Assessment of air quality in Northern China by using the COSMO-ART model in conjunction with satellite and ground-based data

Luftverschmutzung durch Aerosole ist eines der größten Umweltprobleme in der chinesischen Hauptstadt Peking. Insbesondere Mineralstaub, welcher oft aus den weitläufigen asiatischen Trockengebieten in das Stadtgebiet eingetragen wird, führt zu einer drastischen Verschlechterung der Luftqualität. Diese Arbeit ist eine detaillierte Studie über die raumzeitliche Dynamik dieses eingetragenen Mineralstaubs sowie dessen physikalische Interaktion mit lokal produzierten anthropogenen Partikeln

Model prediction and climatology of aerosol optical depth (τ550) and angstrom exponent (α470-660) over three aerosol robotic network stations in Sub-Saharan Africa using moderate resolution imaging spectroradiometer data

The spatial and temporal variations of aerosol optical depth at 550 nm (τ550) and Angstrom exponent derived from 470 and 660 nm (α470-660) over Nairobi (NAI), Skukuza (SKU) and Ilorin (ILO) Aerosol Robotic Network (AERONET) stations in sub-Saharan Africa, as recorded by Moderate Resolution Imaging Spectroradiometer (MODIS) satellites for fifteen years (2000-2015), were examined in relation to their climatologies and prediction. The MODIS measurements of τ550 and α470-660 from aqua (MYD04) and terra (MOD04) satellites were used in this study. Retrievals of τ550 for both satellites were validated with AERONET τ550 for the same period. The validation results showed that they compare favourably over the three stations, but MOD04 performed better than MYD04 data in NAI and ILO for τ550. This shows that the τ550 of NAI and ILO are best captured using the MOD04 data while that of SKU is best with MYD04. It was also discovered that MODIS underestimated AERONET τ550 data over NAI and SKU. The most polluted station is ILO while the least polluted one is NAI. Similarly, the station with the highest concentration of absorbing aerosols is NAI and the least was observed in ILO. The aerosol climatology shows that the most polluted months in NAI, SKU and ILO are October, June and March respectively. On the other hand, February, November and March has the highest amount of scattering aerosols in the atmosphere for NAI, SKU and ILO respectively. The highest amount of absorbing aerosols was found, respectively, in the months of June, June and August. The generated time series (TS) models are all good, though a general underestimation of the parameters by the models was also observed. Keywords: Aerosol optical depth, Angstrom exponent, MODIS, Time series, sub-Saharan Afric

A Geostatistical Data Fusion Technique for Merging Remote Sensing and Ground-Based Observations of Aerosol Optical Thickness

Particles in the atmosphere reflect incoming sunlight, tending to cool the Earth below. Some particles, such as soot, also absorb sunlight, which tens to warm the ambient atmosphere. Aerosol optical depth (AOD) is a measure of the amount of particulate matter in the atmosphere, and is a key input to computer models that simulate and predict Earth's changing climate. The global AOD products from the Multi-angle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS), both of which fly on the NASA Earth Observing System's Terra satellite, provide complementary views of the particles in the atmosphere. Whereas MODIS offers global coverage about four times as frequent as MISR, the multi-angle data makes it possible to separate the surface and atmospheric contributions to the observed top-of-atmosphere radiances, and also to more effectively discriminate particle type. Surface-based AERONET sun photometers retrieve AOD with smaller uncertainties than the satellite instruments, but only at a few fixed locations. So there are clear reasons to combine these data sets in a way that takes advantage of their respective strengths. This paper represents an effort at combining MISR, MODIS and AERONET AOD products over the continental US, using a common spatial statistical technique called kriging. The technique uses the correlation between the satellite data and the "ground-truth" sun photometer observations to assign uncertainty to the satellite data on a region-by-region basis. The larger fraction of the sun photometer variance that is duplicated by the satellite data, the higher the confidence assigned to the satellite data in that region. In the Western and Central US, MISR AOD correlation with AERONET are significantly higher than those with MODIS, likely due to bright surfaces in these regions, which pose greater challenges for the single-view MODIS retrievals. In the east, MODIS correlations are higher, due to more frequent sampling of the varying AOD. These results demonstrate how the MISR and MODIS aerosol products are complementary. The underlying technique also provides one method for combining these products in such a way that takes advantage of the strengths of each, in the places and times when they are maximal, and in addition, yields an estimate of the associated uncertainties in space and time

Evaluation of aerosol optical thickness over Malaysia based on multi-source ground and satellite data

