Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador.

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach.

Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.

Polycyclic Aromatic Hydrocarbons in Araucaria heterophylla Needles in Urban Areas: Evaluation of Sources and Road Characteristics.

Araucaria heterophylla needles were collected in urban areas of the city of Quito, Ecuador, to analyze the relationship between the concentration of polycyclic aromatic hydrocarbons (PAHs) with different emission sources and road characteristics. The PAHs were analyzed by high-performance liquid chromatography (HPLC) and included naphthalene (Naph), benzo[a]anthracene (BaA), chrysene (Chry), and benzo[a]pyrene (BaP), which are related to the sources considered in this work. The results indicated that some streets with moderate and low traffic intensity had higher total concentrations of PAHs than streets with high traffic intensity, showing the importance of non-traffic related emission sources and road characteristics on PAH emissions. All the studied PAHs were associated with traffic emissions, although Naph and BaP were more associated with acceleration and braking activities, while BaA and Chry also seemed to come from restaurant emissions. The presence of gas stations was also important in the emission of PAHs. Road capacity seems to have a higher effect on pollutant emission than road gradient and urban forms. The outcomes of this study are expected to facilitate the diagnostics of the concentration of PAHs in urban areas, which contribute to the design of strategies for the mitigation of pollution by PAHs in urban environment.

Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito.

Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO2, PM2.5, SO2, and O3) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: -48.75%, for CO, -45.76%, for SO2, -42.17%, for PM2.5, and -63.98%, for NO2. The reduction of this latter gas has induced an increase of O3 by +26.54%.

The effect of national protest in Ecuador on PM pollution.

Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall - 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

Condiciones de trabajo asociadas a síntomas respiratorios por exposición a residuos de plaguicidas / Working conditions associated with respiratory symptoms due to exposure to pesticide residues

INTRODUCCIÓN. La exposición a plaguicidas de trabajadores agrícolas y productores ha sido causal de aparición de síntomas respiratorios teniendo el Ecuador el 62% de población rural dedicada a esta actividad. OBJETIVO. Identificar y evaluar las condiciones de trabajo asociadas a síntomas respiratorios por exposición a residuos de plaguicidas. MATERIALES Y MÉTODOS. Estudio analítico transversal. Población de 140 y muestra de 102 trabajadores de la empresa Condimensa. Los datos fueron recolectados mediante el cuestionario de salud respiratoria de la European Comunity Respiratory Health Survey en Latinoamérica segunda versión, en septiembre de 2020. RESULTADOS. Se encontró una relación estadísticamente significativa entre flema crónica y sexo con unA Prueba Exacta de Fisher (p=0,015), la manipulación de sustancias nocivas o toxicas (p=0,001), y la condición de exposición química (p=0,0006). Mediante análisis de regresión logística se determinó que la manipulación de sustancias nocivas o tóxicas (Odds Ratio 5.50, Intervalo de Confianza 95% 1.58 ­ 19.17), y estar expuesto a químicos (Odds Ratio 7.00, Intervalo de Confianza 95% 2.11 ­ 23.22), fueron factores de riesgo para el desarrollo de síntomas respiratorios: flema crónica, tos crónica, sibilancia, opresión en el pecho, disnea crónica, bronquitis crónica. CONCLUSIÓN. Se registró y evaluó las condiciones de trabajo asociadas a síntomas respiratorios por exposición a residuos; y, la evidencia fue fuerte para la exposición residual a plaguicidas.

INTRODUCTION.Worldwide Exposure to pesticides in agricultural workers and producers has been the cause of the appearance of respiratory symptoms. Ecuador having 62% of the rural population dedicated to this activity. OBJETIVE. Identify and evaluate the working conditions associated with respiratory symptoms due to exposure to pesticide residues. MATERIALS AND METHODS. Cross-sectional analytical study. Population of 140 and sample of 102 workers of the Condimensa company. The data were collected using the respiratory health questionnaire of the European Community Respiratory Health Survey in Latin America, second version, in september 2020. RESULTS. A statistically significant relationship between chronic phlegm and sex type was found with a Fisher exact (p=0,015), the handling of harmful or toxic substances Fisher exact (p = 0.001), and the condition of chemical exposure a Fisher Exact Test (p=0,0006). Through logistic regression analysis, it was determined that the handling of harmful or toxic substances (Odds Ratio 5.50, Confidence Interval 95% 1.58 - 19.17), and being exposed to chemicals (Odds Ratio 7.00, Confidence Interval 95% 2.11 - 23.22), were risk factors for the development of respiratory symptoms: chronic phlegm, chronic cough, wheezing, chest tightness, chronic dyspnea, chronic bronchitis. CONCLUSION. The working conditions associated with respiratory symptoms due to exposure to residues associated with chronic phlegm were recorded and evaluated; and the evidence was strong for residual pesticide exposure.

Assessing the COVID-19 Impact on Air Quality: A Machine Learning Approach.

The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross-validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by -53 ± 2%, -45 ± 11%, -30 ± 13%, and -15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic-busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.

Assessment of metals in PM10 filters and Araucaria heterophylla needles in two areas of Quito, Ecuador.

