During the last two decades, Global Positioning System (GPS) geodetic‐grade receivers and accelerometers have been implemented in Structural Health Monitoring (SHM). Most recently, the use of sensors integrated in smartphones has been evolving. Although some of their capabilities are validated for small and local structures, there is a gap in knowledge about the use of sensors embedded in smartphones and other electronic devices for SHM of complex structures as bridges. To contribute in this area, this paper demonstrates the application of GPS receivers, accelerometers, and smartphones, integrating a smart sensor for the SHM of bridges. In order to validate its capabilities, the alternative smart sensor is used to study a particular bridge with vibration problems. Semistatic and dynamic displacements are obtained by means of GPS measurements. Accelerations in three directions of the bridge are determined using the accelerometer and the smartphone. Based on the results of the alternative smart sensor, inappropriate structural behavior is detected in the vertical direction of the bridge. In addition, dynamic characteristics are extracted using the smart sensor applying the Fast Fourier Transformation (FFT) and periodogram to the structural responses. As a result, it is verified the applicability of the fused smart sensor for SHM on real‐scale bridges.

The influence of solar activity on the ionosphere, a critical area of investigation due to its relevance to the Sun–Earth relationship, has been extensively examined through various methodologies. The ability of solar events to induce disturbances in both the ionosphere and the geomagnetic field is widely acknowledged. This specific study focused on sporadic incidents resulting from X-class solar flares that occurred between 2021 and 2022. Utilizing a methodology that involved analyzing data at 5Hz intervals using wavelet algorithms, the data from the GNSS stations of the National Autonomous University of Mexico (UNAM) were investigated. The primary emphasis was on deducing the Total Electron Content (TEC) within the ionosphere. Subsequently, this parameter for each satellite during instances of solar flares was analyzed. The approach uncovered disruptions in the ionosphere triggered by solar flares, even in cases where events transpired at the periphery of the solar disk and were of magnitudes smaller than X2.

Northwestern Mexico experiences large variations in water vapor on seasonal time scales in association with the North American monsoon, as well as during the monsoon associated with upper-tropospheric troughs, mesoscale convective systems, tropical easterly waves, and tropical cyclones. Together these events provide more than half of the annual rainfall to the region. A sufficient density of meteorological observations is required to properly observe, understand, and forecast the important processes contributing to the development of organized convection over northwestern Mexico. The stability of observations over long time periods is also of interest to monitor seasonal and longer-time-scale variability in the water cycle. For more than a decade, the U.S. Global Positioning System (GPS) has been used to obtain tropospheric precipitable water vapor (PWV) for applications in the atmospheric sciences. There is particular interest in establishing these systems where conventional operational meteorological networks are not possible due to the lack of financial or human resources to support the network. Here, we provide an overview of the North American Monsoon GPS Transect Experiment 2013 in northwestern Mexico for the study of mesoscale processes and the impact of PWV observations on high-resolution model forecasts of organized convective events during the 2013 monsoon. Some highlights are presented, as well as a look forward at GPS networks with surface meteorology (GPS-Met) planned for the region that will be capable of capturing a wider range of water vapor variability in both space and time across Mexico and into the southwestern United States.

It has been proven that precise point positioning (PPP) is a well-established technique to obtain high-precision positioning in the order between centimeters and millimeters. In this context, different studies have been carried out to evaluate the performance of PPP in static mode as a possible alternative to the relative method. However, only a few studies have evaluated the performance of a large number of different open-source software programs and have focused extensively on online free PPP services. Therefore, in this paper, a comprehensive comparison of processing in static mode between different open-source software and the online free PPP services is developed. For the evaluation, different GNSS observation files collected at 45 International GNSS Service (IGS) stations distributed worldwide were processed in static PPP mode. Within this frame of reference, ten open-source PPP software and five online free PPP services were studied. The results from the processing strategy demonstrate that it is possible to obtain precision in the order of millimeters with both open-source software and online PPP services. In addition, online PPP services experienced better performance than some other specialized PPP software. In summary, the results show that the daily solutions for the E (East), N (North), and U (Up) components estimated by the ten open-source software and by the five online free PPP services can reach millimeter precision for some stations. Among the open-source software, the PRIDE-PPPAR presented the best performance with a Root Mean Square Error (RMSE) of 5.52, 5.40, and 6.79 mm in the E, N, and U components, respectively. Alternatively, in the case of the online free PPP services, the APPS and CSRS-PPP produced the most accurate results, with RMSE values less than 12 mm for the three components. Finally, the open-source software and online free PPP services experienced similar positioning performance in the horizontal and vertical components, demonstrating that both can be implemented in static mode without compromising the accuracy of the measurement.