Carlos Abraham Ochoa Moya

Ports are major but often under characterized drivers of urban air pollution in coastal cities, where maritime activity interacts with urban emissions and local meteorology. Here, we investigate the coupling between carbonaceous aerosols and airborne microplastics (MPs) in a port-influenced urban environment in Veracruz, Mexico. PM 10 and PM 2.5 samples were collected in a coastal urban area adjacent to port operations and analyzed for elemental composition (X-ray fluorescence), carbon fractions (organic, elemental, and total carbon by thermalcoulombimetry), and airborne MPs (optical microscopy with micro-FTIR confirmation).Carbonaceous aerosols dominated the fine particle fraction, with mean PM 2.5 concentrations of 11.9 (2.4) µg m -3 for organic carbon and 3.0 (1.2) µg m -3 for elemental carbon. Airborne MPs were consistently detected at concentrations of 6.7 (0.5) MPs m -3 , with fibers as the prevailing morphology. A strong positive correlation (r = 0.81) between MPs abundance and organic carbon indicates that airborne MPs co-vary with carbonaceous aerosols and contribute to the refractory organic carbon pool in the urban atmosphere. Elemental patterns and sulfur enrichment further support the influence of port-related combustion and cargo-handling activities, modulated by coastal wind regimes.These results indicate that port activity sustains a persistent urban background of carbonaceous aerosols and airborne microplastics in coastal cities, highlighting ports as long-term sources shaping atmospheric composition and exposure in nearby urban neighborhoods. This integrated perspective emphasizes the role of port infrastructure in coastal urban climate systems and underscores the need to consider microplastics alongside conventional carbonaceous aerosols in assessments of urban air quality and population exposure.

The assessment of wind energy resources in large regions presents a substantial challenge for energy system operators due to the intricacies involved in accurately describing all atmospheric circulation patterns and, even more so, modeling their inherent dynamic. This research primarily aimed to identify the prevailing synoptic weather patterns over Mexico and surrounding regions, thereby enabling the assessment of their influence on the wind field and energy production in Mexico. This study employed ERA5-reanalysis daily mean sea level pressure anomalies data to classify large-scale weather patterns, and two widely recognized clustering techniques, $K$-means and self-organized maps, were utilized for this purpose. Additionally, the study developed a probabilistic approach to establish a regional wind-speed model that facilitates the estimation of factors associated with the potential wind energy production and their relationships with the fluctuations in large-scale weather patterns over the Mexican region. The research findings underscore the crucial influence of these weather patterns on the energy production and emphasize the necessity of a comprehensive study within a forecasting framework to enhance the current Mexican electrical system’s operation.

A supervised machine-learning regression framework is presented for forecasting wind and photovoltaic (PV) power generation by integrating local and synoptic-scale meteorological information. The approach is evaluated across multiple sites, including 39 wind and 18 PV stations in Mexico, and 3 wind and 8 PV stations in China. For each station, an XGBoost regression model is trained to predict hourly energy production using local meteorological variables, derived from ERA5 reanalysis data for Mexico and on-site measurements for the Chinese stations.To assess the added value of large-scale atmospheric information, dimensionally reduced synoptic-scale predictors extracted from ERA5 using self-organizing maps and principal component analysis are incorporated. These predictors are designed to represent dominant atmospheric circulation patterns potentially influencing local renewable energy production. Model performance is assessed through station-specific cross-validation, comparing configurations with and without synoptic-scale features across multiple predictor combinations.Results indicate that the inclusion of synoptic-scale atmospheric patterns can improve short-term power forecasts at several locations, although the overall gains are generally modest. The analysis suggests that improvements in local meteorological inputs are likely to yield larger increases in forecast skill than further refinement of synoptic-scale representations. Nevertheless, the proposed framework demonstrates clear operational relevance: when customized for individual stations, synoptic-scale information can contribute to improved forecasting performance while maintaining the computational efficiency of machine-learning-based methods.

The orographic effect on the spatial structure of precipitation is a fundamental problem in hydrometeorology that still requires a better understanding of the physical processes involved in the emergence of rainfall patterns and their complex statistical structure. In tropical regions, where meteorological measurements are notoriously sparse and data quality control is often poor or missing, the study of precipitation modeling and prediction is challenging. This research aims to show an innovative approach based on a random cascade downscaling method to generate high‐resolution precipitation products from coarse‐scale precipitation products. This approach also includes a topographic enhancement function for describing the altitudinal variability of precipitation and a numerical diffusion filter to lessen the blockiness problem of random cascades. The suggested approach was applied to analyze some long‐term precipitation statistics in the metropolitan area of Mexico City. The model result agrees closely with the temporal statistics of the selected precipitation products and reflects complex orographic constraints. The proposed downscaling approach becomes an alternative to expensive computational methods and allows urban hydrology applications and analysis of small watersheds to incorporate the effects of complex orography.

