Quantifying the contribution of environmental variables to cyclists' exposure to PM2.5 using machine learning techniques.

Cyclists are particularly vulnerable to travel-related exposure to air pollution. Understanding the factors that increase exposure is crucial for promoting healthier urban environments. Machine learning models have successfully predicted air pollutant concentrations, but they tend to be less interpretable than classical statistical ones, such as linear models. This study aimed to develop a predictive model to assess cyclists' exposure to fine particulate matter (PM2.5) in urban environments. The model was generated using geo-temporally referenced data and machine learning techniques. We explored several models and found that the gradient boosting machine learning model best fitted the PM2.5 predictions, with a minimum root mean square error value of 5.62 µg m-3. The variables with greatest influence on cyclist exposure were the temporal ones (month, day of the week, and time of the day), followed by meteorological variables, such as temperature, relative humidity, wind speed, wind direction, and atmospheric pressure. Additionally, we considered relevant attributes, which are partially linked to spatial characteristics. These attributes encompass street typology, vegetation density, and the flow of vehicles on a particular street, which quantifies the number of vehicles passing a given point per minute. Mean PM2.5 concentration was lower in bicycle paths away from vehicular traffic than in bike lanes along streets. These outcomes underscore the need to thoughtfully design public transportation routes, including bus routes, concerning the network of bicycle pathways. Such strategic planning attempts to improve the air quality in urban landscapes.

Environmental, meteorological and pandemic restriction-related variables affecting SARS-CoV-2 cases.

Three years have passed since the outbreak of Coronavirus Disease 2019 (COVID-19) brought the world to standstill. In most countries, the restrictions have ended, and the immunity of the population has increased; however, the possibility of new dangerous variants emerging remains. Therefore, it is crucial to develop tools to study and forecast the dynamics of future pandemics. In this study, a generalized additive model (GAM) was developed to evaluate the impact of meteorological and environmental variables, along with pandemic-related restrictions, on the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Córdoba, Argentina. The results revealed that mean temperature and vegetation cover were the most significant predictors affecting SARS-CoV-2 cases, followed by government restriction phases, days of the week, and hours of sunlight. Although fine particulate matter (PM2.5) and NO2 were less related, they improved the model's predictive power, and a 1-day lag enhanced accuracy metrics. The models exhibited strong adjusted coefficients of determination (R2adj) but did not perform as well in terms of root-mean-square error (RMSE). This suggests that the number of cases may not be the primary variable for controlling the spread of the disease. Furthermore, the increase in positive cases related to policy interventions may indicate the presence of lockdown fatigue. This study highlights the potential of data science as a management tool for identifying crucial variables that influence epidemiological patterns and can be monitored to prevent an overload in the healthcare system.

Hepatic alterations associated with fine particulate matter exposure.

Several studies have pointed to fine particulate matter (PM2.5) as the main responsible for air pollution toxic effects. Indeed, PM2.5 may not only cause respiratory and cardiovascular abnormalities but it may also affect other organs such as the liver. Be that as it may, only a few studies have evaluated the PM2.5 effects on hepatic tissue. Moreover, most of them have not analyzed the relationship between particles composition and toxicological effects. In this study, healthy rats were subjected to urban levels of PM2.5 particles in order to assess their structural and functional effects on the liver. During the exposure periods, mean PM2.5 concentrations were slightly higher than the value suggested by the daily guideline of the World Health Organization. The exposed rats showed a hepatic increase of Cr, Zn, Fe, Ba, Tl and Pb levels. This group also showed leukocyte infiltration, sinusoidal dilation, hydropic inclusions and alterations in carbohydrates distribution. These histologic lesions were accompanied by serological changes, such as increase of total cholesterol and triglycerides, as well as genotoxic damage in their nuclei. We also observed significant associations between several biomarkers and PM2.5 composition. Our results show that exposure to low levels of PM2.5 might cause histologic and serological changes in liver tissue, suggesting that PM2.5 toxicity is influenced not only by their concentration but also by their composition and the exposure frequency.

QuEChER method for air microbiological monitoring in hospital environments.

INTRODUCTION: Nosocomial pathogens have become a priority issue for public health, since they are responsible for increased morbidity and mortality in hospitalized patients and the development of multi-resistant microorganisms, as well. Recent studies found strong evidence that airborne transmission plays a key role in many nosocomial infections. Thus, we aim to develop a QuEChER methodology for the characterization of airborne microbial levels, analyzing potential variables that modify the air microbiological load. METHODOLOGY: Particulate matter levels and suspended and settled bioaerosols were determined simultaneously employing optical sensors, Harvard impactors and settle plates, respectively. Environmental variables were also measured at different sites during different working shifts and seasons. RESULTS: We found a straightforward relationship between airborne particles, air exchange rates, and people influx. Levels of suspended microorganisms were related to fine particulate matter concentration, CO2 and ambient temperature. A positive linear relationship (R2 = 0.9356) was also found between fine particulate matter and CO2 levels and air microbial load. CONCLUSION: The QuEChER methodology is an effective methodology that could be used to improve the surveillance of nosocomial pathogens in developing countries hospitals where air quality is scarcely controlled.

Biomonitoring levels and trends of PAHs and synthetic musks associated with land use in urban environments.

Polycyclic aromatic hydrocarbons (PAHs) are some of the most studied organic compounds in urban environments, due to their known adverse effects on human health and persistence in environmental matrices. During the last decade, new groups of organic compounds with an intensive use worldwide such as synthetic musks have been raising the interest of the scientific community given their toxicity and health effects. However, literature is still scarce in studies dealing with their concentration in the environment, especially in developing countries, where they are even more rare or non-existing at all. We employed leaves of Ligustrum lucidum to assess the concentrations of PAHs and synthetic musks in different land use areas in Cordoba city, therefore contributing with environmental information in Argentina. We found higher levels of PAHs in urban and industrial areas than in the peri-urban sampling sites, naphthalene being one of the dominant PAHs in all sampling areas. Regarding synthetic musk fragrances, polycyclic musks were the most contributing compounds and the highest levels found in industrial areas as well. A high environmental risk could be expected due to the frequent occurrence of galaxolide in addition to the high hazardous potential of phantolide, which was present in 50% of the samples. The results of the present study indicate that leaves of an urban ubiquitous tree can be used to assess the spatial behavior of both "classic" and "emerging" organic pollutants, allowing an assessment of urban air quality in areas where common air sampling devices are unavailable.

Exposure to polycyclic aromatic hydrocarbons in urban environments: health risk assessment by age groups.

A detailed investigation was conducted into the concentration of polycyclic aromatic hydrocarbons (PAHs) associated with PM10 particles collected during 2012 in an urban area in Cordoba, Argentina. Their composition was studied and the lifetime lung cancer risk resulting from exposure to total and individual PAHs was estimated. Samples of PM10 were collected daily on fiber glass filters with PAHs being extracted with methylene chloride and analyzed by HPLC. Mean PAH concentrations were higher during autumn and winter. In contrast, during warm months, high ambient temperature and wind speed contributed to a decrease in the PAH ambient concentrations. The PAH levels found in the present study were within the range of those reported in other polluted urban areas. However risk factors calculated for exposure to individual and cumulative PAHs exceeded the carcinogenic benchmark level of 1×10(-6) early in childhood, implying that these PAH concentrations represent a serious risk to public health.