UV Aerosol Index Research Articles

Impact of the ozone monitoring instrument row anomaly on the long-term record of aerosol products

Abstract. Since about three years after the launch the Ozone Monitoring Instrument (OMI) on the EOS-Aura satellite, the sensor's viewing capability has been affected by what is believed to be an internal obstruction that has reduced OMI's spatial coverage. It currently affects about half of the instrument's 60 viewing positions. In this work we carry out an analysis to assess the effect of the reduced spatial coverage on the monthly average values of retrieved aerosol optical depth (AOD), single scattering albedo (SSA) and the UV Aerosol Index (UVAI) using the 2005–2007 three-year period prior to the onset of the row anomaly. Regional monthly average values calculated using viewing positions 1 through 30 were compared to similarly obtained values using positions 31 through 60, with the expectation of finding close agreement between the two calculations. As expected, mean monthly values of AOD and SSA obtained with these two scattering-angle dependent subsets of OMI observations agreed over regions where carbonaceous or sulphate aerosol particles are the predominant aerosol type. However, over arid regions, where desert dust is the main aerosol type, significant differences between the two sets of calculated regional mean values of AOD were observed. As it turned out, the difference in retrieved desert dust AOD between the scattering-angle dependent observation subsets was due to the incorrect representation of desert dust scattering phase function. A sensitivity analysis using radiative transfer calculations demonstrated that the source of the observed AOD bias was the spherical shape assumption of desert dust particles. A similar analysis in terms of UVAI yielded large differences in the monthly mean values for the two sets of calculations over cloudy regions. On the contrary, in arid regions with minimum cloud presence, the resulting UVAI monthly average values for the two sets of observations were in very close agreement. The discrepancy under cloudy conditions was found to be caused by the parameterization of clouds as opaque Lambertian reflectors. When properly accounting for cloud scattering effects using Mie theory, the observed UVAI angular bias was significantly reduced. The analysis discussed here has uncovered important algorithmic deficiencies associated with the model representation of the angular dependence of scattering effects of desert dust aerosols and cloud droplets. The resulting improvements in the handling of desert dust and cloud scattering have been incorporated in an improved version of the OMAERUV algorithm.

Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets.

Air pollution is one of the major environmental concerns in Vietnam. In this study, we assess the current status of air pollution over Hanoi, Vietnam using multiple different satellite datasets and weather information, and assess the potential to capture rice residue burning emissions with satellite data in a cloud-covered region. We used a timeseries of Ozone Monitoring Instrument (OMI) Ultraviolet Aerosol Index (UVAI) satellite data to characterize absorbing aerosols related to biomass burning. We also tested a timeseries of 3-hourly MERRA-2 reanalysis Black Carbon (BC) concentration data for 5 years from 2012–2016 and explored pollution trends over time. We then used MODIS active fires, and synoptic wind patterns to attribute variability in Hanoi pollution to different sources. Because Hanoi is within the Red River Delta where rice residue burning is prominent, we explored trends to see if the residue burning signal is evident in the UVAI or BC data. Further, as the region experiences monsoon-influenced rainfall patterns, we adjusted the BC data based on daily rainfall amounts. Results indicated forest biomass burning from Northwest Vietnam and Laos impacts Hanoi air quality during the peak UVAI months of March and April. Whereas, during local rice residue burning months of June and October, no increase in UVAI is observed, with slight BC increase in October only. During the peak BC months of December and January, wind patterns indicated pollutant transport from southern China megacity areas. Results also indicated severe pollution episodes during December 2013 and January 2014. We observed significantly higher BC concentrations during nighttime than daytime with peaks generally between 2130 and 0030 local time. Our results highlight the need for better air pollution monitoring systems to capture episodic pollution events and their surface-level impacts, such as rice residue burning in cloud-prone regions in general and Hanoi, Vietnam in particular.

