Emissions, Transport, and Evolution of Atmospheric Pollutants from China: An Observational Study

China's air pollution issue, a byproduct of recent phenomenal economic growth, has received increasing attention in light of its local and large-scale impacts. I investigated the emissions, transport, and evolution of pollutants from China using measurements near some source regions in northern China in 2005. Surface pollution near Beijing in March was overall heavy but changed dramatically, as passing mid-latitude cyclones led to fast transitions between polluted prefrontal and clean postfrontal conditions. Large differences found between measurements and inventories suggest substantial uncertainties in emission estimates. Small, coal-fired boilers are shown unlikely to be the major source of inventory error; experiments measuring traffic emissions are called for. Ground-level aerosols absorb light and are from both wind-blown dust and anthropogenic emissions. Their effects on climate are to be further studied. Pollutants at higher altitudes are more likely to have large-scale impact than pollutants that remain near the surface. The aircraft campaign in April was among the first efforts to measure the vertical distribution of pollutants over inland China. The largest pollutant levels observed in the free troposphere during the campaign were related to dry convective lofting over an industrial region. This differs from earlier experiments over the Pacific, which recognized the warm conveyor belt (WCB) as the main lofting mechanism. Dry convection over the continent may be followed by WCB lifting as the systems move out over the ocean. Their relative roles are yet to be determined. Analyses of meteorological and satellite cloud data reveal the importance of in-cloud processing in oxidizing SO2 transported behind cold fronts. Through integration of satellite sensors, in-situ measurements, trajectory and chemical transport models, I tracked a pollution plume as it traveled away from source region. The decay of SO2 in the plume over three days was quantified, suggesting an SO2 lifetime of 1-4 d. Formation of sulfate and loss of dust together changed the aerosol loading of the plume. This analysis showcases the potential for employing satellites to trace transport events and pollutant evolution, and highlights the main uncertainties in quantitative application of satellite data

Investigating the enhancement of air pollutant predictions and understanding air quality disparities across racial, ethnic, and economic lines at US public schools

2022 Spring.Includes bibliographical references.Ambient air pollution has significant health and economic impacts worldwide. Even in the most developed countries, monitoring networks often lack the spatiotemporal density to resolve air pollution gradients. Though air pollution affects the entire population, it can disproportionately affect the disadvantaged and vulnerable communities in society. Pollutants such as fine particulate matter (PM2.5), nitrogen oxides (NO and NO2), and ozone, which have a variety of anthropogenic and natural sources, have garnered substantial research attention over the last few decades. Over half the world and over 80% of Americans live in urban areas, and yet many cities only have one or several air quality monitors, which limits our ability to capture differences in exposure within cities and estimate the resulting health impacts. Improving sub-city air pollution estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM2.5, providing a better understanding of communities at-risk to urban air pollution. Biomass burning is a source of PM2.5 air pollution that can impact both urban and rural areas, but quantifying the health impacts of PM2.5 from biomass burning can be even more difficult than from urban sources. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke, especially near the source fires. Due to limitations of both urban and rural monitoring networks several techniques have been developed to supplement and enhance air pollution estimates. For example, satellite aerosol optical depth (AOD) can be used to fill spatial gaps in PM monitoring networks, but AOD can be disconnected from time-resolved surface-level PM in a multitude of ways, including the limited overpass times of most satellites, daytime-only measurements, cloud cover, surface reflectivity, and lack of vertical-profile information. Observations of smoke plume height (PH) may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. Low-cost sensor networks have been rapidly expanding to provide higher density air pollution monitoring. Finally, both geophysical modeling, statistical techniques such as machine learning and data mining, and combinations of all of the aforementioned datasets have been increasingly used to enhance surface observations. In this dissertation, we explore several of these different data sources and techniques for estimating air pollution and determining community exposure concentrations. In the first chapter of this dissertation, we assess PH characteristics from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and evaluate its correlation with co-located PM2.5 and AOD measurements. PH is generally highest over the western US. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events. Next, to estimate spatiotemporal variability in PM2.5, we use machine learning (Random Forests; RFs) and concurrent PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM2.5 measurements from the Environmental Protection Agency's (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74-0.84 for CEAMS; R2=0.68-0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM22.5 measurements. We find that the most important predictors of PM2.5 are factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors are less important than spatiotemporal predictors because temporal variability exceeds spatial variability in our dataset. Finally, although concurrent AOD is an important predictor in our RF model for hourly PM2.5, it does not improve model performance during our campaign period in Denver. Regardless, we find that low-cost PM2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM2.5 over wintertime Denver. We also explore how the RF model performance and interpretation changes based on different model configurations and data processing. Finally, we use high resolution PM2.5 and nitrogen dioxide (NO2) estimates to investigate socioeconomic disparities in air quality at public schools in the contiguous US. We find that Black and African American, Hispanic, and Asian or Pacific Islander students are more likely to attend schools in locations where the ambient concentrations of NO2 and PM2.5 are above the World Health Organization's (WHO) guidelines for annual-average air quality. Specifically, we find that ~95% of students that identified as Asian or Pacific Islander, 94% of students that identified as Hispanic, and 89% of students that identified as Black or African American, attended schools in locations where the 2019 ambient concentrations were above the WHO guideline for NO2 (10 μg m-3 or ~5.2 ppbv). Conversely, only 83% of students that identified as white and 82% of those that identified as Native American attended schools in 2019 where the ambient NO2 concentrations were above the WHO guideline. Similar disparities are found in annually averaged ambient PM2.5 across racial and ethnic groups, where students that identified as white (95%) and Native American (83%) had a smallest percentage of students above the WHO guideline (5 μg m-3), compared to students that identified with minoritized groups (98-99%). Furthermore, the disparity between white students and other minoritized groups, other than Native Americans, is larger at higher PM2.5 concentrations. Students that attend schools where a higher percentage of students are eligible for free or reduced meals, which we use as a proxy for poverty, are also more likely to attend schools where the ambient air pollutant concentrations exceed WHO guidelines. These disparities also tend to increase in magnitude at higher concentrations of NO2 and PM2.5. We investigate the intersectionality of disparities across racial/ethnic and poverty lines by quantifying the mean difference between the lowest and highest poverty schools, and the most and least white schools in each state, finding that most states have disparities above 1 ppbv of NO2 and 0.5 μg m-3 of PM2.5 across both. We also identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we also highlight that disparities do not only exist across an urban and non-urban divide, but also within urban areas

