Scintillation due to ionospheric plasma irregularities remains a challenging task for the space science community as it can severely threaten the dynamic systems relying on space-based navigation services. In the present paper, we probe the ionospheric current and plasma irregularity characteristics from a latitudinal arrangement of magnetometers and Global Navigation Satellite System (GNSS) stations from the equator to the far low latitude location over the Indian longitudes, during the severe space weather events of 6–10 September 2017 that are associated with the strongest and consecutive solar flares in the 24th solar cycle. The night-time influence of partial ring current signatures in ASYH and the daytime influence of the disturbances in the ionospheric E region electric currents (D_iono) are highlighted during the event. The total electron content (TEC) from the latitudinal GNSS observables indicate a perturbed equatorial ionization anomaly (EIA) condition on 7 September, due to a sequence of M-class solar flares and associated prompt penetration electric fields (PPEFs), whereas the suppressed EIA on 8 September with an inverted equatorial electrojet (EEJ) suggests the driving disturbance dynamo electric current (Ddyn) corresponding to disturbance dynamo electric fields (DDEFs) penetration in the E region and additional contributions from the plausible storm-time compositional changes (O/N2) in the F-region. The concurrent analysis of the D_iono and EEJ strengths help in identifying the pre-reversal effect (PRE) condition to seed the development of equatorial plasma bubbles (EPBs) during the local evening sector on the storm day. The severity of ionospheric irregularities at different latitudes is revealed from the occurrence rate of the rate of change of TEC index (ROTI) variations. Further, the investigations of the hourly maximum absolute error (MAE) and root mean square error (RMSE) of ROTI from the reference quiet days’ levels and the timestamps of ROTI peak magnitudes substantiate the severity, latitudinal time lag in the peak of irregularity, and poleward expansion of EPBs and associated scintillations. The key findings from this study strengthen the understanding of evolution and the drifting characteristics of plasma irregularities over the Indian low latitudes.

Groundwater quality assessment is crucial for sustainable water resource management and public health protection. This study evaluated the Water Quality Index (WQI) of groundwater in the southern part of Gujarat focusing on the Valsad District. Groundwater in this region occurs in porous, unconsolidated formations and fracture formations, both under groundwater table conditions and confined aquifers. Various parameters including Nitrate (NO₃¯), pH, Calcium (Ca²⁺), Electrical Conductivity (EC), Total Hardness (TH), Magnesium (Mg²⁺), Total Dissolved Solids (TDS), Potassium (K⁺), Sodium (Na⁺), Sulphate (SO₄²⁻), Chloride (Cl¯), Bicarbonate (HCO₃¯), Silicate (SiO₄⁴⁻), and Fluoride (F¯) were analyzed to assess groundwater quality. Results indicate that most of the parameters fell within acceptable permissible limits for drinking water, except for Muli and Nanaponda villages with the parameters Cl¯, EC, and TDS exceeding the permissible limit. The WQI analysis revealed that 31.25% of water samples from different villages were found in the excellent category (WQI < 25). About 68.75% of samples from 16 villages were classified as good quality category (WQI ∼ 25–50). Overall, the WQI ranged from 14.20 to 41.98, suggesting that groundwater in the Valsad district is suitable for drinking. The Piper diagram analysis of water samples collected from the field indicated unique geochemical compositions and good water. The diagram revealed that the Ca²⁺ was the predominant cation, followed by K⁺, Na⁺, and Mg²⁺. Among the anions, the HCO₃⁻ showed the highest concentrations, followed by SO₄²⁻, NO₃⁻, and Cl⁻. This dominance pattern demonstrated that the weathering of minerals significantly influenced the groundwater. This study recommends remediation for areas with reduced water quality to address geogenic and anthropogenic contamination.

Global Navigation Satellite System (GNSS)- and Remote Sensing (RS)-based Earth observations have a significant approach on the monitoring of natural disasters. Since the evolution and appearance of earthquake precursors exhibit complex behavior, the need for different methods on multiple satellite data for earthquake precursors is vital for prior and after the impending main shock. This study provided a new approach of deep machine learning (ML)-based detection of ionosphere and atmosphere precursors. In this study, we investigate multi-parameter precursors of different physical nature defining the states of ionosphere and atmosphere associated with the event in Japan on 13 February 2021 (M_w 7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.