The reliability of Araucaria heterophylla needles as a biomonitor was evaluated by analyzing the concentration of metals in PM10 filters and in Araucaria heterophylla needles. The sampling campaign was carried out at two sites in the city of Quito, Ecuador, in 2017-2019. Concentrations of Cr, Cu, K, Mn, Pb, Zn, Ca, Fe, Al and Mg were determined in PM10 filters and in Araucaria heterophylla needles using an Inductively Coupled Plasma - Optical Emission Spectroscopy (ICP-OES). The annual mean concentrations of PM10 ranged between 24.9 and 26.3 µg m-3, exceeding the limit established by the World Health Organization (20 µg m-3). Statistical analyses, performed for the PM10 filters, showed that dust resuspension and anthropogenic activities were important sources for PM10 emissions in the city. Metals related to natural emissions (Ca, Mg, K, Al and Fe) dominated in both types of samples, while the minor metals were those related to anthropogenic emissions (Zn, Cu, Cr and Pb). The former were positively associated with the needle samples, while the latter were associated with PM10 filters. This work not only improved scientific knowledge on the concentrations of PM10 and metals in the Andean city of Quito, but also greatly contributed to the progress of research on the use of Araucaria heterophylla needles as a biomonitor.

Seasonal variations in PM10 inorganic composition in the Andean city.

Particulate matter (PM) is one of the key pollutants causing health risks worldwide. While the preoccupation for increased concentrations of these particles mainly depends on their sources and thus chemical composition, some regions are yet not well investigated. In this work the composition of chemical elements of atmospheric PM10 (particles with aerodynamic diameters ≤ 10 µm), collected at the urban and suburban sites in high elevation tropical city, were chemically analysed during the dry and wet seasons of 2017-2018. A large fraction (~ 68%) of PM10 composition in Quito, Ecuador is accounted for by water-soluble ions and 16 elements analysed using UV/VIS spectrophotometer and Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Hierarchical clustering analysis was performed to study a correlation between the chemical composition of urban pollution and meteorological parameters. The suburban area displays an increase in PM10 concentrations and natural elemental markers during the dry (increased wind intensity, resuspension of soil dust) season. Meanwhile, densely urbanized area shows increased total PM10 concentrations and anthropogenic elemental markers during the wet season, which may point to the worsened combustion and traffic conditions. This might indicate the prevalence of cardiovascular and respiratory problems in motorized areas of the cities in the developing world.

Clinical, molecular, and epidemiological characterization of the SARS-CoV-2 virus and the Coronavirus Disease 2019 (COVID-19), a comprehensive literature review.

Coronaviruses are an extensive family of viruses that can cause disease in both animals and humans. The current classification of coronaviruses recognizes 39 species in 27 subgenera that belong to the family Coronaviridae. From those, at least 7 coronaviruses are known to cause respiratory infections in humans. Four of these viruses can cause common cold-like symptoms. Those that infect animals can evolve and become infectious to humans. Three recent examples of these viral jumps include SARS CoV, MERS-CoV and SARS CoV-2 virus. They are responsible for causing severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and the most recently discovered coronavirus disease during 2019 (COVID-19). COVID-19, a respiratory disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) on 11 March 2020. The rapid spread of the disease has taken the scientific and medical community by surprise. Latest figures from 20 May 2020 show more than 5 million people had been infected with the virus, causing more than 330,000 deaths in over 210 countries worldwide. The large amount of information received daily relating to COVID-19 is so abundant and dynamic that medical staff, health authorities, academics and the media are not able to keep up with this new pandemic. In order to offer a clear insight of the extensive literature available, we have conducted a comprehensive literature review of the SARS CoV-2 Virus and the Coronavirus Diseases 2019 (COVID-19).

Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina.

In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5 µm) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built. Different techniques have been applied for the construction of the confidence intervals, and routes around the park and through the middle of it have been used to build the confidence intervals and classify this urban park in accordance with categories established by the Quito air quality index. These intervals have been based on the following estimators: the mean and standard deviation, median and median absolute deviation, median and semi interquartile range, a-trimmed mean and Winsorized standard error of order a, location and scale estimators based on the Andrew's wave, biweight location and scale estimators, and estimators based on the bootstrap-t method. The results of the classification of the park and its surrounding streets showed that, in terms of air pollution by PM2.5, the park is not at caution levels. The results of the classification of the routes that were followed through the park and its surrounding streets showed that, in terms of air pollution by PM2.5, these routes are at either desirable, acceptable or caution levels. Therefore, this urban park is actually removing or attenuating unwanted PM2.5 concentration measurements.

Radon (222Rn) concentrations in the touristic Jumandy cave in the Amazon region of Ecuador.

This work consists of the detection and quantification of the concentration levels of radioactive gas radon-222 (222Rn) of natural origin, as well as the determination of the critical points and the estimation of the effective dose absorbed by the tourists and guides inside the Jumandy cavern in Napo, Ecuador. According to the feasibility map of uranium of Ecuador, the study area is located in one of the top-priority areas for obtaining uranium, suggesting possible radioactivity in this unstudied region. The measurements were carried out from July to October of 2017, in three different monitoring points inside the cavern. The average radon concentrations measured in the cavern exceeded the maximum recommended environmental level by a factor of 28, and the effective dose absorbed by the guides exceeded the recommended maximum by a f actor of 10. Meteorological parameters such as temperature and relative humidity have an impact on the 222Rn concentrations in different parts of the cave.