Socioeconomic development role in hospitalization related to air pollution and meteorology: A study case in southern Brazil.

Air pollution is one of the foremost environmental threats to human health. However, the meteorological and social factors that lead to respiratory and cardiovascular diseases have not been fully elucidated. In this study, we use Principal Component Analysis and Generalized Linear Model (PCA-GLM) to investigate the combined effect of socioeconomic development and air pollution on cardiorespiratory hospitalization in southern Brazil. This region has the highest rates of hospitalization by cardiorespiratory diseases in the country. We analyze three main sources of data: (i) air pollutants density from TROPOMI/Sentinel-5p satellite; (ii) temperature, humidity, and planetary boundary layer height (PBLH) modeled with the Weather Research Forecast model; and (iii) hospitalization by cardiorespiratory diseases obtained from the Brazilian National Health System. We estimate the Relative Risk (RR) using the PCA-GLM coefficients and interquartile variations of air pollutants density and meteorological parameters. Our results show that the population living in colder and drier municipalities is more prone to cardiorespiratory hospitalization. Regarding respiratory hospitalization, municipalities with lower socioeconomic development are more sensitive to meteorology and pollution variability than highly developed ones. In less developed municipalities, we observe the highest rates of cardiorespiratory hospitalization even if air pollution is low, which we interpret in terms of higher vulnerability. The RR analysis suggests that air pollution is an important environmental risk to cardiovascular diseases and respiratory diseases is more sensitive to air pollution and meteorology than cardiovascular ones. Our findings corroborate the mounting evidence that social vulnerability is a significant factor affecting the increase of cardiorespiratory hospitalization in the world.

Hysteresis analysis to quantify and qualify the sediment dynamics: state of the art.

This work is a review of the use of hysteresis to quantify sediment discharge dynamics. We reviewed 71 journal articles from the year 1953 to the present day focusing on two topics: the factors that influence hysteresis; and hysteresis quantification. The main factors influencing hysteresis are: (a) magnitude and sequence of events; (b) sediment particle size distribution; (c) basin size; and (d) land use and sediment source. Hysteresis quantification can be done using several different methods that can be grouped as: (a) hysteresis indexes; (b) statistical analysis; and (c) uncertainty analysis. Most studies were conducted in Western Europe and the USA. The studies, in general, show how the factors listed above influence the shape and patterns of hysteresis. However, the sediment dynamics are complex, and the hysteresis patterns may be linked to many other factors, such as slope and drainage systems. The quantification of hysteresis still appears, mainly with the hysteresis index and statistical analysis. Therefore, there are still many other factors that influence hysteresis patterns, as well as hysteresis rates and uncertainty analyses.

Statistical analysis and estimation of annual suspended sediments of major rivers in Japan.

We evaluate the spatiotemporal trends of recent suspended sediment conditions in Japanese rivers. Statistical and spatiotemporal trend analysis is conducted on the 92 major rivers in Japan based on water quality monitoring data from 1992 to 2005. The Mann-Kendall non-parametric method was used to investigate the spatial and temporal trends for the suspended sediment indicator. Results show that the mean concentration of suspended sediments in Japanese rivers has generally declined in recent years, although there are still water quality problems at some monitoring sites (Kanto, Chubu, Kinki and Kyushu regions). A positive relationship between observed yearly discharge and suspended sediment load was found. Land use maps with 100 meter spatial resolution were used to apply an empirical model and develop a regression model for estimating annual suspended sediment loads directly from land use and hydrologic data. Rivers were assigned to three groups according to statistical cluster analysis of suspended sediment (SS) concentration. The correlation between the simulation result from the empirical model and the observed data had R(2) values of 0.62 and 0.71 for groups 2 and 3, and the correlation between the simulation result from the regression model and the observed data had R(2) values of 0.48 and 0.34 for groups 2 and 3. Results show that the proposed simulation technique can be used to predict the pollutant loads to river basins in Japan. Results also suggest prioritization methods and strategies that policy-makers can use to address suspended sediment pollution in rivers and water quality management in general.