Agricultural Burning and Air Quality over Northern India: A Synergistic Analysis using NASA’s A-train Satellite Data and Ground Measurements

In the recent years, New Delhi, the capital city of India, has ranked among the most polluted cities in the world regarding its air quality related to the submicron Particulate Matter (PM(sub 2.5)). Using NASA's A-train satellite data (MODIS, OMI, and CALIOP), ground-level PM2.5 measured in New Delhi (2013-2016), and back-trajectory calculations, we show that the PM(sub 2.5). over New Delhi is strongly affected by the agricultural fires in the northwestern Indian states of Punjab and Haryana during the post-monsoon season (October and November). The mass concentration of PM2.5 escalates from ~50 μg m(exp -3) measured prior to the onset of residue burning in early October to as high as 300 μg m(exp -3) (24-hour averaged, 7- day running mean) during the peak burning period in early November. A linear regression analysis reveals that the variations in PM(sub 2.5) over New Delhi can be attributed to the concurrent changes in the satellite retrievals of fire counts and aerosols over the crop burning area. The back-trajectory analysis shows that most clusters (> 80%) of the northwesterly flow near the ground intercepted the crop burning region before arriving at the receptor location in New Delhi; this further corroborates the transport patterns inferred from the satellite data. A 15-year long satellite record (2002-2016) reveals an increasing trend in agricultural fires (~617 per year) and aerosol loading (0.031 and 0.04 per year in aerosol optical depth and UV aerosol index) in November. Increasing levels of crop residue burning and resulting particulate matter pollution at an alarming rate over northern India is a pressing concern demanding corrective measures to substantially reduce or completely diminish the crop burning through an effective residue management system.

Analysis of remotely sensed and surface data of aerosols and meteorology for the Mexico Megalopolis Area between 2003 and 2015.

This paper presents an aerosol characterization study from 2003 to 2015 for the Mexico City Metropolitan Area using remotely sensed aerosol data, ground-based measurements, air mass trajectory modeling, aerosol chemical composition modeling, and reanalysis data for the broader Megalopolis of Central Mexico region. The most extensive biomass burning emissions occur between March and May concurrent with the highest aerosol optical depth, ultraviolet aerosol index, and surface particulate matter (PM) mass concentration values. A notable enhancement in coarse PM levels is observed during vehicular rush hour periods on weekdays versus weekends owing to nonengine-related emissions such as resuspended dust. Among wet deposition species measured, PM2.5, PM10, and PMcoarse (PM10-PM2.5) were best correlated with NH4+, SO42-, and Ca2+, suggesting that the latter three constituents are important components of the aerosol seeding raindrops that eventually deposit to the surface in the study region. Reductions in surface PM mass concentrations were observed in 2014-2015 owing to reduced regional biomass burning as compared to 2003-2013.

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies.

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA's latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA-1), including aerosol assimilation for the entire period. MERRA-2 assimilates bias-corrected aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer and the Advanced Very High Resolution Radiometer instruments. Additionally, MERRA-2 assimilates (non bias corrected) AOD from the Multiangle Imaging SpectroRadiometer over bright surfaces and AOD from Aerosol Robotic Network sunphotometer stations. This paper, the second of a pair, summarizes the efforts to assess the quality of the MERRA-2 aerosol products. First, MERRA-2 aerosols are evaluated using independent observations. It is shown that the MERRA-2 absorption aerosol optical depth (AAOD) and ultraviolet aerosol index (AI) compare well with Ozone Monitoring Instrument observations. Next, aerosol vertical structure and surface fine particulate matter (PM2.5) are evaluated using available satellite, aircraft, and ground-based observations. While MERRA-2 generally compares well to these observations, the assimilation cannot correct for all deficiencies in the model (e.g., missing emissions). Such deficiencies can explain many of the biases with observations. Finally, a focus is placed on several major aerosol events to illustrate successes and weaknesses of the AOD assimilation: the Mount Pinatubo eruption, a Saharan dust transport episode, the California Rim Fire, and an extreme pollution event over China. The article concludes with a summary that points to best practices for using the MERRA-2 aerosol reanalysis in future studies.