This study evaluates the spatiotemporal distribution of aerosol optical thickness (AOT) over Malaysia. The significance of aerosols in regional and global climate change assessment has become a pressing topic in recent climate discussions. Two different approaches are used in measuring AOT; satellite imagery and ground measurement approaches. However, the satellite approach is deemed the best way for monitoring the patterns and transport of aerosols largely due to its extensive spatial coverage and reliable repetitive measurements. The data in this study were obtained from a Sea-viewing Wide Field-of-view Sensor (SeaWiFS), a Multi-angle Imaging Spectroradiometer (MISR), and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors based on a NASA-operated Giovanni portal. Ground-based Aerosol Robotic Network (AERONET) datasets from two sites over the study area were also used. The results show that the highest AOT ground values of 1.93 and 2.00 were recorded in September 2015, at USM station and Kuching station, respectively. Throughout the 15 years of recorded data, the monthly average value of AOT reached its highest values in September, October, and November. In these months, the value of AOT went above 0.40, unlike in other months of the year. Significantly, the results indicate that Malaysian air quality can be evaluated based on AOT values, as these show the variation in optical properties of aerosol

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

Air Quality over China

The strong economic growth in China in recent decades, together with meteorological factors, has resulted in serious air pollution problems, in particular over large industrialized areas with high population density. To reduce the concentrations of pollutants, air pollution control policies have been successfully implemented, resulting in the gradual decrease of air pollution in China during the last decade, as evidenced from both satellite and ground-based measurements. The aims of the Dragon 4 project “Air quality over China” were the determination of trends in the concentrations of aerosols and trace gases, quantification of emissions using a top-down approach and gain a better understanding of the sources, transport and underlying processes contributing to air pollution. This was achieved through (a) satellite observations of trace gases and aerosols to study the temporal and spatial variability of air pollutants; (b) derivation of trace gas emissions from satellite observations to study sources of air pollution and improve air quality modeling; and (c) study effects of haze on air quality. In these studies, the satellite observations are complemented with ground-based observations and modeling

Downscaling Aerosol Optical Thickness from Satellite Observations: Physics and Machine Learning Approaches

In recent years, the satellite observation of aerosol properties has been greatly improved. As a result, the derivation of Aerosol Optical Thickness (AOT), one of the most popular atmospheric parameters used in air pollution monitoring, over ocean and continents from satellite observations shows comparable quality to ground-based measurements. Satellite AOT products is often applied for monitoring at global scale because of its coarse spatial resolution. However, monitoring at local scale such as over cities requires more detailed AOT information. The increase spatial resolution to suitable level has potential for applications of air pollution monitoring at global-to-local scale, detecting emission sources, deciding pollution management strategies, localizing aerosol estimation, etc. In this thesis, we investigated, proposed, implemented and validated algorithms to derive AOT maps with spatial resolution increased up to 1×1 km2 from MODerate resolution Imaging Spectrometer (MODIS) observations provided by National Aeronautics and Space Administration (NASA), while MODIS standard aerosol products provide maps at 10×10 km2 of spatial resolution. The solutions are considered on two perspectives: dynamical downscaling by improving the algorithm for remote sensing of tropospheric aerosol from MODIS and statistical downscaling using Support Vector Regression

Remote sensed and in situ constraints on processes affecting tropical tropospheric ozone

We use a global chemical transport model (GEOS-Chem) to evaluate the consistency of satellite measurements of lightning flashes and ozone precursors with in situ measurements of tropical tropospheric ozone. The measurements are tropospheric O₃, NO₂, and HCHO columns from the GOME satellite instrument, lightning flashes from the OTD and LIS satellite instruments, profiles of O₃, CO, and relative humidity from the MOZAIC aircraft program, and profiles of O₃ from the SHADOZ ozonesonde network. We interpret these multiple data sources with our model to better understand what controls tropical tropospheric ozone. Tropical tropospheric ozone is mainly affected by lightning NO_x and convection in the upper troposphere and by surface emissions in the lower troposphere. Scaling the spatial distribution of lightning in the model to the observed flashes improves the simulation of O₃ in the upper troposphere by 5–20 ppbv versus in situ observations and by 1–4 Dobson Units versus GOME retrievals of tropospheric O₃ columns. A lightning source strength of 6±2 Tg N/yr best represents in situ observations from aircraft and ozonesonde. Tropospheric NO₂ and HCHO columns from GOME are applied to provide top-down constraints on emission inventories of NO_x (biomass burning and soils) and VOCs (biomass burning). The top-down biomass burning inventory is larger than the bottom-up inventory by a factor of 2 for HCHO and alkenes, and by a factor of 2.6 for NO_x over northern equatorial Africa. These emissions increase lower tropospheric O₃ by 5–20 ppbv, improving the simulation versus aircraft observations, and by 4 Dobson Units versus GOME observations of tropospheric O₃ columns. Emission factors in the a posteriori inventory are more consistent with a recent compilation from in situ measurements. The ozone simulation using two different dynamical schemes (GEOS-3 and GEOS-4) is evaluated versus observations; GEOS-4 better represents O₃ observations by 5–15 ppbv, reflecting enhanced convective detrainment in the upper troposphere. Heterogeneous uptake of HNO₃ on aerosols reduces simulated O₃ by 5–7 ppbv, reducing a model bias versus in situ observations over and downwind of deserts. Exclusion of HO₂ uptake on aerosols increases O₃ by 5 ppbv in biomass burning regions, reducing a model bias versus MOZAIC aircraft measurements