The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM_2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM_2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM_2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM_2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R² of 0.47), wind speed (R² of 0.59) and temperature (R² of 0.64).

High concentrations of particulate matter (PM) could significantly reduce the quality of useful life and human life expectancy. The origin, control, and management of the problem has made great steps in recent decades. However, the problem is still prominent in developing countries. In fact, often the number and spatial distribution of the air quality monitoring stations does not have an appropriate design, misleading decision makers. In the present research, an innovative assessment is proposed of the environmental, health and economic benefits corresponding to a 20% reduction in the PM_2.5 concentration in the urban area of Cartagena de Indias, Colombia. Cases of mortality and morbidity attributable to fine particles (PM_2.5) were estimated, with particular emphasis on mortality, emergency room visits and hospitalizations from respiratory diseases, in addition to their economic assessment using BenMAP-CE^®. The novelty of using BenMAP-CE^® in studying respiratory diseases and PM_2.5 exposure in developing countries lies in its ability to provide a comprehensive assessment of the health impacts of air pollution in these regions. This approach can aid in the development of evidence-based policy and intervention strategies to mitigate the impact of air pollution on respiratory health. Several concentration-response (C-R) functions were implemented to find PM_2.5 attributable mortality cases of ischemic heart and cardiopulmonary disease, lung cancer, respiratory and cardiovascular disease, as well as cases of morbidity episodes related to asthma exacerbation and emergency room/hospitalization care for respiratory disease. A 20% reduction would have avoided 104 cases of premature death among the population older than 30 in Cartagena, and around 65 cases of premature mortality without external causes.

The increase in airborne pollution in large cities since the mid-20th century has had a physiologically proven impact on respiratory health, resulting in the irritation and corrosion of the alveolar wall. One of the demographics of the population most affected by this problem is children. This study focuses on the relationship between particulate matter of 2.5 µm (PM_2.5) and childhood asthma, which is one of the main respiratory diseases identified in developing countries. The city of Cartagena de Indias, Colombia, is taken as a case study. A relevant correlation between childhood asthma and PM_2.5 is found. Incidence series of paediatric asthma on a monthly scale and PM_2.5 records in the city of Cartagena are considered. As is common in developing countries, the series was incomplete due to a lack of experts and insufficient economical resources. Therefore, several statistical and analytical processes were applied to provide sufficient quality to the series. An improvement of the time scale of the records was carried out, as well as the completion (statistical imputation) of missing data due to low statistical significance, by applying Rstudio^®, PAST^® and SPSS^®. The last phases consisted of the determination of the main factors that cause childhood asthma incidence, the estimation of the correlation between asthma incidence and PM_2.5, as well as the estimation of health impact. A reduction in PM_2.5 concentration was simulated using BenMap-CE software to reach safe levels according to the WHO guidelines on air quality to identify preventable cases of childhood asthma, as air pollution has been found to be related to this disease. In addition, a log-linear model was applied to determine the number of hospital visits avoided after reducing the levels of PM_2.5 concentration to the maximum levels recommended by WHO. The results showed a good agreement between childhood asthma incidence and PM_2.5 pollutants in the spectral analysis (75% coincidence) and Chi² (85.5% of coincidence) assessments, while visual correlation, mean and linear regression showed lower relations (61.0%, 55.5% and 0.48%, respectively). A reduction to a safe level of 5 µg/m³ would lead to a reduction of 240 annual cases of childhood asthma (95% CI: 137–330).

Climate patterns are natural processes that drive climate variability in the short, medium, and long term. Characterizing the patterns behind climate variability is essential to understand the functioning of the regional atmospheric system. Since investigations typically reveal only the link and extent of the influence of climate patterns in specific regions, the magnitude of that influence in meteorological records usually remains unclear. The central Peruvian Andes are affected by most of the common climate patterns of tropical areas, such as Intertropical Convergence Zone (ITCZ), Sea Surface Temperature (SST), solar irradiance, Madden Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO), and El Niño Southern Oscillation (ENSO). They are also affected by regional processes that are exclusive from South America, such as the South American Low-Level Jet (SALLJ), South American Monsoon System (SAMS), Bolivian High (BH), and Humboldt Current. The aim of this research is to study the climate variability of precipitation, maximum and minimum temperature records over Cordillera Blanca (Peru), and its relationship with the intensity and periodicity of the common climate patterns that affect this region. To achieve this aim, a spectral analysis based on Lomb’s Periodogram was performed over meteorological records (1986–2019) and over different climate pattern indexes. Results show a coincidence in periodicity between MJO and SALLJ, with monthly cycles for precipitation and temperature (27-day, 56-day, and 90-day cycles). Moreover, the most intense periodicities, such as annual (365 days) and biannual (182 and 122 days) cycles in meteorological variables, possibly would be led by ITCZ and ENSO together, as well as a combination of the Humboldt Current and SALLJ. Additionally, interannual periodicities (3-year, 4.5-year, 5.6–7-year and 11-year cycles) would have coincidence with the ENSO–solar combination, while the longest cycles (16 years) could match PDO variability.

Climate forcings are natural processes that drive climate variability in the short, medium and long term. Characterizing the forcings behind climate variability is important to understand the functioning of the regional atmospheric system. Since investigations typically reveal only the link and extent of the influence of climate forcings in specific regions, the magnitude of that influence in meteorological records remains usually unclear. The central peruvian Andes are affected by most of the common climate forcings of tropical areas, such as Intertropical Convergence Zone (ITCZ), Sea Surface Temperature, Solar irradiance, Madden Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO) and El Niño Southern Oscillation (ENSO). They are also affected by regional processes that are exclusive from South America, such as South American Low Level Jet (SALLJ), South American Monsoon System, Bolivian High and Humboldt Current. The aim of this research is to study the climate variability of precipitation, maximum temperature and minimum temperature records over Cordillera Blanca (Peru) in order to link this climate variability with the intensity and periodicity of the common climate forcings that affects this region. To achieve this aim, a spectral analysis based on Lomb’s Periodogram has been performed over meteorological records (1986 - 2019 period) and over different climate forcing Indexes. Results show a coincidence in periodicity between MJO and SALLJ with monthly cycles for precipitation and temperature (27 days, 56 days, 90 days cycles). The most intense periodicities, such as annual (365 days) and biannual (182 and 122 days) cycles in meteorological variables, possibly would be led by ITCZ and ENSO together, as well as a combination of Humboldt current and SALLJ. Interannual periodicities (3 years, 4.5 years, 5.6 - 7 years and 11 years cycles) would have coincidence with the ENSO - Solar combination, while the longest cycles (16 years) could match PDO variability.

Glacial ice cores are evidence of past environmental conditions through gases and elements trapped inside the drilled material. As Central Andes in the southamerican region are very sensitive to climate changes, a great history of temperature and precipitation variability could be found related to massive Ice Caps. Available oxygen isotope data from glacial ice cores conducted over the last three decades in the Central Andes are reviewed to analyze climate variability over the past seven millennia, a period characterized globally by remarkable climatic stability. The analysis allowed for the identification of climate cycles ranging from secular to millennial in time. These cycles have periods of 1.3, 0.87, 0.67, 0.46, and 0.25 kiloyears (ky.). A series of regional thermal minima and maxima are also defined. This variability in the Andean climate during the mid- and late Holocene is interpreted as being strongly controlled by changes in solar activity, in particular, the forcing of "grand solar minima" is recognized. Likewise, less frequent climate changes could be correlated with Bond cycles and increased or decreased activity of the thermohaline circulation.