In recent years, the study of microplastics has garnered increased interest due to their impact on ecosystems and human health. However, most research has focused on characterizing this emerging contaminant in sedimentary, biological, and aqueous matrices, with very few studies dedicated to determining its presence in atmospheric aerosol. To address this knowledge gap, a campaign was conducted from May 23 to June 7, 2023, to collect microplastic samples in PM2.5 particulate matter using an active method with a Teflon filter at a flow rate of 16 L/min in the Port of Veracruz. Under the BA310Pol optical microscope at 5x and 10x magnification, each filter was analyzed to determine the presence and morphology of microplastics. The average concentration of microplastics was 0.041 MP m-3 ± 7.06, with 292 particles identified as fibers, film-like, fragments, films, and spheres. Among these, fibers and film-like particles were the most abundant. Chemical characterization was performed using FTIR, revealing PET as the more dominant plastic in the samples. On the other hand, toxic pollutants can adsorb to microplastic particles, so the presence of PAHs was also determined.The benzo[a]anthracene, chrysene, and benzo[a]pyrene are the most abundant. The findings highlight the necessity for further research into the presence of microplastics in the air, particularly in the breathable fraction, as it poses a potential threat to public health.

This work aims to define a procedure to declare rainy season onset and withdrawal dates for the Mexico Valley Basin, located in central Mexico. The onset/withdrawal is obtained using only precipitation for the study period 1981–2020. The onset is defined as the first day, between May 1st and July 15th, of the first 20 consecutive days having a 20-day average precipitation over the Basin of at least 2.5 mm/day. The withdrawal is defined as the last day, between September 1st and November 15th, of the last 20 consecutive days having a 20-day average precipitation of at least 1.7 mm/day. The mean onset is June 6th, with a standard deviation of 14.3 days; the mean withdrawal date is October 15th, with a standard deviation of 16.1 days; and the average length of the rainy season is 131 days, with a standard deviation of 22.7 days. These criteria maximize the precipitation change slope during onset/withdrawal. We categorized pre- and post-onset/withdrawal periods to investigate mean circulation characteristic changes. Besides a stark increase (decrease) in rainfall over the Basin during onset (withdrawal), we found that vertically integrated moisture transport over the Caribbean Low-Level Jet core region increases (decreases). The onset/withdrawal dates derived show interannual trends, while a late (early) withdrawal is associated with a positive (negative) ENSO Index, and a strong (weak) Caribbean Low Level Jet (CLLJ) is associated with a late (early) onset.

In this study, two classifications of 20 Weather Types (WTs) were used to identify large-scale and synoptic-scale patterns over the Middle Americas region (MAR) that comprises Mexico, intra-American seas, Central America, and northern South America. The Self-Organizing Maps (SOM) method was used to detect both classifications using standardized Mean Sea-Level Pressure (MSLP) anomalies from the ERA-Interim (ERA-I) reanalysis and the Community Earth System Model-Large Ensemble (CESM-LE) in the historical period and its future projection under an RCP8.5 scenario. The WTs obtained with the CESM-LE in the historical period were assigned to each day of the future projection. Averages of the days belonging to each WT of the historical period were compared between both classifications (ERA-I and CESM-LE) employing seasonal and monthly frequencies of occurrence, correspondence in days of occurrence, Pearson's spatial correlation, and position changes of high-pressure semi-permanent centers such as the North Atlantic Subtropical High (NASH) and the North Pacific High (NPH). From precipitation and MSLP, it was observed that WTs obtained with CESM-LE showed a marked seasonality in their temporal distribution, mainly in the wet period (May–October), similar to the ERA-I classification. Three characteristic phenomena of MAR were the North American Monsoon System (NAMS), the Mid-Summer Drought (MSD), and the Caribbean Low-Level Jet (CLLJ). The CESM-LE adequately represented these phenomena in the historical period compared with ERA-I. Regarding the future projection, the CESM-LE ensemble showed that the spatial patterns were very similar in the historical period. However, differences in precipitation between August and September decreased. To assess the effect of internal climate variability of the CESM-LE, we analyzed the spatial average of precipitation in two regions: NAMS and MSD, for the 34 members of the ensemble in the future projection. In the CLLJ region, differences between the historical and the future projection in terms of averages of the zonal-wind component and precipitation were less than 1 %. This analysis showed that the SOM technique detected the signal of climate change on a regional scale without being affected by the internal global variability of the model. Therefore, SOM emerged as a useful tool for the analysis of numerical experiments such as CESM-LE.

