This chapter discusses efforts to measure surface observations of air pollution at the country-scale. The countries with the most comprehensive regulatory systems to monitor air pollution are the older industrial nations such as countries in the United Kingdom and the United States. Recent proliferation of low-cost air quality monitors (LCAQM) are making near-real-time air pollution monitoring more prevalent across the globe. While unique challenges exist between regulatory and LCAQM data access and usability, there are common challenges in using these data for decision support and research applications. This chapter discusses common statistical methods for estimating air pollution including spatial interpolation methods, statistical regression methods, machine learning, and chemical transport modeling.

Whistler mode chorus waves scatter magnetospheric electrons and cause precipitation into the Earth’s atmosphere. Previous measurements showed that nightside chorus waves are indeed responsible for diffuse/pulsating aurora. Although chorus waves and electron precipitation have also been detected on the dayside, their link has not been illustrated (or demonstrated) in detail compared to the nightside observations. Conventional low-altitude satellite observations do not well resolve the energy range of 10–100 keV, hampering verification on resonance condition with chorus waves. In this paper we report observations of energetic electrons with energies of 30–100 keV that were made by the electron sensor installed on the NASA’s sounding rocket RockSat-XN. It was launched from the Andøya Space Center on the dayside (MLT ~ 11 h) at the L-value of ~ 7 on 13 January 2019. Transient electron precipitation was observed at ~ 50 keV with the duration of <100 s. A ground station at Kola peninsula in Russia near the rocket’s footprint observed intermittent emissions of whistler-mode waves simultaneously with the rocket observations. The energy of precipitating electrons is consistent with those derived from the quasi-linear theory of pitch angle scattering by chorus waves through cyclotron resonance, assuming a typical dayside magnetospheric electron density. Precise interaction region is discussed based on the obtained energy spectrum below 100 keV.

Attempts to determine physical property across thrust faults at subduction zones through drilling, logging and core sampling have been limited and restricted to exhumed accretionary prisms or shallow parts of active wedges. However, characterizing porosity evolution across the sedimentary section entering subduction zones and accreted sediments is crucial to understand deformation history at accretionary margins through determination of sediment trajectories, quantification of transported volumes of sediments and fluids with related mechanical responses and understanding deformation processes in and around fault zones. International Ocean Discovery Program Expeditions 372 and 375 drilled, logged and cored the entering basin (Site U1520) and active Pāpaku thrust (Site U1518) few kilometers landward of the northern Hikurangi margin deformation front where tsunami earthquakes and recurrent slow slip events occur. Here, we examine physical properties evolution across the Pāpaku thrust at Site U1518 including geophysical logging data, pore size distribution obtained by combining Nuclear Magnetic Resonance and Mercury Injection Capillary Pressure, and interstitial porosity that is representative of sediment compaction state, and compare with that of Site U1520. Interstitial porosity is determined by correcting total connected porosity from clay-bound water content based on cation exchange capacity. We evidence strong variations of physical properties across the thrust fault, with lower porosity, higher P-wave velocity and resistivity in the hanging-wall than in the footwall. We suggest that the porosity pattern at the Pāpaku thrust evidences differences in maximum burial depth with an overcompacted hanging-wall that has been uplifted, thrusted and concomitantly eroded above a nearly normally consolidated younger footwall.

Instrumental records of mean annual temperature extend back to the seventeenth and eighteenth centuries at multiple sites in Europe. For such long time series, we expect and find that histograms of mean annual temperature data become skewed towards higher temperatures with time because of global warming. However, we also find that skewness changed abruptly and started increasing between 1985 and 1991 (95% confidence) at 17 sites. We argue that this finding may imply an abrupt change in the climate system affecting Europe which probably occurred at this time. One possible cause is a climate tipping point having been passed. Of known tipping elements, we find Arctic sea ice loss, potentially linked to reduced sulfate aerosol emissions and coupled to temperature by an albedo feedback mechanism, a likely candidate. This is based on good correlations of sea ice extent and sulfate aerosol emissions with skewness of mean annual temperature data.

