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Drivers of PM2.5 Episodes and Exceedance in India: A Synthesis From the COALESCE Network

JGR–Atmospheres - Sat, 07/20/2024 - 15:34

Abstract

Emission sources influencing high particulate air pollution levels and related mortality in India have been studied earlier on country-wide and sub-national scales. Here, we use novel data sets of emissions (for 2019) and observations created under the Carbonaceous Aerosol Emissions, Source Apportionment, and Climate Impacts network in India (Venkataraman et al., 2020, https://doi.org/10.1175/bams-d-19-0030.1) in WRF-Chem simulations to evaluate drivers of high PM2.5 levels during episodes and in airsheds with different pollution levels. We identify airsheds in “extreme” (110–140 μg/m3), “severe” (80–110 μg/m3) and “significant” (40–80 μg/m3) exceedance of the Indian annual ambient air quality standard (National Ambient Air Quality Standards [NAAQS]) of 40 μg/m3 for PM2.5. We find that primary organic matter and anthropogenic mineral matter (largely coal fly-ash) drive high PM2.5 levels, both annually and during high PM2.5 episodes. PM2.5 episodes are driven by organic aerosol in north India (Mohali) in wintertime but are additionally influenced by mineral matter and secondary inorganics in central (Bhopal), south India (Mysuru) and eastern India (Shyamnagar). Across airsheds in exceedance of the NAAQS and during high PM2.5 episodes, primary PM2.5 emissions arise largely from the residential sector (50%–75%). Formal sector emissions (industry, thermal power and transport; 40%–55%) drive airshed and episode scale PM2.5 exceedance in northern and eastern India. Agricultural residue burning emissions predominate (50%–75%) on episode scales, both in northern and central India, but not on annual scales. Interestingly, residential sector emissions strongly influence (60%–90%) airsheds in compliance with the NAAQS (annual mean PM2.5 < 40 μg/m3), implying the need for modern residential energy transitions for the reduction of ambient air pollution across India.

Disentangling Forced Trends in the North Atlantic Jet From Natural Variability Using Deep Learning

JGR–Atmospheres - Sat, 07/20/2024 - 14:44

Abstract

Regional weather variability and extremes over Europe are strongly linked to variations in the North Atlantic jet stream, especially during the winter season. Projections of the evolution of the North Atlantic jet are essential for estimating the regional impacts of climate change. Therefore, separating forced trends in the North Atlantic jet from its natural variability is an extremely relevant task. Here, a deep learning based method, the Latent Linear Adjustment Autoencoder (LLAE), is used for this purpose on an ensemble of fully-coupled climate simulations. The LLAE is based on a variational autoencoder and an additional linear component. The model uses detrended temperature and geopotential to predict the component of the zonal wind associated with natural variability. The residual between this prediction and the original wind field is interpreted as the forced component of the jet. The method is first tested for the geostrophic wind for which the forced trend can be obtained analytically from the difference between geostrophic wind computed from detrended and full geopotential. Despite the large variability of the total trends, the LLAE is shown to be effective in extracting the forced component of the trend for each individual ensemble member in both geostrophic and full wind fields. The LLAE-derived forced trend shows an increase in the upper-level zonal wind speed along a southwest–northeast oriented band over the ocean and a jet extension toward Europe. These are common characteristics over different periods and show some similarities to the upper-level zonal wind speed trend obtained from the ERA5 reanalysis.

An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data

JGR–Atmospheres - Sat, 07/20/2024 - 14:34

Abstract

In ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso-scale Ensemble Prediction System (MEPS) for three TCs: “Dolphin” (2020), “Nepartak” (2021), and “Meari” (2022). Taking advantage of the independence of the density-based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one.