Aeolian dust deposition rates in south-western Iran

The annual atmospheric dust-load originating in the so-called Dust Belt ‎, which ranges from the ‎Sahara desert and the Arabian peninsula to the arid lowlands of Central Asia and the deserts of ‎northern China, impacts the air quality and the climate worldwide. Iran as a whole, and especially the ‎southwestern regions of the country, most affected by dust, with frequent dust storms characterized ‎by annual mean concentrations of more than 100 µg/m³ of suspended dust. Although aeolian dust is a ‎highly relevant problem in Iran, there is a lack of comprehensive regional studies on this topic. The ‎central aim of the study presented here is therefore the spatiotemporal analyses and classification of ‎dust events, the chemical composition of the dust, and the connections between regional and seasonal ‎climate variation and dust deposition rates in four sub-regions of Iran. This comprehensive approach is ‎based on the maximum mean dust concentration and the seasonality of dust events. The results are ‎provided new and valuable insights into the dust deposition and its related processes in the study area.‎ The study area covers 8.43% of Iran (about 117,000 km2), located between 45°30′00″ E 35°00′00″ N ‎and 49°30′00″ E 30°00′00″ N including Kermanshah, Lorestan and Khuzestan. The fieldwork area is ‎characterized by the rolling mountainous terrain about 4000 m above sea level (a.s.l) in the north and ‎east, plains and marshlands in the south. Study area has also located in dry climate and hot summer ‎conditions in the south, cold and hot desert climates in the west. The studies on aeolian dust in ‎southwestern Iran are based solely on ground deposition rates from 2014 to 2017‎‏.‏ To address the connections between the Ground observation of dust Deposition Rates (GDR), climate ‎zones, and weather patterns, a comparative analysis with various data sets was conducted. Both ‎gravimetric and directional dust samplers (10 each) were installed to record the monthly GDR between ‎‎2014 and 2017. The sampler design was deliberately kept simple to ensure long-term durability and ‎easy maintenance. The collected dust samples were analyzed for their chemical composition using ‎Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The ten sampling sites were also classified ‎by their land use / land cover (LULC) for a more detailed data interpretation. The observation data ‎during two typical dust cases (spring 2014 and winter 2015), have furthermore been compared with ‎the spatiotemporal dust concentration and dust load over the study area. Comparing the results of the ‎monthly mean Aerosol Optical Thickness (AOT) derived from the Moderate Resolution Imaging ‎Spectroradiometer (MODIS) and GDR data, using enhancement algorithms were applied in order to ‎investigate the spatiotemporal distribution of dust events. To demonstrate the aerosol movement, a ‎HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used for tracing the ‎investigated dust events. The time-space consistency between AOT and GDR, in agreement with the ‎HYSPLIT model output was the basis for an improved estimation of the dust deposition rate from ‎separate thickness layers. Finally, by comparing the high temporal and maximum seasonal deposition ‎rates, using MODIS and GDR data, the impact of the regional climate on the deposition rates of ‎aeolian dust was assessed, which allows insights in potential future dust emission scenarios in times of ‎climate change. ‎ A major finding shows the impact of dust events on the environment and considers the influence of ‎geographical factors, such as weathering, and climate pattern over aeolian dust deposition rates. In ‎more detail, finding to address the first objective suggested that contributors of the elemental ‎concentrations are associated with elements emanating from local industrial and commercial activities ‎‎(Cr, V, and Cd). The dominant variables (K, Zn) strongly influence the aerosol composition values and ‎represent the dust transport route. Inter –element relationships shows that the highest proportion (80%) ‎of dust samples subjected to Airborne Metals Regulations are formed under local and regional ‎conditions. Besides, the analyses indicate that the WRF-Chem model adequately simulates the ‎evolution, spatial distribution and load of dust over the study area. Hence, the model performance has ‎been evaluated by GDR. It showed different values of GDR highly depending on LULC pattern. Due to ‎the fact, that there is no way to isolate each individual area from the effects of either anthropogenic ‎sources or natural weathering processes, developing guidance on the priorities of expanding projects ‎and preventative actions towards potential dust