Klyuchevskoy and surrounding volcanoes in central Kamchatka form the Northern Group of Volcanoes (NGV), which is an area of the particularly diverse and intensive Pleistocene-Holocene volcanism. In this study, we present a new seismic tomographic model of the crust and uppermost mantle beneath NGV based on local earthquake data recorded by several permanent and temporary seismic networks including a large-scale KISS experiment that was conducted in 2015-2016 by an international scientific consortium. The new model has for Kamchatka an unprecedented resolution and reveals many features associated with the present and past volcanic activity within the NGV. In the upper crust, we found several prominent high-velocity anomalies interpreted as traces of large basaltic shield volcanoes, which were hidden by more recent volcanic structures and sediments. For the mantle structures, we found that the entire system of NGV was fed by an asthenospheric flow arriving through a slab window located below the Kamchatka-Aleutian junction. The interaction of the hot asthenospheric material with fluids released from the slab determines the particular volcanic activity within the NVG. We argue that the eastern branch of the Central Kamchatka Depression, which is associated with a prominent low-velocity anomaly in the uppermost mantle, was formed as a recent rift zone separating the NGV from the Kamchatka Eastern Ranges.

Akutan Island hosts a volcano considered as one of most active in the Aleutians Arc. Besides the regular eruption activity, Akutan in 1996 experienced a remarkable seismic unrest with earthquakes reaching the magnitude of M 5.3. We build a new tomography model including the 3D distributions of the , and ratio based on arrival time data from more than 4000 local earthquakes. In this model, we reveal a columnar anomaly of high ratio with the top boundary at a depth of 5-6 km below surface, which represents a steady conduit feeding the Akutan volcano. At a depth of ~4 km, the deep conduit is split in two branches, one of which ascend to the summit area and another one directs to a fumarole field at the flank of the volcano. This structure explains distinct geochemical features of emitted gases in these two areas. In the upper part of the velocity model, the highly heterogeneous structures are associated with interactions of shallow magmatic sources, meteoric and magmatic fluids, as well as degassing. Besides the prominent anomaly associated with the shallow magma reservoir beneath the caldera and the active cone, we observe several areas with high ratio, some of which are interpreted as shallow magma storages, and some as zones of meteoric water penetration. We propose a scenario of abrupt fluid ejection from the deep magma conduit that led to the seismic unrest and strong ground deformations in the Akutan area in 1996.

The 2B-CLDCLASS-LIDAR R05 (2BCL5) and the raDAR/liDAR (DARDAR) satellite retrievals of cloud occurrence are compared as a function of altitude and latitude. The largest disparities are observed at low altitudes over high southern latitudes. These datasets are cross referenced to ground–based measurements from the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) campaign at McMurdo Station, Antarctica. Compared to AWARE observations, both 2BCL5 and DARDAR underestimate cloud occurrence below 1.5 km, with 2BCL5 and DARDAR distinguishing roughly one third of cloud occurrences observed by AWARE at 0.5 km. While DARDAR identifies greater cloud occurrences than 2BCL5 below 1.5 km, cloud occurrence values for the two datasets have similar differences relative to ground-based measurements. Therefore, the DARDAR retrievals of greater cloud occurrence at low altitudes are likely due to a larger quantity of false positives associated with radar ground clutter or attenuated lidar retrievals. DARDAR cloud occurrences match better with AWARE than 2BCL5 above 5 km. However, the likely underestimation of ground-based measurements at higher altitudes suggests DARDAR may underestimate high level cloud occurrence. Finally, both datasets indicate the presence of liquid containing clouds at temperatures within the homogeneous freezing regime, despite the fact that the ECMWF-AUX dataset implemented in their processing clearly indicates temperatures below -38 °C. Using AWARE radiosonde (ECMWF-AUX) temperature data, we find that 2BCL5 detects 13.3% (13.8%) of mixed phase clouds below -38 °C, while DARDAR detects 5.7% (6.6%) of mixed phase and 1.1% (1.3%) of liquid phase clouds below -38 °C.