Detection of Absorbing Aerosol Using Single Near-UV Radiance Measurements from a Cloud and Aerosol Imager

The Ultra-Violet Aerosol Index (UVAI) is a practical parameter for detecting aerosols that absorb UV radiation, especially where other aerosol retrievals fail, such as over bright surfaces (e.g., deserts and clouds). However, typical UVAI retrieval requires at least two UV channels, while several satellite instruments, such as the Thermal And Near infrared Sensor for carbon Observation Cloud and Aerosol Imager (TANSO-CAI) instrument onboard a Greenhouse gases Observing SATellite (GOSAT), provide single channel UV radiances. In this study, a new UVAI retrieval method was developed which uses a single UV channel. A single channel aerosol index (SAI) is defined to measure the extent to which an absorbing aerosol state differs from its state with minimized absorption by aerosol. The SAI qualitatively represents absorbing aerosols by considering a 30-day minimum composite and the variability in aerosol absorption. This study examines the feasibility of detecting absorbing aerosols using a UV-constrained satellite, focusing on those which have a single UV channel. The Vector LInearized pseudo-spherical Discrete Ordinate Radiative Transfer (VLIDORT) was used to test the sensitivity of the SAI and UVAI to aerosol optical properties. The theoretical calculations showed that highly absorbing aerosols have a meaningful correlation with SAI. The retrieved SAI from OMI and operational OMI UVAI were also in good agreement when UVAI values were greater than 0.7 (the absorption criteria of UVAI). The retrieved SAI from the TANSO-CAI data was compared with operational OMI UVAI data, demonstrating a reasonable agreement and low rate of false detection for cases of absorbing aerosols in East Asia. The SAI retrieved from TANSO-CAI was in better agreement with OMI UVAI, particularly for the values greater than the absorbing threshold value of 0.7.

Intercomparison and assessment of long-term (2004–2013) multiple satellite aerosol products over two contrasting sites in South Africa

To build a long-term database and improve the accuracy of the satellite products used for aerosol studies, there is a need to carry out intercomparison and validation of these satellite observations with ground-based measurements. With this objective, we estimated the long-term inter-annual variations and percentage change in trends of aerosol optical depth (AOD) retrieved from MODerate resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) sensors for a 10-year period during 2004–2013 over two distinct sites namely, Skukuza (SKZ; 24.99°S, 31.58°E) and Richards Bay (RBAY; 28.8°S, 21.1°E) in South Africa. The validation performed over SKZ site shows that MISR was better correlated with AErosol RObotic NETwork (AERONET) when compared to Terra and Aqua satellites of MODIS. Later both the MODIS products (Terra and Aqua) were compared on the annual and seasonal basis to derive the relationship between them through scattering plot. The long-term regression analysis performed at these sites shows that the annual trends were decreasing, with the MODIS products underestimating MISR. This is due to difficulties of the MODIS algorithm when dealing with highly complex surface reflectance conditions and aerosol model assumptions. Also, the temporal variations of AOD derived from the two sensors noticed maximum in spring (September/October) and minimum in winter (June). Further, the Ultra-Violet Aerosol Index (UVAI) retrieved from the Ozone Monitoring Instrument (OMI) at the two locations for 9 years (2005–2013) showed a significant increasing trend with a high value of +0.009yr−1 at SKZ than +0.006yr−1 at RBAY during the study period, which is due to the transport of dust and smoke particles.

Interpreting the ultraviolet aerosol index observed with the OMI satellite instrument to understand absorption by organic aerosols: implications for atmospheric oxidation and direct radiative effects

Abstract. Satellite observations of the ultraviolet aerosol index (UVAI) are sensitive to absorption of solar radiation by aerosols; this absorption affects photolysis frequencies and radiative forcing. We develop a global simulation of the UVAI using the 3-D chemical transport model GEOS-Chem coupled with the Vector Linearized Discrete Ordinate Radiative Transfer model (VLIDORT). The simulation is applied to interpret UVAI observations from the Ozone Monitoring Instrument (OMI) for the year 2007. Simulated and observed values are highly consistent in regions where mineral dust dominates the UVAI, but a large negative bias (−0.32 to −0.97) exists between simulated and observed values in biomass burning regions. We determine effective optical properties for absorbing organic aerosol, known as brown carbon (BrC), and implement them into GEOS-Chem to better represent observed UVAI values over biomass burning regions. The inclusion of absorbing BrC decreases the mean bias between simulated and OMI UVAI values from −0.57 to −0.09 over West Africa in January, from −0.32 to +0.0002 over South Asia in April, from −0.97 to −0.22 over southern Africa in July, and from −0.50 to +0.33 over South America in September. The spectral dependence of absorption after including BrC in the model is broadly consistent with reported observations for biomass burning aerosol, with absorbing Ångström exponent (AAE) values ranging from 2.9 in the ultraviolet (UV) to 1.3 across the UV–Near IR spectrum. We assess the effect of the additional UV absorption by BrC on atmospheric photochemistry by examining tropospheric hydroxyl radical (OH) concentrations in GEOS-Chem. The inclusion of BrC decreases OH by up to 30 % over South America in September, up to 20 % over southern Africa in July, and up to 15 % over other biomass burning regions. Global annual mean OH concentrations in GEOS-Chem decrease due to the presence of absorbing BrC, increasing the methyl chloroform lifetime from 5.62 to 5.68 years, thus reducing the bias against observed values. We calculate the direct radiative effect (DRE) of BrC using GEOS-Chem coupled with the radiative transfer model RRTMG (GC-RT). Treating organic aerosol as containing more strongly absorbing BrC changes the global annual mean all-sky top of atmosphere (TOA) DRE by +0.03 W m−2 and all-sky surface DRE by −0.08 W m−2. Regional changes of up to +0.3 W m−2 at TOA and down to −1.5 W m−2 at the surface are found over major biomass burning regions.

