy), suggests that the vertical profile of RH derived via ground level observation and aerosol size should be considered and applied to models in future studies, which utilize satellite data for air pollution monitoring and controlling.

Underground and ground-level particulate matter concentrations in an Italian metro system

All around the world, many studies and experimental results have assessed elevated concentrations of Particulate Matter (PM) in underground metro systems, with non-negligible implications for human health due to protracted exposure to fine particles. Starting from this consideration, an intensive particulate sampling campaign was carried out in January 2014 measuring the PM concentrations in the Naples (Italy) Metro Line 1, both at station platforms and inside trains. Naples Metro Line 1 is about 18 km long, with 17 stations (3 ground-level and 14 below-ground ones). Experimental results show that the average PM10 concentrations measured in the underground station platforms range between 172 and 262μg/m3 whilst the average PM2.5 concentrations range between 45 and 60μg/m3. By contrast, in ground-level stations no significant difference between stations platforms and urban environment measurements was observed. Furthermore, a direct correlation between trains passage and PM concentrations was observed, with an increase up to 42% above the average value. This correlation is possibly caused by the re-suspension of the particles due to the turbulence induced by trains. The main original finding was the real-time estimations of PM levels inside the trains travelling both in ground-level and underground sections of Line 1. The results show that high concentrations of both PM10 (average values between 58μg/m3 and 138μg/m3) and PM2.5 (average values between 18μg/m3 and 36μg/m3) were also measured inside trains. Furthermore, measurements show that windows left open on trains caused the increase in PM concentrations inside trains in the underground section, while in the ground-level section the clean air entering the trains produced an environmental “washing effect”. Finally, it was estimated that every passenger spends on average about 70 min per day exposed to high levels of PM.

Integration of measurements and model simulations to characterize Eyjafjallajökull volcanic aerosols over south-eastern Italy

Abstract. Volcanic aerosols resulting from the Eyjafjallajökull eruption were detected in south-eastern Italy from 20 to 22 April 2010, at a distance of approximately 4000 km from the volcano, and have been characterized by lidar, sun/sky photometer, and surface in-situ measurements. Volcanic particles added to the pre-existing aerosol load and measurement data allow quantifying the impact of volcanic particles on the aerosol vertical distribution, lidar ratios, the aerosol size distribution, and the ground-level particulate-matter concentrations. Lidar measurements reveal that backscatter coefficients by volcanic particles were about one order of magnitude smaller over south-eastern Italy than over Central Europe. Mean lidar ratios at 355 nm were equal to 64 ± 5 sr inside the volcanic aerosol layer and were characterized by smaller values (47 ± 2 sr) in the underlying layer on 20 April, 19:30 UTC. Lidar ratios and their dependence with the height reduced in the following days, mainly because of the variability of the volcanic particle contributions. Size distributions from sun/sky photometer measurements reveal the presence of volcanic particles with radii r &gt; 0.5 μm on 21 April and that the contribution of coarse volcanic particles increased from 20 to 22 April. The aerosol fine mode fraction from sun/sky photometer measurements varied between values of 0.85 and 0.94 on 20 April and decreased to values between 0.25 and 0.82 on 22 April. Surface measurements of particle size distributions were in good accordance with column averaged particle size distributions from sun/sky photometer measurements. PM1/PM2.5 mass concentration ratios of 0.69, 0.66, and 0.60 on 20, 21, and 22 April, respectively, support the increase of super-micron particles at ground. Measurements from the Regional Air Quality Agency show that PM10 mass concentrations on 20, 21, and 22 April 2010 were enhanced in the entire Apulia Region. More specifically, PM10 mass concentrations have on average increased over Apulia Region 22%, 50%, and 28% on 20, 21, and 22 April, respectively, compared to values on 19 April. Finally, the comparison of measurement data with numerical simulations by the FLEXPART dispersion model demonstrates the ability of FLEXPART to model the advection of the volcanic ash over the 4000 km from the Eyjafjallajökull volcano to Southern Italy.

Night-Time Ground Hyperspectral Imaging for Urban-Scale Remote Sensing of Ambient PM. I. Aerosol Optical Thickness Acquisition

Aerosol loadings in vertical atmospheric columns are commonly measured by satellite-borne or ground instruments that remotely sense the spectral extinction through the optical path. However, correlations of these integrated measurements with ground-level particulate matter concentrations are highly influenced by local meteorology, seasonality, and the surface albedo. Moreover, as most measurements are based on solar radiation, they are limited to daytime. To account for these limitations, we study the feasibility of using a ground hyperspectral camera for acquiring images of artificial light sources through horizontal urban-scale open paths during the night, in order to retrieve the apparent spectral aerosol optical thickness. Laboratory-scale measurements demonstrated a linear response of the camera and set the spectral operational range. A procedure for night-time imaging of illuminating targets through ambient open paths has been developed to enable a consistent selection of pixels for analysis, provid...