deposition from natural and dominant sources may be ‎a subject of institutional interest. ‎ The results of direct measurements of dust deposition, which are typically made by passive sampling ‎techniques (ground-based observations), along with analyzed data from AOT, represent the second ‎objective to understand the spatiotemporal pattern of the points with the same variation. The ‎corresponding points headed to find moving air mass trajectories, using HYSPLIT were proven to be a ‎discriminator of their local and regional origin of aeolian dust. Furthermore, the seasonal deposition rate ‎varied from 8.4 g/m2/month in the summer to 3.5 g/m2/month in the spring. Despite all the advances ‎of AOT, under certain circumstances, the ground-based solutions were able to represent aerosol ‎conditions over the research area, tested in the southwestern regions of Iran. And that is when the low ‎number of observations is a commonly acknowledged drawback of GDR.‎ In addition, the peak of the seasonal deposition rates (t/km2/month) occurred in [arid desert hot-BWh, ‎‎8.4], [arid steppe hot-BSh, 6.6], and [hot and dry summer-Csa, 3.5] climate regions. Thus, the third ‎objective response was‏ ‏detected as the highest deposition rates of dust BWh >BSh >Csa throughout ‎the year, once the annual mean deposition rates (t/km2/year) are 100.80 for [BWh], 79.27 for [BSh], ‎and 39.60 for [Csa]. The knowledge gained on the dust deposition processes, together with the ‎feedback from the climate pattern, will provide insights into the records of data for developing new ‎sources, deposition rates and their climate offsets. Taking this in mind, having information about the ‎ground deposition rates in the study region could make the estimations more accurate, while finding an ‎appropriate algorithm is necessary to enhance the affected areas exposed to the dust. In order to ‎assess the impact of dust events on human health, environment and the damage to the various ‎business sectors of the country’s economy, additional studies with adequate modelling tools are ‎needed. ‎ Due to this date, the data holding organizations are somewhat reluctant to make their data available to ‎other parties. This work is also a step toward an institutional suggestion to gain benefit from information ‎exchange amongst data holding organizations, providers and users. The need for capacity building and ‎strong policy for implementing user-friendly geo information portal‏ ‏is essential.

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

Remote sensing of night lights: a review and an outlook for the future

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordRemote sensing of night light emissions in the visible band offers a unique opportunity to directly observe human activity from space. This has allowed a host of applications including mapping urban areas, estimating population and GDP, monitoring disasters and conflicts. More recently, remotely sensed night lights data have found use in understanding the environmental impacts of light emissions (light pollution), including their impacts on human health. In this review, we outline the historical development of night-time optical sensors up to the current state of the art sensors, highlight various applications of night light data, discuss the special challenges associated with remote sensing of night lights with a focus on the limitations of current sensors, and provide an outlook for the future of remote sensing of night lights. While the paper mainly focuses on space borne remote sensing, ground based sensing of night-time brightness for studies on astronomical and ecological light pollution, as well as for calibration and validation of space borne data, are also discussed. Although the development of night light sensors lags behind day-time sensors, we demonstrate that the field is in a stage of rapid development. The worldwide transition to LED lights poses a particular challenge for remote sensing of night lights, and strongly highlights the need for a new generation of space borne night lights instruments. This work shows that future sensors are needed to monitor temporal changes during the night (for example from a geostationary platform or constellation of satellites), and to better understand the angular patterns of light emission (roughly analogous to the BRDF in daylight sensing). Perhaps most importantly, we make the case that higher spatial resolution and multispectral sensors covering the range from blue to NIR are needed to more effectively identify lighting technologies, map urban functions, and monitor energy use.European Union Horizon 2020Helmholtz AssociationNatural Environment Research Council (NERC)Chinese Academy of ScienceLeibniz AssociationIGB Leibniz Institut

