Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

News media and meteorological bulletins generally report on the ultraviolet index (UVI) to prevent overexposure to the sun. However, this information does not reflect the accumulated damage to the skin induced by the total amount of UVR (ultraviolet radiation) accumulated over time, known as erythemal dose (ED). This study analyzed the UVI and ED measured at ±30 and ±60 minutes around solar noon (SN) in Arica, North of Chile. The results show that SN ED exceeded the Minimum Erythema Dose (MED) for all the skin types. During the summer, shorts exposures to sunlight for up to 1 hour may result in sun exposure up to 10 times greater than the acceptable occupational safe limit for unprotected human skin.

This study presents a synoptic classification at surface and at 500 hPa during winter (June, July, and August) over Brazil, in order to identify the main synoptic-scale meteorological systems that influence the weather of this period. Through principal components analysis for the 1979-2020 period, five main synoptic patterns were identified, which predominated during winter and are practically the same for each of the three analyzed months. The most frequent synoptic pattern is associated with the climatological mean-field, represented by two of the most characteristic systems of low-level atmospheric circulation in South America: the South Atlantic Subtropical High and the North-Western Argentinean Low. There are two other synoptic patterns related to a classical cold front over the southern and southeastern regions of Brazil. Finally, two other synoptic patterns are associated with a blocking anticyclone and a cyclogenetic process over the Atlantic Ocean, respectively, both of which correspond to the most frequent synoptic pattern related to cold waves and cold surges (friagens) over Brazil.

One of the impacts of climate change is an increase in the frequency and intensity of hydrometeorological disasters such as prolonged droughts. Borneo is one of the areas that is threatened by drought due to climate change, so it is important to know the mitigation and adaptation measures. In this study, the dynamical downscaling method by Conformal Cubic Atmospheric Model (CCAM) will be used to find out the potential for drought events in Borneo based on the RCP 4.5 scenario. As a result, in total, the annual rainfall in Borneo in 2021-2050 will increase when compared to 1991-2020. This means that, on average, the rainfall in Borneo will increase. However, the increase on the annual rainfall amount does not make Borneo free from possibilities of drought disasters in the future. Areas in southern Borneo such as Banjarmasin, Pangkalan Bun, and Pontianak are estimated to have experience more frequent meteorological drought events in 2021-2050 compared to 1991-2020. The southern part of Borneo is also expected to experience longer consecutive dry days in 2021-2050 compared to 1991-2020.

The implicit and unconditionally stable numerical method proposed in Skiba (2015) is applied to solve linear advection-diffusion-reaction problems and nonlinear diffusion-reaction problems on a sphere. Numerical experiments carried out on a high-resolution spherical mesh show the effectiveness of the method in modelling linear advection-diffusion processes on a sphere (dispersion of pollution in the atmosphere), and nonlinear diffusion processes (propagation of nonlinear temperature waves, blow-up regimes of combustion, and chemical reactions in the Gray-Scott model). The method correctly describes the mass balance of a substance in forced and dissipative systems and conserves the total mass and norm of the solution in the absence of forcing and dissipation.

In its Nationally Determined Contribution (NDC), Mexico is committed to reducing unconditionally 22 and 51% of its emissions of greenhouse gases (GHGs) and black carbon, respectively, by 2030, with an emission peak in 2026. Additional reductions of 36 and 70%, respectively, are proposed conditioned to support from other parties. In this work, the percentage of reduction to reach the emission mitigation targets that Mexico proposed in its NDC is estimated. The results show that in order to meet its unconditional NDC, Mexico should start mitigation in 2020 with a 1.5% reduction rate until 2030 and a 3.3% reduction rate by 2050, to reach an emission peak in 2023. To meet the conditional NDC, a 3.1% emission reduction rate until 2030 should be applied, with peak emission in 2021, and 5.8% from 2030 to 2050. In none of these estimates an emission peak in 2026 matches the NDC mitigation options. Furthermore, none of the emissions reduction pathways estimated in this study fulfills the conditional or unconditional contribution and peaks in 2026 at the same time. Mexico has a long history in international climate policy and is a key emerging economy among the top 15 highest GHG emitters. If Mexico does not achieve its NDC, the international implications, both political and climatic, could put the NDC model at risk if there are more large emitters that do not comply with their contribution.

Epidemiological studies on air pollution in Mexico often use the environmental concentrations of pollutants as measured by monitors closest to the home of participants as exposure proxies, yet this approach does not account for the space gradients of pollutants and ignores intra-city human mobility. This study aimed to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in ~16 500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants, and used secondary source information on geographical and meteorological variables as well as other pollutants to fit two generalized additive models capable of predicting monthly PM2.5 and NO2 concentrations during the 2004-2019 period. Both models were evaluated through 10-fold cross-validation, and showed high predictive accuracy with out-of-sample data and no overfitting (CV-RMSE = 0.102 for PM2.5 and CV-RMSE = 4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) µg m–3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a promising alternative for estimating PM2.5 and NO2 exposure with high spatiotemporal resolution for epidemiological studies in the Mexico City Metropolitan Area.