We propose an approach to upscale solute transport in spatially periodic porous media. Our methodology relies on pore scale information to predict large scale transport features, including explicit reconstruction of the solute plume, breakthrough curves at fixed distances, and spatial spreading transverse to the main flow direction. The proposed approach is grounded on the recently proposed trajectory-based Spatial Markov model (tSMM), which upscales transport based on information collected from advective-diffusive particle trajectories across one periodic element. In previous works, this model has been applied solely to one-dimensional transport in a single periodic pore geometry. In this work we extend the tSMM to the prediction of multi-dimensional solute plumes. This is obtained by analyzing the joint space-time probability distribution associated with discrete particles, as yielded by the tSMM. By comparing numerical results from fully resolved simulations and predictions obtained with the tSMM over a wide range of Péclet numbers, we demonstrate that the proposed approach is suitable for modeling transport of conservative and linearly decaying solute species in a realistic pore space and showcase the applicability of the model to predict steady state solute plumes. Additionally, we evaluate the model performance as a function of numerical parameters employed in the tSMM parameterization.

The increasing frequency of devastating floods from heavy rainfall associated with climate change has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with 50 cm RMSE for about 10 m of stage peaks as well as accurately simulate stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision we find the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well simulated, the physical concepts of runoff appear to be soundly embedded in the MLP.

A contemporary and decisive optimization algorithm is developed for inverting gravity anomalies due to listric faults. The cross-section of listric faults are generally concave up, and the dip of the fault plane gradually decreases with depth. Quadratic Bezier curves are utilized to represent the curvature of the fault plane. The densities of sediment deposition are assumed to be known and can take any functional form of depth. By constraining the density, a global optimization algorithm is adopted to estimate the fault structure by inverting control point parameters of Bezier curves. The presented algorithm is implemented in two different synthetic models having fixed and depth varying density contrasts. The robustness of the algorithm is authenticated by incorporating white Gaussian noise into synthetic gravity anomalies. A detailed uncertainty appraisal is also performed to justify the reliability of the algorithm. Finally, a real structure is reconstructed using observed gravity anomalies, and the estimated structure is verified with the structure obtained in previously published literature. Furthermore, a Matlab based GUI is developed such that any user can estimate real listric fault structure without any computational difficulties.

A heat danger day is defined as an extreme when the heat stress index (a combined temperature and humidity measure) exceeding 41 ℃, warranting public heat alerts. This study assesses future heat risk (i.e., heat danger days times the population at risk) based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) projections. In recent decades (1995-2014) China’s urban agglomerations (Beijing-Tianjin-Hebei, Yangtze River Delta, Middle Yangtze River, Chongqing-Chengdu, and Pearl River Delta) experienced no more than 3 heat danger days per year, but this number is projected to increase to 3-13 days during the population explosion period (2041-2060) under the high-emission pathways (SSP3-7.0 and SSP5-8.5). This increase will result in approximately 260 million people in these agglomerations facing more than 3 heat danger days annually, accounting for 19% of the total population of China, and will double the current level of overall heat risk. During the period 2081-2100, there will be 8-67 heat danger days per year, 60-90% of the urban agglomerations will exceed the current baseline number, and nearly 310 million people (39% of the total China population) will be exposed to the danger, with the overall heat risk exceeding 18 times the present level. The greatest risk is projected in the Pearl River Delta region with 67 heat danger days to occur annually under SSP5-8.5. With 65 million people (68% of the total population) experiencing increased heat danger days, the overall heat risk in the region will swell by a factor of 50. Conversely, under the low-emission pathways (SSP1-2.6 and SSP2-4.5), the annual heat danger days will remain similar to the present level or increase slightly. The result indicates the need to develop strategic plans to avoid the increased heat risk of urban agglomerations under high emission-population pathways.