Atmospheric changes observed during April 2015 Nepal earthquake

A massive earthquake shook Nepal on 25 April 2015, with a moment magnitude of 7.9Mw, its hypocenter at a depth of 10km. Atmospheric changes that precede an earthquake might offer the hope of early warning and evacuation. Although the existence of such precursory signals is highly controversial, an attempt has been made to investigate the atmospheric changes from two months prior, to five months following this deadly earthquake. Aerosol optical depth (AOD) and columnar ozone were found to be higher by 40% and 6% respectively prior to the occurrence of the earthquake. The UV aerosol index (UVI), AOD and columnar NO2 increased, while columnar ozone and sea level pressure dropped following the earthquake.

Analysis of the origin of peak aerosol optical depth in springtime over the Gulf of Tonkin

By aggregating MODIS (moderate-resolution imaging spectroradiometer) AOD (aerosol optical depth) and OMI (ozone monitoring instrument) UVAI (ultra violet aerosol index) datasets over 2010–2014, it was found that peak aerosol loading in seasonal variation occurred annually in spring over the Gulf of Tonkin (17–23°N, 105–110°E). The vertical structure of the aerosol extinction coefficient retrieved from the spaceborne lidar CALIOP (cloud-aerosol lidar with orthogonal polarization) showed that the springtime peak AOD could be attributed to an abrupt increase in aerosol loading between altitudes of 2 and 5km. In contrast, aerosol loading in the low atmosphere (below 1km) was only half of that in winter. Wind fields in the low and high atmosphere exhibited opposite transportation patterns in spring over the Gulf of Tonkin, implying different sources for each level. By comparing the emission inventory of anthropogenic sources with biomass burning, and analyzing the seasonal variation of the vertical structure of aerosols over the Northern Indo-China Peninsula (NIC), it was concluded that biomass burning emissions contributed to high aerosol loading in spring. The relatively high topography and the high surface temperature in spring made planetary boundary layer height greater than 3km over NIC. In addition, small-scale cumulus convection frequently occurred, facilitating pollutant rising to over 3km, which was a height favoring long-range transport. Thus, pollutants emitted from biomass burning over NIC in spring were raised to the high atmosphere, then experienced long-range transport, leading to the increase in aerosol loading at high altitudes over the Gulf of Tonkin during spring.

Vegetation fires, absorbing aerosols and smoke plume characteristics in diverse biomass burning regions of Asia