Lower Atmosphere Meteorology

The Atmosphere Special Issue “Lower Atmosphere Meteorology” deals with the meteorological processes that occur in the layer of the atmosphere close to the surface. The interaction between the biosphere and the atmosphere is made through the lower layer and can greatly influence living beings and materials. The analysis of the meteorological parameters provides a better understanding of processes within the lower atmosphere and involved in air pollution, climate, and weather. The mixed layer height, the wind speed, and the air parcel trajectory have a relevant interest due to their marked impact on population and energy production. The research also comprises aerosols, clouds, and precipitation, analysing their spatiotemporal variations. This issue addresses features of gases in the atmosphere and anthropogenic greenhouse emission estimates, which are also conditioned by the lower atmosphere meteorology

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Assessing sustainable development in industrial regions towards smart built environment management using Earth observation big data

This thesis investigates the sustainability of nationwide industrial regions using Earth observation big data, from environmental and socio-economic perspectives. The research contributes to spatial methodology design and decision-making support. New spatial methods, including the robust geographical detector and the concept of geocomplexity, are proposed to demonstrate the spatial properties of industrial sustainability. The study delivers scientific decision-making advice to industry stakeholders and policymakers for the post-construction assessment and future planning phases. The research has been published in prestigious geography journals, demonstrating its success

Studies of global cloud field using measurements of GOME, SCIAMACHY and GOME-2

Tropospheric clouds are main players in the Earth climate system. Characterization of long-term global and regional cloud properties aims to support trace-gases retrieval, radiative budget assessment, and analysis of interactions with particles in the atmosphere. The information needed for the determination of cloud properties can be optimally obtained with satellite remote sensing systems. This is because the amount of reflected solar light depends both on macro- and micro-physical characteristics of clouds. At the time of writing, the spaceborne nadir-viewing Global Ozone Monitoring Experiment (GOME), together with the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and GOME-2, make available a unique record of almost 17 years (June 1996 throughout May 2012) of global top-of-atmosphere (TOA) reflectances and form the observational basis of this work. They probe the atmosphere in the ultraviolet, visible and infrared regions of the electromagnetic spectrum. Specifically, in order to infer cloud properties such as optical thickness (COT), spherical albedo (CA), cloud base (CBH) and cloud top (CTH) height, TOA reflectances have been selected inside and around the strong absorption band of molecular oxygen in the wavelength range at 758-772 nm (the O2 A-band). The retrieval is accomplished using the Semi-Analytical CloUd Retrieval Algorithm (SACURA). The physical framework relies on the asymptotic parameterizations of radiative transfer. The generated record has been throughly verified against synthetic datasets as function of cloud and surface parameters, sensing geometries, and instrumental specifications and validated against ground-based retrievals. The error budget analysis shows that SACURA retrieves CTH with an average accuracy of ±400 m, COT within ±20% (given that COT > 5) and places CTH closer to ground-based radar-derived CTH, as compared to independent satellite-based retrievals. In the considered time period the global average CTH is 5.2±3.0 km, for a corresponding average COT of 20.5±16.1 and CA of 0.62±0.11. Using linear least-squares techniques, global trend in deseasonalized CTH has been found to be -1.78±2.14 m * year-1 in the latitude belt ±60°, with diverging tendency over land ( 0.27±3.2 m * year-1) and water (-2.51±2.8 m * year-1) masses. The El Nino-Southern Oscillation (ENSO), observed through CTH and cloud fraction (CF) values over the Pacific Ocean, pulls clouds to lower altitudes. It is argued that ENSO must be removed for trend analysis. The global ENSO-cleaned trend in CTH amounts to -0.49±2.22 m * year-1. At a global scale, no explicit patterns of statistically significant trends (at 95% confidence level, estimated with bootstrap resampling technique) have been found, which are representative of peculiar natural climate variability. One exception is the Sahara region, which exhibits the strongest upward trend in CTH, sustained by an increasing trend in water vapor. Indeed, the representativeness of every trend is affected by the record length under study. 17 years of cloud data still might not be enough to provide any decisive answer to current open questions involving clouds. The algorithm used in this work can be applied to measurements provided by future planned Earth's observation missions. In this way, the existing cloud record will be extended and attribution of cloud property changes to natural or human causes and assessment of cloud feedback sign within the climate system can be investigated