Osmium Transport and Enrichment From the Lithosphere to the Hydrosphere: New Perspectives From Hydrothermal Experiments and Geochemical Modeling

JGR–Solid Earth - Sat, 07/20/2024 - 07:34

Abstract

Metal complexation and speciation is the primary process responsible for metal transport and circulation in hydrothermal systems, during which stable and soluble metal complexes play a pivotal role. Here, we investigate the speciation of Os and the thermodynamic stability of Os(IV)-Cl complexes in chloride-bearing solutions at temperatures ranging from 150 to 600°C and pressure of 100 MPa through hydrolysis experiments. The results show that the dominant species of Os is OsCl6 2− at temperatures between 150 and 450°C and 100 MPa, gradually converting into Os(IV)-OH-Cl and Os(II)-Cl complexes over 450°C. The equilibrium constant (ln K) (K = [HCl]4 ⨯ [Cl−]2/[OsCl6 2−]) between OsCl6 2− and water molecule is determined as ln K = (50.43 ± 4.633) − (54223 ± 2525.6)/T, and Δ r H m Θ and Δ r S m Θ are inferred to be (450.8 ± 21.00) kJ · mol−1 and (419.3 ± 38.52) J · mol−1 · K−1. Furthermore, the formation constant (ln β) of OsCl6 2− exhibits a change from −0.097 to −0.104 as temperatures increase from 150 to 400°C, while the change values in standard Gibbs free energy (Δ r G m Θ ) for the hydrolysis reactions decrease with rising temperature, suggesting a temperature-dependent thermodynamic stability of OsCl6 2−. Geochemical modeling further demonstrates that high solubility of OsCl6 2− could exist in low-temperature and acidic fluids (≤300°C and pH < 5), or relatively high-temperature and acidic-neutral fluids (>300°C and pH < 7), primarily influenced by the Cl concentration. Acidic and near-neutral fluids with high Cl concentration venting in the mid-ocean ridge, back-arc, and sediment-hosted systems contribute more to dissolving and transporting Os from the lithosphere to the hydrosphere, thereby impacting the global ocean dissolved Os budget.

A Numerical Consideration on the Correlation Between Magnitude of Earthquakes and Current Intensity Causing ULF Electromagnetic Wave Emission

Radio Science - Sat, 07/20/2024 - 07:00

Abstract

Numerous studies have reported anomalous ultralow frequency (ULF) electromagnetic fields preceding earthquakes. In this paper, we estimate the current intensity responsible for generating the earthquake-related ULF fields under the assumption that the origin is a current flowing at the hypocenter and that it has the same frequency dependence for all cases. To estimate current intensity, we perform ULF electromagnetic field simulations with an absorbing boundary condition developed in this study, taking into account the conductivity distribution of the Earth's crust. We analyze 11 earthquakes, including those that occurred in Loma Prieta, Spitak, Guam, Biak, Kagoshima, Iwateken Nairiku Hokubu, Izu swarm, Jammu and Kashmir, Alum Rock, Wenchuan, and L’Aquila. Our results show that, for nine out of the 11 events, there is a positive correlation between current intensity and earthquake magnitude, suggesting that the measured ULF fields originate from seismic activity and supporting our assumptions.

A two-fault detection and elimination approach to the receiver autonomous integrity monitoring

GPS Solutions - Sat, 07/20/2024 - 00:00

Abstract

The Receiver Autonomous Integrity Monitoring (RAIM) technique is very important for the GNSS receiver to detect faulty measurement sources before they mislead the positioning solution. Based on the parity RAIM method, a simple algorithm to detect and eliminate one fault is provided. However, there is no such a simple algorithm to detect and eliminate two faults. A general approach is traversing all possible fault cases and calculate the sum of the squares of the residual errors (SSE) of each case by solving the observation equation. However, solving the observation equation takes a lot of computation. This paper proposes a detection and elimination algorithm that can deal with no more than 2 faults, based on the least-squares-residual method. The new algorithm avoids solving the observation equation for each possible fault cases, and has very low computational complexity. Simulations are done to evaluate the performance of the algorithm, and the effect of the satellite geometry conditions on the algorithm is also discussed.