In this study, we explored the relationships between the satellite-retrieved fire counts (FC), fire radiative power (FRP) and aerosol indices using multi-satellite datasets at a daily time-step covering ten different biomass burning regions in Asia. We first assessed the variations in MODIS-retrieved aerosol optical depths (AOD’s) in agriculture, forests, plantation and peat land burning regions and then used MODIS FC and FRP (hereafter FC/FRP) to explain the variations in AOD characteristics. Results suggest that tropical broadleaf forests in Laos burn more intensively than the other vegetation fires. FC/FRP-AOD correlations in different agricultural residue burning regions did not exceed 20% whereas in forest regions they reached 40%. To specifically account for absorbing aerosols, we used Ozone Monitoring Instrument-derived aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI). Results suggest relatively high AAOD and UVAI values in forest fires compared with peat and agriculture fires. Further, FC/FRP could explain a maximum of 29% and 53% of AAOD variations, whereas FC/FRP could explain at most 33% and 51% of the variation in agricultural and forest biomass burning regions, respectively. Relatively, UVAI was found to be a better indicator than AOD and AAOD in both agriculture and forest biomass burning plumes. Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations data showed vertically elevated aerosol profiles greater than 3.2–5.3 km altitude in the forest fire plumes compared to 2.2–3.9 km and less than 1 km in agriculture and peat-land fires, respectively. We infer the need to assimilate smoke plume height information for effective characterization of pollutants from different sources.

A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances

Abstract. Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broadband effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g., AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

AbstractThis study extends the application of the previously developed Aerosol Single‐scattering albedo and layer Height Estimation (ASHE) algorithm, which was originally applied to smoke aerosols only, to both smoke and dust aerosols by including nonspherical dust properties in the retrieval process. The main purpose of the algorithm is to derive aerosol height information over wide areas using aerosol products from multiple satellite sensors simultaneously: aerosol optical depth (AOD) and Ångström exponent from the Visible Infrared Imaging Radiometer Suite (VIIRS), UV aerosol index from the Ozone Mapping and Profiler Suite (OMPS), and total backscatter coefficient profile from the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP). The case studies suggest that the ASHE algorithm performs well for both smoke and dust aerosols, showing root‐mean‐square error of the retrieved aerosol height as compared to CALIOP observations from 0.58 to 1.31 km and mean bias from −0.70 to 1.13 km. In addition, the algorithm shows the ability to retrieve single‐scattering albedo to within 0.03 of Aerosol Robotic Network inversion data for moderate to thick aerosol loadings (AOD of ~1.0). For typical single‐layered aerosol cases, the estimated uncertainty in the retrieved height ranges from 1.20 to 1.80 km over land and from 1.15 to 1.58 km over ocean when favorable conditions are met. Larger errors are observed for multilayered aerosol events, due to the limited sensitivities of the passive sensors to such cases.

Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis

Abstract. A radiative transfer interface has been developed to simulate the UV aerosol index (AI) from the NASA Goddard Earth Observing System version 5 (GEOS-5) aerosol assimilated fields. The purpose of this work is to use the AI and aerosol absorption optical depth (AAOD) derived from the Ozone Monitoring Instrument (OMI) measurements as independent validation for the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Since AI is dependent on aerosol concentration, optical properties and altitude of the aerosol layer, we make use of complementary observations to fully diagnose the model, including AOD from the Multi-angle Imaging SpectroRadiometer (MISR), aerosol retrievals from the AErosol RObotic NETwork (AERONET) and attenuated backscatter coefficients from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to ascertain potential misplacement of plume height by the model. By sampling dust, biomass burning and pollution events in 2007 we have compared model-produced AI and AAOD with the corresponding OMI products, identifying regions where the model representation of absorbing aerosols was deficient. As a result of this study over the Saharan dust region, we have obtained a new set of dust aerosol optical properties that retains consistency with the MODIS AOD data that were assimilated, while resulting in better agreement with aerosol absorption measurements from OMI. The analysis conducted over the southern African and South American biomass burning regions indicates that revising the spectrally dependent aerosol absorption properties in the near-UV region improves the modeled-observed AI comparisons. Finally, during a period where the Asian region was mainly dominated by anthropogenic aerosols, we have performed a qualitative analysis in which the specification of anthropogenic emissions in GEOS-5 is adjusted to provide insight into discrepancies observed in AI comparisons.