Despite the importance of turbidity currents in environmental and resource geology, their flow conditions and mechanisms are not well understood. This study proposes a novel method for the inverse analysis of turbidity currents using a deep learning neural network (DNN) to better explore the properties of turbidity currents. The aim of this study is to verify the DNN inverse method using numerical and flume experiment datasets. Numerical datasets of turbidites were generated with a forward model. Then, the DNN model was trained to find the functional relationship between flow conditions and turbidites by processing the numerical datasets. The performance of the trained DNN model was evaluated with 2000 numerical test datasets and 5 experiment datasets. Inverse analysis results on numerical test datasets indicated that flow conditions can be reconstructed from depositional characteristics of turbidites. For experimental turbidites, spatial distributions of grain size and thickness were consistent with the sample values. Concerning hydraulic conditions, flow depth H, layer-averaged velocity U, and flow duration Td were reconstructed with a certain level of deviation. Greater discrepancies between the measured and reconstructed values of flow concentration were observed relative to the former three parameters (H, U, Td), which may be attributed to difficulties in measuring the flow concentration during experiments. The precision of the reconstructions for experimental datasets was estimated using Jackknife resampling. Although the DNN model did not provide perfect reconstruction, it proved to be a significant advance for the inverse analysis of turbidity currents.

A sequential inversion methodology for combining geophysical data types of different resolutions is developed and applied to monitoring of large-scale CO2 injection. The methodology is a two-step approach within the Bayesian framework where lower resolution data are inverted first, and subsequently used in the generation of the prior model for inversion of the higher resolution data. For the application of CO2 monitoring, the first step is done with either controlled-source electromagnetic (CSEM) or gravimetric data, while the second step is done with seismic amplitude-versus-offset (AVO) data. The Bayesian inverse problems are solved by sampling the posterior probability distributions using either the ensemble Kalman filter or ensemble smoother with multiple data assimilation. A carefully designed parameterization is used to represent the unknown geophysical parameters: electric conductivity, density, and seismic velocity. The parameterization is well suited for identification of CO2 plume location and variation of geophysical parameters within the regions corresponding to inside and outside of the plume. The inversion methodology is applied to a synthetic monitoring test case where geophysical data are made from fluid-flow simulation of large-scale CO2 sequestration in the Skade formation in the North Sea. The numerical experiments show that seismic AVO inversion results are improved with the sequential inversion methodology using prior information from either CSEM or gravimetric inversion.

Interferometric Synthetic Aperture Radar (InSAR) phase unwrapping error is a major limiting factor on the InSAR-derived tectonic deformation velocity. This is particularly the case when atmospheric turbulence, large deformation gradient and strong phase noise exist. To address limitations of existing phase unwrapping error correction methods which are not supported for multi-looked InSAR data, here we present a new algorithm that integrates decorrelation phase correction, triplet phase closure (TPC) test and integer linear programming (ILP) to overcome this limit. The rationale behind is that we mitigate the phase inconsistence using decorrelation correction and then detect the phase unwrapping error magnitude using TPC. Next we borrow the ILP from Compressed Sensing that converts the phase unwrapping error correction to a sparse signal recovery problem. We demonstrated the validity of our method by using synthetic data and 5-years Sentinel-1 real data covering the Central San Andreas Fault creep section, where exists obvious tectonic deformation, strong atmospheric disturbance and decorrelated scatterers, and the inverted long-term creep model constrained by InSAR velocity after correction shows a lower uncertainty than that constrained by the uncorrected one.

We present the Malawi Active Fault Database (MAFD), a geospatial database of 114 active fault traces in Malawi, and in neighboring Tanzania and Mozambique. The MAFD has been developed from a multidisciplinary dataset: high resolution digital elevation models, field observations, aeromagnetic and gravity data, and seismic reflection surveys from offshore Lake Malawi. Active faults longer than 50 km are found throughout Malawi, where seismic risk is increasing due to its rapidly growing population and its seismically vulnerable building stock. The MAFD also provides an opportunity to investigate the population of normal faults in an incipient continental rift. We find that the null hypothesis that the distribution of fault lengths in the MAFD is described by a power law cannot be rejected. Furthermore, a power-law distribution of faults in Malawi is consistent with its thick seismogenic crust (~35 km), and low (<8%) regional extensional strain that is predominantly (50-75%) accommodated across relatively long hard-linked border faults. Cumulatively, the data and inferences drawn from the MAFD highlight the importance of integrating onshore and offshore geological and geophysical data to develop active fault databases along the East African Rift and similar continental settings, both to understand the regional seismic hazard and tectonic evolution.

