The relationship among air pollution, meteorological factors and COVID-19 in the Brussels Capital Region.

In great metropoles, there is a need for a better understanding of the spread of COVID-19 in an outdoor context with environmental parameters. Many studies on this topic have been carried out worldwide. However, there is conflicting evidence regarding the influence of environmental variables on the transmission, hospitalizations and deaths from COVID-19, even though there are plausible scientific explanations that support this, especially air quality and meteorological factors. Different urban contexts, methodological approaches and even the limitations of ecological studies are some possible explanations for this issue. That is why methodological experimentations in different regions of the world are important so that scientific knowledge can advance in this aspect. This research analyses the relationship between air pollution, meteorological factors and COVID-19 in the Brussels Capital Region. We use a data mining approach that is capable of extracting patterns in large databases with diverse taxonomies. Data on air pollution, meteorological, and epidemiological variables were processed in time series for the multivariate analysis and the classification based on association. The environmental variables associated with COVID-19-related deaths, cases and hospitalization were PM2.5, O3, NO2, black carbon, radiation, air pressure, wind speed, dew point, temperature and precipitation. These environmental variables combined with epidemiological factors were able to predict intervals of hospitalization, cases and deaths from COVID-19. These findings confirm the influence of meteorological and air quality variables in the Brussels region on deaths and cases of COVID-19 and can guide public policies and provide useful insights for high-level governmental decision-making concerning COVID-19. However, it is necessary to consider intrinsic elements of this study that may have influenced our results, such as the use of air quality aggregated data, ecological fallacy, focus on acute effects in the time-series study, the underreporting of COVID-19, and the lack of behavioral factors.

Optimization of water quality monitoring programs by data mining.

Water quality monitoring programs are essential planning and management tools, but they face many challenges in the developing world. The scarcity of financial and human resources and the unavailability of infrastructure often make it impossible to meet the legal requirements of water monitoring. Many approaches to optimizing water quality monitoring programs have already been proposed. However, few investigations have developed and tested data mining for this purpose. This article has developed data-based models to reduce the number of water quality parameters of monitoring programs using data mining. The objective was to extract patterns from the database, expressed by association rules, which together with field parameters, measured with automatic probes, can estimate laboratory variables. This approach was applied in 35 monitoring stations along 27 river basins throughout Brazil. The data are from fifty years of monitoring (1971-2021), constituting 6328 observations of 60 water quality parameters investigated in different environmental contexts, water quality, and the structuring of monitoring programs. With the applied approach it was possible to estimate 56% of the laboratory parameters in the monitoring stations investigated. The influence of environmental characteristics on the optimization capacity of monitoring programs was evident. The methodology used was not influenced by different water quality levels and anthropogenic impacts. However, the number of parameters was the most influential element in optimization. Monitoring programs with 20 or more water quality variables have the highest potential (≥44%) of optimization by this methodology. Results demonstrate that this approach is a promising alternative that can reduce the frequency of analyses measured in the laboratory and increase the spatial and temporal coverage of water quality monitoring networks.

Management of pharmaceutical micropollutants discharged in urban waters: 30 years of systematic review looking at opportunities for developing countries.

Pharmaceutical micropollutants' contamination of urban waters has been studied globally for decades, but the concentration of innovations in management initiatives is still in developed economies. The gap between the locus of innovations in pharmaceuticals and the relative stagnation in less developed economies to manage waste originating in this activity seems fruitful for investigations on innovation in integrated micropollutant management strategies. These tensions allow for advances in current knowledge for environmental management and, particularly, finding solutions for the contamination by pharmaceutical micropollutants of urban water bodies in developing countries. We aim to list the main strategies for managing pharmaceutical micropollutants discussed to point out opportunities for developing countries to advance in this direction. Methodologically, we conducted a systematic literature review from 1990 to 2020, covering 3027 documents on "pharmaceutical micropollutants management." The framework formed by the macro-approach to integrated management operationalized by the dimensional micro-approaches: technical, organizational, community, and governmental allowed us to understand that (1) the management of pharmaceutical micropollutants tends to occur through a technical approach centered on the removal of aquatic matrices, green chemistry, and urine diversion; (2) management with an organizational approach has enabled removing drugs from water bodies by drug take-back program, collaborative projects, drug use reduction, and better organizational practices; (3) the community approach have helped minimize this type of pollution by reducing the consumption of medicines and the proper destination for medicines that are no longer in use. Finally, the government management approach emerges as a source of legal, economic, and informational instruments to reduce pollution by pharmaceutical micropollutants. Furthermore, these management approaches allowed us to identify 15 opportunities for possible adjustments for developing societies. These opportunities can be promising for practices and research and, in the medium term, contribute to minimizing pollution by pharmaceutical micropollutants in urban waters.

Data based model for predicting COVID-19 morbidity and mortality in metropolis.

There is an ongoing need for scientific analysis to help governments and public health authorities make decisions regarding the COVID-19 pandemic. This article presents a methodology based on data mining that can offer support for coping with epidemic diseases. The methodological approach was applied in São Paulo, Rio de Janeiro and Manaus, the cities in Brazil with the most COVID-19 deaths until the first half of 2021. We aimed to predict the evolution of COVID-19 in metropolises and identify air quality and meteorological variables correlated with confirmed cases and deaths. The statistical analyses indicated the most important explanatory environmental variables, while the cluster analyses showed the potential best input variables for the forecasting models. The forecast models were built by two different algorithms and their results have been compared. The relationship between epidemiological and environmental variables was particular to each of the three cities studied. Low solar radiation periods predicted in Manaus can guide managers to likely increase deaths due to COVID-19. In São Paulo, an increase in the mortality rate can be indicated by drought periods. The developed models can predict new cases and deaths by COVID-19 in studied cities. Furthermore, the methodological approach can be applied in other cities and for other epidemic diseases.