The forecasting of ionospheric electron density has been of great interest to the research scientists and engineers’ community as it significantly influences satellite-based navigation, positioning, and communication applications under the influence of space weather. Hence, the present paper adopts a long short-term memory (LSTM) deep learning network model to forecast the ionospheric total electron content (TEC) by exploiting global positioning system (GPS) observables, at a low latitude Indian location in Bangalore (IISC; Geographic 13.03° N and 77.57° E), during the 24th solar cycle. The proposed model uses about eight years of GPS-TEC data (from 2009 to 2017) for training and validation, whereas the data for 2018 was used for independent testing and forecasting of TEC. Apart from the input TEC parameters, the model considers sequential data of solar and geophysical indices to realize the effects. The performance of the model is evaluated by comparing the forecasted TEC values with the observed and global empirical ionosphere model (international reference ionosphere; IRI-2016) through a set of validation metrics. The analysis of the results during the test period showed that LSTM output closely followed the observed GPS-TEC data with a relatively minimal root mean square error (RMSE) of 1.6149 and the highest correlation coefficient (CC) of 0.992, as compared to IRI-2016. Furthermore, the day-to-day performance of LSTM was validated during the year 2018, inferring that the proposed model outcomes are significantly better than IRI-2016 at the considered location. Implementation of the model at other latitudinal locations of the region is suggested for an efficient regional forecast of TEC across the Indian region. The present work complements efforts towards establishing an efficient regional forecasting system for indices of ionospheric delays and irregularities, which are responsible for degrading static, as well as dynamic, space-based navigation system performances.

Identifying atmospheric and ionospheric anomalies based on remote sensing satellites has contributed highly to develop the hypothesis of lithosphere-atmosphere-ionosphere coupling (LAIC) over the Earthquake (EQ) epicenter during the seismic preparation period. This paper has investigated the variations of potential EQ precursor in daytime and nighttime Land Surface Temperature (LST) before and after the 2019 Pakistan EQ from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite. The nighttime LST values of MODIS exhibit temporal anomalies during nighttime period within a time window of 5 days before and after the main shock day. Furthermore, the LST values predicted by Artificial Neural Network (ANN) validate the significant enhancement in nighttime time series of MODIS. The nighttime LST anomalies obtained from the observation and ANN prediction are more than 20% and 7% of normal distribution beyond the confidence bounds, respectively, within 5 days after the main shock. Likewise, the ionospheric anomaly from daily Total Electron Content (TEC) values at Sukkur Global Positioning System (GPS) station confirms the EQ associated ionospheric perturbations on the day after the main shock. The Global Ionospheric Maps (GIMs) also show the TEC anomalies during 1000-1400 LT on September 25, 2019.

The recent advances in satellite-based earthquakes (EQs) precursors provide an opportunity to correlate the seismic variation on lithosphere with atmosphere during the EQ preparation period through a rigorous atmospheric monitoring system. In the present study, seismic-induced thermal anomalies from cloud-free satellite thermal images of Moderate Resolution Imaging Spectroradiometer-Land Surface Temperature (MODIS-LST) are analyzed within a time interval of three months (precedent two months and succeeding one month to each EQ day) of 13 Mw ≥ 6.0 terrestrial EQs during 2000-2019. All these EQs occur in low vegetation and no snow cover regions except Mw 6.7, Siberia Russia event. Remote sensing data show evidence of significant perturbation with reference to confidence bounds in LST within 5-20 time window upon the antecedent and the descendant of EQ day. The studied thermal anomalies are obtained from LST values over the epicenter region. This work endorses the performance of MODIS-LST for detecting EQ-induced thermal anomalies in terrestrial regions with no vegetation and snow cover and also assisting to the development of lithosphere-atmosphere hypothesis over the epicenter region.