Hydrofluorocarbons (HFCs) have been increasingly replacing chlorofluorocarbons and hydrochlorofluorocarbons. Although their ozone-depleting potential is negligible, as potent greenhouse gases they indirectly influence stratospheric ozone recovery. Measurements and model projections must continue to evaluate HFC limitation measures and assess the long-term impact of HFCs on the atmospheric radiation budget and stratospheric ozone. In this study, we present multi-member ensemble simulations designed to estimate the impact of HFCs on stratospheric temperature, ozone and circulation changes at the end of the century. We compared simulations with and without HFCs for two three-dimensional chemistry-climate models that use the same chemistry module but different physical schemes. At low and mid-latitudes, temperature and ozone responses were comparable for both models and in general agreement with previous studies. HFCs induced a marked temperature increase up to 5 hPa and vertically alternating positive and negative ozone anomalies. We explained this pattern by competing effects of vertical motion (low and middle stratosphere) and temperature (upper stratosphere) anomalies. At northern high latitudes, there were strong discrepancies with previous studies and between the models themselves, attributed to differences in ozone anomalies caused by wave activity during winter. Quantitatively, we found a net positive, but small, HFC impact on total ozone amounts. Largest anomalies were less than 1% in the winter polar stratosphere. Our results indicate that increasing HFC amounts will likely have a limited impact on stratospheric ozone recovery within this century, with large uncertainty in the polar regions.

Induced polarization (IP) is increasingly applied for hydrological, environmental and agricultural purposes. Interpretation of IP data is based on understanding the relationship between the IP signature and the porous media property of interest. Mechanistic models on the IP phenomenon rely on the Poisson-Nernst-Plank equations, where diffusion and electromigration fluxes are the driving forces of charge transport and are directly related to IP. However, to our knowledge, the impact of advection flux on IP was not investigated experimentally and was not considered in any IP model. In this work, we measured the spectral IP (SIP) signature of porous media under varying flow conditions, in addition to developing and solving a model for SIP signature of porous media, which takes flow into consideration. The experimental and the model results demonstrate that as bulk velocity increases, polarization and relaxation time decrease. Using a numerical model, we established that fluid flow near the particle deforms the electrical double layer (EDL) structure, accounting for the observed reduction in polarization. We found a qualitative agreement between the model and the measurements. Still, the model overestimates the impact of flow rate on SIP signature, which we explain in terms of the flow boundary conditions. Overall, our results demonstrate the sensitivity of the SIP signature to fluid flow, highlighting the need to consider fluid velocity in the interpretation of the SIP signature of porous media, and opening an exciting new direction for noninvasive measurements of fluid flow at the EDL scale.

The standard Polar Cap (PC) indices, PCN (North) based on magnetic data from Qaanaaq in Greenland and PCS (South) based on data from Vostok in Antarctica, have been submitted from the Arctic and Antarctic Research Institute (AARI) in St. Petersburg, Russia, the Danish Meteorological Institute (DMI), and the Danish Space Research Institute (DTU Space) in different versions. In order to consolidate PCS indices based on Vostok data or replace poor or missing index data, derivation procedures have been developed to generate alternative PCS index values based on data from Dome Concordia (Dome-C) magnetic observations from epoch 2009-2020 of solar cycle 24. The reference levels and calibration parameters needed for calculations of Dome-C-based PCS values in post-event and real-time versions are defined and explained in the present work. Assessment of the new PCS index has shown its unprecedented high relevance. Part of the methods used here such as the quiet reference level construction and the correlation and regression procedures used for calculations of scaling parameters deviate from corresponding features considered inadequate of the IAGA-endorsed PC index derivation methods