Climatological data with unreliable or missing values is an important area of research, and multiple methods are available to fill in missing data and evaluate data quality. Our study aims to compare the performance of different methods for estimating missing values explicitly designed for precipitation and multipurpose hydrological data. The climate variable used for the analysis was daily precipitation. We considered two different climate and orographic regions to evaluate the effects of altitude, precipitation regime, and percentage of missing data on the Mean Absolute Error of imputed values and performed a homogeneity evaluation of meteorological stations. We excluded meteorological stations with more than 25% missing data from the analysis. In the semi-arid region, ReddPrec (optimal for nine stations) and GCIDW (optimal for eight stations) were the best-performing methods for the 23 stations, with average MAE values of 1.63 mm/day and 1.46 mm/day, respectively. In the humid region, GCIDW was optimal in ~59% of stations, EM in ~24%, and ReddPrec in ~17%, with average MAE values of ~6.0 mm/day, 6.5 mm/day, and ~9.8 mm/day, respectively. This research makes a valuable contribution to identifying the most appropriate methods to impute daily precipitation in different climatic regions of Mexico based on efficiency indicators and homogeneity evaluation.

The first direct current observations (with the Lowering Acoustic Doppler Current Profiler [LADCP] and surface drifters) in Bahía de La Paz, in the southwestern Gulf of California (GC), concur with previous reports that the main dynamical feature during summer is a closed cyclonic circulation. However, we found that geostrophic calculations overestimate the speed of the orbital velocity: actual speeds (0.20-0.25 m s–1) were ~25-40% lower than those estimated from geostrophic balance (0.25-0.35 m s–1). The reason is that the centrifugal force cannot be neglected in this case. The mean rotation period during ship-borne observations in August 2004 was ~1.4 days, but it varied in the time that surface drifters were inside the bay, from ~1-2 days in June-July to ~2.5-3 days in September-October. The analysis of satellite data (wind velocity, sea surface temperature and chlorophyll) shows that from May to September the wind stress curl is strong and cyclonic, and the surface of the bay is cooler and richer than the adjacent Gulf of California waters, which could be attributed to the positive wind stress curl. This positive wind stress curl on the bay is part of a larger-scale positive wind stress curl distribution that surrounds the southern part of the Baja California Peninsula during summer, probably enhanced in the bay by local topography features. Although there is an exchange of water between the bay and the GC, its effect on the dynamics is poorly known. 

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants, affecting human health (increasing susceptibility to respiratory infections) and the environment (soil and water acidification). In many regions of Brazil, NO2 measurements at ground level meet difficulties because monitoring stations are few and unevenly distributed. Satellite observations combined with machine learning models can mitigate this lack of data. This paper report results from an investigation on the ability of a machine learning approach (a non-linear statistical Random Forest algorithm, hereafter RF) to reconstruct the long-term spatiotemporal ground-level NO2 from 2006 to 2019 using as input parameters NO2 data retrieved from the Ozone Monitoring Instrument (OMI) sensor aboard Aura satellite, besides meteorological covariates and localized ground-level NO2 measurements. Results show that the RF model predicts NO2 with an accuracy expressed by an R2 = 0.68 correlation based on a 10-fold cross-validation. The model also predicted a mean NO2 concentration of 18.73 ± 3.86 μg m–3. The total NO2 concentration over the entire region analyzed showed a decreasing trend (2.9 μg m–3 yr–1), being 2006 the year with the higher concentrations and 2017 with the lowest. This study suggests that non-linear statistical algorithm reconstructions using RF can be complementary tools to in situ and satellite observations for NO2 mapping.

This study presents a first simultaneous trend and magnitude assessment of air pollutants (CO, H2S, SO2, NO2, NO, NOx, O3 and PM10) and meteorological variables (rainfall [RF], relative humidity [RH], atmospheric pressure [PR], temperature [TC], wind speed [WS], and wind direction [WD]) in the city of Haqel and at four different locations in the city of Jeddah, Saudi Arabia, for a continuous 5-year period (2008-2012). The spatio-temporal co-variations of air pollutants in terms of their diurnal, weekly, seasonal and annual cycles, and their relationship with meteorological conditions, along with the estimates of the weekend effect, are described. A decreasing annual trend was observed for most air pollutants analyzed except for O3 and PM10. The CO, NO2, NO and NOx displayed a strong weekend effect. A percentile-based change analysis displayed an increase in concentrations for O3 (PM10) in the lower (higher) percentiles from the first to second half of the study period. The study identified 12 cyclonic weather events during the 5-year time period associated with high PM10 concentrations (> 500 µg m–3) relative to a mean value of 102 µg m–3, with a standard deviation value of 179 µg m–3. The study also analyzed the impacts of several mid-latitude anti-cyclonic events on air pollutant concentrations and found a significant change in air pollutant concentrations (CO, SO2, NO2, NO, NOx, O3 and PM10) and meteorological variables (RH, PR, TC, WS, and WD) associated with stagnant upper air conditions during the atmospheric blocking.