Implications of MODIS impression of aerosol loading over urban and rural settlements in Nigeria: Possible links to energy consumption patterns in the country

A study of aerosol loading patterns in some selected cities in Nigeria was carried out using MODIS, TOMS/OMI AND AIRS satellite imageries for a period of 10 years. The results showed that an aerosol optical depth (AOD) loading obtained ranged from 0.02–0.9, UV aerosol index (AI) and carbon monoxide (CO) results ranged from 1.32– 2.43 and 2.22–2.6 molecule/cm2, respectively. The CO data was used to infer the presence of carbonecous aerosols from biomass, fossil combustion and industrial activities. This result indicates that areas with higher AOD and AI do not correspond in high CO loading. From the HYSPLIT and HAT analysis conducted it showed that advection plays important role in the dispersion of aerosols. This implies that aerosols can reside in a place remote from where they are generated. Also, the high concentration of CO aerosol in the southern cities suggests a high rate of industrial pollution as a result of fossil fuel burning, vehicular emissions, high population density and gas flaring. Therefore, emphasis should be on the need to switch to renewable energy options as an alternative to fossil fuel. Furthermore, plans for mitigations should not be limited to industrialized cities only but extended to other cities which might be bearing the real brunt of industrial emissions as shown in this work.

Discuss on Satellite-Based Particulate Matter Monitoring Technique

Abstract. Satellite measurements for atmospheric pollutants monitoring provide full mapping, large spatial coverage, and high spatial resolution. Retrieved aerosol optical depth (AOD) from satellite data as the key parameter has been used in the study on particulate matter (PM) distributions which is complementary to ground-based measurements. Based on the empirical relations between aerosol optical properties and PM10 or PM2.5 concentration and its influencing factors, combining the Beijing-Tianjin-Hebei regional PM distribution feature, the specifications including bands, viewing angles and polarization measuring requirements of the on-orbit PM monitoring instrument are discussed. The instrument is designed to obtain the data for retrieving atmospheric AOD, shape and size of particles, refractive index, aerosol single scattering albedo (SSA), etc. The major pollutant PM2.5 concentration can be retrieved from the remote sensing data especially, and its global distribution can be mapped as well. The progress of conventional aerosol retrieval method using visible data is presented along with its limitation for serious haze-fog condition. Adding ultra violet (UV) bands to obtain UV aerosol index (UVAI) is useful for monitoring the main constituents of haze.

An overview of regional and local characteristics of aerosols in South Africa using satellite, ground, and modeling data.

We present a comprehensive overview of particulate air quality across the five major metropolitan areas of South Africa (Cape Town, Bloemfontein, Johannesburg and Tshwane (Gauteng Province), the Industrial Highveld Air Quality Priority Area (HVAPA), and Durban), based on a decadal (1 January 2000 to 31 December 2009) aerosol climatology from multiple satellite platforms and detailed analysis of ground-based data from 19 sites throughout Gauteng Province. Satellite analysis was based on aerosol optical depth (AOD) from MODIS Aqua and Terra (550 nm) and MISR (555 nm) platforms, Ångström Exponent (α) from MODIS Aqua (550/865 nm) and Terra (470/660 nm), ultraviolet aerosol index (UVAI) from TOMS, and results from the Goddard Ozone Chemistry Aerosol Radiation and Transport (GOCART) model. At continentally influenced sites, AOD, α, and UVAI reach maxima (0.12-0.20, 1.0-1.8, and 1.0-1.2, respectively) during austral spring (September-October), coinciding with a period of enhanced dust generation and the maximum integrated intensity of close-proximity and subtropical fires identified by MODIS Fire Information for Resource Management System (FIRMS). Minima in AOD, α, and UVAI occur during winter. Results from ground monitoring indicate that low-income township sites experience by far the worst particulate air quality in South Africa, with seasonally averaged PM10 concentrations as much as 136 % higher in townships that in industrial areas. We report poor agreement between satellite and ground aerosol measurements, with maximum surface aerosol concentrations coinciding with minima in AOD, α, and UVAI. This result suggests that remotely sensed data are not an appropriate surrogate for ground air quality in metropolitan South Africa.