In this study, two classifications of 20 Weather Types (WTs) were used to identify large-scale and synoptic-scale patterns over the Middle Americas region (MAR) that comprises Mexico, intra-American seas, Central America, and northern South America. The Self-Organizing Maps (SOM) method was used to detect both classifications using standardized Mean Sea-Level Pressure (MSLP) anomalies from the ERA-Interim (ERA-I) reanalysis and the Community Earth System Model-Large Ensemble (CESM-LE) in the historical period and its future projection under an RCP8.5 scenario. The WTs obtained with the CESM-LE in the historical period were assigned to each day of the future projection. Averages of the days belonging to each WT of the historical period were compared between both classifications (ERA-I and CESM-LE) employing seasonal and monthly frequencies of occurrence, correspondence in days of occurrence, Pearson's spatial correlation, and position changes of high-pressure semi-permanent centers such as the North Atlantic Subtropical High (NASH) and the North Pacific High (NPH). From precipitation and MSLP, it was observed that WTs obtained with CESM-LE showed a marked seasonality in their temporal distribution, mainly in the wet period (May–October), similar to the ERA-I classification. Three characteristic phenomena of MAR were the North American Monsoon System (NAMS), the Mid-Summer Drought (MSD), and the Caribbean Low-Level Jet (CLLJ). The CESM-LE adequately represented these phenomena in the historical period compared with ERA-I. Regarding the future projection, the CESM-LE ensemble showed that the spatial patterns were very similar in the historical period. However, differences in precipitation between August and September decreased. To assess the effect of internal climate variability of the CESM-LE, we analyzed the spatial average of precipitation in two regions: NAMS and MSD, for the 34 members of the ensemble in the future projection. In the CLLJ region, differences between the historical and the future projection in terms of averages of the zonal-wind component and precipitation were less than 1 %. This analysis showed that the SOM technique detected the signal of climate change on a regional scale without being affected by the internal global variability of the model. Therefore, SOM emerged as a useful tool for the analysis of numerical experiments such as CESM-LE.

Mesoscale Convective Systems (MCS) may vary greatly with respect to their morphology, propagation mechanism, intensity, and under which synoptic-scale conditions as a function of topographic complexity. In this study, we develop a long-term climatology of MCS during the North American Monsoon focusing on MCS morphology, lifecycle, and intensity as well as possible propagation mechanisms. We employ an MCS tracking and classification technique based on 23 years (1995 to 2017) of GOES IR satellite data. MCS intensity is also gauged with 7 years (2011 to 2017) of Vaisala GLD360 lightning data and, finally, monthly and interannual variability in synoptic conditions are examined with ERA5 reanalysis data. Our results based on 1594 identified MCS reveal that 98% are morphologically classified as Persistent Elongated Convective Systems. During the 23 summers (June through September) observed, the number of MCS varied considerably, averaging 70 MCS with minimum of 41 and maximum of 94. MCS typically have an average duration of around 8 h ± with a 2 h standard deviation. Propagation speeds, estimated with Hovmöller diagrams in addition to MCS centroid initial and final position, vary slightly depending on the trajectory. A notable result suggests that MCS propagation speeds are more consistent density currents or cold pools and not gravity waves nor steering-level winds. The results of this study could also provide a dataset for examining larger-scale controls on MCS frequency in addition to assesing convective parameterization and convective-resolving models in regions of complex topography.

Land use land cover (LULC) significantly impacts local circulation in the Mexico Basin, particularly wind field circulations such as gap winds, convergence lines, and thermally induced upslope/downslope wind. A case study with a high-pressure system over the Mexico Basin isolates the influence of large-scale synoptic forcing. Numerical simulations reveal a wind system composed of meridional circulation and a zonal component. Thermal pressure gradients between the Mexico basin and its colder surroundings create near-surface convergence lines as part of the meridional circulation. Experiments show that the intensity and organization of meridional circulations and downslope winds increase when LULC changes from natural and cultivated land to urban. Zonal circulation exhibits a typical circulation pattern with the upslope flow and descending motion in the middle of the basin. Large values of moist static energy are near the surface where air parcels pick up energy from the surface either as fluxes of enthalpy or latent heat. Surface heat fluxes and stored energy in the ground are drivers of local circulation, which is more evident in zonal circulation patterns.