Airborne particulate matter integral assessment in Magdalena department, Colombia: Patterns, health impact, and policy management.

The relevance of atmospheric particulate matter (PM) to health and the environment is widely known. Long-term studies are necessary for understanding current and future trends in air quality management. This study aimed to assess the long-term PM concentration in the Magdalena department (Colombia). It focused on the following aspects: i) spatiotemporal patterns, ii) correlation with meteorology, iii) compliance with standards, iv) temporal trends over time, v) impact on health, and vi) impact of policy management. Fifteen stations from 2003 to 2021 were analyzed. Spearman-Rho and Mann-Kendall methods were used to correlate concentration with meteorology. The temporal and five-year moving trends were determined, and the trend magnitude was calculated using Teil-Sen. Acute respiratory infection odd ratios and risk of cancer associated with PM concentration were used to assess the impact on health. The study found that the maximum PM10 concentration was 194.5 µg/m3, and the minimum was 3 µg/m3. In all stations, a negative correlation was observed between PM10 and atmospheric water content, while the wind speed and temperature showed a positive correlation. The global trends indicated an increasing value, with five fluctuations in five-year moving trends, consistent with PM sources and socio-economic behavior. PM concentrations were found to comply with national standard; however, the results showed a potential impact on population health. The management regulation had a limited impact on increasing concentration. Considering that national regulations tend to converge towards WHO standards, the study area must create a management program to ensure compliance.

National Health Systems and COVID-19 Death Toll Doubling Time.

Coronavirus disease 2019 (COVID-19) has placed stress on all National Health Systems (NHSs) worldwide. Recent studies on the disease have evaluated different variables, namely, quarantine models, mitigation efforts, damage to mental health, mortality of the population with chronic diseases, diagnosis, use of masks and social distancing, and mortality based on age. This study focused on the four NHSs recognized by the WHO. These systems are as follows: (1) The Beveridge model, (2) the Bismarck model, (3) the National Health Insurance (NHI) model, and (4) the "Out-of-Pocket" model. The study analyzes the response of the health systems to the pandemic by comparing the time in days required to double the number of disease-related deaths. The statistical analysis was limited to 56 countries representing 70% of the global population. Each country was grouped into the health system defined by the WHO. The study compared the median death toll DT, between health systems using Mood's median test method. The results show high variability of the temporal trends in each group; none of the health systems for the three analyzed periods maintain stable interquartile ranges (IQRs). Nevertheless, the results obtained show similar medians between the study groups. The COVID-19 pandemic saturates health systems regardless of their management structures, and the result measured with the time for doubling death rate variable is similar among the four NHSs.

The ideal frequency of temperature data collection in compostability experiments on domestic organic residues.

One of the main analytical variable to indicate the evolution and the phases of the composting process is temperature, whose constant monitoring is fundamental for decision making. However, studies usually perform collection of temperature data with a daily frequency due to the operational difficulty in obtaining this information from manually collected samples. Thus, the aim of this study was to determine the ideal frequency of temperature data collection in composting layers. Eight composting layers containing tree prunings + domestic organic residues were installed and four temperature sensors were installed in each layer. The temperature data were collected and recorded from minute to minute by means of a datalogger developed with an Arduino board during 70 days of composting. Thus, the collected temperatures were used as a pilot sample, and therefore the ideal temperature collection rate was estimated for different estimation error limits. No significant difference was found between the different collection times according to the Kruskal-Wallis test at a significance level of 5%. Therefore, the ideal collection frequency can be determined from the error limit of temperature estimation that is acceptable to the researcher.