Identifying fire plumes in the Arctic with tropospheric FTIR measurements and transport models

Abstract. We investigate Arctic tropospheric composition using ground-based Fourier transform infrared (FTIR) solar absorption spectra, recorded at the Polar Environment Atmospheric Research Laboratory (PEARL, Eureka, Nunavut, Canada, 80°05' N, 86°42' W) and at Thule (Greenland, 76°53' N, −68°74' W) from 2008 to 2012. The target species, carbon monoxide (CO), hydrogen cyanide (HCN), ethane (C2H6), acetylene (C2H2), formic acid (HCOOH), and formaldehyde (H2CO) are emitted by biomass burning and can be transported from mid-latitudes to the Arctic. By detecting simultaneous enhancements of three biomass burning tracers (HCN, CO, and C2H6), ten and eight fire events are identified at Eureka and Thule, respectively, within the 5-year FTIR time series. Analyses of Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model back-trajectories coupled with Moderate Resolution Imaging Spectroradiometer (MODIS) fire hotspot data, Stochastic Time-Inverted Lagrangian Transport (STILT) model footprints, and Ozone Monitoring Instrument (OMI) UV aerosol index maps, are used to attribute burning source regions and travel time durations of the plumes. By taking into account the effect of aging of the smoke plumes, measured FTIR enhancement ratios were corrected to obtain emission ratios and equivalent emission factors. The means of emission factors for extratropical forest estimated with the two FTIR data sets are 0.40 ± 0.21 g kg−1 for HCN, 1.24 ± 0.71 g kg−1 for C2H6, 0.34 ± 0.21 g kg−1 for C2H2, and 2.92 ± 1.30 g kg−1 for HCOOH. The emission factor for CH3OH estimated at Eureka is 3.44 ± 1.68 g kg−1. To improve our knowledge concerning the dynamical and chemical processes associated with Arctic pollution from fires, the two sets of FTIR measurements were compared to the Model for OZone And Related chemical Tracers, version 4 (MOZART-4). Seasonal cycles and day-to-day variabilities were compared to assess the ability of the model to reproduce emissions from fires and their transport. Good agreement in winter confirms that transport is well implemented in the model. For C2H6, however, the lower wintertime concentration estimated by the model as compared to the FTIR observations highlights an underestimation of its emission. Results show that modeled and measured total columns are correlated (linear correlation coefficient r > 0.6 for all gases except for H2CO at Eureka and HCOOH at Thule), but suggest a general underestimation of the concentrations in the model for all seven tropospheric species in the high Arctic.

Impact of Meteorological Parameters and Gaseous Pollutants on PM2.5 and PM10 Mass Concentrations during 2010 in Xi’an, China

ABSTRACTMass concentrations of PM2.5 and PM10 from the six urban/rural sampling sites of Xi’an were obtained during two weeks of every month corresponding to January, April, July and October during 2010, together with the six meteorological parameters and the data of two precursors. The result showed that the average annual mass concentrations of PM2.5 and PM10 were 140.9 ± 108.9 µg m–3 and 257.8 ± 194.7 µg m–3, respectively. Basin terrain constrains the diffusion of PM2.5 and PM10 concentration spatially. High concentrations in wintertime and low concentrations in summertime are due to seasonal variations of meteorological parameters and cyclic changes of precursors (SO2 and NO2). Stepwise Multiple Linear Regression (MLR) analysis indicates that relative humidity is the main factor influencing on meteorological parameter. Entry MLR analysis suggests that SO2 from local coal-burning power plants is still the primary pollutant. Trajectory cluster results of PM2.5 at BRR indicate that the entrained urban pollutants carried by the westerly or winter monsoon forms the dominant regional pollution sources in winter and spring. Ultraviolet (UV) aerosol index verified the source and pathway of dust storm in spring.

Analysis of a severe prolonged regional haze episode in the Yangtze River Delta, China

A severe prolonged regional haze episode occurred over the Yangtze River Delta (YRD) from 2 to 14 December 2013. In this paper, we have discussed the probable cause of haze episode and aerosol characteristics. Analysis of visibility and meteorological parameters suggest that the meteorological conditions play a very important role in the accumulation of aerosol particles that cause haze. The height of the planetary boundary layer (PBL) is considered as one of the key factors together with other factors in the haze formation.The Principal Component Analysis (PCA) clearly shows accumulation process of air pollutants from vehicular, industrial and anthropogenic sources are major contributors for the haze formation. The aerosol optical properties (aerosol optical depth – AOD, Ångström exponent – α and Ultra Violet Aerosol Index – UVAI) are analyzed to study the temporal and spatial variations of aerosol loadings. The results show concentrations of fine mode particles during the haze episode, the dust could have been also entered in the region from the northwestern China. Analysis of vertical aerosol profiles indicate that dust aerosols concentrated in the lower part of the haze layers.