Analysis of PM2.5 and Meteorological Variables Using Enhanced Geospatial Techniques in Developing Countries: A Case Study of Cartagena de Indias City (Colombia)

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 (PM2.5) 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 PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, 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 PM2.5 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)

Environmental and Health Benefits Assessment of Reducing PM2.5 Concentrations in Urban Areas in Developing Countries: Case Study Cartagena de Indias

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 PM2.5 concentration in the urban area of Cartagena de Indias, Colombia. Cases of mortality and morbidity attributable to fine particles (PM2.5) 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 PM2.5 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 PM2.5 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

Climate patterns and their influence in the Cordillera Blanca, Peru, deduced from spectral analysis techniques

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 variabilit

Spatiotemporal Analysis of PM2.5 Concentrations on the Incidence of Childhood Asthma in Developing Countries: Case Study of Cartagena de Indias, Colombia

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 (PM2.5) 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 PM2.5 is found. Incidence series of paediatric asthma on a monthly scale and PM2.5 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 PM2.5, as well as the estimation of health impact. A reduction in PM2.5 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 PM2.5 concentration to the maximum levels recommended by WHO. The results showed a good agreement between childhood asthma incidence and PM2.5 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 and Their Influence in the Cordillera Blanca, Peru, Deduced from Spectral Analysis Techniques

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 temperatura 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

Spatiotemporal Analysis of PM2.5 Concentrations on the Incidence of Childhood Asthma in Developing Countries: Case Study of Cartagena de Indias, Colombia

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 (PM2.5) 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 PM2.5 is found. Incidence series of paediatric asthma on a monthly scale and PM2.5 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 PM2.5, as well as the estimation of health impact. A reduction in PM2.5 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 PM2.5 concentration to the maximum levels recommended by WHO. The results showed a good agreement between childhood asthma incidence and PM2.5 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)

Analysis of PM2.5 and Meteorological Variables Using Enhanced Geospatial Techniques in Developing Countries: A Case Study of Cartagena de Indias City (Colombia)

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 (PM2.5) 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 PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, 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 PM2.5 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)

Estimation of the vehicle emission factor in different areas of Cartagena de Indias

In most of the cities of the Colombian Caribbean, the emission factors associated to road traffic have not yet been estimated, due to the shortage of technical and economic resources. The authorities of some municipalities across Colombia have developed emission inventories adopting emission factors from other countries and, although these inventories are theoretically approximate, results indicate that road traffic is a source of emission of significant amounts of pollutants into the atmosphere. Studies conducted in 2013 by the Institute of Immunological Research (IIR), associated with the University of Cartagena, determined that the concentrations of CO and PM2.5 which were recorded throughout the city generally increased in the vicinity of the main roads. The present study aims to estimate the concentration of air pollutants generated by road traffic on the main roads of the city of Cartagena de Indias, Colombia, taking into account the critical points of increased vehicular flow. The upper limits of the emission factors values applying the inverse modeling technique were estimated for CO and PM2.5 considering average concentrations obtained for 24 hours of the pollutants that represent a greater threat to public health, as well as effects of weather conditions and urban morphology. This study is a starting point to determine the magnitude of the emission associated with road traffic in Cartagena and also provides technical support to be able to identify approximately the impact of different vehicle sources in the city. Finally, this article aims to propitiate applicable tools for the authorities to develop effective mitigation and/or control strategies pointed at minimizing the impact of vehicle emissions on the Cartagena inhabitants’ health.En la mayoría de las ciudades del Caribe Colombiano aún no se han estimado los factores de emisión asociados al tráfico de vehículos, debido a la escasez de recursos técnicos y económicos. Las autoridades de algunos municipios colombianos han desarrollado inventarios de emisiones adoptando la metodología de factores de emisión de otros países y, aunque estos inventarios son aproximaciones de la realidad, los resultados indicarían que el tráfico supone una fuente de emisión de cantidades significativas de contaminantes del aire. Estudios realizados en 2013 por el Instituto de Investigaciones Inmunológicas, asociado a la Universidad de Cartagena, determinaron que las concentraciones de CO y PM2.5 que se registraron a lo largo de la ciudad generalmente aumentaban en las cercanías de las vías principales de la ciudad.El presente estudio contempla como objetivo principal estimar la concentración de contaminantes atmosféricos generados por el tráfico en las principales vías de Cartagena, Colombia, teniendo en cuenta las áreas más críticas de congestión de vehículos. Se estimaron los valores límites superiores de los factores de emisión para el CO y el PM2.5 aplicando la técnica de modelación inversa considerando las concentraciones medias diarias, las condiciones meteorológicas y la morfología urbana. Este estudio es un punto de partida para determinar la magnitud de las emisiones asociadas con el tráfico en Cartagena y también proporciona una base técnica para poder identificar de un modo aproximado el impacto de las diferentes fuentes de vehículos en la ciudad. Finalmente, este artículo de investigación tiene como objetivo ofrecer herramientas para que las autoridades desarrollen estrategias efectivas de mitigación y/o control destinadas a minimizar el impacto de las emisiones vehiculares en la salud de los habitantes de Cartagena

Climate Patterns and Their Influence in the Cordillera Blanca, Peru, Deduced from Spectral Analysis Techniques

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